diff --git a/TAA2/.ipynb_checkpoints/tutorial1-checkpoint.ipynb b/TAA2/.ipynb_checkpoints/tutorial1-checkpoint.ipynb deleted file mode 100644 index 6525cf1..0000000 --- a/TAA2/.ipynb_checkpoints/tutorial1-checkpoint.ipynb +++ /dev/null @@ -1,2401 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "jTmaE4gi47C9" - }, - "source": [ - "# Tutorial 1: Working with OpenStreetMap\n", - "\n", - "Within this tutorial, we will explore the power of OpenStreetMap. We will learn how to extract information from OpenStreetMap, how you can explore and visualize this, and how to use it for some basic analysis.\n", - "\n", - "### Important before we start\n", - "---\n", - "Make sure that you save this file before you continue, else you will lose everything. To do so, go to **Bestand/File** and click on **Een kopie opslaan in Drive/Save a Copy on Drive**!\n", - "\n", - "Now, rename the file into Week5_Tutorial1.ipynb. You can do so by clicking on the name in the top of this screen." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "KqMg54GZ47DB", - "tags": [] - }, - "source": [ - "## Learning Objectives\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JC0K9vub47DC" - }, - "source": [ - "- To understand the use of **OSMnx** to extract geospatial data from OpenStreetmap.\n", - "- To know how to rasterize vector data through using **Geocube**.\n", - "- To know how to visualise vector and raster data.\n", - "- To understand the basic functioning of **Matplotlib** to create a map.\n", - "- To know how one can generate routes between two points using **NetworkX**.\n", - "- To visualize networks on an interactive map." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "W6efV5lD47DC" - }, - "source": [ - "

Tutorial Outline

\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "xVGN7_hz47DD", - "tags": [] - }, - "source": [ - "## 1.Introducing the packages\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "mr4J-7kq47DD" - }, - "source": [ - "Within this tutorial, we are going to make use of the following packages: \n", - "\n", - "[**GeoPandas**](https://geopandas.org/) is a Python package that extends the datatypes used by pandas to allow spatial operations on geometric types.\n", - "\n", - "[**OSMnx**](https://osmnx.readthedocs.io/) is a Python package that lets you download geospatial data from OpenStreetMap and model, project, visualize, and analyze real-world street networks and any other geospatial geometries. You can download and model walkable, drivable, or bikeable urban networks with a single line of Python code then easily analyze and visualize them. You can just as easily download and work with other infrastructure types, amenities/points of interest, building footprints, elevation data, street bearings/orientations, and speed/travel time.\n", - "\n", - "[**NetworkX**](https://networkx.org/) is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.\n", - "\n", - "[**Matplotlib**](https://matplotlib.org/) is a comprehensive Python package for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible.\n", - "\n", - "[**Geocube**](https://corteva.github.io/geocube) is a Python package to convert geopandas vector data into rasterized data.\n", - "\n", - "[**xarray**](https://docs.xarray.dev/) is a Python package that allows for easy and efficient use of multi-dimensional arrays.\n", - "\n", - "*We will first need to install these packages in the cell below. Uncomment them to make sure we can pip install them*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 31325, - "status": "ok", - "timestamp": 1675086280074, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "VD7sW5hx47DE", - "outputId": "36a15ed9-7936-4f89-9284-6562c265d75c" - }, - "outputs": [], - "source": [ - "!pip install osmnx\n", - "!pip install geocube\n", - "!pip install contextily" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "eVsADIc847DG" - }, - "source": [ - "Now we will import these packages in the cell below:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "executionInfo": { - "elapsed": 3237, - "status": "ok", - "timestamp": 1675086283294, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "EJeE3bc047DG" - }, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'geocube'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[3], line 15\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpatches\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Patch\n\u001b[0;32m 14\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m---> 15\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgeocube\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m make_geocube\n\u001b[0;32m 17\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39mrun_line_magic(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmatplotlib\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124minline\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'geocube'" - ] - } - ], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore') \n", - "\n", - "import osmnx as ox\n", - "import numpy as np\n", - "import networkx as nx\n", - "import contextily as cx\n", - "import matplotlib\n", - "import geopandas as gpd\n", - "import osm_flex.extract as ex\n", - "\n", - "from matplotlib.colors import LinearSegmentedColormap,ListedColormap\n", - "from matplotlib.patches import Patch\n", - "import matplotlib.pyplot as plt\n", - "from geocube.api.core import make_geocube\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "YPgVj6al47DH" - }, - "source": [ - "## 2. Extract and visualize land-use information from OpenStreetMap\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Uhh94J5Z47DI" - }, - "source": [ - "The first step is to define which area you want to focus on. In the cell below, you will now read \"Zoeterwoude, The Netherlands\". Change this to any area or municipality in the Netherlands that (1) you can think of and (2) will work. \n", - "\n", - "In some cases, the function does not recognize the location. You could either try a different phrasing or try a different location. Many parts of the Netherlands should work." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "executionInfo": { - "elapsed": 1398, - "status": "ok", - "timestamp": 1675086381517, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "9cijX5J047DI" - }, - "outputs": [], - "source": [ - "place_name = \"Kampen, The Netherlands\"\n", - "area = ox.geocode_to_gdf(place_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kX3feV7O47DJ" - }, - "source": [ - "Now let us visualize the bounding box of the area. As you will notice, we also estimate the size of the area. If the area size is above 50km2, or when you have many elements within your area (for example the amsterdam city centre), extracting the data from OpenStreetMap may take a little while. " - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 608 - }, - "executionInfo": { - "elapsed": 5155, - "status": "ok", - "timestamp": 1675086404602, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "JFBsptnt47DJ", - "outputId": "3a628a04-9ade-43dd-f684-efae51795dc5", - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\eks510\\AppData\\Local\\Temp\\ipykernel_24312\\2701094748.py:8: FutureWarning: Calling int on a single element Series is deprecated and will raise a TypeError in the future. Use int(ser.iloc[0]) instead\n", - " size = int(area_to_check.area/1e6)\n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Kampen, The Netherlands. Total area: 218 km2')" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "area_to_check = area.to_crs(epsg=3857)\n", - "ax = area_to_check.plot(figsize=(10, 10), color=\"none\", edgecolor=\"k\", linewidth=4)\n", - "ax.set_xticks([])\n", - "ax.set_yticks([])\n", - "ax.set_axis_off()\n", - "cx.add_basemap(ax, zoom=11)\n", - "\n", - "size = int(area_to_check.area/1e6)\n", - "\n", - "ax.set_title(\"{}. Total area: {} km2\".format(place_name,size),fontweight='bold')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "pT5odIz-6fki" - }, - "source": [ - "
\n", - "Question 1: To make sure we understand which area you focus on, please submit the figure that outlines your area.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6zbwxAev47DL" - }, - "source": [ - "Now we are satisfied with the selected area, we are going to extract the land-use information from OpenStreetMap. To find the right information from OpenStreetMap, we use **tags**.\n", - "\n", - "As you will see in the cell below, we use the tags *\"landuse\"* and *\"natural\"*. We need to use the *\"natural\"* tag to ensure we also obtain water bodies and other natural elements. " - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": { - "executionInfo": { - "elapsed": 104940, - "status": "ok", - "timestamp": 1675086538272, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "pWxGFjgN47DL" - }, - "outputs": [], - "source": [ - "tags = {'landuse': True, 'natural': True} \n", - "landuse = ox.features_from_place(place_name, tags)" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [], - "source": [ - "# from urllib import request\n", - "\n", - "# remote_url = 'https://github.com/VU-IVM/DamageScanner/raw/develop/data/kampen/exposure/kampen_landuse.gpkg'\n", - "# file = 'kampen_landuse.gpkg'\n", - "\n", - "# request.urlretrieve(remote_url, file)\n", - "#landuse = gpd.GeoDataFrame.from_file('kampen_landuse.gpkg')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "brNqZmi547DM" - }, - "source": [ - "To ensure we really only get the area that we want, we use geopandas's `clip` function to only keep the area we want. This function does exactly the same as the `clip` function in QGIS." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Anrn7wiL47DM" - }, - "source": [ - "When we want to visualize or analyse the data, we want all information in a single column. However, at the moment, all information that was tagged as *\"natural\"*, has no information stored in the *\"landuse\"* tags. It is, however, very convenient if we can just use a single column for further exploration of the data. \n", - "\n", - "To overcome this issue, we need to add the missing information to the landuse column, as done below. Let's first have a look which categories we have in the **natural** column. " - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
osm_idosm_way_idnamelandusenaturalgeometry
01290848NoneNoneNonewaterMULTIPOLYGON (((5.92830 52.55048, 5.92835 52.5...
11290851NoneGat van SeveningenNonewaterMULTIPOLYGON (((5.90809 52.56782, 5.90811 52.5...
21300414NoneNonegrassNoneMULTIPOLYGON (((5.98407 52.53105, 5.98421 52.5...
31300416NoneNoneNonewaterMULTIPOLYGON (((5.98667 52.53165, 5.98656 52.5...
41300424NoneNonegrassNoneMULTIPOLYGON (((5.98652 52.53149, 5.98641 52.5...
.....................
4601None1133167548NonegrassNoneMULTIPOLYGON (((5.92780 52.53297, 5.92785 52.5...
4602None1133167549NonegrassNoneMULTIPOLYGON (((5.92772 52.53299, 5.92778 52.5...
4603None1133167616NonegrassNoneMULTIPOLYGON (((5.92763 52.53281, 5.92767 52.5...
4604None1133167617NonegrassNoneMULTIPOLYGON (((5.92755 52.53283, 5.92760 52.5...
4605None1133169701NonegrassNoneMULTIPOLYGON (((5.92796 52.53322, 5.92803 52.5...
\n", - "

4606 rows × 6 columns

\n", - "
" - ], - "text/plain": [ - " osm_id osm_way_id name landuse natural \\\n", - "0 1290848 None None None water \n", - "1 1290851 None Gat van Seveningen None water \n", - "2 1300414 None None grass None \n", - "3 1300416 None None None water \n", - "4 1300424 None None grass None \n", - "... ... ... ... ... ... \n", - "4601 None 1133167548 None grass None \n", - "4602 None 1133167549 None grass None \n", - "4603 None 1133167616 None grass None \n", - "4604 None 1133167617 None grass None \n", - "4605 None 1133169701 None grass None \n", - "\n", - " geometry \n", - "0 MULTIPOLYGON (((5.92830 52.55048, 5.92835 52.5... \n", - "1 MULTIPOLYGON (((5.90809 52.56782, 5.90811 52.5... \n", - "2 MULTIPOLYGON (((5.98407 52.53105, 5.98421 52.5... \n", - "3 MULTIPOLYGON (((5.98667 52.53165, 5.98656 52.5... \n", - "4 MULTIPOLYGON (((5.98652 52.53149, 5.98641 52.5... \n", - "... ... \n", - "4601 MULTIPOLYGON (((5.92780 52.53297, 5.92785 52.5... \n", - "4602 MULTIPOLYGON (((5.92772 52.53299, 5.92778 52.5... \n", - "4603 MULTIPOLYGON (((5.92763 52.53281, 5.92767 52.5... \n", - "4604 MULTIPOLYGON (((5.92755 52.53283, 5.92760 52.5... \n", - "4605 MULTIPOLYGON (((5.92796 52.53322, 5.92803 52.5... \n", - "\n", - "[4606 rows x 6 columns]" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "landuse#.natural.unique()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And now we can add them to the **landuse** column. We made a start, but its up to you to fill in the rest." - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": { - "executionInfo": { - "elapsed": 429, - "status": "ok", - "timestamp": 1675086633493, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "nlyHmEzg47DM" - }, - "outputs": [], - "source": [ - "landuse.loc[landuse.natural=='water','landuse'] = 'water'\n", - "landuse.loc[landuse.natural=='wetland','landuse'] = 'wetlands'\n", - "\n", - "\n", - "landuse = landuse.dropna(subset=['landuse'])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FOhhcbFs8Nsz" - }, - "source": [ - "
\n", - "Question 2: Please provide in the answer box in Canvas the code that you used to make sure that all land uses are now registered within the landuse column.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Ii4rjR3q47DN" - }, - "source": [ - "Our next step is to prepare the visualisation of a map. What better way to explore land-use information than plotting it on a map? \n", - "\n", - "As you will see below, we can create a dictionary with color codes that will color each land-use class based on the color code provided in this dictionary." - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": { - "executionInfo": { - "elapsed": 864, - "status": "ok", - "timestamp": 1675086777083, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "urAI5MAG47DN" - }, - "outputs": [], - "source": [ - "color_dict = { \"grass\":'#c3eead', \"railway\": \"#000000\",\n", - " \"forest\":'#1c7426', \"orchard\":'#fe6729',\n", - " \"residential\":'#f13013', \"industrial\":'#0f045c',\n", - " \"retail\":'#b71456', \"education\":'#d61181', \n", - " \"commercial\":'#981cb8', \"farmland\":'#fcfcb9',\n", - " \"cemetery\":'#c39797', \"construction\":'#c0c0c0',\n", - " \"meadow\":'#c3eead', \"farmyard\":'#fcfcb9',\n", - " \"plant_nursery\":'#eaffe2', \"scrub\":'#98574d',\n", - " \"allotments\":'#fbffe2', \"reservoir\":'#8af4f2',\n", - " \"static_caravan\":'#ff3a55', \"wetlands\": \"#c9f5e5\",\n", - " \"water\": \"#c9e5f5\", \"beach\": \"#ffeead\",\n", - " \"landfill\" : \"#B08C4D\", \"recreation_ground\" : \"#c3eead\",\n", - " \"brownfield\" : \"#B08C4D\", \"village_green\" : \"#f13013\" ,\n", - " \"military\": \"#52514E\", \"garden\" : '#c3eead'\n", - " } " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Daf-4BMa47DN" - }, - "source": [ - "Unfortunately, OpenSteetMap very often contains elements that have a unique tag. As such, it may be the case that some of our land-use categories are not in the dictionary yet. \n", - "\n", - "Let's first create an overview of the unique land-use categories within our data through using the `.unique()` function within our dataframe:" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 221, - "status": "ok", - "timestamp": 1675086893656, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "h8t5a6_Z47DN", - "outputId": "9e17ba91-d0a4-4dcc-df05-8483f352a228" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['water', 'grass', 'forest', 'orchard', 'wetlands', 'construction',\n", - " 'industrial', 'residential', 'retail', 'meadow', 'cemetery',\n", - " 'farmland', 'farmyard', 'allotments', 'commercial', 'education',\n", - " 'static_caravan', 'railway'], dtype=object)" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "landuse.landuse.unique()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "h0p7fYBd47DN" - }, - "source": [ - "Ofcourse we can visually compare the array above with our color_dict, but it is much quicker to use `Sets` to check if there is anything missing:" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 248, - "status": "ok", - "timestamp": 1675086896661, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "UZaHsgNq47DO", - "outputId": "8a7f6f9e-8bde-4c5c-8012-6ed7f8b071ed" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "set()" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "set(landuse.landuse.unique())-set(color_dict)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EtMHGvQk47DO" - }, - "source": [ - "In case anything is missing, add them to the color_dict dictionairy and re-run that cell. \n", - "\n", - "
\n", - "Question 3: Show us in Canvas (i) which land-use categories you had to add, and (ii) how your final color dictionary looks like.\n", - "
\n", - "\n", - "```{tip}\n", - "You can easily find hexcodes online to find the right colour for each land-use category. Just google hexcodes!\n", - "```\n", - "\n", - "\n", - "Our next step is to make sure that we can connect our color codes to our dataframe with land-use categories." - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": { - "executionInfo": { - "elapsed": 208, - "status": "ok", - "timestamp": 1675086997214, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "Fkkqz3Px47DO" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\eks510\\.conda\\envs\\py311\\Lib\\site-packages\\geopandas\\geodataframe.py:1543: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " super().__setitem__(key, value)\n" - ] - } - ], - "source": [ - "color_dict = {key: color_dict[key]\n", - " for key in color_dict if key not in list(set(color_dict)-set(landuse.landuse.unique()))}\n", - "\n", - "map_dict = dict(zip(color_dict.keys(),[x for x in range(len(color_dict))]))\n", - "\n", - "landuse['col_landuse'] = landuse.landuse.apply(lambda x: color_dict[x])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "NxWuztp347DO" - }, - "source": [ - "Now we can plot the figure!\n", - "\n", - "As you will see in the cell below, we first state that we want to create a figure with a specific figure size. You can change the dimensions to your liking." - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 608 - }, - "executionInfo": { - "elapsed": 1825, - "status": "ok", - "timestamp": 1675087046285, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "NH6j-qJ147DO", - "outputId": "23c77638-0509-4bb3-dc19-904f586b2a70" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Kampen, The Netherlands')" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 1,figsize=(12,10))\n", - "\n", - "# add color scheme\n", - "color_scheme_map = list(color_dict.values())\n", - "cmap = LinearSegmentedColormap.from_list(name='landuse',\n", - " colors=color_scheme_map) \n", - "\n", - "# and plot the land-use map.\n", - "landuse.plot(color=landuse['col_landuse'],ax=ax,linewidth=0)\n", - "\n", - "# remove the ax labels\n", - "ax.set_xticks([])\n", - "ax.set_yticks([])\n", - "ax.set_axis_off()\n", - "\n", - "# add a legend:\n", - "legend_elements = []\n", - "for iter_,item in enumerate(color_dict):\n", - " legend_elements.append(Patch(facecolor=color_scheme_map[iter_],label=item)) \n", - "\n", - "ax.legend(handles=legend_elements,edgecolor='black',facecolor='#fefdfd',prop={'size':12},loc=(1.02,0.2)) \n", - "\n", - "# add a title\n", - "ax.set_title(place_name,fontweight='bold')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EGwHPQEL9_hD" - }, - "source": [ - "
\n", - "Question 4: Please upload a figure of your land-use map, using OpenStreetMap. \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EzQP70Om47DO" - }, - "source": [ - "## 3. Rasterize land-use information\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4w-Py4fw47DP" - }, - "source": [ - "As you have noticed already during the lecture, and as we will again see next week when using the Google Earth Engine, most land-use data is in raster format. \n", - "\n", - "In OpenStreetMap everything is stored in vector format. As such, the land-use information we extracted from OpenStreetMap is also in vector format. While it is not always necessary to have this information in raster format, it is useful to know how to convert your data into a raster format.\n", - "\n", - "To do so, we can make use of the **GeoCube** package, which is a newly developed Python package that can very easily convert vector data into a raster format." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "PXETqUIN47DP" - }, - "source": [ - "The first thing we will need to do is to define all the unique land-use classes and store them in a dictionary:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "executionInfo": { - "elapsed": 290, - "status": "ok", - "timestamp": 1675087253309, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "4st7ApDp47DP" - }, - "outputs": [], - "source": [ - "categorical_enums = {'landuse': landuse.landuse.drop_duplicates().values.tolist()\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4_qzExh447DP" - }, - "source": [ - "And now we simply use the `make_geocube()` function to convert our vector data into raster data. \n", - "\n", - "In the `make_geocube()` function, we have to specify several arguments:\n", - "\n", - "- Through the `vector_data` argument we have to state which dataframe we want to rasterize.\n", - "- Through the `output_crs` argument we have to state the coordinate reference system (CRS). We use the OpenStreetMap default EPSG:4326.\n", - "- Through the `resolution` argument we have to state the resolution. In our case, we will have to set this in degrees. 0.01 degrees is equivalent to roughly 10km around the equator. \n", - "- Through the `categorical_enums` argument we specify the different land-use categories.\n", - "\n", - "Play around with the different resolutions to find the level of detail. The higher the resolution (i.e., the more zeros behind the comma), the longer it will take to rasterize." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "executionInfo": { - "elapsed": 551, - "status": "ok", - "timestamp": 1675087257707, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "wjGoNrwg47DP", - "tags": [] - }, - "outputs": [], - "source": [ - "landuse_grid = make_geocube(\n", - " vector_data=XXXX,\n", - " output_crs=\"epsg:4326\",\n", - " resolution=(-XXXX, XXXX),\n", - " categorical_enums=categorical_enums\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "wx4FAAsg47DP" - }, - "source": [ - "Let's explore what this function has given us:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 389 - }, - "executionInfo": { - "elapsed": 429, - "status": "ok", - "timestamp": 1675087480922, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "JqdlWZUe47DQ", - "outputId": "8880c112-9200-4613-ef45-01e8b88c0bde" - }, - "outputs": [], - "source": [ - "landuse_grid[\"landuse\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UB4gbheF47DQ" - }, - "source": [ - "The output above is a typical output of the **xarray** package. \n", - "\n", - "- The `array` shows the numpy array with the actual values. As you can see, the rasterization process has used the value `-1` for NoData. \n", - "- The `Coordinates` table shows the x (longitude) and y (latitude) coordinates of the array. It has the exact same size as the `array` with land-use values.\n", - "- The `Attributes` table specifies the NoData value (the `_FillValue` element, which indeed shows `-1`) and the name of the dataset.\n", - "\n", - "Now let's plot the data to see the result!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 608 - }, - "executionInfo": { - "elapsed": 1852, - "status": "ok", - "timestamp": 1675087494788, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "extRLk1W47DQ", - "outputId": "19fe91a4-5785-494e-a370-9408a563a70a" - }, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(1, 1,figsize=(14,10))\n", - "\n", - "landuse_grid[\"landuse\"].plot(ax=ax,vmin=0,vmax=15,levels=15,cmap='tab20')\n", - "\n", - "# remove the ax labels\n", - "ax.set_xticks([])\n", - "ax.set_yticks([])\n", - "ax.set_axis_off()\n", - "\n", - "#add a title\n", - "\n", - "ax.set_title('')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "94GLYG5K47DQ" - }, - "source": [ - "As we can see in the figure above, the land-use categories have turned into numbers, instead of land-use categories described by a string value. \n", - "\n", - "This is of course a lot harder to interpret. Let's re-do some parts to make sure we can properly link them back to the original data.\n", - "\n", - "To do so, we will first need to make sure that we know which values (numbers) are connected to each land-use category. Instead of trying to match, let's predefine this ourselves!\n", - "\n", - "We will start with creating a dictionary that allows us to couple a number to each category:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "executionInfo": { - "elapsed": 209, - "status": "ok", - "timestamp": 1675087654241, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "iSHZ4nq247DQ" - }, - "outputs": [], - "source": [ - "value_dict = dict(zip(landuse.landuse.unique(),np.arange(0,len(landuse.landuse.unique()),1)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "executionInfo": { - "elapsed": 2, - "status": "ok", - "timestamp": 1675087655102, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "ZCng7Xyk47DQ" - }, - "outputs": [], - "source": [ - "value_dict['nodata'] = -1" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "zv1-QoKv47DR" - }, - "source": [ - "And we now use this dictionary to add a new column to the dataframe with the values:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "executionInfo": { - "elapsed": 904, - "status": "ok", - "timestamp": 1675087658329, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "9c2byjZn47DR" - }, - "outputs": [], - "source": [ - "landuse['landuse_value'] = landuse.landuse.apply(lambda x: value_dict[x])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FP4pzj2A47DR" - }, - "source": [ - "Now let us use the `make_geocube()` function again to rasterize." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "executionInfo": { - "elapsed": 1101, - "status": "ok", - "timestamp": 1675087662054, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "Z-1RmkFC47DR" - }, - "outputs": [], - "source": [ - "landuse_valued = make_geocube(\n", - " vector_data=XXXX,\n", - " output_crs=XXXX,\n", - " resolution=(-XXXX, XXXX),\n", - " categorical_enums={'landuse_value': landuse.landuse_value.drop_duplicates().values.tolist()\n", - "}\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "V0-NKtL147DR" - }, - "source": [ - "And let's use the original `color_dict` dictionary to find the right hex codes for each of the land-use categories" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "executionInfo": { - "elapsed": 227, - "status": "ok", - "timestamp": 1675087671651, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "4FKdnZ4V47DR" - }, - "outputs": [], - "source": [ - "unique_classes = landuse.landuse.drop_duplicates().values.tolist()\n", - "colormap_raster = [color_dict[lu_class] for lu_class in unique_classes] " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "be1TcNAK47DR" - }, - "source": [ - "To plot the new result:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 608 - }, - "executionInfo": { - "elapsed": 1116, - "status": "ok", - "timestamp": 1675087675785, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "qBo9kmWy47DR", - "outputId": "269fe715-c71f-4f82-ca71-021b67a3ccf7" - }, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(1, 1,figsize=(14,10))\n", - "\n", - "landuse_valued[\"landuse_value\"].plot(ax=ax,vmin=0,vmax=19,levels=len(unique_classes),colors=colormap_raster)\n", - "\n", - "# remove the ax labels\n", - "ax.set_xticks([])\n", - "ax.set_yticks([])\n", - "ax.set_axis_off()\n", - "\n", - "# add title\n", - "ax.set_title('')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "cO2YPNMU-9hQ" - }, - "source": [ - "
\n", - "Question 5: In the rasterization process, we use the `.make_geocube()` function. Please elaborate on the following: i)why is it important to specify the right coordinate system? What could happen if you choose the wrong coordinate system? ii) which resolution did you choose and why? iii)Why did the first result did not give us the right output with the correct colors? How did you solve this? \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dx5ZK8Vm47DS" - }, - "source": [ - "But to be honest, this legend is still not entirely what we are looking for. So let's do some Python magic to get a legend like we desire when plotting a land-use map" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "executionInfo": { - "elapsed": 242, - "status": "ok", - "timestamp": 1675087680453, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "plSLRJp947DS" - }, - "outputs": [], - "source": [ - "unique_classes = landuse.landuse.drop_duplicates().values.tolist()\n", - "colormap_raster = [color_dict[lu_class] for lu_class in unique_classes] \n", - "color_dict_raster = dict(zip(np.arange(-1,len(landuse.landuse.unique())+1,1),['#ffffff']+colormap_raster))\n", - "\n", - "# We create a colormar from our list of colors\n", - "cm = ListedColormap([color_dict_raster[x] for x in color_dict_raster.keys()])\n", - "\n", - "# Let's also define the description of each category. Order should be respected here!\n", - "labels = np.array(['nodata'] + unique_classes)\n", - "len_lab = len(labels)\n", - "\n", - "# prepare normalizer\n", - "## Prepare bins for the normalizer\n", - "norm_bins = np.sort([*color_dict_raster.keys()]) + 0.5\n", - "norm_bins = np.insert(norm_bins, 0, np.min(norm_bins) - 1.0)\n", - "\n", - "## Make normalizer and formatter\n", - "norm = matplotlib.colors.BoundaryNorm(norm_bins, len_lab, clip=True)\n", - "fmt = matplotlib.ticker.FuncFormatter(lambda x, pos: labels[norm(x)])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "MRsijBkf47DS" - }, - "source": [ - "Let's plot the map again!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 591 - }, - "executionInfo": { - "elapsed": 1663, - "status": "ok", - "timestamp": 1675087684895, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "odFEVMUc47DS", - "outputId": "31905199-3798-4cf3-b4c6-effb7dacff10" - }, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(1, 1,figsize=(14,10))\n", - "\n", - "ax = landuse_valued[\"landuse_value\"].plot(levels=len(unique_classes), cmap=cm, norm=norm)\n", - "\n", - "# remove the ax labels\n", - "diff = norm_bins[1:] - norm_bins[:-1]\n", - "tickz = norm_bins[:-1] + diff / 2\n", - "cb = fig.colorbar(ax, format=fmt, ticks=tickz)\n", - "\n", - "# set title again\n", - "fig.axes[0].set_title('')\n", - "\n", - "fig.axes[0].set_xticks([])\n", - "fig.axes[0].set_yticks([])\n", - "fig.axes[0].set_axis_off()\n", - "\n", - "# for some weird reason we get two colorbars, so we remove one:\n", - "fig.delaxes(fig.axes[1])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "u9yzw5hE47DS" - }, - "source": [ - "## 4. Extracting buildings from OpenStreetMap\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WMOSa6JF47DS" - }, - "source": [ - "There is a lot more data to extract from OpenStreetMap besides land-use information. Let's extract some building data. To do so, we use the *\"building\"* tag." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "executionInfo": { - "elapsed": 119709, - "status": "ok", - "timestamp": 1675087861528, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "B6EiF_dN47DS" - }, - "outputs": [], - "source": [ - "tags = {\"building\": True}\n", - "buildings = ox.features_from_place(place_name, tags)" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [], - "source": [ - "# from urllib import request\n", - "\n", - "# remote_url = 'https://github.com/VU-IVM/DamageScanner/raw/develop/data/kampen/exposure/kampen_buildings.gpkg'\n", - "# file = 'kampen_buildings.gpkg'\n", - "# \n", - "# #request.urlretrieve(remote_url, file)\n", - "# buildings = gpd.GeoDataFrame.from_file('kampen_buildings.gpkg')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "bon7osXA47DT" - }, - "source": [ - "Now let's see what information is actually extracted:" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 765 - }, - "executionInfo": { - "elapsed": 37, - "status": "ok", - "timestamp": 1675087861529, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "6HdghuMv47DT", - "outputId": "dbec25d2-8c62-4420-f125-b1a4ac80c318" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
osm_idosm_way_idnametypeaerowayamenityadmin_levelbarrierboundarybuilding...layerheightbrand:wikidatabrandsocial_facility:forsocial_facilitystart_datesource:dateref:baggeometry
03515957NoneNonemultipolygonNoneNoneNoneNoneNoneyes...NoneNoneNoneNoneNoneNone19612017-05-20166100000023440MULTIPOLYGON (((5.93640 52.56451, 5.93599 52.5...
13591734NoneNonemultipolygonNoneNoneNoneNoneNoneyes...NoneNoneNoneNoneNoneNone20042014-02-11166100000007784MULTIPOLYGON (((5.90453 52.54767, 5.90450 52.5...
23591735NoneNonemultipolygonNoneNoneNoneNoneNoneyes...NoneNoneNoneNoneNoneNone19792014-02-11166100000002207MULTIPOLYGON (((5.89202 52.55474, 5.89196 52.5...
33592277NoneNonemultipolygonNoneNoneNoneNoneNoneyes...NoneNoneNoneNoneNoneNone19702014-02-11166100000000530MULTIPOLYGON (((5.87884 52.57550, 5.87880 52.5...
43592410NoneNonemultipolygonNoneNoneNoneNoneNonehouse...NoneNoneNoneNoneNoneNone19502014-02-11166100000009298MULTIPOLYGON (((5.90615 52.56335, 5.90616 52.5...
\n", - "

5 rows × 85 columns

\n", - "
" - ], - "text/plain": [ - " osm_id osm_way_id name type aeroway amenity admin_level barrier \\\n", - "0 3515957 None None multipolygon None None None None \n", - "1 3591734 None None multipolygon None None None None \n", - "2 3591735 None None multipolygon None None None None \n", - "3 3592277 None None multipolygon None None None None \n", - "4 3592410 None None multipolygon None None None None \n", - "\n", - " boundary building ... layer height brand:wikidata brand \\\n", - "0 None yes ... None None None None \n", - "1 None yes ... None None None None \n", - "2 None yes ... None None None None \n", - "3 None yes ... None None None None \n", - "4 None house ... None None None None \n", - "\n", - " social_facility:for social_facility start_date source:date ref:bag \\\n", - "0 None None 1961 2017-05-20 166100000023440 \n", - "1 None None 2004 2014-02-11 166100000007784 \n", - "2 None None 1979 2014-02-11 166100000002207 \n", - "3 None None 1970 2014-02-11 166100000000530 \n", - "4 None None 1950 2014-02-11 166100000009298 \n", - "\n", - " geometry \n", - "0 MULTIPOLYGON (((5.93640 52.56451, 5.93599 52.5... \n", - "1 MULTIPOLYGON (((5.90453 52.54767, 5.90450 52.5... \n", - "2 MULTIPOLYGON (((5.89202 52.55474, 5.89196 52.5... \n", - "3 MULTIPOLYGON (((5.87884 52.57550, 5.87880 52.5... \n", - "4 MULTIPOLYGON (((5.90615 52.56335, 5.90616 52.5... \n", - "\n", - "[5 rows x 85 columns]" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "buildings.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "p8dhZx1n47DT" - }, - "source": [ - "As you notice in the output of the cell above, there are many columns which just contain \"NaN\". And there even seem to be to many columns to even visualize properly in one view.\n", - "\n", - "Let's check what information is collected for the different buildings:" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 35, - "status": "ok", - "timestamp": 1675087861529, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "W3ZpsIkO47DT", - "outputId": "385616f8-25fe-4675-f5f0-16d7fa9b0df7" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['osm_id', 'osm_way_id', 'name', 'type', 'aeroway', 'amenity',\n", - " 'admin_level', 'barrier', 'boundary', 'building', 'craft', 'geological',\n", - " 'historic', 'land_area', 'landuse', 'leisure', 'man_made', 'military',\n", - " 'natural', 'office', 'place', 'shop', 'sport', 'tourism', 'other_tags',\n", - " 'covered', 'substation', 'parking', 'bridge:support', 'construction',\n", - " 'building_1', 'seamark:type', 'seamark:small_craft_facility:category',\n", - " 'wikimedia_commons', 'tower:type', 'toilets:wheelchair', 'alt_name',\n", - " 'phone', 'mdb_id', 'dhm_id', 'takeaway', 'smoking', 'opening_hours',\n", - " 'internet_access', 'drive_through', 'description', 'delivery',\n", - " 'cuisine', 'contact:website', 'contact:phone', 'brand:wikipedia',\n", - " 'addr:country', 'url', 'roof:levels', 'building:levels', 'website',\n", - " 'power', 'roof:material', 'roof:height', 'roof:colour',\n", - " 'building:material', 'building:colour', 'ref:rce', 'heritage:operator',\n", - " 'wikipedia', 'wikidata', 'heritage', 'roof:shape', 'religion',\n", - " 'denomination', 'addr:street', 'addr:postcode', 'addr:housenumber',\n", - " 'addr:city', 'wheelchair', 'layer', 'height', 'brand:wikidata', 'brand',\n", - " 'social_facility:for', 'social_facility', 'start_date', 'source:date',\n", - " 'ref:bag', 'geometry'],\n", - " dtype='object')" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "buildings.columns" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "sH7NTKENA4WO" - }, - "source": [ - "
\n", - "Question 6: Let's have a look at the extracted building information. Please describe in your own words the information it contains. Is there specific information that suprises you to see, and do you think anything is missing that you expected? \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "8OHDhc-047DT" - }, - "source": [ - "## 5. Analyze and visualize building stock\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Z37JRRc747DT" - }, - "source": [ - "One interesting column is called `start_date`. This shows the building year per building. \n", - "\n", - "Let's explore this year of building a bit more.\n", - "\n", - "First, it would be interesting to get an idea how many buildings are build in each year through using the `value_counts()` function. Normally, that functions ranks the values in descending order (high to low). We are more interested in how this has developed over time. So we use the `sort_index()` function to sort the values by year. Add these two functions in the cell below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "executionInfo": { - "elapsed": 924, - "status": "ok", - "timestamp": 1675087884735, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "lNSe826i47DT" - }, - "outputs": [], - "source": [ - "building_year = buildings.start_date. XXXX" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "X6b_T5xs47DU" - }, - "source": [ - "There is not better way to further explore this years than through plotting it. Don't forget to add things such as a x label, y label and title. Have a look at some of the matplotlib [tutorials](https://matplotlib.org/stable/tutorials/introductory/quick_start.html). Note that you need to look at the code that also uses subplots and where they use the `ax` option." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "executionInfo": { - "elapsed": 1636, - "status": "ok", - "timestamp": 1675087889714, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "fmk6m78I47DU", - "outputId": "78f3ea6a-284d-495f-b596-12db32305128" - }, - "outputs": [], - "source": [ - "fig,ax = plt.subplots(1,1,figsize=(5,18))\n", - "\n", - "building_year.plot(kind='barh',ax=ax)\n", - "\n", - "ax.tick_params(axis='y', which='major', labelsize=7)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "AwRyhvNeBn0G" - }, - "source": [ - "
\n", - "Question 7: Please upload a figure that shows the development of building stock over the years in your region of interest. Make sure it contains all the necessary elements (labels on the axis, title, etc.)\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "wrcM1p-m47DU" - }, - "source": [ - "What we also noticed is that quite some buildings are identified as 'yes'. This is not very useful as it does not really say much about the use of the building. \n", - "\n", - "Let's see for how many buildings this is the case: " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 205, - "status": "ok", - "timestamp": 1675087945407, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "_4eKR2Bo47DU", - "outputId": "54e29c44-9717-4eee-984f-9555659317be" - }, - "outputs": [], - "source": [ - "buildings.building.value_counts()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "vF6WJ9_k47DU" - }, - "source": [ - "Now let's visualize the buildings again. We need to create a similar color dictionary as we did for the land-use categories. Now its up to you to make it!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "executionInfo": { - "elapsed": 226, - "status": "ok", - "timestamp": 1675087956546, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "RYRxKXxd47DU" - }, - "outputs": [], - "source": [ - "color_dict = { 'yes' : \"#f1134b\", \n", - " 'house':'#f13013', \n", - " 'industrial':'#0f045c',\n", - " 'farm':'#fcfcb9', \n", - " 'bungalow':'#f13013',\n", - " 'service':'#CB8DDB' }" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "executionInfo": { - "elapsed": 232, - "status": "ok", - "timestamp": 1675087958726, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "YjRwC-K-47DU" - }, - "outputs": [], - "source": [ - "# Remove multiple keys from dictionary\n", - "color_dict = {key: color_dict[key]\n", - " for key in color_dict if key not in list(set(color_dict)-set(buildings.building.unique()))}\n", - "\n", - "map_dict = dict(zip(color_dict.keys(),[x for x in range(len(color_dict))]))\n", - "buildings['col_landuse'] =buildings.building.apply(lambda x: color_dict[x])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "wGh7rbnB47DU" - }, - "source": [ - "And plot the figure in the same manner!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 616 - }, - "executionInfo": { - "elapsed": 3651, - "status": "ok", - "timestamp": 1675087966347, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "oDfamvIV47DV", - "outputId": "dbcee552-e955-4d2b-ddda-909ab4265105" - }, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(1, 1,figsize=(12,10))\n", - "\n", - "# add color scheme\n", - "color_scheme_map = list(color_dict.values())\n", - "cmap = LinearSegmentedColormap.from_list(name='landuse',\n", - " colors=color_scheme_map) \n", - "\n", - "# and plot the land-use map.\n", - "buildings.plot(color=buildings['col_landuse'],ax=ax,linewidth=0)\n", - "\n", - "# remove the ax labels\n", - "ax.set_xticks([])\n", - "ax.set_yticks([])\n", - "ax.set_axis_off()\n", - "\n", - "# add a legend:\n", - "legend_elements = []\n", - "for iter_,item in enumerate(color_dict):\n", - " legend_elements.append(Patch(facecolor=color_scheme_map[iter_],label=item)) \n", - "\n", - "ax.legend(handles=legend_elements,edgecolor='black',facecolor='#fefdfd',prop={'size':12},loc=(1.02,0.2)) \n", - "\n", - "# add a title\n", - "ax.set_title(place_name,fontweight='bold')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "IJalDDJvB6Yd" - }, - "source": [ - "
\n", - "Question 8: Please upload a figure of your building stock map of your region of interest. Make sure that the interpretation is clear. If necessary, merge multiple categories into one (i.e., when some categories only contain 1 or 2 buildings).\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3NeIaiLO47DV" - }, - "source": [ - "## 6. Extracting roads from OpenStreetMap\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "HpWnKfIk47DV" - }, - "source": [ - "Let's continue (and end) this tutorial with the core data in OpenStreetMap (it is even in the name): roads!\n", - "\n", - "Now, instead of using tags, we want to identify what type of roads we would like to extract. Let's first only extract roads that can be used to drive.\n", - "\n", - "The `graph_from_place()` function returns a `NetworkX` Graph element. You can read more about these graph elements in the introduction page of [NetworkX](https://networkx.org/documentation/stable/reference/introduction.html)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "executionInfo": { - "elapsed": 13403, - "status": "ok", - "timestamp": 1675088179196, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "MpjxUadp47DV" - }, - "outputs": [], - "source": [ - "G = ox.graph_from_place(place_name, network_type=\"drive\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "NA_HAmHx47DV" - }, - "source": [ - "Unfortunately, it is bit difficult to easily view all the roads within such a Graph element. To be able to explore the data, we are going to convert it to a `Geopandas GeoDataFrame`, using the `to_pandas_edgelist()` function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 15, - "status": "ok", - "timestamp": 1675088179197, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "4dMXZb_v47DV", - "outputId": "e615d5f8-6b96-4a32-f114-ce38b1481b70" - }, - "outputs": [], - "source": [ - "roads = gpd.GeoDataFrame(nx.to_pandas_edgelist(G))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "yds5NBlF47DV" - }, - "source": [ - "In some cases, roads are classified with more than one category. If that is the case, they are captured within a `list`. To overcome this issue, we specify that we want the entire `highway` column as a `string` dtype." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "executionInfo": { - "elapsed": 294, - "status": "ok", - "timestamp": 1675088191403, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "NxTg1jb847DV" - }, - "outputs": [], - "source": [ - "roads.highway = roads.highway.astype('str')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rbzl5JPR47DW" - }, - "source": [ - "Now we can create a plot to see how the road network is configured." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 575 - }, - "executionInfo": { - "elapsed": 888, - "status": "ok", - "timestamp": 1675088196301, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "uyo3Ggpc47DW", - "outputId": "e58c915a-b085-498c-e7ea-8d62f237d417" - }, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(1, 1,figsize=(12,10))\n", - "\n", - "\n", - "roads.plot(column='highway',legend=True,ax=ax,legend_kwds={'loc': 'lower right'});\n", - "\n", - "\n", - "# remove the ax labels\n", - "ax.set_xticks([])\n", - "ax.set_yticks([])\n", - "ax.set_axis_off()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "M18fuTWM47DW", - "tags": [] - }, - "source": [ - "It would also be interesting to explore the network a little but more interactively. **OSMnx** has a function called `plot_graph_folium()`, which allow us to use the [folium](https://python-visualization.github.io/folium/quickstart.html#Getting-Started) package to plot data interactively on a map. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 866 - }, - "executionInfo": { - "elapsed": 1720, - "status": "ok", - "timestamp": 1675088204394, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "lpWHR0Ez47DW", - "outputId": "38be3bea-b399-4e8f-a081-210baa559ef7" - }, - "outputs": [], - "source": [ - "m1 = ox.plot_graph_folium(G, popup_attribute=\"highway\", weight=2, color=\"#8b0000\")\n", - "m1" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "M6-fqL9L47DW", - "tags": [] - }, - "source": [ - "## 7. Plot Routes Using OpenStreetMap and Folium\n", - "
" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "gm8NIAR947DW" - }, - "source": [ - "One of the exiting things we can do with this data is that we can compute and plot routes between two points on a map.\n", - "\n", - "Let's first select two random start and end points from the graph and compute the shortest route between them through using the `shortest_path()` function of the `NetworkX` package.\n", - "\n", - "The function `ox.nearest_nodes()` looks for the nearest point in your network based on a `X` and `Y` coordinate. For example, in the code below, the origin node is based on the northwestern corner of your bounding box, whereas the destination node is based on the coordinates of the southeastern corner of your bounding box. \n", - "\n", - "So this can also be rewritten as:\n", - "\n", - "```\n", - "origin_node = ox.nearest_nodes(G,4.65465, 56.6778) \n", - "destination_node = ox.nearest_nodes(G,4.61055, 59.5487) \n", - "route = nx.shortest_path(G, origin_node, destination_node) \n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Zi_xhqXTCi3h" - }, - "source": [ - "
\n", - "Question 9: The last element of this tutorial is to play around with routing. Please explain in your own words what the .shortest_path() algorithm does. Include the term 'Dijkstra algorithm' in your answer.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "executionInfo": { - "elapsed": 837, - "status": "ok", - "timestamp": 1675088236066, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "Dr7Ftbbh47DW" - }, - "outputs": [], - "source": [ - "origin_node = ox.nearest_nodes(G,area['bbox_west'].values[0], area['bbox_north'].values[0])\n", - "destination_node = ox.nearest_nodes(G,area['bbox_east'].values[0], area['bbox_south'].values[0])\n", - "route = nx.shortest_path(G, origin_node, destination_node)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4UuCS8G247DW" - }, - "source": [ - "We can plot the route with folium. Like above, you can pass keyword args along to folium PolyLine to style the lines." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 866 - }, - "executionInfo": { - "elapsed": 240, - "status": "ok", - "timestamp": 1675088397348, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "7QrZ6Gpi47DX", - "outputId": "33fecead-a6d7-4090-99da-e09d0c34081a" - }, - "outputs": [], - "source": [ - "m2 = ox.plot_route_folium(G, route, weight=10)\n", - "m2" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "eedHqPli47DX" - }, - "source": [ - "Plot the route with folium on top of the previously created map\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 866 - }, - "executionInfo": { - "elapsed": 2518, - "status": "ok", - "timestamp": 1675088413924, - "user": { - "displayName": "RA Odongo", - "userId": "17326618845752559881" - }, - "user_tz": -60 - }, - "id": "aHzxZAbZ47DX", - "outputId": "01ee7d97-4792-41fb-925c-c44fb9b15a3f" - }, - "outputs": [], - "source": [ - "m3 = ox.plot_route_folium(G, route, route_map=m1, popup_attribute=\"name\", weight=7)\n", - "m3" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Question 10: Please add one more routes on a map and upload the resulting figure here.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "colab": { - "provenance": [ - { - "file_id": "https://github.com/ElcoK/BigData_AED/blob/main/week4/tutorial1.ipynb", - "timestamp": 1675085725524 - } - ] - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - }, - "vscode": { - "interpreter": { - "hash": "f323064ae63d54ed8d769390a968e914fbf7abacffc63e116cd2e04a08ed2d24" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/TAA2/.ipynb_checkpoints/tutorial2-checkpoint.ipynb b/TAA2/.ipynb_checkpoints/tutorial2-checkpoint.ipynb deleted file mode 100644 index 917e728..0000000 --- a/TAA2/.ipynb_checkpoints/tutorial2-checkpoint.ipynb +++ /dev/null @@ -1,1850 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "s1nFKY6h-g35", - "tags": [] - }, - "source": [ - "# Tutorial 2: Natural Hazard Risk Assessment" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "F5jQ4EU--g38" - }, - "source": [ - "In the second tutorial of this week, we are going to use publicly available hazard data and exposure data to do a risk assessment for an area of choice within Europe. More specifically we will look at damage due to wind storms and flooding. \n", - "\n", - "We will use both Copernicus Land Cover data and OpenStreetMap to estimate the potential damage of natural hazards to the built environment. We will use Copernicus Land Cover data to estimate the damage to specific land-uses, whereas we will use OpenStreetMap to assess the potential damage to the road system." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "K66fASIyP--9" - }, - "source": [ - "### Important before we start\n", - "---\n", - "Make sure that you save this file before you continue, else you will lose everything. To do so, go to **Bestand/File** and click on **Een kopie opslaan in Drive/Save a Copy on Drive**!\n", - "\n", - "Now, rename the file into Week4_Tutorial2.ipynb. You can do so by clicking on the name in the top of this screen." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "hkXRFEqH-g38" - }, - "source": [ - "## Learning Objectives\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WDQJZqvL-g39" - }, - "source": [ - "- To know how to download data from the Copernicus Climate Data Store using the `cdsapi` and access it through Python.\n", - "- To be able to open and visualize this hazard data.\n", - "- To know how to access and open information from the Copernicus Land Monitoring System. Specifically the Corine Land Cover data.\n", - "- To understand the basic approach of a natural hazard risk assessment.\n", - "- To be able to use the `DamageScanner` to do a damage assessment.\n", - "- To interpret and compare the damage estimates." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4KseyvEh-g39" - }, - "source": [ - "

Tutorial Outline

\n", - "
\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ysRSLb-6-g3-" - }, - "source": [ - "## 1.Introducing the packages\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "h3aMPYWo-g3-" - }, - "source": [ - "Within this tutorial, we are going to make use of the following packages: \n", - "\n", - "[**GeoPandas**](https://geopandas.org/) is a Python packagee that extends the datatypes used by pandas to allow spatial operations on geometric types.\n", - "\n", - "[**OSMnx**](https://osmnx.readthedocs.io/) is a Python package that lets you download geospatial data from OpenStreetMap and model, project, visualize, and analyze real-world street networks and any other geospatial geometries. You can download and model walkable, drivable, or bikeable urban networks with a single line of Python code then easily analyze and visualize them. You can just as easily download and work with other infrastructure types, amenities/points of interest, building footprints, elevation data, street bearings/orientations, and speed/travel time.\n", - "\n", - "[**xarray**](https://docs.xarray.dev/) is a Python package that allows for easy and efficient use of multi-dimensional arrays.\n", - "\n", - "[**Matplotlib**](https://matplotlib.org/) is a comprehensive Python package for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible.\n", - "\n", - "*We will first need to install the missing packages in the cell below. Uncomment them to make sure we can pip install them*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "xAk93LFz-g3-", - "outputId": "b0dca5b3-3894-457e-8f2e-e0188b7b8706" - }, - "outputs": [], - "source": [ - "!pip install geopandas\n", - "!pip install pygeos\n", - "!pip install osmnx\n", - "!pip install xarray\n", - "!pip install rasterio\n", - "!pip install rioxarray\n", - "!pip install cdsapi" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "T28OUcKK-g3_" - }, - "source": [ - "As you may or may not have seen while installing, there was a warning that we need to restart our runtime. To do so, click on **Runtime** in the topbar menu and click on **Runtime opnieuw starten**/**Restart runtime**.\n", - "\n", - "Now we will import these packages in the cell below:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "HNZcbQQ_-g4A", - "outputId": "2e8b1214-69e9-40f5-bc84-d02672bf3a5d" - }, - "outputs": [], - "source": [ - "import os\n", - "import rasterio\n", - "import cdsapi\n", - "import pygeos \n", - "import rioxarray\n", - "import matplotlib\n", - "import urllib3\n", - "import pyproj\n", - "\n", - "import osmnx as ox\n", - "import numpy as np\n", - "import xarray as xr\n", - "import geopandas as gpd\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import networkx as nx\n", - "\n", - "from matplotlib.colors import LinearSegmentedColormap,ListedColormap\n", - "from matplotlib.patches import Patch\n", - "from zipfile import ZipFile\n", - "from matplotlib import rcParams, cycler\n", - "from io import BytesIO\n", - "from urllib.request import urlopen\n", - "from zipfile import ZipFile\n", - "from tqdm import tqdm\n", - "\n", - "urllib3.disable_warnings()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TJr_yUdjFMKz" - }, - "source": [ - "### Connect to google drive\n", - "---\n", - "To be able to read the data from Google Drive, we need to *mount* our Drive to this notebook.\n", - "\n", - "As you can see in the cell below, make sure that in your **My Drive** folder, you have created a **BigData** folder and within that folder, you have created a **Week4_Data** folder in which you can store the files that are required to run this analysis.\n", - "\n", - "Please go the URL when its prompted in the box underneath the following cell, and copy the authorization code in that box." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "-Xqbo0nMFTkd", - "outputId": "8ce3d2fd-fb29-44f4-b874-a77a9199a41f" - }, - "outputs": [], - "source": [ - "from google.colab import drive\n", - "drive.mount('/content/gdrive/')\n", - "\n", - "import sys\n", - "sys.path.append(\"/content/gdrive/My Drive/BigData/\")\n", - "\n", - "data_path = os.path.join('/content/gdrive/My Drive/BigData/','Week4_Data')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "fqDSHMah-g4A", - "tags": [] - }, - "source": [ - "## 2. Downloading and accessing natural hazard data\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "1YWXcMRV-g4A" - }, - "source": [ - "We are going to perform a damage assessment using both windstorm data and flood data for Europe.\n", - "\n", - "### Windstorm Data\n", - "\n", - "The windstorm data will be downloaded from the [Copernicus Climate Data Store](https://cds.climate.copernicus.eu/). As we have seen during the lecture, and as you can also see by browsing on this website, there is an awful lot of climate data available through this Data Store. As such, it is very valuable to understand how to access and download this information to use within an analysis. To keep things simple, we only download one dataset today: [A winter windstorm](https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-european-wind-storm-indicators?tab=overview). \n", - "\n", - "We will do so using an **API**, which is the acronym for application programming interface. It is a software intermediary that allows two applications to talk to each other. APIs are an accessible way to extract and share data within and across organizations. APIs are all around us. Every time you use a rideshare app, send a mobile payment, or change the thermostat temperature from your phone, you’re using an API.\n", - "\n", - "However, before we can access this **API**, we need to take a few steps. Most importantly, we need to register ourselves on the [Copernicus Climate Data Store](https://cds.climate.copernicus.eu/) portal. To do so, we need to register, as explained in the video clip below:\n", - "\n", - "\n", - "
\n", - "\n", - "Now, the next step is to access the API. You can now login on the website of the [Copernicus Climate Data Store](https://cds.climate.copernicus.eu/). After you login, you can click on your name in the top right corner of the webpage (next to the login button). On the personal page that has just opened, you will find your user ID (**uid**) and your personal **API**. You need to add those in the cell below to be able to download the windstorm.\n", - "\n", - "As you can see in the cell below, we download a specific windstorm that has occured on the seventh of February in 2020. This is storm [Ciara](https://en.wikipedia.org/wiki/Storm_Ciara). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "42ejGQJF-g4B", - "outputId": "2936be1c-6e11-4d67-91cc-e339af3e661b" - }, - "outputs": [], - "source": [ - "uid = XXX\n", - "apikey = 'XXX'\n", - "\n", - "c = cdsapi.Client(key=f\"{uid}:{apikey}\", url=\"https://cds.climate.copernicus.eu/api/v2\")\n", - "\n", - "c.retrieve(\n", - " 'sis-european-wind-storm-indicators',\n", - " {\n", - " 'variable': 'all',\n", - " 'format': 'zip',\n", - " 'product': 'windstorm_footprints',\n", - " 'year': '2013',\n", - " 'month': '10',\n", - " 'day': '28',\n", - " },\n", - " 'Carmen.zip')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "0RKepWbV-g4B", - "tags": [] - }, - "source": [ - "### Flood Data\n", - "\n", - "The flood data we will extract from a repository maintained by the European Commission Joint Research Centre. We will download river flood hazard maps from their [Flood Data Collection](https://data.jrc.ec.europa.eu/dataset/1d128b6c-a4ee-4858-9e34-6210707f3c81). \n", - "\n", - "Here we do not need to use an API and we also do not need to register ourselves, so we can download any of the files directly. To do so, we use the `urllib` package." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 363 - }, - "id": "gdS3cvIv-g4B", - "outputId": "233e924d-c368-471b-9590-5b13c9e4e806" - }, - "outputs": [], - "source": [ - "## this is the link to the 1/100 flood map for Europe\n", - "zipurl = 'https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/FLOODS/EuropeanMaps/floodMap_RP100.zip'\n", - "\n", - "# and now we open and extract the data\n", - "with urlopen(zipurl) as zipresp:\n", - " with ZipFile(BytesIO(zipresp.read())) as zfile:\n", - " zfile.extractall(data_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "IJrBotZevbZ6" - }, - "source": [ - "The download and zip in the cell above sometimes does not work. If that is indeed the case (e.g., when it seems to remain stuck), download the files manually through the link and upload them in the data folder for this week (as explained at the start of this tutorial.)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GCBoCWv2-g4B" - }, - "source": [ - "### Set location to explore\n", - "---\n", - "Before we continue, we need to specify our location of interest. This should be a province that will have some flooding and relative high wind speeds occuring (else we will find zero damage).\n", - "\n", - "Specify the region in the cell below by using the `geocode_to_gdf()` function again. I have chosen Gelderland, but feel free to choose a different region. It would be good to double check later on whether there is actually some high wind speeds and flooding in your chosen area." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "bQfZLu6T-g4B" - }, - "outputs": [], - "source": [ - "place_name = \"Gelderland, The Netherlands\"\n", - "area = ox.geocode_to_gdf(place_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "qES1iP8N-g4C" - }, - "source": [ - "## 3. Explore the natural hazard data\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "v89JIHRz-g4C" - }, - "source": [ - "As you can see in the section above, we have downloaded the storm footprint in a zipfile. Let's open the zipfile and load the dataset using the `xarray` package through the `open_dataset()` function." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "I8FlFSFw-g4C" - }, - "source": [ - "### Windstorm Data\n", - "---" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "UFcP1DTs-g4C" - }, - "outputs": [], - "source": [ - "with ZipFile('Carmen.zip') as zf:\n", - " \n", - " # Let's get the filename first\n", - " file = zf.namelist()[0]\n", - " \n", - " # And now we can open and select the file within Python\n", - " with zf.open(file) as f:\n", - " windstorm_europe = xr.open_dataset(f)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "KFZEcL_C-g4C" - }, - "source": [ - "Let's have a look at the storm we have downloaded!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 270 - }, - "id": "90SX9QOB-g4C", - "outputId": "1e159968-32e6-4375-9d36-559864415b85" - }, - "outputs": [], - "source": [ - "windstorm_europe" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Xz30IPy7QAiY" - }, - "source": [ - "
\n", - "Question 1: Describe windstorm Carmen. When did this event happen, which areas were most affected? Can you say something about the maximum wind speeds in different areas, based on the plot? And what does FX mean?\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "zLb6mFA9-g4D" - }, - "source": [ - "Unfortunately, our data does not have a proper coordinate system defined yet. As such, we will need to use the `rio.write_crs()` function to set the coordinate system to **EPSG:4326** (the standard global coordinate reference system). \n", - "\n", - "We also need to make sure that the functions will know what the exact parameters are that we have to use for our spatial dimenions (e.g. longitude and latitude). It prefers to be named `x` and `y`. So we use the `rename()` function before we use the `set_spatial_dims()` function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 291 - }, - "id": "WqAmuTVo-g4D", - "outputId": "0337b286-bc80-4d33-f182-f35d0458686f" - }, - "outputs": [], - "source": [ - "windstorm_europe.rio.write_crs(4326, inplace=True)\n", - "windstorm_europe = windstorm_europe.rename({'Latitude': 'y','Longitude': 'x'})\n", - "windstorm_europe.rio.set_spatial_dims(x_dim=\"x\",y_dim=\"y\", inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "sHxrXrOkRNqA" - }, - "source": [ - "
\n", - "Question 2: Climate data is often stored as a netCDF file. Please describe what a netCDF file is. Which information is stored in the netCDF file we have downloaded for the windstorm? What type of metadata does it contain?\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "E2cHd5hV-g4D" - }, - "source": [ - "Following, we also make sure it will be in the European coordinate system **EPSG:3035** to ensure we can easily use it together with the other data. To do so, we use the `reproject()` function. You can simple add the number of the coordinate system." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "AqZvdyGk-g4D" - }, - "outputs": [], - "source": [ - "windstorm_europe = windstorm_europe.rio.reproject(XXXX)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6o_aa1sp-g4D" - }, - "source": [ - "Now we have all the information to clip the windstorm data to our area of interest:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "NRQV7Io3-g4E" - }, - "outputs": [], - "source": [ - "windstorm_map = windstorm_europe.rio.clip(area.envelope.values, area.crs)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WlWwtOKH-g4E" - }, - "source": [ - "And let's have a look as well by using the `plot()` function. Please note that the legend is in meters per second." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 324 - }, - "id": "AzolOfUC-g4E", - "outputId": "3de9b3d2-8907-48ee-b828-4b03a097a80d" - }, - "outputs": [], - "source": [ - "windstorm_map['FX']. XXXX" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "oMTrBOtzReVz" - }, - "source": [ - "
\n", - "Question 3: Upload the windstorm map of your chosen area.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5jQ2_51Z-g4E" - }, - "source": [ - "### Flood data\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "btJy-1aR-g4E" - }, - "source": [ - "And similarly, we want to open the flood map. But now we do not have to unzip the file anymore and we can directly open it through using `xarray`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "oRvMEvkm-g4E" - }, - "outputs": [], - "source": [ - "flood_map_path = os.path.join(data_path,'floodmap_EFAS_RP100_C.tif')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 328 - }, - "id": "eEA1EKlt-g4E", - "outputId": "1968dec7-b036-4df4-bd9f-b16c41102b33" - }, - "outputs": [], - "source": [ - "flood_map = xr.open_dataset(flood_map_path, engine=\"rasterio\")\n", - "flood_map" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "O4EIxBK_-g4F" - }, - "source": [ - "And let's make sure we set all the variables and the CRS correctly again to be able to open the data properly. Note that we now use **EPSG:3035**. This is the standard coordinate system for Europe, in meters (instead of degrees)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 291 - }, - "id": "LxTaPlzW-g4F", - "outputId": "8c9d5bcb-3ceb-4628-88fb-0aabe161365f", - "tags": [] - }, - "outputs": [], - "source": [ - "flood_map.rio.write_crs(3035, inplace=True)\n", - "flood_map.rio.set_spatial_dims(x_dim=\"x\",y_dim=\"y\", inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "utiDVMpJ-g4F" - }, - "source": [ - "Now it is pretty difficult to explore the data for our area of interest, so let's clip the flood data. \n", - "\n", - "We want to clip our flood data to our chosen area. The code, however, is very inefficient and will run into memories issues on Google Colab. As such, we first need to clip it by using a bounding box, followed by the actual clip.\n", - "\n", - "
\n", - "Question 4: Please provide the lines of code below in which you show how you have clipped the flood map to your area.\n", - "
\n", - "\n", - "*A few hints*:\n", - "\n", - "* carefully read the documentation of the `.clip_box()` function of rioxarray. Which information do you need? \n", - "* is the GeoDataFrame of your region (the area GeoDataframe) in the same coordinate system? Perhaps you need to convert it using the `.to_crs()` function. \n", - "* how do you get the bounds from your area GeoDataFrame? \n", - "* The final step of the clip would be to use the `.rio.clip()` function, using the actual area file and the flood map clipped to the bounding box.\n", - "\n", - "As you will see, we first clip it very efficiently using the bounding box. After that, we do an exact clip." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "PuJ290bGISlL" - }, - "outputs": [], - "source": [ - "min_lon = area.to_crs(epsg=3035).bounds.minx.values[0]\n", - "min_lat = area.to_crs(epsg=3035).bounds.miny\n", - "max_lon = area.to_crs(epsg=3035).bounds\n", - "max_lat = area.to_crs(epsg=3035).\n", - "\n", - "flood_map_area = flood_map.rio.clip_box(minx=.... )\n", - "flood_map_area = flood_map_area.rio.clip(area.XXXX.values, area.crs)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "xNjj8RT--g4F" - }, - "source": [ - "And let's have a look as well. Please note that the legend is in meters." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 324 - }, - "id": "v_wldK5x-g4F", - "outputId": "15c3b526-d094-40e1-dae1-9dbfe57339ea" - }, - "outputs": [], - "source": [ - "flood_map_area['band_data'].plot(cmap='Blues',vmax=10)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "iiOgZMi0-g4F", - "tags": [] - }, - "source": [ - "## 4. Download and access Copernicus Land Cover data\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JTp2SMuK-g4F" - }, - "source": [ - "Unfortunately, there is no API option to download the [Corine Land Cover](https://land.copernicus.eu/pan-european/corine-land-cover) data. We will have to download the data from the website first.\n", - "\n", - "To do so, we will first have to register ourselves again on the website. Please find in the video clip below how to register yourself on the website of the [Copernicus Land Monitoring Service](https://land.copernicus.eu/):\n", - "\n", - "\n", - "\n", - "Now click on the Login button in the top right corner to login on the website. There are many interesting datasets on this website, but we just want to download the Corine Land Cover data, and specifically the latest version: [Corine Land Cover 2018](https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=download). To do so, please select the **Corine Land Cover - 100 meter**. Now click on the large green Download button. Your download should start any minute.\n", - "\n", - "Slightly annoying, the file you have downloaded is double zipped. Its slightly inconvenient to open this through Python and within Google Drive. So let's unzip it twice outside of Python and drop the data into this week's data directory, as specified at the start of this tutorial when we mounted our Google Drive. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "e3OV0J1N-g4G" - }, - "outputs": [], - "source": [ - "CLC_location = os.path.join(data_path,'U2018_CLC2018_V2020_20u1.tif')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "stGRY3U5-g4G", - "outputId": "afaec16f-d144-46ba-af4c-6cc03df68763" - }, - "outputs": [], - "source": [ - "CLC = xr.open_dataset(CLC_location, engine=\"rasterio\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EF_na6jnKAvZ" - }, - "source": [ - "Similarly to the flood map data, we need to do a two-stage clip again (like we did before in this tutorial to ensure we get only our area of interest without exceeding our RAM." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "LHBLJONMJ_Zf" - }, - "outputs": [], - "source": [ - "CLC_region = CLC.rio.clip_box(minx=.....,)\n", - "CLC_region = CLC_region.rio.clip(area.geometry.values,area.crs)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 291 - }, - "id": "CE5AhRYt-g4G", - "outputId": "861397ac-9256-4e1d-a876-c17f0d18cfca" - }, - "outputs": [], - "source": [ - "CLC_region = CLC_region.rename({'x': 'lat','y': 'lon'})\n", - "CLC_region.rio.set_spatial_dims(x_dim=\"lat\",y_dim=\"lon\", inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dGDKag4cKTnO" - }, - "source": [ - "And now we create a *color_dict* again, similarly as we did for the raster data in the previous tutorial " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "g7I4fbKs-g4G" - }, - "outputs": [], - "source": [ - "CLC_values = [111, 112, 121, 122, 123, 124, 131, 132, 133, 141, 142, 211, 212, 213, 221, 222, 223, 231, 241, 242,\n", - " 243, 244, 311, 312, 313, 321, 322, 323, 324, 331, 332, 333, 334, 335, 411, 412, 421, 422, 423, 511, 512, 521, 522, 523]\n", - "\n", - "CLC_colors = ['#E6004D', '#FF0000', '#CC4DF2', '#CC0000', '#E6CCCC', '#E6CCE6', '#A600CC', '#A64DCC', '#FF4DFF', '#FFA6FF', '#FFE6FF', '#FFFFA8', '#FFFF00', '#E6E600',\n", - " '#E68000', '#F2A64D', '#E6A600', '#E6E64D', '#FFE6A6', '#FFE64D', '#E6CC4D', '#F2CCA6', '#80FF00', '#00A600',\n", - " '#4DFF00', '#CCF24D', '#A6FF80', '#A6E64D', '#A6F200', '#E6E6E6', '#CCCCCC', '#CCFFCC', '#000000', '#A6E6CC',\n", - " '#A6A6FF', '#4D4DFF', '#CCCCFF', '#E6E6FF', '#A6A6E6', '#00CCF2', '#80F2E6', '#00FFA6', '#A6FFE6', '#E6F2FF']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Y9Ye19av-g4G" - }, - "outputs": [], - "source": [ - "color_dict_raster = dict(zip(CLC_values,CLC_colors))\n", - "\n", - "# We create a colormar from our list of colors\n", - "cm = ListedColormap(CLC_colors)\n", - "\n", - "# Let's also define the description of each category : 1 (blue) is Sea; 2 (red) is burnt, etc... Order should be respected here ! Or using another dict maybe could help.\n", - "labels = np.array(CLC_values)\n", - "len_lab = len(labels)\n", - "\n", - "# prepare normalizer\n", - "## Prepare bins for the normalizer\n", - "norm_bins = np.sort([*color_dict_raster.keys()]) + 0.5\n", - "norm_bins = np.insert(norm_bins, 0, np.min(norm_bins) - 1.0)\n", - "\n", - "## Make normalizer and formatter\n", - "norm = matplotlib.colors.BoundaryNorm(norm_bins, len_lab, clip=True)\n", - "fmt = matplotlib.ticker.FuncFormatter(lambda x, pos: labels[norm(x)])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5CQoFmdqKcMe" - }, - "source": [ - "And let's plot the Corine Land Cover data for our area of interest" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 650 - }, - "id": "JIPpIZRh-g4G", - "outputId": "59ecd822-c679-42a5-eb5f-ead83aee5102" - }, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(1, 1,figsize=(14,10))\n", - "\n", - "CLC_region[\"band_data\"].plot(ax=ax,levels=len(CLC_colors),colors=CLC_colors)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "AdSaPHzIfNbi" - }, - "source": [ - "
\n", - "Question 5: Describe the different land-use classes within your region that you see on the Corine Land Cover map.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9zbsc7d_-g4G", - "tags": [] - }, - "source": [ - "## 5. Perform a damage assessment using Coring Land Cover\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Agxq2HqY-g4H" - }, - "source": [ - "To calculate the potential damage to both windstorms and floods, we use stage-damage curves, which relate the intensity of the hazard to the fraction of maximum damage that can be sustained by a certain land use. As you can see on the Corine Land Cover map that we just plotted, there are a lot of land use classes (44), though not all will suffer damage from either the windstorm or the flood event. For each of the land-use classes a curve and a maximum damage number are assigned.\n", - "\n", - "To Assess the damage for both the flood and windstorm event, we are going to make use of the [DamageScanner](https://damagescanner.readthedocs.io/en/latest/), which is a tool to calculate potential flood damages based on inundation depth and land use using depth-damage curves in the Netherlands. The DamageScanner was originally developed for the 'Netherlands Later' project [(Klijn et al., 2007)](https://www.rivm.nl/bibliotheek/digitaaldepot/WL_rapport_Overstromingsrisicos_Nederland.pdf). The original land-use classes were based on the Land-Use Scanner in order to evaluate the effect of future land-use change on flood damages. We have tailored the input of the DamageScanner to make sure it can estimate the damages using Corine Land Cover.\n", - "\n", - "Because the simplicity of the model, we can use this for any raster-based hazard map with some level of intensity. Hence, we can use it for both hazards." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5m_RAcp_fraF" - }, - "source": [ - "
\n", - "Question 6: Describe in your own words what the `DamageScanner()` function does. Please walk us through the different steps. Which inputs do you need to be able to run this damage assessment?\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "jDrTp44Q-g4H" - }, - "outputs": [], - "source": [ - "def DamageScanner(landuse_map,inun_map,curve_path,maxdam_path,cellsize=100):\n", - " \n", - " # load land-use map\n", - " landuse = landuse_map.copy()\n", - " \n", - " # Load inundation map\n", - " inundation = inun_map.copy()\n", - " \n", - " inundation = np.nan_to_num(inundation) \n", - "\n", - " # Load curves\n", - " if isinstance(curve_path, pd.DataFrame):\n", - " curves = curve_path.values \n", - " elif isinstance(curve_path, np.ndarray):\n", - " curves = curve_path\n", - "\n", - " #Load maximum damages\n", - " if isinstance(maxdam_path, pd.DataFrame):\n", - " maxdam = maxdam_path.values \n", - " elif isinstance(maxdam_path, np.ndarray):\n", - " maxdam = maxdam_path\n", - " \n", - " # Speed up calculation by only considering feasible points\n", - " inun = inundation * (inundation>=0) + 0\n", - " inun[inun>=curves[:,0].max()] = curves[:,0].max()\n", - " waterdepth = inun[inun>0]\n", - " landuse = landuse[inun>0]\n", - "\n", - " # Calculate damage per land-use class for structures\n", - " numberofclasses = len(maxdam)\n", - " alldamage = np.zeros(landuse.shape[0])\n", - " damagebin = np.zeros((numberofclasses, 4,))\n", - " for i in range(0,numberofclasses):\n", - " n = maxdam[i,0]\n", - " damagebin[i,0] = n\n", - " wd = waterdepth[landuse==n]\n", - " alpha = np.interp(wd,((curves[:,0])),curves[:,i+1])\n", - " damage = alpha*(maxdam[i,1]*cellsize)\n", - " damagebin[i,1] = sum(damage)\n", - " damagebin[i,2] = len(wd)\n", - " if len(wd) == 0:\n", - " damagebin[i,3] = 0\n", - " else:\n", - " damagebin[i,3] = np.mean(wd)\n", - " alldamage[landuse==n] = damage\n", - "\n", - " # create pandas dataframe with output\n", - " loss_df = pd.DataFrame(damagebin.astype(float),columns=['landuse','losses','area','avg_depth']).groupby('landuse').sum()\n", - " \n", - " # return output\n", - " return loss_df.sum().values[0],loss_df" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Y7PB8oJz-g4H" - }, - "source": [ - "### Windstorm Damage\n", - "---\n", - "To estimate the potential damage of our windstorm, we use the vulnerability curves developed by [Yamin et al. (2014)](https://www.sciencedirect.com/science/article/pii/S2212420914000466). Following [Yamin et al. (2014)](https://www.sciencedirect.com/science/article/pii/S2212420914000466), we will apply a sigmoidal vulnerability function satisfying two constraints: (i) a minimum threshold for the occurrence of damage with an upper bound of 100% direct damage; (ii) a high power-law function for the slope, describing an increase in damage with increasing wind speeds. Due to the limited amount of vulnerability curves available for windstorm damage, we will use the damage curve that represents low-rise *reinforced masonry* buildings for all land-use classes that may contain buildings. Obviously, this is a large oversimplification of the real world, but this should be sufficient for this exercise. When doing a proper stand-alone windstorm risk assessment, one should take more effort in collecting the right vulnerability curves for different building types. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "-RxvAEQh-g4H", - "tags": [] - }, - "outputs": [], - "source": [ - "wind_curves = pd.read_excel(\"https://github.com/ElcoK/BigData_AED/raw/main/week4/damage_curves.xlsx\",sheet_name='wind_curves')\n", - "maxdam = pd.read_excel(\"https://github.com/ElcoK/BigData_AED/raw/main/week4/damage_curves.xlsx\",sheet_name='maxdam')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "uLZ7vl1w-g4H" - }, - "source": [ - "Unfortunately, we run into a *classic* problem when we want to overlay the windstorm data with the Corine Land Cover data. The windstorm data is not only stored in a different coordinate system (we had to convert it from **EPSG:4326** to **EPSG:3035**), it is in a different resolution (**1km** instead of the **100m** of Corine Land Cover). \n", - "\n", - "Let's first have a look how our clipped data look's like. If you have decided to use Gelderland, you will see that we have 102 columns (our Lattitude/lat) and 74 rows (our Longitude/lon). If you scroll above to our Corine Land Cover data, you see that dimensions are different: 1270 columns (Lattitude/lat/x) and 870 rows (Longitude/lon/y). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 291 - }, - "id": "gG2OXOySj8Ra", - "outputId": "67135491-52de-4571-f8c9-7b6a74e19b66" - }, - "outputs": [], - "source": [ - "windstorm_map" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "igfFBqcK-g4H" - }, - "source": [ - "The first thing we are going to do is try to make sure our data will be in the correct resolution (moving from **1km** to **100m**). To do so, we will use the `rio.reproject()` function. You will see that specify the resolution as **100**. Because **EPSG:3035** is a coordinate system in meters, we can simply use meters to define the resolution. We use the `rio.clip()` function to make sure we clip it again to our area of interest. The function below (`match_rasters`) will do the hard work for us. Please note all the input variables to understand what's happening." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Kud2CWEDhz1O" - }, - "outputs": [], - "source": [ - "def match_rasters(hazard,landuse,haz_crs=3035,lu_crs=3035,resolution=100,hazard_col=['FX']):\n", - " \"\"\"\n", - " Clips, reprojections, and matches the resolutions of two rasters, `hazard` and `landuse`,\n", - " to prepare them for further analysis.\n", - "\n", - " Parameters\n", - " ----------\n", - " hazard : xarray.DataArray\n", - " A 2D or 3D array containing hazard data.\n", - " landuse : xarray.DataArray\n", - " A 2D array containing land use data.\n", - " haz_crs : int, optional\n", - " The CRS of `hazard`. Default is EPSG:3035.\n", - " lu_crs : int, optional\n", - " The CRS of `landuse`. Default is EPSG:3035.\n", - " resolution : float, optional\n", - " The desired resolution in meters for both `hazard` and `landuse` after reprojection. Default is 100.\n", - " hazard_col : list, optional\n", - " A list of column names or indices for the hazard variable. Default is ['FX'].\n", - "\n", - " Returns\n", - " -------\n", - " tuple\n", - " A tuple containing two xarray.DataArray objects:\n", - " - The land use variable with matching resolution and dimensions to the hazard variable.\n", - " - The hazard variable clipped to the extent of the land use variable, with matching resolution and dimensions.\n", - " \"\"\"\n", - " \n", - " # Set the crs of the hazard variable to haz_crs\n", - " hazard.rio.write_crs(haz_crs, inplace=True)\n", - "\n", - " # Set the x and y dimensions in the hazard variable to 'x' and 'y' respectively\n", - " hazard.rio.set_spatial_dims(x_dim=\"x\",y_dim=\"y\", inplace=True)\n", - "\n", - " # Reproject the landuse variable from EPSG:4326 to EPSG:3857\n", - " landuse = CLC_region.rio.reproject(\"EPSG:3857\",resolution=resolution)\n", - "\n", - " # Get the minimum longitude and latitude values in the landuse variable\n", - " min_lon = landuse.x.min().to_dict()['data']\n", - " min_lat = landuse.y.min().to_dict()['data']\n", - "\n", - " # Get the maximum longitude and latitude values in the landuse variable\n", - " max_lon = landuse.x.max().to_dict()['data']\n", - " max_lat = landuse.y.max().to_dict()['data']\n", - "\n", - " # Create a bounding box using the minimum and maximum latitude and longitude values\n", - " area = gpd.GeoDataFrame([pygeos.box(min_lon,min_lat,max_lon, max_lat)],columns=['geometry'])\n", - "\n", - " # Set the crs of the bounding box to EPSG:3857\n", - " area.crs = 'epsg:3857'\n", - "\n", - " # Convert the crs of the bounding box to EPSG:4326\n", - " area = area.to_crs(f'epsg:{haz_crs}')\n", - "\n", - " # Clip the hazard variable to the extent of the bounding box\n", - " hazard = hazard.rio.clip(area.geometry.values, area.crs)\n", - "\n", - " # Reproject the hazard variable to EPSG:3857 with the desired resolution\n", - " hazard = hazard.rio.reproject(\"EPSG:3857\",resolution=resolution)\n", - "\n", - " # Clip the hazard variable again to the extent of the bounding box\n", - " hazard = hazard.rio.clip(area.geometry.values, area.crs)\n", - "\n", - " # If the hazard variable has fewer columns and rows than the landuse variable, reproject the landuse variable to match the hazard variable\n", - " if (len(hazard.x)len(landuse.x)) & (len(hazard.y)>len(landuse.y)):\n", - " hazard = hazard.rio.reproject_match(landuse)\n", - "\n", - " # return the new landuse and hazard map\n", - " return landuse,hazard" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Vkf6YKPZ-g4I" - }, - "source": [ - "Now let's run the `match_rasters` function and let it do its magic." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "v8NW3c1Q-g4I" - }, - "outputs": [], - "source": [ - "CLC_region_wind, windstorm = match_rasters(windstorm_europe,\n", - " CLC_region,\n", - " haz_crs=3035,\n", - " lu_crs=3035,\n", - " resolution=100,\n", - " hazard_col=['FX'])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GgcwJe_6nJip" - }, - "source": [ - "And let's have a look if the two rasters are now the same extend:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 291 - }, - "id": "vzMbkiSLldlQ", - "outputId": "6e73f8b1-33ad-4a7c-b95c-d320b6c75439" - }, - "outputs": [], - "source": [ - "CLC_region_wind" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 291 - }, - "id": "DXnxCBS_ldWg", - "outputId": "ec49b756-0fd9-4d49-f9ff-9f68a52bf83c" - }, - "outputs": [], - "source": [ - "windstorm" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6123eX9C-g4J" - }, - "source": [ - "It worked! And to double check, let's also plot it:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 324 - }, - "id": "Aeay_slW-g4J", - "outputId": "11424ad3-2a00-49db-db13-b4db8e73671e" - }, - "outputs": [], - "source": [ - "windstorm.FX.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JlZF-cs4gSuu" - }, - "source": [ - "
\n", - "Question 7: Describe the various steps you have taken to make sure that the windstorm map is now exactly the same extent as the corine land cover map. Feel free to include lines of code in your answer and also describe the different functions you have used along the way.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "LW158xPh-g4J" - }, - "source": [ - "Now its finally time to do our damage assessment! To do so, we need to convert our data to `numpy.arrays()` to do our calculation:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "QZIzWIeP-g4J" - }, - "outputs": [], - "source": [ - "landuse_map = CLC_region_wind['band_data'].to_numpy()[0,:,:]\n", - "wind_map = windstorm['FX'].to_numpy()[0,:,:]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "aBqqRqbkmA1Y", - "outputId": "709d6c91-4ad9-4e27-e6a9-e6201df32dc7" - }, - "outputs": [], - "source": [ - "wind_map.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "J9QHyhSU-g4J" - }, - "source": [ - "And remember that our windstorm data was stored in **m/s**. Hence, we need to convert it to **km/h**:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "GqdUCXD_-g4J" - }, - "outputs": [], - "source": [ - "wind_map_kmh = wind_map*XXX" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ln7NqRB1-g4J" - }, - "source": [ - "And now let's run the DamageScanner to obtain the damage results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "y_g0pj1h-g4J", - "tags": [] - }, - "outputs": [], - "source": [ - "wind_damage_CLC = DamageScanner(landuse_map,wind_map_kmh,wind_curves,maxdam)[1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "-6DbFD_JeFA1", - "outputId": "fb251350-8885-4dde-e665-893fa04cde6e" - }, - "outputs": [], - "source": [ - "wind_damage_CLC" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5UNySYvk-g4J", - "tags": [] - }, - "source": [ - "### Flood Damage\n", - "---\n", - "To Assess the flood damage, we are again going to make use of the [DamageScanner](https://damagescanner.readthedocs.io/en/latest/). The Corine Land Cover data is widely used in European flood risk assessments. As such, we can simply make use of pre-developed curves. We are using the damage curves as developed by Huizinga et al. (2007). Again, let's first load the maximum damages and the depth-damage curves:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "ua2xyAGW-g4J" - }, - "outputs": [], - "source": [ - "flood_curves = pd.read_excel(\"https://github.com/ElcoK/BigData_AED/raw/main/week4/damage_curves.xlsx\",sheet_name='flood_curves',engine='openpyxl')\n", - "maxdam = pd.read_excel(\"https://github.com/ElcoK/BigData_AED/raw/main/week4/damage_curves.xlsx\",sheet_name='maxdam')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "HT54wRvs-g4K" - }, - "source": [ - "And convert our data to `numpy.arrays()` to do our calculation:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "qzXKNmg2-g4K" - }, - "outputs": [], - "source": [ - "landuse_map = CLC_region['band_data'].to_numpy()\n", - "flood_map = flood_map_area['band_data'].to_numpy()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ttGra99k-g4K" - }, - "source": [ - "And now let's run the DamageScanner to obtain the damage results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "2qL8UATu-g4K" - }, - "outputs": [], - "source": [ - "flood_damage_CLC = DamageScanner(landuse_map,flood_map,flood_curves,maxdam)[1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "WnWu6AMUeFA2", - "outputId": "a16faddd-7a66-40e4-f103-9a1c1f723735" - }, - "outputs": [], - "source": [ - "flood_damage_CLC" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "hGWhg9fogvM3" - }, - "source": [ - "
\n", - "Question 8: Describe the results of the flood and wind damage assessments. Do you notice any differences between the outcomes? Do you observe specific land-use classes that are severely damaged?\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "E0ohOKwd-g4K" - }, - "source": [ - "## 6. Perform a damage assessment of the road network using OpenStreetMap\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "bKFmTKpj-g4K" - }, - "source": [ - "Generally, wind damage does not cause much damage to roads. There will be clean-up cost of the trees that will fall on the roads, but structural damage is rare. As such, we will only do a flood damage assessment for the road network of our region.\n", - "\n", - "To do so, we first need to extract the roads again. We will use the `graph_from_place()` function again to do so. However, the area will be to large to extract roads, so we will focus our analysis on the main network." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "CUqFG7AD-g4K" - }, - "outputs": [], - "source": [ - "cf = '[\"highway\"~\"trunk|motorway|primary|secondary\"]'\n", - "G = ox.graph_from_place(place_name, network_type=\"drive\", custom_filter=cf)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "modIJTEz-g4K" - }, - "source": [ - "And convert the road network to a `geodataframe`, as done in the previous tutorial as well." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "sxOBriok-g4K", - "outputId": "6ddc37c2-d8d1-4222-f989-08a2f9d08d43" - }, - "outputs": [], - "source": [ - "roads = gpd.GeoDataFrame(nx.to_pandas_edgelist(G))\n", - "roads.highway = roads.highway.astype('str')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "VIaMGLxA-g4K" - }, - "source": [ - "And lets have a look at the data:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 495 - }, - "id": "dx_299FS-g4L", - "outputId": "ffdab479-6a25-4794-da3a-cbacf2accaa4" - }, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(1, 1,figsize=(12,10))\n", - "\n", - "\n", - "roads.plot(column='highway',legend=True,ax=ax,legend_kwds={'loc': 'lower right'});\n", - "\n", - "\n", - "# remove the ax labels\n", - "ax.set_xticks([])\n", - "ax.set_yticks([])\n", - "ax.set_axis_off()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "PSGo7dC3-g4L" - }, - "source": [ - "It is actually quite inconvenient to have all these lists in the data for when we want to do the damage assessment. Let's clean this up a bit. To do so, we first make sure that all the lists are represented as actual lists, and not lists wrapped within a string." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "S_LZSRI6-g4L" - }, - "outputs": [], - "source": [ - "roads.highway = roads.highway.apply(lambda x: x.strip('][').split(', '))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "cwQKiRDd-g4L" - }, - "source": [ - "Now we just need to grab the first element of each of the lists." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "qe86tcET-g4L" - }, - "outputs": [], - "source": [ - "roads.highway = roads.highway.apply(lambda x: x[0] if isinstance(x, list) else x)\n", - "roads.highway = roads.highway.str.replace(\"'\",\"\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TkyDDDIP-g4L" - }, - "source": [ - "And let's have a look whether this worked:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 495 - }, - "id": "f5qPSBmq-g4L", - "outputId": "6ac152f2-14f1-4e6c-c53a-9d68db347ce6" - }, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(1, 1,figsize=(12,10))\n", - "\n", - "roads.plot(column='highway',legend=True,ax=ax,legend_kwds={'loc': 'upper left','ncol':1});\n", - "\n", - "# remove the ax labels\n", - "ax.set_xticks([])\n", - "ax.set_yticks([])\n", - "ax.set_axis_off()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "u0crazq8iQjQ" - }, - "source": [ - "
\n", - "Question 9: Upload a figure of the cleaned road network (e.g. in which you do not see any of the listed road types anymore)\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "J63sExRp-g4L" - }, - "source": [ - "Nice! now let's start with the damage calculation. As you already have may have noticed, our data is now not stored in raster format, but in vector format. One way to deal with this issue is to convert our vector data to raster data, but we will lose a lot of information and detail. As such, we will perform the damage assessment on the road elements, using the xarray flood map.\n", - "\n", - "Let's start with preparing the flood data into vector format:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "uHmaZFXV-g4L" - }, - "outputs": [], - "source": [ - "# get the mean values\n", - "flood_map_vector = flood_map_area['band_data'].to_dataframe().reset_index()\n", - "\n", - "# create geometry values and drop lat lon columns\n", - "flood_map_vector['geometry'] = [pygeos.points(x) for x in list(zip(flood_map_vector['x'],flood_map_vector['y']))]\n", - "flood_map_vector = flood_map_vector.drop(['x','y','band','spatial_ref'],axis=1)\n", - "\n", - "# drop all non values to reduce size\n", - "flood_map_vector = flood_map_vector.loc[~flood_map_vector['band_data'].isna()].reset_index(drop=True)\n", - "\n", - "# and turn them into squares again:\n", - "flood_map_vector.geometry= pygeos.buffer(flood_map_vector.geometry,radius=100/2,cap_style='square').values" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "sKb-ig4Q-g4M" - }, - "source": [ - "And let's plot the results:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 306 - }, - "id": "LL3YU6r1-g4M", - "outputId": "e0a9e61f-e376-436c-a11a-fa58651bf15a" - }, - "outputs": [], - "source": [ - "gpd.GeoDataFrame(flood_map_vector.copy()).plot(column='band_data',cmap='Blues',vmax=5,linewidth=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XBsxnhjN-g4M" - }, - "source": [ - "We will need a bunch of functions to make sure we can do our calculations. They are specified below. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "T-XfgGLB-g4M" - }, - "outputs": [], - "source": [ - "def reproject(df_ds,current_crs=\"epsg:4326\",approximate_crs = \"epsg:3035\"):\n", - " geometries = df_ds['geometry']\n", - " coords = pygeos.get_coordinates(geometries)\n", - " transformer=pyproj.Transformer.from_crs(current_crs, approximate_crs,always_xy=True)\n", - " new_coords = transformer.transform(coords[:, 0], coords[:, 1])\n", - " \n", - " return pygeos.set_coordinates(geometries.copy(), np.array(new_coords).T) \n", - "\n", - "def buffer_assets(assets,buffer_size=100):\n", - " assets['buffered'] = pygeos.buffer(assets.geometry.values,buffer_size)\n", - " return assets\n", - "\n", - "def overlay_hazard_assets(df_ds,assets):\n", - "\n", - " #overlay \n", - " hazard_tree = pygeos.STRtree(df_ds.geometry.values)\n", - " if (pygeos.get_type_id(assets.iloc[0].geometry) == 3) | (pygeos.get_type_id(assets.iloc[0].geometry) == 6):\n", - " return hazard_tree.query_bulk(assets.geometry,predicate='intersects') \n", - " else:\n", - " return hazard_tree.query_bulk(assets.buffered,predicate='intersects')\n", - " \n", - "def get_damage_per_asset(asset,df_ds,assets):\n", - " # find the exact hazard overlays:\n", - " get_hazard_points = df_ds.iloc[asset[1]['hazard_point'].values].reset_index()\n", - " get_hazard_points = get_hazard_points.loc[pygeos.intersects(get_hazard_points.geometry.values,assets.iloc[asset[0]].geometry)]\n", - "\n", - " asset_geom = assets.iloc[asset[0]].geometry\n", - "\n", - " maxdam_asset = 100\n", - " hazard_intensity = np.arange(0,10,0.1) \n", - " fragility_values = np.arange(0,1,0.01) \n", - " \n", - " if len(get_hazard_points) == 0:\n", - " return asset[0],0\n", - " else:\n", - " get_hazard_points['overlay_meters'] = pygeos.length(pygeos.intersection(get_hazard_points.geometry.values,asset_geom))\n", - " return asset[0],np.sum((np.interp(get_hazard_points.band_data.values,hazard_intensity,fragility_values))*get_hazard_points.overlay_meters*maxdam_asset)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "og2Bkcv--g4M" - }, - "source": [ - "Now we need to make sure that the road data is the same coordinate system. To do so, we will use the **pygeos** package. This is a much faster package compared to **GeoPandas**. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "NvtDuspN-g4M", - "outputId": "8d920938-ab07-42f9-bb88-34483e751c3f" - }, - "outputs": [], - "source": [ - "roads_pg = pd.DataFrame(roads.copy())\n", - "roads_pg.geometry = pygeos.from_shapely(roads_pg.geometry)\n", - "roads_pg.geometry = reproject(roads_pg)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4JT25WTv-g4M" - }, - "source": [ - "And we can now overlay the roads with the flood data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "8rtBYbX_-g4M" - }, - "outputs": [], - "source": [ - "overlay_roads = pd.DataFrame(overlay_hazard_assets(flood_map_vector,buffer_assets(roads_pg)).T,columns=['asset','hazard_point'])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s82DyD_y-g4M" - }, - "source": [ - "And estimate the damages" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "LGqPFklh-g4N", - "outputId": "207937c9-dcc5-41a5-ebc4-76aa03003022" - }, - "outputs": [], - "source": [ - "collect_output = []\n", - "for asset in tqdm(overlay_roads.groupby('asset'),total=len(overlay_roads.asset.unique()),\n", - " desc='polyline damage calculation for'):\n", - " collect_output.append(get_damage_per_asset(asset,flood_map_vector,roads_pg))\n", - " \n", - "damaged_roads = roads.merge(pd.DataFrame(collect_output,columns=['index','damage']),\n", - " left_index=True,right_on='index')[['highway','geometry','damage']]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RFGZxWl7i7pQ" - }, - "source": [ - "
\n", - "Question 10: Describe the various steps we have taken to perform the damage assessment on the road network. How is this approach different compared to the raster-based approach? Highlight the differences you find most important. Include any line of code you may want to include to make your story clear.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "B5jpsbyC-g4N" - }, - "source": [ - "And let's plot the results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 514 - }, - "id": "n25j-3wG-g4N", - "outputId": "b926a8e7-7e51-4434-f3b8-d61e6278bc24" - }, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(1, 1,figsize=(12,10))\n", - "\n", - "damaged_roads.plot(column='damage',cmap='Reds',ax=ax);" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "bTfmvwW2jchB" - }, - "source": [ - "
\n", - "Question 11: Describe the most severely damaged parts of the road network. Use Google Maps to identify these roads. Are you surprised by the results?\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "SG3FSqsLeFA5" - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "colab": { - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.9" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/TAA4/.ipynb_checkpoints/TAA4_V2-checkpoint.ipynb b/TAA4/.ipynb_checkpoints/TAA4_V2-checkpoint.ipynb new file mode 100644 index 0000000..1ebc7c0 --- /dev/null +++ b/TAA4/.ipynb_checkpoints/TAA4_V2-checkpoint.ipynb @@ -0,0 +1,3330 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ee037ada-1068-4f35-91ae-30c2ea52ca01", + "metadata": { + "id": "ee037ada-1068-4f35-91ae-30c2ea52ca01" + }, + "source": [ + "# TAA4: Accessibility to healthcare facilities" + ] + }, + { + "cell_type": "markdown", + "id": "c6036316-479d-4438-b9e7-e31cf7e9051c", + "metadata": { + "id": "c6036316-479d-4438-b9e7-e31cf7e9051c" + }, + "source": [ + "[explain assignment]\n", + "\n", + "### Important before we start\n", + "---\n", + "Make sure that you save this file before you continue, else you will lose everything. To do so, go to **Bestand/File** and click on **Een kopie opslaan in Drive/Save a Copy on Drive**!" + ] + }, + { + "cell_type": "markdown", + "id": "75f3efb3-f86a-443e-b87a-22653c771143", + "metadata": { + "id": "75f3efb3-f86a-443e-b87a-22653c771143" + }, + "source": [ + "## Learning Objectives\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "59d989b6-9cc8-4a39-a17b-c96c463711dd", + "metadata": { + "id": "59d989b6-9cc8-4a39-a17b-c96c463711dd" + }, + "source": [ + "## Prepare the packages\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b06c7b0f-2f83-4f86-ab73-0e8d889bd164", + "metadata": { + "id": "b06c7b0f-2f83-4f86-ab73-0e8d889bd164" + }, + "outputs": [], + "source": [ + "!pip install rasterio\n", + "!pip install rioxarray\n", + "!pip install contextily\n", + "!pip install osm_flex" + ] + }, + { + "cell_type": "markdown", + "id": "bee1cfab-03df-433e-913e-62de5c0076f4", + "metadata": { + "id": "bee1cfab-03df-433e-913e-62de5c0076f4" + }, + "source": [ + "Now we will import these packages in the cell below:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ffc10f5f-a43d-4f8f-91b1-ac7afc347ab4", + "metadata": { + "id": "ffc10f5f-a43d-4f8f-91b1-ac7afc347ab4" + }, + "outputs": [], + "source": [ + "import os,sys\n", + "import requests\n", + "import shapely\n", + "import random\n", + "import sklearn\n", + "\n", + "import xarray as xr\n", + "import rioxarray as rxr\n", + "import pandas as pd\n", + "import geopandas as gpd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import contextily as cx\n", + "import rasterio as rio\n", + "\n", + "from pathlib import Path\n", + "from rasterio.enums import Resampling\n", + "from sklearn.cluster import KMeans\n", + "from shapely.geometry import Point\n", + "from scipy.spatial.distance import cdist\n", + "from osm_flex import download\n", + "from zipfile import ZipFile\n", + "from io import BytesIO\n", + "from urllib.request import urlopen\n", + "from datetime import datetime\n", + "from tqdm import tqdm # fancy progress bar package\n", + "from IPython.display import clear_output\n", + "\n", + "\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "import ee\n", + "import geemap\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "bb50fef4-f456-46ca-aff7-a838766fb127", + "metadata": { + "id": "bb50fef4-f456-46ca-aff7-a838766fb127" + }, + "source": [ + "## 2. Data download and preparation\n", + "\n", + "Define a country of your interest and a size for gridding and a randomSeed" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "da8b4246-1b98-455c-89ce-999e21e5dd27", + "metadata": { + "id": "da8b4246-1b98-455c-89ce-999e21e5dd27" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "country_full_name = 'Luxembourg'\n", + "country_iso3 = 'LUX'\n", + "upscale_factor = 10 #Km\n", + "random_seed= 1" + ] + }, + { + "cell_type": "markdown", + "id": "6d8c4878-9662-4de4-9c69-b9ec0af9cda3", + "metadata": { + "id": "6d8c4878-9662-4de4-9c69-b9ec0af9cda3" + }, + "source": [ + "Download the population data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6eb55f91-caba-443b-a72b-688bce077b6b", + "metadata": { + "id": "6eb55f91-caba-443b-a72b-688bce077b6b" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "url = \"https://data.worldpop.org/GIS/Population/Global_2000_2020/2018/0_Mosaicked/ppp_2018_1km_Aggregated.tif\"\n", + "\n", + "file_name = 'ppp_2018_1km_Aggregated.tif'\n", + "#open(file_name, 'wb').write(requests.get(url).content)\n", + "\n", + "file_name = \"C:\\\\Data\\\\Global_Geospatial\\\\worldpop\\\\ppp_2018_1km_Aggregated.tif\"\n", + "\n", + "\n", + "world_pop_glob =xr.open_dataset(file_name,engine='rasterio')" + ] + }, + { + "cell_type": "markdown", + "id": "84d4cc8e-d8fc-495a-9337-bfb06958a553", + "metadata": { + "id": "84d4cc8e-d8fc-495a-9337-bfb06958a553" + }, + "source": [ + "Download a file with country borders. We use Natural Earth." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "36d356c0-167b-4d4c-bfa1-67e6f3dfee46", + "metadata": { + "id": "36d356c0-167b-4d4c-bfa1-67e6f3dfee46" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "world = gpd.read_file(\"https://github.com/nvkelso/natural-earth-vector/raw/master/10m_cultural/ne_10m_admin_0_countries.shp\")" + ] + }, + { + "cell_type": "markdown", + "id": "f1b63ad2-1a42-4549-b979-66eeb78618e2", + "metadata": { + "id": "f1b63ad2-1a42-4549-b979-66eeb78618e2" + }, + "source": [ + "And we want to take the country boundaries and geometry" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5bdadd64-1091-4659-b4a8-ab1c2a43cf4c", + "metadata": { + "id": "5bdadd64-1091-4659-b4a8-ab1c2a43cf4c" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "country_bounds = world.loc[world.ADM0_ISO == country_iso3].bounds\n", + "country_geom = world.loc[world.ADM0_ISO == country_iso3].geometry" + ] + }, + { + "cell_type": "markdown", + "id": "2d131107-2f4f-4f39-9850-df988197ee62", + "metadata": { + "id": "2d131107-2f4f-4f39-9850-df988197ee62" + }, + "source": [ + "Now we use this to clip the population data from worldpop, just for your country" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3cee77c1-0fcc-4151-b5f1-5c67870b5ab4", + "metadata": { + "id": "3cee77c1-0fcc-4151-b5f1-5c67870b5ab4" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# clip to country\n", + "world_pop_national = world_pop_glob.rio.clip_box(minx=country_bounds.minx.values[0],\n", + " miny=country_bounds.miny.values[0],\n", + " maxx=country_bounds.maxx.values[0],\n", + " maxy=country_bounds.maxy.values[0]\n", + " )\n", + "world_pop_national = world_pop_national.rio.clip(country_geom.values, world_pop_glob.rio.crs, drop=False)" + ] + }, + { + "cell_type": "markdown", + "id": "ab6155b2-3e5b-4f0d-85b1-0f45d5b5f31f", + "metadata": { + "id": "ab6155b2-3e5b-4f0d-85b1-0f45d5b5f31f" + }, + "source": [ + "The worldpop data, however, is stored as 1km by 1km grid. This will be too computationally intensive if we would use that resolution. As such, we reproject the to a lower resolution. This will help us to perform the analyis more smoothly. We use the *upscale_factor* as defined at the start of this subsection." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "0834501f-2e00-4dce-b91c-8101fcdffb74", + "metadata": { + "id": "0834501f-2e00-4dce-b91c-8101fcdffb74" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "new_width = int(world_pop_national.rio.width / upscale_factor)\n", + "new_height = int(world_pop_national.rio.height / upscale_factor)\n", + "\n", + "worldpop_Grided = world_pop_national.rio.reproject(\n", + " world_pop_national.rio.crs,\n", + " shape=(new_height, new_width),\n", + " resampling=Resampling.sum,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "256c59b1-c8cb-40ef-9d4d-6c30e05e2861", + "metadata": { + "id": "256c59b1-c8cb-40ef-9d4d-6c30e05e2861" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The output is df_pop_LUX as a dataframe of the population data\n" + ] + } + ], + "source": [ + "df_worldpop_ = worldpop_Grided.band_data.to_dataframe()\n", + "df_worldpop_ = df_worldpop_.loc[~df_worldpop_.band_data.isna()].reset_index(drop=False)\n", + "\n", + "# create geometry values and drop lat lon columns\n", + "df_worldpop_['geometry'] = shapely.points(np.array(list(zip(df_worldpop_['x'],df_worldpop_['y']))))\n", + "\n", + "df_worldpop_ = gpd.GeoDataFrame(df_worldpop_.drop(['y','x','spatial_ref','band'],axis=1))\n", + "\n", + "# dynamically create a variable name for the DataFrame\n", + "globals()[f'df_pop_{country_iso3}'] = gpd.GeoDataFrame(df_worldpop_)\n", + "\n", + "# dynamically create a print statement that reflects the current country code\n", + "print(f\"The output is df_pop_{country_iso3} as a dataframe of the population data\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f0ce8240-0a9f-446a-a2fd-c38d2c7416b9", + "metadata": { + "id": "f0ce8240-0a9f-446a-a2fd-c38d2c7416b9", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_worldpop_.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "02dbdbed-5e0a-425d-88c8-548d57c92d7d", + "metadata": { + "id": "02dbdbed-5e0a-425d-88c8-548d57c92d7d" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "55" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(df_worldpop_)" + ] + }, + { + "cell_type": "markdown", + "id": "32dab221-7a28-4719-a180-dc8a91c17b48", + "metadata": { + "id": "32dab221-7a28-4719-a180-dc8a91c17b48" + }, + "source": [ + "Our next step is to extract information of healthcare facilities for the country of interest. We do so using OpenStreetMap. With the latest version of geopandas, it is now possible to directly read **osm.pbf** files from OpenStreetMap.\n", + "\n", + "Healthcare facilities are stored as *multipolygons* within OpenStreetMap, and we want to download all clinics and hospitals." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "dbf8e926-52cc-4411-bc68-263f7cafaf1f", + "metadata": { + "id": "dbf8e926-52cc-4411-bc68-263f7cafaf1f" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\eks510\\.conda\\envs\\pygis\\Lib\\site-packages\\pyogrio\\raw.py:196: RuntimeWarning: Non closed ring detected. To avoid accepting it, set the OGR_GEOMETRY_ACCEPT_UNCLOSED_RING configuration option to NO\n", + " return ogr_read(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: total: 12 s\n", + "Wall time: 12.2 s\n" + ] + } + ], + "source": [ + "%%time\n", + "Country_GeofabrikData_path = download.get_country_geofabrik(country_iso3)\n", + "#Country_GeofabrikData_path = \"C:\\\\Data\\\\country_osm\\\\albania-latest.osm.pbf\"\n", + "\n", + "HealthCenters = gpd.read_file(Country_GeofabrikData_path, layer=\"multipolygons\")\n", + "sub_types =['clinic', 'hospital']\n", + "HealthCenters = HealthCenters[HealthCenters['amenity'].isin(sub_types)].reset_index(drop=True)\n", + "HealthCenters = HealthCenters.to_crs(3857)\n", + "\n", + "# to convert polygons to their centroids\n", + "HealthCenters_centroids = HealthCenters.copy()\n", + "HealthCenters_centroids['geometry'] = HealthCenters.centroid\n", + "\n", + "HealthCenters_centroids=HealthCenters_centroids.to_crs(4326)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "cfbc0e8e-bf63-42c9-b9d0-3a095c0f8ca6", + "metadata": { + "id": "cfbc0e8e-bf63-42c9-b9d0-3a095c0f8ca6" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
osm_idosm_way_idnametypeaerowayamenityadmin_levelbarrierboundarybuilding...man_mademilitarynaturalofficeplaceshopsporttourismother_tagsgeometry
07591385NoneZithaKlinikmultipolygonNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"healthcare\"=>\"hospital\",\"operator\"=>\"Hôpitaux...MULTIPOLYGON (((682338.374 6377939.045, 682324...
117514812NoneHôpital KirchbergmultipolygonNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"healthcare\"=>\"hospital\",\"operator\"=>\"Hôpitaux...MULTIPOLYGON (((687383.596 6382856.727, 687394...
2None41407070Centre Hospitalier de LuxembourgNoneNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"healthcare\"=>\"hospital\",\"operator\"=>\"Centre H...MULTIPOLYGON (((679155.827 6380157.554, 679142...
3None56104142SénologieNoneNonehospitalNoneNoneNonehospital...NoneNoneNoneNoneNoneNoneNoneNoneNoneMULTIPOLYGON (((682993.032 6382614.167, 682977...
4None72872456Centre Hospitalier Émile MayrischNoneNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"healthcare\"=>\"hospital\",\"short_name\"=>\"CHEM\",...MULTIPOLYGON (((665744.712 6360608.875, 665747...
5None112389436Hôpital de la ville de DudelangeNoneNonehospitalNoneNoneNoneyes...NoneNoneNoneNoneNoneNoneNoneNone\"addr:city\"=>\"Dudelange\",\"addr:housenumber\"=>\"...MULTIPOLYGON (((677744.73 6355108.857, 677802....
6None189452987CHL EichNoneNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"addr:city\"=>\"Luxembourg\",\"addr:country\"=>\"LU\"...MULTIPOLYGON (((682944.085 6382574.363, 682938...
7None298655535Centre Médical de SteinselNoneNoneclinicNoneNoneNoneyes...NoneNoneNoneNoneNoneNoneNoneNone\"addr:city\"=>\"Steinsel\",\"addr:country\"=>\"LU\",\"...MULTIPOLYGON (((681904.484 6390283.25, 681900....
8None381951079Clinique Sainte MarieNoneNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"emergency\"=>\"yes\",\"healthcare\"=>\"hospital\",\"o...MULTIPOLYGON (((666416.525 6360368.193, 666413...
9None426571994Centre de réhabilitationNoneNonehospitalNoneNoneNoneyes...NoneNoneNoneNoneNoneNoneNoneNone\"addr:city\"=>\"Colpach-Bas\",\"addr:housenumber\"=...MULTIPOLYGON (((648445.596 6404692.12, 648459....
10None469054630Hôpital intercommunal de SteinfortNoneNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"healthcare\"=>\"hospital\"MULTIPOLYGON (((658123.935 6387823.318, 658132...
11None570707211Hôpital Intercommunal Princesse Marie-AstridNoneNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"healthcare\"=>\"hospital\"MULTIPOLYGON (((655334.714 6365873.121, 655368...
12None784862436Centre hospitalier Neuro-Psychiatrique (CHNP)NoneNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"healthcare\"=>\"hospital\"MULTIPOLYGON (((678665.866 6419086.54, 678705....
13None784866880RehazenterNoneNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"healthcare\"=>\"hospital\"MULTIPOLYGON (((687456.721 6382232.099, 687457...
14None884212046Centre Hospitalier du NordNoneNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"contact:email\"=>\"chdn@chdn.lu\",\"contact:fax\"=...MULTIPOLYGON (((678507.013 6421193.856, 678560...
15None887577792Centre Hospitalier du NordNoneNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"addr:city\"=>\"Wiltz\",\"addr:country\"=>\"LU\",\"add...MULTIPOLYGON (((660490.343 6440253.818, 660477...
\n", + "

16 rows × 26 columns

\n", + "
" + ], + "text/plain": [ + " osm_id osm_way_id name \\\n", + "0 7591385 None ZithaKlinik \n", + "1 17514812 None Hôpital Kirchberg \n", + "2 None 41407070 Centre Hospitalier de Luxembourg \n", + "3 None 56104142 Sénologie \n", + "4 None 72872456 Centre Hospitalier Émile Mayrisch \n", + "5 None 112389436 Hôpital de la ville de Dudelange \n", + "6 None 189452987 CHL Eich \n", + "7 None 298655535 Centre Médical de Steinsel \n", + "8 None 381951079 Clinique Sainte Marie \n", + "9 None 426571994 Centre de réhabilitation \n", + "10 None 469054630 Hôpital intercommunal de Steinfort \n", + "11 None 570707211 Hôpital Intercommunal Princesse Marie-Astrid \n", + "12 None 784862436 Centre hospitalier Neuro-Psychiatrique (CHNP) \n", + "13 None 784866880 Rehazenter \n", + "14 None 884212046 Centre Hospitalier du Nord \n", + "15 None 887577792 Centre Hospitalier du Nord \n", + "\n", + " type aeroway amenity admin_level barrier boundary building \\\n", + "0 multipolygon None hospital None None None None \n", + "1 multipolygon None hospital None None None None \n", + "2 None None hospital None None None None \n", + "3 None None hospital None None None hospital \n", + "4 None None hospital None None None None \n", + "5 None None hospital None None None yes \n", + "6 None None hospital None None None None \n", + "7 None None clinic None None None yes \n", + "8 None None hospital None None None None \n", + "9 None None hospital None None None yes \n", + "10 None None hospital None None None None \n", + "11 None None hospital None None None None \n", + "12 None None hospital None None None None \n", + "13 None None hospital None None None None \n", + "14 None None hospital None None None None \n", + "15 None None hospital None None None None \n", + "\n", + " ... man_made military natural office place shop sport tourism \\\n", + "0 ... None None None None None None None None \n", + "1 ... None None None None None None None None \n", + "2 ... None None None None None None None None \n", + "3 ... None None None None None None None None \n", + "4 ... None None None None None None None None \n", + "5 ... None None None None None None None None \n", + "6 ... None None None None None None None None \n", + "7 ... None None None None None None None None \n", + "8 ... None None None None None None None None \n", + "9 ... None None None None None None None None \n", + "10 ... None None None None None None None None \n", + "11 ... None None None None None None None None \n", + "12 ... None None None None None None None None \n", + "13 ... None None None None None None None None \n", + "14 ... None None None None None None None None \n", + "15 ... None None None None None None None None \n", + "\n", + " other_tags \\\n", + "0 \"healthcare\"=>\"hospital\",\"operator\"=>\"Hôpitaux... \n", + "1 \"healthcare\"=>\"hospital\",\"operator\"=>\"Hôpitaux... \n", + "2 \"healthcare\"=>\"hospital\",\"operator\"=>\"Centre H... \n", + "3 None \n", + "4 \"healthcare\"=>\"hospital\",\"short_name\"=>\"CHEM\",... \n", + "5 \"addr:city\"=>\"Dudelange\",\"addr:housenumber\"=>\"... \n", + "6 \"addr:city\"=>\"Luxembourg\",\"addr:country\"=>\"LU\"... \n", + "7 \"addr:city\"=>\"Steinsel\",\"addr:country\"=>\"LU\",\"... \n", + "8 \"emergency\"=>\"yes\",\"healthcare\"=>\"hospital\",\"o... \n", + "9 \"addr:city\"=>\"Colpach-Bas\",\"addr:housenumber\"=... \n", + "10 \"healthcare\"=>\"hospital\" \n", + "11 \"healthcare\"=>\"hospital\" \n", + "12 \"healthcare\"=>\"hospital\" \n", + "13 \"healthcare\"=>\"hospital\" \n", + "14 \"contact:email\"=>\"chdn@chdn.lu\",\"contact:fax\"=... \n", + "15 \"addr:city\"=>\"Wiltz\",\"addr:country\"=>\"LU\",\"add... \n", + "\n", + " geometry \n", + "0 MULTIPOLYGON (((682338.374 6377939.045, 682324... \n", + "1 MULTIPOLYGON (((687383.596 6382856.727, 687394... \n", + "2 MULTIPOLYGON (((679155.827 6380157.554, 679142... \n", + "3 MULTIPOLYGON (((682993.032 6382614.167, 682977... \n", + "4 MULTIPOLYGON (((665744.712 6360608.875, 665747... \n", + "5 MULTIPOLYGON (((677744.73 6355108.857, 677802.... \n", + "6 MULTIPOLYGON (((682944.085 6382574.363, 682938... \n", + "7 MULTIPOLYGON (((681904.484 6390283.25, 681900.... \n", + "8 MULTIPOLYGON (((666416.525 6360368.193, 666413... \n", + "9 MULTIPOLYGON (((648445.596 6404692.12, 648459.... \n", + "10 MULTIPOLYGON (((658123.935 6387823.318, 658132... \n", + "11 MULTIPOLYGON (((655334.714 6365873.121, 655368... \n", + "12 MULTIPOLYGON (((678665.866 6419086.54, 678705.... \n", + "13 MULTIPOLYGON (((687456.721 6382232.099, 687457... \n", + "14 MULTIPOLYGON (((678507.013 6421193.856, 678560... \n", + "15 MULTIPOLYGON (((660490.343 6440253.818, 660477... \n", + "\n", + "[16 rows x 26 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "HealthCenters" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "14c6ab08-56b0-49a2-8955-74a0bd8b6b7c", + "metadata": { + "id": "14c6ab08-56b0-49a2-8955-74a0bd8b6b7c" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The output is HealthCenters_centroids as a dataframe of the Health Centers\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"The output is HealthCenters_centroids as a dataframe of the Health Centers\")\n", + "\n", + "#plotting\n", + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "HealthCenters_centroids.plot(ax=ax, color='red', markersize=10, alpha=0.7)\n", + "\n", + "# temporarily reprojects to EPSG:3857 to add the basemap (contextily requires it)\n", + "#cx.add_basemap(ax, crs='EPSG:4326', source=cx.providers.OpenStreetMap.Mapnik, zoom=8)\n", + "\n", + "ax.set_title(f'Locations of Hospitals and Clinics in {country_iso3}', fontsize=15)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "dc39cef6-eac9-40a0-a737-bac3cd5bc2a1", + "metadata": { + "id": "dc39cef6-eac9-40a0-a737-bac3cd5bc2a1" + }, + "source": [ + "## 3. Classification of rural and urban areas" + ] + }, + { + "cell_type": "markdown", + "id": "zotYyVnD4Jt2", + "metadata": { + "id": "zotYyVnD4Jt2" + }, + "source": [ + "## Set-up" + ] + }, + { + "cell_type": "markdown", + "id": "kzGnaMdm4Gpu", + "metadata": { + "id": "kzGnaMdm4Gpu" + }, + "source": [ + "Fix seed" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "JS9on1714Ega", + "metadata": { + "id": "JS9on1714Ega" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "### Set global seeds ###\n", + "seed = random_seed\n", + "np.random.seed(seed)\n", + "random.seed(seed)\n", + "os.environ['PYTHONHASHSEED'] = str(seed)" + ] + }, + { + "cell_type": "markdown", + "id": "-l1Sg8b54mQl", + "metadata": { + "id": "-l1Sg8b54mQl" + }, + "source": [ + "Set-up EE environment" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "lm4NPROn4loe", + "metadata": { + "id": "lm4NPROn4loe" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ee.Authenticate()\n", + "# ee.Initialize(project=\"YOUR_PROJECT_NAME\")\n", + "ee.Initialize(project=\"proj-gis-1234\")" + ] + }, + { + "cell_type": "markdown", + "id": "_tY_sxXy4SPA", + "metadata": { + "id": "_tY_sxXy4SPA" + }, + "source": [ + "## Set-up train/val data" + ] + }, + { + "cell_type": "markdown", + "id": "GRJQm_UT5uhV", + "metadata": { + "id": "GRJQm_UT5uhV" + }, + "source": [ + "Load training data - 2018, CLC" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "_I9OT5iF4iPe", + "metadata": { + "id": "_I9OT5iF4iPe" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define the region of interest (ROI)\n", + "netherlands = ee.FeatureCollection(\"FAO/GAUL/2015/level0\") \\\n", + " .filter(ee.Filter.eq('ADM0_NAME', 'Luxembourg'))\n", + "\n", + "# Load Sentinel-2 Image Collection\n", + "sentinel2 = ee.ImageCollection(\"COPERNICUS/S2_HARMONIZED\") \\\n", + " .filterBounds(netherlands.geometry()) \\\n", + " .filterDate('2018-06-01', '2018-08-31') \\\n", + " .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 10))\n", + "\n", + "# Create a median composite to reduce cloud cover\n", + "mosaic_image = sentinel2.median().clip(netherlands.geometry())" + ] + }, + { + "cell_type": "markdown", + "id": "FPiX1Imk6Oom", + "metadata": { + "id": "FPiX1Imk6Oom" + }, + "source": [ + "Show on map to verify that it's loaded" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "gUE7-YsH5oim", + "metadata": { + "id": "gUE7-YsH5oim" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "44826237f81e4246a0ce32e290adaf62", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map(center=[51.37, 4.999999999999999], controls=(WidgetControl(options=['position', 'transparent_bg'], widget=…" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "point = ee.Geometry.Point([5.0, 51.37])\n", + "bands = ['B4', 'B3', 'B2'] # Fill in this list yourself\n", + "vis_params = {'max': 3000, 'bands':bands} # Limit upper range so you can see detail\n", + "\n", + "map = geemap.Map(height=800,width=700,center=[52.37,4.5],zoom=7)\n", + "map.centerObject(point, 8)\n", + "map.addLayer(mosaic_image, vis_params, \"Sentinel-2_2018\")\n", + "map" + ] + }, + { + "cell_type": "markdown", + "id": "8NEzm7xf7weH", + "metadata": { + "id": "8NEzm7xf7weH" + }, + "source": [ + "Calculate variables to include" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "cjMQF_E57zIw", + "metadata": { + "id": "cjMQF_E57zIw" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def make_s2_variables(s2_image):\n", + " # Calculate additional spectral indices\n", + " dvi = s2_image.select('B5').subtract(s2_image.select('B4')).rename('DVI')\n", + " ndvi = s2_image.normalizedDifference(['B5', 'B4']).rename('NDVI')\n", + " ndwi = s2_image.normalizedDifference(['B3', 'B5']).rename('NDWI')\n", + "\n", + " # Add indices to the image\n", + " s2_image = s2_image.addBands([dvi, ndvi, ndwi])\n", + "\n", + " # Define neighborhood size (e.g., 3x3)\n", + " kernel = ee.Kernel.square(radius=1)\n", + "\n", + " # Calculate neighborhood statistics\n", + " neighborhood_vars = []\n", + " for band in ['DVI', 'NDVI', 'NDWI']:\n", + " mean = s2_image.select(band).reduceNeighborhood(\n", + " reducer=ee.Reducer.mean(),\n", + " kernel=kernel\n", + " ).rename(f'{band}_mean')\n", + "\n", + " std_dev = s2_image.select(band).reduceNeighborhood(\n", + " reducer=ee.Reducer.stdDev(),\n", + " kernel=kernel\n", + " ).rename(f'{band}_stdDev')\n", + "\n", + " # Add additional statistics here, such as min, max, etc., if desired.\n", + "\n", + " # Append neighborhood bands to the list\n", + " neighborhood_vars.extend([mean, std_dev])\n", + "\n", + " # Add neighborhood statistics to the image\n", + " s2_image = s2_image.addBands(neighborhood_vars)\n", + "\n", + " return s2_image" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "oCmPdaR_74Ml", + "metadata": { + "id": "oCmPdaR_74Ml" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mosaic_image = make_s2_variables(mosaic_image)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "sTScJDPp8dew", + "metadata": { + "id": "sTScJDPp8dew" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Use these bands for prediction\n", + "bands = ['B2', 'B3', 'B4', 'B5', 'B6', 'B7']\n", + "indices = ['NDVI', 'DVI', 'NDWI'] # Add your index(es) band here\n", + "img_bands = [*bands, *indices]" + ] + }, + { + "cell_type": "markdown", + "id": "44mziLGt8j4C", + "metadata": { + "id": "44mziLGt8j4C" + }, + "source": [ + "Sample CORINE" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "Pacg8UcH8lHe", + "metadata": { + "id": "Pacg8UcH8lHe" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "CLC = ee.Image('COPERNICUS/CORINE/V20/100m/2012').select('landcover').clip(mosaic_image.geometry())\n", + "lc_points = CLC.sample(\n", + " **{\n", + " 'region': mosaic_image.geometry(),\n", + " 'scale': 30,\n", + " 'numPixels': 10000,\n", + " 'seed': 0,\n", + " 'geometries': True,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "n7hzNmOt8tY5", + "metadata": { + "id": "n7hzNmOt8tY5" + }, + "source": [ + "Reclassify to binary urban/rural" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "Hm4xWGNS8ugv", + "metadata": { + "id": "Hm4xWGNS8ugv" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def generalize_clc_class(feature):\n", + " lc_value = ee.String(feature.get('landcover'))\n", + "\n", + " # Check if the first character is '1'\n", + " set_value = ee.Algorithms.If(lc_value.slice(0, 1).equals('1'), 1, 0)\n", + "\n", + " # Set the new binary value for the 'landcover' property\n", + " return feature.set('landcover', set_value)\n", + "lc_reference_pts = lc_points.map(generalize_clc_class)" + ] + }, + { + "cell_type": "markdown", + "id": "th9Dl6mW9FII", + "metadata": { + "id": "th9Dl6mW9FII" + }, + "source": [ + "Make train/validation splits" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "X1tMXV4w9LFb", + "metadata": { + "id": "X1tMXV4w9LFb" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define the land cover labels column\n", + "label_col = 'landcover'\n", + "\n", + "# Filter points by label\n", + "positive_points = lc_reference_pts.filter(ee.Filter.eq(label_col, 1))\n", + "negative_points = lc_reference_pts.filter(ee.Filter.eq(label_col, 0))\n", + "\n", + "# Allow a maximum of 2-to-1 difference in negative vs positive class sampling\n", + "positive_sample = positive_points.randomColumn('random').limit(positive_points.size())\n", + "negative_sample = negative_points.randomColumn('random').limit(positive_points.size().multiply(ee.Number(2)))\n", + "\n", + "# Merge the samples\n", + "balanced_sample = positive_sample.merge(negative_sample)\n", + "\n", + "# Split into training and validation sets\n", + "training_sample = balanced_sample.filter('random <= 0.8')\n", + "validation_sample = balanced_sample.filter('random > 0.8')\n", + "\n", + "# Sample regions for training and validation datasets\n", + "train_data = mosaic_image.select(img_bands).sampleRegions(\n", + " collection=training_sample, properties=[label_col], scale=100\n", + ")\n", + "\n", + "val_data = mosaic_image.select(img_bands).sampleRegions(\n", + " collection=validation_sample, properties=[label_col], scale=100\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "uS-6YzaM9P5-", + "metadata": { + "id": "uS-6YzaM9P5-" + }, + "source": [ + "## Optimize on train/val" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "Hv5M5UkT9X39", + "metadata": { + "id": "Hv5M5UkT9X39" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Train the model\n", + "classifier = ee.Classifier.smileRandomForest(numberOfTrees=100, minLeafPopulation=2, maxNodes=50)\n", + "trained_classifier = classifier.train(features=train_data, classProperty=label_col, inputProperties=img_bands)\n", + "\n", + "# Apply the classifier to the validation data\n", + "classified_val = val_data.classify(trained_classifier)" + ] + }, + { + "cell_type": "markdown", + "id": "_r0pZTPq97XH", + "metadata": { + "id": "_r0pZTPq97XH" + }, + "source": [ + "Calculate metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "q7SJuNRd9Zmm", + "metadata": { + "id": "q7SJuNRd9Zmm" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Validation Metrics:\n", + "Accuracy: 0.8302828618968386\n", + "Precision: 0.9427860696517413\n", + "Recall: 0.8275109170305677\n", + "Kappa: 0.587547433861722\n" + ] + } + ], + "source": [ + "# Calculate metrics\n", + "confusion_matrix = classified_val.errorMatrix(label_col, 'classification')\n", + "val_accuracy = confusion_matrix.accuracy()\n", + "precision = confusion_matrix.producersAccuracy().get([0, 0]) # Replace [0, 0] with the indices for the desired class if needed\n", + "recall = confusion_matrix.consumersAccuracy().get([0, 0]) # Replace [0, 0] with the indices for the desired class if needed\n", + "kappa = confusion_matrix.kappa()\n", + "\n", + "# Package all metrics into a single dictionary\n", + "metrics = ee.Dictionary({\n", + " 'Accuracy': val_accuracy,\n", + " 'Precision': precision,\n", + " 'Recall': recall,\n", + " 'Kappa': kappa\n", + "})\n", + "\n", + "# Retrieve all metrics in one call\n", + "metrics_info = metrics.getInfo()\n", + "\n", + "# Print all metrics\n", + "print('Validation Metrics:')\n", + "print(f\"Accuracy: {metrics_info['Accuracy']}\")\n", + "print(f\"Precision: {metrics_info['Precision']}\")\n", + "print(f\"Recall: {metrics_info['Recall']}\")\n", + "print(f\"Kappa: {metrics_info['Kappa']}\")" + ] + }, + { + "cell_type": "markdown", + "id": "BnOR_Ouk-CFv", + "metadata": { + "id": "BnOR_Ouk-CFv" + }, + "source": [ + "## Run on test set" + ] + }, + { + "cell_type": "markdown", + "id": "YjIzJ6rR-mje", + "metadata": { + "id": "YjIzJ6rR-mje" + }, + "source": [ + "Load August 2024 test image" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "cL9G2aPS-ba2", + "metadata": { + "id": "cL9G2aPS-ba2" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define the region of interest (ROI)\n", + "netherlands = ee.FeatureCollection(\"FAO/GAUL/2015/level0\") \\\n", + " .filter(ee.Filter.eq('ADM0_NAME', 'Luxembourg'))\n", + "\n", + "# Load Sentinel-2 Image Collection\n", + "test_sentinel2 = ee.ImageCollection(\"COPERNICUS/S2_HARMONIZED\") \\\n", + " .filterBounds(netherlands.geometry()) \\\n", + " .filterDate('2024-08-01', '2024-08-31') \\\n", + " .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 10))\n", + "\n", + "# Create a median composite to reduce cloud cover\n", + "test_mosaic_image = sentinel2.median().clip(netherlands.geometry())\n", + "\n", + "# Add variables\n", + "test_mosaic_image = make_s2_variables(test_mosaic_image)" + ] + }, + { + "cell_type": "markdown", + "id": "sx8nFANn-o_2", + "metadata": { + "id": "sx8nFANn-o_2" + }, + "source": [ + "Sample small amount of pixels for testing" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "xjwC0pny-rJu", + "metadata": { + "id": "xjwC0pny-rJu" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "CLC = ee.Image('COPERNICUS/CORINE/V20/100m/2018').select('landcover').clip(test_mosaic_image.geometry())\n", + "test_lc_points = CLC.sample(\n", + " **{\n", + " 'region': test_mosaic_image.geometry(),\n", + " 'scale': 30, #check what the resolution of corine land cover is\n", + " 'numPixels': 10000,\n", + " 'seed': 0,\n", + " 'geometries': True, # Set this to False to ignore geometries\n", + " }\n", + ")\n", + "test_lc_reference_pts = test_lc_points.map(generalize_clc_class) # Make sure to reclassify\n", + "\n", + "test_data = test_mosaic_image.select(img_bands).sampleRegions(\n", + " **{'collection': test_lc_reference_pts, 'properties': [label_col], 'scale': 100}\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "LxwRLRfN-zD9", + "metadata": { + "id": "LxwRLRfN-zD9" + }, + "source": [ + "Test set metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "T9eLm7JA-0HB", + "metadata": { + "id": "T9eLm7JA-0HB" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Validation Metrics:\n", + "Accuracy: 0.9283\n", + "Precision: 0.9593205185516317\n", + "Recall: 0.9605012867852747\n", + "Kappa: 0.6209046600069962\n" + ] + } + ], + "source": [ + "classified_test = test_data.classify(trained_classifier)\n", + "\n", + "# Calculate the confusion matrix and server-side metrics\n", + "confusion_matrix = classified_test.errorMatrix(label_col, 'classification')\n", + "test_accuracy = confusion_matrix.accuracy()\n", + "precision = confusion_matrix.producersAccuracy().get([0, 0]) # Replace [0, 0] with the indices for the desired class if needed\n", + "recall = confusion_matrix.consumersAccuracy().get([0, 0]) # Replace [0, 0] with the indices for the desired class if needed\n", + "kappa = confusion_matrix.kappa()\n", + "\n", + "# Package all metrics into a single dictionary\n", + "metrics = ee.Dictionary({\n", + " 'Accuracy': test_accuracy,\n", + " 'Precision': precision,\n", + " 'Recall': recall,\n", + " 'Kappa': kappa\n", + "})\n", + "\n", + "# Retrieve all metrics in one call\n", + "metrics_info = metrics.getInfo()\n", + "\n", + "# Print all metrics\n", + "print('Validation Metrics:')\n", + "print(f\"Accuracy: {metrics_info['Accuracy']}\")\n", + "print(f\"Precision: {metrics_info['Precision']}\")\n", + "print(f\"Recall: {metrics_info['Recall']}\")\n", + "print(f\"Kappa: {metrics_info['Kappa']}\")" + ] + }, + { + "cell_type": "markdown", + "id": "ZMXJCmTj-9YV", + "metadata": { + "id": "ZMXJCmTj-9YV" + }, + "source": [ + "Run classifier on entire test image" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "waPHnNLv-76V", + "metadata": { + "id": "waPHnNLv-76V" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2ad623644ac342c2a5c5b7c1eab7e8ee", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map(center=[51.37, 4.999999999999999], controls=(WidgetControl(options=['position', 'transparent_bg'], widget=…" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Classify the test image\n", + "classified_image = test_mosaic_image.classify(trained_classifier)\n", + "\n", + "# Define the color mapping dictionary\n", + "clc_colors = {\n", + " 0: '#FFFFFF', # Rural\n", + " 1: '#000000', # Urban\n", + "}\n", + "\n", + "# Convert string labels to numeric codes\n", + "def classify_to_numeric(image):\n", + " # Create a dictionary that maps string labels to numeric values\n", + " label_to_numeric = {label: index for index, label in enumerate(clc_colors.keys())}\n", + "\n", + " # Convert string label to numeric value\n", + " return image.remap(\n", + " list(label_to_numeric.keys()),\n", + " list(label_to_numeric.values())\n", + " )\n", + "\n", + "# Convert the classified image\n", + "numeric_classified_image = classify_to_numeric(classified_image)\n", + "\n", + "# Generate a palette for visualization\n", + "palette = [clc_colors[label] for label in clc_colors.keys()]\n", + "\n", + "# Add the numeric classified image to the map\n", + "map.addLayer(numeric_classified_image, {'palette': palette, 'min': 0, 'max': len(clc_colors) - 1}, 'Classified Image')\n", + "map" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "p-rnLVS8-bdZ", + "metadata": { + "id": "p-rnLVS8-bdZ" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "22c88ff1bebd46198ed0d15e024120e6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map(center=[51.37, 4.999999999999999], controls=(WidgetControl(options=['position', 'transparent_bg'], widget=…" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "point = ee.Geometry.Point([5.0, 51.37])\n", + "bands = ['B4', 'B3', 'B2'] # Fill in this list yourself\n", + "vis_params = {'max': 3000, 'bands':bands} # Limit upper range so you can see detail\n", + "\n", + "map = geemap.Map(height=800,width=700,center=[52.37,4.5],zoom=7)\n", + "map.centerObject(point, 8)\n", + "map.addLayer(test_mosaic_image, vis_params, \"test-Sentinel-2_2024\")\n", + "map" + ] + }, + { + "cell_type": "markdown", + "id": "LPkrvHduHyDh", + "metadata": { + "id": "LPkrvHduHyDh" + }, + "source": [ + "Load computed layer to local environment" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "m1FjLJVzH2Zt", + "metadata": { + "id": "m1FjLJVzH2Zt" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating URL ...\n", + "Downloading data from https://earthengine.googleapis.com/v1/projects/proj-gis-1234/thumbnails/b7555458ba76e1713bf16d9bb30011ed-50ef1678dd59a39ff576ec1ccb0c387e:getPixels\n", + "Please wait ...\n", + "Data downloaded to C:\\projects\\UNIGIS_ProgrammingGIS\\TAA4\\urban_rural_raster.tif\n" + ] + } + ], + "source": [ + "geemap.ee_export_image(classified_image, filename='urban_rural_raster.tif', scale=1000, file_per_band=False)\n", + "urban_raster = rio.open('urban_rural_raster.tif')" + ] + }, + { + "cell_type": "markdown", + "id": "309a00a2-8aa1-4091-b575-31d56622fd16", + "metadata": {}, + "source": [ + "## 3. Overlay urban and rural classification with population and Healthcare information" + ] + }, + { + "cell_type": "markdown", + "id": "7055a7db-636f-4a56-a954-62ed2546328e", + "metadata": {}, + "source": [ + "1 is urban, 0 is rural" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "f456e034-5e81-4d99-9093-0380af66e8f9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "urban_rural = xr.open_dataarray('urban_rural_raster.tif')\n", + "\n", + "values = urban_rural.sel(\n", + " {\n", + " urban_rural.rio.x_dim: xr.DataArray(df_worldpop_.geometry.x),\n", + " urban_rural.rio.y_dim: xr.DataArray(df_worldpop_.geometry.y),\n", + " },\n", + " method=\"nearest\",\n", + " ).values[0]\n", + "df_worldpop_[\"urban_rural\"] = values\n", + "df_worldpop_[\"urban_rural\"] = df_worldpop_[\"urban_rural\"].fillna(0)" + ] + }, + { + "cell_type": "markdown", + "id": "573f10a4-d0bb-4675-903f-71ffbe45358f", + "metadata": { + "id": "573f10a4-d0bb-4675-903f-71ffbe45358f" + }, + "source": [ + "## 4. Clustering of Healthcare centres and population" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "09511e34-85cc-43f8-b994-a5d3f53431c2", + "metadata": { + "id": "09511e34-85cc-43f8-b994-a5d3f53431c2" + }, + "outputs": [], + "source": [ + "### 1st step: Add Local_ID to the HealthCenters_centroids GeoDataFrame\n", + "HealthCenters_centroids['Local_ID'] = range(1, len(HealthCenters_centroids) + 1)\n", + "\n", + "# 2nd step Ensure both GeoDataFrames are in the same CRS (EPSG:4326)\n", + "HealthCenters_centroids = HealthCenters_centroids.to_crs(epsg=4326)\n", + "\n", + "# Convert geometries to a list of coordinates (for KMeans)\n", + "pop_coords = np.array([(geom.x, geom.y) for geom in df_worldpop_['geometry']])\n", + "pop_band_data = df_worldpop_['band_data'].values\n", + "\n", + "# Extract the hospital coordinates\n", + "hospital_coords = np.array([(geom.x, geom.y) for geom in HealthCenters_centroids['geometry']])\n", + "hospital_local_ids = HealthCenters_centroids['Local_ID'].values\n", + "hospital_geometries = HealthCenters_centroids['geometry'].values\n", + "\n", + "### 3rd step 3: K-Means\n", + "kmeans = KMeans(n_clusters=len(hospital_coords), random_state=random_seed, init=hospital_coords, n_init=1) # is using hospital locations as initial centers\n", + "df_worldpop_['cluster'] = kmeans.fit_predict(pop_coords)\n", + "\n", + "# get cluster centers (latitude and longitude)\n", + "cluster_centers = kmeans.cluster_centers_\n", + "\n", + "### 4th step: calculate the sum of population in each cluster\n", + "df_worldpop_['cluster_population'] = df_worldpop_.groupby('cluster')['band_data'].transform('sum')\n", + "\n", + "# Create a new DataFrame for clusters and their population sums\n", + "clusters_df = pd.DataFrame({\n", + " 'cluster': range(len(cluster_centers)),\n", + " 'geometry': [Point(x, y) for x, y in cluster_centers],\n", + " 'population': df_worldpop_.groupby('cluster')['band_data'].sum().values\n", + "})\n", + "\n", + "### 5th step: assign the nearest hospital to the related cluster\n", + "# distances between each cluster center and each health facility center\n", + "distances = cdist(cluster_centers, hospital_coords, metric='euclidean')\n", + "\n", + "# the index of the nearest hospital for each cluster\n", + "nearest_hospital_idx = distances.argmin(axis=1)\n", + "\n", + "# assign the nearest hospital Local_ID and geometry to each cluster center\n", + "clusters_df['nearest_hospital_local_id'] = [hospital_local_ids[idx] for idx in nearest_hospital_idx]\n", + "clusters_df['nearest_hospital_geometry'] = [hospital_geometries[idx] for idx in nearest_hospital_idx]\n", + "\n", + "### 6th step: convert to GeoDataFrame and set CRS\n", + "clusters_gdf = gpd.GeoDataFrame(clusters_df, geometry='geometry')\n", + "clusters_gdf.set_crs(epsg=4326, inplace=True)\n", + "\n", + "# check the content of the resutled clusters dataframe\n", + "clusters_gdf" + ] + }, + { + "cell_type": "markdown", + "id": "5299738d-567d-4473-aaa2-095dede18b92", + "metadata": { + "id": "5299738d-567d-4473-aaa2-095dede18b92" + }, + "source": [ + "## 5. Explore and evaluate baseline results\n", + "\n", + "### Plot clusters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4735e1af-5573-4baf-83d7-6644ac7a3ad5", + "metadata": { + "id": "4735e1af-5573-4baf-83d7-6644ac7a3ad5" + }, + "outputs": [], + "source": [ + "clusters_gdf['x'] = clusters_gdf.geometry.x\n", + "clusters_gdf['y'] = clusters_gdf.geometry.y\n", + "\n", + "\n", + "HealthCenters_centroids['x'] = HealthCenters_centroids.geometry.x\n", + "HealthCenters_centroids['y'] = HealthCenters_centroids.geometry.y\n", + "\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 8))\n", + "\n", + "\n", + "ax.scatter(clusters_gdf['x'], clusters_gdf['y'], color='black', marker='o', s=20, label='Cluster Centers')\n", + "\n", + "\n", + "for x, y, label in zip(clusters_gdf['x'], clusters_gdf['y'], clusters_gdf['cluster']):\n", + " ax.text(x, y, str(label), fontsize=12, color='k')\n", + "\n", + "ax.scatter(HealthCenters_centroids['x'], HealthCenters_centroids['y'], color='red', marker='+', s=100, label='Hospitals')\n", + "\n", + "\n", + "plt.title(\"Cluster Centers with their IDs and Hospital Locations\")\n", + "plt.xlabel(\"Longitude\")\n", + "plt.ylabel(\"Latitude\")\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "e3d8e5a3-ffa1-4b1a-a3e6-0e569346a78c", + "metadata": { + "id": "e3d8e5a3-ffa1-4b1a-a3e6-0e569346a78c" + }, + "source": [ + "### Identify and plot population per healthcare facility" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc10ce38-c177-4925-a677-82c5770e9034", + "metadata": { + "id": "dc10ce38-c177-4925-a677-82c5770e9034" + }, + "outputs": [], + "source": [ + "# to calculate the total population in demand of services from each hospital\n", + "hospital_population = clusters_gdf.groupby('nearest_hospital_local_id')['population'].sum().reset_index()\n", + "\n", + "hospital_population_merged = HealthCenters_centroids.merge(hospital_population, left_on='Local_ID', right_on='nearest_hospital_local_id', how='left')\n", + "\n", + "hospital_population_merged[['Local_ID', 'population']].sort_values('population',ascending=False).dropna()" + ] + }, + { + "cell_type": "markdown", + "id": "12b83859-6dfc-4b28-8fd6-297b66ed737a", + "metadata": { + "id": "12b83859-6dfc-4b28-8fd6-297b66ed737a", + "scrolled": true + }, + "source": [ + "hospital_population_merged.plot('population')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb07e73c-84a2-466f-a644-4fa313b82dec", + "metadata": { + "id": "bb07e73c-84a2-466f-a644-4fa313b82dec" + }, + "outputs": [], + "source": [ + "assigned_hospitals = clusters_gdf['nearest_hospital_local_id'].unique()\n", + "\n", + "unassigned_hospitals = HealthCenters_centroids[~HealthCenters_centroids['Local_ID'].isin(assigned_hospitals)]\n", + "\n", + "if unassigned_hospitals.empty:\n", + " print(\"All hospitals are assigned to at least one cluster.\")\n", + "else:\n", + " print(\"following hospitals are not assigned to any cluster:\")\n", + " print(unassigned_hospitals[['Local_ID', 'geometry']])" + ] + }, + { + "cell_type": "markdown", + "id": "033c1875-c35a-4a76-8e80-1a364b5d1d1d", + "metadata": { + "id": "033c1875-c35a-4a76-8e80-1a364b5d1d1d" + }, + "source": [ + "### Assess distance to hospital from each population grid" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "281204e5-8a50-4225-9521-3313a1bbf3ba", + "metadata": { + "id": "281204e5-8a50-4225-9521-3313a1bbf3ba" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "3740a61b-f60c-427c-aab5-6e6e552dc57d", + "metadata": { + "id": "3740a61b-f60c-427c-aab5-6e6e552dc57d" + }, + "source": [ + "## 6. Natural hazard disruption" + ] + }, + { + "cell_type": "markdown", + "id": "52d30a9c-2515-4aff-ae5f-9a15cd343061", + "metadata": { + "id": "52d30a9c-2515-4aff-ae5f-9a15cd343061" + }, + "source": [ + "### Download flood data\n", + "The flood data we will extract from a repository maintained by the European Commission Joint Research Centre. We will download river flood hazard maps from their [Flood Data Collection](https://data.jrc.ec.europa.eu/dataset/1d128b6c-a4ee-4858-9e34-6210707f3c81).\n", + "\n", + "Here we do not need to use an API and we also do not need to register ourselves, so we can download any of the files directly. To do so, we use the `urllib` package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "47875fb5-c347-4ef7-bd27-ee0ef75f29af", + "metadata": { + "id": "47875fb5-c347-4ef7-bd27-ee0ef75f29af" + }, + "outputs": [], + "source": [ + "## this is the link to the 1/100 flood map for Europe\n", + "zipurl = 'https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/FLOODS/EuropeanMaps/floodMap_RP100.zip'\n", + "\n", + "# The path where the downloaded flood map will be extracted, this is the folder of this Google Collaboratory instance. NOTE: a new instance will have this directory be cleared.\n", + "data_path = \"\"\n", + "\n", + "# and now we open and extract the data\n", + "with urlopen(zipurl) as zipresp:\n", + " with ZipFile(BytesIO(zipresp.read())) as zfile:\n", + " zfile.extractall(data_path)" + ] + }, + { + "cell_type": "markdown", + "id": "b0ff5f7a-fc94-4429-82bb-2a94da72a887", + "metadata": { + "id": "b0ff5f7a-fc94-4429-82bb-2a94da72a887" + }, + "source": [ + "### Overlay flood data with population centroids" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e73f0bce-ad12-4be5-acbb-33dd7e2fa18d", + "metadata": { + "id": "e73f0bce-ad12-4be5-acbb-33dd7e2fa18d" + }, + "outputs": [], + "source": [ + "flood_map_path = \"floodmap_EFAS_RP100_C.tif\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "28d110b6-0ef4-41c6-acb2-8673597decd2", + "metadata": { + "id": "28d110b6-0ef4-41c6-acb2-8673597decd2" + }, + "outputs": [], + "source": [ + "flood_map = xr.open_dataset(flood_map_path, engine=\"rasterio\")\n", + "flood_map" + ] + }, + { + "cell_type": "markdown", + "id": "399149ac-8ee4-4459-971e-9a2003ab4b4e", + "metadata": { + "id": "399149ac-8ee4-4459-971e-9a2003ab4b4e" + }, + "source": [ + "### Overlay flood data with healthcare facilities" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "024a09f7-6f1c-4e15-a810-cf407daa2a48", + "metadata": { + "id": "024a09f7-6f1c-4e15-a810-cf407daa2a48" + }, + "outputs": [], + "source": [ + "def _get_damage_per_object(asset, curves, cell_area_m2):\n", + " \"\"\"\n", + " Calculate damage for a given asset based on hazard information.\n", + " Arguments:\n", + " *asset*: Tuple containing information about the asset. It includes:\n", + " - Index or identifier of the asset (asset[0]).\n", + " - Asset-specific information, including hazard points (asset[1]['hazard_point']).\n", + " *maxdam_dict*: Maximum damage value.\n", + " Returns:\n", + " *tuple*: A tuple containing the asset index or identifier and the calculated damage.\n", + " \"\"\"\n", + "\n", + " if asset.geometry.geom_type in (\"Polygon\", \"MultiPolygon\"):\n", + " coverage = asset[\"coverage\"] * cell_area_m2\n", + " elif asset.geometry.geom_type in (\"LineString\", \"MultiLineString\"):\n", + " coverage = asset[\"coverage\"]\n", + " elif asset.geometry.geom_type in (\"Point\"):\n", + " coverage = 1\n", + " else:\n", + " raise ValueError(f\"Geometry type {asset.geometry.geom_type} not supported\")\n", + "\n", + " return (\n", + " np.sum(\n", + " np.interp(\n", + " asset[\"values\"], curves.index, curves[asset[\"amenity\"]].values\n", + " )\n", + " * coverage\n", + " )\n", + " * asset[\"maximum_damage\"]\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48ec7696-a117-4302-9276-ac37fa369cd2", + "metadata": { + "id": "48ec7696-a117-4302-9276-ac37fa369cd2" + }, + "outputs": [], + "source": [ + "maxdam = {\"hospital\":2000,\n", + " \"clinic\":1500,\n", + "}\n", + "\n", + "curves = np.array(\n", + " [[0,0],\n", + " [50,0.2],\n", + " [100,0.4],\n", + " [150,0.6],\n", + " [200,0.8],\n", + " [250,1]])\n", + "\n", + "curves = np.concatenate((curves,\n", + " np.transpose(np.array([curves[:,1]]*(len(maxdam)-1)))),\n", + " axis=1)\n", + "\n", + "curves = pd.DataFrame(curves)\n", + "curves.columns = ['depth']+list(maxdam.keys())\n", + "curves.set_index('depth',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26e1e323-d30b-4e40-8a89-a25d1e475234", + "metadata": { + "id": "26e1e323-d30b-4e40-8a89-a25d1e475234", + "scrolled": true + }, + "outputs": [], + "source": [ + "values_and_coverage_per_object = exact_extract(\n", + " flood_map,\n", + " HealthCenters,\n", + " [\"coverage\", \"values\"],\n", + " output=\"pandas\",\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "811245a1-d794-4b0d-b604-1d31c2507d97", + "metadata": { + "id": "811245a1-d794-4b0d-b604-1d31c2507d97" + }, + "outputs": [], + "source": [ + "HealthCenters = HealthCenters.merge(values_and_coverage_per_object,left_index=True,right_index=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "115d6cc9-aeb9-4497-9c0e-78f7c8ef19b8", + "metadata": { + "id": "115d6cc9-aeb9-4497-9c0e-78f7c8ef19b8" + }, + "outputs": [], + "source": [ + "HealthCenters['maximum_damage'] = HealthCenters.amenity.apply(lambda x: maxdam[x])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "291a4fba-40c1-4397-a7f2-8b1665ca7ef5", + "metadata": { + "id": "291a4fba-40c1-4397-a7f2-8b1665ca7ef5" + }, + "outputs": [], + "source": [ + "HealthCenters['damage'] = HealthCenters.apply(\n", + " lambda _object: _get_damage_per_object(_object, curves, cell_area_m2=100*100),\n", + " axis=1,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "913f9757-3151-46f9-9427-f54fa58d8beb", + "metadata": { + "id": "913f9757-3151-46f9-9427-f54fa58d8beb" + }, + "outputs": [], + "source": [ + "damage" + ] + }, + { + "cell_type": "markdown", + "id": "af40b670-4810-473b-81d8-7466852d85a1", + "metadata": { + "id": "af40b670-4810-473b-81d8-7466852d85a1" + }, + "source": [ + "### Recompute clustering without affected healthcare facilities" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "151a2c6a-9f28-41ad-a78c-38517e5545fd", + "metadata": { + "id": "151a2c6a-9f28-41ad-a78c-38517e5545fd" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "160dafed-34d4-44c8-8d9d-aa44c8dfb621", + "metadata": { + "id": "160dafed-34d4-44c8-8d9d-aa44c8dfb621" + }, + "source": [ + "## 7. Visualize and summarize final results" + ] + }, + { + "cell_type": "markdown", + "id": "c412b628-014e-41ba-8c41-0722098ad006", + "metadata": { + "id": "c412b628-014e-41ba-8c41-0722098ad006" + }, + "source": [ + "- population affected (and changed distance / hospital allocation)\n", + "- hospitals affected\n", + "- differences in urban and rural accessibility" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9463fcad-2b88-44c7-9389-8c7c9a94cbf4", + "metadata": { + "id": "9463fcad-2b88-44c7-9389-8c7c9a94cbf4" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [ + "573f10a4-d0bb-4675-903f-71ffbe45358f", + "5299738d-567d-4473-aaa2-095dede18b92" + ], + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/TAA4/.ipynb_checkpoints/TAA4_V3 (1)-checkpoint.ipynb b/TAA4/.ipynb_checkpoints/TAA4_V3 (1)-checkpoint.ipynb new file mode 100644 index 0000000..0ab6cc3 --- /dev/null +++ b/TAA4/.ipynb_checkpoints/TAA4_V3 (1)-checkpoint.ipynb @@ -0,0 +1,4895 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ee037ada-1068-4f35-91ae-30c2ea52ca01", + "metadata": { + "id": "ee037ada-1068-4f35-91ae-30c2ea52ca01" + }, + "source": [ + "# TAA4: Accessibility to healthcare facilities" + ] + }, + { + "cell_type": "markdown", + "id": "c6036316-479d-4438-b9e7-e31cf7e9051c", + "metadata": { + "id": "c6036316-479d-4438-b9e7-e31cf7e9051c" + }, + "source": [ + "[explain assignment]\n", + "\n", + "### Important before we start\n", + "---\n", + "Make sure that you save this file before you continue, else you will lose everything. To do so, go to **Bestand/File** and click on **Een kopie opslaan in Drive/Save a Copy on Drive**!" + ] + }, + { + "cell_type": "markdown", + "id": "75f3efb3-f86a-443e-b87a-22653c771143", + "metadata": { + "id": "75f3efb3-f86a-443e-b87a-22653c771143" + }, + "source": [ + "## Learning Objectives\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "59d989b6-9cc8-4a39-a17b-c96c463711dd", + "metadata": { + "id": "59d989b6-9cc8-4a39-a17b-c96c463711dd" + }, + "source": [ + "## Prepare the packages\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b06c7b0f-2f83-4f86-ab73-0e8d889bd164", + "metadata": { + "id": "b06c7b0f-2f83-4f86-ab73-0e8d889bd164" + }, + "outputs": [], + "source": [ + "!pip install rasterio\n", + "!pip install rioxarray\n", + "!pip install contextily\n", + "!pip install osm_flex" + ] + }, + { + "cell_type": "markdown", + "id": "bee1cfab-03df-433e-913e-62de5c0076f4", + "metadata": { + "id": "bee1cfab-03df-433e-913e-62de5c0076f4" + }, + "source": [ + "Now we will import these packages in the cell below:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ffc10f5f-a43d-4f8f-91b1-ac7afc347ab4", + "metadata": { + "id": "ffc10f5f-a43d-4f8f-91b1-ac7afc347ab4" + }, + "outputs": [], + "source": [ + "import os,sys\n", + "import requests\n", + "import shapely\n", + "import random\n", + "import sklearn\n", + "\n", + "import xarray as xr\n", + "import rioxarray as rxr\n", + "import pandas as pd\n", + "import geopandas as gpd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import contextily as cx\n", + "import rasterio as rio\n", + "\n", + "from pathlib import Path\n", + "from rasterio.enums import Resampling\n", + "from sklearn.cluster import KMeans\n", + "from shapely.geometry import Point\n", + "from scipy.spatial.distance import cdist\n", + "from osm_flex import download\n", + "from zipfile import ZipFile\n", + "from io import BytesIO\n", + "from urllib.request import urlopen\n", + "from datetime import datetime\n", + "from tqdm import tqdm # fancy progress bar package\n", + "from IPython.display import clear_output\n", + "\n", + "\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "import ee\n", + "import geemap\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "bb50fef4-f456-46ca-aff7-a838766fb127", + "metadata": { + "id": "bb50fef4-f456-46ca-aff7-a838766fb127" + }, + "source": [ + "## 2. Data download and preparation\n", + "\n", + "Define a country of your interest and a size for gridding and a randomSeed" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "da8b4246-1b98-455c-89ce-999e21e5dd27", + "metadata": { + "id": "da8b4246-1b98-455c-89ce-999e21e5dd27" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "country_full_name = 'Luxembourg'\n", + "country_iso3 = 'LUX'\n", + "upscale_factor = 10 #Km\n", + "random_seed= 1" + ] + }, + { + "cell_type": "markdown", + "id": "6d8c4878-9662-4de4-9c69-b9ec0af9cda3", + "metadata": { + "id": "6d8c4878-9662-4de4-9c69-b9ec0af9cda3" + }, + "source": [ + "Download the population data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6eb55f91-caba-443b-a72b-688bce077b6b", + "metadata": { + "id": "6eb55f91-caba-443b-a72b-688bce077b6b" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "url = \"https://data.worldpop.org/GIS/Population/Global_2000_2020/2018/0_Mosaicked/ppp_2018_1km_Aggregated.tif\"\n", + "\n", + "file_name = 'ppp_2018_1km_Aggregated.tif'\n", + "\n", + "open(file_name, 'wb').write(requests.get(url).content)\n", + "\n", + "# file_name = \"C:\\\\Data\\\\Global_Geospatial\\\\worldpop\\\\ppp_2018_1km_Aggregated.tif\"\n", + "\n", + "\n", + "world_pop_glob =xr.open_dataset(file_name,engine='rasterio')" + ] + }, + { + "cell_type": "markdown", + "id": "84d4cc8e-d8fc-495a-9337-bfb06958a553", + "metadata": { + "id": "84d4cc8e-d8fc-495a-9337-bfb06958a553" + }, + "source": [ + "Download a file with country borders. We use Natural Earth." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "36d356c0-167b-4d4c-bfa1-67e6f3dfee46", + "metadata": { + "id": "36d356c0-167b-4d4c-bfa1-67e6f3dfee46" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "world = gpd.read_file(\"https://github.com/nvkelso/natural-earth-vector/raw/master/10m_cultural/ne_10m_admin_0_countries.shp\")" + ] + }, + { + "cell_type": "markdown", + "id": "f1b63ad2-1a42-4549-b979-66eeb78618e2", + "metadata": { + "id": "f1b63ad2-1a42-4549-b979-66eeb78618e2" + }, + "source": [ + "And we want to take the country boundaries and geometry" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5bdadd64-1091-4659-b4a8-ab1c2a43cf4c", + "metadata": { + "id": "5bdadd64-1091-4659-b4a8-ab1c2a43cf4c" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "country_bounds = world.loc[world.ADM0_ISO == country_iso3].bounds\n", + "country_geom = world.loc[world.ADM0_ISO == country_iso3].geometry" + ] + }, + { + "cell_type": "markdown", + "id": "2d131107-2f4f-4f39-9850-df988197ee62", + "metadata": { + "id": "2d131107-2f4f-4f39-9850-df988197ee62" + }, + "source": [ + "Now we use this to clip the population data from worldpop, just for your country" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3cee77c1-0fcc-4151-b5f1-5c67870b5ab4", + "metadata": { + "id": "3cee77c1-0fcc-4151-b5f1-5c67870b5ab4" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# clip to country\n", + "world_pop_national = world_pop_glob.rio.clip_box(minx=country_bounds.minx.values[0],\n", + " miny=country_bounds.miny.values[0],\n", + " maxx=country_bounds.maxx.values[0],\n", + " maxy=country_bounds.maxy.values[0]\n", + " )\n", + "world_pop_national = world_pop_national.rio.clip(country_geom.values, world_pop_glob.rio.crs, drop=False)" + ] + }, + { + "cell_type": "markdown", + "id": "ab6155b2-3e5b-4f0d-85b1-0f45d5b5f31f", + "metadata": { + "id": "ab6155b2-3e5b-4f0d-85b1-0f45d5b5f31f" + }, + "source": [ + "The worldpop data, however, is stored as 1km by 1km grid. This will be too computationally intensive if we would use that resolution. As such, we reproject the to a lower resolution. This will help us to perform the analyis more smoothly. We use the *upscale_factor* as defined at the start of this subsection." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "0834501f-2e00-4dce-b91c-8101fcdffb74", + "metadata": { + "id": "0834501f-2e00-4dce-b91c-8101fcdffb74" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "new_width = int(world_pop_national.rio.width / upscale_factor)\n", + "new_height = int(world_pop_national.rio.height / upscale_factor)\n", + "\n", + "worldpop_Grided = world_pop_national.rio.reproject(\n", + " world_pop_national.rio.crs,\n", + " shape=(new_height, new_width),\n", + " resampling=Resampling.sum,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9ab78ba1-1d06-4a76-8092-b989a5842f00", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "256c59b1-c8cb-40ef-9d4d-6c30e05e2861", + "metadata": { + "id": "256c59b1-c8cb-40ef-9d4d-6c30e05e2861" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The output is df_pop_LUX as a dataframe of the population data of Luxembourg\n" + ] + } + ], + "source": [ + "df_worldpop_ = worldpop_Grided.band_data.to_dataframe()\n", + "df_worldpop_ = df_worldpop_.loc[~df_worldpop_.band_data.isna()].reset_index(drop=False)\n", + "\n", + "# create geometry values and drop lat lon columns\n", + "df_worldpop_['geometry'] = shapely.points(np.array(list(zip(df_worldpop_['x'],df_worldpop_['y']))))\n", + "\n", + "df_worldpop_ = gpd.GeoDataFrame(df_worldpop_.drop(['y','x','spatial_ref','band'],axis=1))\n", + "\n", + "# dynamically create a variable name for the DataFrame\n", + "globals()[f'df_pop_{country_iso3}'] = gpd.GeoDataFrame(df_worldpop_)\n", + "\n", + "# dynamically create a print statement that reflects the current country code\n", + "print(f\"The output is df_pop_{country_iso3} as a dataframe of the population data of {country_full_name}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "971cf678-5a60-4b22-af71-5bb7d9ab2302", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = globals()[f'df_pop_{country_iso3}'].plot()\n", + "ax.set_title(f'Population Points of {country_full_name}')\n", + "ax.set_xlabel('Longitude')\n", + "ax.set_ylabel('Latitude')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "02dbdbed-5e0a-425d-88c8-548d57c92d7d", + "metadata": { + "id": "02dbdbed-5e0a-425d-88c8-548d57c92d7d" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "55" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(globals()[f'df_pop_{country_iso3}'])" + ] + }, + { + "cell_type": "markdown", + "id": "32dab221-7a28-4719-a180-dc8a91c17b48", + "metadata": { + "id": "32dab221-7a28-4719-a180-dc8a91c17b48" + }, + "source": [ + "Our next step is to extract information of healthcare facilities for the country of interest. We do so using OpenStreetMap. With the latest version of geopandas, it is now possible to directly read **osm.pbf** files from OpenStreetMap.\n", + "\n", + "Healthcare facilities are stored as *multipolygons* within OpenStreetMap, and we want to download all clinics and hospitals." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "dbf8e926-52cc-4411-bc68-263f7cafaf1f", + "metadata": { + "id": "dbf8e926-52cc-4411-bc68-263f7cafaf1f" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\lif475\\AppData\\Local\\miniforge3\\envs\\pygis\\Lib\\site-packages\\pyogrio\\raw.py:196: RuntimeWarning: Non closed ring detected. To avoid accepting it, set the OGR_GEOMETRY_ACCEPT_UNCLOSED_RING configuration option to NO\n", + " return ogr_read(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: total: 45.5 s\n", + "Wall time: 1min 14s\n" + ] + } + ], + "source": [ + "%%time\n", + "Country_GeofabrikData_path = download.get_country_geofabrik(country_iso3)\n", + "#Country_GeofabrikData_path = \"C:\\\\Data\\\\country_osm\\\\albania-latest.osm.pbf\"\n", + "\n", + "HealthCenters = gpd.read_file(Country_GeofabrikData_path, layer=\"multipolygons\")\n", + "sub_types =['clinic', 'hospital']\n", + "HealthCenters = HealthCenters[HealthCenters['amenity'].isin(sub_types)].reset_index(drop=True)\n", + "HealthCenters = HealthCenters.to_crs(3857)\n", + "\n", + "# to convert polygons to their centroids\n", + "HealthCenters_centroids = HealthCenters.copy()\n", + "HealthCenters_centroids['geometry'] = HealthCenters.centroid\n", + "\n", + "HealthCenters_centroids=HealthCenters_centroids.to_crs(4326)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "cfbc0e8e-bf63-42c9-b9d0-3a095c0f8ca6", + "metadata": { + "id": "cfbc0e8e-bf63-42c9-b9d0-3a095c0f8ca6" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
osm_idosm_way_idnametypeaerowayamenityadmin_levelbarrierboundarybuilding...man_mademilitarynaturalofficeplaceshopsporttourismother_tagsgeometry
07591385NoneZithaKlinikmultipolygonNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"healthcare\"=>\"hospital\",\"operator\"=>\"Hôpitaux...MULTIPOLYGON (((682338.374 6377939.045, 682324...
117514812NoneHôpital KirchbergmultipolygonNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"healthcare\"=>\"hospital\",\"operator\"=>\"Hôpitaux...MULTIPOLYGON (((687383.596 6382856.727, 687394...
2None41407070Centre Hospitalier de LuxembourgNoneNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"healthcare\"=>\"hospital\",\"operator\"=>\"Centre H...MULTIPOLYGON (((679155.827 6380157.554, 679142...
3None56104142SénologieNoneNonehospitalNoneNoneNonehospital...NoneNoneNoneNoneNoneNoneNoneNoneNoneMULTIPOLYGON (((682993.032 6382614.167, 682977...
4None72872456Centre Hospitalier Émile MayrischNoneNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"healthcare\"=>\"hospital\",\"short_name\"=>\"CHEM\",...MULTIPOLYGON (((665744.712 6360608.875, 665747...
5None112389436Hôpital de la ville de DudelangeNoneNonehospitalNoneNoneNoneyes...NoneNoneNoneNoneNoneNoneNoneNone\"addr:city\"=>\"Dudelange\",\"addr:housenumber\"=>\"...MULTIPOLYGON (((677744.73 6355108.857, 677802....
6None189452987CHL EichNoneNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"addr:city\"=>\"Luxembourg\",\"addr:country\"=>\"LU\"...MULTIPOLYGON (((682944.085 6382574.363, 682938...
7None298655535Centre Médical de SteinselNoneNoneclinicNoneNoneNoneyes...NoneNoneNoneNoneNoneNoneNoneNone\"addr:city\"=>\"Steinsel\",\"addr:country\"=>\"LU\",\"...MULTIPOLYGON (((681904.484 6390283.25, 681900....
8None381951079Clinique Sainte MarieNoneNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"emergency\"=>\"yes\",\"healthcare\"=>\"hospital\",\"o...MULTIPOLYGON (((666416.525 6360368.193, 666413...
9None426571994Centre de réhabilitationNoneNonehospitalNoneNoneNoneyes...NoneNoneNoneNoneNoneNoneNoneNone\"addr:city\"=>\"Colpach-Bas\",\"addr:housenumber\"=...MULTIPOLYGON (((648445.596 6404692.12, 648459....
10None469054630Hôpital intercommunal de SteinfortNoneNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"healthcare\"=>\"hospital\"MULTIPOLYGON (((658123.935 6387823.318, 658132...
11None570707211Hôpital Intercommunal Princesse Marie-AstridNoneNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"healthcare\"=>\"hospital\"MULTIPOLYGON (((655334.714 6365873.121, 655368...
12None784862436Centre hospitalier Neuro-Psychiatrique (CHNP)NoneNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"healthcare\"=>\"hospital\"MULTIPOLYGON (((678665.866 6419086.54, 678705....
13None784866880RehazenterNoneNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"healthcare\"=>\"hospital\"MULTIPOLYGON (((687456.721 6382232.099, 687457...
14None884212046Centre Hospitalier du NordNoneNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"contact:email\"=>\"chdn@chdn.lu\",\"contact:fax\"=...MULTIPOLYGON (((678507.013 6421193.856, 678560...
15None887577792Centre Hospitalier du NordNoneNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"addr:city\"=>\"Wiltz\",\"addr:country\"=>\"LU\",\"add...MULTIPOLYGON (((660490.343 6440253.818, 660477...
\n", + "

16 rows × 26 columns

\n", + "
" + ], + "text/plain": [ + " osm_id osm_way_id name \\\n", + "0 7591385 None ZithaKlinik \n", + "1 17514812 None Hôpital Kirchberg \n", + "2 None 41407070 Centre Hospitalier de Luxembourg \n", + "3 None 56104142 Sénologie \n", + "4 None 72872456 Centre Hospitalier Émile Mayrisch \n", + "5 None 112389436 Hôpital de la ville de Dudelange \n", + "6 None 189452987 CHL Eich \n", + "7 None 298655535 Centre Médical de Steinsel \n", + "8 None 381951079 Clinique Sainte Marie \n", + "9 None 426571994 Centre de réhabilitation \n", + "10 None 469054630 Hôpital intercommunal de Steinfort \n", + "11 None 570707211 Hôpital Intercommunal Princesse Marie-Astrid \n", + "12 None 784862436 Centre hospitalier Neuro-Psychiatrique (CHNP) \n", + "13 None 784866880 Rehazenter \n", + "14 None 884212046 Centre Hospitalier du Nord \n", + "15 None 887577792 Centre Hospitalier du Nord \n", + "\n", + " type aeroway amenity admin_level barrier boundary building \\\n", + "0 multipolygon None hospital None None None None \n", + "1 multipolygon None hospital None None None None \n", + "2 None None hospital None None None None \n", + "3 None None hospital None None None hospital \n", + "4 None None hospital None None None None \n", + "5 None None hospital None None None yes \n", + "6 None None hospital None None None None \n", + "7 None None clinic None None None yes \n", + "8 None None hospital None None None None \n", + "9 None None hospital None None None yes \n", + "10 None None hospital None None None None \n", + "11 None None hospital None None None None \n", + "12 None None hospital None None None None \n", + "13 None None hospital None None None None \n", + "14 None None hospital None None None None \n", + "15 None None hospital None None None None \n", + "\n", + " ... man_made military natural office place shop sport tourism \\\n", + "0 ... None None None None None None None None \n", + "1 ... None None None None None None None None \n", + "2 ... None None None None None None None None \n", + "3 ... None None None None None None None None \n", + "4 ... None None None None None None None None \n", + "5 ... None None None None None None None None \n", + "6 ... None None None None None None None None \n", + "7 ... None None None None None None None None \n", + "8 ... None None None None None None None None \n", + "9 ... None None None None None None None None \n", + "10 ... None None None None None None None None \n", + "11 ... None None None None None None None None \n", + "12 ... None None None None None None None None \n", + "13 ... None None None None None None None None \n", + "14 ... None None None None None None None None \n", + "15 ... None None None None None None None None \n", + "\n", + " other_tags \\\n", + "0 \"healthcare\"=>\"hospital\",\"operator\"=>\"Hôpitaux... \n", + "1 \"healthcare\"=>\"hospital\",\"operator\"=>\"Hôpitaux... \n", + "2 \"healthcare\"=>\"hospital\",\"operator\"=>\"Centre H... \n", + "3 None \n", + "4 \"healthcare\"=>\"hospital\",\"short_name\"=>\"CHEM\",... \n", + "5 \"addr:city\"=>\"Dudelange\",\"addr:housenumber\"=>\"... \n", + "6 \"addr:city\"=>\"Luxembourg\",\"addr:country\"=>\"LU\"... \n", + "7 \"addr:city\"=>\"Steinsel\",\"addr:country\"=>\"LU\",\"... \n", + "8 \"emergency\"=>\"yes\",\"healthcare\"=>\"hospital\",\"o... \n", + "9 \"addr:city\"=>\"Colpach-Bas\",\"addr:housenumber\"=... \n", + "10 \"healthcare\"=>\"hospital\" \n", + "11 \"healthcare\"=>\"hospital\" \n", + "12 \"healthcare\"=>\"hospital\" \n", + "13 \"healthcare\"=>\"hospital\" \n", + "14 \"contact:email\"=>\"chdn@chdn.lu\",\"contact:fax\"=... \n", + "15 \"addr:city\"=>\"Wiltz\",\"addr:country\"=>\"LU\",\"add... \n", + "\n", + " geometry \n", + "0 MULTIPOLYGON (((682338.374 6377939.045, 682324... \n", + "1 MULTIPOLYGON (((687383.596 6382856.727, 687394... \n", + "2 MULTIPOLYGON (((679155.827 6380157.554, 679142... \n", + "3 MULTIPOLYGON (((682993.032 6382614.167, 682977... \n", + "4 MULTIPOLYGON (((665744.712 6360608.875, 665747... \n", + "5 MULTIPOLYGON (((677744.73 6355108.857, 677802.... \n", + "6 MULTIPOLYGON (((682944.085 6382574.363, 682938... \n", + "7 MULTIPOLYGON (((681904.484 6390283.25, 681900.... \n", + "8 MULTIPOLYGON (((666416.525 6360368.193, 666413... \n", + "9 MULTIPOLYGON (((648445.596 6404692.12, 648459.... \n", + "10 MULTIPOLYGON (((658123.935 6387823.318, 658132... \n", + "11 MULTIPOLYGON (((655334.714 6365873.121, 655368... \n", + "12 MULTIPOLYGON (((678665.866 6419086.54, 678705.... \n", + "13 MULTIPOLYGON (((687456.721 6382232.099, 687457... \n", + "14 MULTIPOLYGON (((678507.013 6421193.856, 678560... \n", + "15 MULTIPOLYGON (((660490.343 6440253.818, 660477... \n", + "\n", + "[16 rows x 26 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "HealthCenters" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "14c6ab08-56b0-49a2-8955-74a0bd8b6b7c", + "metadata": { + "id": "14c6ab08-56b0-49a2-8955-74a0bd8b6b7c" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The output is HealthCenters_centroids as a dataframe of the Health Centers\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"The output is HealthCenters_centroids as a dataframe of the Health Centers\")\n", + "\n", + "#plotting\n", + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "HealthCenters_centroids.plot(ax=ax, color='red', markersize=10, alpha=0.7)\n", + "\n", + "# temporarily reprojects to EPSG:3857 to add the basemap (contextily requires it)\n", + "#cx.add_basemap(ax, crs='EPSG:4326', source=cx.providers.OpenStreetMap.Mapnik, zoom=8)\n", + "\n", + "ax.set_title(f'Locations of Hospitals and Clinics in {country_full_name}', fontsize=15)\n", + "\n", + "ax.set_xlabel('Longitude')\n", + "ax.set_ylabel('Latitude')\n", + "plt.show()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "dc39cef6-eac9-40a0-a737-bac3cd5bc2a1", + "metadata": { + "id": "dc39cef6-eac9-40a0-a737-bac3cd5bc2a1" + }, + "source": [ + "## 3. Classification of rural and urban areas" + ] + }, + { + "cell_type": "markdown", + "id": "zotYyVnD4Jt2", + "metadata": { + "id": "zotYyVnD4Jt2" + }, + "source": [ + "## Set-up" + ] + }, + { + "cell_type": "markdown", + "id": "kzGnaMdm4Gpu", + "metadata": { + "id": "kzGnaMdm4Gpu" + }, + "source": [ + "Fix seed" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "JS9on1714Ega", + "metadata": { + "id": "JS9on1714Ega" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "### Set global seeds ###\n", + "seed = random_seed\n", + "np.random.seed(seed)\n", + "random.seed(seed)\n", + "os.environ['PYTHONHASHSEED'] = str(seed)" + ] + }, + { + "cell_type": "markdown", + "id": "-l1Sg8b54mQl", + "metadata": { + "id": "-l1Sg8b54mQl" + }, + "source": [ + "Set-up EE environment" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "11e5d73b-1a5b-4b7e-bcfd-7f0d8c75e67a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ee.Authenticate()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "3b64489e-3685-483e-b3ca-862c059b8b53", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ee.Initialize()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "lm4NPROn4loe", + "metadata": { + "id": "lm4NPROn4loe" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ee.Authenticate()\n", + "# ee.Initialize(project=\"YOUR_PROJECT_NAME\")\n", + "ee.Initialize()\n", + "# ee.Initialize(project=\"proj-gis-1234\")" + ] + }, + { + "cell_type": "markdown", + "id": "_tY_sxXy4SPA", + "metadata": { + "id": "_tY_sxXy4SPA" + }, + "source": [ + "## Set-up train/val data" + ] + }, + { + "cell_type": "markdown", + "id": "GRJQm_UT5uhV", + "metadata": { + "id": "GRJQm_UT5uhV" + }, + "source": [ + "Load training data - 2018, CLC" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "_I9OT5iF4iPe", + "metadata": { + "id": "_I9OT5iF4iPe" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define the region of interest (ROI)\n", + "\n", + "# netherlands = ee.FeatureCollection(\"FAO/GAUL/2015/level0\") \\\n", + "# .filter(ee.Filter.eq('ADM0_NAME', 'Luxembourg'))\n", + "\n", + "country_ROI = ee.FeatureCollection(\"FAO/GAUL/2015/level0\") \\\n", + " .filter(ee.Filter.eq('ADM0_NAME', country_full_name))\n", + "\n", + "# Load Sentinel-2 Image Collection\n", + "\n", + "# sentinel2 = ee.ImageCollection(\"COPERNICUS/S2_HARMONIZED\") \\\n", + "# .filterBounds(netherlands.geometry()) \\\n", + "# .filterDate('2018-06-01', '2018-08-31') \\\n", + "# .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 10))\n", + "\n", + "sentinel2 = ee.ImageCollection(\"COPERNICUS/S2_HARMONIZED\") \\\n", + " .filterBounds(country_ROI.geometry()) \\\n", + " .filterDate('2018-06-01', '2018-08-31') \\\n", + " .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 10))\n", + "\n", + "# Create a median composite to reduce cloud cover\n", + "\n", + "# mosaic_image = sentinel2.median().clip(netherlands.geometry())\n", + "\n", + "mosaic_image = sentinel2.median().clip(country_ROI.geometry())\n", + "# print(mosaic_image.getInfo())" + ] + }, + { + "cell_type": "markdown", + "id": "FPiX1Imk6Oom", + "metadata": { + "id": "FPiX1Imk6Oom" + }, + "source": [ + "Show on map to verify that it's loaded" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "gUE7-YsH5oim", + "metadata": { + "id": "gUE7-YsH5oim" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "28477c2e658b47e8ac4978d5a4c0a786", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map(center=[51.37, 4.999999999999999], controls=(WidgetControl(options=['position', 'transparent_bg'], widget=…" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# point = ee.Geometry.Point([5.0, 51.37])\n", + "# bands = ['B4', 'B3', 'B2'] # Fill in this list yourself\n", + "# vis_params = {'max': 3000, 'bands':bands} # Limit upper range so you can see detail\n", + "\n", + "# map = geemap.Map(height=800,width=700,center=[52.37,4.5],zoom=7)\n", + "# map.centerObject(point, 8)\n", + "# map.addLayer(mosaic_image, vis_params, \"Sentinel-2_2018\")\n", + "# map" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "8695048c-7977-441e-b5ba-478ff30fb05d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# from ipywidgets import Widget\n", + "# Widget.widget_types\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "f260da2e-5799-4dbb-ab09-e3e01a637e56", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# !jupyter nbextension enable --py --sys-prefix ipyleaflet\n", + "# !jupyter nbextension enable --py --sys-prefix widgetsnbextension\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "c9346505-212e-496f-a45b-2cae417f196b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# jupyter nbextension enable --py --sys-prefix ipyleaflet\n", + "# jupyter nbextension enable --py --sys-prefix widgetsnbextension\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "b6a80c9d-fe85-493c-a337-ae669250e54f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "eb2c61d83e6a426c97721f9cf16be8fc", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map(center=[49.77709292899147, 6.095157795443893], controls=(WidgetControl(options=['position', 'transparent_b…" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bands = ['B4', 'B3', 'B2'] \n", + "vis_params = {'max': 3000, 'bands': bands} \n", + "\n", + "map = geemap.Map(height=800, width=700)\n", + "\n", + "# dynamically center the map on the selected country (ROI)\n", + "map.centerObject(country_ROI.geometry(), 8)\n", + "\n", + "# Add the mosaic image layer for the selected country\n", + "map.addLayer(mosaic_image, vis_params, \"Sentinel-2_2018\")\n", + "\n", + "map" + ] + }, + { + "cell_type": "markdown", + "id": "8NEzm7xf7weH", + "metadata": { + "id": "8NEzm7xf7weH" + }, + "source": [ + "Calculate variables to include" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "cjMQF_E57zIw", + "metadata": { + "id": "cjMQF_E57zIw" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def make_s2_variables(s2_image):\n", + " # Calculate additional spectral indices\n", + " dvi = s2_image.select('B5').subtract(s2_image.select('B4')).rename('DVI')\n", + " ndvi = s2_image.normalizedDifference(['B5', 'B4']).rename('NDVI')\n", + " ndwi = s2_image.normalizedDifference(['B3', 'B5']).rename('NDWI')\n", + "\n", + " # Add indices to the image\n", + " s2_image = s2_image.addBands([dvi, ndvi, ndwi])\n", + "\n", + " # Define neighborhood size (e.g., 3x3)\n", + " kernel = ee.Kernel.square(radius=1)\n", + "\n", + " # Calculate neighborhood statistics\n", + " neighborhood_vars = []\n", + " for band in ['DVI', 'NDVI', 'NDWI']:\n", + " mean = s2_image.select(band).reduceNeighborhood(\n", + " reducer=ee.Reducer.mean(),\n", + " kernel=kernel\n", + " ).rename(f'{band}_mean')\n", + "\n", + " std_dev = s2_image.select(band).reduceNeighborhood(\n", + " reducer=ee.Reducer.stdDev(),\n", + " kernel=kernel\n", + " ).rename(f'{band}_stdDev')\n", + "\n", + " # Add additional statistics here, such as min, max, etc., if desired.\n", + "\n", + " # Append neighborhood bands to the list\n", + " neighborhood_vars.extend([mean, std_dev])\n", + "\n", + " # Add neighborhood statistics to the image\n", + " s2_image = s2_image.addBands(neighborhood_vars)\n", + "\n", + " return s2_image" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "oCmPdaR_74Ml", + "metadata": { + "id": "oCmPdaR_74Ml" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mosaic_image = make_s2_variables(mosaic_image)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "sTScJDPp8dew", + "metadata": { + "id": "sTScJDPp8dew" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Use these bands for prediction\n", + "bands = ['B2', 'B3', 'B4', 'B5', 'B6', 'B7']\n", + "indices = ['NDVI', 'DVI', 'NDWI'] # Add your index(es) band here\n", + "img_bands = [*bands, *indices]" + ] + }, + { + "cell_type": "markdown", + "id": "44mziLGt8j4C", + "metadata": { + "id": "44mziLGt8j4C" + }, + "source": [ + "Sample CORINE" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "Pacg8UcH8lHe", + "metadata": { + "id": "Pacg8UcH8lHe" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "CLC = ee.Image('COPERNICUS/CORINE/V20/100m/2012').select('landcover').clip(mosaic_image.geometry())\n", + "lc_points = CLC.sample(\n", + " **{\n", + " 'region': mosaic_image.geometry(),\n", + " 'scale': 30,\n", + " 'numPixels': 10000,\n", + " 'seed': 0,\n", + " 'geometries': True,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "n7hzNmOt8tY5", + "metadata": { + "id": "n7hzNmOt8tY5" + }, + "source": [ + "Reclassify to binary urban/rural" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "Hm4xWGNS8ugv", + "metadata": { + "id": "Hm4xWGNS8ugv" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def generalize_clc_class(feature):\n", + " lc_value = ee.String(feature.get('landcover'))\n", + "\n", + " # Check if the first character is '1'\n", + " set_value = ee.Algorithms.If(lc_value.slice(0, 1).equals('1'), 1, 0)\n", + "\n", + " # Set the new binary value for the 'landcover' property\n", + " return feature.set('landcover', set_value)\n", + "lc_reference_pts = lc_points.map(generalize_clc_class)" + ] + }, + { + "cell_type": "markdown", + "id": "th9Dl6mW9FII", + "metadata": { + "id": "th9Dl6mW9FII" + }, + "source": [ + "Make train/validation splits" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "X1tMXV4w9LFb", + "metadata": { + "id": "X1tMXV4w9LFb" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define the land cover labels column\n", + "label_col = 'landcover'\n", + "\n", + "# Filter points by label\n", + "positive_points = lc_reference_pts.filter(ee.Filter.eq(label_col, 1))\n", + "negative_points = lc_reference_pts.filter(ee.Filter.eq(label_col, 0))\n", + "\n", + "# Allow a maximum of 2-to-1 difference in negative vs positive class sampling\n", + "positive_sample = positive_points.randomColumn('random').limit(positive_points.size())\n", + "negative_sample = negative_points.randomColumn('random').limit(positive_points.size().multiply(ee.Number(2)))\n", + "\n", + "# Merge the samples\n", + "balanced_sample = positive_sample.merge(negative_sample)\n", + "\n", + "# Split into training and validation sets\n", + "training_sample = balanced_sample.filter('random <= 0.8')\n", + "validation_sample = balanced_sample.filter('random > 0.8')\n", + "\n", + "# Sample regions for training and validation datasets\n", + "train_data = mosaic_image.select(img_bands).sampleRegions(\n", + " collection=training_sample, properties=[label_col], scale=100\n", + ")\n", + "\n", + "val_data = mosaic_image.select(img_bands).sampleRegions(\n", + " collection=validation_sample, properties=[label_col], scale=100\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "uS-6YzaM9P5-", + "metadata": { + "id": "uS-6YzaM9P5-" + }, + "source": [ + "## Optimize on train/val" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "Hv5M5UkT9X39", + "metadata": { + "id": "Hv5M5UkT9X39" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Train the model\n", + "classifier = ee.Classifier.smileRandomForest(numberOfTrees=100, minLeafPopulation=2, maxNodes=50)\n", + "trained_classifier = classifier.train(features=train_data, classProperty=label_col, inputProperties=img_bands)\n", + "\n", + "# Apply the classifier to the validation data\n", + "classified_val = val_data.classify(trained_classifier)" + ] + }, + { + "cell_type": "markdown", + "id": "_r0pZTPq97XH", + "metadata": { + "id": "_r0pZTPq97XH" + }, + "source": [ + "Calculate metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "q7SJuNRd9Zmm", + "metadata": { + "id": "q7SJuNRd9Zmm" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Validation Metrics:\n", + "Accuracy: 0.8302828618968386\n", + "Precision: 0.9427860696517413\n", + "Recall: 0.8275109170305677\n", + "Kappa: 0.587547433861722\n" + ] + } + ], + "source": [ + "# Calculate metrics\n", + "confusion_matrix = classified_val.errorMatrix(label_col, 'classification')\n", + "val_accuracy = confusion_matrix.accuracy()\n", + "precision = confusion_matrix.producersAccuracy().get([0, 0]) # Replace [0, 0] with the indices for the desired class if needed\n", + "recall = confusion_matrix.consumersAccuracy().get([0, 0]) # Replace [0, 0] with the indices for the desired class if needed\n", + "kappa = confusion_matrix.kappa()\n", + "\n", + "# Package all metrics into a single dictionary\n", + "metrics = ee.Dictionary({\n", + " 'Accuracy': val_accuracy,\n", + " 'Precision': precision,\n", + " 'Recall': recall,\n", + " 'Kappa': kappa\n", + "})\n", + "\n", + "# Retrieve all metrics in one call\n", + "metrics_info = metrics.getInfo()\n", + "\n", + "# Print all metrics\n", + "print('Validation Metrics:')\n", + "print(f\"Accuracy: {metrics_info['Accuracy']}\")\n", + "print(f\"Precision: {metrics_info['Precision']}\")\n", + "print(f\"Recall: {metrics_info['Recall']}\")\n", + "print(f\"Kappa: {metrics_info['Kappa']}\")" + ] + }, + { + "cell_type": "markdown", + "id": "BnOR_Ouk-CFv", + "metadata": { + "id": "BnOR_Ouk-CFv" + }, + "source": [ + "## Run on test set" + ] + }, + { + "cell_type": "markdown", + "id": "YjIzJ6rR-mje", + "metadata": { + "id": "YjIzJ6rR-mje" + }, + "source": [ + "Load August 2024 test image" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "cL9G2aPS-ba2", + "metadata": { + "id": "cL9G2aPS-ba2" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define the region of interest (ROI)\n", + "netherlands = ee.FeatureCollection(\"FAO/GAUL/2015/level0\") \\\n", + " .filter(ee.Filter.eq('ADM0_NAME', 'Luxembourg'))\n", + "\n", + "# Load Sentinel-2 Image Collection\n", + "test_sentinel2 = ee.ImageCollection(\"COPERNICUS/S2_HARMONIZED\") \\\n", + " .filterBounds(netherlands.geometry()) \\\n", + " .filterDate('2024-08-01', '2024-08-31') \\\n", + " .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 10))\n", + "\n", + "# Create a median composite to reduce cloud cover\n", + "test_mosaic_image = sentinel2.median().clip(netherlands.geometry())\n", + "\n", + "# Add variables\n", + "test_mosaic_image = make_s2_variables(test_mosaic_image)" + ] + }, + { + "cell_type": "markdown", + "id": "sx8nFANn-o_2", + "metadata": { + "id": "sx8nFANn-o_2" + }, + "source": [ + "Sample small amount of pixels for testing" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "xjwC0pny-rJu", + "metadata": { + "id": "xjwC0pny-rJu" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "CLC = ee.Image('COPERNICUS/CORINE/V20/100m/2018').select('landcover').clip(test_mosaic_image.geometry())\n", + "test_lc_points = CLC.sample(\n", + " **{\n", + " 'region': test_mosaic_image.geometry(),\n", + " 'scale': 30, #check what the resolution of corine land cover is\n", + " 'numPixels': 10000,\n", + " 'seed': 0,\n", + " 'geometries': True, # Set this to False to ignore geometries\n", + " }\n", + ")\n", + "test_lc_reference_pts = test_lc_points.map(generalize_clc_class) # Make sure to reclassify\n", + "\n", + "test_data = test_mosaic_image.select(img_bands).sampleRegions(\n", + " **{'collection': test_lc_reference_pts, 'properties': [label_col], 'scale': 100}\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "LxwRLRfN-zD9", + "metadata": { + "id": "LxwRLRfN-zD9" + }, + "source": [ + "Test set metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "T9eLm7JA-0HB", + "metadata": { + "id": "T9eLm7JA-0HB" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Validation Metrics:\n", + "Accuracy: 0.9283\n", + "Precision: 0.9593205185516317\n", + "Recall: 0.9605012867852747\n", + "Kappa: 0.6209046600069962\n" + ] + } + ], + "source": [ + "classified_test = test_data.classify(trained_classifier)\n", + "\n", + "# Calculate the confusion matrix and server-side metrics\n", + "confusion_matrix = classified_test.errorMatrix(label_col, 'classification')\n", + "test_accuracy = confusion_matrix.accuracy()\n", + "precision = confusion_matrix.producersAccuracy().get([0, 0]) # Replace [0, 0] with the indices for the desired class if needed\n", + "recall = confusion_matrix.consumersAccuracy().get([0, 0]) # Replace [0, 0] with the indices for the desired class if needed\n", + "kappa = confusion_matrix.kappa()\n", + "\n", + "# Package all metrics into a single dictionary\n", + "metrics = ee.Dictionary({\n", + " 'Accuracy': test_accuracy,\n", + " 'Precision': precision,\n", + " 'Recall': recall,\n", + " 'Kappa': kappa\n", + "})\n", + "\n", + "# Retrieve all metrics in one call\n", + "metrics_info = metrics.getInfo()\n", + "\n", + "# Print all metrics\n", + "print('Validation Metrics:')\n", + "print(f\"Accuracy: {metrics_info['Accuracy']}\")\n", + "print(f\"Precision: {metrics_info['Precision']}\")\n", + "print(f\"Recall: {metrics_info['Recall']}\")\n", + "print(f\"Kappa: {metrics_info['Kappa']}\")" + ] + }, + { + "cell_type": "markdown", + "id": "ZMXJCmTj-9YV", + "metadata": { + "id": "ZMXJCmTj-9YV" + }, + "source": [ + "Run classifier on entire test image" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "waPHnNLv-76V", + "metadata": { + "id": "waPHnNLv-76V" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "eb2c61d83e6a426c97721f9cf16be8fc", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map(center=[49.77709292899147, 6.095157795443893], controls=(WidgetControl(options=['position', 'transparent_b…" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Classify the test image\n", + "classified_image = test_mosaic_image.classify(trained_classifier)\n", + "\n", + "# Define the color mapping dictionary\n", + "clc_colors = {\n", + " 0: '#FFFFFF', # Rural\n", + " 1: '#000000', # Urban\n", + "}\n", + "\n", + "# Convert string labels to numeric codes\n", + "def classify_to_numeric(image):\n", + " # Create a dictionary that maps string labels to numeric values\n", + " label_to_numeric = {label: index for index, label in enumerate(clc_colors.keys())}\n", + "\n", + " # Convert string label to numeric value\n", + " return image.remap(\n", + " list(label_to_numeric.keys()),\n", + " list(label_to_numeric.values())\n", + " )\n", + "\n", + "# Convert the classified image\n", + "numeric_classified_image = classify_to_numeric(classified_image)\n", + "\n", + "# Generate a palette for visualization\n", + "palette = [clc_colors[label] for label in clc_colors.keys()]\n", + "\n", + "# Add the numeric classified image to the map\n", + "map.addLayer(numeric_classified_image, {'palette': palette, 'min': 0, 'max': len(clc_colors) - 1}, 'Classified Image')\n", + "map" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "p-rnLVS8-bdZ", + "metadata": { + "id": "p-rnLVS8-bdZ" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "22c88ff1bebd46198ed0d15e024120e6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map(center=[51.37, 4.999999999999999], controls=(WidgetControl(options=['position', 'transparent_bg'], widget=…" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# point = ee.Geometry.Point([5.0, 51.37])\n", + "# bands = ['B4', 'B3', 'B2'] # Fill in this list yourself\n", + "# vis_params = {'max': 3000, 'bands':bands} # Limit upper range so you can see detail\n", + "\n", + "# map = geemap.Map(height=800,width=700,center=[52.37,4.5],zoom=7)\n", + "# map.centerObject(point, 8)\n", + "# map.addLayer(test_mosaic_image, vis_params, \"test-Sentinel-2_2024\")\n", + "# map" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "883eb0bb-2196-4acc-bbb8-1fdc13448a66", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ad9d5eb56d864f548bb04947c83ff33d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map(center=[49.77709292899147, 6.095157795443893], controls=(WidgetControl(options=['position', 'transparent_b…" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "country_center = country_ROI.geometry().centroid()\n", + "\n", + "\n", + "bands = ['B4', 'B3', 'B2'] # Fill in the bands yourself\n", + "vis_params = {'max': 3000, 'bands': bands} # Limit upper range so you can see detail\n", + "\n", + "\n", + "map = geemap.Map(height=800, width=700)\n", + "\n", + "# dynamically center the map on the selected country's center\n", + "map.centerObject(country_ROI.geometry(), 8) \n", + "\n", + "map.addLayer(test_mosaic_image, vis_params, \"test-Sentinel-2_2024\")\n", + "\n", + "map" + ] + }, + { + "cell_type": "markdown", + "id": "LPkrvHduHyDh", + "metadata": { + "id": "LPkrvHduHyDh" + }, + "source": [ + "Load computed layer to local environment" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "m1FjLJVzH2Zt", + "metadata": { + "id": "m1FjLJVzH2Zt" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating URL ...\n", + "Downloading data from https://earthengine.googleapis.com/v1/projects/earthengine-legacy/thumbnails/a14d3f6634316aa0f4ba8aa3d1d2dbff-6ff583178c3d996d7033c97edff50aa8:getPixels\n", + "Please wait ...\n", + "Data downloaded to C:\\Users\\lif475\\OneDrive - Vrije Universiteit Amsterdam\\Documents\\BigDatainSustainabilitySciences\\urban_rural_raster.tif\n" + ] + } + ], + "source": [ + "geemap.ee_export_image(classified_image, filename='urban_rural_raster.tif', scale=1000, file_per_band=False)\n", + "urban_raster = rio.open('urban_rural_raster.tif')" + ] + }, + { + "cell_type": "markdown", + "id": "309a00a2-8aa1-4091-b575-31d56622fd16", + "metadata": {}, + "source": [ + "## 3. Overlay urban and rural classification with population and Healthcare information" + ] + }, + { + "cell_type": "markdown", + "id": "7055a7db-636f-4a56-a954-62ed2546328e", + "metadata": {}, + "source": [ + "1 is urban, 0 is rural" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "f456e034-5e81-4d99-9093-0380af66e8f9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "urban_rural = xr.open_dataarray('urban_rural_raster.tif')\n", + "\n", + "values = urban_rural.sel(\n", + " {\n", + " urban_rural.rio.x_dim: xr.DataArray(df_worldpop_.geometry.x),\n", + " urban_rural.rio.y_dim: xr.DataArray(df_worldpop_.geometry.y),\n", + " },\n", + " method=\"nearest\",\n", + " ).values[0]\n", + "df_worldpop_[\"urban_rural\"] = values\n", + "df_worldpop_[\"urban_rural\"] = df_worldpop_[\"urban_rural\"].fillna(0)" + ] + }, + { + "cell_type": "markdown", + "id": "573f10a4-d0bb-4675-903f-71ffbe45358f", + "metadata": { + "id": "573f10a4-d0bb-4675-903f-71ffbe45358f" + }, + "source": [ + "## 4. Clustering of Healthcare centres and population" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "09511e34-85cc-43f8-b994-a5d3f53431c2", + "metadata": { + "id": "09511e34-85cc-43f8-b994-a5d3f53431c2" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
clustergeometrypopulationnearest_hospital_local_idnearest_hospital_geometry
00POINT (6.15153 49.58031)128651.3125001POINT (6.128297306122917 49.60370244201384)
11POINT (6.32931 49.67302)15717.3925782POINT (6.176428597947581 49.63239294648283)
22POINT (6.01819 49.71938)18540.7695318POINT (6.125497137582533 49.67548286167413)
33POINT (6.40338 49.79663)9557.7675782POINT (6.176428597947581 49.63239294648283)
44POINT (5.92931 49.53396)73094.47656212POINT (5.886631289416359 49.53288914400383)
55POINT (6.15153 49.4876)35154.4765626POINT (6.088783750195296 49.47060587499345)
66POINT (6.46264 49.67302)5642.8691412POINT (6.176428597947581 49.63239294648283)
77POINT (6.13671 49.70392)62590.1953128POINT (6.125497137582533 49.67548286167413)
88POINT (6.01819 49.53396)61558.2968759POINT (5.985821590636188 49.50122369975853)
99POINT (5.81079 49.87389)13944.09765610POINT (5.8253568378630085 49.75912164639861)
1010POINT (5.88486 49.71938)25226.89453111POINT (5.912548337712089 49.66095224406815)
1111POINT (5.79597 49.53396)33223.04687512POINT (5.886631289416359 49.53288914400383)
1212POINT (6.24042 49.81208)23956.86718813POINT (6.096093002090682 49.84290986563605)
1313POINT (6.32931 49.53396)21879.98828114POINT (6.173860418724089 49.62791052823996)
1414POINT (6.09227 49.93569)28763.84375015POINT (6.095520633026102 49.85367960759491)
1515POINT (5.95153 50.07862)20551.43945316POINT (5.934042808032211 49.96544643315656)
\n", + "
" + ], + "text/plain": [ + " cluster geometry population \\\n", + "0 0 POINT (6.15153 49.58031) 128651.312500 \n", + "1 1 POINT (6.32931 49.67302) 15717.392578 \n", + "2 2 POINT (6.01819 49.71938) 18540.769531 \n", + "3 3 POINT (6.40338 49.79663) 9557.767578 \n", + "4 4 POINT (5.92931 49.53396) 73094.476562 \n", + "5 5 POINT (6.15153 49.4876) 35154.476562 \n", + "6 6 POINT (6.46264 49.67302) 5642.869141 \n", + "7 7 POINT (6.13671 49.70392) 62590.195312 \n", + "8 8 POINT (6.01819 49.53396) 61558.296875 \n", + "9 9 POINT (5.81079 49.87389) 13944.097656 \n", + "10 10 POINT (5.88486 49.71938) 25226.894531 \n", + "11 11 POINT (5.79597 49.53396) 33223.046875 \n", + "12 12 POINT (6.24042 49.81208) 23956.867188 \n", + "13 13 POINT (6.32931 49.53396) 21879.988281 \n", + "14 14 POINT (6.09227 49.93569) 28763.843750 \n", + "15 15 POINT (5.95153 50.07862) 20551.439453 \n", + "\n", + " nearest_hospital_local_id nearest_hospital_geometry \n", + "0 1 POINT (6.128297306122917 49.60370244201384) \n", + "1 2 POINT (6.176428597947581 49.63239294648283) \n", + "2 8 POINT (6.125497137582533 49.67548286167413) \n", + "3 2 POINT (6.176428597947581 49.63239294648283) \n", + "4 12 POINT (5.886631289416359 49.53288914400383) \n", + "5 6 POINT (6.088783750195296 49.47060587499345) \n", + "6 2 POINT (6.176428597947581 49.63239294648283) \n", + "7 8 POINT (6.125497137582533 49.67548286167413) \n", + "8 9 POINT (5.985821590636188 49.50122369975853) \n", + "9 10 POINT (5.8253568378630085 49.75912164639861) \n", + "10 11 POINT (5.912548337712089 49.66095224406815) \n", + "11 12 POINT (5.886631289416359 49.53288914400383) \n", + "12 13 POINT (6.096093002090682 49.84290986563605) \n", + "13 14 POINT (6.173860418724089 49.62791052823996) \n", + "14 15 POINT (6.095520633026102 49.85367960759491) \n", + "15 16 POINT (5.934042808032211 49.96544643315656) " + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "### 1st step: Add Local_ID to the HealthCenters_centroids GeoDataFrame\n", + "HealthCenters_centroids['Local_ID'] = range(1, len(HealthCenters_centroids) + 1)\n", + "\n", + "# 2nd step Ensure both GeoDataFrames are in the same CRS (EPSG:4326)\n", + "HealthCenters_centroids = HealthCenters_centroids.to_crs(epsg=4326)\n", + "\n", + "# Convert geometries to a list of coordinates (for KMeans)\n", + "pop_coords = np.array([(geom.x, geom.y) for geom in df_worldpop_['geometry']])\n", + "pop_band_data = df_worldpop_['band_data'].values\n", + "\n", + "# Extract the hospital coordinates\n", + "hospital_coords = np.array([(geom.x, geom.y) for geom in HealthCenters_centroids['geometry']])\n", + "hospital_local_ids = HealthCenters_centroids['Local_ID'].values\n", + "hospital_geometries = HealthCenters_centroids['geometry'].values\n", + "\n", + "### 3rd step 3: K-Means\n", + "kmeans = KMeans(n_clusters=len(hospital_coords), random_state=random_seed, init=hospital_coords, n_init=1) # is using hospital locations as initial centers\n", + "df_worldpop_['cluster'] = kmeans.fit_predict(pop_coords)\n", + "\n", + "# get cluster centers (latitude and longitude)\n", + "cluster_centers = kmeans.cluster_centers_\n", + "\n", + "### 4th step: calculate the sum of population in each cluster\n", + "df_worldpop_['cluster_population'] = df_worldpop_.groupby('cluster')['band_data'].transform('sum')\n", + "\n", + "# Create a new DataFrame for clusters and their population sums\n", + "clusters_df = pd.DataFrame({\n", + " 'cluster': range(len(cluster_centers)),\n", + " 'geometry': [Point(x, y) for x, y in cluster_centers],\n", + " 'population': df_worldpop_.groupby('cluster')['band_data'].sum().values\n", + "})\n", + "\n", + "### 5th step: assign the nearest hospital to the related cluster\n", + "# distances between each cluster center and each health facility center\n", + "distances = cdist(cluster_centers, hospital_coords, metric='euclidean')\n", + "\n", + "# the index of the nearest hospital for each cluster\n", + "nearest_hospital_idx = distances.argmin(axis=1)\n", + "\n", + "# assign the nearest hospital Local_ID and geometry to each cluster center\n", + "clusters_df['nearest_hospital_local_id'] = [hospital_local_ids[idx] for idx in nearest_hospital_idx]\n", + "clusters_df['nearest_hospital_geometry'] = [hospital_geometries[idx] for idx in nearest_hospital_idx]\n", + "\n", + "### 6th step: convert to GeoDataFrame and set CRS\n", + "clusters_gdf = gpd.GeoDataFrame(clusters_df, geometry='geometry')\n", + "clusters_gdf.set_crs(epsg=4326, inplace=True)\n", + "\n", + "# check the content of the resutled clusters dataframe\n", + "clusters_gdf" + ] + }, + { + "cell_type": "markdown", + "id": "5299738d-567d-4473-aaa2-095dede18b92", + "metadata": { + "id": "5299738d-567d-4473-aaa2-095dede18b92" + }, + "source": [ + "## 5. Explore and evaluate baseline results\n", + "\n", + "### Plot clusters" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "4735e1af-5573-4baf-83d7-6644ac7a3ad5", + "metadata": { + "id": "4735e1af-5573-4baf-83d7-6644ac7a3ad5" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "clusters_gdf['x'] = clusters_gdf.geometry.x\n", + "clusters_gdf['y'] = clusters_gdf.geometry.y\n", + "\n", + "\n", + "HealthCenters_centroids['x'] = HealthCenters_centroids.geometry.x\n", + "HealthCenters_centroids['y'] = HealthCenters_centroids.geometry.y\n", + "\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 8))\n", + "\n", + "\n", + "ax.scatter(clusters_gdf['x'], clusters_gdf['y'], color='black', marker='o', s=20, label='Cluster Centers')\n", + "\n", + "\n", + "for x, y, label in zip(clusters_gdf['x'], clusters_gdf['y'], clusters_gdf['cluster']):\n", + " ax.text(x, y, str(label), fontsize=12, color='k')\n", + "\n", + "ax.scatter(HealthCenters_centroids['x'], HealthCenters_centroids['y'], color='red', marker='+', s=100, label='Hospitals')\n", + "\n", + "\n", + "plt.title(\"Cluster Centers with their IDs and Hospital Locations\")\n", + "plt.xlabel(\"Longitude\")\n", + "plt.ylabel(\"Latitude\")\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "e3d8e5a3-ffa1-4b1a-a3e6-0e569346a78c", + "metadata": { + "id": "e3d8e5a3-ffa1-4b1a-a3e6-0e569346a78c" + }, + "source": [ + "### Identify and plot population per healthcare facility" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "dc10ce38-c177-4925-a677-82c5770e9034", + "metadata": { + "id": "dc10ce38-c177-4925-a677-82c5770e9034" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Local_IDpopulation
01128651.312500
1112106317.523438
7881130.968750
8961558.296875
5635154.476562
1230918.029297
141528763.843750
101125226.894531
121323956.867188
131421879.988281
151620551.439453
91013944.097656
\n", + "
" + ], + "text/plain": [ + " Local_ID population\n", + "0 1 128651.312500\n", + "11 12 106317.523438\n", + "7 8 81130.968750\n", + "8 9 61558.296875\n", + "5 6 35154.476562\n", + "1 2 30918.029297\n", + "14 15 28763.843750\n", + "10 11 25226.894531\n", + "12 13 23956.867188\n", + "13 14 21879.988281\n", + "15 16 20551.439453\n", + "9 10 13944.097656" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# to calculate the total population in demand of services from each hospital\n", + "hospital_population = clusters_gdf.groupby('nearest_hospital_local_id')['population'].sum().reset_index()\n", + "\n", + "hospital_population_merged = HealthCenters_centroids.merge(hospital_population, left_on='Local_ID', right_on='nearest_hospital_local_id', how='left')\n", + "\n", + "hospital_population_merged[['Local_ID', 'population']].sort_values('population',ascending=False).dropna()" + ] + }, + { + "cell_type": "markdown", + "id": "12b83859-6dfc-4b28-8fd6-297b66ed737a", + "metadata": { + "id": "12b83859-6dfc-4b28-8fd6-297b66ed737a", + "scrolled": true + }, + "source": [ + "hospital_population_merged.plot('population')" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "bb07e73c-84a2-466f-a644-4fa313b82dec", + "metadata": { + "id": "bb07e73c-84a2-466f-a644-4fa313b82dec" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "following hospitals are not assigned to any cluster:\n", + " Local_ID geometry\n", + "2 3 POINT (6.1006 49.61807)\n", + "3 4 POINT (6.13542 49.63111)\n", + "4 5 POINT (5.98155 49.5017)\n", + "6 7 POINT (6.13554 49.6317)\n" + ] + } + ], + "source": [ + "assigned_hospitals = clusters_gdf['nearest_hospital_local_id'].unique()\n", + "\n", + "unassigned_hospitals = HealthCenters_centroids[~HealthCenters_centroids['Local_ID'].isin(assigned_hospitals)]\n", + "\n", + "if unassigned_hospitals.empty:\n", + " print(\"All hospitals are assigned to at least one cluster.\")\n", + "else:\n", + " print(\"following hospitals are not assigned to any cluster:\")\n", + " print(unassigned_hospitals[['Local_ID', 'geometry']])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "281204e5-8a50-4225-9521-3313a1bbf3ba", + "metadata": { + "id": "281204e5-8a50-4225-9521-3313a1bbf3ba" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "3740a61b-f60c-427c-aab5-6e6e552dc57d", + "metadata": { + "id": "3740a61b-f60c-427c-aab5-6e6e552dc57d" + }, + "source": [ + "## 6. Natural hazard disruption" + ] + }, + { + "cell_type": "markdown", + "id": "52d30a9c-2515-4aff-ae5f-9a15cd343061", + "metadata": { + "id": "52d30a9c-2515-4aff-ae5f-9a15cd343061" + }, + "source": [ + "### Download flood data\n", + "The flood data we will extract from a repository maintained by the European Commission Joint Research Centre. We will download river flood hazard maps from their [Flood Data Collection](https://data.jrc.ec.europa.eu/dataset/1d128b6c-a4ee-4858-9e34-6210707f3c81).\n", + "\n", + "Here we do not need to use an API and we also do not need to register ourselves, so we can download any of the files directly. To do so, we use the `urllib` package." + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "47875fb5-c347-4ef7-bd27-ee0ef75f29af", + "metadata": { + "id": "47875fb5-c347-4ef7-bd27-ee0ef75f29af" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## this is the link to the 1/100 flood map for Europe\n", + "zipurl = 'https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/FLOODS/EuropeanMaps/floodMap_RP500.zip'\n", + "\n", + "# The path where the downloaded flood map will be extracted, this is the folder of this Google Collaboratory instance. NOTE: a new instance will have this directory be cleared.\n", + "# data_path = \"\"\n", + "data_path =r'C:\\Users\\lif475\\OneDrive - Vrije Universiteit Amsterdam\\Documents\\BigDatainSustainabilityScience'\n", + "# and now we open and extract the data\n", + "with urlopen(zipurl) as zipresp:\n", + " with ZipFile(BytesIO(zipresp.read())) as zfile:\n", + " zfile.extractall(data_path)" + ] + }, + { + "cell_type": "markdown", + "id": "b0ff5f7a-fc94-4429-82bb-2a94da72a887", + "metadata": { + "id": "b0ff5f7a-fc94-4429-82bb-2a94da72a887" + }, + "source": [ + "### Overlay flood data with population centroids" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "e73f0bce-ad12-4be5-acbb-33dd7e2fa18d", + "metadata": { + "id": "e73f0bce-ad12-4be5-acbb-33dd7e2fa18d" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "flood_map_path = \"floodmap_EFAS_RP500_C.tif\"" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "28d110b6-0ef4-41c6-acb2-8673597decd2", + "metadata": { + "id": "28d110b6-0ef4-41c6-acb2-8673597decd2" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset> Size: 12GB\n",
+       "Dimensions:      (band: 1, x: 63976, y: 45242)\n",
+       "Coordinates:\n",
+       "  * band         (band) int64 8B 1\n",
+       "  * x            (x) float64 512kB 9.19e+05 9.19e+05 ... 7.316e+06 7.316e+06\n",
+       "  * y            (y) float64 362kB 5.441e+06 5.44e+06 ... 9.166e+05 9.164e+05\n",
+       "    spatial_ref  int64 8B ...\n",
+       "Data variables:\n",
+       "    band_data    (band, y, x) float32 12GB ...
" + ], + "text/plain": [ + " Size: 12GB\n", + "Dimensions: (band: 1, x: 63976, y: 45242)\n", + "Coordinates:\n", + " * band (band) int64 8B 1\n", + " * x (x) float64 512kB 9.19e+05 9.19e+05 ... 7.316e+06 7.316e+06\n", + " * y (y) float64 362kB 5.441e+06 5.44e+06 ... 9.166e+05 9.164e+05\n", + " spatial_ref int64 8B ...\n", + "Data variables:\n", + " band_data (band, y, x) float32 12GB ..." + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "flood_map = xr.open_dataset(flood_map_path, engine=\"rasterio\")\n", + "flood_map" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "f14da004-413e-4856-80e6-fdaa544aca9d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "KeyboardInterrupt\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "flood_map_data = flood_map['band_data'].isel(band=0)\n", + "\n", + "plt.figure(figsize=(10, 8))\n", + "flood_map_data.plot(cmap='Blues')\n", + "plt.title(\"Flood Map RP500\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "399149ac-8ee4-4459-971e-9a2003ab4b4e", + "metadata": { + "id": "399149ac-8ee4-4459-971e-9a2003ab4b4e" + }, + "source": [ + "### Overlay flood data with healthcare facilities" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "024a09f7-6f1c-4e15-a810-cf407daa2a48", + "metadata": { + "id": "024a09f7-6f1c-4e15-a810-cf407daa2a48" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def _get_damage_per_object(asset, curves, cell_area_m2):\n", + " \"\"\"\n", + " Calculate damage for a given asset based on hazard information.\n", + " Arguments:\n", + " *asset*: Tuple containing information about the asset. It includes:\n", + " - Index or identifier of the asset (asset[0]).\n", + " - Asset-specific information, including hazard points (asset[1]['hazard_point']).\n", + " *maxdam_dict*: Maximum damage value.\n", + " Returns:\n", + " *tuple*: A tuple containing the asset index or identifier and the calculated damage.\n", + " \"\"\"\n", + "\n", + " if asset.geometry.geom_type in (\"Polygon\", \"MultiPolygon\"):\n", + " coverage = asset[\"coverage\"] * cell_area_m2\n", + " elif asset.geometry.geom_type in (\"LineString\", \"MultiLineString\"):\n", + " coverage = asset[\"coverage\"]\n", + " elif asset.geometry.geom_type in (\"Point\"):\n", + " coverage = 1\n", + " else:\n", + " raise ValueError(f\"Geometry type {asset.geometry.geom_type} not supported\")\n", + "\n", + " return (\n", + " np.sum(\n", + " np.interp(\n", + " asset[\"values\"], curves.index, curves[asset[\"amenity\"]].values\n", + " )\n", + " * coverage\n", + " )\n", + " * asset[\"maximum_damage\"]\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "48ec7696-a117-4302-9276-ac37fa369cd2", + "metadata": { + "id": "48ec7696-a117-4302-9276-ac37fa369cd2" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "maxdam = {\"hospital\":2000,\n", + " \"clinic\":1500,\n", + "}\n", + "\n", + "curves = np.array(\n", + " [[0,0],\n", + " [50,0.2],\n", + " [100,0.4],\n", + " [150,0.6],\n", + " [200,0.8],\n", + " [250,1]])\n", + "\n", + "curves = np.concatenate((curves,\n", + " np.transpose(np.array([curves[:,1]]*(len(maxdam)-1)))),\n", + " axis=1)\n", + "\n", + "curves = pd.DataFrame(curves)\n", + "curves.columns = ['depth']+list(maxdam.keys())\n", + "curves.set_index('depth',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "26e1e323-d30b-4e40-8a89-a25d1e475234", + "metadata": { + "id": "26e1e323-d30b-4e40-8a89-a25d1e475234", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "NameError", + "evalue": "name 'exact_extract' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[80], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m values_and_coverage_per_object \u001b[38;5;241m=\u001b[39m \u001b[43mexact_extract\u001b[49m(\n\u001b[0;32m 2\u001b[0m flood_map,\n\u001b[0;32m 3\u001b[0m HealthCenters,\n\u001b[0;32m 4\u001b[0m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcoverage\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalues\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m 5\u001b[0m output\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpandas\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 6\u001b[0m )\n", + "\u001b[1;31mNameError\u001b[0m: name 'exact_extract' is not defined" + ] + } + ], + "source": [ + "values_and_coverage_per_object = exact_extract(\n", + " flood_map,\n", + " HealthCenters,\n", + " [\"coverage\", \"values\"],\n", + " output=\"pandas\",\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "811245a1-d794-4b0d-b604-1d31c2507d97", + "metadata": { + "id": "811245a1-d794-4b0d-b604-1d31c2507d97" + }, + "outputs": [], + "source": [ + "HealthCenters = HealthCenters.merge(values_and_coverage_per_object,left_index=True,right_index=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "115d6cc9-aeb9-4497-9c0e-78f7c8ef19b8", + "metadata": { + "id": "115d6cc9-aeb9-4497-9c0e-78f7c8ef19b8" + }, + "outputs": [], + "source": [ + "HealthCenters['maximum_damage'] = HealthCenters.amenity.apply(lambda x: maxdam[x])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "291a4fba-40c1-4397-a7f2-8b1665ca7ef5", + "metadata": { + "id": "291a4fba-40c1-4397-a7f2-8b1665ca7ef5" + }, + "outputs": [], + "source": [ + "HealthCenters['damage'] = HealthCenters.apply(\n", + " lambda _object: _get_damage_per_object(_object, curves, cell_area_m2=100*100),\n", + " axis=1,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "913f9757-3151-46f9-9427-f54fa58d8beb", + "metadata": { + "id": "913f9757-3151-46f9-9427-f54fa58d8beb" + }, + "outputs": [], + "source": [ + "damage" + ] + }, + { + "cell_type": "markdown", + "id": "af40b670-4810-473b-81d8-7466852d85a1", + "metadata": { + "id": "af40b670-4810-473b-81d8-7466852d85a1" + }, + "source": [ + "### Recompute clustering without affected healthcare facilities" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "151a2c6a-9f28-41ad-a78c-38517e5545fd", + "metadata": { + "id": "151a2c6a-9f28-41ad-a78c-38517e5545fd" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "160dafed-34d4-44c8-8d9d-aa44c8dfb621", + "metadata": { + "id": "160dafed-34d4-44c8-8d9d-aa44c8dfb621" + }, + "source": [ + "## 7. Visualize and summarize final results" + ] + }, + { + "cell_type": "markdown", + "id": "c412b628-014e-41ba-8c41-0722098ad006", + "metadata": { + "id": "c412b628-014e-41ba-8c41-0722098ad006" + }, + "source": [ + "- population affected (and changed distance / hospital allocation)\n", + "- hospitals affected\n", + "- differences in urban and rural accessibility" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9463fcad-2b88-44c7-9389-8c7c9a94cbf4", + "metadata": { + "id": "9463fcad-2b88-44c7-9389-8c7c9a94cbf4" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [ + "573f10a4-d0bb-4675-903f-71ffbe45358f", + "5299738d-567d-4473-aaa2-095dede18b92" + ], + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/TAA4/.ipynb_checkpoints/TAA4_V5-checkpoint.ipynb b/TAA4/.ipynb_checkpoints/TAA4_V5-checkpoint.ipynb new file mode 100644 index 0000000..db938d2 --- /dev/null +++ b/TAA4/.ipynb_checkpoints/TAA4_V5-checkpoint.ipynb @@ -0,0 +1,4916 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ee037ada-1068-4f35-91ae-30c2ea52ca01", + "metadata": { + "id": "ee037ada-1068-4f35-91ae-30c2ea52ca01" + }, + "source": [ + "# TAA4: Accessibility to healthcare facilities" + ] + }, + { + "cell_type": "markdown", + "id": "c6036316-479d-4438-b9e7-e31cf7e9051c", + "metadata": { + "id": "c6036316-479d-4438-b9e7-e31cf7e9051c" + }, + "source": [ + "In this tutorial, we wrap up what you have learned in TAA1-3 by demonstrating their applications and connections. For a European country of your choice, we collect data on the population and their health facilities. Using a classification method, we add rural and urban features to the population data points. Then, with a clustering algorithm, we group population points based on their coordinates and assign each cluster to its closest hospitals. This allows us to calculate the total urban and rural demand for each hospital. Next, we assess the impact of flooding on the hospitals, distinguishing between damaged and undamaged facilities.\n", + "\n", + "In the aftermath of the flood, the number of hospitals in service has changed. Your task will be to repeat the clustering process, assign the new clusters to the intact hospitals, and then calculate their urban and rural demand in the post-flood conditions.\n", + "\n", + "### Important before we start\n", + "---\n", + "Make sure that you save this file before you continue, else you will lose everything. To do so, go to **Bestand/File** and click on **Een kopie opslaan in Drive/Save a Copy on Drive**!" + ] + }, + { + "cell_type": "markdown", + "id": "75f3efb3-f86a-443e-b87a-22653c771143", + "metadata": { + "id": "75f3efb3-f86a-443e-b87a-22653c771143" + }, + "source": [ + "## Learning Objectives\n", + "\n", + "- To extract, prepare and manipulate geospatial information\n", + "\n", + "- To run a classification algorithm to identify urban and rural land use.\n", + "\n", + "- To overlay raster and vector information.\n", + "\n", + "- To cluster different geospatial layers.\n", + "\n", + "- To visualise geospatial information.\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "59d989b6-9cc8-4a39-a17b-c96c463711dd", + "metadata": { + "id": "59d989b6-9cc8-4a39-a17b-c96c463711dd" + }, + "source": [ + "## Prepare the packages\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b06c7b0f-2f83-4f86-ab73-0e8d889bd164", + "metadata": { + "id": "b06c7b0f-2f83-4f86-ab73-0e8d889bd164" + }, + "outputs": [], + "source": [ + "!pip install -q rasterio rioxarray contextily osm_flex exact_extract" + ] + }, + { + "cell_type": "markdown", + "id": "bee1cfab-03df-433e-913e-62de5c0076f4", + "metadata": { + "id": "bee1cfab-03df-433e-913e-62de5c0076f4" + }, + "source": [ + "Now we will import these packages in the cell below:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ffc10f5f-a43d-4f8f-91b1-ac7afc347ab4", + "metadata": { + "id": "ffc10f5f-a43d-4f8f-91b1-ac7afc347ab4" + }, + "outputs": [], + "source": [ + "# Standard Library Imports\n", + "import os\n", + "import sys\n", + "from pathlib import Path\n", + "from datetime import datetime\n", + "from zipfile import ZipFile\n", + "from io import BytesIO\n", + "import random\n", + "import requests\n", + "from urllib.request import urlopen\n", + "\n", + "# Data Manipulation and Analysis\n", + "import numpy as np\n", + "import pandas as pd\n", + "import geopandas as gpd\n", + "import xarray as xr\n", + "import rioxarray as rxr\n", + "\n", + "# Machine Learning\n", + "import sklearn # General import if other sklearn modules are needed\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.cluster import KMeans\n", + "\n", + "# Geometry and Spatial Analysis\n", + "import shapely\n", + "from shapely.geometry import Point\n", + "import rasterio as rio\n", + "from rasterio.enums import Resampling\n", + "from scipy.spatial.distance import cdist\n", + "\n", + "# Earth Engine and Geospatial Libraries\n", + "import ee\n", + "import geemap\n", + "import contextily as cx\n", + "import osm_flex\n", + "from osm_flex import download\n", + "from exactextract import exact_extract\n", + "\n", + "# Visualization\n", + "import matplotlib.pyplot as plt\n", + "import contextily as ctx\n", + "from tqdm import tqdm \n", + "from IPython.display import clear_output" + ] + }, + { + "cell_type": "markdown", + "id": "bb50fef4-f456-46ca-aff7-a838766fb127", + "metadata": { + "id": "bb50fef4-f456-46ca-aff7-a838766fb127" + }, + "source": [ + "## 2. Data download and preparation\n", + "\n", + "Define a country of your interest and a size for gridding and a randomSeed" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "da8b4246-1b98-455c-89ce-999e21e5dd27", + "metadata": { + "id": "da8b4246-1b98-455c-89ce-999e21e5dd27", + "outputId": "19481c7f-23a6-4e4d-d34e-caed04646a65" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "country_full_name = 'Slovenia'\n", + "country_iso3 = 'SVN'\n", + "upscale_factor = 10 #Km\n", + "\n", + "### Set global seeds ###\n", + "random_seed = 1\n", + "np.random.seed(random_seed)\n", + "random.seed(random_seed)\n", + "os.environ['PYTHONHASHSEED'] = str(random_seed)" + ] + }, + { + "cell_type": "markdown", + "id": "6d8c4878-9662-4de4-9c69-b9ec0af9cda3", + "metadata": { + "id": "6d8c4878-9662-4de4-9c69-b9ec0af9cda3" + }, + "source": [ + "Here, we download the population data from WorldPop, an open source platform. Select the country of interest from the WorldPop [website](https://hub.worldpop.org/geodata/listing?id=62) and add the link to the URL below." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6eb55f91-caba-443b-a72b-688bce077b6b", + "metadata": { + "id": "6eb55f91-caba-443b-a72b-688bce077b6b", + "outputId": "5cdbc571-9669-4108-9cec-2d204189b5a3" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "url = \"https://data.worldpop.org/GIS/Population/Global_2000_2020/2018/0_Mosaicked/ppp_2018_1km_Aggregated.tif\"\n", + "\n", + "file_name = 'ppp_2018_1km_Aggregated.tif'\n", + "\n", + "#open(file_name, 'wb').write(requests.get(url).content)\n", + "\n", + "file_name = \"C:\\\\Data\\\\Global_Geospatial\\\\worldpop\\\\ppp_2018_1km_Aggregated.tif\"\n", + "\n", + "\n", + "world_pop_glob = xr.open_dataset(file_name,engine='rasterio')" + ] + }, + { + "cell_type": "markdown", + "id": "84d4cc8e-d8fc-495a-9337-bfb06958a553", + "metadata": { + "id": "84d4cc8e-d8fc-495a-9337-bfb06958a553" + }, + "source": [ + "Now, we use a file with country borders from Natural Earth, to get boundries of the country of your interest." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "36d356c0-167b-4d4c-bfa1-67e6f3dfee46", + "metadata": { + "id": "36d356c0-167b-4d4c-bfa1-67e6f3dfee46", + "outputId": "ac5d3f50-fac8-4159-ffec-41b3d4147127" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "world = gpd.read_file(\"https://github.com/nvkelso/natural-earth-vector/raw/master/10m_cultural/ne_10m_admin_0_countries.shp\")\n", + "# And we want to take the country boundaries and geometry\n", + "country_bounds = world.loc[world.ADM0_ISO == country_iso3].bounds\n", + "country_geom = world.loc[world.ADM0_ISO == country_iso3].geometry" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d0dbb83b-a8b4-48ee-821e-3d156677455f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "103 POLYGON ((13.64292 45.45943, 13.64282 45.45945...\n", + "Name: geometry, dtype: geometry" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "country_geom" + ] + }, + { + "cell_type": "markdown", + "id": "2d131107-2f4f-4f39-9850-df988197ee62", + "metadata": { + "id": "2d131107-2f4f-4f39-9850-df988197ee62" + }, + "source": [ + "Now, we use the derived boundries to clip the population data from worldpop, to get the population of our coutnry." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3cee77c1-0fcc-4151-b5f1-5c67870b5ab4", + "metadata": { + "id": "3cee77c1-0fcc-4151-b5f1-5c67870b5ab4", + "outputId": "b2ac98f6-4aab-456d-ffe1-beb2dca3562b" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# clip to country\n", + "world_pop_national = world_pop_glob.rio.clip_box(minx=country_bounds.minx.values[0],\n", + " miny=country_bounds.miny.values[0],\n", + " maxx=country_bounds.maxx.values[0],\n", + " maxy=country_bounds.maxy.values[0]\n", + " )\n", + "world_pop_national = world_pop_national.rio.clip(country_geom.values, world_pop_glob.rio.crs, drop=False)" + ] + }, + { + "cell_type": "markdown", + "id": "ab6155b2-3e5b-4f0d-85b1-0f45d5b5f31f", + "metadata": { + "id": "ab6155b2-3e5b-4f0d-85b1-0f45d5b5f31f" + }, + "source": [ + "The worldpop data, however, is stored as 1km by 1km grid. This will be too computationally intensive if we would use that resolution. As such, we reproject the to a lower resolution. This will help us to perform the analyis more smoothly. We use the *upscale_factor* as defined at the start of this subsection." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "0834501f-2e00-4dce-b91c-8101fcdffb74", + "metadata": { + "id": "0834501f-2e00-4dce-b91c-8101fcdffb74", + "outputId": "9249e16c-68b7-4761-a8ad-e36a1f3175d2" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "new_width = int(world_pop_national.rio.width / upscale_factor)\n", + "new_height = int(world_pop_national.rio.height / upscale_factor)\n", + "\n", + "worldpop_Grided = world_pop_national.rio.reproject(\n", + " world_pop_national.rio.crs,\n", + " shape=(new_height, new_width),\n", + " resampling=Resampling.sum,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "17062d77-f2a0-47ff-ac8b-759d09fc8b97", + "metadata": { + "id": "9ab78ba1-1d06-4a76-8092-b989a5842f00" + }, + "source": [ + "Now we remove the missing data from our data points and create a GeoDataFrame for our country. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "256c59b1-c8cb-40ef-9d4d-6c30e05e2861", + "metadata": { + "id": "256c59b1-c8cb-40ef-9d4d-6c30e05e2861", + "outputId": "5da3bf85-5663-49bd-803c-0046f63ec4ac" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The output is df_pop_SVN as a dataframe of the population data of Slovenia\n" + ] + } + ], + "source": [ + "df_worldpop_ = worldpop_Grided.band_data.to_dataframe()\n", + "df_worldpop_ = df_worldpop_.loc[~df_worldpop_.band_data.isna()].reset_index(drop=False)\n", + "\n", + "# create geometry values and drop lat lon columns\n", + "df_worldpop_['geometry'] = shapely.points(np.array(list(zip(df_worldpop_['x'],df_worldpop_['y']))))\n", + "\n", + "df_worldpop_ = gpd.GeoDataFrame(df_worldpop_.drop(['y','x','spatial_ref','band'],axis=1))\n", + "\n", + "# dynamically create a variable name for the DataFrame\n", + "globals()[f'df_pop_{country_iso3}'] = gpd.GeoDataFrame(df_worldpop_)\n", + "\n", + "# dynamically create a print statement that reflects the current country code\n", + "print(f\"The output is df_pop_{country_iso3} as a dataframe of the population data of {country_full_name}\")" + ] + }, + { + "cell_type": "markdown", + "id": "9d91ef78-094c-40f4-8142-3f100934ccbc", + "metadata": {}, + "source": [ + "And Lets plot the population points of our country of interest" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "971cf678-5a60-4b22-af71-5bb7d9ab2302", + "metadata": { + "id": "971cf678-5a60-4b22-af71-5bb7d9ab2302", + "outputId": "001a9a7f-be5c-4df3-cc5a-28e44e39d1c7" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = globals()[f'df_pop_{country_iso3}'].plot()\n", + "ax.set_title(f'Population Points of {country_full_name}')\n", + "ax.set_xlabel('Longitude')\n", + "ax.set_ylabel('Latitude')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "02dbdbed-5e0a-425d-88c8-548d57c92d7d", + "metadata": { + "id": "02dbdbed-5e0a-425d-88c8-548d57c92d7d", + "outputId": "81bf44e3-0117-4979-dfff-ae1d7842470f" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "396" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(globals()[f'df_pop_{country_iso3}'])" + ] + }, + { + "cell_type": "markdown", + "id": "32dab221-7a28-4719-a180-dc8a91c17b48", + "metadata": { + "id": "32dab221-7a28-4719-a180-dc8a91c17b48" + }, + "source": [ + "Our next step is to extract information of healthcare facilities for the country of interest. We do so using OpenStreetMap. With the latest version of geopandas, it is now possible to directly read **osm.pbf** files from OpenStreetMap.\n", + "\n", + "Healthcare facilities are stored as *multipolygons* within OpenStreetMap, and we want to download all clinics and hospitals." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "dbf8e926-52cc-4411-bc68-263f7cafaf1f", + "metadata": { + "id": "dbf8e926-52cc-4411-bc68-263f7cafaf1f", + "outputId": "41a6e348-4234-4890-fb96-a4dd0943c47d" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: total: 0 ns\n", + "Wall time: 0 ns\n" + ] + } + ], + "source": [ + "%%time\n", + "Country_GeofabrikData_path = download.get_country_geofabrik(country_iso3)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "22f32bc2-8a4b-4af8-a8c6-2494ed27ee89", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\eks510\\.conda\\envs\\pygis\\Lib\\site-packages\\pyogrio\\raw.py:196: RuntimeWarning: Non closed ring detected. To avoid accepting it, set the OGR_GEOMETRY_ACCEPT_UNCLOSED_RING configuration option to NO\n", + " return ogr_read(\n" + ] + } + ], + "source": [ + "HealthCenters = gpd.read_file(Country_GeofabrikData_path, layer=\"multipolygons\")\n", + "sub_types =['clinic', 'hospital']\n", + "HealthCenters = HealthCenters[HealthCenters['amenity'].isin(sub_types)].reset_index(drop=True)\n", + "HealthCenters = HealthCenters.to_crs(3857)\n", + "\n", + "# to convert polygons to their centroids\n", + "HealthCenters_centroids = HealthCenters.copy()\n", + "HealthCenters_centroids['geometry'] = HealthCenters.centroid\n", + "\n", + "HealthCenters_centroids=HealthCenters_centroids.to_crs(4326)" + ] + }, + { + "cell_type": "markdown", + "id": "c57009a2-caa8-4963-b37b-a1a5503a6be9", + "metadata": {}, + "source": [ + "Let's check the content of our generated HealthCenters_centroids GeoDataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "cfbc0e8e-bf63-42c9-b9d0-3a095c0f8ca6", + "metadata": { + "id": "cfbc0e8e-bf63-42c9-b9d0-3a095c0f8ca6", + "outputId": "5468bc08-d8e2-4404-8f28-fdd3a7186a41" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
osm_idosm_way_idnametypeaerowayamenityadmin_levelbarrierboundarybuilding...man_mademilitarynaturalofficeplaceshopsporttourismother_tagsgeometry
016172NonePorodnišnica LjubljanamultipolygonNonehospitalNoneNoneNonehospital...NoneNoneNoneNoneNoneNoneNoneNone\"addr:city\"=>\"Ljubljana\",\"addr:housenumber\"=>\"...MULTIPOLYGON (((1616891.57 5788942.854, 161685...
11735820NoneREHA Radkersburg Klinik Maria TheresiamultipolygonNoneclinicNoneNoneNoneyes...NoneNoneNoneNoneNoneNoneNoneNone\"addr:city\"=>\"Bad Radkersburg\",\"addr:housenumb...MULTIPOLYGON (((1778635.005 5891252.415, 17786...
22226607NoneUniverzitetni rehabilitacijski inštitut Republ...multipolygonNonehospitalNoneNoneNonehospital...NoneNoneNoneNoneNoneNoneNoneNone\"addr:city\"=>\"Ljubljana\",\"addr:housenumber\"=>\"...MULTIPOLYGON (((1616842.545 5791255.347, 16168...
33449006NoneZdravstveni dom Ljubljana - RudnikmultipolygonNoneclinicNoneNoneNoneyes...NoneNoneNoneNoneNoneNoneNoneNone\"addr:city\"=>\"Ljubljana\",\"addr:housenumber\"=>\"...MULTIPOLYGON (((1616791.327 5786197.784, 16167...
45229321NonePsihiatrična bolnišnicamultipolygonNonehospitalNoneNoneNoneyes...NoneNoneNoneNoneNoneNoneNoneNone\"addr:city\"=>\"Begunje na Gorenjskem\",\"addr:hou...MULTIPOLYGON (((1580944.926 5840918.384, 15808...
\n", + "

5 rows × 26 columns

\n", + "
" + ], + "text/plain": [ + " osm_id osm_way_id name \\\n", + "0 16172 None Porodnišnica Ljubljana \n", + "1 1735820 None REHA Radkersburg Klinik Maria Theresia \n", + "2 2226607 None Univerzitetni rehabilitacijski inštitut Republ... \n", + "3 3449006 None Zdravstveni dom Ljubljana - Rudnik \n", + "4 5229321 None Psihiatrična bolnišnica \n", + "\n", + " type aeroway amenity admin_level barrier boundary building ... \\\n", + "0 multipolygon None hospital None None None hospital ... \n", + "1 multipolygon None clinic None None None yes ... \n", + "2 multipolygon None hospital None None None hospital ... \n", + "3 multipolygon None clinic None None None yes ... \n", + "4 multipolygon None hospital None None None yes ... \n", + "\n", + " man_made military natural office place shop sport tourism \\\n", + "0 None None None None None None None None \n", + "1 None None None None None None None None \n", + "2 None None None None None None None None \n", + "3 None None None None None None None None \n", + "4 None None None None None None None None \n", + "\n", + " other_tags \\\n", + "0 \"addr:city\"=>\"Ljubljana\",\"addr:housenumber\"=>\"... \n", + "1 \"addr:city\"=>\"Bad Radkersburg\",\"addr:housenumb... \n", + "2 \"addr:city\"=>\"Ljubljana\",\"addr:housenumber\"=>\"... \n", + "3 \"addr:city\"=>\"Ljubljana\",\"addr:housenumber\"=>\"... \n", + "4 \"addr:city\"=>\"Begunje na Gorenjskem\",\"addr:hou... \n", + "\n", + " geometry \n", + "0 MULTIPOLYGON (((1616891.57 5788942.854, 161685... \n", + "1 MULTIPOLYGON (((1778635.005 5891252.415, 17786... \n", + "2 MULTIPOLYGON (((1616842.545 5791255.347, 16168... \n", + "3 MULTIPOLYGON (((1616791.327 5786197.784, 16167... \n", + "4 MULTIPOLYGON (((1580944.926 5840918.384, 15808... \n", + "\n", + "[5 rows x 26 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "HealthCenters.head()" + ] + }, + { + "cell_type": "markdown", + "id": "c364c46d-40ca-47a3-babe-c0ee55ecb880", + "metadata": {}, + "source": [ + "And let's visualize the hospitals locations." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "14c6ab08-56b0-49a2-8955-74a0bd8b6b7c", + "metadata": { + "id": "14c6ab08-56b0-49a2-8955-74a0bd8b6b7c", + "outputId": "2560df0b-9f7b-4972-fcf4-1e25bd6b7217" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The output is HealthCenters_centroids as a dataframe of the Health Centers\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"The output is HealthCenters_centroids as a dataframe of the Health Centers\")\n", + "\n", + "#plotting\n", + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "HealthCenters_centroids.plot(ax=ax, color='red', markersize=10, alpha=0.7)\n", + "\n", + "# temporarily reprojects to EPSG:3857 to add the basemap (contextily requires it)\n", + "#cx.add_basemap(ax, crs='EPSG:4326', source=cx.providers.OpenStreetMap.Mapnik, zoom=8)\n", + "\n", + "ax.set_title(f'Locations of Hospitals and Clinics in {country_full_name}', fontsize=15)\n", + "\n", + "ax.set_xlabel('Longitude')\n", + "ax.set_ylabel('Latitude')\n", + "plt.show()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "dc39cef6-eac9-40a0-a737-bac3cd5bc2a1", + "metadata": { + "id": "dc39cef6-eac9-40a0-a737-bac3cd5bc2a1" + }, + "source": [ + "## 3. Classification of rural and urban areas" + ] + }, + { + "cell_type": "markdown", + "id": "M2QYz4j0EBah", + "metadata": { + "id": "M2QYz4j0EBah" + }, + "source": [ + "As you remember, we checked the content of the population data, and there was no information regarding urban or rural areas. Therefore, we need to add this information to our dataset. So, we will use an approach similar to what you learned in TAA1, but for the sake of processing efficiency, we will use Earth Engine's built-in classifier packages rather than scikit-learn. In this situation we are interested in only two land cover classes: rural and urban. We will have to reclassify the raster, train a model to distinguish between these timesteps, and finally perform the classification on recent images.\n", + "\n", + " We have pre-filled most of the code in this section, and you will use a few new tricks to perform the classification. Beyond that, we challenge you to leverage what you have learned in TAA1 to improve the performance of the model, by any means you see fit. You will be evaluated on your reasoning, as well as the creativity of the approaches." + ] + }, + { + "cell_type": "markdown", + "id": "zotYyVnD4Jt2", + "metadata": { + "id": "zotYyVnD4Jt2" + }, + "source": [ + "### Set-up" + ] + }, + { + "cell_type": "markdown", + "id": "-l1Sg8b54mQl", + "metadata": { + "id": "-l1Sg8b54mQl" + }, + "source": [ + "First, set up your Earth Engine environment as you did before." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "i1hx3v6pEMva", + "metadata": { + "id": "i1hx3v6pEMva" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ee.Authenticate()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "a76108be-d449-46a6-98e9-be3f17ae1888", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ee.Initialize(project=\"proj-gis-1234\")" + ] + }, + { + "cell_type": "markdown", + "id": "_tY_sxXy4SPA", + "metadata": { + "id": "_tY_sxXy4SPA" + }, + "source": [ + "### Set-up training & validation data" + ] + }, + { + "cell_type": "markdown", + "id": "rToXuPnoET3C", + "metadata": { + "id": "rToXuPnoET3C" + }, + "source": [ + "We will make use of the following data sources:\n", + "1. Administrative country boundary to clip to the area of interest \n", + "2. Sentinel-2 satellite images at 10x10m resolution\n", + "3. CORINE Land Cover (CLC)\n", + "\n", + "For the satellite image, we take the S2 median image over the summer to improve sensitivity to single-capture conditions. The median image is taken over the summer, so that we exclude seasonal dynamics. Finally, we clip this image to the country extent.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "_I9OT5iF4iPe", + "metadata": { + "id": "_I9OT5iF4iPe", + "outputId": "65deb07b-7f0a-4382-b519-459ab71bbb7e" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define the region of interest (ROI)\n", + "country_ROI = ee.FeatureCollection(\"FAO/GAUL/2015/level0\") \\\n", + " .filter(ee.Filter.eq('ADM0_NAME', country_full_name))\n", + "\n", + "# Load Sentinel-2 Image Collection\n", + "sentinel2 = ee.ImageCollection(\"COPERNICUS/S2_HARMONIZED\") \\\n", + " .filterBounds(country_ROI.geometry()) \\\n", + " .filterDate('2018-06-01', '2018-08-31') \\\n", + " .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 10))\n", + "\n", + "# Create a median composite to reduce cloud cover\n", + "mosaic_image = sentinel2.median().clip(country_ROI.geometry())" + ] + }, + { + "cell_type": "markdown", + "id": "FPiX1Imk6Oom", + "metadata": { + "id": "FPiX1Imk6Oom" + }, + "source": [ + "Show on map to verify that it's loaded" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "98176961-f52f-4859-8ff9-9b55d2eb7347", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1397cb265f0847a09763b85957dcf482", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map(center=[46.12445795068446, 14.826893950462633], controls=(WidgetControl(options=['position', 'transparent_…" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bands = ['B4', 'B3', 'B2'] # Fill in this list yourself\n", + "vis_params = {'max': 3000, 'bands': bands} # Limit upper range so you can see detail\n", + "\n", + "map = geemap.Map(height=800, width=700, zoom=7)\n", + "\n", + "# dynamically center the map on the selected country (ROI)\n", + "map.centerObject(country_ROI.geometry(), 8)\n", + "\n", + "# adding the mosaic image layer for the selected country\n", + "map.addLayer(mosaic_image, vis_params, \"Sentinel-2_2018\")\n", + "\n", + "map" + ] + }, + { + "cell_type": "markdown", + "id": "c9NfiE5dFr19", + "metadata": { + "id": "c9NfiE5dFr19" + }, + "source": [ + "### Calculate image variables to include" + ] + }, + { + "cell_type": "markdown", + "id": "1gGMF-NEGN84", + "metadata": { + "id": "1gGMF-NEGN84" + }, + "source": [ + "Next, we will compute variables to use for our classifier. We have provided the examples you've already seen for TAA1, but we challenge you to add your own variables. You can get inspiration from anywhere, but be sure that you are able to explain the reasoning behind including each variable." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "cjMQF_E57zIw", + "metadata": { + "id": "cjMQF_E57zIw", + "outputId": "95107040-bb80-4b50-ea20-9ac63bd3c95f" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def make_s2_variables(s2_image):\n", + " # Calculate additional spectral indices\n", + " dvi = s2_image.select('B5').subtract(s2_image.select('B4')).rename('DVI')\n", + " ndvi = s2_image.normalizedDifference(['B5', 'B4']).rename('NDVI')\n", + " ndwi = s2_image.normalizedDifference(['B3', 'B5']).rename('NDWI')\n", + "\n", + " # Add indices to the image\n", + " s2_image = s2_image.addBands([dvi, ndvi, ndwi])\n", + "\n", + " '''\n", + " ToDo: Add your own variables!\n", + "\n", + " Think about what you learned in TAA1 - how else can you compute information\n", + " from the spectral bands to include? Look at some papers for inspiration,\n", + " or try something out yourself. There's more than just indices that can help here!\n", + "\n", + " Don't forget to add them to the image after you create them!\n", + " '''\n", + " ### TEACHER EXAMPLE - REMOVE BEFORE GOES LIVE. ###\n", + "\n", + " # Define neighborhood size (e.g., 3x3)\n", + " kernel = ee.Kernel.square(radius=1)\n", + "\n", + " # Calculate neighborhood statistics\n", + " neighborhood_vars = []\n", + " for band in ['DVI', 'NDVI', 'NDWI']:\n", + " mean = s2_image.select(band).reduceNeighborhood(\n", + " reducer=ee.Reducer.mean(),\n", + " kernel=kernel\n", + " ).rename(f'{band}_mean')\n", + "\n", + " std_dev = s2_image.select(band).reduceNeighborhood(\n", + " reducer=ee.Reducer.stdDev(),\n", + " kernel=kernel\n", + " ).rename(f'{band}_stdDev')\n", + "\n", + " # Add additional statistics here, such as min, max, etc., if desired.\n", + "\n", + " # Append neighborhood bands to the list\n", + " neighborhood_vars.extend([mean, std_dev])\n", + "\n", + " # Add neighborhood statistics to the image\n", + " s2_image = s2_image.addBands(neighborhood_vars)\n", + "\n", + " ### End of teaching example ###\n", + "\n", + " return s2_image" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "oCmPdaR_74Ml", + "metadata": { + "id": "oCmPdaR_74Ml", + "outputId": "f971149c-89b2-421c-ebe1-2fb3ddfb51ff" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mosaic_image = make_s2_variables(mosaic_image)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "sTScJDPp8dew", + "metadata": { + "id": "sTScJDPp8dew", + "outputId": "59b81db2-2a46-4f87-e968-a772e927eec8" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Use these bands for prediction\n", + "# DON'T FORGET to add your own variables when you make them!\n", + "bands = ['B2', 'B3', 'B4', 'B5', 'B6', 'B7']\n", + "indices = ['NDVI', 'DVI']\n", + "img_bands = [*bands, *indices]" + ] + }, + { + "cell_type": "markdown", + "id": "44mziLGt8j4C", + "metadata": { + "id": "44mziLGt8j4C" + }, + "source": [ + "### Load CLC2018 and sample data\n" + ] + }, + { + "cell_type": "markdown", + "id": "K62rE0S-F5X2", + "metadata": { + "id": "K62rE0S-F5X2" + }, + "source": [ + "Now, let's load labels from CORINE and classify the model." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "Pacg8UcH8lHe", + "metadata": { + "id": "Pacg8UcH8lHe", + "outputId": "4cd47f65-1213-4c47-9c69-7e8161b96f86" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "CLC = ee.Image('COPERNICUS/CORINE/V20/100m/2018').select('landcover').clip(mosaic_image.geometry())\n", + "lc_points = CLC.sample(\n", + " **{\n", + " 'region': mosaic_image.geometry(),\n", + " 'scale': 30,\n", + " 'numPixels': 10000, # Change this as you see fit\n", + " 'seed': random_seed,\n", + " 'geometries': True,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "n7hzNmOt8tY5", + "metadata": { + "id": "n7hzNmOt8tY5" + }, + "source": [ + "Next, let's reclassify the dataset to a binary urban/rural dataset. The choice how to do this is yours, and can be as easy or complicated as you like it to be. As a reminder, CLC consists of a [3-digit hierarchy](https://land.copernicus.eu/content/corine-land-cover-nomenclature-guidelines/html/), so you can pick which classes you want to include and exclude." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "Hm4xWGNS8ugv", + "metadata": { + "id": "Hm4xWGNS8ugv", + "outputId": "ade49efe-bc51-4e15-f485-c016104a56c6" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def generalize_clc_class(feature):\n", + " lc_value = ee.String(feature.get('landcover'))\n", + "\n", + " # Check if the first character is '1'\n", + " set_value = ee.Algorithms.If(lc_value.slice(0, 1).equals('1'), 1, 0)\n", + "\n", + " # Set the new binary value for the 'landcover' property\n", + " return feature.set('landcover', set_value)\n", + "lc_reference_pts = lc_points.map(generalize_clc_class)" + ] + }, + { + "cell_type": "markdown", + "id": "Y0v4HOG6GtZq", + "metadata": { + "id": "Y0v4HOG6GtZq" + }, + "source": [ + "**Q: Briefly explain which classes you included for the urban/rural re-classification.**" + ] + }, + { + "cell_type": "markdown", + "id": "th9Dl6mW9FII", + "metadata": { + "id": "th9Dl6mW9FII" + }, + "source": [ + "Let's make training/validation splits. You can customize this however you see fit, but we suggest balanced sampling, where you control how many positive and how many negative samples are included during training/validation, so as to not over/under-sample one or the other." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "X1tMXV4w9LFb", + "metadata": { + "id": "X1tMXV4w9LFb", + "outputId": "d52bc7b8-4fc3-4c3b-92c9-24abd3def05b" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define the land cover labels column\n", + "label_col = 'landcover'\n", + "\n", + "## TEACHING EXAMPLE, REMOVE BEFORE GOING LIVE ##\n", + "# Filter points by label\n", + "positive_points = lc_reference_pts.filter(ee.Filter.eq(label_col, 1))\n", + "negative_points = lc_reference_pts.filter(ee.Filter.eq(label_col, 0))\n", + "\n", + "# Allow a maximum of 2-to-1 difference in negative vs positive class sampling\n", + "positive_sample = positive_points.randomColumn('random').limit(positive_points.size())\n", + "negative_sample = negative_points.randomColumn('random').limit(positive_points.size().multiply(ee.Number(2)))\n", + "\n", + "# Merge the samples\n", + "balanced_sample = positive_sample.merge(negative_sample)\n", + "\n", + "## End of teaching example ##\n", + "\n", + "# Split into training and validation sets\n", + "training_sample = balanced_sample.filter('random <= 0.8')\n", + "validation_sample = balanced_sample.filter('random > 0.8')\n", + "\n", + "# Sample regions for training and validation datasets\n", + "train_data = mosaic_image.select(img_bands).sampleRegions(\n", + " collection=training_sample, properties=[label_col], scale=100\n", + ")\n", + "\n", + "val_data = mosaic_image.select(img_bands).sampleRegions(\n", + " collection=validation_sample, properties=[label_col], scale=100\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "LONawc_nGj3L", + "metadata": { + "id": "LONawc_nGj3L" + }, + "source": [ + "**Q: Describe your sampling approach. Which approach did you go with, and what is the rationale behind it?**" + ] + }, + { + "cell_type": "markdown", + "id": "uS-6YzaM9P5-", + "metadata": { + "id": "uS-6YzaM9P5-" + }, + "source": [ + "### Optimize on training & validation set" + ] + }, + { + "cell_type": "markdown", + "id": "H3G8LxVmG7d6", + "metadata": { + "id": "H3G8LxVmG7d6" + }, + "source": [ + "Now that we have sampled data, we will iteratively improve the model. How you want to approach this is up to you, we only provide the basic process here. In TAA1 we taught you a few methods and concepts that you can build on. To give some suggestions:\n", + "1. Analyze the confusion matrix\n", + "2. Look at different metrics that might be more informative - for instance, precision and recall\n", + "3. Look at redundant variables - you learned this in scikit-learn, but EE has options for this too if you want to search for it.\n", + "\n", + "It's up to you how to approach this, but we recommend to document what worked and what didn't work so that you have an easier time reporting your results." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "Hv5M5UkT9X39", + "metadata": { + "id": "Hv5M5UkT9X39", + "outputId": "ac04f2d4-d0e6-4007-f6c0-e61fb97a2e83" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Train the model\n", + "classifier = ee.Classifier.smileRandomForest(numberOfTrees=100, minLeafPopulation=2, maxNodes=50)\n", + "trained_classifier = classifier.train(features=train_data, classProperty=label_col, inputProperties=img_bands)\n", + "\n", + "# Apply the classifier to the validation data\n", + "classified_val = val_data.classify(trained_classifier)" + ] + }, + { + "cell_type": "markdown", + "id": "_r0pZTPq97XH", + "metadata": { + "id": "_r0pZTPq97XH" + }, + "source": [ + "Now, let's analyze your results." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "q7SJuNRd9Zmm", + "metadata": { + "id": "q7SJuNRd9Zmm", + "outputId": "356fee26-dffb-4987-bf85-469a2b4cb693" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Validation Metrics:\n", + "Accuracy: 0.8556701030927835\n", + "Kappa: 0.6687804878048779\n" + ] + } + ], + "source": [ + "# Calculate metrics\n", + "confusion_matrix = classified_val.errorMatrix(label_col, 'classification')\n", + "val_accuracy = confusion_matrix.accuracy()\n", + "kappa = confusion_matrix.kappa()\n", + "\n", + "# Package all metrics into a single dictionary\n", + "metrics = ee.Dictionary({\n", + " 'Accuracy': val_accuracy,\n", + " 'Kappa': kappa\n", + "})\n", + "\n", + "# Retrieve all metrics in one call\n", + "metrics_info = metrics.getInfo()\n", + "\n", + "# Print all metrics\n", + "print('Validation Metrics:')\n", + "print(f\"Accuracy: {metrics_info['Accuracy']}\")\n", + "print(f\"Kappa: {metrics_info['Kappa']}\")" + ] + }, + { + "cell_type": "markdown", + "id": "u-QKNQK5AL43", + "metadata": { + "id": "u-QKNQK5AL43" + }, + "source": [ + "**Q: Describe the changes you made to the 'standard' approach.**\n", + "1. **Why did you make these changes**\n", + "2. **which impact did each change have on your analysis?**\n", + "3. **Which things did you try that did not work out?**" + ] + }, + { + "cell_type": "markdown", + "id": "BnOR_Ouk-CFv", + "metadata": { + "id": "BnOR_Ouk-CFv" + }, + "source": [ + "### Run on test set" + ] + }, + { + "cell_type": "markdown", + "id": "YjIzJ6rR-mje", + "metadata": { + "id": "YjIzJ6rR-mje" + }, + "source": [ + "Now, we load recent test images to classify recent urban extent. We take a median image over August 2024." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "cL9G2aPS-ba2", + "metadata": { + "id": "cL9G2aPS-ba2" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load Sentinel-2 Image Collection\n", + "test_sentinel2 = ee.ImageCollection(\"COPERNICUS/S2_HARMONIZED\") \\\n", + " .filterBounds(country_ROI.geometry()) \\\n", + " .filterDate('2024-08-01', '2024-08-31') \\\n", + " .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 10))\n", + "\n", + "# Create a median composite to reduce cloud cover\n", + "test_mosaic_image = sentinel2.median().clip(country_ROI.geometry())\n", + "\n", + "# Add variables\n", + "test_mosaic_image = make_s2_variables(test_mosaic_image)\n", + "\n", + "# Add to map object\n", + "map.addLayer(test_mosaic_image, vis_params, \"test-Sentinel-2-2024\")" + ] + }, + { + "cell_type": "markdown", + "id": "ZMXJCmTj-9YV", + "metadata": { + "id": "ZMXJCmTj-9YV" + }, + "source": [ + "Let's visualize the results calculated over the entire test image" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "waPHnNLv-76V", + "metadata": { + "colab": { + "referenced_widgets": [ + "eb2c61d83e6a426c97721f9cf16be8fc" + ] + }, + "id": "waPHnNLv-76V", + "outputId": "b8f65f6f-ca43-46f1-b91f-aa582c9a33ef" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1397cb265f0847a09763b85957dcf482", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map(center=[46.12445795068446, 14.826893950462633], controls=(WidgetControl(options=['position', 'transparent_…" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Classify the test image\n", + "classified_image = test_mosaic_image.classify(trained_classifier)\n", + "\n", + "# Define the color mapping dictionary\n", + "clc_colors = {\n", + " 0: '#FFFFFF', # Rural\n", + " 1: '#000000', # Urban\n", + "}\n", + "\n", + "# Convert string labels to numeric codes\n", + "def classify_to_numeric(image):\n", + " # Create a dictionary that maps string labels to numeric values\n", + " label_to_numeric = {label: index for index, label in enumerate(clc_colors.keys())}\n", + "\n", + " # Convert string label to numeric value\n", + " return image.remap(\n", + " list(label_to_numeric.keys()),\n", + " list(label_to_numeric.values())\n", + " )\n", + "\n", + "# Convert the classified image\n", + "numeric_classified_image = classify_to_numeric(classified_image)\n", + "\n", + "# Generate a palette for visualization\n", + "palette = [clc_colors[label] for label in clc_colors.keys()]\n", + "\n", + "# Add the numeric classified image to the map\n", + "map.addLayer(numeric_classified_image, {'palette': palette, 'min': 0, 'max': len(clc_colors) - 1}, 'Classified Image')\n", + "map" + ] + }, + { + "cell_type": "markdown", + "id": "pJVtw5Y9IVes", + "metadata": { + "id": "pJVtw5Y9IVes" + }, + "source": [ + "There are no metrics to calculate here - after all, we're trying to update our reference data. Instead, visually evaluate if you're happy with the results. When you're happy with the results, proceed to the next block to export the data from Earth Engine into our local environment.\n", + "\n", + "(**hint:** don't go for perfect, this is very hard to achieve in the current setting)\n", + "\n", + "**Q: Describe your map and your results in general.**\n", + "1. **Where do you still see errors? Are these systemic?**\n", + "2. **How could you further improve your product to clean these up?** **bold text**\n", + "3. **Was there anything you wanted to try, but were unable to?**" + ] + }, + { + "cell_type": "markdown", + "id": "LPkrvHduHyDh", + "metadata": { + "id": "LPkrvHduHyDh" + }, + "source": [ + "### Export resulting image" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "m1FjLJVzH2Zt", + "metadata": { + "id": "m1FjLJVzH2Zt", + "outputId": "930bd2b1-83ff-4ea3-c851-12142a86a63c" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating URL ...\n", + "Downloading data from https://earthengine.googleapis.com/v1/projects/proj-gis-1234/thumbnails/5318c58b433613d47dc90c8602bd6264-893516433a0289f1d648527a6e51611d:getPixels\n", + "Please wait ...\n", + "Data downloaded to C:\\projects\\UNIGIS_ProgrammingGIS\\TAA4\\urban_rural_raster.tif\n" + ] + } + ], + "source": [ + "geemap.ee_export_image(classified_image, filename='urban_rural_raster.tif', scale=1000, file_per_band=False)\n", + "urban_raster = xr.open_dataset('urban_rural_raster.tif', engine='rasterio')" + ] + }, + { + "cell_type": "markdown", + "id": "309a00a2-8aa1-4091-b575-31d56622fd16", + "metadata": { + "id": "309a00a2-8aa1-4091-b575-31d56622fd16" + }, + "source": [ + "### 3. Overlay urban and rural classification with population and Healthcare information" + ] + }, + { + "cell_type": "markdown", + "id": "cfa24afd-0400-4462-ab77-62d5fd126853", + "metadata": {}, + "source": [ + "In this step, we overlay the resulting urban/rural raster image from the previous section onto our population data point. a binary categorical variable, unban_rural, will be added to each data point, where 1 represents urban and 0 represents rural." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "f456e034-5e81-4d99-9093-0380af66e8f9", + "metadata": { + "id": "f456e034-5e81-4d99-9093-0380af66e8f9", + "outputId": "d397d580-707f-411c-a5af-82887241d4bd" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "urban_rural = xr.open_dataarray('urban_rural_raster.tif')\n", + "\n", + "values = urban_rural.sel(\n", + " {\n", + " urban_rural.rio.x_dim: xr.DataArray(df_worldpop_.geometry.x),\n", + " urban_rural.rio.y_dim: xr.DataArray(df_worldpop_.geometry.y),\n", + " },\n", + " method=\"nearest\",\n", + " ).values[0]\n", + "df_worldpop_[\"urban_rural\"] = values\n", + "df_worldpop_[\"urban_rural\"] = df_worldpop_[\"urban_rural\"].fillna(0)" + ] + }, + { + "cell_type": "markdown", + "id": "33b230bc-dbfe-436f-aefb-acd3de96f909", + "metadata": {}, + "source": [ + "lets check the resulted calssified population, as mentioned about 1 measn urban and 0 rural." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "bf032653-2165-4676-9ad3-e55700c6b8e8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
band_datageometryurban_rural
0512.969238POINT (15.96057 46.8236)0.0
13930.664307POINT (16.04593 46.8236)0.0
22205.554199POINT (16.13129 46.8236)0.0
31943.986572POINT (16.21666 46.8236)0.0
4836.774170POINT (16.30202 46.8236)0.0
............
391101.199600POINT (15.02161 45.4589)0.0
392446.684387POINT (15.10697 45.4589)0.0
3931087.734619POINT (15.19233 45.4589)0.0
3941046.664062POINT (15.27769 45.4589)0.0
395155.921677POINT (15.36305 45.4589)0.0
\n", + "

396 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " band_data geometry urban_rural\n", + "0 512.969238 POINT (15.96057 46.8236) 0.0\n", + "1 3930.664307 POINT (16.04593 46.8236) 0.0\n", + "2 2205.554199 POINT (16.13129 46.8236) 0.0\n", + "3 1943.986572 POINT (16.21666 46.8236) 0.0\n", + "4 836.774170 POINT (16.30202 46.8236) 0.0\n", + ".. ... ... ...\n", + "391 101.199600 POINT (15.02161 45.4589) 0.0\n", + "392 446.684387 POINT (15.10697 45.4589) 0.0\n", + "393 1087.734619 POINT (15.19233 45.4589) 0.0\n", + "394 1046.664062 POINT (15.27769 45.4589) 0.0\n", + "395 155.921677 POINT (15.36305 45.4589) 0.0\n", + "\n", + "[396 rows x 3 columns]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_worldpop_" + ] + }, + { + "cell_type": "markdown", + "id": "573f10a4-d0bb-4675-903f-71ffbe45358f", + "metadata": { + "id": "573f10a4-d0bb-4675-903f-71ffbe45358f" + }, + "source": [ + "## 4. Clustering population data points and assigning them to their nearest hospital." + ] + }, + { + "cell_type": "markdown", + "id": "32b0f3b2-85c6-43ee-b80d-dd3106d809dd", + "metadata": {}, + "source": [ + "In this section, we first add a local_ID to all hospitals to make sure each one has a uniqe id to be refered to later. then we create clusters of population. in this regard, we use K-means clustering algorithm with k equal to the numebr of hospitals. then calculate the sum of rural population each cluster as well as the urban population. then we calcualte the eucleadin distance of each center of cluster to all hospitals and assinge the hospila that has the minimum distance to them." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "e56a71da-0062-4325-8246-d8de4ceb9135", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "133" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(HealthCenters_centroids)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "8991a94b-75bb-4a65-b81f-8800eb23752a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
clustergeometryurban_populationrural_populationnearest_hospital_local_idnearest_hospital_geometry
00POINT (15.36305 45.4589)0.000000155.92167773POINT (15.163850749114253 45.79989695241292)
11POINT (15.99472 46.75537)3826.31665013469.5839842POINT (15.978238324664575 46.68750277238846)
22POINT (15.27769 45.4589)0.0000001046.66406273POINT (15.163850749114253 45.79989695241292)
33POINT (14.50945 45.97066)10871.8242190.0000004POINT (14.523534544937489 46.03654961029273)
44POINT (14.21922 46.38007)20575.24218813451.0517585POINT (14.201134898326943 46.376751864014345)
.....................
128128POINT (14.50945 46.35449)0.0000001238.608643129POINT (14.486722993533334 46.24971688580778)
129129POINT (13.78389 46.43978)0.0000003545.973633130POINT (13.782413390607312 46.48538871591214)
130130POINT (14.38141 46.43978)0.0000005411.233398131POINT (14.332917646680235 46.32902384898702)
131131POINT (14.33873 46.22654)47305.28515629385.140625132POINT (14.353454222758952 46.24864076461883)
132132POINT (13.91193 45.50154)0.0000002524.85302743POINT (13.732211026809601 45.53942988832533)
\n", + "

133 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " cluster geometry urban_population rural_population \\\n", + "0 0 POINT (15.36305 45.4589) 0.000000 155.921677 \n", + "1 1 POINT (15.99472 46.75537) 3826.316650 13469.583984 \n", + "2 2 POINT (15.27769 45.4589) 0.000000 1046.664062 \n", + "3 3 POINT (14.50945 45.97066) 10871.824219 0.000000 \n", + "4 4 POINT (14.21922 46.38007) 20575.242188 13451.051758 \n", + ".. ... ... ... ... \n", + "128 128 POINT (14.50945 46.35449) 0.000000 1238.608643 \n", + "129 129 POINT (13.78389 46.43978) 0.000000 3545.973633 \n", + "130 130 POINT (14.38141 46.43978) 0.000000 5411.233398 \n", + "131 131 POINT (14.33873 46.22654) 47305.285156 29385.140625 \n", + "132 132 POINT (13.91193 45.50154) 0.000000 2524.853027 \n", + "\n", + " nearest_hospital_local_id nearest_hospital_geometry \n", + "0 73 POINT (15.163850749114253 45.79989695241292) \n", + "1 2 POINT (15.978238324664575 46.68750277238846) \n", + "2 73 POINT (15.163850749114253 45.79989695241292) \n", + "3 4 POINT (14.523534544937489 46.03654961029273) \n", + "4 5 POINT (14.201134898326943 46.376751864014345) \n", + ".. ... ... \n", + "128 129 POINT (14.486722993533334 46.24971688580778) \n", + "129 130 POINT (13.782413390607312 46.48538871591214) \n", + "130 131 POINT (14.332917646680235 46.32902384898702) \n", + "131 132 POINT (14.353454222758952 46.24864076461883) \n", + "132 43 POINT (13.732211026809601 45.53942988832533) \n", + "\n", + "[133 rows x 6 columns]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "### 1st step: Add Local_ID to the HealthCenters_centroids GeoDataFrame\n", + "HealthCenters_centroids['Local_ID'] = range(1, len(HealthCenters_centroids) + 1)\n", + "\n", + "# 2nd step: Ensure both GeoDataFrames are in the same CRS (EPSG:4326)\n", + "HealthCenters_centroids = HealthCenters_centroids.to_crs(epsg=4326)\n", + "\n", + "# Convert geometries to a list of coordinates (for KMeans)\n", + "pop_coords = np.array([(geom.x, geom.y) for geom in df_worldpop_['geometry']])\n", + "pop_band_data = df_worldpop_['band_data'].values\n", + "\n", + "# Extract the hospital coordinates\n", + "hospital_coords = np.array([(geom.x, geom.y) for geom in HealthCenters_centroids['geometry']])\n", + "hospital_local_ids = HealthCenters_centroids['Local_ID'].values\n", + "hospital_geometries = HealthCenters_centroids['geometry'].values\n", + "\n", + "### 3rd step: K-Means\n", + "# Ensure that the number of clusters is equal to the number of hospital coordinates\n", + "n_clusters = len(hospital_coords)\n", + "\n", + "kmeans = KMeans(n_clusters=n_clusters, random_state=random_seed, init=hospital_coords, n_init=1) # using hospital locations as initial centers\n", + "df_worldpop_['cluster'] = kmeans.fit_predict(pop_coords)\n", + "\n", + "# Get cluster centers (latitude & longitude)\n", + "cluster_centers = kmeans.cluster_centers_\n", + "\n", + "### 4th step: calculate the sum of urban and rural populations in each cluster\n", + "# group by cluster and urban/rural status then sum up the populations\n", + "\n", + "# rural population (urban_rural = 0)\n", + "rural_population_by_cluster = df_worldpop_[df_worldpop_['urban_rural'] == 0.0].groupby('cluster')['band_data'].sum().reindex(range(n_clusters), fill_value=0)\n", + "\n", + "# urban population (urban_rural = 1)\n", + "urban_population_by_cluster = df_worldpop_[df_worldpop_['urban_rural'] == 1.0].groupby('cluster')['band_data'].sum().reindex(range(n_clusters), fill_value=0)\n", + "\n", + "# to create a new DataFrame for clusters with the total urban and rural population\n", + "clusters_df = pd.DataFrame({\n", + " 'cluster': range(n_clusters),\n", + " 'geometry': [Point(x, y) for x, y in cluster_centers],\n", + " 'urban_population': urban_population_by_cluster.values,\n", + " 'rural_population': rural_population_by_cluster.values\n", + "})\n", + "\n", + "### 5th step: assign the nearest hospital to the related cluster\n", + "# distances between each cluster center and each health facility center\n", + "distances = cdist(cluster_centers, hospital_coords, metric='euclidean')\n", + "\n", + "# the index of the nearest hospital for each cluster\n", + "nearest_hospital_idx = distances.argmin(axis=1)\n", + "\n", + "# assign the nearest hospital Local_ID and geometry to each cluster center\n", + "clusters_df['nearest_hospital_local_id'] = [hospital_local_ids[idx] for idx in nearest_hospital_idx]\n", + "clusters_df['nearest_hospital_geometry'] = [hospital_geometries[idx] for idx in nearest_hospital_idx]\n", + "\n", + "### 6th step: convert to GeoDataFrame and set CRS\n", + "clusters_gdf = gpd.GeoDataFrame(clusters_df, geometry='geometry')\n", + "clusters_gdf.set_crs(epsg=4326, inplace=True)\n", + "\n", + "# review the content of the resulting clusters dataframe\n", + "clusters_gdf\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "5299738d-567d-4473-aaa2-095dede18b92", + "metadata": { + "id": "5299738d-567d-4473-aaa2-095dede18b92" + }, + "source": [ + "## 5. Explore and evaluate baseline results\n", + "\n", + "let's Plot cluster centers and the location of hospitals" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "4735e1af-5573-4baf-83d7-6644ac7a3ad5", + "metadata": { + "id": "4735e1af-5573-4baf-83d7-6644ac7a3ad5", + "outputId": "cee4726f-b707-4b29-c1b1-d1495557475d" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "clusters_gdf['x'] = clusters_gdf.geometry.x\n", + "clusters_gdf['y'] = clusters_gdf.geometry.y\n", + "\n", + "\n", + "HealthCenters_centroids['x'] = HealthCenters_centroids.geometry.x\n", + "HealthCenters_centroids['y'] = HealthCenters_centroids.geometry.y\n", + "\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 8))\n", + "\n", + "\n", + "ax.scatter(clusters_gdf['x'], clusters_gdf['y'], color='black', marker='o', s=20, label='Cluster Centers')\n", + "\n", + "\n", + "for x, y, label in zip(clusters_gdf['x'], clusters_gdf['y'], clusters_gdf['cluster']):\n", + " ax.text(x, y, str(label), fontsize=12, color='k')\n", + "\n", + "ax.scatter(HealthCenters_centroids['x'], HealthCenters_centroids['y'], color='red', marker='+', s=100, label='Hospitals')\n", + "\n", + "\n", + "plt.title(\"Cluster Centers with their IDs and Hospital Locations\")\n", + "plt.xlabel(\"Longitude\")\n", + "plt.ylabel(\"Latitude\")\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "e3d8e5a3-ffa1-4b1a-a3e6-0e569346a78c", + "metadata": { + "id": "e3d8e5a3-ffa1-4b1a-a3e6-0e569346a78c" + }, + "source": [ + "### Identify and plot population per healthcare facility" + ] + }, + { + "cell_type": "markdown", + "id": "b2ea7390-b44c-4bbc-9cf4-7b40556e5e8d", + "metadata": {}, + "source": [ + "Here, we calculate the urban and rural demand for services for each hospital by summing the populations in each cluster assigned to the hospitals. We also calculate the total demand for each hospital." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "cf27813b-e51f-49cd-811d-e95fb1f1983c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Local_IDurban_populationrural_populationtotal_population
3839167343.8437500.000000167343.843750
484946885.50000070193.351562117078.851562
252676068.71875011647.86523487716.585938
161766169.15625020895.76953187064.921875
13113247305.28515629385.14062576690.421875
...............
1011020.0000003403.6149903403.614990
1211222615.232422354.3833922969.615723
40410.0000002423.2248542423.224854
1081090.0000001852.2803961852.280396
1281290.0000001437.8864751437.886475
\n", + "

77 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " Local_ID urban_population rural_population total_population\n", + "38 39 167343.843750 0.000000 167343.843750\n", + "48 49 46885.500000 70193.351562 117078.851562\n", + "25 26 76068.718750 11647.865234 87716.585938\n", + "16 17 66169.156250 20895.769531 87064.921875\n", + "131 132 47305.285156 29385.140625 76690.421875\n", + ".. ... ... ... ...\n", + "101 102 0.000000 3403.614990 3403.614990\n", + "121 122 2615.232422 354.383392 2969.615723\n", + "40 41 0.000000 2423.224854 2423.224854\n", + "108 109 0.000000 1852.280396 1852.280396\n", + "128 129 0.000000 1437.886475 1437.886475\n", + "\n", + "[77 rows x 4 columns]" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# calculateing the total rural and urban demand (population) of each hospital\n", + "hospital_population = clusters_gdf.groupby('nearest_hospital_local_id')[['urban_population', 'rural_population']].sum().reset_index()\n", + "\n", + "# now we merge the population data back to the HealthCenters_centroids GeoDataFrame\n", + "hospital_population_merged = HealthCenters_centroids.merge(hospital_population, left_on='Local_ID', right_on='nearest_hospital_local_id', how='left')\n", + "\n", + "# also we sum up rural and urban demand to get the total demand of each hospital as well\n", + "hospital_population_merged['total_population'] = hospital_population_merged['urban_population'] + hospital_population_merged['rural_population']\n", + "\n", + "# display the demand of hospitals\n", + "hospital_population_merged[['Local_ID', 'urban_population', 'rural_population', 'total_population']].sort_values('total_population', ascending=False).dropna()\n" + ] + }, + { + "cell_type": "markdown", + "id": "12b83859-6dfc-4b28-8fd6-297b66ed737a", + "metadata": { + "id": "12b83859-6dfc-4b28-8fd6-297b66ed737a", + "scrolled": true + }, + "source": [ + "hospital_population_merged.plot('population')" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "d9e2d924-4ad9-440b-9f46-409878af679c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "Index(['osm_id', 'osm_way_id', 'name', 'type', 'aeroway', 'amenity',\n", + " 'admin_level', 'barrier', 'boundary', 'building', 'craft', 'geological',\n", + " 'historic', 'land_area', 'landuse', 'leisure', 'man_made', 'military',\n", + " 'natural', 'office', 'place', 'shop', 'sport', 'tourism', 'other_tags',\n", + " 'geometry', 'Local_ID', 'x', 'y', 'nearest_hospital_local_id',\n", + " 'urban_population', 'rural_population', 'total_population'],\n", + " dtype='object')" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hospital_population_merged.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "bc00dbb0-81fb-431b-ba56-37e07aa9907a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "\n", + "\n", + "hospital_population_merged.plot(\n", + " ax=ax,\n", + " column='total_population',\n", + " legend=True, \n", + " legend_kwds={'label': \"Total Population\", 'orientation': \"horizontal\"},\n", + " markersize=hospital_population_merged['total_population']/1000 , \n", + " alpha=0.8,\n", + " cmap='viridis_r',\n", + " edgecolor='black' \n", + ")\n", + "\n", + "\n", + "# Add a title and labels to the axes\n", + "ax.set_title('Hospital locations and their total demand', fontsize=16)\n", + "ax.set_xlabel('Longitude', fontsize=12)\n", + "ax.set_ylabel('Latitude', fontsize=12)\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "c95fa7e9-46bf-4588-9bcd-7149d77d3e38", + "metadata": {}, + "source": [ + "Now we check if there is any hospital that has not been assigned to any population cluster." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "bb07e73c-84a2-466f-a644-4fa313b82dec", + "metadata": { + "id": "bb07e73c-84a2-466f-a644-4fa313b82dec", + "outputId": "10ee77ab-180c-4ee4-a5fa-ca59f4ae757e" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "following hospitals are not assigned to any cluster:\n", + " Local_ID geometry\n", + "0 1 POINT (14.52441 46.05365)\n", + "2 3 POINT (14.52484 46.06755)\n", + "6 7 POINT (14.48788 46.06866)\n", + "7 8 POINT (14.48717 46.06762)\n", + "8 9 POINT (14.52093 46.05413)\n", + "9 10 POINT (14.5237 46.05333)\n", + "10 11 POINT (14.52078 46.05266)\n", + "11 12 POINT (14.52033 46.05172)\n", + "13 14 POINT (14.56972 46.05215)\n", + "21 22 POINT (14.52243 46.05235)\n", + "22 23 POINT (14.52138 46.05506)\n", + "23 24 POINT (14.51099 46.06451)\n", + "26 27 POINT (15.87645 46.42654)\n", + "28 29 POINT (14.5239 46.05406)\n", + "29 30 POINT (14.52124 46.05119)\n", + "30 31 POINT (14.5225 46.05157)\n", + "31 32 POINT (14.52194 46.05188)\n", + "32 33 POINT (14.52217 46.05327)\n", + "33 34 POINT (14.52236 46.05607)\n", + "34 35 POINT (14.51737 46.05131)\n", + "37 38 POINT (14.52526 46.05426)\n", + "39 40 POINT (14.52839 46.05496)\n", + "43 44 POINT (14.4811 46.04615)\n", + "44 45 POINT (14.53185 46.10335)\n", + "45 46 POINT (14.53148 46.05589)\n", + "46 47 POINT (14.50068 46.06068)\n", + "47 48 POINT (14.51968 46.05196)\n", + "49 50 POINT (15.08172 46.5083)\n", + "51 52 POINT (14.55865 46.0535)\n", + "52 53 POINT (14.57055 46.05288)\n", + "53 54 POINT (14.57102 46.05314)\n", + "54 55 POINT (14.57156 46.05365)\n", + "55 56 POINT (14.57166 46.05253)\n", + "56 57 POINT (14.57013 46.05332)\n", + "62 63 POINT (15.67162 46.52356)\n", + "67 68 POINT (14.46488 46.09857)\n", + "73 74 POINT (15.16206 45.80063)\n", + "79 80 POINT (14.48296 46.0712)\n", + "80 81 POINT (14.59646 46.13959)\n", + "81 82 POINT (14.51998 46.05327)\n", + "82 83 POINT (14.52931 46.05476)\n", + "83 84 POINT (14.50254 46.06452)\n", + "84 85 POINT (15.64027 46.23881)\n", + "89 90 POINT (13.64537 45.95796)\n", + "92 93 POINT (14.35333 46.249)\n", + "96 97 POINT (15.08171 46.50819)\n", + "97 98 POINT (14.35409 46.24867)\n", + "112 113 POINT (14.48694 46.04654)\n", + "113 114 POINT (14.52171 46.05584)\n", + "114 115 POINT (14.48726 46.06773)\n", + "119 120 POINT (16.14167 46.40654)\n", + "122 123 POINT (13.68777 45.5443)\n", + "123 124 POINT (14.35315 46.27635)\n", + "124 125 POINT (14.5258 46.05186)\n", + "127 128 POINT (14.487 46.0465)\n", + "132 133 POINT (14.10892 46.37141)\n" + ] + } + ], + "source": [ + "assigned_hospitals = clusters_gdf['nearest_hospital_local_id'].unique()\n", + "\n", + "unassigned_hospitals = HealthCenters_centroids[~HealthCenters_centroids['Local_ID'].isin(assigned_hospitals)]\n", + "\n", + "if unassigned_hospitals.empty:\n", + " print(\"All hospitals are assigned to at least one cluster.\")\n", + "else:\n", + " print(\"following hospitals are not assigned to any cluster:\")\n", + " print(unassigned_hospitals[['Local_ID', 'geometry']])" + ] + }, + { + "cell_type": "markdown", + "id": "3740a61b-f60c-427c-aab5-6e6e552dc57d", + "metadata": { + "id": "3740a61b-f60c-427c-aab5-6e6e552dc57d" + }, + "source": [ + "## 6. Natural hazard disruption" + ] + }, + { + "cell_type": "markdown", + "id": "52d30a9c-2515-4aff-ae5f-9a15cd343061", + "metadata": { + "id": "52d30a9c-2515-4aff-ae5f-9a15cd343061" + }, + "source": [ + "### Download flood data\n", + "The flood data we will extract from a repository maintained by the World Resources Institute. We will download river flood hazard maps from their [Flood Data Collection](https://wri-projects.s3.amazonaws.com/AqueductFloodTool/download/v2/index.html).\n", + "\n", + "Here we do not need to use an API and we also do not need to register ourselves, so we can download any of the files directly. In case you do not have any flood impacts. You could select a more extreme future scenario. But let's first download the 1/1000 river flood event under historic conditions. " + ] + }, + { + "cell_type": "markdown", + "id": "b0ff5f7a-fc94-4429-82bb-2a94da72a887", + "metadata": { + "id": "b0ff5f7a-fc94-4429-82bb-2a94da72a887" + }, + "source": [ + "### Overlay flood data with Hospitals" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "e73f0bce-ad12-4be5-acbb-33dd7e2fa18d", + "metadata": { + "id": "e73f0bce-ad12-4be5-acbb-33dd7e2fa18d", + "outputId": "ec5a636a-dac4-4098-a442-1a515598df95" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "flood_map_path = \"https://wri-projects.s3.amazonaws.com/AqueductFloodTool/download/v2/inunriver_historical_000000000WATCH_1980_rp01000.tif\"" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "28d110b6-0ef4-41c6-acb2-8673597decd2", + "metadata": { + "id": "28d110b6-0ef4-41c6-acb2-8673597decd2", + "outputId": "d23d720d-52bf-42cb-e706-b71f0e2a8a1a" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset> Size: 4GB\n",
+       "Dimensions:      (band: 1, x: 43200, y: 21600)\n",
+       "Coordinates:\n",
+       "  * band         (band) int32 4B 1\n",
+       "  * x            (x) float64 346kB -180.0 -180.0 -180.0 ... 180.0 180.0 180.0\n",
+       "  * y            (y) float64 173kB 90.0 89.99 89.98 ... -89.98 -89.99 -90.0\n",
+       "    spatial_ref  int32 4B ...\n",
+       "Data variables:\n",
+       "    band_data    (band, y, x) float32 4GB ...
" + ], + "text/plain": [ + " Size: 4GB\n", + "Dimensions: (band: 1, x: 43200, y: 21600)\n", + "Coordinates:\n", + " * band (band) int32 4B 1\n", + " * x (x) float64 346kB -180.0 -180.0 -180.0 ... 180.0 180.0 180.0\n", + " * y (y) float64 173kB 90.0 89.99 89.98 ... -89.98 -89.99 -90.0\n", + " spatial_ref int32 4B ...\n", + "Data variables:\n", + " band_data (band, y, x) float32 4GB ..." + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "flood_map = xr.open_dataset(flood_map_path, engine=\"rasterio\")\n", + "flood_map" + ] + }, + { + "cell_type": "markdown", + "id": "b21c691f-5789-4b0d-a5ec-da785b94c833", + "metadata": {}, + "source": [ + "As you can see, this is a very large dataset again. Let's make our life a little bit more relaxed by clipping the map to our country of interest." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "f14da004-413e-4856-80e6-fdaa544aca9d", + "metadata": { + "id": "f14da004-413e-4856-80e6-fdaa544aca9d", + "outputId": "3696782e-4378-42fc-9e84-2599abeef710" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "min_lon = country_geom.bounds.minx.values[0]\n", + "min_lat = country_geom.bounds.miny.values[0] #complete function\n", + "max_lon = country_geom.bounds.maxx.values[0] #complete function\n", + "max_lat = country_geom.bounds.maxy.values[0]#complete function\n", + "\n", + "flood_map_area = flood_map.rio.clip_box(minx=min_lon,miny=min_lat,maxx=max_lon,maxy=max_lat) #complete function" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "ecf80c30-9ba1-426c-9990-cf3ea07e499b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "flood_map_area.band_data.plot(cmap='Blues')" + ] + }, + { + "cell_type": "markdown", + "id": "399149ac-8ee4-4459-971e-9a2003ab4b4e", + "metadata": { + "id": "399149ac-8ee4-4459-971e-9a2003ab4b4e" + }, + "source": [ + "### Overlay flood data with healthcare facilities" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "024a09f7-6f1c-4e15-a810-cf407daa2a48", + "metadata": { + "id": "024a09f7-6f1c-4e15-a810-cf407daa2a48", + "outputId": "a33183f3-464f-4abf-fe29-436436f65671" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def _get_damage_per_object(asset, curves, cell_area_m2):\n", + " \"\"\"\n", + " Calculate damage for a given asset based on hazard information.\n", + " Arguments:\n", + " *asset*: Tuple containing information about the asset. It includes:\n", + " - Index or identifier of the asset (asset[0]).\n", + " - Asset-specific information, including hazard points (asset[1]['hazard_point']).\n", + " *maxdam_dict*: Maximum damage value.\n", + " Returns:\n", + " *tuple*: A tuple containing the asset index or identifier and the calculated damage.\n", + " \"\"\"\n", + "\n", + " if asset.geometry.geom_type in (\"Polygon\", \"MultiPolygon\"):\n", + " coverage = asset[\"coverage\"] * cell_area_m2\n", + " elif asset.geometry.geom_type in (\"LineString\", \"MultiLineString\"):\n", + " coverage = asset[\"coverage\"]\n", + " elif asset.geometry.geom_type in (\"Point\"):\n", + " coverage = 1\n", + " else:\n", + " raise ValueError(f\"Geometry type {asset.geometry.geom_type} not supported\")\n", + "\n", + " return (\n", + " np.sum(\n", + " np.interp(\n", + " asset[\"values\"], curves.index, curves[asset[\"amenity\"]].values\n", + " )\n", + " * coverage\n", + " )\n", + " * asset[\"maximum_damage\"]\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "48ec7696-a117-4302-9276-ac37fa369cd2", + "metadata": { + "id": "48ec7696-a117-4302-9276-ac37fa369cd2", + "outputId": "88db2c6c-ffd8-4b7d-80b6-2c3bbf33c165" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "maxdam = {\"hospital\":2000,\n", + " \"clinic\":1500,\n", + "}\n", + "\n", + "curves = np.array(\n", + " [[0,0],\n", + " [50,0.2],\n", + " [100,0.4],\n", + " [150,0.6],\n", + " [200,0.8],\n", + " [250,1]])\n", + "\n", + "curves = np.concatenate((curves,\n", + " np.transpose(np.array([curves[:,1]]*(len(maxdam)-1)))),\n", + " axis=1)\n", + "\n", + "curves = pd.DataFrame(curves)\n", + "curves.columns = ['depth']+list(maxdam.keys())\n", + "curves.set_index('depth',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "26e1e323-d30b-4e40-8a89-a25d1e475234", + "metadata": { + "id": "26e1e323-d30b-4e40-8a89-a25d1e475234", + "outputId": "70b41a67-b5e6-49fb-cd9e-9366d1bf31e5", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "values_and_coverage_per_object = exact_extract(\n", + " flood_map,\n", + " HealthCenters.to_crs(4326),\n", + " [\"coverage\", \"values\"],\n", + " output=\"pandas\",\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "2460afd7-dc6c-4b37-9e77-0550fcacf24d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
coveragevalues
0[0.0031160966027528048][0.0]
1[0.008566441014409065][0.31174808740615845]
2[0.012850387953221798, 0.008853942155838013, 0...[0.0, 0.0, 0.0, 0.0]
3[0.00234486092813313][0.0]
4[0.00516355037689209][0.0]
.........
128[0.0008400809019804001][0.0]
129[0.0007367293583229184][0.0]
130[0.00482288608327508, 0.0020372953731566668][0.0, 0.0]
131[0.0023558475077152252][0.0]
132[0.002978724194690585][0.0]
\n", + "

133 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " coverage values\n", + "0 [0.0031160966027528048] [0.0]\n", + "1 [0.008566441014409065] [0.31174808740615845]\n", + "2 [0.012850387953221798, 0.008853942155838013, 0... [0.0, 0.0, 0.0, 0.0]\n", + "3 [0.00234486092813313] [0.0]\n", + "4 [0.00516355037689209] [0.0]\n", + ".. ... ...\n", + "128 [0.0008400809019804001] [0.0]\n", + "129 [0.0007367293583229184] [0.0]\n", + "130 [0.00482288608327508, 0.0020372953731566668] [0.0, 0.0]\n", + "131 [0.0023558475077152252] [0.0]\n", + "132 [0.002978724194690585] [0.0]\n", + "\n", + "[133 rows x 2 columns]" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "values_and_coverage_per_object" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "811245a1-d794-4b0d-b604-1d31c2507d97", + "metadata": { + "id": "811245a1-d794-4b0d-b604-1d31c2507d97" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "HealthCenters = HealthCenters.merge(values_and_coverage_per_object,left_index=True,right_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "115d6cc9-aeb9-4497-9c0e-78f7c8ef19b8", + "metadata": { + "id": "115d6cc9-aeb9-4497-9c0e-78f7c8ef19b8" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "HealthCenters['maximum_damage'] = HealthCenters.amenity.apply(lambda x: maxdam[x])" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "291a4fba-40c1-4397-a7f2-8b1665ca7ef5", + "metadata": { + "id": "291a4fba-40c1-4397-a7f2-8b1665ca7ef5" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "HealthCenters['damage'] = HealthCenters.apply(\n", + " lambda _object: _get_damage_per_object(_object, curves, cell_area_m2=100*100),\n", + " axis=1,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "913f9757-3151-46f9-9427-f54fa58d8beb", + "metadata": { + "id": "913f9757-3151-46f9-9427-f54fa58d8beb" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0 0.000000\n", + "93 0.000000\n", + "92 0.000000\n", + "91 0.000000\n", + "90 0.000000\n", + " ... \n", + "76 86.627588\n", + "41 148.154755\n", + "1 160.234296\n", + "120 326.382010\n", + "12 2793.266143\n", + "Name: damage, Length: 133, dtype: float64" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "HealthCenters['damage'].sort_values(ascending=True)" + ] + }, + { + "cell_type": "markdown", + "id": "437b207f-7c43-4846-b9ba-63e416ff92cf", + "metadata": { + "id": "160dafed-34d4-44c8-8d9d-aa44c8dfb621" + }, + "source": [ + "# 7. Your Final Task" + ] + }, + { + "cell_type": "markdown", + "id": "c412b628-014e-41ba-8c41-0722098ad006", + "metadata": { + "id": "c412b628-014e-41ba-8c41-0722098ad006" + }, + "source": [ + "As you saw, due to a flood with a 1000-year return period, some hospitals may be out of service. Therefore, we need to estimate the post-flood urban/rural demand for services from the hospitals that remain operational.\n", + "\n", + "### Your task here will be: \n", + "- Create new clusters of populations and assign them to the remaining hospitals, then determine the post-disaster demand for these hospitals.\n", + "- Calcuate the urban, rural and total demand (population in need of services) for each hospital.\n", + "- Plot the remaining hospitals vs their total population in need of service.\n", + "- Let us know how many hospitals were affected by the flood and the total number of rural residents who need to find an alternative hospital." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f9203aa-b2e2-4207-ad93-bf71e1ff5636", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [ + "dc39cef6-eac9-40a0-a737-bac3cd5bc2a1", + "zotYyVnD4Jt2", + "_tY_sxXy4SPA", + "c9NfiE5dFr19", + "44mziLGt8j4C", + "uS-6YzaM9P5-", + "BnOR_Ouk-CFv", + "573f10a4-d0bb-4675-903f-71ffbe45358f", + "5299738d-567d-4473-aaa2-095dede18b92" + ], + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/TAA4/.ipynb_checkpoints/tutorial-checkpoint.ipynb b/TAA4/.ipynb_checkpoints/tutorial-checkpoint.ipynb new file mode 100644 index 0000000..a9cf903 --- /dev/null +++ b/TAA4/.ipynb_checkpoints/tutorial-checkpoint.ipynb @@ -0,0 +1,2188 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ee037ada-1068-4f35-91ae-30c2ea52ca01", + "metadata": {}, + "source": [ + "# TAA4: Accessibility to healthcare facilities" + ] + }, + { + "cell_type": "markdown", + "id": "c6036316-479d-4438-b9e7-e31cf7e9051c", + "metadata": {}, + "source": [ + "[explain assignment]\n", + "\n", + "### Important before we start\n", + "---\n", + "Make sure that you save this file before you continue, else you will lose everything. To do so, go to **Bestand/File** and click on **Een kopie opslaan in Drive/Save a Copy on Drive**!" + ] + }, + { + "cell_type": "markdown", + "id": "75f3efb3-f86a-443e-b87a-22653c771143", + "metadata": {}, + "source": [ + "## Learning Objectives\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "59d989b6-9cc8-4a39-a17b-c96c463711dd", + "metadata": {}, + "source": [ + "## Prepare the packages\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b06c7b0f-2f83-4f86-ab73-0e8d889bd164", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install rasterio\n", + "!pip install rioxarray\n", + "!pip install contextily\n", + "!pip install osm_flex " + ] + }, + { + "cell_type": "markdown", + "id": "bee1cfab-03df-433e-913e-62de5c0076f4", + "metadata": {}, + "source": [ + "Now we will import these packages in the cell below:" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "id": "ffc10f5f-a43d-4f8f-91b1-ac7afc347ab4", + "metadata": {}, + "outputs": [], + "source": [ + "import os,sys\n", + "import requests\n", + "import shapely\n", + "\n", + "import xarray as xr\n", + "import rioxarray as rxr\n", + "import pandas as pd\n", + "import geopandas as gpd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import contextily as cx\n", + "\n", + "from pathlib import Path\n", + "from rasterio.enums import Resampling\n", + "from sklearn.cluster import KMeans\n", + "from shapely.geometry import Point\n", + "from scipy.spatial.distance import cdist\n", + "from osm_flex import download\n", + "from zipfile import ZipFile\n", + "from io import BytesIO\n", + "from urllib.request import urlopen" + ] + }, + { + "cell_type": "markdown", + "id": "bb50fef4-f456-46ca-aff7-a838766fb127", + "metadata": {}, + "source": [ + "## 2. Data download and preparation \n", + "\n", + "Define a country of your interest and a size for gridding and a randomSeed" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "id": "da8b4246-1b98-455c-89ce-999e21e5dd27", + "metadata": {}, + "outputs": [], + "source": [ + "country_full_name = 'Luxembourg'\n", + "country_iso3 = 'LUX'\n", + "upscale_factor = 10 #Km\n", + "random_seed= 1" + ] + }, + { + "cell_type": "markdown", + "id": "6d8c4878-9662-4de4-9c69-b9ec0af9cda3", + "metadata": {}, + "source": [ + "Download the population data" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "id": "6eb55f91-caba-443b-a72b-688bce077b6b", + "metadata": {}, + "outputs": [], + "source": [ + "url = \"https://data.worldpop.org/GIS/Population/Global_2000_2020/2018/0_Mosaicked/ppp_2018_1km_Aggregated.tif\"\n", + "\n", + "file_name = 'ppp_2018_1km_Aggregated.tif'\n", + "#open(file_name, 'wb').write(requests.get(url).content)\n", + "\n", + "file_name = \"C:\\\\Data\\\\Global_Geospatial\\\\worldpop\\\\ppp_2018_1km_Aggregated.tif\"\n", + "\n", + "world_pop_glob =xr.open_dataset(file_name,engine='rasterio')" + ] + }, + { + "cell_type": "markdown", + "id": "84d4cc8e-d8fc-495a-9337-bfb06958a553", + "metadata": {}, + "source": [ + "Download a file with country borders. We use Natural Earth." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "36d356c0-167b-4d4c-bfa1-67e6f3dfee46", + "metadata": {}, + "outputs": [], + "source": [ + "#world = gpd.read_file(\"https://github.com/nvkelso/natural-earth-vector/raw/master/10m_cultural/ne_10m_admin_0_countries.shp\")\n", + "world = gpd.read_file(\"C:\\\\Data\\\\Global_Geospatial\\\\shapefiles\\\\ne_10m_admin_0_countries.shp\")" + ] + }, + { + "cell_type": "markdown", + "id": "f1b63ad2-1a42-4549-b979-66eeb78618e2", + "metadata": {}, + "source": [ + "And we want to take the country boundaries and geometry" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "id": "5bdadd64-1091-4659-b4a8-ab1c2a43cf4c", + "metadata": {}, + "outputs": [], + "source": [ + "country_bounds = world.loc[world.ADM0_ISO == country_iso3].bounds\n", + "country_geom = world.loc[world.ADM0_ISO == country_iso3].geometry" + ] + }, + { + "cell_type": "markdown", + "id": "2d131107-2f4f-4f39-9850-df988197ee62", + "metadata": {}, + "source": [ + "Now we use this to clip the population data from worldpop, just for your country" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "id": "3cee77c1-0fcc-4151-b5f1-5c67870b5ab4", + "metadata": {}, + "outputs": [], + "source": [ + "# clip to country\n", + "world_pop_national = world_pop_glob.rio.clip_box(minx=country_bounds.minx.values[0],\n", + " miny=country_bounds.miny.values[0],\n", + " maxx=country_bounds.maxx.values[0],\n", + " maxy=country_bounds.maxy.values[0]\n", + " )\n", + "world_pop_national = world_pop_national.rio.clip(country_geom.values, world_pop_glob.rio.crs, drop=False)" + ] + }, + { + "cell_type": "markdown", + "id": "ab6155b2-3e5b-4f0d-85b1-0f45d5b5f31f", + "metadata": {}, + "source": [ + "The worldpop data, however, is stored as 1km by 1km grid. This will be too computationally intensive if we would use that resolution. As such, we reproject the to a lower resolution. This will help us to perform the analyis more smoothly. We use the *upscale_factor* as defined at the start of this subsection." + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "id": "0834501f-2e00-4dce-b91c-8101fcdffb74", + "metadata": {}, + "outputs": [], + "source": [ + "new_width = int(world_pop_national.rio.width / upscale_factor)\n", + "new_height = int(world_pop_national.rio.height / upscale_factor)\n", + "\n", + "worldpop_Grided = world_pop_national.rio.reproject(\n", + " world_pop_national.rio.crs,\n", + " shape=(new_height, new_width),\n", + " resampling=Resampling.sum,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "id": "256c59b1-c8cb-40ef-9d4d-6c30e05e2861", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The output is df_pop_LUX as a dataframe of the population data\n" + ] + } + ], + "source": [ + "df_worldpop_ = worldpop_Grided.band_data.to_dataframe()\n", + "df_worldpop_ = df_worldpop_.loc[~df_worldpop_.band_data.isna()].reset_index(drop=False)\n", + "\n", + "# create geometry values and drop lat lon columns\n", + "df_worldpop_['geometry'] = shapely.points(np.array(list(zip(df_worldpop_['x'],df_worldpop_['y']))))\n", + "\n", + "df_worldpop_ = gpd.GeoDataFrame(df_worldpop_.drop(['y','x','spatial_ref','band'],axis=1))\n", + "\n", + "# dynamically create a variable name for the DataFrame\n", + "globals()[f'df_pop_{country_iso3}'] = gpd.GeoDataFrame(df_worldpop_)\n", + "\n", + "# dynamically create a print statement that reflects the current country code\n", + "print(f\"The output is df_pop_{country_iso3} as a dataframe of the population data\")" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "id": "f0ce8240-0a9f-446a-a2fd-c38d2c7416b9", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 149, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdEAAAGdCAYAAABNWUmMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAu/klEQVR4nO3dfXRU5YHH8d/khYTEZCRsQiYYYuxyiDGgvAvY4h4DgpiK9bSiYIRaX7EGXVfMCgfwhVhb0bVKKhyxKgotYEVaROjpQQ8qBgQsENekJl0iTsgqOAlSJm1y9w+WqUNIyDxJnsyQ7+ece4555t6b32OG/ObO3HvjchzHEQAACFlUdwcAACBSUaIAABiiRAEAMESJAgBgiBIFAMAQJQoAgCFKFAAAQ5QoAACGYro7QGdpbm7WF198oaSkJLlcru6OAwCIYI7jqKGhQRkZGYqKav1486wp0S+++EKZmZndHQMAcBapqanReeed1+rjZ02JJiUlSTox4eTk5G5OAwCIZPX19crMzAx0S2vOmhI9+RZucnIyJQoA6BRn+niQE4sAADBEiQIAYIgSBQDAECUKAIAhShQAAEOUKAAAhihRAAAMUaIAABg6a2620JM1NTsqqz6suobjSkuK16jsFEVHhf/9g8ltV6TmBsIZJRrhNu3zatGGcnl9xwNjHne8FhTkalKepxuTtY3cdkVqbiDcuRzHcbo7RGeor6+X2+2Wz+frMbf927TPqztX7tKpP8CTxxalM4aF5S9IctsVqbmB7tTeTuEz0QjV1Oxo0YbyFr8YJQXGFm0oV1NzeL1GIrddkZobiBSUaIQqqz4c9NbcqRxJXt9xlVUftheqHchtV6TmBiIFJRqh6hpa/8Vosp4t5LYrUnMDkYISjVBpSfGdup4t5LYrUnMDkYISjVCjslPkccertQsUXDpx9uWo7BSbsc6I3HZFam4gUlCiESo6yqUFBbmS1OIX5MmvFxTkht11gOS2K1JzA5GCEo1gk/I8Kp0xTOnu4Lfi0t3xYX3ZArntitTcQCTgOtGzQKTeiYbcdkVqbqA7tLdTKFEAAE7BzRYAAOhilCgAAIYoUQAADFGiAAAYokQBADBEiQIAYIgSBQDAECUKAIChkEp04cKFcrlcQUt6enrgccdxtHDhQmVkZKh37966/PLLtX///jb3uX//fl133XU6//zz5XK59PTTTxtNBAAA20I+Er3ooovk9XoDy969ewOPPfHEE1qyZImeffZZ7dixQ+np6ZowYYIaGhpa3d+xY8d0wQUX6PHHHw8qZAAAwl1MyBvExJy27BzH0dNPP62HHnpIP/jBDyRJL730kvr166fXXntNt99++2n3N3LkSI0cOVKS9OCDD4YaBwCAbhPykWhlZaUyMjKUnZ2tadOmqaqqSpJUXV2t2tpaTZw4MbBuXFycxo8fr/fff7/zEv8/v9+v+vr6oAUAAJtCKtHRo0fr5Zdf1ttvv63ly5ertrZWY8eO1VdffaXa2lpJUr9+/YK26devX+CxzlRSUiK32x1YMjMzO/17AADQlpBKdPLkybruuus0ePBg5efn6w9/+IOkE2/bnuRyBf9pJcdxWox1huLiYvl8vsBSU1PT6d8DAIC2dOgSl8TERA0ePFiVlZWBz0lPPeqsq6trcXTaGeLi4pScnBy0AABgU4dK1O/365NPPpHH41F2drbS09O1ZcuWwOONjY165513NHbs2A4HBQAg3IR0du7999+vgoICDRgwQHV1dXr00UdVX1+vm2++WS6XS3PmzNHixYs1cOBADRw4UIsXL1ZCQoJuvPHGwD4KCwvVv39/lZSUSDpRtOXl5YH/PnjwoPbs2aNzzjlH//qv/9qJUwUAoHOFVKKff/65brjhBn355ZdKTU3VpZdequ3btysrK0uS9MADD+hvf/ub7rrrLh05ckSjR4/W5s2blZSUFNjHgQMHFBX1zwPgL774QkOHDg18/Ytf/EK/+MUvNH78eG3durWD0wMAoOu4HMdxujtEZ6ivr5fb7ZbP5+PzUQBAh7S3U7h3LgAAhihRAAAMUaIAABiiRAEAMESJAgBgiBIFAMAQJQoAgCFKFAAAQ5QoAACGKFEAAAxRogAAGKJEAQAwRIkCAGCIEgUAwBAlCgCAIUoUAABDlCgAAIZiujsAOq6p2VFZ9WHVNRxXWlK8RmWnKDrK1d2xzojcdkVqbiCcUaIRbtM+rxZtKJfXdzww5nHHa0FBribleboxWdvIbVek5gbCnctxHKe7Q3SG+vp6ud1u+Xw+JScnd3ccKzbt8+rOlbt06g/w5LFF6YxhYfkLktx2RWpuoDu1t1P4TDRCNTU7WrShvMUvRkmBsUUbytXUHF6vkchtV6TmBiIFJRqhyqoPB701dypHktd3XGXVh+2Fagdy2xWpuYFIQYlGqLqG1n8xmqxnC7ntitTcQKSgRCNUWlJ8p65nC7ntitTcQKSgRCPUqOwUedzxau0CBZdOnH05KjvFZqwzIrddkZobiBSUaISKjnJpQUGuJLX4BXny6wUFuWF3HSC57YrU3ECkoEQj2KQ8j0pnDFO6O/ituHR3fFhftkBuuyI1NxAJuE70LBCpd6Iht12RmhvoDu3tFEoUAIBTcLMFAAC6GCUKAIAhShQAAEOUKAAAhihRAAAMUaIAABiiRAEAMESJAgBgiBIFAMAQJQoAgCFKFAAAQ5QoAACGQirRhQsXyuVyBS3p6emBxx3H0cKFC5WRkaHevXvr8ssv1/79+8+433Xr1ik3N1dxcXHKzc3V7373u9BnAgCAZSEfiV500UXyer2BZe/evYHHnnjiCS1ZskTPPvusduzYofT0dE2YMEENDQ2t7u+DDz7Q9ddfr5tuukkff/yxbrrpJv3oRz/Shx9+aDYjAAAsCelPoS1cuFBvvPGG9uzZ0+Ixx3GUkZGhOXPmaO7cuZIkv9+vfv366Wc/+5luv/320+7z+uuvV319vd56663A2KRJk9SnTx+tWrWq3RPhT6EBADpLl/0ptMrKSmVkZCg7O1vTpk1TVVWVJKm6ulq1tbWaOHFiYN24uDiNHz9e77//fqv7++CDD4K2kaQrr7yyzW2kEwVdX18ftAAAYFNIJTp69Gi9/PLLevvtt7V8+XLV1tZq7Nix+uqrr1RbWytJ6tevX9A2/fr1Czx2OrW1tSFvI0klJSVyu92BJTMzM5SpAADQYSGV6OTJk3Xddddp8ODBys/P1x/+8AdJ0ksvvRRYx+VyBW3jOE6LsVOZbFNcXCyfzxdYampqQpkKAAAd1qFLXBITEzV48GBVVlYGztI99Qiyrq6uxZHmt6Wnp4e8jXTireLk5OSgBQAAmzpUon6/X5988ok8Ho+ys7OVnp6uLVu2BB5vbGzUO++8o7Fjx7a6jzFjxgRtI0mbN29ucxsAAMJBTCgr33///SooKNCAAQNUV1enRx99VPX19br55pvlcrk0Z84cLV68WAMHDtTAgQO1ePFiJSQk6MYbbwzso7CwUP3791dJSYkkqaioSN/73vf0s5/9TNdcc43Wr1+vP/7xj9q2bVvnzhQAgE4WUol+/vnnuuGGG/Tll18qNTVVl156qbZv366srCxJ0gMPPKC//e1vuuuuu3TkyBGNHj1amzdvVlJSUmAfBw4cUFTUPw+Ax44dq9WrV2vevHmaP3++vvOd7+g3v/mNRo8e3UlTBACga4R0nWg44zpRAEBn6bLrRAEAwAmUKAAAhihRAAAMUaIAABiiRAEAMESJAgBgiBIFAMAQJQoAgCFKFAAAQ5QoAACGKFEAAAxRogAAGKJEAQAwFNKfQjvbNTU7Kqs+rLqG40pLiteo7BRFR7m6O9YZkdsuctsVqbnRM1Ci/2/TPq8WbSiX13c8MOZxx2tBQa4m5Xm6MVnbyG0Xue2K1NzoOfh7ojrxD/XOlbt06v+Ik691S2cMC8t/sOS2i9x2RWpunB34e6Lt1NTsaNGG8hb/UCUFxhZtKFdTc3i91iC3XeS2K1Jzo+fp8SVaVn046K2iUzmSvL7jKqs+bC9UO5DbLnLbFam50fP0+BKta2j9H6rJeraQ2y5y2xWpudHz9PgSTUuK79T1bCG3XeS2K1Jzo+fp8SU6KjtFHne8Wjth3qUTZwOOyk6xGeuMyG0Xue2K1NzoeXp8iUZHubSgIFeSWvyDPfn1goLcsLsujdx2kduuSM2NnqfHl6gkTcrzqHTGMKW7g98aSnfHh/Vp9OS2i9x2RWpu9CxcJ/otkXpnFHLbRW67IjU3Ilt7O4USBQDgFNxsAQCALkaJAgBgiBIFAMAQJQoAgCFKFAAAQ5QoAACGKFEAAAxRogAAGKJEAQAwRIkCAGCIEgUAwBAlCgCAIUoUAABDlCgAAIYoUQAADFGiAAAY6lCJlpSUyOVyac6cOYGxQ4cOaebMmcrIyFBCQoImTZqkysrKNvfz97//XQ8//LC+853vKD4+XhdffLE2bdrUkWgAAHQ54xLdsWOHli1bpiFDhgTGHMfR1KlTVVVVpfXr12v37t3KyspSfn6+vvnmm1b3NW/ePD3//PP65S9/qfLyct1xxx269tprtXv3btN4AAB0OaMSPXr0qKZPn67ly5erT58+gfHKykpt375dpaWlGjlypAYNGqSlS5fq6NGjWrVqVav7e+WVV/Sf//mfuuqqq3TBBRfozjvv1JVXXqknn3zSJB4AAFYYlejs2bM1ZcoU5efnB437/X5JUnx8fGAsOjpavXr10rZt21rdn9/vD9pGknr37n3Gberr64MWAABsCrlEV69erV27dqmkpKTFYzk5OcrKylJxcbGOHDmixsZGPf7446qtrZXX6211n1deeaWWLFmiyspKNTc3a8uWLVq/fn2b25SUlMjtdgeWzMzMUKcCAECHhFSiNTU1Kioq0sqVK1scOUpSbGys1q1bp4qKCqWkpCghIUFbt27V5MmTFR0d3ep+/+u//ksDBw5UTk6OevXqpbvvvluzZs1qc5vi4mL5fL7AUlNTE8pUAADoMJfjOE57V37jjTd07bXXBpVbU1OTXC6XoqKi5Pf7A4/5fD41NjYqNTVVo0eP1ogRI/Tcc8+1uf/jx4/rq6++UkZGhh588EH9/ve/1/79+9uVrb6+Xm63Wz6fT8nJye2dEgAALbS3U2JC2ekVV1yhvXv3Bo3NmjVLOTk5mjt3blC5ut1uSSdONtq5c6ceeeSRM+4/Pj5e/fv319///netW7dOP/rRj0KJBwCAVSGVaFJSkvLy8oLGEhMT1bdv38D4mjVrlJqaqgEDBmjv3r0qKirS1KlTNXHixMA2hYWF6t+/f+Bz1Q8//FAHDx7UJZdcooMHD2rhwoVqbm7WAw880NH5AQDQZUIq0fbwer267777dOjQIXk8HhUWFmr+/PlB6xw4cEBRUf/8OPb48eOaN2+eqqqqdM455+iqq67SK6+8onPPPbez4wEA0GlC+kw0nPGZKACgs7S3U7h3LgAAhihRAAAMUaIAABiiRAEAMESJAgBgiBIFAMAQJQoAgCFKFAAAQ5QoAACGOv22f5GsqdlRWfVh1TUcV1pSvEZlpyg6ytXdsc6I3HaR2y5y2xWpubsLJfr/Nu3zatGGcnl9xwNjHne8FhTkalKepxuTtY3cdpHbLnLbFam5uxP3ztWJJ86dK3fp1P8RJ197lc4YFpZPIHLbRW67yG1XpObuKtw7t52amh0t2lDe4okjKTC2aEO5mprD67UGue0it13ktitSc4eDHl+iZdWHg966OJUjyes7rrLqw/ZCtQO57SK3XeS2K1Jzh4MeX6J1Da0/cUzWs4XcdpHbLnLbFam5w0GPL9G0pPhOXc8WcttFbrvIbVek5g4HPb5ER2WnyOOOV2sncLt04uy0UdkpNmOdEbntIrdd5LYrUnOHgx5fotFRLi0oyJWkFk+gk18vKMgNu+ukyG0Xue0it12Rmjsc9PgSlaRJeR6VzhimdHfwWxXp7viwPq2b3HaR2y5y2xWpubsb14l+S6TeqYPcdpHbLnLbFam5O1t7O4USBQDgFNxsAQCALkaJAgBgiBIFAMAQJQoAgCFKFAAAQ5QoAACGKFEAAAxRogAAGKJEAQAwRIkCAGCIEgUAwBAlCgCAIUoUAABDlCgAAIYoUQAADFGiAAAYokQBADBEiQIAYIgSBQDAECUKAIChDpVoSUmJXC6X5syZExg7dOiQZs6cqYyMDCUkJGjSpEmqrKw8476efvppDRo0SL1791ZmZqbuvfdeHT9+vCPxAADoUjGmG+7YsUPLli3TkCFDAmOO42jq1KmKjY3V+vXrlZycrCVLlig/P1/l5eVKTEw87b5effVVPfjgg1qxYoXGjh2riooKzZw5U5L01FNPmUYEAKBLGR2JHj16VNOnT9fy5cvVp0+fwHhlZaW2b9+u0tJSjRw5UoMGDdLSpUt19OhRrVq1qtX9ffDBBxo3bpxuvPFGnX/++Zo4caJuuOEG7dy50yQeAABWGJXo7NmzNWXKFOXn5weN+/1+SVJ8fHxgLDo6Wr169dK2bdta3d9ll12mjz76SGVlZZKkqqoqbdy4UVOmTGl1G7/fr/r6+qAFAACbQn47d/Xq1dq1a5d27NjR4rGcnBxlZWWpuLhYzz//vBITE7VkyRLV1tbK6/W2us9p06bpf//3f3XZZZfJcRz94x//0J133qkHH3yw1W1KSkq0aNGiUOMDANBpQjoSrampUVFRkVauXBl0tHlSbGys1q1bp4qKCqWkpCghIUFbt27V5MmTFR0d3ep+t27dqscee0xLly7Vrl279Prrr+v3v/+9HnnkkVa3KS4uls/nCyw1NTWhTAUAgA5zOY7jtHflN954Q9dee21QITY1NcnlcikqKkp+vz/wmM/nU2Njo1JTUzV69GiNGDFCzz333Gn3+93vfleXXnqpfv7znwfGVq5cqdtuu01Hjx5VVNSZu76+vl5ut1s+n0/JycntnRIAAC20t1NCejv3iiuu0N69e4PGZs2apZycHM2dOzeoXN1ut6QTJxvt3LmzzaPKY8eOtSjK6OhoOY6jEDoeAACrQirRpKQk5eXlBY0lJiaqb9++gfE1a9YoNTVVAwYM0N69e1VUVKSpU6dq4sSJgW0KCwvVv39/lZSUSJIKCgq0ZMkSDR06VKNHj9Zf/vIXzZ8/X9///vfbfBsYAIDuZHydaGu8Xq/uu+8+HTp0SB6PR4WFhZo/f37QOgcOHAg68pw3b55cLpfmzZungwcPKjU1VQUFBXrsscc6Ox4AAJ0mpM9EwxmfiQIAOkt7O4V75wIAYIgSBQDAECUKAIAhShQAAEOUKAAAhjr9EpdI1tTsqKz6sOoajistKV6jslMUHeXq7lhnRG67yG0Xue0id2go0f+3aZ9XizaUy+v75x8C97jjtaAgV5PyPN2YrG3ktovcdpHbLnKHjutEdeIHcOfKXTr1f8TJ1zClM4aF5ROI3HaR2y5y20XuYFwn2k5NzY4WbShv8QOQFBhbtKFcTc3h9VqD3HaR2y5y20Vucz2+RMuqDwe9BXAqR5LXd1xl1YfthWoHcttFbrvIbRe5zfX4Eq1raP0HYLKeLeS2i9x2kdsucpvr8SWaltTyj4t3ZD1byG0Xue0it13kNtfjS3RUdoo87ni1diK0SyfO8hqVnWIz1hmR2y5y20Vuu8htrseXaHSUSwsKciWpxQ/i5NcLCnLD7jopcttFbrvIbRe5zfX4EpWkSXkelc4YpnR38CF/ujs+bE/rlshtG7ntIrdd5DbDdaLfwp067CK3XeS2i9x2dXbu9nYKJQoAwCm42QIAAF2MEgUAwBAlCgCAIUoUAABDlCgAAIYoUQAADFGiAAAYokQBADBEiQIAYIgSBQDAECUKAIAhShQAAEOUKAAAhihRAAAMUaIAABiiRAEAMESJAgBgiBIFAMAQJQoAgCFKFAAAQ5QoAACGKFEAAAxRogAAGOpQiZaUlMjlcmnOnDmBsUOHDmnmzJnKyMhQQkKCJk2apMrKyjb3c/nll8vlcrVYpkyZ0pF4AAB0KeMS3bFjh5YtW6YhQ4YExhzH0dSpU1VVVaX169dr9+7dysrKUn5+vr755ptW9/X666/L6/UGln379ik6Olo//OEPTeMBANDljEr06NGjmj59upYvX64+ffoExisrK7V9+3aVlpZq5MiRGjRokJYuXaqjR49q1apVre4vJSVF6enpgWXLli1KSEigRAEAYc2oRGfPnq0pU6YoPz8/aNzv90uS4uPjA2PR0dHq1auXtm3b1u79v/DCC5o2bZoSExNN4gEAYEVMqBusXr1au3bt0o4dO1o8lpOTo6ysLBUXF+v5559XYmKilixZotraWnm93nbtv6ysTPv27dMLL7zQ5np+vz9Q2pJUX18f2kQAAOigkI5Ea2pqVFRUpJUrVwYdbZ4UGxurdevWqaKiQikpKUpISNDWrVs1efJkRUdHt+t7vPDCC8rLy9OoUaPaXK+kpERutzuwZGZmhjIVAAA6zOU4jtPeld944w1de+21QYXY1NQkl8ulqKgo+f3+wGM+n0+NjY1KTU3V6NGjNWLECD333HNt7v/YsWPyeDx6+OGHVVRU1Oa6pzsSzczMlM/nU3JycnunBABAC/X19XK73WfslJDezr3iiiu0d+/eoLFZs2YpJydHc+fODSpXt9st6cTJRjt37tQjjzxyxv3/9re/ld/v14wZM864blxcnOLi4kKJDwBApwqpRJOSkpSXlxc0lpiYqL59+wbG16xZo9TUVA0YMEB79+5VUVGRpk6dqokTJwa2KSwsVP/+/VVSUhK0rxdeeEFTp05V3759TecDAIA1IZ9YdCZer1f33XefDh06JI/Ho8LCQs2fPz9onQMHDigqKvjj2IqKCm3btk2bN2/u7EgAAHSJkD4TDWftff8aAIAzaW+ncO9cAAAMUaIAABjq9M9EYV9Ts6Oy6sOqaziutKR4jcpOUXSUq7tjnRG57SK3XeS2q7tyU6IRbtM+rxZtKJfXdzww5nHHa0FBribleboxWdvIbRe57SK3Xd2ZmxOLItimfV7duXKXTv0BnnztVTpjWFg+8cltF7ntIrddXZWbE4vOck3NjhZtKG/xxJEUGFu0oVxNzeH1GoncdpHbLnLbFQ65KdEIVVZ9OOiti1M5kry+4yqrPmwvVDuQ2y5y20Vuu8IhNyUaoeoaWn/imKxnC7ntIrdd5LYrHHJTohEqLanlX9HpyHq2kNsucttFbrvCITclGqFGZafI445Xaydwu3Ti7LRR2Sk2Y50Rue0it13ktiscclOiESo6yqUFBbmS1OIJdPLrBQW5YXd9F7ntIrdd5LYrHHJTohFsUp5HpTOGKd0d/FZFujs+bE9Hl8htG7ntIrdd3Z2b60TPAtxhxC5y20Vuu8h9Qns7hRIFAOAU3GwBAIAuRokCAGCIEgUAwBAlCgCAIUoUAABDlCgAAIYoUQAADFGiAAAYokQBADBEiQIAYIgSBQDAECUKAIAhShQAAEOUKAAAhihRAAAMUaIAABiiRAEAMESJAgBgiBIFAMAQJQoAgCFKFAAAQ5QoAACGKFEAAAxRogAAGKJEAQAwRIkCAGCIEgUAwFCHSrSkpEQul0tz5swJjB06dEgzZ85URkaGEhISNGnSJFVWVp5xX19//bVmz54tj8ej+Ph4XXjhhdq4cWNH4gEA0KViTDfcsWOHli1bpiFDhgTGHMfR1KlTFRsbq/Xr1ys5OVlLlixRfn6+ysvLlZiYeNp9NTY2asKECUpLS9PatWt13nnnqaamRklJSabxAADockYlevToUU2fPl3Lly/Xo48+GhivrKzU9u3btW/fPl100UWSpKVLlyotLU2rVq3ST37yk9Pub8WKFTp8+LDef/99xcbGSpKysrJMogEAYI3R27mzZ8/WlClTlJ+fHzTu9/slSfHx8YGx6Oho9erVS9u2bWt1f2+++abGjBmj2bNnq1+/fsrLy9PixYvV1NTU6jZ+v1/19fVBCwAANoVcoqtXr9auXbtUUlLS4rGcnBxlZWWpuLhYR44cUWNjox5//HHV1tbK6/W2us+qqiqtXbtWTU1N2rhxo+bNm6cnn3xSjz32WKvblJSUyO12B5bMzMxQpwIAQIeEVKI1NTUqKirSypUrg442T4qNjdW6detUUVGhlJQUJSQkaOvWrZo8ebKio6Nb3W9zc7PS0tK0bNkyDR8+XNOmTdNDDz2k0tLSVrcpLi6Wz+cLLDU1NaFMBQCADgvpM9GPPvpIdXV1Gj58eGCsqalJ7777rp599ln5/X4NHz5ce/bskc/nU2Njo1JTUzV69GiNGDGi1f16PB7FxsYGFe2FF16o2tpaNTY2qlevXi22iYuLU1xcXCjxAQDoVCEdiV5xxRXau3ev9uzZE1hGjBih6dOna8+ePUEl6Ha7lZqaqsrKSu3cuVPXXHNNq/sdN26c/vKXv6i5uTkwVlFRIY/Hc9oCBQAgHIR0JJqUlKS8vLygscTERPXt2zcwvmbNGqWmpmrAgAHau3evioqKNHXqVE2cODGwTWFhofr37x/4XPXOO+/UL3/5SxUVFemnP/2pKisrtXjxYt1zzz0dnR8AAF3G+DrR1ni9Xt133306dOiQPB6PCgsLNX/+/KB1Dhw4oKiofx4EZ2ZmavPmzbr33ns1ZMgQ9e/fX0VFRZo7d25nx2tTU7OjsurDqms4rrSkeI3KTlF0lMtqBhPktovcdpHbrkjN3V1cjuM43R2iM9TX18vtdsvn8yk5OTnk7Tft82rRhnJ5fccDYx53vBYU5GpSnqczo3YqcttFbrvIbVek5u4K7e0USlQnnjh3rtylU/9HnHztVTpjWFg+gchtF7ntIrddkZq7q7S3U3r8Deibmh0t2lDe4okjKTC2aEO5mprD67UGue0it13ktitSc4eDHl+iZdWHg966OJUjyes7rrLqw/ZCtQO57SK3XeS2K1Jzh4MeX6J1Da0/cUzWs4XcdpHbLnLbFam5w0GPL9G0pJZ3XurIeraQ2y5y20VuuyI1dzjo8SU6KjtFHne8WjuB26UTZ6eNyk6xGeuMyG0Xue0it12Rmjsc9PgSjY5yaUFBriS1eAKd/HpBQW7YXSdFbrvIbRe57YrU3OGgx5eoJE3K86h0xjClu4Pfqkh3x4f1ad3ktovcdpHbrkjN3d24TvRbIvVOHeS2i9x2kduuSM3d2bjZAgAAhrjZAgAAXYwSBQDAECUKAIAhShQAAEOUKAAAhihRAAAMUaIAABiiRAEAMESJAgBgiBIFAMAQJQoAgCFKFAAAQ5QoAACGKFEAAAxRogAAGKJEAQAwRIkCAGCIEgUAwBAlCgCAIUoUAABDlCgAAIYoUQAADFGiAAAYokQBADBEiQIAYIgSBQDAECUKAIAhShQAAEOUKAAAhihRAAAMUaIAABjqUImWlJTI5XJpzpw5gbFDhw5p5syZysjIUEJCgiZNmqTKyso29/PrX/9aLperxXL8+PGOxAMAoEsZl+iOHTu0bNkyDRkyJDDmOI6mTp2qqqoqrV+/Xrt371ZWVpby8/P1zTfftLm/5ORkeb3eoCU+Pt40HgAAXc6oRI8eParp06dr+fLl6tOnT2C8srJS27dvV2lpqUaOHKlBgwZp6dKlOnr0qFatWtXmPl0ul9LT04MWAADCmVGJzp49W1OmTFF+fn7QuN/vl6SgI8jo6Gj16tVL27Zta3OfR48eVVZWls477zxdffXV2r17d5vr+/1+1dfXBy0AANgUcomuXr1au3btUklJSYvHcnJylJWVpeLiYh05ckSNjY16/PHHVVtbK6/X2+o+c3Jy9Otf/1pvvvmmVq1apfj4eI0bN67Nz1JLSkrkdrsDS2ZmZqhTAQCgQ1yO4zjtXbmmpkYjRozQ5s2bdfHFF0uSLr/8cl1yySV6+umnJUkfffSRbrnlFn388ceKjo5Wfn6+oqJOdPXGjRvb9X2am5s1bNgwfe9739Mzzzxz2nX8fn/gyFeS6uvrlZmZKZ/Pp+Tk5PZOKUhTs6Oy6sOqaziutKR4jcpOUXSUy2hfNpHbLnLbRW67IjV3Z6uvr5fb7T5jp8SEstOPPvpIdXV1Gj58eGCsqalJ7777rp599ln5/X4NHz5ce/bskc/nU2Njo1JTUzV69GiNGDGi3d8nKipKI0eObPNINC4uTnFxcaHEb9OmfV4t2lAur++fZwR73PFaUJCrSXmeTvs+nY3cdpHbLnLbFam5u1NIR6INDQ36n//5n6CxWbNmKScnR3PnzlVeXl6LbSorK5WTk6O33npLEydObNf3cRxHo0aN0uDBg7VixYp2bdPeVw2ns2mfV3eu3KVT/0ecfO1VOmNYWD6ByG0Xue0it12RmrurtLdTQvpMNCkpSXl5eUFLYmKi+vbtGyjQNWvWaOvWrYHLXCZMmKCpU6cGFWhhYaGKi4sDXy9atEhvv/22qqqqtGfPHt1yyy3as2eP7rjjjlDnHbKmZkeLNpS3eOJICowt2lCupuZ2v9awgtx2kdsuctsVqbnDQaffscjr9eqmm25STk6O7rnnHt10000tLm85cOBA0IlGX3/9tW677TZdeOGFmjhxog4ePKh3331Xo0aN6ux4LZRVHw566+JUjiSv77jKqg93eZZQkNsucttFbrsiNXc4COkz0dPZunVr0Nf33HOP7rnnnpC2eeqpp/TUU091NIqRuob23RWpvevZQm67yG0Xue2K1NzhoMffOzctqX13RWrveraQ2y5y20VuuyI1dzjo8SU6KjtFHne8WjuB26UTZ6eNyk6xGeuMyG0Xue0it12Rmjsc9PgSjY5yaUFBriS1eAKd/HpBQW7YXSdFbrvIbRe57YrU3OGgx5eoJE3K86h0xjClu4Pfqkh3x4f1ad3ktovcdpHbrkjN3d1Cuk40nHXkOtGTIvVOHeS2i9x2kduuSM3d2drbKZQoAACn6JKbLQAAgH+iRAEAMESJAgBgiBIFAMAQJQoAgCFKFAAAQ5QoAACGKFEAAAxRogAAGOrw3xMNFydvvFRfX9/NSQAAke5kl5zppn5nTYk2NDRIkjIzM7s5CQDgbNHQ0CC3293q42fNvXObm5v1xRdfKCkpSS5XeN8sub6+XpmZmaqpqTkr7vPLfMLb2TSfs2kuEvMJZ47jqKGhQRkZGYqKav2Tz7PmSDQqKkrnnXded8cISXJycsQ/0b6N+YS3s2k+Z9NcJOYTrto6Aj2JE4sAADBEiQIAYIgS7QZxcXFasGCB4uLiujtKp2A+4e1sms/ZNBeJ+ZwNzpoTiwAAsI0jUQAADFGiAAAYokQBADBEiQIAYIgS7WQLFy6Uy+UKWtLT09vc5tVXX9XFF1+shIQEeTwezZo1S1999ZWlxG0zmc9zzz2nCy+8UL1799agQYP08ssvW0rbPgcPHtSMGTPUt29fJSQk6JJLLtFHH33U5jbvvPOOhg8frvj4eF1wwQX61a9+ZSlt20Kdi9fr1Y033qhBgwYpKipKc+bMsRe2HUKdz+uvv64JEyYoNTVVycnJGjNmjN5++22LidsW6ny2bdumcePGqW/fvurdu7dycnL01FNPWUzcNpN/Oye99957iomJ0SWXXNK1IS07a+5YFE4uuugi/fGPfwx8HR0d3eq627ZtU2FhoZ566ikVFBTo4MGDuuOOO/STn/xEv/vd72zEPaNQ5lNaWqri4mItX75cI0eOVFlZmW699Vb16dNHBQUFNuK26ciRIxo3bpz+7d/+TW+99ZbS0tL02Wef6dxzz211m+rqal111VW69dZbtXLlSr333nu66667lJqaquuuu85e+FOYzMXv9ys1NVUPPfRQWP1ylszm8+6772rChAlavHixzj33XL344osqKCjQhx9+qKFDh9oLfxom80lMTNTdd9+tIUOGKDExUdu2bdPtt9+uxMRE3XbbbfbCn4bJfE7y+XwqLCzUFVdcoUOHDnV9WJscdKoFCxY4F198cbvX//nPf+5ccMEFQWPPPPOMc95553VyMjOhzmfMmDHO/fffHzRWVFTkjBs3rpOTmZk7d65z2WWXhbTNAw884OTk5ASN3X777c6ll17amdFCZjKXbxs/frxTVFTUeYE6qKPzOSk3N9dZtGhRJyTqmM6az7XXXuvMmDGjExJ1TEfmc/311zvz5s0L+fdJJODt3C5QWVmpjIwMZWdna9q0aaqqqmp13bFjx+rzzz/Xxo0b5TiODh06pLVr12rKlCkWE7ctlPn4/X7Fx8cHjfXu3VtlZWX6+9//3tVRz+jNN9/UiBEj9MMf/lBpaWkaOnSoli9f3uY2H3zwgSZOnBg0duWVV2rnzp3dOieTuYSzzphPc3OzGhoalJKS0kUp268z5rN79269//77Gj9+fBelbD/T+bz44ov67LPPtGDBAgspu0F3t/jZZuPGjc7atWudP//5z86WLVuc8ePHO/369XO+/PLLVrdZs2aNc8455zgxMTGOJOf73/++09jYaDF160KdT3FxsZOenu7s3LnTaW5udnbs2OGkpaU5kpwvvvjCcvqW4uLinLi4OKe4uNjZtWuX86tf/cqJj493XnrppVa3GThwoPPYY48Fjb333nvdPieTuXxbuB2JdnQ+juM4TzzxhJOSkuIcOnSoC5O2T0fm079/f6dXr15OVFSU8/DDD1tIe2Ym86moqHDS0tKcTz/91HGc0N/ZigSUaBc7evSo069fP+fJJ5887eP79+93PB6P88QTTzgff/yxs2nTJmfw4MHOj3/8Y8tJ2+dM8zl27Jgza9YsJyYmxomOjnYyMjKcBx54wJEUFr/YYmNjnTFjxgSN/fSnP23zrdmBAwc6ixcvDhrbtm2bI8nxer1dkrM9TObybeFWoh2dz2uvveYkJCQ4W7Zs6Yp4IevIfKqqqpw///nPzrJly5yUlBTntdde66qY7RbqfP7xj384I0aMcEpLSwNjZ2OJ8nZuF0tMTNTgwYNVWVl52sdLSko0btw4/cd//IeGDBmiK6+8UkuXLtWKFSvk9Xotpz2zM82nd+/eWrFihY4dO6a//vWvOnDggM4//3wlJSXpX/7lXyynbcnj8Sg3Nzdo7MILL9SBAwda3SY9PV21tbVBY3V1dYqJiVHfvn27JGd7mMwlnHVkPr/5zW90yy236Le//a3y8/O7KmJIOjKf7OxsDR48WLfeeqvuvfdeLVy4sItStl+o82loaNDOnTt19913KyYmRjExMXr44Yf18ccfKyYmRn/6059sxO5ynJ3bxfx+vz755BN997vfPe3jx44dU0xM8I/h5NmvThje1vhM8zkpNjY28PddV69erauvvrrNP2xry7hx4/Tpp58GjVVUVCgrK6vVbcaMGaMNGzYEjW3evFkjRoxQbGxsl+RsD5O5hDPT+axatUo//vGPtWrVqrA6l6Czfj6O48jv93dmNCOhzic5OVl79+4NGlu6dKn+9Kc/ae3atcrOzu6yrFZ196Hw2ebf//3fna1btzpVVVXO9u3bnauvvtpJSkpy/vrXvzqO4zgPPvigc9NNNwXWf/HFF52YmBhn6dKlzmeffeZs27bNGTFihDNq1KjumkKQUOfz6aefOq+88opTUVHhfPjhh87111/vpKSkONXV1d00g2BlZWVOTEyM89hjjzmVlZXOq6++6iQkJDgrV64MrHPqnKqqqpyEhATn3nvvdcrLy50XXnjBiY2NddauXdsdUwgwmYvjOM7u3bud3bt3O8OHD3duvPFGZ/fu3c7+/fttx2/BZD6vvfaaExMT4zz33HOO1+sNLF9//XV3TCGIyXyeffZZ580333QqKiqciooKZ8WKFU5ycrLz0EMPdccUgpg+377tbHw7lxLtZNdff73j8Xic2NhYJyMjw/nBD34Q9Avq5ptvdsaPHx+0zTPPPOPk5uY6vXv3djwejzN9+nTn888/t5z89EKdT3l5uXPJJZc4vXv3dpKTk51rrrnG+e///u9uSN66DRs2OHl5eU5cXJyTk5PjLFu2LOjx0/2Mtm7d6gwdOtTp1auXc/755wd9ztOdTOYiqcWSlZVlL3QbQp3P+PHjTzufm2++2W7wVoQ6n2eeeca56KKLnISEBCc5OdkZOnSos3TpUqepqcly8tMzeb5929lYovwpNAAADHX/h1QAAEQoShQAAEOUKAAAhihRAAAMUaIAABiiRAEAMESJAgBgiBIFAMAQJQoAgCFKFAAAQ5QoAACGKFEAAAz9H2cIXBgFlfLqAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_worldpop_.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "02dbdbed-5e0a-425d-88c8-548d57c92d7d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "499" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(df_worldpop_)" + ] + }, + { + "cell_type": "markdown", + "id": "32dab221-7a28-4719-a180-dc8a91c17b48", + "metadata": {}, + "source": [ + "Our next step is to extract information of healthcare facilities for the country of interest. We do so using OpenStreetMap. With the latest version of geopandas, it is now possible to directly read **osm.pbf** files from OpenStreetMap. \n", + "\n", + "Healthcare facilities are stored as *multipolygons* within OpenStreetMap, and we want to download all clinics and hospitals. " + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "id": "dbf8e926-52cc-4411-bc68-263f7cafaf1f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\eks510\\.conda\\envs\\pygis\\Lib\\site-packages\\pyogrio\\raw.py:196: RuntimeWarning: Non closed ring detected. To avoid accepting it, set the OGR_GEOMETRY_ACCEPT_UNCLOSED_RING configuration option to NO\n", + " return ogr_read(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: total: 13.1 s\n", + "Wall time: 47.1 s\n" + ] + } + ], + "source": [ + "%%time\n", + "Country_GeofabrikData_path = download.get_country_geofabrik(country_iso3)\n", + "#Country_GeofabrikData_path = \"C:\\\\Data\\\\country_osm\\\\albania-latest.osm.pbf\"\n", + "\n", + "HealthCenters = gpd.read_file(Country_GeofabrikData_path, layer=\"multipolygons\")\n", + "sub_types =['clinic', 'hospital']\n", + "HealthCenters = HealthCenters[HealthCenters['amenity'].isin(sub_types)].reset_index(drop=True)\n", + "HealthCenters = HealthCenters.to_crs(3857)\n", + "\n", + "# to convert polygons to their centroids\n", + "HealthCenters_centroids = HealthCenters.copy()\n", + "HealthCenters_centroids['geometry'] = HealthCenters.centroid\n", + "\n", + "HealthCenters_centroids=HealthCenters_centroids.to_crs(4326)" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "id": "cfbc0e8e-bf63-42c9-b9d0-3a095c0f8ca6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
osm_idosm_way_idnametypeaerowayamenityadmin_levelbarrierboundarybuilding...man_mademilitarynaturalofficeplaceshopsporttourismother_tagsgeometry
07591385NoneZithaKlinikmultipolygonNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"healthcare\"=>\"hospital\",\"operator\"=>\"Hôpitaux...MULTIPOLYGON (((682338.374 6377939.045, 682324...
117514812NoneHôpital KirchbergmultipolygonNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"healthcare\"=>\"hospital\",\"operator\"=>\"Hôpitaux...MULTIPOLYGON (((687383.596 6382856.727, 687394...
2None41407070Centre Hospitalier de LuxembourgNoneNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"healthcare\"=>\"hospital\",\"operator\"=>\"Centre H...MULTIPOLYGON (((679155.827 6380157.554, 679142...
3None56104142SénologieNoneNonehospitalNoneNoneNonehospital...NoneNoneNoneNoneNoneNoneNoneNoneNoneMULTIPOLYGON (((682993.032 6382614.167, 682977...
4None72872456Centre Hospitalier Émile MayrischNoneNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"healthcare\"=>\"hospital\",\"short_name\"=>\"CHEM\",...MULTIPOLYGON (((665744.712 6360608.875, 665747...
5None112389436Hôpital de la ville de DudelangeNoneNonehospitalNoneNoneNoneyes...NoneNoneNoneNoneNoneNoneNoneNone\"addr:city\"=>\"Dudelange\",\"addr:housenumber\"=>\"...MULTIPOLYGON (((677744.73 6355108.857, 677802....
6None189452987CHL EichNoneNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"addr:city\"=>\"Luxembourg\",\"addr:country\"=>\"LU\"...MULTIPOLYGON (((682944.085 6382574.363, 682938...
7None298655535Centre Médical de SteinselNoneNoneclinicNoneNoneNoneyes...NoneNoneNoneNoneNoneNoneNoneNone\"addr:city\"=>\"Steinsel\",\"addr:country\"=>\"LU\",\"...MULTIPOLYGON (((681904.484 6390283.25, 681900....
8None381951079Clinique Sainte MarieNoneNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"emergency\"=>\"yes\",\"healthcare\"=>\"hospital\",\"o...MULTIPOLYGON (((666416.525 6360368.193, 666413...
9None426571994Centre de réhabilitationNoneNonehospitalNoneNoneNoneyes...NoneNoneNoneNoneNoneNoneNoneNone\"addr:city\"=>\"Colpach-Bas\",\"addr:housenumber\"=...MULTIPOLYGON (((648445.596 6404692.12, 648459....
10None469054630Hôpital intercommunal de SteinfortNoneNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"healthcare\"=>\"hospital\"MULTIPOLYGON (((658123.935 6387823.318, 658132...
11None570707211Hôpital Intercommunal Princesse Marie-AstridNoneNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"healthcare\"=>\"hospital\"MULTIPOLYGON (((655334.714 6365873.121, 655368...
12None784862436Centre hospitalier Neuro-Psychiatrique (CHNP)NoneNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"healthcare\"=>\"hospital\"MULTIPOLYGON (((678665.866 6419086.54, 678705....
13None784866880RehazenterNoneNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"healthcare\"=>\"hospital\"MULTIPOLYGON (((687456.721 6382232.099, 687457...
14None884212046Centre Hospitalier du NordNoneNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"contact:email\"=>\"chdn@chdn.lu\",\"contact:fax\"=...MULTIPOLYGON (((678507.013 6421193.856, 678560...
15None887577792Centre Hospitalier du NordNoneNonehospitalNoneNoneNoneNone...NoneNoneNoneNoneNoneNoneNoneNone\"addr:city\"=>\"Wiltz\",\"addr:country\"=>\"LU\",\"add...MULTIPOLYGON (((660490.343 6440253.818, 660477...
\n", + "

16 rows × 26 columns

\n", + "
" + ], + "text/plain": [ + " osm_id osm_way_id name \\\n", + "0 7591385 None ZithaKlinik \n", + "1 17514812 None Hôpital Kirchberg \n", + "2 None 41407070 Centre Hospitalier de Luxembourg \n", + "3 None 56104142 Sénologie \n", + "4 None 72872456 Centre Hospitalier Émile Mayrisch \n", + "5 None 112389436 Hôpital de la ville de Dudelange \n", + "6 None 189452987 CHL Eich \n", + "7 None 298655535 Centre Médical de Steinsel \n", + "8 None 381951079 Clinique Sainte Marie \n", + "9 None 426571994 Centre de réhabilitation \n", + "10 None 469054630 Hôpital intercommunal de Steinfort \n", + "11 None 570707211 Hôpital Intercommunal Princesse Marie-Astrid \n", + "12 None 784862436 Centre hospitalier Neuro-Psychiatrique (CHNP) \n", + "13 None 784866880 Rehazenter \n", + "14 None 884212046 Centre Hospitalier du Nord \n", + "15 None 887577792 Centre Hospitalier du Nord \n", + "\n", + " type aeroway amenity admin_level barrier boundary building \\\n", + "0 multipolygon None hospital None None None None \n", + "1 multipolygon None hospital None None None None \n", + "2 None None hospital None None None None \n", + "3 None None hospital None None None hospital \n", + "4 None None hospital None None None None \n", + "5 None None hospital None None None yes \n", + "6 None None hospital None None None None \n", + "7 None None clinic None None None yes \n", + "8 None None hospital None None None None \n", + "9 None None hospital None None None yes \n", + "10 None None hospital None None None None \n", + "11 None None hospital None None None None \n", + "12 None None hospital None None None None \n", + "13 None None hospital None None None None \n", + "14 None None hospital None None None None \n", + "15 None None hospital None None None None \n", + "\n", + " ... man_made military natural office place shop sport tourism \\\n", + "0 ... None None None None None None None None \n", + "1 ... None None None None None None None None \n", + "2 ... None None None None None None None None \n", + "3 ... None None None None None None None None \n", + "4 ... None None None None None None None None \n", + "5 ... None None None None None None None None \n", + "6 ... None None None None None None None None \n", + "7 ... None None None None None None None None \n", + "8 ... None None None None None None None None \n", + "9 ... None None None None None None None None \n", + "10 ... None None None None None None None None \n", + "11 ... None None None None None None None None \n", + "12 ... None None None None None None None None \n", + "13 ... None None None None None None None None \n", + "14 ... None None None None None None None None \n", + "15 ... None None None None None None None None \n", + "\n", + " other_tags \\\n", + "0 \"healthcare\"=>\"hospital\",\"operator\"=>\"Hôpitaux... \n", + "1 \"healthcare\"=>\"hospital\",\"operator\"=>\"Hôpitaux... \n", + "2 \"healthcare\"=>\"hospital\",\"operator\"=>\"Centre H... \n", + "3 None \n", + "4 \"healthcare\"=>\"hospital\",\"short_name\"=>\"CHEM\",... \n", + "5 \"addr:city\"=>\"Dudelange\",\"addr:housenumber\"=>\"... \n", + "6 \"addr:city\"=>\"Luxembourg\",\"addr:country\"=>\"LU\"... \n", + "7 \"addr:city\"=>\"Steinsel\",\"addr:country\"=>\"LU\",\"... \n", + "8 \"emergency\"=>\"yes\",\"healthcare\"=>\"hospital\",\"o... \n", + "9 \"addr:city\"=>\"Colpach-Bas\",\"addr:housenumber\"=... \n", + "10 \"healthcare\"=>\"hospital\" \n", + "11 \"healthcare\"=>\"hospital\" \n", + "12 \"healthcare\"=>\"hospital\" \n", + "13 \"healthcare\"=>\"hospital\" \n", + "14 \"contact:email\"=>\"chdn@chdn.lu\",\"contact:fax\"=... \n", + "15 \"addr:city\"=>\"Wiltz\",\"addr:country\"=>\"LU\",\"add... \n", + "\n", + " geometry \n", + "0 MULTIPOLYGON (((682338.374 6377939.045, 682324... \n", + "1 MULTIPOLYGON (((687383.596 6382856.727, 687394... \n", + "2 MULTIPOLYGON (((679155.827 6380157.554, 679142... \n", + "3 MULTIPOLYGON (((682993.032 6382614.167, 682977... \n", + "4 MULTIPOLYGON (((665744.712 6360608.875, 665747... \n", + "5 MULTIPOLYGON (((677744.73 6355108.857, 677802.... \n", + "6 MULTIPOLYGON (((682944.085 6382574.363, 682938... \n", + "7 MULTIPOLYGON (((681904.484 6390283.25, 681900.... \n", + "8 MULTIPOLYGON (((666416.525 6360368.193, 666413... \n", + "9 MULTIPOLYGON (((648445.596 6404692.12, 648459.... \n", + "10 MULTIPOLYGON (((658123.935 6387823.318, 658132... \n", + "11 MULTIPOLYGON (((655334.714 6365873.121, 655368... \n", + "12 MULTIPOLYGON (((678665.866 6419086.54, 678705.... \n", + "13 MULTIPOLYGON (((687456.721 6382232.099, 687457... \n", + "14 MULTIPOLYGON (((678507.013 6421193.856, 678560... \n", + "15 MULTIPOLYGON (((660490.343 6440253.818, 660477... \n", + "\n", + "[16 rows x 26 columns]" + ] + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "HealthCenters" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "id": "14c6ab08-56b0-49a2-8955-74a0bd8b6b7c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The output is HealthCenters_centroids as a dataframe of the Health Centers\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"The output is HealthCenters_centroids as a dataframe of the Health Centers\")\n", + "\n", + "#plotting\n", + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "HealthCenters_centroids.plot(ax=ax, color='red', markersize=10, alpha=0.7)\n", + "\n", + "# temporarily reprojects to EPSG:3857 to add the basemap (contextily requires it)\n", + "#cx.add_basemap(ax, crs='EPSG:4326', source=cx.providers.OpenStreetMap.Mapnik, zoom=8)\n", + "\n", + "ax.set_title(f'Locations of Hospitals and Clinics in {country_iso3}', fontsize=15)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "dc39cef6-eac9-40a0-a737-bac3cd5bc2a1", + "metadata": {}, + "source": [ + "## 3. Classification of rural and urban areas" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c59b420-aaaf-45d6-afff-41e9ef162818", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "573f10a4-d0bb-4675-903f-71ffbe45358f", + "metadata": {}, + "source": [ + "## 4. Clustering of Healthcare centres and population" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "id": "09511e34-85cc-43f8-b994-a5d3f53431c2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\eks510\\.conda\\envs\\pygis\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
clustergeometrypopulationnearest_hospital_local_idnearest_hospital_geometry
00POINT (6.15153 49.58031)128651.3125001POINT (6.128297306122917 49.60370244201384)
11POINT (6.32931 49.67302)15717.3925782POINT (6.176428597947581 49.63239294648283)
22POINT (6.01819 49.71938)18540.7695318POINT (6.125497137582533 49.67548286167413)
33POINT (6.40338 49.79663)9557.7675782POINT (6.176428597947581 49.63239294648283)
44POINT (5.92931 49.53396)73094.47656212POINT (5.886631289416359 49.53288914400383)
55POINT (6.15153 49.4876)35154.4765626POINT (6.088783750195296 49.47060587499345)
66POINT (6.46264 49.67302)5642.8691412POINT (6.176428597947581 49.63239294648283)
77POINT (6.13671 49.70392)62590.1953128POINT (6.125497137582533 49.67548286167413)
88POINT (6.01819 49.53396)61558.2968759POINT (5.985821590636188 49.50122369975853)
99POINT (5.81079 49.87389)13944.09765610POINT (5.8253568378630085 49.75912164639861)
1010POINT (5.88486 49.71938)25226.89453111POINT (5.912548337712089 49.66095224406815)
1111POINT (5.79597 49.53396)33223.04687512POINT (5.886631289416359 49.53288914400383)
1212POINT (6.24042 49.81208)23956.86718813POINT (6.096093002090682 49.84290986563605)
1313POINT (6.32931 49.53396)21879.98828114POINT (6.173860418724089 49.62791052823996)
1414POINT (6.09227 49.93569)28763.84375015POINT (6.095520633026102 49.85367960759491)
1515POINT (5.95153 50.07862)20551.43945316POINT (5.934042808032211 49.96544643315656)
\n", + "
" + ], + "text/plain": [ + " cluster geometry population \\\n", + "0 0 POINT (6.15153 49.58031) 128651.312500 \n", + "1 1 POINT (6.32931 49.67302) 15717.392578 \n", + "2 2 POINT (6.01819 49.71938) 18540.769531 \n", + "3 3 POINT (6.40338 49.79663) 9557.767578 \n", + "4 4 POINT (5.92931 49.53396) 73094.476562 \n", + "5 5 POINT (6.15153 49.4876) 35154.476562 \n", + "6 6 POINT (6.46264 49.67302) 5642.869141 \n", + "7 7 POINT (6.13671 49.70392) 62590.195312 \n", + "8 8 POINT (6.01819 49.53396) 61558.296875 \n", + "9 9 POINT (5.81079 49.87389) 13944.097656 \n", + "10 10 POINT (5.88486 49.71938) 25226.894531 \n", + "11 11 POINT (5.79597 49.53396) 33223.046875 \n", + "12 12 POINT (6.24042 49.81208) 23956.867188 \n", + "13 13 POINT (6.32931 49.53396) 21879.988281 \n", + "14 14 POINT (6.09227 49.93569) 28763.843750 \n", + "15 15 POINT (5.95153 50.07862) 20551.439453 \n", + "\n", + " nearest_hospital_local_id nearest_hospital_geometry \n", + "0 1 POINT (6.128297306122917 49.60370244201384) \n", + "1 2 POINT (6.176428597947581 49.63239294648283) \n", + "2 8 POINT (6.125497137582533 49.67548286167413) \n", + "3 2 POINT (6.176428597947581 49.63239294648283) \n", + "4 12 POINT (5.886631289416359 49.53288914400383) \n", + "5 6 POINT (6.088783750195296 49.47060587499345) \n", + "6 2 POINT (6.176428597947581 49.63239294648283) \n", + "7 8 POINT (6.125497137582533 49.67548286167413) \n", + "8 9 POINT (5.985821590636188 49.50122369975853) \n", + "9 10 POINT (5.8253568378630085 49.75912164639861) \n", + "10 11 POINT (5.912548337712089 49.66095224406815) \n", + "11 12 POINT (5.886631289416359 49.53288914400383) \n", + "12 13 POINT (6.096093002090682 49.84290986563605) \n", + "13 14 POINT (6.173860418724089 49.62791052823996) \n", + "14 15 POINT (6.095520633026102 49.85367960759491) \n", + "15 16 POINT (5.934042808032211 49.96544643315656) " + ] + }, + "execution_count": 150, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "### 1st step: Add Local_ID to the HealthCenters_centroids GeoDataFrame\n", + "HealthCenters_centroids['Local_ID'] = range(1, len(HealthCenters_centroids) + 1)\n", + "\n", + "# 2nd step Ensure both GeoDataFrames are in the same CRS (EPSG:4326)\n", + "HealthCenters_centroids = HealthCenters_centroids.to_crs(epsg=4326)\n", + "\n", + "# Convert geometries to a list of coordinates (for KMeans)\n", + "pop_coords = np.array([(geom.x, geom.y) for geom in df_worldpop_['geometry']])\n", + "pop_band_data = df_worldpop_['band_data'].values\n", + "\n", + "# Extract the hospital coordinates\n", + "hospital_coords = np.array([(geom.x, geom.y) for geom in HealthCenters_centroids['geometry']])\n", + "hospital_local_ids = HealthCenters_centroids['Local_ID'].values\n", + "hospital_geometries = HealthCenters_centroids['geometry'].values\n", + "\n", + "### 3rd step 3: K-Means\n", + "kmeans = KMeans(n_clusters=len(hospital_coords), random_state=random_seed, init=hospital_coords, n_init=1) # is using hospital locations as initial centers\n", + "df_worldpop_['cluster'] = kmeans.fit_predict(pop_coords)\n", + "\n", + "# get cluster centers (latitude and longitude)\n", + "cluster_centers = kmeans.cluster_centers_\n", + "\n", + "### 4th step: calculate the sum of population in each cluster\n", + "df_worldpop_['cluster_population'] = df_worldpop_.groupby('cluster')['band_data'].transform('sum')\n", + "\n", + "# Create a new DataFrame for clusters and their population sums\n", + "clusters_df = pd.DataFrame({\n", + " 'cluster': range(len(cluster_centers)),\n", + " 'geometry': [Point(x, y) for x, y in cluster_centers],\n", + " 'population': df_worldpop_.groupby('cluster')['band_data'].sum().values\n", + "})\n", + "\n", + "### 5th step: assign the nearest hospital to the related cluster\n", + "# distances between each cluster center and each health facility center\n", + "distances = cdist(cluster_centers, hospital_coords, metric='euclidean')\n", + "\n", + "# the index of the nearest hospital for each cluster\n", + "nearest_hospital_idx = distances.argmin(axis=1)\n", + "\n", + "# assign the nearest hospital Local_ID and geometry to each cluster center\n", + "clusters_df['nearest_hospital_local_id'] = [hospital_local_ids[idx] for idx in nearest_hospital_idx]\n", + "clusters_df['nearest_hospital_geometry'] = [hospital_geometries[idx] for idx in nearest_hospital_idx]\n", + "\n", + "### 6th step: convert to GeoDataFrame and set CRS\n", + "clusters_gdf = gpd.GeoDataFrame(clusters_df, geometry='geometry')\n", + "clusters_gdf.set_crs(epsg=4326, inplace=True)\n", + "\n", + "# check the content of the resutled clusters dataframe\n", + "clusters_gdf" + ] + }, + { + "cell_type": "markdown", + "id": "5299738d-567d-4473-aaa2-095dede18b92", + "metadata": {}, + "source": [ + "## 5. Explore and evaluate baseline results\n", + "\n", + "### Plot clusters" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "id": "4735e1af-5573-4baf-83d7-6644ac7a3ad5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "clusters_gdf['x'] = clusters_gdf.geometry.x\n", + "clusters_gdf['y'] = clusters_gdf.geometry.y\n", + "\n", + "\n", + "HealthCenters_centroids['x'] = HealthCenters_centroids.geometry.x\n", + "HealthCenters_centroids['y'] = HealthCenters_centroids.geometry.y\n", + "\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 8))\n", + "\n", + "\n", + "ax.scatter(clusters_gdf['x'], clusters_gdf['y'], color='black', marker='o', s=20, label='Cluster Centers')\n", + "\n", + "\n", + "for x, y, label in zip(clusters_gdf['x'], clusters_gdf['y'], clusters_gdf['cluster']):\n", + " ax.text(x, y, str(label), fontsize=12, color='k')\n", + "\n", + "ax.scatter(HealthCenters_centroids['x'], HealthCenters_centroids['y'], color='red', marker='+', s=100, label='Hospitals')\n", + "\n", + "\n", + "plt.title(\"Cluster Centers with their IDs and Hospital Locations\")\n", + "plt.xlabel(\"Longitude\")\n", + "plt.ylabel(\"Latitude\")\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "e3d8e5a3-ffa1-4b1a-a3e6-0e569346a78c", + "metadata": {}, + "source": [ + "### Identify and plot population per healthcare facility " + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "id": "dc10ce38-c177-4925-a677-82c5770e9034", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Local_IDpopulation
01128651.312500
1112106317.523438
7881130.968750
8961558.296875
5635154.476562
1230918.029297
141528763.843750
101125226.894531
121323956.867188
131421879.988281
151620551.439453
91013944.097656
\n", + "
" + ], + "text/plain": [ + " Local_ID population\n", + "0 1 128651.312500\n", + "11 12 106317.523438\n", + "7 8 81130.968750\n", + "8 9 61558.296875\n", + "5 6 35154.476562\n", + "1 2 30918.029297\n", + "14 15 28763.843750\n", + "10 11 25226.894531\n", + "12 13 23956.867188\n", + "13 14 21879.988281\n", + "15 16 20551.439453\n", + "9 10 13944.097656" + ] + }, + "execution_count": 152, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# to calculate the total population in demand of services from each hospital\n", + "hospital_population = clusters_gdf.groupby('nearest_hospital_local_id')['population'].sum().reset_index()\n", + "\n", + "hospital_population_merged = HealthCenters_centroids.merge(hospital_population, left_on='Local_ID', right_on='nearest_hospital_local_id', how='left')\n", + "\n", + "hospital_population_merged[['Local_ID', 'population']].sort_values('population',ascending=False).dropna()" + ] + }, + { + "cell_type": "markdown", + "id": "12b83859-6dfc-4b28-8fd6-297b66ed737a", + "metadata": { + "scrolled": true + }, + "source": [ + "hospital_population_merged.plot('population')" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "id": "bb07e73c-84a2-466f-a644-4fa313b82dec", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "following hospitals are not assigned to any cluster:\n", + " Local_ID geometry\n", + "2 3 POINT (6.1006 49.61807)\n", + "3 4 POINT (6.13542 49.63111)\n", + "4 5 POINT (5.98155 49.5017)\n", + "6 7 POINT (6.13554 49.6317)\n" + ] + } + ], + "source": [ + "assigned_hospitals = clusters_gdf['nearest_hospital_local_id'].unique()\n", + "\n", + "unassigned_hospitals = HealthCenters_centroids[~HealthCenters_centroids['Local_ID'].isin(assigned_hospitals)]\n", + "\n", + "if unassigned_hospitals.empty:\n", + " print(\"All hospitals are assigned to at least one cluster.\")\n", + "else:\n", + " print(\"following hospitals are not assigned to any cluster:\")\n", + " print(unassigned_hospitals[['Local_ID', 'geometry']])" + ] + }, + { + "cell_type": "markdown", + "id": "033c1875-c35a-4a76-8e80-1a364b5d1d1d", + "metadata": {}, + "source": [ + "### Assess distance to hospital from each population grid" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "281204e5-8a50-4225-9521-3313a1bbf3ba", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "3740a61b-f60c-427c-aab5-6e6e552dc57d", + "metadata": {}, + "source": [ + "## 6. Natural hazard disruption" + ] + }, + { + "cell_type": "markdown", + "id": "52d30a9c-2515-4aff-ae5f-9a15cd343061", + "metadata": {}, + "source": [ + "### Download flood data\n", + "The flood data we will extract from a repository maintained by the European Commission Joint Research Centre. We will download river flood hazard maps from their [Flood Data Collection](https://data.jrc.ec.europa.eu/dataset/1d128b6c-a4ee-4858-9e34-6210707f3c81).\n", + "\n", + "Here we do not need to use an API and we also do not need to register ourselves, so we can download any of the files directly. To do so, we use the `urllib` package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "47875fb5-c347-4ef7-bd27-ee0ef75f29af", + "metadata": {}, + "outputs": [], + "source": [ + "## this is the link to the 1/100 flood map for Europe\n", + "zipurl = 'https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/FLOODS/EuropeanMaps/floodMap_RP100.zip'\n", + "\n", + "# The path where the downloaded flood map will be extracted, this is the folder of this Google Collaboratory instance. NOTE: a new instance will have this directory be cleared.\n", + "data_path = \"\"\n", + "\n", + "# and now we open and extract the data\n", + "with urlopen(zipurl) as zipresp:\n", + " with ZipFile(BytesIO(zipresp.read())) as zfile:\n", + " zfile.extractall(data_path)" + ] + }, + { + "cell_type": "markdown", + "id": "b0ff5f7a-fc94-4429-82bb-2a94da72a887", + "metadata": {}, + "source": [ + "### Overlay flood data with population centroids" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "id": "e73f0bce-ad12-4be5-acbb-33dd7e2fa18d", + "metadata": {}, + "outputs": [], + "source": [ + "flood_map_path = \"floodmap_EFAS_RP100_C.tif\"" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "id": "28d110b6-0ef4-41c6-acb2-8673597decd2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset> Size: 12GB\n",
+       "Dimensions:      (band: 1, x: 63976, y: 45242)\n",
+       "Coordinates:\n",
+       "  * band         (band) int32 4B 1\n",
+       "  * x            (x) float64 512kB 9.19e+05 9.19e+05 ... 7.316e+06 7.316e+06\n",
+       "  * y            (y) float64 362kB 5.441e+06 5.44e+06 ... 9.166e+05 9.164e+05\n",
+       "    spatial_ref  int32 4B ...\n",
+       "Data variables:\n",
+       "    band_data    (band, y, x) float32 12GB ...
" + ], + "text/plain": [ + " Size: 12GB\n", + "Dimensions: (band: 1, x: 63976, y: 45242)\n", + "Coordinates:\n", + " * band (band) int32 4B 1\n", + " * x (x) float64 512kB 9.19e+05 9.19e+05 ... 7.316e+06 7.316e+06\n", + " * y (y) float64 362kB 5.441e+06 5.44e+06 ... 9.166e+05 9.164e+05\n", + " spatial_ref int32 4B ...\n", + "Data variables:\n", + " band_data (band, y, x) float32 12GB ..." + ] + }, + "execution_count": 155, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "flood_map = xr.open_dataset(flood_map_path, engine=\"rasterio\")\n", + "flood_map" + ] + }, + { + "cell_type": "markdown", + "id": "399149ac-8ee4-4459-971e-9a2003ab4b4e", + "metadata": {}, + "source": [ + "### Overlay flood data with healthcare facilities" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "id": "024a09f7-6f1c-4e15-a810-cf407daa2a48", + "metadata": {}, + "outputs": [], + "source": [ + "def _get_damage_per_object(asset, curves, cell_area_m2):\n", + " \"\"\"\n", + " Calculate damage for a given asset based on hazard information.\n", + " Arguments:\n", + " *asset*: Tuple containing information about the asset. It includes:\n", + " - Index or identifier of the asset (asset[0]).\n", + " - Asset-specific information, including hazard points (asset[1]['hazard_point']).\n", + " *maxdam_dict*: Maximum damage value.\n", + " Returns:\n", + " *tuple*: A tuple containing the asset index or identifier and the calculated damage.\n", + " \"\"\"\n", + "\n", + " if asset.geometry.geom_type in (\"Polygon\", \"MultiPolygon\"):\n", + " coverage = asset[\"coverage\"] * cell_area_m2\n", + " elif asset.geometry.geom_type in (\"LineString\", \"MultiLineString\"):\n", + " coverage = asset[\"coverage\"]\n", + " elif asset.geometry.geom_type in (\"Point\"):\n", + " coverage = 1\n", + " else:\n", + " raise ValueError(f\"Geometry type {asset.geometry.geom_type} not supported\")\n", + "\n", + " return (\n", + " np.sum(\n", + " np.interp(\n", + " asset[\"values\"], curves.index, curves[asset[\"amenity\"]].values\n", + " )\n", + " * coverage\n", + " )\n", + " * asset[\"maximum_damage\"]\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "id": "48ec7696-a117-4302-9276-ac37fa369cd2", + "metadata": {}, + "outputs": [], + "source": [ + "maxdam = {\"hospital\":2000,\n", + " \"clinic\":1500,\n", + "}\n", + "\n", + "curves = np.array(\n", + " [[0,0],\n", + " [50,0.2],\n", + " [100,0.4],\n", + " [150,0.6],\n", + " [200,0.8],\n", + " [250,1]]) \n", + " \n", + "curves = np.concatenate((curves,\n", + " np.transpose(np.array([curves[:,1]]*(len(maxdam)-1)))),\n", + " axis=1)\n", + "\n", + "curves = pd.DataFrame(curves)\n", + "curves.columns = ['depth']+list(maxdam.keys())\n", + "curves.set_index('depth',inplace=True) " + ] + }, + { + "cell_type": "code", + "execution_count": 180, + "id": "26e1e323-d30b-4e40-8a89-a25d1e475234", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\eks510\\.conda\\envs\\pygis\\Lib\\site-packages\\exactextract\\exact_extract.py:330: RuntimeWarning: Spatial reference system of input features does not exactly match raster.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "values_and_coverage_per_object = exact_extract(\n", + " flood_map,\n", + " HealthCenters,\n", + " [\"coverage\", \"values\"],\n", + " output=\"pandas\",\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 181, + "id": "811245a1-d794-4b0d-b604-1d31c2507d97", + "metadata": {}, + "outputs": [], + "source": [ + "HealthCenters = HealthCenters.merge(values_and_coverage_per_object,left_index=True,right_index=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "id": "115d6cc9-aeb9-4497-9c0e-78f7c8ef19b8", + "metadata": {}, + "outputs": [], + "source": [ + "HealthCenters['maximum_damage'] = HealthCenters.amenity.apply(lambda x: maxdam[x])" + ] + }, + { + "cell_type": "code", + "execution_count": 185, + "id": "291a4fba-40c1-4397-a7f2-8b1665ca7ef5", + "metadata": {}, + "outputs": [], + "source": [ + "HealthCenters['damage'] = HealthCenters.apply(\n", + " lambda _object: _get_damage_per_object(_object, curves, cell_area_m2=100*100),\n", + " axis=1,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 186, + "id": "913f9757-3151-46f9-9427-f54fa58d8beb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0.0\n", + "1 0.0\n", + "2 0.0\n", + "3 0.0\n", + "4 0.0\n", + "5 0.0\n", + "6 0.0\n", + "7 0.0\n", + "8 0.0\n", + "9 0.0\n", + "10 0.0\n", + "11 0.0\n", + "12 0.0\n", + "13 0.0\n", + "14 0.0\n", + "15 0.0\n", + "dtype: float64" + ] + }, + "execution_count": 186, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "damage" + ] + }, + { + "cell_type": "markdown", + "id": "af40b670-4810-473b-81d8-7466852d85a1", + "metadata": {}, + "source": [ + "### Recompute clustering without affected healthcare facilities" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "151a2c6a-9f28-41ad-a78c-38517e5545fd", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "160dafed-34d4-44c8-8d9d-aa44c8dfb621", + "metadata": {}, + "source": [ + "## 7. Visualize and summarize final results" + ] + }, + { + "cell_type": "markdown", + "id": "c412b628-014e-41ba-8c41-0722098ad006", + "metadata": {}, + "source": [ + "- population affected (and changed distance / hospital allocation)\n", + "- hospitals affected\n", + "- differences in urban and rural accessibility " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9463fcad-2b88-44c7-9389-8c7c9a94cbf4", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/TAA4/TAA4.ipynb b/TAA4/TAA4.ipynb new file mode 100644 index 0000000..db938d2 --- /dev/null +++ b/TAA4/TAA4.ipynb @@ -0,0 +1,4916 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ee037ada-1068-4f35-91ae-30c2ea52ca01", + "metadata": { + "id": "ee037ada-1068-4f35-91ae-30c2ea52ca01" + }, + "source": [ + "# TAA4: Accessibility to healthcare facilities" + ] + }, + { + "cell_type": "markdown", + "id": "c6036316-479d-4438-b9e7-e31cf7e9051c", + "metadata": { + "id": "c6036316-479d-4438-b9e7-e31cf7e9051c" + }, + "source": [ + "In this tutorial, we wrap up what you have learned in TAA1-3 by demonstrating their applications and connections. For a European country of your choice, we collect data on the population and their health facilities. Using a classification method, we add rural and urban features to the population data points. Then, with a clustering algorithm, we group population points based on their coordinates and assign each cluster to its closest hospitals. This allows us to calculate the total urban and rural demand for each hospital. Next, we assess the impact of flooding on the hospitals, distinguishing between damaged and undamaged facilities.\n", + "\n", + "In the aftermath of the flood, the number of hospitals in service has changed. Your task will be to repeat the clustering process, assign the new clusters to the intact hospitals, and then calculate their urban and rural demand in the post-flood conditions.\n", + "\n", + "### Important before we start\n", + "---\n", + "Make sure that you save this file before you continue, else you will lose everything. To do so, go to **Bestand/File** and click on **Een kopie opslaan in Drive/Save a Copy on Drive**!" + ] + }, + { + "cell_type": "markdown", + "id": "75f3efb3-f86a-443e-b87a-22653c771143", + "metadata": { + "id": "75f3efb3-f86a-443e-b87a-22653c771143" + }, + "source": [ + "## Learning Objectives\n", + "\n", + "- To extract, prepare and manipulate geospatial information\n", + "\n", + "- To run a classification algorithm to identify urban and rural land use.\n", + "\n", + "- To overlay raster and vector information.\n", + "\n", + "- To cluster different geospatial layers.\n", + "\n", + "- To visualise geospatial information.\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "59d989b6-9cc8-4a39-a17b-c96c463711dd", + "metadata": { + "id": "59d989b6-9cc8-4a39-a17b-c96c463711dd" + }, + "source": [ + "## Prepare the packages\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b06c7b0f-2f83-4f86-ab73-0e8d889bd164", + "metadata": { + "id": "b06c7b0f-2f83-4f86-ab73-0e8d889bd164" + }, + "outputs": [], + "source": [ + "!pip install -q rasterio rioxarray contextily osm_flex exact_extract" + ] + }, + { + "cell_type": "markdown", + "id": "bee1cfab-03df-433e-913e-62de5c0076f4", + "metadata": { + "id": "bee1cfab-03df-433e-913e-62de5c0076f4" + }, + "source": [ + "Now we will import these packages in the cell below:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ffc10f5f-a43d-4f8f-91b1-ac7afc347ab4", + "metadata": { + "id": "ffc10f5f-a43d-4f8f-91b1-ac7afc347ab4" + }, + "outputs": [], + "source": [ + "# Standard Library Imports\n", + "import os\n", + "import sys\n", + "from pathlib import Path\n", + "from datetime import datetime\n", + "from zipfile import ZipFile\n", + "from io import BytesIO\n", + "import random\n", + "import requests\n", + "from urllib.request import urlopen\n", + "\n", + "# Data Manipulation and Analysis\n", + "import numpy as np\n", + "import pandas as pd\n", + "import geopandas as gpd\n", + "import xarray as xr\n", + "import rioxarray as rxr\n", + "\n", + "# Machine Learning\n", + "import sklearn # General import if other sklearn modules are needed\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.cluster import KMeans\n", + "\n", + "# Geometry and Spatial Analysis\n", + "import shapely\n", + "from shapely.geometry import Point\n", + "import rasterio as rio\n", + "from rasterio.enums import Resampling\n", + "from scipy.spatial.distance import cdist\n", + "\n", + "# Earth Engine and Geospatial Libraries\n", + "import ee\n", + "import geemap\n", + "import contextily as cx\n", + "import osm_flex\n", + "from osm_flex import download\n", + "from exactextract import exact_extract\n", + "\n", + "# Visualization\n", + "import matplotlib.pyplot as plt\n", + "import contextily as ctx\n", + "from tqdm import tqdm \n", + "from IPython.display import clear_output" + ] + }, + { + "cell_type": "markdown", + "id": "bb50fef4-f456-46ca-aff7-a838766fb127", + "metadata": { + "id": "bb50fef4-f456-46ca-aff7-a838766fb127" + }, + "source": [ + "## 2. Data download and preparation\n", + "\n", + "Define a country of your interest and a size for gridding and a randomSeed" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "da8b4246-1b98-455c-89ce-999e21e5dd27", + "metadata": { + "id": "da8b4246-1b98-455c-89ce-999e21e5dd27", + "outputId": "19481c7f-23a6-4e4d-d34e-caed04646a65" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "country_full_name = 'Slovenia'\n", + "country_iso3 = 'SVN'\n", + "upscale_factor = 10 #Km\n", + "\n", + "### Set global seeds ###\n", + "random_seed = 1\n", + "np.random.seed(random_seed)\n", + "random.seed(random_seed)\n", + "os.environ['PYTHONHASHSEED'] = str(random_seed)" + ] + }, + { + "cell_type": "markdown", + "id": "6d8c4878-9662-4de4-9c69-b9ec0af9cda3", + "metadata": { + "id": "6d8c4878-9662-4de4-9c69-b9ec0af9cda3" + }, + "source": [ + "Here, we download the population data from WorldPop, an open source platform. Select the country of interest from the WorldPop [website](https://hub.worldpop.org/geodata/listing?id=62) and add the link to the URL below." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6eb55f91-caba-443b-a72b-688bce077b6b", + "metadata": { + "id": "6eb55f91-caba-443b-a72b-688bce077b6b", + "outputId": "5cdbc571-9669-4108-9cec-2d204189b5a3" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "url = \"https://data.worldpop.org/GIS/Population/Global_2000_2020/2018/0_Mosaicked/ppp_2018_1km_Aggregated.tif\"\n", + "\n", + "file_name = 'ppp_2018_1km_Aggregated.tif'\n", + "\n", + "#open(file_name, 'wb').write(requests.get(url).content)\n", + "\n", + "file_name = \"C:\\\\Data\\\\Global_Geospatial\\\\worldpop\\\\ppp_2018_1km_Aggregated.tif\"\n", + "\n", + "\n", + "world_pop_glob = xr.open_dataset(file_name,engine='rasterio')" + ] + }, + { + "cell_type": "markdown", + "id": "84d4cc8e-d8fc-495a-9337-bfb06958a553", + "metadata": { + "id": "84d4cc8e-d8fc-495a-9337-bfb06958a553" + }, + "source": [ + "Now, we use a file with country borders from Natural Earth, to get boundries of the country of your interest." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "36d356c0-167b-4d4c-bfa1-67e6f3dfee46", + "metadata": { + "id": "36d356c0-167b-4d4c-bfa1-67e6f3dfee46", + "outputId": "ac5d3f50-fac8-4159-ffec-41b3d4147127" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "world = gpd.read_file(\"https://github.com/nvkelso/natural-earth-vector/raw/master/10m_cultural/ne_10m_admin_0_countries.shp\")\n", + "# And we want to take the country boundaries and geometry\n", + "country_bounds = world.loc[world.ADM0_ISO == country_iso3].bounds\n", + "country_geom = world.loc[world.ADM0_ISO == country_iso3].geometry" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d0dbb83b-a8b4-48ee-821e-3d156677455f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "103 POLYGON ((13.64292 45.45943, 13.64282 45.45945...\n", + "Name: geometry, dtype: geometry" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "country_geom" + ] + }, + { + "cell_type": "markdown", + "id": "2d131107-2f4f-4f39-9850-df988197ee62", + "metadata": { + "id": "2d131107-2f4f-4f39-9850-df988197ee62" + }, + "source": [ + "Now, we use the derived boundries to clip the population data from worldpop, to get the population of our coutnry." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3cee77c1-0fcc-4151-b5f1-5c67870b5ab4", + "metadata": { + "id": "3cee77c1-0fcc-4151-b5f1-5c67870b5ab4", + "outputId": "b2ac98f6-4aab-456d-ffe1-beb2dca3562b" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# clip to country\n", + "world_pop_national = world_pop_glob.rio.clip_box(minx=country_bounds.minx.values[0],\n", + " miny=country_bounds.miny.values[0],\n", + " maxx=country_bounds.maxx.values[0],\n", + " maxy=country_bounds.maxy.values[0]\n", + " )\n", + "world_pop_national = world_pop_national.rio.clip(country_geom.values, world_pop_glob.rio.crs, drop=False)" + ] + }, + { + "cell_type": "markdown", + "id": "ab6155b2-3e5b-4f0d-85b1-0f45d5b5f31f", + "metadata": { + "id": "ab6155b2-3e5b-4f0d-85b1-0f45d5b5f31f" + }, + "source": [ + "The worldpop data, however, is stored as 1km by 1km grid. This will be too computationally intensive if we would use that resolution. As such, we reproject the to a lower resolution. This will help us to perform the analyis more smoothly. We use the *upscale_factor* as defined at the start of this subsection." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "0834501f-2e00-4dce-b91c-8101fcdffb74", + "metadata": { + "id": "0834501f-2e00-4dce-b91c-8101fcdffb74", + "outputId": "9249e16c-68b7-4761-a8ad-e36a1f3175d2" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "new_width = int(world_pop_national.rio.width / upscale_factor)\n", + "new_height = int(world_pop_national.rio.height / upscale_factor)\n", + "\n", + "worldpop_Grided = world_pop_national.rio.reproject(\n", + " world_pop_national.rio.crs,\n", + " shape=(new_height, new_width),\n", + " resampling=Resampling.sum,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "17062d77-f2a0-47ff-ac8b-759d09fc8b97", + "metadata": { + "id": "9ab78ba1-1d06-4a76-8092-b989a5842f00" + }, + "source": [ + "Now we remove the missing data from our data points and create a GeoDataFrame for our country. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "256c59b1-c8cb-40ef-9d4d-6c30e05e2861", + "metadata": { + "id": "256c59b1-c8cb-40ef-9d4d-6c30e05e2861", + "outputId": "5da3bf85-5663-49bd-803c-0046f63ec4ac" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The output is df_pop_SVN as a dataframe of the population data of Slovenia\n" + ] + } + ], + "source": [ + "df_worldpop_ = worldpop_Grided.band_data.to_dataframe()\n", + "df_worldpop_ = df_worldpop_.loc[~df_worldpop_.band_data.isna()].reset_index(drop=False)\n", + "\n", + "# create geometry values and drop lat lon columns\n", + "df_worldpop_['geometry'] = shapely.points(np.array(list(zip(df_worldpop_['x'],df_worldpop_['y']))))\n", + "\n", + "df_worldpop_ = gpd.GeoDataFrame(df_worldpop_.drop(['y','x','spatial_ref','band'],axis=1))\n", + "\n", + "# dynamically create a variable name for the DataFrame\n", + "globals()[f'df_pop_{country_iso3}'] = gpd.GeoDataFrame(df_worldpop_)\n", + "\n", + "# dynamically create a print statement that reflects the current country code\n", + "print(f\"The output is df_pop_{country_iso3} as a dataframe of the population data of {country_full_name}\")" + ] + }, + { + "cell_type": "markdown", + "id": "9d91ef78-094c-40f4-8142-3f100934ccbc", + "metadata": {}, + "source": [ + "And Lets plot the population points of our country of interest" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "971cf678-5a60-4b22-af71-5bb7d9ab2302", + "metadata": { + "id": "971cf678-5a60-4b22-af71-5bb7d9ab2302", + "outputId": "001a9a7f-be5c-4df3-cc5a-28e44e39d1c7" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = globals()[f'df_pop_{country_iso3}'].plot()\n", + "ax.set_title(f'Population Points of {country_full_name}')\n", + "ax.set_xlabel('Longitude')\n", + "ax.set_ylabel('Latitude')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "02dbdbed-5e0a-425d-88c8-548d57c92d7d", + "metadata": { + "id": "02dbdbed-5e0a-425d-88c8-548d57c92d7d", + "outputId": "81bf44e3-0117-4979-dfff-ae1d7842470f" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "396" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(globals()[f'df_pop_{country_iso3}'])" + ] + }, + { + "cell_type": "markdown", + "id": "32dab221-7a28-4719-a180-dc8a91c17b48", + "metadata": { + "id": "32dab221-7a28-4719-a180-dc8a91c17b48" + }, + "source": [ + "Our next step is to extract information of healthcare facilities for the country of interest. We do so using OpenStreetMap. With the latest version of geopandas, it is now possible to directly read **osm.pbf** files from OpenStreetMap.\n", + "\n", + "Healthcare facilities are stored as *multipolygons* within OpenStreetMap, and we want to download all clinics and hospitals." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "dbf8e926-52cc-4411-bc68-263f7cafaf1f", + "metadata": { + "id": "dbf8e926-52cc-4411-bc68-263f7cafaf1f", + "outputId": "41a6e348-4234-4890-fb96-a4dd0943c47d" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: total: 0 ns\n", + "Wall time: 0 ns\n" + ] + } + ], + "source": [ + "%%time\n", + "Country_GeofabrikData_path = download.get_country_geofabrik(country_iso3)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "22f32bc2-8a4b-4af8-a8c6-2494ed27ee89", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\eks510\\.conda\\envs\\pygis\\Lib\\site-packages\\pyogrio\\raw.py:196: RuntimeWarning: Non closed ring detected. To avoid accepting it, set the OGR_GEOMETRY_ACCEPT_UNCLOSED_RING configuration option to NO\n", + " return ogr_read(\n" + ] + } + ], + "source": [ + "HealthCenters = gpd.read_file(Country_GeofabrikData_path, layer=\"multipolygons\")\n", + "sub_types =['clinic', 'hospital']\n", + "HealthCenters = HealthCenters[HealthCenters['amenity'].isin(sub_types)].reset_index(drop=True)\n", + "HealthCenters = HealthCenters.to_crs(3857)\n", + "\n", + "# to convert polygons to their centroids\n", + "HealthCenters_centroids = HealthCenters.copy()\n", + "HealthCenters_centroids['geometry'] = HealthCenters.centroid\n", + "\n", + "HealthCenters_centroids=HealthCenters_centroids.to_crs(4326)" + ] + }, + { + "cell_type": "markdown", + "id": "c57009a2-caa8-4963-b37b-a1a5503a6be9", + "metadata": {}, + "source": [ + "Let's check the content of our generated HealthCenters_centroids GeoDataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "cfbc0e8e-bf63-42c9-b9d0-3a095c0f8ca6", + "metadata": { + "id": "cfbc0e8e-bf63-42c9-b9d0-3a095c0f8ca6", + "outputId": "5468bc08-d8e2-4404-8f28-fdd3a7186a41" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
osm_idosm_way_idnametypeaerowayamenityadmin_levelbarrierboundarybuilding...man_mademilitarynaturalofficeplaceshopsporttourismother_tagsgeometry
016172NonePorodnišnica LjubljanamultipolygonNonehospitalNoneNoneNonehospital...NoneNoneNoneNoneNoneNoneNoneNone\"addr:city\"=>\"Ljubljana\",\"addr:housenumber\"=>\"...MULTIPOLYGON (((1616891.57 5788942.854, 161685...
11735820NoneREHA Radkersburg Klinik Maria TheresiamultipolygonNoneclinicNoneNoneNoneyes...NoneNoneNoneNoneNoneNoneNoneNone\"addr:city\"=>\"Bad Radkersburg\",\"addr:housenumb...MULTIPOLYGON (((1778635.005 5891252.415, 17786...
22226607NoneUniverzitetni rehabilitacijski inštitut Republ...multipolygonNonehospitalNoneNoneNonehospital...NoneNoneNoneNoneNoneNoneNoneNone\"addr:city\"=>\"Ljubljana\",\"addr:housenumber\"=>\"...MULTIPOLYGON (((1616842.545 5791255.347, 16168...
33449006NoneZdravstveni dom Ljubljana - RudnikmultipolygonNoneclinicNoneNoneNoneyes...NoneNoneNoneNoneNoneNoneNoneNone\"addr:city\"=>\"Ljubljana\",\"addr:housenumber\"=>\"...MULTIPOLYGON (((1616791.327 5786197.784, 16167...
45229321NonePsihiatrična bolnišnicamultipolygonNonehospitalNoneNoneNoneyes...NoneNoneNoneNoneNoneNoneNoneNone\"addr:city\"=>\"Begunje na Gorenjskem\",\"addr:hou...MULTIPOLYGON (((1580944.926 5840918.384, 15808...
\n", + "

5 rows × 26 columns

\n", + "
" + ], + "text/plain": [ + " osm_id osm_way_id name \\\n", + "0 16172 None Porodnišnica Ljubljana \n", + "1 1735820 None REHA Radkersburg Klinik Maria Theresia \n", + "2 2226607 None Univerzitetni rehabilitacijski inštitut Republ... \n", + "3 3449006 None Zdravstveni dom Ljubljana - Rudnik \n", + "4 5229321 None Psihiatrična bolnišnica \n", + "\n", + " type aeroway amenity admin_level barrier boundary building ... \\\n", + "0 multipolygon None hospital None None None hospital ... \n", + "1 multipolygon None clinic None None None yes ... \n", + "2 multipolygon None hospital None None None hospital ... \n", + "3 multipolygon None clinic None None None yes ... \n", + "4 multipolygon None hospital None None None yes ... \n", + "\n", + " man_made military natural office place shop sport tourism \\\n", + "0 None None None None None None None None \n", + "1 None None None None None None None None \n", + "2 None None None None None None None None \n", + "3 None None None None None None None None \n", + "4 None None None None None None None None \n", + "\n", + " other_tags \\\n", + "0 \"addr:city\"=>\"Ljubljana\",\"addr:housenumber\"=>\"... \n", + "1 \"addr:city\"=>\"Bad Radkersburg\",\"addr:housenumb... \n", + "2 \"addr:city\"=>\"Ljubljana\",\"addr:housenumber\"=>\"... \n", + "3 \"addr:city\"=>\"Ljubljana\",\"addr:housenumber\"=>\"... \n", + "4 \"addr:city\"=>\"Begunje na Gorenjskem\",\"addr:hou... \n", + "\n", + " geometry \n", + "0 MULTIPOLYGON (((1616891.57 5788942.854, 161685... \n", + "1 MULTIPOLYGON (((1778635.005 5891252.415, 17786... \n", + "2 MULTIPOLYGON (((1616842.545 5791255.347, 16168... \n", + "3 MULTIPOLYGON (((1616791.327 5786197.784, 16167... \n", + "4 MULTIPOLYGON (((1580944.926 5840918.384, 15808... \n", + "\n", + "[5 rows x 26 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "HealthCenters.head()" + ] + }, + { + "cell_type": "markdown", + "id": "c364c46d-40ca-47a3-babe-c0ee55ecb880", + "metadata": {}, + "source": [ + "And let's visualize the hospitals locations." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "14c6ab08-56b0-49a2-8955-74a0bd8b6b7c", + "metadata": { + "id": "14c6ab08-56b0-49a2-8955-74a0bd8b6b7c", + "outputId": "2560df0b-9f7b-4972-fcf4-1e25bd6b7217" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The output is HealthCenters_centroids as a dataframe of the Health Centers\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"The output is HealthCenters_centroids as a dataframe of the Health Centers\")\n", + "\n", + "#plotting\n", + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "HealthCenters_centroids.plot(ax=ax, color='red', markersize=10, alpha=0.7)\n", + "\n", + "# temporarily reprojects to EPSG:3857 to add the basemap (contextily requires it)\n", + "#cx.add_basemap(ax, crs='EPSG:4326', source=cx.providers.OpenStreetMap.Mapnik, zoom=8)\n", + "\n", + "ax.set_title(f'Locations of Hospitals and Clinics in {country_full_name}', fontsize=15)\n", + "\n", + "ax.set_xlabel('Longitude')\n", + "ax.set_ylabel('Latitude')\n", + "plt.show()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "dc39cef6-eac9-40a0-a737-bac3cd5bc2a1", + "metadata": { + "id": "dc39cef6-eac9-40a0-a737-bac3cd5bc2a1" + }, + "source": [ + "## 3. Classification of rural and urban areas" + ] + }, + { + "cell_type": "markdown", + "id": "M2QYz4j0EBah", + "metadata": { + "id": "M2QYz4j0EBah" + }, + "source": [ + "As you remember, we checked the content of the population data, and there was no information regarding urban or rural areas. Therefore, we need to add this information to our dataset. So, we will use an approach similar to what you learned in TAA1, but for the sake of processing efficiency, we will use Earth Engine's built-in classifier packages rather than scikit-learn. In this situation we are interested in only two land cover classes: rural and urban. We will have to reclassify the raster, train a model to distinguish between these timesteps, and finally perform the classification on recent images.\n", + "\n", + " We have pre-filled most of the code in this section, and you will use a few new tricks to perform the classification. Beyond that, we challenge you to leverage what you have learned in TAA1 to improve the performance of the model, by any means you see fit. You will be evaluated on your reasoning, as well as the creativity of the approaches." + ] + }, + { + "cell_type": "markdown", + "id": "zotYyVnD4Jt2", + "metadata": { + "id": "zotYyVnD4Jt2" + }, + "source": [ + "### Set-up" + ] + }, + { + "cell_type": "markdown", + "id": "-l1Sg8b54mQl", + "metadata": { + "id": "-l1Sg8b54mQl" + }, + "source": [ + "First, set up your Earth Engine environment as you did before." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "i1hx3v6pEMva", + "metadata": { + "id": "i1hx3v6pEMva" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ee.Authenticate()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "a76108be-d449-46a6-98e9-be3f17ae1888", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ee.Initialize(project=\"proj-gis-1234\")" + ] + }, + { + "cell_type": "markdown", + "id": "_tY_sxXy4SPA", + "metadata": { + "id": "_tY_sxXy4SPA" + }, + "source": [ + "### Set-up training & validation data" + ] + }, + { + "cell_type": "markdown", + "id": "rToXuPnoET3C", + "metadata": { + "id": "rToXuPnoET3C" + }, + "source": [ + "We will make use of the following data sources:\n", + "1. Administrative country boundary to clip to the area of interest \n", + "2. Sentinel-2 satellite images at 10x10m resolution\n", + "3. CORINE Land Cover (CLC)\n", + "\n", + "For the satellite image, we take the S2 median image over the summer to improve sensitivity to single-capture conditions. The median image is taken over the summer, so that we exclude seasonal dynamics. Finally, we clip this image to the country extent.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "_I9OT5iF4iPe", + "metadata": { + "id": "_I9OT5iF4iPe", + "outputId": "65deb07b-7f0a-4382-b519-459ab71bbb7e" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define the region of interest (ROI)\n", + "country_ROI = ee.FeatureCollection(\"FAO/GAUL/2015/level0\") \\\n", + " .filter(ee.Filter.eq('ADM0_NAME', country_full_name))\n", + "\n", + "# Load Sentinel-2 Image Collection\n", + "sentinel2 = ee.ImageCollection(\"COPERNICUS/S2_HARMONIZED\") \\\n", + " .filterBounds(country_ROI.geometry()) \\\n", + " .filterDate('2018-06-01', '2018-08-31') \\\n", + " .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 10))\n", + "\n", + "# Create a median composite to reduce cloud cover\n", + "mosaic_image = sentinel2.median().clip(country_ROI.geometry())" + ] + }, + { + "cell_type": "markdown", + "id": "FPiX1Imk6Oom", + "metadata": { + "id": "FPiX1Imk6Oom" + }, + "source": [ + "Show on map to verify that it's loaded" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "98176961-f52f-4859-8ff9-9b55d2eb7347", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1397cb265f0847a09763b85957dcf482", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map(center=[46.12445795068446, 14.826893950462633], controls=(WidgetControl(options=['position', 'transparent_…" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bands = ['B4', 'B3', 'B2'] # Fill in this list yourself\n", + "vis_params = {'max': 3000, 'bands': bands} # Limit upper range so you can see detail\n", + "\n", + "map = geemap.Map(height=800, width=700, zoom=7)\n", + "\n", + "# dynamically center the map on the selected country (ROI)\n", + "map.centerObject(country_ROI.geometry(), 8)\n", + "\n", + "# adding the mosaic image layer for the selected country\n", + "map.addLayer(mosaic_image, vis_params, \"Sentinel-2_2018\")\n", + "\n", + "map" + ] + }, + { + "cell_type": "markdown", + "id": "c9NfiE5dFr19", + "metadata": { + "id": "c9NfiE5dFr19" + }, + "source": [ + "### Calculate image variables to include" + ] + }, + { + "cell_type": "markdown", + "id": "1gGMF-NEGN84", + "metadata": { + "id": "1gGMF-NEGN84" + }, + "source": [ + "Next, we will compute variables to use for our classifier. We have provided the examples you've already seen for TAA1, but we challenge you to add your own variables. You can get inspiration from anywhere, but be sure that you are able to explain the reasoning behind including each variable." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "cjMQF_E57zIw", + "metadata": { + "id": "cjMQF_E57zIw", + "outputId": "95107040-bb80-4b50-ea20-9ac63bd3c95f" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def make_s2_variables(s2_image):\n", + " # Calculate additional spectral indices\n", + " dvi = s2_image.select('B5').subtract(s2_image.select('B4')).rename('DVI')\n", + " ndvi = s2_image.normalizedDifference(['B5', 'B4']).rename('NDVI')\n", + " ndwi = s2_image.normalizedDifference(['B3', 'B5']).rename('NDWI')\n", + "\n", + " # Add indices to the image\n", + " s2_image = s2_image.addBands([dvi, ndvi, ndwi])\n", + "\n", + " '''\n", + " ToDo: Add your own variables!\n", + "\n", + " Think about what you learned in TAA1 - how else can you compute information\n", + " from the spectral bands to include? Look at some papers for inspiration,\n", + " or try something out yourself. There's more than just indices that can help here!\n", + "\n", + " Don't forget to add them to the image after you create them!\n", + " '''\n", + " ### TEACHER EXAMPLE - REMOVE BEFORE GOES LIVE. ###\n", + "\n", + " # Define neighborhood size (e.g., 3x3)\n", + " kernel = ee.Kernel.square(radius=1)\n", + "\n", + " # Calculate neighborhood statistics\n", + " neighborhood_vars = []\n", + " for band in ['DVI', 'NDVI', 'NDWI']:\n", + " mean = s2_image.select(band).reduceNeighborhood(\n", + " reducer=ee.Reducer.mean(),\n", + " kernel=kernel\n", + " ).rename(f'{band}_mean')\n", + "\n", + " std_dev = s2_image.select(band).reduceNeighborhood(\n", + " reducer=ee.Reducer.stdDev(),\n", + " kernel=kernel\n", + " ).rename(f'{band}_stdDev')\n", + "\n", + " # Add additional statistics here, such as min, max, etc., if desired.\n", + "\n", + " # Append neighborhood bands to the list\n", + " neighborhood_vars.extend([mean, std_dev])\n", + "\n", + " # Add neighborhood statistics to the image\n", + " s2_image = s2_image.addBands(neighborhood_vars)\n", + "\n", + " ### End of teaching example ###\n", + "\n", + " return s2_image" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "oCmPdaR_74Ml", + "metadata": { + "id": "oCmPdaR_74Ml", + "outputId": "f971149c-89b2-421c-ebe1-2fb3ddfb51ff" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mosaic_image = make_s2_variables(mosaic_image)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "sTScJDPp8dew", + "metadata": { + "id": "sTScJDPp8dew", + "outputId": "59b81db2-2a46-4f87-e968-a772e927eec8" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Use these bands for prediction\n", + "# DON'T FORGET to add your own variables when you make them!\n", + "bands = ['B2', 'B3', 'B4', 'B5', 'B6', 'B7']\n", + "indices = ['NDVI', 'DVI']\n", + "img_bands = [*bands, *indices]" + ] + }, + { + "cell_type": "markdown", + "id": "44mziLGt8j4C", + "metadata": { + "id": "44mziLGt8j4C" + }, + "source": [ + "### Load CLC2018 and sample data\n" + ] + }, + { + "cell_type": "markdown", + "id": "K62rE0S-F5X2", + "metadata": { + "id": "K62rE0S-F5X2" + }, + "source": [ + "Now, let's load labels from CORINE and classify the model." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "Pacg8UcH8lHe", + "metadata": { + "id": "Pacg8UcH8lHe", + "outputId": "4cd47f65-1213-4c47-9c69-7e8161b96f86" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "CLC = ee.Image('COPERNICUS/CORINE/V20/100m/2018').select('landcover').clip(mosaic_image.geometry())\n", + "lc_points = CLC.sample(\n", + " **{\n", + " 'region': mosaic_image.geometry(),\n", + " 'scale': 30,\n", + " 'numPixels': 10000, # Change this as you see fit\n", + " 'seed': random_seed,\n", + " 'geometries': True,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "n7hzNmOt8tY5", + "metadata": { + "id": "n7hzNmOt8tY5" + }, + "source": [ + "Next, let's reclassify the dataset to a binary urban/rural dataset. The choice how to do this is yours, and can be as easy or complicated as you like it to be. As a reminder, CLC consists of a [3-digit hierarchy](https://land.copernicus.eu/content/corine-land-cover-nomenclature-guidelines/html/), so you can pick which classes you want to include and exclude." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "Hm4xWGNS8ugv", + "metadata": { + "id": "Hm4xWGNS8ugv", + "outputId": "ade49efe-bc51-4e15-f485-c016104a56c6" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def generalize_clc_class(feature):\n", + " lc_value = ee.String(feature.get('landcover'))\n", + "\n", + " # Check if the first character is '1'\n", + " set_value = ee.Algorithms.If(lc_value.slice(0, 1).equals('1'), 1, 0)\n", + "\n", + " # Set the new binary value for the 'landcover' property\n", + " return feature.set('landcover', set_value)\n", + "lc_reference_pts = lc_points.map(generalize_clc_class)" + ] + }, + { + "cell_type": "markdown", + "id": "Y0v4HOG6GtZq", + "metadata": { + "id": "Y0v4HOG6GtZq" + }, + "source": [ + "**Q: Briefly explain which classes you included for the urban/rural re-classification.**" + ] + }, + { + "cell_type": "markdown", + "id": "th9Dl6mW9FII", + "metadata": { + "id": "th9Dl6mW9FII" + }, + "source": [ + "Let's make training/validation splits. You can customize this however you see fit, but we suggest balanced sampling, where you control how many positive and how many negative samples are included during training/validation, so as to not over/under-sample one or the other." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "X1tMXV4w9LFb", + "metadata": { + "id": "X1tMXV4w9LFb", + "outputId": "d52bc7b8-4fc3-4c3b-92c9-24abd3def05b" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define the land cover labels column\n", + "label_col = 'landcover'\n", + "\n", + "## TEACHING EXAMPLE, REMOVE BEFORE GOING LIVE ##\n", + "# Filter points by label\n", + "positive_points = lc_reference_pts.filter(ee.Filter.eq(label_col, 1))\n", + "negative_points = lc_reference_pts.filter(ee.Filter.eq(label_col, 0))\n", + "\n", + "# Allow a maximum of 2-to-1 difference in negative vs positive class sampling\n", + "positive_sample = positive_points.randomColumn('random').limit(positive_points.size())\n", + "negative_sample = negative_points.randomColumn('random').limit(positive_points.size().multiply(ee.Number(2)))\n", + "\n", + "# Merge the samples\n", + "balanced_sample = positive_sample.merge(negative_sample)\n", + "\n", + "## End of teaching example ##\n", + "\n", + "# Split into training and validation sets\n", + "training_sample = balanced_sample.filter('random <= 0.8')\n", + "validation_sample = balanced_sample.filter('random > 0.8')\n", + "\n", + "# Sample regions for training and validation datasets\n", + "train_data = mosaic_image.select(img_bands).sampleRegions(\n", + " collection=training_sample, properties=[label_col], scale=100\n", + ")\n", + "\n", + "val_data = mosaic_image.select(img_bands).sampleRegions(\n", + " collection=validation_sample, properties=[label_col], scale=100\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "LONawc_nGj3L", + "metadata": { + "id": "LONawc_nGj3L" + }, + "source": [ + "**Q: Describe your sampling approach. Which approach did you go with, and what is the rationale behind it?**" + ] + }, + { + "cell_type": "markdown", + "id": "uS-6YzaM9P5-", + "metadata": { + "id": "uS-6YzaM9P5-" + }, + "source": [ + "### Optimize on training & validation set" + ] + }, + { + "cell_type": "markdown", + "id": "H3G8LxVmG7d6", + "metadata": { + "id": "H3G8LxVmG7d6" + }, + "source": [ + "Now that we have sampled data, we will iteratively improve the model. How you want to approach this is up to you, we only provide the basic process here. In TAA1 we taught you a few methods and concepts that you can build on. To give some suggestions:\n", + "1. Analyze the confusion matrix\n", + "2. Look at different metrics that might be more informative - for instance, precision and recall\n", + "3. Look at redundant variables - you learned this in scikit-learn, but EE has options for this too if you want to search for it.\n", + "\n", + "It's up to you how to approach this, but we recommend to document what worked and what didn't work so that you have an easier time reporting your results." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "Hv5M5UkT9X39", + "metadata": { + "id": "Hv5M5UkT9X39", + "outputId": "ac04f2d4-d0e6-4007-f6c0-e61fb97a2e83" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Train the model\n", + "classifier = ee.Classifier.smileRandomForest(numberOfTrees=100, minLeafPopulation=2, maxNodes=50)\n", + "trained_classifier = classifier.train(features=train_data, classProperty=label_col, inputProperties=img_bands)\n", + "\n", + "# Apply the classifier to the validation data\n", + "classified_val = val_data.classify(trained_classifier)" + ] + }, + { + "cell_type": "markdown", + "id": "_r0pZTPq97XH", + "metadata": { + "id": "_r0pZTPq97XH" + }, + "source": [ + "Now, let's analyze your results." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "q7SJuNRd9Zmm", + "metadata": { + "id": "q7SJuNRd9Zmm", + "outputId": "356fee26-dffb-4987-bf85-469a2b4cb693" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Validation Metrics:\n", + "Accuracy: 0.8556701030927835\n", + "Kappa: 0.6687804878048779\n" + ] + } + ], + "source": [ + "# Calculate metrics\n", + "confusion_matrix = classified_val.errorMatrix(label_col, 'classification')\n", + "val_accuracy = confusion_matrix.accuracy()\n", + "kappa = confusion_matrix.kappa()\n", + "\n", + "# Package all metrics into a single dictionary\n", + "metrics = ee.Dictionary({\n", + " 'Accuracy': val_accuracy,\n", + " 'Kappa': kappa\n", + "})\n", + "\n", + "# Retrieve all metrics in one call\n", + "metrics_info = metrics.getInfo()\n", + "\n", + "# Print all metrics\n", + "print('Validation Metrics:')\n", + "print(f\"Accuracy: {metrics_info['Accuracy']}\")\n", + "print(f\"Kappa: {metrics_info['Kappa']}\")" + ] + }, + { + "cell_type": "markdown", + "id": "u-QKNQK5AL43", + "metadata": { + "id": "u-QKNQK5AL43" + }, + "source": [ + "**Q: Describe the changes you made to the 'standard' approach.**\n", + "1. **Why did you make these changes**\n", + "2. **which impact did each change have on your analysis?**\n", + "3. **Which things did you try that did not work out?**" + ] + }, + { + "cell_type": "markdown", + "id": "BnOR_Ouk-CFv", + "metadata": { + "id": "BnOR_Ouk-CFv" + }, + "source": [ + "### Run on test set" + ] + }, + { + "cell_type": "markdown", + "id": "YjIzJ6rR-mje", + "metadata": { + "id": "YjIzJ6rR-mje" + }, + "source": [ + "Now, we load recent test images to classify recent urban extent. We take a median image over August 2024." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "cL9G2aPS-ba2", + "metadata": { + "id": "cL9G2aPS-ba2" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load Sentinel-2 Image Collection\n", + "test_sentinel2 = ee.ImageCollection(\"COPERNICUS/S2_HARMONIZED\") \\\n", + " .filterBounds(country_ROI.geometry()) \\\n", + " .filterDate('2024-08-01', '2024-08-31') \\\n", + " .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 10))\n", + "\n", + "# Create a median composite to reduce cloud cover\n", + "test_mosaic_image = sentinel2.median().clip(country_ROI.geometry())\n", + "\n", + "# Add variables\n", + "test_mosaic_image = make_s2_variables(test_mosaic_image)\n", + "\n", + "# Add to map object\n", + "map.addLayer(test_mosaic_image, vis_params, \"test-Sentinel-2-2024\")" + ] + }, + { + "cell_type": "markdown", + "id": "ZMXJCmTj-9YV", + "metadata": { + "id": "ZMXJCmTj-9YV" + }, + "source": [ + "Let's visualize the results calculated over the entire test image" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "waPHnNLv-76V", + "metadata": { + "colab": { + "referenced_widgets": [ + "eb2c61d83e6a426c97721f9cf16be8fc" + ] + }, + "id": "waPHnNLv-76V", + "outputId": "b8f65f6f-ca43-46f1-b91f-aa582c9a33ef" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1397cb265f0847a09763b85957dcf482", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map(center=[46.12445795068446, 14.826893950462633], controls=(WidgetControl(options=['position', 'transparent_…" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Classify the test image\n", + "classified_image = test_mosaic_image.classify(trained_classifier)\n", + "\n", + "# Define the color mapping dictionary\n", + "clc_colors = {\n", + " 0: '#FFFFFF', # Rural\n", + " 1: '#000000', # Urban\n", + "}\n", + "\n", + "# Convert string labels to numeric codes\n", + "def classify_to_numeric(image):\n", + " # Create a dictionary that maps string labels to numeric values\n", + " label_to_numeric = {label: index for index, label in enumerate(clc_colors.keys())}\n", + "\n", + " # Convert string label to numeric value\n", + " return image.remap(\n", + " list(label_to_numeric.keys()),\n", + " list(label_to_numeric.values())\n", + " )\n", + "\n", + "# Convert the classified image\n", + "numeric_classified_image = classify_to_numeric(classified_image)\n", + "\n", + "# Generate a palette for visualization\n", + "palette = [clc_colors[label] for label in clc_colors.keys()]\n", + "\n", + "# Add the numeric classified image to the map\n", + "map.addLayer(numeric_classified_image, {'palette': palette, 'min': 0, 'max': len(clc_colors) - 1}, 'Classified Image')\n", + "map" + ] + }, + { + "cell_type": "markdown", + "id": "pJVtw5Y9IVes", + "metadata": { + "id": "pJVtw5Y9IVes" + }, + "source": [ + "There are no metrics to calculate here - after all, we're trying to update our reference data. Instead, visually evaluate if you're happy with the results. When you're happy with the results, proceed to the next block to export the data from Earth Engine into our local environment.\n", + "\n", + "(**hint:** don't go for perfect, this is very hard to achieve in the current setting)\n", + "\n", + "**Q: Describe your map and your results in general.**\n", + "1. **Where do you still see errors? Are these systemic?**\n", + "2. **How could you further improve your product to clean these up?** **bold text**\n", + "3. **Was there anything you wanted to try, but were unable to?**" + ] + }, + { + "cell_type": "markdown", + "id": "LPkrvHduHyDh", + "metadata": { + "id": "LPkrvHduHyDh" + }, + "source": [ + "### Export resulting image" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "m1FjLJVzH2Zt", + "metadata": { + "id": "m1FjLJVzH2Zt", + "outputId": "930bd2b1-83ff-4ea3-c851-12142a86a63c" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating URL ...\n", + "Downloading data from https://earthengine.googleapis.com/v1/projects/proj-gis-1234/thumbnails/5318c58b433613d47dc90c8602bd6264-893516433a0289f1d648527a6e51611d:getPixels\n", + "Please wait ...\n", + "Data downloaded to C:\\projects\\UNIGIS_ProgrammingGIS\\TAA4\\urban_rural_raster.tif\n" + ] + } + ], + "source": [ + "geemap.ee_export_image(classified_image, filename='urban_rural_raster.tif', scale=1000, file_per_band=False)\n", + "urban_raster = xr.open_dataset('urban_rural_raster.tif', engine='rasterio')" + ] + }, + { + "cell_type": "markdown", + "id": "309a00a2-8aa1-4091-b575-31d56622fd16", + "metadata": { + "id": "309a00a2-8aa1-4091-b575-31d56622fd16" + }, + "source": [ + "### 3. Overlay urban and rural classification with population and Healthcare information" + ] + }, + { + "cell_type": "markdown", + "id": "cfa24afd-0400-4462-ab77-62d5fd126853", + "metadata": {}, + "source": [ + "In this step, we overlay the resulting urban/rural raster image from the previous section onto our population data point. a binary categorical variable, unban_rural, will be added to each data point, where 1 represents urban and 0 represents rural." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "f456e034-5e81-4d99-9093-0380af66e8f9", + "metadata": { + "id": "f456e034-5e81-4d99-9093-0380af66e8f9", + "outputId": "d397d580-707f-411c-a5af-82887241d4bd" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "urban_rural = xr.open_dataarray('urban_rural_raster.tif')\n", + "\n", + "values = urban_rural.sel(\n", + " {\n", + " urban_rural.rio.x_dim: xr.DataArray(df_worldpop_.geometry.x),\n", + " urban_rural.rio.y_dim: xr.DataArray(df_worldpop_.geometry.y),\n", + " },\n", + " method=\"nearest\",\n", + " ).values[0]\n", + "df_worldpop_[\"urban_rural\"] = values\n", + "df_worldpop_[\"urban_rural\"] = df_worldpop_[\"urban_rural\"].fillna(0)" + ] + }, + { + "cell_type": "markdown", + "id": "33b230bc-dbfe-436f-aefb-acd3de96f909", + "metadata": {}, + "source": [ + "lets check the resulted calssified population, as mentioned about 1 measn urban and 0 rural." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "bf032653-2165-4676-9ad3-e55700c6b8e8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
band_datageometryurban_rural
0512.969238POINT (15.96057 46.8236)0.0
13930.664307POINT (16.04593 46.8236)0.0
22205.554199POINT (16.13129 46.8236)0.0
31943.986572POINT (16.21666 46.8236)0.0
4836.774170POINT (16.30202 46.8236)0.0
............
391101.199600POINT (15.02161 45.4589)0.0
392446.684387POINT (15.10697 45.4589)0.0
3931087.734619POINT (15.19233 45.4589)0.0
3941046.664062POINT (15.27769 45.4589)0.0
395155.921677POINT (15.36305 45.4589)0.0
\n", + "

396 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " band_data geometry urban_rural\n", + "0 512.969238 POINT (15.96057 46.8236) 0.0\n", + "1 3930.664307 POINT (16.04593 46.8236) 0.0\n", + "2 2205.554199 POINT (16.13129 46.8236) 0.0\n", + "3 1943.986572 POINT (16.21666 46.8236) 0.0\n", + "4 836.774170 POINT (16.30202 46.8236) 0.0\n", + ".. ... ... ...\n", + "391 101.199600 POINT (15.02161 45.4589) 0.0\n", + "392 446.684387 POINT (15.10697 45.4589) 0.0\n", + "393 1087.734619 POINT (15.19233 45.4589) 0.0\n", + "394 1046.664062 POINT (15.27769 45.4589) 0.0\n", + "395 155.921677 POINT (15.36305 45.4589) 0.0\n", + "\n", + "[396 rows x 3 columns]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_worldpop_" + ] + }, + { + "cell_type": "markdown", + "id": "573f10a4-d0bb-4675-903f-71ffbe45358f", + "metadata": { + "id": "573f10a4-d0bb-4675-903f-71ffbe45358f" + }, + "source": [ + "## 4. Clustering population data points and assigning them to their nearest hospital." + ] + }, + { + "cell_type": "markdown", + "id": "32b0f3b2-85c6-43ee-b80d-dd3106d809dd", + "metadata": {}, + "source": [ + "In this section, we first add a local_ID to all hospitals to make sure each one has a uniqe id to be refered to later. then we create clusters of population. in this regard, we use K-means clustering algorithm with k equal to the numebr of hospitals. then calculate the sum of rural population each cluster as well as the urban population. then we calcualte the eucleadin distance of each center of cluster to all hospitals and assinge the hospila that has the minimum distance to them." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "e56a71da-0062-4325-8246-d8de4ceb9135", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "133" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(HealthCenters_centroids)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "8991a94b-75bb-4a65-b81f-8800eb23752a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
clustergeometryurban_populationrural_populationnearest_hospital_local_idnearest_hospital_geometry
00POINT (15.36305 45.4589)0.000000155.92167773POINT (15.163850749114253 45.79989695241292)
11POINT (15.99472 46.75537)3826.31665013469.5839842POINT (15.978238324664575 46.68750277238846)
22POINT (15.27769 45.4589)0.0000001046.66406273POINT (15.163850749114253 45.79989695241292)
33POINT (14.50945 45.97066)10871.8242190.0000004POINT (14.523534544937489 46.03654961029273)
44POINT (14.21922 46.38007)20575.24218813451.0517585POINT (14.201134898326943 46.376751864014345)
.....................
128128POINT (14.50945 46.35449)0.0000001238.608643129POINT (14.486722993533334 46.24971688580778)
129129POINT (13.78389 46.43978)0.0000003545.973633130POINT (13.782413390607312 46.48538871591214)
130130POINT (14.38141 46.43978)0.0000005411.233398131POINT (14.332917646680235 46.32902384898702)
131131POINT (14.33873 46.22654)47305.28515629385.140625132POINT (14.353454222758952 46.24864076461883)
132132POINT (13.91193 45.50154)0.0000002524.85302743POINT (13.732211026809601 45.53942988832533)
\n", + "

133 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " cluster geometry urban_population rural_population \\\n", + "0 0 POINT (15.36305 45.4589) 0.000000 155.921677 \n", + "1 1 POINT (15.99472 46.75537) 3826.316650 13469.583984 \n", + "2 2 POINT (15.27769 45.4589) 0.000000 1046.664062 \n", + "3 3 POINT (14.50945 45.97066) 10871.824219 0.000000 \n", + "4 4 POINT (14.21922 46.38007) 20575.242188 13451.051758 \n", + ".. ... ... ... ... \n", + "128 128 POINT (14.50945 46.35449) 0.000000 1238.608643 \n", + "129 129 POINT (13.78389 46.43978) 0.000000 3545.973633 \n", + "130 130 POINT (14.38141 46.43978) 0.000000 5411.233398 \n", + "131 131 POINT (14.33873 46.22654) 47305.285156 29385.140625 \n", + "132 132 POINT (13.91193 45.50154) 0.000000 2524.853027 \n", + "\n", + " nearest_hospital_local_id nearest_hospital_geometry \n", + "0 73 POINT (15.163850749114253 45.79989695241292) \n", + "1 2 POINT (15.978238324664575 46.68750277238846) \n", + "2 73 POINT (15.163850749114253 45.79989695241292) \n", + "3 4 POINT (14.523534544937489 46.03654961029273) \n", + "4 5 POINT (14.201134898326943 46.376751864014345) \n", + ".. ... ... \n", + "128 129 POINT (14.486722993533334 46.24971688580778) \n", + "129 130 POINT (13.782413390607312 46.48538871591214) \n", + "130 131 POINT (14.332917646680235 46.32902384898702) \n", + "131 132 POINT (14.353454222758952 46.24864076461883) \n", + "132 43 POINT (13.732211026809601 45.53942988832533) \n", + "\n", + "[133 rows x 6 columns]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "### 1st step: Add Local_ID to the HealthCenters_centroids GeoDataFrame\n", + "HealthCenters_centroids['Local_ID'] = range(1, len(HealthCenters_centroids) + 1)\n", + "\n", + "# 2nd step: Ensure both GeoDataFrames are in the same CRS (EPSG:4326)\n", + "HealthCenters_centroids = HealthCenters_centroids.to_crs(epsg=4326)\n", + "\n", + "# Convert geometries to a list of coordinates (for KMeans)\n", + "pop_coords = np.array([(geom.x, geom.y) for geom in df_worldpop_['geometry']])\n", + "pop_band_data = df_worldpop_['band_data'].values\n", + "\n", + "# Extract the hospital coordinates\n", + "hospital_coords = np.array([(geom.x, geom.y) for geom in HealthCenters_centroids['geometry']])\n", + "hospital_local_ids = HealthCenters_centroids['Local_ID'].values\n", + "hospital_geometries = HealthCenters_centroids['geometry'].values\n", + "\n", + "### 3rd step: K-Means\n", + "# Ensure that the number of clusters is equal to the number of hospital coordinates\n", + "n_clusters = len(hospital_coords)\n", + "\n", + "kmeans = KMeans(n_clusters=n_clusters, random_state=random_seed, init=hospital_coords, n_init=1) # using hospital locations as initial centers\n", + "df_worldpop_['cluster'] = kmeans.fit_predict(pop_coords)\n", + "\n", + "# Get cluster centers (latitude & longitude)\n", + "cluster_centers = kmeans.cluster_centers_\n", + "\n", + "### 4th step: calculate the sum of urban and rural populations in each cluster\n", + "# group by cluster and urban/rural status then sum up the populations\n", + "\n", + "# rural population (urban_rural = 0)\n", + "rural_population_by_cluster = df_worldpop_[df_worldpop_['urban_rural'] == 0.0].groupby('cluster')['band_data'].sum().reindex(range(n_clusters), fill_value=0)\n", + "\n", + "# urban population (urban_rural = 1)\n", + "urban_population_by_cluster = df_worldpop_[df_worldpop_['urban_rural'] == 1.0].groupby('cluster')['band_data'].sum().reindex(range(n_clusters), fill_value=0)\n", + "\n", + "# to create a new DataFrame for clusters with the total urban and rural population\n", + "clusters_df = pd.DataFrame({\n", + " 'cluster': range(n_clusters),\n", + " 'geometry': [Point(x, y) for x, y in cluster_centers],\n", + " 'urban_population': urban_population_by_cluster.values,\n", + " 'rural_population': rural_population_by_cluster.values\n", + "})\n", + "\n", + "### 5th step: assign the nearest hospital to the related cluster\n", + "# distances between each cluster center and each health facility center\n", + "distances = cdist(cluster_centers, hospital_coords, metric='euclidean')\n", + "\n", + "# the index of the nearest hospital for each cluster\n", + "nearest_hospital_idx = distances.argmin(axis=1)\n", + "\n", + "# assign the nearest hospital Local_ID and geometry to each cluster center\n", + "clusters_df['nearest_hospital_local_id'] = [hospital_local_ids[idx] for idx in nearest_hospital_idx]\n", + "clusters_df['nearest_hospital_geometry'] = [hospital_geometries[idx] for idx in nearest_hospital_idx]\n", + "\n", + "### 6th step: convert to GeoDataFrame and set CRS\n", + "clusters_gdf = gpd.GeoDataFrame(clusters_df, geometry='geometry')\n", + "clusters_gdf.set_crs(epsg=4326, inplace=True)\n", + "\n", + "# review the content of the resulting clusters dataframe\n", + "clusters_gdf\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "5299738d-567d-4473-aaa2-095dede18b92", + "metadata": { + "id": "5299738d-567d-4473-aaa2-095dede18b92" + }, + "source": [ + "## 5. Explore and evaluate baseline results\n", + "\n", + "let's Plot cluster centers and the location of hospitals" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "4735e1af-5573-4baf-83d7-6644ac7a3ad5", + "metadata": { + "id": "4735e1af-5573-4baf-83d7-6644ac7a3ad5", + "outputId": "cee4726f-b707-4b29-c1b1-d1495557475d" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "clusters_gdf['x'] = clusters_gdf.geometry.x\n", + "clusters_gdf['y'] = clusters_gdf.geometry.y\n", + "\n", + "\n", + "HealthCenters_centroids['x'] = HealthCenters_centroids.geometry.x\n", + "HealthCenters_centroids['y'] = HealthCenters_centroids.geometry.y\n", + "\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 8))\n", + "\n", + "\n", + "ax.scatter(clusters_gdf['x'], clusters_gdf['y'], color='black', marker='o', s=20, label='Cluster Centers')\n", + "\n", + "\n", + "for x, y, label in zip(clusters_gdf['x'], clusters_gdf['y'], clusters_gdf['cluster']):\n", + " ax.text(x, y, str(label), fontsize=12, color='k')\n", + "\n", + "ax.scatter(HealthCenters_centroids['x'], HealthCenters_centroids['y'], color='red', marker='+', s=100, label='Hospitals')\n", + "\n", + "\n", + "plt.title(\"Cluster Centers with their IDs and Hospital Locations\")\n", + "plt.xlabel(\"Longitude\")\n", + "plt.ylabel(\"Latitude\")\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "e3d8e5a3-ffa1-4b1a-a3e6-0e569346a78c", + "metadata": { + "id": "e3d8e5a3-ffa1-4b1a-a3e6-0e569346a78c" + }, + "source": [ + "### Identify and plot population per healthcare facility" + ] + }, + { + "cell_type": "markdown", + "id": "b2ea7390-b44c-4bbc-9cf4-7b40556e5e8d", + "metadata": {}, + "source": [ + "Here, we calculate the urban and rural demand for services for each hospital by summing the populations in each cluster assigned to the hospitals. We also calculate the total demand for each hospital." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "cf27813b-e51f-49cd-811d-e95fb1f1983c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Local_IDurban_populationrural_populationtotal_population
3839167343.8437500.000000167343.843750
484946885.50000070193.351562117078.851562
252676068.71875011647.86523487716.585938
161766169.15625020895.76953187064.921875
13113247305.28515629385.14062576690.421875
...............
1011020.0000003403.6149903403.614990
1211222615.232422354.3833922969.615723
40410.0000002423.2248542423.224854
1081090.0000001852.2803961852.280396
1281290.0000001437.8864751437.886475
\n", + "

77 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " Local_ID urban_population rural_population total_population\n", + "38 39 167343.843750 0.000000 167343.843750\n", + "48 49 46885.500000 70193.351562 117078.851562\n", + "25 26 76068.718750 11647.865234 87716.585938\n", + "16 17 66169.156250 20895.769531 87064.921875\n", + "131 132 47305.285156 29385.140625 76690.421875\n", + ".. ... ... ... ...\n", + "101 102 0.000000 3403.614990 3403.614990\n", + "121 122 2615.232422 354.383392 2969.615723\n", + "40 41 0.000000 2423.224854 2423.224854\n", + "108 109 0.000000 1852.280396 1852.280396\n", + "128 129 0.000000 1437.886475 1437.886475\n", + "\n", + "[77 rows x 4 columns]" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# calculateing the total rural and urban demand (population) of each hospital\n", + "hospital_population = clusters_gdf.groupby('nearest_hospital_local_id')[['urban_population', 'rural_population']].sum().reset_index()\n", + "\n", + "# now we merge the population data back to the HealthCenters_centroids GeoDataFrame\n", + "hospital_population_merged = HealthCenters_centroids.merge(hospital_population, left_on='Local_ID', right_on='nearest_hospital_local_id', how='left')\n", + "\n", + "# also we sum up rural and urban demand to get the total demand of each hospital as well\n", + "hospital_population_merged['total_population'] = hospital_population_merged['urban_population'] + hospital_population_merged['rural_population']\n", + "\n", + "# display the demand of hospitals\n", + "hospital_population_merged[['Local_ID', 'urban_population', 'rural_population', 'total_population']].sort_values('total_population', ascending=False).dropna()\n" + ] + }, + { + "cell_type": "markdown", + "id": "12b83859-6dfc-4b28-8fd6-297b66ed737a", + "metadata": { + "id": "12b83859-6dfc-4b28-8fd6-297b66ed737a", + "scrolled": true + }, + "source": [ + "hospital_population_merged.plot('population')" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "d9e2d924-4ad9-440b-9f46-409878af679c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "Index(['osm_id', 'osm_way_id', 'name', 'type', 'aeroway', 'amenity',\n", + " 'admin_level', 'barrier', 'boundary', 'building', 'craft', 'geological',\n", + " 'historic', 'land_area', 'landuse', 'leisure', 'man_made', 'military',\n", + " 'natural', 'office', 'place', 'shop', 'sport', 'tourism', 'other_tags',\n", + " 'geometry', 'Local_ID', 'x', 'y', 'nearest_hospital_local_id',\n", + " 'urban_population', 'rural_population', 'total_population'],\n", + " dtype='object')" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hospital_population_merged.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "bc00dbb0-81fb-431b-ba56-37e07aa9907a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAAKoCAYAAABqel4FAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAACUxklEQVR4nOzdeVgVdf//8deBwyIIiCKCuC+477jvW1rmgplmpqUtd2Z3ZqstlpqF2mZmZmlZ3ZlmmpqppaVoLpW5pWZq7oqIK4jIeub3Rz/OV2THgcPyfFwX1wUzn/mc95yZgfNiZj5jMQzDEAAAAADgljk5ugAAAAAAKC4IWAAAAABgEgIWAAAAAJiEgAUAAAAAJiFgAQAAAIBJCFgAAAAAYBICFgAAAACYhIAFAAAAACYhYAEAAACASQhYAApctWrVZLFYdPz48QJ5vfDwcFksFnXp0iVXyxV0nQXls88+k8Vi0QMPPODoUoql48ePy2KxqFq1aqb2m9f9OK8mTpwoi8WiiRMnFsjrFWb5tU27dOkii8Wi8PBwU/stznjPUBQQsIBCKPWD/WeffZZlu9Q/NMXlA9Dy5cs1ceJE7d6929GlFFlXrlzRxIkTNWPGDEeXglyaMWOGJk6cqCtXrji6lHyXX+saHh6uiRMn8uEbgEMRsAAUuJo1a6pOnTpycXFJM3358uWaNGkSAesWXLlyRZMmTcoyYPn4+KhOnToKDAwsuMKQrRkzZmjSpEmFJmD5+fmpTp068vPzM73v/FrX8PBwTZo0iYAFwKGsji4AQMnz888/O7qEEi00NFShoaGOLgOF3OOPP67HH3/c0WUAQJHDGSwAAAAAMAkBCyjGtm7dqoEDB6pChQpydXVVpUqVNGLECB04cCDD9teuXdPkyZPVuHFjeXp6yt3dXZUrV1aXLl00depUJSUl2dveeNO3YRh6//331ahRI3l4eMjf31/Dhw/XyZMnM3ydmwePSO3r888/lySNHDlSFovF/nXjPWb79u3Tq6++qrZt2yowMFCurq4KDAzUwIEDtXXrVnPeuBxISkrS+++/r1atWsnb21uenp5q0qSJXn/9dcXFxWW6XFxcnN566y21adNGZcqUkYeHh2rXrq3hw4dr48aNadoePXpU06ZNU5cuXVS5cmW5ubmpfPny6t27t1atWpWu7wceeEDVq1eXJJ04cSLNe2ixWOztshvkYv/+/Ro+fLgqVaokV1dXVahQQXfddZd+/fXXDNs/8MAD9nsGIyIiNGrUKAUGBsrd3V0NGjTQBx98kOFyycnJeu+999SqVSt5eXnJzc1NFStWVLt27fTqq6/m6vKx3L5XUtpBI2w2m9577z01bNhQ7u7uqlChgh588EGdP38+09fcuHGjevToIW9vb/n4+Khr165at25djmtOlbo9Tpw4IUmqXr16mu2W0eVuean30qVLeumll9SwYUN5enrKy8tLbdq00dy5c2Wz2dK1z2yQixv3n2vXrunFF19UcHCw3N3dsx2AI7frmpt90WKxaNKkSZKkSZMmpen3xn09L/vKrbhw4YIee+wxBQUFyd3dXXXq1NFrr72W5vdpZn788Uf169dPFSpUkJubmypVqqSRI0fqyJEj6drePBDHvHnz1KxZM3l4eCgoKEhPPPGErl69KklKSUnR22+/rQYNGqhUqVKqVKmSxo8fr8TExHT9Xr9+XQsXLtQ999yjOnXqqHTp0ipdurSaNm2qKVOm6Nq1axnWfuPv+V9//VW33367fH195enpqY4dO2r9+vX58p4BhYIBoNCpWrWqIcmYP39+lu06d+5sSDJeffXVdPNmz55tWCwWQ5Lh7+9vhISEGGXKlDEkGe7u7sb333+fpn1SUpLRpk0bQ5Lh5ORk1KlTxwgJCTEqVqxoODk5GZKMy5cv29sfO3bMkGRUrVrVGD16tCHJqFKlitGiRQvD3d3dkGSUL1/e+PvvvzNdv2PHjhmGYRhnz5412rdvb/j7+xuSjNq1axvt27e3f33yySf2Zbt3725IMsqUKWPUq1fPaN68ueHn52dIMpydnY0FCxake70NGzYYkozOnTtn+X5mV2equLg4o1u3boYkQ5JRr149o3Hjxvb3qWnTpsaFCxfS9XfixAmjXr169uVq165tNG/e3ChbtmyG9T344IOGJKN06dJGcHCwERISYgQGBtqXnzp1apr2r7/+uhESEmJIMtzc3NK8h+3bt7e3mz9/viHJuP/++9PVuGLFCsPNzc3+HoeEhBjly5e37xcff/xxumXuv/9+Q5IxceJEIyAgwHB3dzeaN29uVKxY0V7rlClT0i1311132efXrFnTaNmypVG5cmXD2dnZkGTs2rUr841zk9y+V4aRdr+499577dukQYMGhtVqNSQZDRo0MOLj49Mtu3DhQvv2LleunBESEmKULVvWcHJyMqZOnWo/NnJi9erVRvv27e3ve0hISJrttnPnzluud9++fUZQUJAhyXB1dTXq169v1KxZ0/47YtCgQYbNZkuzzKuvvprh75fU/Wfw4MFG8+bNDYvFYtSrV89o1qyZcdttt5myroaR+32xffv2RuXKlQ1JRuXKldP0+/rrr9vb5WVfufH3XW6cPXvWqFGjhiHJsFqtRtOmTY3atWsbkow777zT6NSpkyHJ2LBhQ7plx44da6/J39/faNasmeHt7W1IMry9vY0tW7ZkWuNTTz1lP64aNmxo3z+6detmpKSkGAMGDLD/7qpTp459PxgxYkS6On755Rd7/ZUqVTJCQkKM2rVr2/ts3ry5ERcXl2651N+f77//vuHi4mKUK1fOaNGiheHj42PvL6P1vpX3DCgsCFhAIXSrAWvXrl32P37Tp083UlJSDMMwjPj4eOOxxx4zJBk+Pj5GRESEfZklS5YYkowmTZoYp06dStNfVFSUMWPGDOPatWv2aal/zK1Wq+Hi4mIsXLjQPu/ChQtGjx49DElGq1at0n1wyyy4pH5Qz2q9v/nmG+PPP/9MM81msxnLly83SpcubXh7exsxMTFp5psdsJ5++mlDklGxYkVjx44d9umHDx826tata//weaPk5GSjRYsW9g+Vf/31V5r5u3btMmbPnp1m2urVq41ff/013fu3adMmIzAw0HB2djb++eefNPNy8kEws4B15swZ+we4sWPHGgkJCYZhGEZKSorx+uuvG5IMFxcXY8+ePWmWS91uLi4uxqBBg9IE8dmzZ9tD/Y3T//jjD/sH4Zvfi+joaGPu3LnGyZMnM12Hm+XlvUrdL1xcXIyKFSsav/32m33ewYMHjUqVKhmSjA8//DDNcqdPnzZKly5tSDLGjx9vJCUlGYZhGImJica4ceMMFxeXPH0Yz2x/u9V6Y2NjjZo1axqSjCeeeMKIjo62z9u/f7/RoEEDQ5Ixa9asNMtlF7CcnZ2N4ODgNNvv+vXrpqxrXvfFzGq+UX4dVxkJDQ21h5Ab9+eff/7Z8PLysu8rN4eFOXPmGJKM6tWrp5mXnJxsTJkyxZBkVKpUKc37fePvZB8fH+Onn36yz9u7d69Rrlw5Q5IxYMAAo1KlSmn+gbFhwwbD1dXVkGTs378/TS3Hjx83Fi9ebFy9ejXN9LNnzxqDBg2y/3PlZqnb2MXFxQgLCzOSk5MNw/j3OBk2bJghyWjdurVp7xlQmBCwgEIo9Q9TTr9u/jCR+serf//+6fq22Wz2D1QTJkywTw8LCzMkGe+9916Oakz9Y576oe1m586ds5/JWr9+fYbrl5eAlZWXX37ZkJTuLJaZASs6Otrw8PAwJBnLli1Lt8zvv/9uSDIsFkuaD2mLFy+2/yc6o7NbuTVv3jxDUpr/zBvGrQWsl156yZD+PQOXkTvuuMOQZAwfPjzN9NTtFhAQYMTGxqZbrnnz5oYk49tvv7VPW7hwoSHJGDduXDZreusye69S9wtJxtKlS9MtN3PmTEOS0a9fvzTTU/ezli1bZvh6jRs3zteAldt6U6eHhoZm2O+ePXsMi8Vi1KhRI8307AKWpDT/YMiN7NY1r/tiTgJWVm7luLrZ4cOH7WeG9u3bl27+O++8Y38fbwwLCQkJRkBAgOHs7JzmjN6NUs/+fvHFF+lqlGS8++676ZZ54YUX7PMz+t11zz33GJKMd955J8frGBcXZ7i6uhq1a9dONy91G/ft2zfdvPPnz9vPTl66dMk+Pa/vGVDYMIogUIjVrl1b/v7+mc7fu3evYmJi0k1fu3atJOm///1vunkWi0VPPPGE/vOf/2jt2rWaPHmyJKly5cqSpFWrVumhhx6Sh4dHjuscM2ZMumn+/v4aNGiQvvzyS/3444/q2rVrjvvLzsmTJ/XVV19p586dunDhgv2+gaioKEnSnj17dO+995r2ejfavHmz4uLiVKVKFfXv3z/d/JYtW6pt27batm2b1q1bp5o1a0qSVqxYIUkaNWqUypUrl+PXO3/+vL766iv99ttvioqKUnx8vCQpOjpa0r/rapbU/SazkePGjh2r1atX29vdbOjQofL09Ew3vWXLltq5c6eOHj1qn5a6v/3888+6dOmSypYte6vl5/m98vX11cCBAzOsW1KauqV/74uRpNGjR2fY32OPPaZHH300byuRA7mt99tvv5UkPfTQQxn217hxY1WrVk1Hjx7V6dOnValSpRzV0aBBAzVv3jw3pefYre6L2SmI42rt2rUyDEOdOnVSgwYN0s1/6KGHMrzvadu2bYqMjFTLli3VrFmzDPvu16+fli5dqo0bN2r48OHp5o8aNSrdtKZNm0qSypYtqwEDBqSb36xZMy1atCjd/iP9e8/fypUrtXbtWh09elSxsbEyDEPSv39TDh8+rLi4uAz/bmS03/n5+alatWo6ePCgjh49qhYtWkjK+3sGFDYELKAQe/HFFzMdiED690HDNw+McOXKFfuN7vXr189wudQ/XIcOHbJPGzBggKpVq6a1a9eqYsWK6t27tzp27KguXbpk+IculYuLi2rVqpXhvHr16qV7nVv1+eef69FHH7V/IMrIpUuXTHu9m6WuS926ddMMHHGjBg0aaNu2bWnWO3VgkTZt2uT4tdauXavBgwfbP/RlxMx1Ta03u/3m3LlziomJkbe3d5r5qWHyZqn/JIiNjbVPa9u2rVq3bq3ffvtNlStXVs+ePdWpUyd17txZzZs3z/S9zcytvFe5qVv6v/cpdf++WWbTzZLbevfu3StJeuWVV/TGG29kuOyFCxckSWfOnMlxwMrP9bzVfTErBXVcZbefeHl5KSgoSMeOHUszPXV7HT9+XB06dMhw2dQBYM6cOZNuXvny5TN8P8qXLy8p8/0ndf7N+8+VK1d0xx13aNu2bRkul+ry5csZBqys9teDBw+meb28vmdAYcMogkAxc+Mfq8zOflWoUEGS7CNKSZKnp6d++eUXjRw5UjabTV9//bUef/xxNWzYUA0aNND333+fYV/lypWTk1PGv0oyep1bceTIET388MOKj4/X008/rV27dikmJkY2m02GYWju3LmSlK8jTaW+v1mdWcxovVPPNJYpUyZHr3PlyhXdc889io6O1ogRI/Trr7/q8uXLSklJkWEY9tHqzFzX7NYtdb2kjLdpRmevJNn3j9T/eKdOW7NmjcaOHatSpUppxYoVevrppxUSEqLq1avrs88+y3Hdt/pe5aZu6f/ep9QPpDe78X3KD7mtNzVI7NixQ1u2bMnwK3V7Xr9+/ZbrMMOt7ouZKcjjKrv9RMp4X0ndXufPn890e+3fv19Sxtsrs6sPUv9pkd38m/efp556Stu2bVOdOnW0dOlSnTlzRgkJCTL+vc1EQUFBksw5vvL6ngGFDQELKGZKly5t/z71krmbnTt3TtK//w28UaVKlfTpp5/q0qVL+vXXXzV16lSFhITor7/+0oABA/Tbb7+l6+vixYsZDvF84+vf/Dp5tXjxYiUlJemee+7RW2+9paZNm8rLy8v+weDUqVOmvE5WUt/fzN5bKeP3N/X7nA49vmbNGl2+fFlt27bVZ599ptatW6tMmTL2DyX5sa7ZrVvqeknmbFNfX1/NmDFD58+f165du/Tee++pa9euOnHihEaOHKklS5bkqJ+Cfq9S36fMhkTPat9whNR6Dx8+bP9QnNlXdsOsF5T82hcLcl/Jbj+RMl6/1OWGDRuW7fbKaAh/MyUnJ2vx4sWS/r3MeeDAgapYsaJcXV3t8yMjI017vby+Z0BhQ8ACipkyZcrY//v3119/Zdgm9b+fwcHBGc63Wq1q3bq1nn/+eW3fvl333HOPUlJS9Omnn6Zrm5SUlOEzWaT/uywus9e5WXaXhaU+N6tdu3YZzjfzfqTMpK7LgQMH0v2nN1VG72/qJU2ZPUvqZqnr2rZt2wzfl8zWNbeX1t0otd7s9psKFSrk6pKs7FgsFjVt2lRPPPGE1q9fr/Hjx0uS/YxkdvL6XuVV6vv0999/Zzg/s+fMZedWtl1WUi+z27dvX770nxfZrWte98Wc/g4piH0lu/0kNjZWp0+fTje9MG2v8+fP69q1aypbtqzq1KmTbv6+ffuUkpJi2uvl9T0DChsCFlAM9erVS5L0/vvvp5tn/P+HAt/YLjup9w1FRERkOH/27Nnppp0/f17ffPONJOm2227L0euUKlVKUuaXKaXOv/G/16n+/vtvrVy5Mkevcys6dOggDw8PnTp1yj5wxY3++OMPbdu2TRaLRT179rRPT72pPPUMYXayWteLFy/qk08+yXK53FzqlSp1f5g1a1aG82fOnJmmXX7Jbn+7WV7fq7xK3Z/nzJmT4fwPP/wwT/3eyrbLSuqAGDNnzsz0nwIFLbt1zeu+eCu/Q8zeV1L3k02bNmUYFOfNm5fhYA0dO3aUn5+f9uzZk+9nqLKT+n7FxMRk+J5Onz7d1NfL63sGFDYELKAYevrpp2W1WrVixQq9/fbb9kv4EhMTNXbsWO3bt08+Pj5pRkF79913NWPGjHQfPE6ePKl58+ZJUoYjhlmtVs2ePdsepqR/bxC/7777FB8fr5CQkByPIFijRg1J//5xzeiDYOoN37Nnz9bu3bvt0w8dOqS7777bftlKfvL29ra/b48//rh27dpln3fkyBHdf//9kqTBgwenubl7wIABCgkJUVRUlO644w4dPHgwTb979uxJ88G8Y8eOkv69LPKnn36yTz979qzuuusuJScnZ1hf+fLl5eXlpaioqFyfSRk9erS8vb21e/dujRs3zv5Bxmazafr06Vq1apVcXFz09NNP56rfjCxYsECvvfaa/YxCqosXL9o/POd0hLq8vld59eijj8rT01O//fabJkyYYO8/KSlJzz77rP3sSm6l7v83D1xzq/7zn/+oRo0a2rBhg4YNG6azZ8+mmR8bG6vFixfrqaeeMvV1s5LduuZ1X0ztd+vWrRlu94LcV2rVqqX+/fvLMAzdf//9ac68hIeHa+LEiXJxcUm3nLu7u31017vvvlvLli1L9/tw3759ev7557VlyxbT6s1ImTJl1KBBAyUnJ6fZDikpKZo2bZq+/vprU3/v5vU9AwqdfB0EHkCe3OqDhg3j3we8pj5PpEKFCkbLli2NMmXKGJIMNzc34/vvv0/TfuzYsfbni1SrVs1o1aqVUbduXcPZ2dmQZDRs2NC4cuWKvf2Nz4UZPXq0/fuQkBCjVKlShiSjXLly6R4ie+P63fwMnH/++cf+sMuqVasaHTt2NDp37mx/H5KSkow2bdrYH3Jar149o2HDhobFYjECAwPtD+C8+flOZj9oOC4uzujatav9/apfv77RpEkT+3vVpEmTDJ91deLECaNOnTr25YKDg40WLVrYHwB6c32pD/GUZNSqVcto2rSpYbVaDS8vL2PGjBmZrtOoUaMM6d+H+4aEhBidO3dO0y6z52AZhmGsWLHCvg18fX2Nli1bGv7+/oYkw8nJyfjoo4/SLZPd88syejbRu+++a1+3oKAgo2XLlkbDhg3trx0UFGScOHEiw/4ykpf3Krv9IqtnH3355Zf248vPz89o2bKlUbZsWcPJycmYOnVqnp6D9cUXX9jXoWHDhvbtlvpA2Fup98CBA0b16tXt27FevXpG69atjeDgYPt+e/NDX7N7DlZG+49Z62oYedsXo6OjDV9fX0OSERgYaLRv397o3LmzERYWZm+Tl30lrw8aPnPmjFGtWjVD+veBu82aNTOCg4MNSUafPn2MTp06ZfpMp/Hjx9vrLFu2rNGyZUujefPmRtmyZe3T16xZk+Mas9t/Mtuu3333nX1fL1u2rBESEmL4+fkZ0r/PUszs92R2zzpL/ft187rfynsGFBacwQKKqdGjR+uXX37RgAEDZLPZtHv3bnl4eOi+++7Tzp071adPnzTtH330UU2cOFGdOnVSUlKSdu/ercuXL6tly5Z6//339fvvv8vHxyfD1/rggw/03nvvycvLS/v27ZOnp6eGDRumHTt25Goo55o1a2rlypXq3LmzLl++rM2bN2vjxo32sxxWq1U//vij/vvf/6pChQr6559/dOXKFT344IPasWOHfTSr/FaqVCn9+OOPeu+99xQSEqITJ07o0KFDql+/vqZMmaKtW7dm+KyrKlWqaMeOHQoLC1Pz5s0VERGhAwcOqGzZsrr//vv12muvpWm/YMECTZgwQdWqVdOJEycUGRmpQYMGafv27WrSpEmm9b333nsaO3asAgICtGfPHm3cuDHHZ0X69eunHTt2aNiwYXJ3d9fu3btlGIZCQ0O1efNmPfLII7l7szJx1113adq0aerZs6ecnZ21d+9enT17Vg0bNtSUKVO0b98+ValSJcf95fW9yqthw4Zp/fr16tq1q+Lj4/X333+rUaNGWrNmjYYMGZKnPocPH6733ntPjRs31pEjR+zbLacDo2Slbt262rNnj6ZOnaqWLVvqzJkz2r17txITE9W5c2e99dZbWrRo0S2/Tk7lZF3zsi96e3tr7dq1uv3225WQkKBt27Zp48aNae7pKch9pWLFivr999/16KOPys/PT3/99ZcMw9DkyZO1bNmyLO8ZCwsL05YtW3TvvffK09NTe/bs0fHjx1WpUiWNGjVKq1atUvfu3U2tNyN9+/bVmjVr1K5dO12/fl0HDx5UrVq19OWXX9rPtJnpVt4zoLCwGEYhuSAbQJFy/PhxVa9eXVWrVk13mRcAAEBJxRksAAAAADAJAQsAAAAATELAAgAAAACTELAAAAAAwCQMcgEAAAAAJuEMFgAAAACYxOroAgorm82miIgIeXl58cwFAAAAoAQzDENXr15VxYoV5eSU9TkqAlYmIiIiVLlyZUeXAQAAAKCQOHXqlCpVqpRlGwJWJry8vCT9+yZ6e3s7uBoAAAAAjhITE6PKlSvbM0JWCFiZSL0s0Nvbm4AFAAAAIEe3DjHIBQAAAACYhIAFAAAAACYhYAEAAACASbgHCwAAAECxl5KSoqSkpEznu7q6ZjsEe04QsAAAAAAUW4ZhKDIyUleuXMmynZOTk6pXry5XV9dbej0CFgAAAIBiKzVc+fv7y8PDI8ORAG02myIiInT27FlVqVIlR6MFZoaABQAAAKBYSklJsYercuXKZdm2fPnyioiIUHJyslxcXPL8mgxyAQAAAKBYSr3nysPDI9u2qZcGpqSk3NJrErAAAAAAFGs5ueTvVi4LvBEBCwAAAABMQsACAAAAAJMQsAAAAADAJAQsAAAAAMWaYRimtMkJhmkHAAAACoGIiAgtWrRIB/7+S0EVK2nw4MGqX7++o8sq0lKHW4+Li1OpUqWybJuYmChJcnZ2vqXXJGABAAAADnb48GGNfHC44nVe1RqV1v5fN2r599/onenvq1u3bo4ur8hydnZWmTJlFBUVJUlZPmj4/Pnz8vDwkNV6axGJgAUAAAA42KxZ70teF/XMm+1VytNFKSk2ff7GH5r21hvq3LnzLZ9VKckCAgIkyR6yMuPk5KQqVarc8nDtBCwAAADAwTZv26jOw4JUyvPfS9qcnZ3UsV91ff7qAZ04cUI1atRwcIVFl8ViUWBgoPz9/e0PHs6Iq6urnJxufYgKAhYAAADgYJ4enrp6JSHNtNjoRFkszvLw8HBQVcWLs7NzgZwJZBRBAAAAwMH6971L2384p/2/R8pmM3T6nyv68ct/1KpFO/slbigaOIMFAAAAONjo0aN16PBBffXGBslpnwybs2pVa6DJk6Y4ujTkEgELAAAAcDAPDw/N+fAj7dmzR4cOHVJgYKDatWvH4BZFEAELAAAAKAQsFouaNm2qpk2bOroU3ALuwQIAAAAAkxCwAAAAAMAkBCwAAAAAMAkBCwAAAABMQsACAAAAAJMQsAAAAADAJAQsAAAAADAJAQsAAAAATFKoAlZYWJgsFouefPLJNNMPHDigfv36ycfHR15eXmrTpo1OnjyZZV9XrlzRmDFjFBgYKHd3d9WrV0+rV6/Ox+oBAAAAlHRWRxeQavv27fr444/VuHHjNNOPHDmiDh066MEHH9SkSZPk4+OjAwcOyN3dPdO+EhMT1bNnT/n7+2vJkiWqVKmSTp06JS8vr/xeDQAAAAAlWKEIWLGxsRo2bJjmzp2rKVOmpJn30ksv6Y477tD06dPt02rUqJFlf59++qkuXbqkrVu3ysXFRZJUtWpV8wsHAAAAgBsUiksEx4wZoz59+qhHjx5ppttsNq1atUrBwcHq1auX/P391bp1ay1fvjzL/r777ju1bdtWY8aMUYUKFdSwYUO98cYbSklJyce1AAAAAFDSOTxgLVq0SDt37lRYWFi6eVFRUYqNjdXUqVPVu3dvrV27VqGhoRo4cKA2btyYaZ9Hjx7VkiVLlJKSotWrV+vll1/W22+/rddffz3TZRISEhQTE5PmCwAAAAByw6GXCJ46dUpjx47V2rVrM7ynymazSZL69++vcePGSZKaNm2qrVu3as6cOercuXOG/dpsNvn7++vjjz+Ws7OzWrRooYiICL355pt65ZVXMlwmLCxMkyZNMmnNAAAAAJREDj2DtWPHDkVFRalFixayWq2yWq3auHGjZs6cKavVqnLlyslqtap+/fpplqtXr16WowgGBgYqODhYzs7OaZaJjIxUYmJihsu88MILio6Otn+dOnXKnJUEAAAAUGI49AxW9+7dtXfv3jTTRo4cqbp16+r555+Xm5ubWrZsqYMHD6Zpc+jQoSwHrWjfvr2++uor2Ww2OTk52ZcJDAyUq6trhsu4ubnJzc3tFtcIAAAAQEnm0IDl5eWlhg0bppnm6empcuXK2ac/++yzGjJkiDp16qSuXbvqhx9+0MqVKxUeHm5fZsSIEQoKCrLfxzV69Gi9//77Gjt2rP773//q8OHDeuONN/TEE08U2LoBAAAAKHkcPshFdkJDQzVnzhxNnz5djRo10rx587R06VJ16NDB3ubkyZM6e/as/efKlStr7dq12r59uxo3bqwnnnhCY8eO1fjx4x2xCgAAAEVaSkqKPvnkE/Xs3V1NQxpp+P33acuWLY4uCyiULIZhGI4uojCKiYmRj4+PoqOj5e3t7ehyAAAAHGby5MlatPxz1e9eQeWr+OjvbWd08e9kffDeR+rYsaOjywPyXW6yQaE/gwUAAADHiYiI0JLli9R5ZLD6jA5Rqz61dd/kzvKr56IPP5rt6PKAQoeABQAAgEz99ddfSrTFq2HHKvZpTk4W1e8QpH1//Wl/rA6AfxGwAAAAkCk/Pz85WZx1/mR0munnT8aorG85WSwWB1UGFE4ELAAAAGSqcePGqlerodZ8uEcn/zqv+GuJ2rn2iPauO6vBd91DwAJu4tBh2gEAAFC4OTk56d2339PYcf/V1y//oRQjWS5Obgq9Y4gefvhhR5cHFDoELAAAAGSpSpUqWvrNMu3atUvnz59X/fr1VaVKlewXBEogAhYAAACy5eTkpBYtWji6DJQgp06dUlJSkmrUqOHoUnKFe7AAAAAAFCqrV69W3/49FTqot7788ktHl5MrBCwAAAAAhcratWvlUyFWlYKTtOaHlY4uJ1cIWAAAAAAKlc6dO+vKOU+dPmRVl849HF1OrnAPFgAAAIBCJTQ0VPXr11dCQoIaNWrk6HJyhYAFAAAAoNCpU6eOo0vIEy4RBAAAAACTELAAAAAAwCQELAAAAAAwCQELAAAAAExCwAIAAAAAkxCwAAAAAMAkBCwAAAAAMAnPwQIAwAQRERH6+++/FR8fL29vbzVv3lweHh6OLgsAUMAIWAAA3ILffvtN/1uwQD9t2qC45EQZhiFni5PK+/hq8ICBGjZsmCpWrOjoMgEABcRiGIbh6CIKo5iYGPn4+Cg6Olre3t6OLgcAUAjNmzdP02a+K1uAjyp0aia/JrXl7OqihMtXdfbXP3Vp235VdPPWnPdnqUmTJo4uFwCQR7nJBgSsTBCwAABZWbx4sV54baLK9ApRtTvay2KxpGuTFBevv+YsVdkryfr6fwtUrVq1gi8UAHDLcpMNGOQCAIBciouL0/QZ76hUmzqq3qdDhuFKklw83NVg9CCdsyRo9ocfFnCVAABHIGABAJBLa9asUdTVK6rWu322ba2l3FShSzOt+ulHnT9/vgCqAwA4EgELAIBcWrVmjdzrVJJ72ZxdQh7YppGuJido/fr1+VwZAMDRCFgAAORS1MXzcvf3zXF7ayl3Wb08dPHixXysCgBQGBCwAADIJavVKiPFlqtljOQUubi45FNFAIDCgoAFAEAu1a0VrGuHTyunA/HGRpyXERuv6tWr53NlAABHI2ABAJBLdw0cKJ2/qiuHT+Wo/ZlNO1WlQqA6d+6cz5UBAByNgAUAQC6FhISocZ16OrbkZyXHJ2bZNvroGcX8dkD33j2ESwQBoAQgYOGWGYahtWvX6tFHH9Z9wwZr1qxZiomJcXRZAJBvLBaL3po6TX7XLdr73kJdO3shXRvDZtO5HQd08IMl6tairUaNGuWASgEABc1i5PQC8hImN09rLum++OILvTdjokKaWuRXzqpNW66rctXW+uzzBXJ1dXV0eQCQbw4ePKjHnviv/jlzUq41A+XbqKac3VyVcDlGl7cfkHN0vO7o1kNT3wiTh4eHo8sFAORRbrIBASsTBKycuX79unrd1lG397iucWNqSpIO/ROrkY/9o9emzNEdd9zh4AoBIH8lJiZqw4YNWvj119q9708lJCbKu3Rp9e7eU4MHD1b9+vVlsVgcXSYA4BbkJhtYC6gmFFNnz57VtWuX1aVDkH1acK3SCqzgpMOHDzuwMgAoGK6ururVq5d69eolSbLZbHJy4gp8ACip+AuAW1KhQgW5u3vp9x1X7NNOno5T5LkUVatWzWF1AYCjEK4AoGTjDBZuiaenp+4d9rDmf/qmzkT8rfJ+rvrx5xhVqtxQt912m6PLAwAAAAoUAQu3bPTo0SpfvryWffu19h26rM7dhujRRx9VqVKlHF0aAAAOFxsbq6NHjyo+Pl7e3t4KDg7mTCdQjDHIRSYY5AIAANyKw4cPa9GiRVq5epmuxcfIMAw5WZxUqWI1DRl0rwYOHCgfHx9HlwkgBxhF0AQELAAAkFffffedXpn8oly8E9S8V5DqtQqUWymroi/Eaef6k/rrlwuq5F9bs9+foxo1aji6XADZIGCZgICF7Fy+fFnfffed1q5dpUuXopSYmKDSpb1Vq1YDDRp0t1q3bs0lIABQAv30008a99zjatjdR33/00TO1vR/C6IvXNf/Jv8m94SKWvC/RfL393dApQByioBlAgIWMhMREaEPP/xQ69Ytl2G7rHbt3FSpkrtcXJwUG5usHTvidOy4RVWq1Ne99z6gQYMGEbQAoIRISkpSr9t7yKtWjIY+3yrLZ6BdvRyvD8dtVr+uw/Xqq68WYJUAcovnYAH55K+//tITTzwii+W4ht/nq9t715G3t0uaNsZDhv76K1bffXdIU6c+oz179mjixIlycXHJpFcAQHGxYcMGRV44pQGvtMn2AdNevu4Kub2SVi1foXHjxvEPXaCY4N/qQA4dPXpUo0c/IP/ypzRndh0NGRyULlxJksViUYMGXnrhhVp68QV/rVv3hSZNmiROFgNA8ff99ysVVM9DFarkLCy16lVNsQmXtWHDhnyuDEBB4QwWkAPJycl68snH5FcuUmFvBMvTM2eHTpfOfrLIotfDvlCjRo00ZMiQfK4UAOBIkefPqnwVzxy3L13GXaW8rIqKisrHqgAUJM5gATmwadMmnT59QM89WzXH4SpV587l1K2Lq7788lPZbLZ8qhAAUBhYnZxls+XuigXDJjk7O+dTRQAKGgELyIHFixeqfj2LatbM+X8lb9S3bwWdOXNQv/76q8mVAQAKk6pVauj0gZgcXxYedfqq4q+mqEqVKvlcGYCCQsACsnHy5Elt/z1cffuWy3Mf9euXVo3qhr755msTKwMAFDYDBw7U5TPJOr7/Qo7a/7bqiPzLBalTp075XBmAgkLAArJx+PBhGUa8WoaUyXMfFotFISEe+uef/eYVBgAodEJCQlSnZkP9MP+AEuOTs2x75shl/bn+vAbfNVSurq4FVCGA/EbAArIRGxsrQykqXfrWxoTx8rLq6tUYk6oCABRGFotFb0yZqvhID3326jZdOnctXRvDMHRwR6S+eOUPNanTRqNGjXJApQDyC6MIAtlwcXGRRU5KTLTJ3T3vNyEnJtjk6upmYmUAgMKobt26mvfRZ3riyTF6f/Rm1Wjuo3qtAuTi7qzoC9e1e32ErpxJVofW3fXWm2/L3d3d0SUDMBEBC8hGuXLlJItVEWfjVaN63ga5kKQzEQkqW7a8iZUBAAqrhg0b6vvv1ujHH3/UosVf6cc5+yUZsjq7qHuXO3X3K4PVsmXLbB9GXJRdu3ZNf/31l1xcXNSwYUNZrXzsRMnAng5ko1mzZipXrpLWrInSmMeq56mPq1eTtXlzvB75z50mVwcAKKw8PDwUGhqq0NBQJScnKyEhQaVKlZKTU/G+Q8Nms+nDDz/UF1/NV8y1K7LIooDyQRr3xNPq27evo8sD8l3xPsIBE7i6uio09F6tXXdN16+n5KmPtWujZMhH/fv3N7k6AEBRYLVa5enpWezDlSTNnDlTsz55R8G9Smvke+1177TW8gyO0wuvPKt169Y5ujwg3xX/oxwwQWhoqBLiS2vpt2dzvezVq8latvyyunXr9+/lhgAAFFNXrlzRl4s+U+tBVdXtvsYqX9lHQbXLKvSp1qrYtJTmfDw7x88IA4oqAhaQA4GBgRr14BP64n8x+unn8zleLi4uRa9O+keJSZX12GOP5WOFAAA43u7duxUbH6NmPWqkmW6xWNS0ezUd/OeALlzI2TPCgKKKe7CAHBo9erSioqI0/c1Pde5cggaGBqpUqcxHFTx+PE7Tpp9Q5Lny+uCDOapcuXIBVgsAQMH79xJIi1KSbenmpaQYslgsJeIySZRsBCwghywWi1555RX5+/vr009m6pslB9TrttK6vbe/KlZ0l4uLRdfiUrTjj2h9t/KC9u5LUWBgHX3yyQcKDg52dPkAAOS7Fi1ayMfTV9vX/KPbRja1T7fZDO344agaN2jG5fIo9iwGF8JmKCYmRj4+PoqOjpa3t7ejy0EhExERoaVLl2r58kW6fPm0ZCRLFkMynCSLu5o376jBg4eqa9eucnFxcXS5AAAUmE8//VRvznxDdbv6qVHnqkqMT9b27//Rhb+T9eHMuWrXrp2jSywwsbGxWrp0qX748XvFxl5Vg/pNNHTovWrSpImjS0Mu5SYbELAyQcBCTiQmJuqPP/7QpUuXlJiYqNKlS6tWrVqqUaNG9gsDAFAMGYahRYsWad78jxV5/owssqhOrfoaN/ZpdejQwdHlFZgrV65o1IMjtHP3VtmMBLm4WuTm5iYXJ1+9OmGqBg4c6OgSkQsELBMQsAAAAPIuOTlZJ0+elIuLiypVqlSsH6qckUmTJmn2R28qMT5BFQOc5evjpGMnkuTjX1ruzoH6YU24/P39HV0mcig32aBQ3WUYFhYmi8WiJ598Ms30AwcOqF+/fvLx8ZGXl5fatGmjkydP5qjPRYsWyWKxaMCAAeYXDAAAgAxZrVbVqFFDlStXLnHhKiEhQYu/WaDr8Qlq1tRNSxZX1uLFVTT+aV9FR13T9cRL+v777x1dJvJJoQlY27dv18cff6zGjRunmX7kyBF16NBBdevWVXh4uPbs2aMJEybI3d092z5PnDihZ555Rh07dsyvsgEAAIA0oqOjdSX6itzcnNS+nYfKlHGWxSL17u0tq9WQi5uhyMhIR5eJfFIoAlZsbKyGDRumuXPnytfXN828l156SXfccYemT5+uZs2aqUaNGurTp0+2p1RTUlI0bNgwTZo0ifthAAAAUGB8fHzk6eGppESbdu6KV9z1f4et37YtTklJUnzcv8/YRPFUKALWmDFj1KdPH/Xo0SPNdJvNplWrVik4OFi9evWSv7+/WrdureXLl2fb5+TJk1W+fHk9+OCDOaohISFBMTExab4AAACA3HJzc9PQe0bIMJz1+454Db//jMY8fkYTJl+Qk5urvDzK684773R0mcgnDg9YixYt0s6dOxUWFpZuXlRUlGJjYzV16lT17t1ba9euVWhoqAYOHKiNGzdm2ueWLVv0ySefaO7cuTmuIywsTD4+PvYvHgoLAACAvHrqqafUvm1n2VJc9Of+RP28KUEJNleV8w3SKxNeV/ny5R1dIvKJQx80fOrUKY0dO1Zr167N8J4qm+3f06n9+/fXuHHjJElNmzbV1q1bNWfOHHXu3DndMlevXtV9992nuXPnys/PL8e1vPDCC3rqqafsP8fExBCyAAAAkCc+Pj5a8s0yLV++XKvXrFTc9VjVr9tEQ4cOVYMGDRxdHvKRQwPWjh07FBUVpRYtWtinpaSkaNOmTZo1a5auXbsmq9Wq+vXrp1muXr162rx5c4Z9HjlyRMePH1ffvn3t01KDmtVq1cGDB1WzZs10y7m5ucnNzc2M1QIAAADk4eGhe++9V/fee6+jS0EBcmjA6t69u/bu3Ztm2siRI1W3bl09//zzcnNzU8uWLXXw4ME0bQ4dOqSqVatm2GfdunXT9fnyyy/r6tWreu+99zgrBQAAACDfODRgeXl5qWHDhmmmeXp6qly5cvbpzz77rIYMGaJOnTqpa9eu+uGHH7Ry5UqFh4fblxkxYoSCgoIUFhYmd3f3dH2WKVNGktJNBwAAAAAzOTRg5URoaKjmzJmjsLAwPfHEE6pTp46WLl2qDh062NucPHlSTk4OH68DAAAAQAlnMQzDcHQRhVFMTIx8fHwUHR0tb29vR5cDAAAAwEFykw047QMAAAAAJiFgAQAAAIBJCFgAAAAAYBICFgAAAACYhIAFAAAAACYhYAEAAACASQhYAAAAAGASAhYAAAAAmMTq6AIAAAAyc/HiRe3Zs0fJyckKCgpS/fr1ZbFYHF0WAGSKgAUAAAqdiIgIzZw5U+t+XqnE5KuSYcjJyVW1azbSIw8/pttuu83RJQJAhghYAACgUDl+/LgefHiEEnVSA0ZWUKtONeTm7qwjf8fo5+8O6Nnxjyki4mU98MADji4VANIhYAEAgELDMAw99/xTcip1Sq9MbSwvH1f7vAbNyqp+U18tX3BM7858Q82aNVOTJk0cWC0ApMcgFwAAoNDYsWOH/j60R8NG10gTrlJZLBYNGFZdfoFJWrRooQMqBICsEbAAAEChsW7dOvkFGqrTqEymbSwWi9rf5qeffl6tlJSUgisOAHKAgAUAAAqNmJgYlS3vnO1IgX4V3JWUnKBr164VUGUAkDMELAAF4vr16zp06JAuX77s6FIAFGJeXl66dD5FhmFk2e5SVIKszq7y8PAooMoAIGcIWADy3V9//aV+/Xpp6D291atXRy1btszRJQEopHr06KELZ6VD+6MzbWMYhjavu6Du3W6X1cp4XQAKFwIWgHz3yivj5Vf2hGa9U1G9eyTqjTde0rlz5xxdFoBCqGXLlgqu1VgLZh/VtatJGbb5/usTOn/GWUOG3FPA1QFA9ghYAPKVYRg6efKoevUoq4YNvDUotKJsKXGKiIhwdGkACiGLxaLp095R4tUAvfbkHq3//rSuXU1SUqJNB/de1qwp+7RywWU9Pvo5NW/e3NHlAkA6nFcHkK8sFosaNGimxUvD5VHKST+HX1Ypj3KqVq2ao0sDUEjVqFFDX3y2SO+9N0NL5v6ghXMiJMOQxeKqGtXqKey10erTp4+jywSADFmM7O4iLaFiYmLk4+Oj6OhoeXt7O7ocoEiLiIjQs88+qb///lNly/pr8uRpatu2raPLAlAEREVFaffu3UpKSlJQUJCaNGmS7QiDAGC23GQDAlYmCFiA+RISEuTq6sqHIwAAUKTkJhtwiSCAAuPm5uboEgAAAPIVg1wAAAAAgEkIWAAAAABgEgIWAAAAAJiEgAUAAAAAJiFgAQAAAIBJCFgAAAAAYBICFgAAAACYhIAFAAAAACYhYAEAAACASQhYAAAAAGASAhYAAAAAmISABQAAAAAmIWABAAAAgEkIWAAAAABgEgIWAAAAAJiEgAUAAAAAJiFgAQAAAIBJCFgAAAAAYBICFgAAAACYhIAFAAAAACaxOroAACVTdHS0VqxYoX379qlMmTK688471bhxY0eXBQAAcEsIWAAK3MmTJzVi1CgdOXdW1sqBsl2O0fyFX+mFcU9p1KhRji4PAPJdcnKynJyc5OTExURAcUPAAlDgpk6fpqNxMar79Gi5lfGWYbPpxI8bNG3Gu+ratauqV6/u6BIBwHSXLl3St99+q2XLFuls5Gk5OTmrRfO2Gjx4qLp27UrYAooJjmQABSomJkbrN22Sf+e2civjLUmyODmpSo/Oum510rp16xxcIQCY7/Dhw7rnnlDNnTdJ9Rqd0JixHhr1sItiYn/SM88+qOeff05JSUmOLhOACTiDBaBAxcfHK8UwZPXwSDPdYnWWxdVV169fd1BlQMlz5swZHT16VB4eHmrSpImsVj4W5IerV6/qv/99RJ5ex/Tme7XlW9bVPu/O/gH6deslTX9jkd55p7yef/55B1YKwAycwQJQoMqXL686NWsq6vedMmw2+/Qrh47I6eo1tWzZ0oHVASVDRESEHnt8jLr36aURjz2kwSOHqUfv2/TNN9/IMAxHl1fsrFy5UhcuHtaESTXThKtUbdqV1T3DfPXtsi91+fJlB1QIwEwELAAFymKx6Kknxsrl5Fn99dHnitjym46u+EEnv/pWPdp3UJs2bbJc/vz583ro4VFq1yFE48Y9qbi4uAKqHCgeLly4oOEj79cPf25RqVZV5dEgUNaafjrtdk3jJ0/Ql19+6egSi53vvluiNu3c5FfeLdM2vfv4y2a7oh9//LEAKwOQHwhYAApct27dNP/DOeoYWFUJP21RuRNn9eyDj+j9mTOzvcn77bff0r7D69W2n5PCty3XvHnzCqhqoHj48ssvdfRShKxlPXUk/E+dPXtV5y/FKepwhOLcbHr3g5m6evWqo8ssVs5GnlbNWh5ZtvH2dlF5f2dFRkYWUFUA8gsXWwNwiHbt2qldu3YyDEMWiyXHy0WcPa2qdUvpjqE19eevF3X27Nl8rBIofr5duUIugd6K3H9SfvffKc+61SRJsXv/0YUFa3TGdl0bNmxQv379HFtoMeLm5p5taLXZDF27liI3t8zPcgEoGjiDBcChchOuJOmO2/vqwG9Jen7oNkUdt6pXr175VBlQ/BiGoYuXLur65Vi5VA20hytJKt2ollwq+ishIV6XLl1yXJHFUPt23fXLxljZbJnf37Z7V7Riol3Url27AqwMQH7gDBaAIuWee+5RQECA/v77b7Vo0YJBMYBcsFgsCgqsqPOnD0kBnunmGzJkJKYoMDDQAdUVX3fffbdWrPhSy5ZE6K7BQenmX49L0RefnlWd4BA1btzYARUCMBMBC0CR06VLF3Xp0sXRZdyyyMhILV26VCkpKerfv7+qVq3q6JJQAgweOEi735iohCOndP3IaZWqWUmSdO3AcV3/57RqBAQWi+OrMKlfv74eemic5s6brtOnjyj0rkBVqeqh5GSbfvv1shYtOKeL5/316SdhuT6rD6DwsRiMx5qhmJgY+fj4KDo6Wt7e3o4uB0Axc+nSJd1zT6jirx+Vs7OhFFtFffXVt6pUqZKjS0Mxd+3aNY0Yeb/WbQpXsouz3IOryJZiU/zBE/JIkb787Avdcccdji6z2DEMQ4sXL9bcebN04cIJeXgaSko0lJLsrqZN22v8+JcUHBzs6DIBZCI32YCAlQkCFoD8tGzZMk2e/LgWfVVH7u5OGjrsbz300GSNGjXK0aWhBIiJidGsWbP06f8+04ULF2WR1KRRY02eOEmdOnVydHnFWlJSkn755RedPn1aLi4uatGiBcEKKAJykw24RBAAHKBUqVKyyEmnTl2Xp6ezkpIkd3d3R5eFEsLb21svvviixo0bp8jISHl6esrf39/RZZUILi4u6tatm6PLAJCPCFgA4ADdunVT02bd9Nz4cEmGatVqybDYKHClSpVS9erVHV0GABQrBCwAcABXV1d99NE8/frrr0pJSVHr1q1VqlQpR5cFAABuUaF6DlZY2L+j5zz55JNpph84cED9+vWTj4+PvLy81KZNG508eTLTfubOnauOHTvK19dXvr6+6tGjh37//fd8rh4AcsfFxUUdO3ZUly5dCFcAABQThSZgbd++XR9//HG65z8cOXJEHTp0UN26dRUeHq49e/ZowoQJWd6rEB4erqFDh2rDhg3atm2bqlSpottuu01nzpzJ79UAAAAAUIIVilEEY2Nj1bx5c82ePVtTpkxR06ZNNWPGDEn/PlTUxcVF//vf//Lcf0pKinx9fTVr1iyNGDEiR8swiiAAAAAAKXfZoFCcwRozZoz69OmjHj16pJlus9m0atUqBQcHq1evXvL391fr1q21fPnyXPUfFxenpKQklS1b1sSqAQAAACAthwesRYsWaefOnQoLC0s3LyoqSrGxsZo6dap69+6ttWvXKjQ0VAMHDtTGjRtz/Brjx49XUFBQugB3o4SEBMXExKT5AgAAAIDccOgogqdOndLYsWO1du3aDO+pstlskqT+/ftr3LhxkqSmTZtq69atmjNnjjp37pzta0yfPl0LFy5UeHh4lvdthYWFadKkSXlcEwAAAABw8BmsHTt2KCoqSi1atJDVapXVatXGjRs1c+ZMWa1WlStXTlarVfXr10+zXL169bIcRTDVW2+9pTfeeENr165NN3jGzV544QVFR0fbv06dOnVL6wYAAACg5HHoGazu3btr7969aaaNHDlSdevW1fPPPy83Nze1bNlSBw8eTNPm0KFDqlq1apZ9v/nmm5oyZYp+/PFHhYSEZFuLm5ub3Nzccr8SAAAAAPD/OTRgeXl5qWHDhmmmeXp6qly5cvbpzz77rIYMGaJOnTqpa9eu+uGHH7Ry5UqFh4fblxkxYoSCgoLs93FNnz5dEyZM0FdffaVq1aopMjJSklS6dGmVLl26YFYOAAAAQInj8EEushMaGqo5c+Zo+vTpatSokebNm6elS5eqQ4cO9jYnT57U2bNn7T/Pnj1biYmJGjRokAIDA+1fb731liNWAQAAAEAJUSieg1UY8RwsAAAAAFIRfA4WAAAAABQHtxSwzp8/rxdeeEFt27ZVcHCw9u/fL0n66KOPtGvXLlMKBAAAAICiIs8B69ixY2rSpIlmzpwpi8WiI0eOKCEhQZL0559/aubMmaYVCQAAAABFQZ4D1nPPPacyZcro8OHD2rRpk268latDhw7asmWLKQUCAAAAQFGR52Haf/75Z3344YeqWLGiUlJS0swLDAxURETELRcHAAAAAEVJngNWfHy8ypYtm+G8a9euycmJ8TMAAMXD5cuXtXr1ap05c0be3t7q3bu3qlWr5uiyAACFUJ4DVp06dfTTTz+pZ8+e6eZt2rQp3QOEAQAoipYtW6aJYa/rckKcXMuXUdKVWL3z4SyNuPsevfjii/xDEQCQRp4D1sMPP6ynnnpKFStW1LBhwyRJiYmJWrJkiWbPnq1Zs2aZViQAAI6wZcsWPT9xglxCaqtJv65yKe0hW1Kyzm7drblff6kyZcro8ccfd3SZAIBC5JYeNPzII49o3rx5cnJyks1mk5OTkwzD0MMPP6w5c+aYWWeB40HDAICRDz6oTZH/qPG4+2SxWNLMO7pig6x/HNHGdT/L09PTQRUCAApCbrJBns9gSdLHH3+sUaNGadWqVTp37pz8/Px05513ql27drfSLQAADhcXF6dtf/wu/0Gd04UrSQps10R/rd+hXbt2qUOHDg6oEABQGN1SwJKkNm3aqE2bNmbUAgBAoZGcnCxDhpzdXDOc7+zmJkNSUlJSwRYGACjUbjlgAQCKJsMwtGvXLv3www+6evWqqlWrpv79+ysgIMDRpRUKXl5eqlm1uo7uOST/FvXTzT+/56A8XdxUv376eQCAkitXQx85OTnJ2dk5x18AgMIpMTFRzzzztO5/cIhW/zJff575Th9+NlW339lD3333naPLKxQsFotG3DtM8XuP6Nz2fWnmxZ4+p7Ort+iOHj1VoUIFB1UIACiMcnUG65VXXklzHfr8+fMVGxurvn37KiAgQGfPntX3338vT09PjRo1yvRiAQDm+OCDD7Ru0zLd82wdNW4fKIvFovjryfruk/2aMOl5Va9eXY0aNXJ0mQ43ePBg7d+/XwsWLFXkhj/kUS1QCeevKOHwabVu0FgTXnrZ0SUCAAqZXAWsiRMn2r9/++23FRAQoJ9++kmlS5e2T7969ap69OghDw8P04oEAJgnLi5Oi5d+pQ4DAtWkQ0X7dPdSVg16rLGO7t2sr776SmFhYQ6ssnBwcnLS5MmTddttt2nJt0t17ORJ+ZWtojsnjdYdd9whd3d3R5cIAChk8nwP1uzZs/Xmm2+mCVfSv9esP/fcc3rmmWf07LPP3nKBQEGLi4vTpk2blJiYqDZt2sjf39/RJQGmOnz4sK5eu6SmHZqmm+fkZFGjdn7aseXXgi+skLJYLOrYsaM6duzo6FIAAEVAngPWmTNnZLVmvLjValVkZGSeiwIcJSYmRiNHDdfhIzsl2VS2TBV9/NHnCg4OdnRpgGn+vUfWosSElAznJyakyGp1KdiiAAAoJnI1yMWN6tWrp3feeSfd8LSJiYl6++23Vbdu3VsuDihoCxYs0OmIXZo+p44+/rqxSnlHaObMGY4uCzBVnTp1VKF8Jf229mS6eQnxyfrzlwvq0qmHAyoDAKDoy/MZrClTpmjAgAGqUaOGBg4cqICAAEVGRurbb79VZGSkli9fbmKZQMG4cOGCygc6q1KVf+8hDK7voXPHzzm4KsBcLi4uevCBR/TGmxPk5fu3ugyoIQ8vV0Uci9ayjw7ImlJOQ4cOdXSZAAAUSXkOWH369NEPP/ygl156SR988IFsNpssFotatWql+fPnq0cP/vuJoqdFixZasuxTLZh3VGXKumrL+lgNGcSDtFH8DB06VLGxsfr4k1n6ZdlmuZVy1vWrhoICqmn2+2+rcuXKji4RAIAiyWIYhnGrncTFxeny5cvy9fUtNqMHxsTEyMfHR9HR0fL29nZ0OSgghmHo448/1vzPPlRSUpJu69lXr746kZHCUGxdunRJGzZsUExMjKpVq6aOHTtmen8tAAAlVW6ygSkBqzgiYJVsNptNNpuND5oAAADIVTbI86fHyZMnZznfYrFowoQJee0ecCgnJyc5OeV5DBgAAACUUHk+g5Xdh0+LxaKUlIyHAC4KOIMFAAAAQMpdNsjzv+hTL6G68evChQuaN2+eGjZsqOPHj+e1awAAAAAokky9Bqps2bIaNWqU7r33Xj3xxBNmdg0AAAAAhV6+3GTSqlUr/fzzz/nRNQAAAAAUWvkSsPbs2aPSpUvnR9cAAAAAUGjleRTBL774It20hIQE/fnnn/r0009133333VJhAAAAAFDU5DlgPfDAAxlOd3d313333ae33norr10DAAAAQJGU54B17NixdNPc3d1VoUKFWyoIAAAAAIqqPAcsi8WiwMBAubi4pJuXnJysiIgIValS5ZaKAwAAAICiJM+DXFSvXl27du3KcN6ePXtUvXr1PBcFAAAAAEVRngOWYRiZzktJSZHFYslr1wAAAABQJN3SMO0ZhaiEhAStWbNGfn5+t9I1AAAAABQ5uboHa9KkSZo8ebKkf8NVmzZtMm370EMP3VplAAAAAFDE5CpgtWrVSo899pgMw9Ds2bM1aNCgdKMGurm5qVGjRrr33ntNLRQAAAAACrtcBazbb79dt99+uyTp2rVreuWVVxjMAgAAAAD+vzwP0z5//nwz6wAAAACAIi9XAWvTpk1q3ry5SpcurU2bNmXbvlOnTnkuDAAAAACKGouR1XjrN3FyctKvv/6qVq1aycnJKdOh2A3DkMViUUpKimmFFrSYmBj5+PgoOjpa3t7eji4HAAAAgIPkJhvk6gzWhg0bVL9+fUnS+vXredYVAAAAANwgV2ewSpLCcgYrKSlJGzdu1PHjx1W1alV16dJFLi4uDqsHKEni4uK0fft2+fr6qnHjxo4uBwAAOEhuskGeHzTcrVs3/f333xnOO3TokLp165bXrvH/nT9/XkPvuUvjn3tQixa8pheef0j3DBmoqKgoR5cGFHtxcXEadu99Gnnvg7p7wGDNmzfP0SUBAIAiIM8BKzw8XDExMRnOu3r1qjZu3JjnovCvN9+crtjo3friw9r64Ztm+uLD2oq7+qfefHO6o0sDir1t27Zpz/Y9almmk3wTK+iTjz9xdEkAAKAIyHPAysrZs2fl4eGRH12XGImJidoYvkb3Diqv4JqlJUnBNUvr3kHltTF8jRITEx1cIVC8+fr6yupq1amYY4pJuqyy5co5uiQAAFAE5GqQixUrVmjFihX2n1977TWVL18+TZvr168rPDxczZo1M6fCEiwlJUVurs5pprm7Oclms8lmszmoKqBkaNasmcY+84Q+nTtfVfyDFDbtDUeXBAAAioBcBay//vpL33zzjSTJYrFo/fr1cnJKexLMzc1NjRo10nvvvWdelSWQq6urWrfpoq+XrVbXjn4q6+uqS5cT9fWyKLVs1Uvu7u6OLhEo1iwWi8aMGaMxY8Y4uhQAAFCE5HkUwRufiVUcFYZRBI8dO6b/PHK/rsYcV+3qLjp8LFGlvarqo4+/UI0aNRxSE1DS2Gw2bdq0SVu3bpW7u7t69+5tf1wFAAAoGXKTDRimPROFIWBJ0uXLl7Vy5Ur7MO39+vWTr6+vw+oBSpKUlBQ988xTCt+4QpUqG4qLM3T5opvGjXtFw4cPd3R5AACggBCwTFBYAhYAx1m9erVefOlRvfhKgNq0KyubzdD8eSe0aoWTVq78WYGBgY4uEQAAFIACeQ6WJH355ZcKCQmRp6ennJ2d030BQFEWHh6uuvUsatOurCTJycmie++rLFlitHnzZgdXBwAACqM8B6zvvvtOI0eOVLNmzXT9+nWNHDlSQ4cOlaenp2rXrq1XXnnFzDoBoMBZrVYlJ6edlmIzZLMZ/BMJAABkKM8Ba+rUqXrqqac0Z84cSdJjjz2mL7/8UocOHVJKSooqV65sWpEA4Ajdu3fXkX8s+nHNOdlshuLjU/TJRydktZZV586dHV0eAAAohHI1TPuNDh48qEmTJslisUiSkv//v3kDAgL08ssv680339SoUaPMqRJAiXD06FEtWbJE68I3KPbaNZXx9lHvHj101113qVKlSgVeT9euXTWg/0jNnvmFvvz8ghITpKREL73yyusqx4OHAQBABvIcsFJSUuTq6ionJyd5enoqMjLSPq9KlSo6evSoKQUCKP5sNpvefPNNfbLgf4p3dVHpRnXkUrqirsRc1Vuff6oPP/1EYx8drUcffdT+T52C4OTkpFdeeUWhoaHasmWL3N3dddtttykoKKjAagAAAEVLngNW9erVFRERIUlq0qSJFi5cqH79+kmSlixZwuhaAHJs2rRp+nDBF/Lr3UW12obIyfp/v5pS+t6mM+FbNW3WTEnS6NGjC7Q2i8Wixo0bq3HjxgX6ugAAoGjK8z1Y3bt3108//SRJGjt2rL7++mvVqlVL9evX15w5c/Too4+aViSA4mv//v365KsFKt+nuyp2bJMmXEmSs4uLqvTsLO+ubTXjw9k6efKkgyoFAADIXp7PYL3++utKSEiQJN19991ydnbWggULZLFY9Pjjj+vOO+80rUgAxdfixYuVWNpdAW1aZNkuqEs77du6Q99++62efPLJgikOAAAgl/J8BsvNzS3NQ7YGDhyopUuXasmSJapQoYKqV69uSoEAirc1P/+kMs0ayeKU9a8jZxcXlW5UVz/8/zPnAAAAhdEtPWgYAG6FzWbT1atX5erjlaP2rj5euhJ9JX+LAgAAuAWFKmCFhYXJYrGku/znwIED6tevn3x8fOTl5aU2bdpkex/G0qVLVb9+fbm5ual+/fpatmxZPlYOIC+cnJzk4eGppNhrOWqfFHtNpUuXzueqAAAA8q7QBKzt27fr448/TjdS15EjR9ShQwfVrVtX4eHh2rNnjyZMmCB3d/dM+9q2bZuGDBmi4cOHa8+ePRo+fLgGDx6s3377Lb9XA0Aude/USVd27ZdhGFm2syUnK3bv3+rRuUvBFAYAAJAHhSJgxcbGatiwYZo7d658fX3TzHvppZd0xx13aPr06WrWrJlq1KihPn36yN/fP9P+ZsyYoZ49e+qFF15Q3bp19cILL6h79+6aMWNGPq8JgNwaMniwnK9c1YXd+7NsF/nrTrklJGnQoEEFVBkAAEDu5WoUwZ07d+aoXW4fMjxmzBj16dNHPXr00JQpU+zTbTabVq1apeeee069evXSrl27VL16db3wwgsaMGBApv1t27ZN48aNSzOtV69eWQashIQE+6iIkhQTE5OrdQCQN82bN9fdd/bVV0tXSpL8mjZI8zBhw2bTud936cLq9Rp93wjVqlXLUaUCAABkK1cBKyQkJM0Hn8wYhpGjdpK0aNEi7dy5U9u3b083LyoqSrGxsZo6daqmTJmiadOm6YcfftDAgQO1YcMGde7cOcM+IyMjVaFChTTTKlSooMjIyEzrCAsL06RJk3JUMwDzWCwWvTZ5siTpmyUrFfnzZpVp1kAupT2VGB2j6F375HI1To8MHabnnnvOwdUCAABkLVcBa/78+aa++KlTpzR27FitXbs2w3uqbDabJKl///72M1JNmzbV1q1bNWfOnEwDlqR0AS+70PfCCy/oqaeesv8cExOjypUr52p9AOSNq6urpoaFafDdd+vrxYv1U/gGXYuPl6eHp0b06K2777473f2ZAAAAhVGuAtb9999v6ovv2LFDUVFRatHi/x4wmpKSok2bNmnWrFm6du2arFar6tevn2a5evXqafPmzZn2GxAQkO5sVVRUVLqzWjdyc3OTm5tbHtcEwK2yWCxq0aKF/feBzWaTUzbPxgIAAChsHPrppXv37tq7d692795t/woJCdGwYcO0e/duubm5qWXLljp48GCa5Q4dOqSqVatm2m/btm21bt26NNPWrl2rdu3a5ct6ADAf4QoAABRFuTqDZTYvLy81bNgwzTRPT0+VK1fOPv3ZZ5/VkCFD1KlTJ3Xt2lU//PCDVq5cqfDwcPsyI0aMUFBQkMLCwiRJY8eOVadOnTRt2jT1799fK1as0E8//ZTlWS8AAAAAuFWF/l/EoaGhmjNnjqZPn65GjRpp3rx5Wrp0qTp06GBvc/LkSZ09e9b+c7t27bRo0SLNnz9fjRs31meffaavv/5arVu3dsQqAAAAACghLEZ2T/csoWJiYuTj46Po6Gh5e3s7uhyg0IqJidG5c+fk7OyswMBAlSpVytElAQAAmCo32cChlwgCKLp27dqlRYsWau36NUpM/vcZct6ePup/51265557VK1aNccWCAAA4ACcwcoEZ7CAzM2fP1/vzJwmr4oWtehdWUG1y8pmM/TPrnPavfaMnBJK6+1pM7J8lAIAAEBRkZtsQMDKRFELWFevXpWrq6suXryo5cuX69SpU3JxcVHr1q3Vs2dPubq6OrpEFBMrVqzQixOfVZu7K6rrPfXTPV8uOTFFS9/ZrjO7kzV/7v94fpUJbDabTp8+LYvFoqCgIEZYBACggBGwTFBUAta1a9f0zHPPKnxLuK5cuKTS7lLFABfVDXZV7DWb/j6cIl/fKnptyptq27ato8tFEZecnKxet/eQd71rGjSuVebtklI099mNalK5qz6YNbsAKyxeDMPQwoUL9fmXn+rM2VOSpKqVaujBkQ8rNDQ0y4enAwAA83APVgkyY8YM/fzHz3Kv5C6P67G6f7CHHhhWXX5+ZSRJJ0/Haebs4xo37hHNmfM/NW3a1KH1omgLDw9XxPlTuuPFrMO61cVZbfrW0I+zN+jkyZOqUqVKAVVYvLz99tv69MsPVb9rWQ1+uIEMm7R7w0m9PPk5nTt3TqNHj3Z0iQAA4CZcZ1LE/bn/T5WtU06xx8/oqXEVdHsvLxm2RPv8KpU8FDaprmpVu6qZM991YKUoDnbu3CnfIDcFVPPJtm3DDpWVlHJdu3fvzv/CiqGjR4/q86/mqdvI6rprbEvVbhag4BYBGvxMK3W4p5LmzHtfERERji4TAADchIBVxFWvUl1HNx+Rn3eyOrZ1l5FipLvfysXFSfcNqag9u7fqyJEjDqoUxUF8fLxcSznnqK2Lm7OcrBbFx8fnc1XF06pVq+RSOkWt76iZbl77AcEyXBL0ww8/OKAyAACQFQJWEffUU0+ptNxVv7qUdOW6fH3KyMcn/dmFkGZlJMUTsHBLfH19FXM+XrYUW7Zto8/HyUixqEyZMvlfWDF08eJFlQlwl9UlfaB1dbfKu7y7Lly44IDKAABAVghYRZy/v7969eotDw8f1apZW0FBQVIGN74nJtkkWWS1ctsd8q5nz55KjHHS39vPZtv2j7VHVaZ0OXXo0KEAKit+KlasqAun4pQYn5xuXtzVRF2JvP7v8Q4AAAoVAlYx0LJla+3ea1NisnOG4UqS1m+8ICfn0mrUqFEBV4fipG7dumrRpJXCvzqohOtJmba7eDZWO9ec0YC+g+Th4VGAFRYfffv2lVNSKa1f+JduHOzVMAz99OU+uTt56/bbb3dghQAAICMErGJgwIABSjHK6OP5x5XRqPsXLiZoweIode3aR+XLl3dAhShOJrz8qowr3vp8whZFHo9OM88wDP2z+5y+eHmrqvrX06OPPuqgKou+wMBAPT32ee34LkrzX9qs3384qt9WH9G853/RvrVX9MJzE1S2bFlHlwkAAG7Cc7AyUVSeg5Vq6dKleuP18erc3tB9Qyqrfl0vJSSk6Kfw85r/ZaSSjZr67LOFCggIcHSpKAb+/vtvPfn0Ezpx5ogC65RSYC0fGYahY7svKeZsipo3bq13354hPz8/R5da5G3cuFGfff6pduzeLossahXSViMfGKV27do5tK4TJ07oq6++0h+7tsvDw1O333a7QkNDVapUKYfWBQBAfuBBwyYoKgHr3LlziouLU2BgoDZs2KDZH7yriIhDcrEmKyVFMuShtu166sUXX1ZgYKCjy0UxkpycrE2bNunbZUt1+sxJOTs7K7hWXd1992A1a9aMh+CaLCkpSRZL4biPcufOnfrP448o3npNQS0CFB+ToLO7otSifkvN/WiePD09HV0iAACmImCZoLAHrC1btujDOR/oz/07ZBg2eXn6aEC/wXr00Ue1f/9+nTp1SlarVS1btlTlypUdXS6AYsJms+nO/nfqcqlzuvOlnnJx+zfwnT92UStf+UljRz3FA5ABAMUOAcsEhTlg/fDDD3r+pXGqVE/qcEclefm66eCuC9ry/TnVqdZKn8ybz8ACAPLFnj17NOT+wer9akdVrJf2kuMNH2+RcdBVP6z60UHVAQCQP3KTDRx/rQlyJT4+Xm9Mm6z67V01/OkmcnL69zKsWg3LqWmHQM18+nctXrxYDzzwgGMLBVAsRUdHy2akyCcg/R8Xb38vHf890gFVAQBQeDCKYBETHh6ui5cjdMd9te3hKlVQdW816uCjZSu+cVB1AIq72rVry9XZTcf+OJlmumEYOrkzQg3qNnRQZQAAFA4ErCImMjJSpUo7qXxgxjeRV67lrYiI0wVcFYCSIjAwUHf07KPtC/bq0JajsqXYFBd9Xb989puuHIrViPtGOLpEAAAciksEi5iyZcsqPs6mKxfjVaace7r5507Fqlw5nnUFIP+8+sqruv5CnH5+/ydt+uA3GTbJ291HE557RV26dHF0eQAAOBQBq4jp1q2bvKaV00/f/KNBj6a9FOfSuTjt2nhZj4x42EHVASgJPD099f7MWTp06JB2794td3d3de7cWT4+Po4uDShwJ0+e1Lfffqv9+/9USkqygoKqKjQ0lMdVACUYowhmojCPIvjVV18p7M1X1KiTpzr2qSpvXzf9veu81n19Un6edfTlFwtVpkwZR5cJAECxlZSUpDfeeEMrVixQKc/ratLMVVYXiw4fTFRkhFUNG7TW22+/J39/f0eXCsAEDNNugsIcsCRp+fLl+vCj9xUReUKGUuTiXEpdO/XS+PEv8MscAIB8ZLPZNH7881q/YZFG/sdP3Xr6y83t39vaDcPQn7ujNevd0/J0b6DPP18oX19fB1cM4FYRsExQ2AOWJKWkpGj//v26fv26qlevTrACAKAAbNy4UWPH3a9nX/JX2/blMmxzLjJez/z3H/Xv+18999xzBVwhALPlJhswimAR5uzsrMaNG6t169aEKwAACsjixQtVK1iZhitJqhDgrl59vPXdyq8VFxdXgNUBcDQCFgAAQA4lJibq1183qmuP7C/769ajvGJjL2jXrl0FUBmAwoKABQAAkEPx8fEyjBT5lHHJtm2Zsi6SUnTt2rX8LwxAoUHAAgAAyCFPT085W10VFZmQbdtzkQmSxcrIvkAJQ8ACAADIIWdnZ3Xreod+XntF2Y0TtnbNOfmVq6xmzZoVUHUACgMCFgAAQC4MGXKPIk676PsVkZm2OXwwVhvWxmlg6FC5uGR/OSGA4oOABQAAkAvNmzfXiOFjNP+jy/r0o+O6cOH/Lhe8HpeiH1dFauKLx1SvbieNGjXKgZUCcASrowsAbkViYqK+//57/fHHH/L29lb//v1Vr149R5cFACjmnnzySZUpU0affDpLq747rOo1nGW1WnTqZLISEzx1W89hmjDhVbm7uzu6VAAFjAcNZ6IoPGi4pEtMTNSjjz6sXXs2qGYdJ126YFP0JU+9PuVd9e7d29HlAQBKgLi4OK1Zs0Z//fWXUlJSFBQUpDvvvFOBgYGOLg2AiXKTDQhYmSBgFX7Lly/XxMlPaNJbVVWnvrdSUgy9N/WQjhwI0JrV6+Xq6uroEgEAAFAM5CYbcA8Wiqw//vhDNes4q079f3dyZ2eL7hgQqIuXInTs2DEHVwcAAICSiICFIqtMmTK6EJWi5GSbfVrk2XhZZJWPj48DKwMAAEBJRcBCkdWvXz/FRpfWu28c0v4/o7Vh3Tn9b26kOrTvoYCAAEeXBwAAgBKIgIUiKzg4WNOnva9TRyrptfFn9fG7MQppOkBTpoQ5ujQAAACUUAxykQkGuSg6kpOTdfz4cXl7e8vf39/R5QAAAKCYyU024DlYKPKsVqtq1arl6DIAAAAALhEEAAAAALMQsAAAAADAJAQsAAAAADAJAQsAAAAATELAAgAAAACTELAAAAAAwCQELAAAAAAwCQELAAAAAExCwAIAAAAAkxCwAAAAAMAkBCwAAAAAMAkBCwAAAABMQsACAAAAAJMQsAAAAADAJAQsAAAAADAJAQsAAAAATELAAgAAAACTELAAAAAAwCQELAAAAAAwCQELAAAAAExCwAIAAAAAkxSqgBUWFiaLxaInn3zSPu2BBx6QxWJJ89WmTZts+5oxY4bq1KmjUqVKqXLlyho3bpzi4+PzsXoAAMxhGIbCw8P13/+OUb8Bd+ihh0fpu+++U3JysqNLAwBkw+roAlJt375dH3/8sRo3bpxuXu/evTV//nz7z66urln2tWDBAo0fP16ffvqp2rVrp0OHDumBBx6QJL377rum1g0AgJkMw9C0adP05dfz5F9dCqjpqpNn/tYLr67XTz+t1dtvvysXFxdHlwkAyEShCFixsbEaNmyY5s6dqylTpqSb7+bmpoCAgBz3t23bNrVv31733nuvJKlatWoaOnSofv/9d9NqBgAgP2zevFkLvp6nnveXU2DdFFmcJSOllM4ft2nNx6v0zTft7H/fAACFT6G4RHDMmDHq06ePevTokeH88PBw+fv7Kzg4WA8//LCioqKy7K9Dhw7asWOHPVAdPXpUq1evVp8+fUyvHQAAMy1b9q0q1HRWtcYucvV0VvmqPipTsbTKV3NS7RA3LV222NElAgCy4PAzWIsWLdLOnTu1ffv2DOfffvvtuvvuu1W1alUdO3ZMEyZMULdu3bRjxw65ublluMw999yj8+fPq0OHDjIMQ8nJyRo9erTGjx+faR0JCQlKSEiw/xwTE3NrKwYAQB6cjjihysFeMgybnJydJIvk7GyRIalSLW/9tuyEo0sEAGTBoQHr1KlTGjt2rNauXSt3d/cM2wwZMsT+fcOGDRUSEqKqVatq1apVGjhwYIbLhIeH6/XXX9fs2bPVunVr/fPPPxo7dqwCAwM1YcKEDJcJCwvTpEmTbn2lAAC4BRXKV9ThYwfUuUwFRZ2PVEpSipITbXJzcdPFs9cUUCHI0SUCALLg0EsEd+zYoaioKLVo0UJWq1VWq1UbN27UzJkzZbValZKSkm6ZwMBAVa1aVYcPH8603wkTJmj48OF66KGH1KhRI4WGhuqNN95QWFiYbDZbhsu88MILio6Otn+dOnXKtPUEACCn+vcfoNN/JyriULICA4JUyrm0fL3KypLoq/2bYxTa/25HlwgAyIJDz2B1795de/fuTTNt5MiRqlu3rp5//nk5OzunW+bixYs6deqUAgMDM+03Li5OTk5ps6Ozs7MMw5BhGBku4+bmluklhwAAFJRu3bqpb++79dX0xQoO8VTVuj6KPHFJ+7dFq0WjTho6dKijSwQAZMGhAcvLy0sNGzZMM83T01PlypVTw4YNFRsbq4kTJ+quu+5SYGCgjh8/rhdffFF+fn4KDQ21LzNixAgFBQUpLCxMktS3b1+98847atasmf0SwQkTJqhfv34ZhjYAAAoLJycnvf76G2rTpq2+WbJIO1YeV/ny1fTsE3fp7rvvzvSSegBA4eDwQS6y4uzsrL179+qLL77QlStXFBgYqK5du+rrr7+Wl5eXvd3JkyfTnLF6+eWXZbFY9PLLL+vMmTMqX768+vbtq9dff90RqwEAQK44OTmpf//+6t+/v6NLAQDkksXI7Jq5Ei4mJkY+Pj6Kjo6Wt7e3o8sBAAAA4CC5yQaF+gwWAHMlJydr48aNunjxourWravGjRs7uiQAAIBihYAFlBCJiYkaM+ZR/fbHz7I4pchilNITj7+gUaNGObo0AACAYsOhw7QDKDiLFi3Sjj0/a/y0mpq3oqVuv9tTM2dN04kTPLQUAADALAQsoIQ4fvy4gqq6qE7DMnJysqhbnyDZjHgCFgAAgIkIWEAJERQUpLOnknTiyFVJ0rbwc3KyuCkoKMjBlQEAABQf3IMFlBBDhw7VTz//qElP/ioPT4viYq168IGxqlmzpqNLAwAAKDYIWEAJ4eHhofmffqE1a9bo4sWLqlevntq3b+/osgAAAIoVAhZQgri7uys0NNTRZQAAABRb3IMFAAAAACYhYAEAAACASQhYAAAAAGASAhYAAAAAmISABQAAAAAmIWABAAAAgEkIWAAAAABgEgIWAAAAAJiEgAUAAAAAJiFgAQAAAIBJCFgAAAAAYBICFgAAAACYhIAFAAAAACYhYAEAAACASQhYAAAAAGASAhYAAAAAmISABQAAAAAmIWABAAAAgEkIWAAAAABgEgIWAAAAAJiEgAUAAAAAJiFgAQAAAIBJCFgAAAAAYBICFgAAAACYhIAFAAAAACYhYAEAAACASQhYAAAAAGASAhYAAAAAmISABQAAAAAmIWABAAAAgEkIWAAAAABgEgIWAAAAAJiEgAUAAAAAJiFgAQAAAIBJrI4uAABQsly7dk2///67rly5ImdnZ/n7+6tly5ZydnZ2dGkAANwyAhYAoEAcPXpUX3/9tZZ//62ir12SoRTJkJwsVlUOqKa77xqiu+66S2XLlnV0qQAA5JnFMAzD0UUURjExMfLx8VF0dLS8vb0dXQ4AFGkrVqzQK6+9JKfSiWrUs7KadKshbz8PGTZD509Ga8cPh3Xwl3PyK11Rs96brUaNGjm6ZAAA7HKTDQhYmSBgAYA5vvvuO7346nOq072cej3cQs7WjG//jYtJ0JKwLUqIcNfnn/xPderUKeBKAQDIWG6yAYNcAADyTUREhF597WXV6VZOtz8akmm4kiQPbzfd82onWf2v66lnnpTNZivASgEAMAcBCwCQb5YsWaIU1+vq+WAzWSyWbNu7ulvV65GmOnrqsDZv3lwAFQIAYC4CFgAgXyQmJmrJsq9Vr0ugXNxyPqZSxdrlVK5GKX3zzeJ8rA4AgPxBwAIA5Iv9+/fr/OUoNe5SPdfLNuhSWZu2hislJSUfKgMAIP8QsAAA+SImJkaGUlS6bKlcL+tVtpRSjBRdu3YtHyoDACD/ELAAAPnCxcVFkkUpybk/C5WSmCKLLLJaeVwjAKBoIWABAPJFYGCgrBYXnf3ncq6XjThySb4+ZVWqVO7PfgEA4EgELABAvqhevbqaN2mpXT8ezdVySQnJOhAeobsG3J2jkQcBAChMCFgAgHxzz+ChOv3nFZ07nvOzWHvWH5Nx3VV33XVXPlYGAED+IGABAPJNjx491KB2Uy0N+1XRF7IfsOL43nPa+NnfuqvfYFWqVKkAKgQAwFwELABAvnF1ddUH78+Wv1s1/e/5jfpry0mlJNvStYuPTdS25X9r6ZTf1blVT7344osOqBYAgFtnMQzDcHQRhVFMTIx8fHwUHR0tb29vR5cDAEXahQsX9PKEl7T5141y8bGpbqeK8vHzkM1mKOrEFR3eEiUXw0ODQ4fq2Wef/f8jEAIAUDjkJhsQsDJBwAIA8/3zzz9asmSJ1q3/QZevXJazs7MC/APVv2+oBgwYID8/P0eXCABAOgQsExCwAAAAAEi5ywbcgwUAAAAAJilUASssLEwWi0VPPvmkfdoDDzwgi8WS5qtNmzbZ9nXlyhWNGTNGgYGBcnd3V7169bR69ep8rB4AAABASWd1dAGptm/fro8//liNGzdON693796aP3++/WdXV9cs+0pMTFTPnj3l7++vJUuWqFKlSjp16pS8vLxMrxsAAAAAUhWKgBUbG6thw4Zp7ty5mjJlSrr5bm5uCggIyHF/n376qS5duqStW7faR6KqWrWqafUCAAAAQEYKxSWCY8aMUZ8+fdSjR48M54eHh8vf31/BwcF6+OGHFRUVlWV/3333ndq2basxY8aoQoUKatiwod544w2lpKRkukxCQoJiYmLSfAEAAABAbjj8DNaiRYu0c+dObd++PcP5t99+u+6++25VrVpVx44d04QJE9StWzft2LFDbm5uGS5z9OhRrV+/XsOGDdPq1at1+PBhjRkzRsnJyXrllVcyXCYsLEyTJk0ybb0AAAAAlDwOHab91KlTCgkJ0dq1a9WkSRNJUpcuXdS0aVPNmDEjw2XOnj2rqlWratGiRRo4cGCGbYKDgxUfH69jx47J2dlZkvTOO+/ozTff1NmzZzNcJiEhQQkJCfafY2JiVLlyZYZpBwAAAEq43AzT7tAzWDt27FBUVJRatGhhn5aSkqJNmzZp1qxZSkhIsAekVIGBgapataoOHz6cab+BgYFycXFJs2y9evUUGRmpxMTEDAfJcHNzy/SMWElz8OBBHTlyRL6+vmrVqlW6bQAAAAAgYw4NWN27d9fevXvTTBs5cqTq1q2r559/PsMP9hcvXtSpU6cUGBiYab/t27fXV199JZvNJienf28zO3TokAIDA7MdgbAkO3/+vMaPf1Z7dm2WFC/JRf4VamjS5Klq2bKlo8sDAAAACj2HDnLh5eWlhg0bpvny9PRUuXLl1LBhQ8XGxuqZZ57Rtm3bdPz4cYWHh6tv377y8/NTaGiovZ8RI0bohRdesP88evRoXbx4UWPHjtWhQ4e0atUqvfHGGxozZowjVrNIsNlsGvvEaEWc3KC3JlbULytb6H+za6lq4Ak9OfYRnTx50tElAgAAAIVeoRhFMDPOzs7au3ev+vfvr+DgYN1///0KDg7Wtm3b0jzT6uTJk2nurapcubLWrl2r7du3q3HjxnriiSc0duxYjR8/3hGrUSRs27ZNhw7u0Bsv1VKndn5yc3NWnVpeentyA3m4X9LXX3/t6BIBAACAQs/howjeLDw83P59qVKl9OOPP+ZqmVRt27bVr7/+amJlxdvevXtV1tdQ4wZpb9pzd3dWh1ae2vvnDgdVBgAAABQdhfoMFgqOh4eH4uJsio+3pZt34VKS3Et5OqAqAAAAoGghYEGS1KNHDyUkeerLJWnvtfpzf7R+25Go3r37OKgyAAAAoOgodJcIwjEqVqyoBx8aq7kfT9ef+/eqVXMfnTgVpx/WX1Ojpl115513OrpEAAAAoNBz6IOGC7PcPEysuDAMQ+vWrdPCr77QkSN/q2xZP/Xtd7eGDRsmd3d3R5cHAAAAOERusgEBKxMlMWABAAAASC832YB7sAAAAADAJAQsAAAAADAJAQsAAAAATELAAgAAAACTELAAAAAAwCQELAAAAAAwCQELAAAAAExCwEI6NptNc+bMUf9BoXrk0Ud0+PBhR5cEAAAAFAkELKTzv//9T29+OENnysYr/MgOPTT6EcXFxTm6LAAAAKDQI2Ahnd+3b1epuv5q/lAvNR7VQ2eizurYsWOOLgsAAAAo9AhYSKdypUqKO3ZBp7Yd0NF1u1TK1V0VKlRwdFkAAABAoWd1dAEofEaPHq2//j6g3+dvlodbKYVNnCI/Pz9HlwUAAAAUegQspOPj46PP53+my5cvy9PTU25ubo4uCQAAACgSCFjIkMViUdmyZR1dBgAAAFCkcA8WAAAAAJiEgAUAAAAAJiFgAQAAAIBJCFgAAAAAYBICFgAAAACYhIAFAAAAACYhYAEAAACASQhYAAAAAGASAhYAAAAAmISABQAAAAAmIWABAAAAgEkIWAAAAABgEgIWAAAAAJiEgAUAAAAAJrE6uoDCyjAMSVJMTIyDKwEAAADgSKmZIDUjZIWAlYmrV69KkipXruzgSgAAAAAUBlevXpWPj0+WbSxGTmJYCWSz2RQRESEvLy9ZLJY082JiYlS5cmWdOnVK3t7eDqoQBYXtXbKwvUsWtnfJwvYuWdjeJUt+b2/DMHT16lVVrFhRTk5Z32XFGaxMODk5qVKlSlm28fb25oAtQdjeJQvbu2Rhe5csbO+She1dsuTn9s7uzFUqBrkAAAAAAJMQsAAAAADAJASsPHBzc9Orr74qNzc3R5eCAsD2LlnY3iUL27tkYXuXLGzvkqUwbW8GuQAAAAAAk3AGCwAAAABMQsACAAAAAJMQsAAAAADAJAQsAAAAADAJAQsAAAAATFLiA9amTZvUt29fVaxYURaLRcuXL08zf+LEiapbt648PT3l6+urHj166Lfffsuyz88++0wWiyXdV3x8fD6uCXIiu+19o//85z+yWCyaMWNGtv0uXbpU9evXl5ubm+rXr69ly5aZVzTyLD+2N8d34ZXd9n7ggQfSbbc2bdpk2y/Hd+GUH9ub47vwysnv8wMHDqhfv37y8fGRl5eX2rRpo5MnT2bZL8d34ZQf27sgj+8SH7CuXbumJk2aaNasWRnODw4O1qxZs7R3715t3rxZ1apV02233abz589n2a+3t7fOnj2b5svd3T0/VgG5kN32TrV8+XL99ttvqlixYrZ9btu2TUOGDNHw4cO1Z88eDR8+XIMHD842iCP/5cf2lji+C6ucbO/evXun2W6rV6/Osk+O78IrP7a3xPFdWGW3vY8cOaIOHTqobt26Cg8P1549ezRhwoQstx3Hd+GVH9tbKsDj24CdJGPZsmVZtomOjjYkGT/99FOmbebPn2/4+PiYWxxMl9n2Pn36tBEUFGTs27fPqFq1qvHuu+9m2c/gwYON3r17p5nWq1cv45577jGxWtwqs7Y3x3fRkNH2vv/++43+/fvnqh+O76LBrO3N8V00ZLS9hwwZYtx333256ofju2gwa3sX5PFd4s9g5UZiYqI+/vhj+fj4qEmTJlm2jY2NVdWqVVWpUiXdeeed2rVrVwFViVths9k0fPhwPfvss2rQoEGOltm2bZtuu+22NNN69eqlrVu35keJMFFetrfE8V2UhYeHy9/fX8HBwXr44YcVFRWVZXuO76Itt9tb4vguimw2m1atWqXg4GD16tVL/v7+at26dZaXhUsc30VVXre3VHDHNwErB77//nuVLl1a7u7uevfdd7Vu3Tr5+fll2r5u3br67LPP9N1332nhwoVyd3dX+/btdfjw4QKsGnkxbdo0Wa1WPfHEEzleJjIyUhUqVEgzrUKFCoqMjDS7PJgsL9ub47vouv3227VgwQKtX79eb7/9trZv365u3bopISEh02U4vouuvGxvju+iKSoqSrGxsZo6dap69+6ttWvXKjQ0VAMHDtTGjRszXY7ju2jK6/YuyOPbanqPxVDXrl21e/duXbhwQXPnzrVfn+vv759h+zZt2qS5kbZ9+/Zq3ry53n//fc2cObOgykYu7dixQ++995527twpi8WSq2Vvbm8YRq77QMHK6/bm+C66hgwZYv++YcOGCgkJUdWqVbVq1SoNHDgw0+U4voumvGxvju+iyWazSZL69++vcePGSZKaNm2qrVu3as6cOercuXOmy3J8Fz153d4FeXxzBisHPD09VatWLbVp00affPKJrFarPvnkkxwv7+TkpJYtW/IfsELul19+UVRUlKpUqSKr1Sqr1aoTJ07o6aefVrVq1TJdLiAgIN1/u6KiotL9VwyFS1639804vouuwMBAVa1aNcttx/FdfORke9+M47to8PPzk9VqVf369dNMr1evXpajynF8F0153d43y8/jm4CVB4ZhZHmJQUbtd+/ercDAwHysCrdq+PDh+vPPP7V79277V8WKFfXss8/qxx9/zHS5tm3bat26dWmmrV27Vu3atcvvknEL8rq9b8bxXXRdvHhRp06dynLbcXwXHznZ3jfj+C4aXF1d1bJlSx08eDDN9EOHDqlq1aqZLsfxXTTldXvfLD+P7xJ/iWBsbKz++ecf+8/Hjh3T7t27VbZsWZUrV06vv/66+vXrp8DAQF28eFGzZ8/W6dOndffdd9uXGTFihIKCghQWFiZJmjRpktq0aaPatWsrJiZGM2fO1O7du/XBBx8U+Pohray2d5UqVVSuXLk07V1cXBQQEKA6derYp928vceOHatOnTpp2rRp6t+/v1asWKGffvpJmzdvLpiVQqbyY3tzfBdeWW3vsmXLauLEibrrrrsUGBio48eP68UXX5Sfn59CQ0Pty3B8Fx35sb05vguv7H6fP/vssxoyZIg6deqkrl276ocfftDKlSsVHh5uX4bju+jIj+1doMd3gYxVWIht2LDBkJTu6/777zeuX79uhIaGGhUrVjRcXV2NwMBAo1+/fsbvv/+epo/OnTsb999/v/3nJ5980qhSpYrh6upqlC9f3rjtttuMrVu3FvCaISNZbe+MZDRs983b2zAM45tvvjHq1KljuLi4GHXr1jWWLl2aPyuAXMmP7c3xXXhltb3j4uKM2267zShfvrzh4uJiVKlSxbj//vuNkydPpumD47voyI/tzfFdeOXk9/knn3xi1KpVy3B3dzeaNGliLF++PE0fHN9FR35s74I8vi2GYRjmxzYAAAAAKHm4BwsAAAAATELAAgAAAACTELAAAAAAwCQELAAAAAAwCQELAAAAAExCwAIAAAAAkxCwAAAAAMAkBCwAgMN99tlnslgs+uOPPxxdSjrh4eGyWCwKDw+3T1u9erUmTpyYL683ceJEWSyWfOkbAJD/CFgAAGShefPm2rZtm5o3b26ftnr1ak2aNMmBVQEACiurowsAAKAw8/b2Vps2bRxdBgCgiOAMFgCgSNi8ebO6d+8uLy8veXh4qF27dlq1alWaNqmXGm7YsEGjR4+Wn5+fypUrp4EDByoiIiJN24SEBD399NMKCAiQh4eHOnXqpB07dqhatWp64IEH7O1uvkTwgQce0AcffCBJslgs9q/jx4/r+PHjslgs+uyzz9LVb7FY0l1WuGrVKjVt2lRubm6qXr263nrrrQzX3TAMzZ49W02bNlWpUqXk6+urQYMG6ejRo7l7EwEA+Y6ABQAo9DZu3Khu3bopOjpan3zyiRYuXCgvLy/17dtXX3/9dbr2Dz30kFxcXPTVV19p+vTpCg8P13333ZemzciRIzVjxgyNHDlSK1as0F133aXQ0FBduXIly1omTJigQYMGSZK2bdtm/woMDMzVOv3888/q37+/vLy8tGjRIr355ptavHix5s+fn67tf/7zHz355JPq0aOHli9frtmzZ2v//v1q166dzp07l6vXBQDkLy4RBAAUeuPHj5evr6/Cw8NVunRpSdKdd96ppk2b6plnntHgwYPTDAzRu3dvzZw50/7zpUuX9NxzzykyMlIBAQH666+/tHDhQj3//PMKCwuTJPXs2VMVKlTQ0KFDs6ylZs2aqlChgiTd0qWDL730kipUqKB169bJ3d1dktSrVy9Vq1YtTbtff/1Vc+fO1dtvv62nnnrKPr1jx44KDg7WO++8o2nTpuW5DgCAuTiDBQAo1K5du6bffvtNgwYNsocrSXJ2dtbw4cN1+vRpHTx4MM0y/fr1S/Nz48aNJUknTpyQ9O8ZMUkaPHhwmnaDBg2S1Zr//3u8du2atm/froEDB9rDlST7Wbkbff/997JYLLrvvvuUnJxs/woICFCTJk3SjG4IAHA8zmABAAq1y5cvyzCMDC/Bq1ixoiTp4sWLaaaXK1cuzc9ubm6SpOvXr6dpn3omKpXVak23bH64fPmybDabAgIC0s27edq5c+dkGEa6WlPVqFEjX2oEAOQNAQsAUKj5+vrKyclJZ8+eTTcvdeAKPz+/XPWZGqLOnTunoKAg+/Tk5OR0YS03Us9GJSQkpJl+c5++vr6yWCyKjIxM18fN0/z8/GSxWPTLL7/Yg+KNMpoGAHAcLhEEABRqnp6eat26tb799lv7GShJstls+vLLL1WpUiUFBwfnqs9OnTpJUroBMpYsWaLk5ORsl7/5jFiqChUqyN3dXX/++Wea6StWrEjzs6enp1q1aqVvv/1W8fHx9ulXr17VypUr07S98847ZRiGzpw5o5CQkHRfjRo1yrZeAEDB4QwWAKDQWL9+vY4fP55uelhYmHr27KmuXbvqmWeekaurq2bPnq19+/Zp4cKFaQa4yIkGDRpo6NChevvtt+Xs7Kxu3bpp//79evvtt+Xj4yMnp6z//5gaaqZNm6bbb79dzs7Oaty4sVxdXXXffffp008/Vc2aNdWkSRP9/vvv+uqrr9L18dprr6l3797q2bOnnn76aaWkpGjatGny9PTUpUuX7O3at2+vRx55RCNHjtQff/yhTp06ydPTU2fPntXmzZvVqFEjjR49OlfrDwDIPwQsAECh8fzzz2c4/dixY1q/fr1effVVPfDAA7LZbGrSpIm+++473XnnnXl6rfnz5yswMFCffPKJ3n33XTVt2lSLFy9W7969VaZMmSyXvffee7VlyxbNnj1bkydPlmEYOnbsmKpVq6a3335bkjR9+nTFxsaqW7du+v7779ONDtizZ08tX75cL7/8soYMGaKAgAA99thjun79uiZNmpSm7UcffaQ2bdroo48+0uzZs2Wz2VSxYkW1b99erVq1ytP6AwDyh8UwDMPRRQAAUBhs3bpV7du314IFC3Tvvfc6uhwAQBFEwAIAlEjr1q3Ttm3b1KJFC5UqVUp79uzR1KlT5ePjoz///DPN8OkAAOQUlwgCAEokb29vrV27VjNmzNDVq1fl5+en22+/XWFhYYQrAECecQYLAAAAAEzCMO0AAAAAYBICFgAAAACYhIAFAAAAACYhYAEAAACASQhYAAAAAGASAhYAAAAAmISABQAAAAAmIWABAAAAgEkIWAAAAABgEgIWAAAAAJiEgAUAAAAAJiFgAQAAAIBJCFgAAAAAYBICFgAAAACYhIAFAAAAACYhYAEAAACASQhYAAAAAGASAhYAAAAAmISABQAAAAAmIWABAAAAgEkIWAAAAABgEgIWAAAAAJiEgAUAAAAAJiFgAQAAAIBJCFgAAAAAYBICFgAAAACYhIAFAAAAACYhYAEAAACASQhYAAAAAGASAhYAAAAAmISABQAAAAAmIWABAAAAgEkIWAAAAABgEgIWAAAAAJiEgAUAAAAAJiFgAQAAAIBJCFgAAAAAYBICFgAAAACYhIAFAAAAACYhYAEAAACASQhYAAAAAGASAhYAAAAAmISABQAAAAAmIWABAAAAgEkIWAAAAABgEgIWAAAAAJiEgAUAAAAAJiFgAQAAAIBJCFgAAAAAYBICFgAAAACYhIAFAAAAACYhYAEAAACASQhYAAAAAGASAhYAAAAAmISABQAAAAAmIWABAAAAgEkIWAAAAABgEgIWAAAAAJiEgAUAAAAAJiFgAQAAAIBJCFgAAAAAYBICFgAAAACYhIAFAAAAACYhYAEAAACASQhYAAAAAGASAhYAAAAAmISABQAAAAAmIWABAAAAgEkIWAAAAABgEqujCyjM4uPjlZiY6OgyAAAAADiYq6ur3N3ds21HwMpEfHy8qlctrcioFEeXAgAAAMDBAgICdOzYsWxDFgErE4mJiYqMStGJHdXk7eUkmwz7vBTDZv/+xuk22W5oY2TSJuPv0y9zw/Q0r60M29hk+b/vb2iTcsP0lDRtMp6ecsNVo0ZmbYz/a2PLpP/M2thu6D9tmxunZ1yP7Yb2aaZLN3yfWf8Z15a2/4zrTPu6/9cm/bxMpt/wvZHZe5TpOmf23mW8LY1Mar1xn7Bl+r5Ysm1jZLIP2TJZF1um7bP//sajI9fL5qCGdO0yWd7IrCZl8nppls34tXLWPpNlM6k/7Yzsa8isjTJrn5PXVWb1ZF9bZm0saX9NZtou89fLQb83v0ZqmzSvm/1rZdpnpm0y7jMn/eSof5nU5pbWMZ9eI5d93ii3r3sr62xJcwDmQw03uuG1Mu3Hlsn0tL9Y0nSbs5py8Nppf6ln3ObGF85Rnxl/KMrsfc+8zxum2zKenvl2zUmbTPrMtOYcfJ+TZaWb3utMf9ln0t6WgzY5Wc9M+smkTyOz181Bn0Zmr5VJzUYm65vR9GQlaXPkaiUmJhKwbpW3l5O8vZxvCk//Nz9tYNINbYwM26Qo4+npl7lheg4CVmYfpm8lYGXaJrNglKMQZn7AShMMTApYmb9uVgEr48CRkzrMCliZ15N9eMo0YOUoMJkfsG4pnJkYsDILQJZMApYllwErt+3T/v3K+AO6kYM+cx2wcjC92AWsXL5WfgesXPevHLQpwLBh6mvkss98CTc5WjazD9/5UVsOAklhD1i57vP/tXfvwVHV5x/HPwtJNoGE5ZKGsISrAYEkBASKXNpIsY3DTXGqQjFCbTvSFgIWI05pC+PUEqZYoE7V2gtOBYXpEChYCyQUMQxBOiQpgYKiDaBcDLUhgAok7PP7w/H8siGbC5wkkL5fM5nZ/Z7nfM93P2x28+QsJw1pOBoyZ7XxG2mwGtsweRpZ39h9P9/YuHmDXuxDNCuhmrOQczagYao2Z1CDFQhx3BBPZmvQk7xaI9WY8VCvr7XgIhcAAAAA4BIaLAAAAABwCQ0WAAAAALiEBgsAAAAAXEKDBQAAAAAuocECAAAAAJfQYAEAAACAS2iwAAAAAMAlNFgAAAAA4BIaLAAAAABwCQ0WAAAAALiEBgsAAAAAXEKDBQAAAAAuocECAAAAAJfQYAEAAACAS2iwAAAAAMAlNFgAAAAA4BIaLAAAAABwCQ0WAAAAALiEBgsAAAAAXEKDBQAAAAAuocECAAAAAJfQYAEAAACAS2iwAAAAAMAlNFgAAAAA4BIaLAAAAABwCQ0WAAAAALiEBgsAAAAAXEKDBQAAAAAuocECAAAAAJfQYAEAAACAS2iwAAAAAMAlNFgAAAAA4BIaLAAAAABwCQ0WAAAAALgkrKUXcLM7fyEgSQrInLGrFnBuVx8PKFCtxkLU1H772n2qjQcdW7XWBOT5/9vVaq5WG78aVFP7+NVqc1qomqA1WIia6o/FU3t9iIyuBh23Wo1Z7ePV1hyo9juDq9am2rin1vGr1eqrZxIIGq9e//81124LMV7ttoVYR/C/R/1rqp5R9RoLsdbqz4lAyFw89dZYiOdQIMRjCYSsr/929e+ORu/bgDVcUxdifwu1JoU4XtC+tR+rYfUh9g2x/uAN9a8hVI1C1TfkuAq1nvrXFqrGE/wyGbIu9PEaMG/NY3xRE3Tc+o8Vcs6QNbXP2ZB5GjS/XKq5ocfYRMdo5JzVNfa4N/KYPUHfgE2whuqqHSvkPIEQ48EvLEHTNmxNDTh28It67TXVD9ygOWv/oShU7qHnrDYeqH089L9rQ2pCzBlyzQ243ZB9pRpZh3yxD1EfaEBNQx5niHlCzGmhjmv1r61h+1avr33O2sarVKmGosEKISIiQvHx8eo17FhLLwUAAABAC4uPj1dERES9dR6zmq0uvnDp0iVduXKlpZfR6pw/f149evTQBx98oA4dOrT0cv4nkHnLIPfmR+Ytg9ybH5m3DHJvfjdT5hEREYqMjKy3jjNYdYiMjGxQiLg+HTp0aPFvlP81ZN4yyL35kXnLIPfmR+Ytg9yb362UORe5AAAAAACX0GABAAAAgEtosNDsvF6vFi9eLK/X29JL+Z9B5i2D3JsfmbcMcm9+ZN4yyL353YqZc5ELAAAAAHAJZ7AAAAAAwCU0WAAAAADgEhosAAAAAHAJDRYAAAAAuIQGC/VaunSpRowYoZiYGMXFxem+++7TO++8E1RjZlqyZIn8fr+ioqJ011136dChQ0E1ly9f1ty5cxUbG6v27dtrypQp+vDDD4NqysvLlZGRIZ/PJ5/Pp4yMDJ07dy6o5sSJE5o8ebLat2+v2NhYZWZm6sqVK03y2G8WS5culcfj0fz5850xMm8aJ0+e1MMPP6wuXbqoXbt2GjJkiPbv3+9sJ3d3VVVV6Sc/+Yn69OmjqKgo9e3bV08//bQCgYBTQ+Y37q233tLkyZPl9/vl8Xi0adOmoO03W8YlJSVKS0tTVFSUunfvrqefflq32jW56sq8srJSCxcuVEpKitq3by+/369HHnlEp06dCpqDzBunvud5dY899pg8Ho9WrlwZNE7mjdeQ3A8fPqwpU6bI5/MpJiZGd955p06cOOFsb3W5G1CP9PR0W716tR08eNCKi4tt4sSJ1rNnT7t48aJTk52dbTExMbZhwwYrKSmxhx56yLp162bnz593ambPnm3du3e33NxcKywstHHjxllqaqpVVVU5Nffcc48lJyfbnj17bM+ePZacnGyTJk1ytldVVVlycrKNGzfOCgsLLTc31/x+v82ZM6d5wmgB+/bts969e9vgwYNt3rx5zjiZu++///2v9erVy2bNmmVvv/22lZaWWl5enr333ntODbm76+c//7l16dLFXn/9dSstLbU///nPFh0dbStXrnRqyPzGvfHGG7Zo0SLbsGGDSbKNGzcGbb+ZMq6oqLCuXbvatGnTrKSkxDZs2GAxMTG2fPnypguoCdSV+blz5+zuu++29evX25EjR6ygoMBGjhxpw4YNC5qDzBunvuf5FzZu3Gipqanm9/ttxYoVQdvIvPHqy/29996zzp07W1ZWlhUWFtr7779vr7/+un300UdOTWvLnQYLjVZWVmaSbNeuXWZmFggELD4+3rKzs52aS5cumc/nsxdffNHMPn8zCQ8Pt3Xr1jk1J0+etDZt2tjWrVvNzOxf//qXSbK9e/c6NQUFBSbJjhw5YmaffxO3adPGTp486dS89tpr5vV6raKioukedAu5cOGC9evXz3Jzcy0tLc1psMi8aSxcuNDGjh0bcju5u2/ixIn26KOPBo3df//99vDDD5sZmTeFmj8A3WwZP//88+bz+ezSpUtOzdKlS83v91sgEHAxieZT1w/7X9i3b59JsuPHj5sZmd+oUJl/+OGH1r17dzt48KD16tUrqMEi8xtXW+4PPfSQ85pem9aYOx8RRKNVVFRIkjp37ixJKi0t1ZkzZ/SNb3zDqfF6vUpLS9OePXskSfv371dlZWVQjd/vV3JyslNTUFAgn8+nkSNHOjV33nmnfD5fUE1ycrL8fr9Tk56ersuXLwd9jKu1+OEPf6iJEyfq7rvvDhon86axefNmDR8+XA888IDi4uI0dOhQ/e53v3O2k7v7xo4dqx07dujdd9+VJP3zn//U7t27NWHCBElk3hxutowLCgqUlpYW9EdF09PTderUKR07dsz9AG4SFRUV8ng86tixoyQybwqBQEAZGRnKyspSUlLSNdvJ3H2BQEB//etf1b9/f6WnpysuLk4jR44M+hhha8ydBguNYmb60Y9+pLFjxyo5OVmSdObMGUlS165dg2q7du3qbDtz5owiIiLUqVOnOmvi4uKuOWZcXFxQTc3jdOrUSREREU5Na7Fu3ToVFhZq6dKl12wj86bx73//Wy+88IL69eunbdu2afbs2crMzNSf/vQnSeTeFBYuXKjp06drwIABCg8P19ChQzV//nxNnz5dEpk3h5st49pqvrjfWv8dLl26pKeeekrf+ta31KFDB0lk3hSWLVumsLAwZWZm1rqdzN1XVlamixcvKjs7W/fcc4+2b9+uqVOn6v7779euXbsktc7cw1ybCf8T5syZowMHDmj37t3XbPN4PEH3zeyasZpq1tRWfz01t7oPPvhA8+bN0/bt2xUZGRmyjszdFQgENHz4cP3iF7+QJA0dOlSHDh3SCy+8oEceecSpI3f3rF+/XmvWrNGrr76qpKQkFRcXa/78+fL7/Zo5c6ZTR+ZN72bKuLa1hNr3VldZWalp06YpEAjo+eefr7eezK/P/v37tWrVKhUWFjb6MZH59fvigkX33nuvHn/8cUnSkCFDtGfPHr344otKS0sLue+tnDtnsNBgc+fO1ebNm7Vz504lJCQ44/Hx8ZKu7fzLysqc3wrEx8frypUrKi8vr7Pmo48+uua4Z8+eDaqpeZzy8nJVVlZe8xuJW9n+/ftVVlamYcOGKSwsTGFhYdq1a5d+/etfKywsLORvW8j8xnTr1k2DBg0KGhs4cKBzpSOe6+7LysrSU089pWnTpiklJUUZGRl6/PHHnTO3ZN70braMa6spKyuTdO1ZtltdZWWlHnzwQZWWlio3N9c5eyWRudvy8/NVVlamnj17Ou+rx48f14IFC9S7d29JZN4UYmNjFRYWVu97a2vLnQYL9TIzzZkzRzk5Ofr73/+uPn36BG3v06eP4uPjlZub64xduXJFu3bt0ujRoyVJw4YNU3h4eFDN6dOndfDgQadm1KhRqqio0L59+5yat99+WxUVFUE1Bw8e1OnTp52a7du3y+v1atiwYe4/+BYyfvx4lZSUqLi42PkaPny4ZsyYoeLiYvXt25fMm8CYMWOu+RME7777rnr16iWJ53pT+PTTT9WmTfBbUdu2bZ3fepJ507vZMh41apTeeuutoEsrb9++XX6/3/lBuDX4ork6evSo8vLy1KVLl6DtZO6ujIwMHThwIOh91e/3KysrS9u2bZNE5k0hIiJCI0aMqPO9tVXm7trlMtBqff/73zefz2dvvvmmnT592vn69NNPnZrs7Gzz+XyWk5NjJSUlNn369Fov8ZuQkGB5eXlWWFhoX/va12q9BOfgwYOtoKDACgoKLCUlpdZLcI4fP94KCwstLy/PEhISWsVllOtT/SqCZmTeFPbt22dhYWH2zDPP2NGjR23t2rXWrl07W7NmjVND7u6aOXOmde/e3blMe05OjsXGxtqTTz7p1JD5jbtw4YIVFRVZUVGRSbJf/epXVlRU5Fyx7mbK+Ny5c9a1a1ebPn26lZSUWE5OjnXo0OGWu3x1XZlXVlbalClTLCEhwYqLi4PeWy9fvuzMQeaNU9/zvKaaVxE0I/PrUV/uOTk5Fh4ebi+99JIdPXrUnnvuOWvbtq3l5+c7c7S23GmwUC9JtX6tXr3aqQkEArZ48WKLj483r9drX/3qV62kpCRons8++8zmzJljnTt3tqioKJs0aZKdOHEiqObjjz+2GTNmWExMjMXExNiMGTOsvLw8qOb48eM2ceJEi4qKss6dO9ucOXOCLrfZWtVssMi8aWzZssWSk5PN6/XagAED7KWXXgraTu7uOn/+vM2bN8969uxpkZGR1rdvX1u0aFHQD5lkfuN27txZ6+v4zJkzzezmy/jAgQP2la98xbxer8XHx9uSJUtuuUtX15V5aWlpyPfWnTt3OnOQeePU9zyvqbYGi8wbryG5/+EPf7DExESLjIy01NRU27RpU9AcrS13j9kt+CejAQAAAOAmxP/BAgAAAACX0GABAAAAgEtosAAAAADAJTRYAAAAAOASGiwAAAAAcAkNFgAAAAC4hAYLAAAAAFxCgwUAuKX17t1bK1eubOll1GvJkiUaMmTITTMPAKBp0GABAFzh8Xjq/Jo1a1a9+2/atMn1dS1ZssRZQ9u2bdWjRw9997vf1dmzZ10/lttqy+SJJ57Qjh07WmZBAIB6hbX0AgAArcPp06ed2+vXr9fPfvYzvfPOO85YVFRUSyxLkpSUlKS8vDxdvXpVRUVF+s53vqOTJ0/qb3/7W4ut6XpFR0crOjq6pZcBAAiBM1gAAFfEx8c7Xz6fTx6PJ2js1Vdf1W233aaIiAjdfvvteuWVV5x9e/fuLUmaOnWqPB6Pc//999/Xvffeq65duyo6OlojRoxQXl5eo9cWFham+Ph4de/eXZMmTVJmZqa2b9+uzz77TIFAQE8//bQSEhLk9Xo1ZMgQbd261dn32LFj8ng8WrdunUaPHq3IyEglJSXpzTffdGpefvlldezYMeiYmzZtksfjCbmmf/zjH/r617+u2NhY+Xw+paWlqbCwsN5Man5EsKHrz8nJ0bhx49SuXTulpqaqoKCg0TkCAOpHgwUAaHIbN27UvHnztGDBAh08eFCPPfaYvv3tb2vnzp2SPm82JGn16tU6ffq0c//ixYuaMGGC8vLyVFRUpPT0dE2ePFknTpy4ofVERUUpEAioqqpKq1at0rPPPqvly5frwIEDSk9P15QpU3T06NGgfbKysrRgwQIVFRVp9OjRmjJlij7++OPrXsOFCxc0c+ZM5efna+/everXr58mTJigCxcuSAqdSU0NXf+iRYv0xBNPqLi4WP3799f06dNVVVV13esHAIRgAAC4bPXq1ebz+Zz7o0ePtu9973tBNQ888IBNmDDBuS/JNm7cWO/cgwYNsueee86536tXL1uxYkXI+sWLF1tqaqpz//Dhw5aYmGhf/vKXzczM7/fbM888E7TPiBEj7Ac/+IGZmZWWlpoky87OdrZXVlZaQkKCLVu2rNbHa2a2ceNGq/42W3MdNVVVVVlMTIxt2bLFGastk5rzNHT9v//9753thw4dMkl2+PDhkOsBAFwfzmABAJrc4cOHNWbMmKCxMWPG6PDhw3Xu98knn+jJJ5/UoEGD1LFjR0VHR+vIkSONPoNVUlKi6OhoRUVFadCgQerRo4fWrl2r8+fP69SpUw1a26hRo5zbYWFhGj58eL3rr0tZWZlmz56t/v37y+fzyefz6eLFi416bI1Z/+DBg53b3bp1c9YAAHAXF7kAADSLmv8fyczq/D9K0ucfy9u2bZuWL1+uxMRERUVF6Zvf/KauXLnSqGPffvvt2rx5s9q2bSu/3y+v1yvp8wbletdWfb82bdrIzIK2VVZW1rnvrFmzdPbsWa1cuVK9evWS1+vVqFGjGv3Yqq/jC7WtPzw8/Jr6QCDQ6GMBAOrGGSwAQJMbOHCgdu/eHTS2Z88eDRw40LkfHh6uq1evBtXk5+dr1qxZmjp1qlJSUhQfH69jx441+vgRERFKTExUnz59nOZKkjp06CC/31/v2iRp7969zu2qqirt379fAwYMkCR96Utf0oULF/TJJ584NcXFxXWuKT8/X5mZmZowYYKSkpLk9Xr1n//8J6imtkyqa8z6AQDNgzNYAIAml5WVpQcffFB33HGHxo8fry1btignJyfoioC9e/fWjh07NGbMGHm9XnXq1EmJiYnKycnR5MmT5fF49NOf/tT1sy5ZWVlavHixbrvtNg0ZMkSrV69WcXGx1q5dG1T3m9/8Rv369dPAgQO1YsUKlZeX69FHH5UkjRw5Uu3atdOPf/xjzZ07V/v27dPLL79c53ETExP1yiuvaPjw4Tp//ryysrKuuZR9bZlc7/oBAM2DM1gAgCZ33333adWqVfrlL3+ppKQk/fa3v9Xq1at11113OTXPPvuscnNz1aNHDw0dOlSStGLFCnXq1EmjR4/W5MmTlZ6erjvuuMPVtWVmZmrBggVasGCBUlJStHXrVm3evFn9+vULqsvOztayZcuUmpqq/Px8/eUvf1FsbKwkqXPnzlqzZo3eeOMNpaSk6LXXXtOSJUvqPO4f//hHlZeXa+jQocrIyFBmZqbi4uKCamrL5HrXDwBoHh6r+aFxAADgOHbsmPr06aOioqKgvz8FAEBtOIMFAAAAAC6hwQIAAAAAl/ARQQAAAABwCWewAAAAAMAlNFgAAAAA4BIaLAAAAABwCQ0WAAAAALiEBgsAAAAAXEKDBQAAAAAuocECAAAAAJfQYAEAAACAS2iwAAAAAMAl/wcnBeJer/LIiwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "\n", + "\n", + "hospital_population_merged.plot(\n", + " ax=ax,\n", + " column='total_population',\n", + " legend=True, \n", + " legend_kwds={'label': \"Total Population\", 'orientation': \"horizontal\"},\n", + " markersize=hospital_population_merged['total_population']/1000 , \n", + " alpha=0.8,\n", + " cmap='viridis_r',\n", + " edgecolor='black' \n", + ")\n", + "\n", + "\n", + "# Add a title and labels to the axes\n", + "ax.set_title('Hospital locations and their total demand', fontsize=16)\n", + "ax.set_xlabel('Longitude', fontsize=12)\n", + "ax.set_ylabel('Latitude', fontsize=12)\n", + "plt.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "c95fa7e9-46bf-4588-9bcd-7149d77d3e38", + "metadata": {}, + "source": [ + "Now we check if there is any hospital that has not been assigned to any population cluster." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "bb07e73c-84a2-466f-a644-4fa313b82dec", + "metadata": { + "id": "bb07e73c-84a2-466f-a644-4fa313b82dec", + "outputId": "10ee77ab-180c-4ee4-a5fa-ca59f4ae757e" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "following hospitals are not assigned to any cluster:\n", + " Local_ID geometry\n", + "0 1 POINT (14.52441 46.05365)\n", + "2 3 POINT (14.52484 46.06755)\n", + "6 7 POINT (14.48788 46.06866)\n", + "7 8 POINT (14.48717 46.06762)\n", + "8 9 POINT (14.52093 46.05413)\n", + "9 10 POINT (14.5237 46.05333)\n", + "10 11 POINT (14.52078 46.05266)\n", + "11 12 POINT (14.52033 46.05172)\n", + "13 14 POINT (14.56972 46.05215)\n", + "21 22 POINT (14.52243 46.05235)\n", + "22 23 POINT (14.52138 46.05506)\n", + "23 24 POINT (14.51099 46.06451)\n", + "26 27 POINT (15.87645 46.42654)\n", + "28 29 POINT (14.5239 46.05406)\n", + "29 30 POINT (14.52124 46.05119)\n", + "30 31 POINT (14.5225 46.05157)\n", + "31 32 POINT (14.52194 46.05188)\n", + "32 33 POINT (14.52217 46.05327)\n", + "33 34 POINT (14.52236 46.05607)\n", + "34 35 POINT (14.51737 46.05131)\n", + "37 38 POINT (14.52526 46.05426)\n", + "39 40 POINT (14.52839 46.05496)\n", + "43 44 POINT (14.4811 46.04615)\n", + "44 45 POINT (14.53185 46.10335)\n", + "45 46 POINT (14.53148 46.05589)\n", + "46 47 POINT (14.50068 46.06068)\n", + "47 48 POINT (14.51968 46.05196)\n", + "49 50 POINT (15.08172 46.5083)\n", + "51 52 POINT (14.55865 46.0535)\n", + "52 53 POINT (14.57055 46.05288)\n", + "53 54 POINT (14.57102 46.05314)\n", + "54 55 POINT (14.57156 46.05365)\n", + "55 56 POINT (14.57166 46.05253)\n", + "56 57 POINT (14.57013 46.05332)\n", + "62 63 POINT (15.67162 46.52356)\n", + "67 68 POINT (14.46488 46.09857)\n", + "73 74 POINT (15.16206 45.80063)\n", + "79 80 POINT (14.48296 46.0712)\n", + "80 81 POINT (14.59646 46.13959)\n", + "81 82 POINT (14.51998 46.05327)\n", + "82 83 POINT (14.52931 46.05476)\n", + "83 84 POINT (14.50254 46.06452)\n", + "84 85 POINT (15.64027 46.23881)\n", + "89 90 POINT (13.64537 45.95796)\n", + "92 93 POINT (14.35333 46.249)\n", + "96 97 POINT (15.08171 46.50819)\n", + "97 98 POINT (14.35409 46.24867)\n", + "112 113 POINT (14.48694 46.04654)\n", + "113 114 POINT (14.52171 46.05584)\n", + "114 115 POINT (14.48726 46.06773)\n", + "119 120 POINT (16.14167 46.40654)\n", + "122 123 POINT (13.68777 45.5443)\n", + "123 124 POINT (14.35315 46.27635)\n", + "124 125 POINT (14.5258 46.05186)\n", + "127 128 POINT (14.487 46.0465)\n", + "132 133 POINT (14.10892 46.37141)\n" + ] + } + ], + "source": [ + "assigned_hospitals = clusters_gdf['nearest_hospital_local_id'].unique()\n", + "\n", + "unassigned_hospitals = HealthCenters_centroids[~HealthCenters_centroids['Local_ID'].isin(assigned_hospitals)]\n", + "\n", + "if unassigned_hospitals.empty:\n", + " print(\"All hospitals are assigned to at least one cluster.\")\n", + "else:\n", + " print(\"following hospitals are not assigned to any cluster:\")\n", + " print(unassigned_hospitals[['Local_ID', 'geometry']])" + ] + }, + { + "cell_type": "markdown", + "id": "3740a61b-f60c-427c-aab5-6e6e552dc57d", + "metadata": { + "id": "3740a61b-f60c-427c-aab5-6e6e552dc57d" + }, + "source": [ + "## 6. Natural hazard disruption" + ] + }, + { + "cell_type": "markdown", + "id": "52d30a9c-2515-4aff-ae5f-9a15cd343061", + "metadata": { + "id": "52d30a9c-2515-4aff-ae5f-9a15cd343061" + }, + "source": [ + "### Download flood data\n", + "The flood data we will extract from a repository maintained by the World Resources Institute. We will download river flood hazard maps from their [Flood Data Collection](https://wri-projects.s3.amazonaws.com/AqueductFloodTool/download/v2/index.html).\n", + "\n", + "Here we do not need to use an API and we also do not need to register ourselves, so we can download any of the files directly. In case you do not have any flood impacts. You could select a more extreme future scenario. But let's first download the 1/1000 river flood event under historic conditions. " + ] + }, + { + "cell_type": "markdown", + "id": "b0ff5f7a-fc94-4429-82bb-2a94da72a887", + "metadata": { + "id": "b0ff5f7a-fc94-4429-82bb-2a94da72a887" + }, + "source": [ + "### Overlay flood data with Hospitals" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "e73f0bce-ad12-4be5-acbb-33dd7e2fa18d", + "metadata": { + "id": "e73f0bce-ad12-4be5-acbb-33dd7e2fa18d", + "outputId": "ec5a636a-dac4-4098-a442-1a515598df95" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "flood_map_path = \"https://wri-projects.s3.amazonaws.com/AqueductFloodTool/download/v2/inunriver_historical_000000000WATCH_1980_rp01000.tif\"" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "28d110b6-0ef4-41c6-acb2-8673597decd2", + "metadata": { + "id": "28d110b6-0ef4-41c6-acb2-8673597decd2", + "outputId": "d23d720d-52bf-42cb-e706-b71f0e2a8a1a" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset> Size: 4GB\n",
+       "Dimensions:      (band: 1, x: 43200, y: 21600)\n",
+       "Coordinates:\n",
+       "  * band         (band) int32 4B 1\n",
+       "  * x            (x) float64 346kB -180.0 -180.0 -180.0 ... 180.0 180.0 180.0\n",
+       "  * y            (y) float64 173kB 90.0 89.99 89.98 ... -89.98 -89.99 -90.0\n",
+       "    spatial_ref  int32 4B ...\n",
+       "Data variables:\n",
+       "    band_data    (band, y, x) float32 4GB ...
" + ], + "text/plain": [ + " Size: 4GB\n", + "Dimensions: (band: 1, x: 43200, y: 21600)\n", + "Coordinates:\n", + " * band (band) int32 4B 1\n", + " * x (x) float64 346kB -180.0 -180.0 -180.0 ... 180.0 180.0 180.0\n", + " * y (y) float64 173kB 90.0 89.99 89.98 ... -89.98 -89.99 -90.0\n", + " spatial_ref int32 4B ...\n", + "Data variables:\n", + " band_data (band, y, x) float32 4GB ..." + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "flood_map = xr.open_dataset(flood_map_path, engine=\"rasterio\")\n", + "flood_map" + ] + }, + { + "cell_type": "markdown", + "id": "b21c691f-5789-4b0d-a5ec-da785b94c833", + "metadata": {}, + "source": [ + "As you can see, this is a very large dataset again. Let's make our life a little bit more relaxed by clipping the map to our country of interest." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "f14da004-413e-4856-80e6-fdaa544aca9d", + "metadata": { + "id": "f14da004-413e-4856-80e6-fdaa544aca9d", + "outputId": "3696782e-4378-42fc-9e84-2599abeef710" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "min_lon = country_geom.bounds.minx.values[0]\n", + "min_lat = country_geom.bounds.miny.values[0] #complete function\n", + "max_lon = country_geom.bounds.maxx.values[0] #complete function\n", + "max_lat = country_geom.bounds.maxy.values[0]#complete function\n", + "\n", + "flood_map_area = flood_map.rio.clip_box(minx=min_lon,miny=min_lat,maxx=max_lon,maxy=max_lat) #complete function" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "ecf80c30-9ba1-426c-9990-cf3ea07e499b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "flood_map_area.band_data.plot(cmap='Blues')" + ] + }, + { + "cell_type": "markdown", + "id": "399149ac-8ee4-4459-971e-9a2003ab4b4e", + "metadata": { + "id": "399149ac-8ee4-4459-971e-9a2003ab4b4e" + }, + "source": [ + "### Overlay flood data with healthcare facilities" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "024a09f7-6f1c-4e15-a810-cf407daa2a48", + "metadata": { + "id": "024a09f7-6f1c-4e15-a810-cf407daa2a48", + "outputId": "a33183f3-464f-4abf-fe29-436436f65671" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def _get_damage_per_object(asset, curves, cell_area_m2):\n", + " \"\"\"\n", + " Calculate damage for a given asset based on hazard information.\n", + " Arguments:\n", + " *asset*: Tuple containing information about the asset. It includes:\n", + " - Index or identifier of the asset (asset[0]).\n", + " - Asset-specific information, including hazard points (asset[1]['hazard_point']).\n", + " *maxdam_dict*: Maximum damage value.\n", + " Returns:\n", + " *tuple*: A tuple containing the asset index or identifier and the calculated damage.\n", + " \"\"\"\n", + "\n", + " if asset.geometry.geom_type in (\"Polygon\", \"MultiPolygon\"):\n", + " coverage = asset[\"coverage\"] * cell_area_m2\n", + " elif asset.geometry.geom_type in (\"LineString\", \"MultiLineString\"):\n", + " coverage = asset[\"coverage\"]\n", + " elif asset.geometry.geom_type in (\"Point\"):\n", + " coverage = 1\n", + " else:\n", + " raise ValueError(f\"Geometry type {asset.geometry.geom_type} not supported\")\n", + "\n", + " return (\n", + " np.sum(\n", + " np.interp(\n", + " asset[\"values\"], curves.index, curves[asset[\"amenity\"]].values\n", + " )\n", + " * coverage\n", + " )\n", + " * asset[\"maximum_damage\"]\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "48ec7696-a117-4302-9276-ac37fa369cd2", + "metadata": { + "id": "48ec7696-a117-4302-9276-ac37fa369cd2", + "outputId": "88db2c6c-ffd8-4b7d-80b6-2c3bbf33c165" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "maxdam = {\"hospital\":2000,\n", + " \"clinic\":1500,\n", + "}\n", + "\n", + "curves = np.array(\n", + " [[0,0],\n", + " [50,0.2],\n", + " [100,0.4],\n", + " [150,0.6],\n", + " [200,0.8],\n", + " [250,1]])\n", + "\n", + "curves = np.concatenate((curves,\n", + " np.transpose(np.array([curves[:,1]]*(len(maxdam)-1)))),\n", + " axis=1)\n", + "\n", + "curves = pd.DataFrame(curves)\n", + "curves.columns = ['depth']+list(maxdam.keys())\n", + "curves.set_index('depth',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "26e1e323-d30b-4e40-8a89-a25d1e475234", + "metadata": { + "id": "26e1e323-d30b-4e40-8a89-a25d1e475234", + "outputId": "70b41a67-b5e6-49fb-cd9e-9366d1bf31e5", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "values_and_coverage_per_object = exact_extract(\n", + " flood_map,\n", + " HealthCenters.to_crs(4326),\n", + " [\"coverage\", \"values\"],\n", + " output=\"pandas\",\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "2460afd7-dc6c-4b37-9e77-0550fcacf24d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
coveragevalues
0[0.0031160966027528048][0.0]
1[0.008566441014409065][0.31174808740615845]
2[0.012850387953221798, 0.008853942155838013, 0...[0.0, 0.0, 0.0, 0.0]
3[0.00234486092813313][0.0]
4[0.00516355037689209][0.0]
.........
128[0.0008400809019804001][0.0]
129[0.0007367293583229184][0.0]
130[0.00482288608327508, 0.0020372953731566668][0.0, 0.0]
131[0.0023558475077152252][0.0]
132[0.002978724194690585][0.0]
\n", + "

133 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " coverage values\n", + "0 [0.0031160966027528048] [0.0]\n", + "1 [0.008566441014409065] [0.31174808740615845]\n", + "2 [0.012850387953221798, 0.008853942155838013, 0... [0.0, 0.0, 0.0, 0.0]\n", + "3 [0.00234486092813313] [0.0]\n", + "4 [0.00516355037689209] [0.0]\n", + ".. ... ...\n", + "128 [0.0008400809019804001] [0.0]\n", + "129 [0.0007367293583229184] [0.0]\n", + "130 [0.00482288608327508, 0.0020372953731566668] [0.0, 0.0]\n", + "131 [0.0023558475077152252] [0.0]\n", + "132 [0.002978724194690585] [0.0]\n", + "\n", + "[133 rows x 2 columns]" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "values_and_coverage_per_object" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "811245a1-d794-4b0d-b604-1d31c2507d97", + "metadata": { + "id": "811245a1-d794-4b0d-b604-1d31c2507d97" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "HealthCenters = HealthCenters.merge(values_and_coverage_per_object,left_index=True,right_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "115d6cc9-aeb9-4497-9c0e-78f7c8ef19b8", + "metadata": { + "id": "115d6cc9-aeb9-4497-9c0e-78f7c8ef19b8" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "HealthCenters['maximum_damage'] = HealthCenters.amenity.apply(lambda x: maxdam[x])" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "291a4fba-40c1-4397-a7f2-8b1665ca7ef5", + "metadata": { + "id": "291a4fba-40c1-4397-a7f2-8b1665ca7ef5" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "HealthCenters['damage'] = HealthCenters.apply(\n", + " lambda _object: _get_damage_per_object(_object, curves, cell_area_m2=100*100),\n", + " axis=1,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "913f9757-3151-46f9-9427-f54fa58d8beb", + "metadata": { + "id": "913f9757-3151-46f9-9427-f54fa58d8beb" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0 0.000000\n", + "93 0.000000\n", + "92 0.000000\n", + "91 0.000000\n", + "90 0.000000\n", + " ... \n", + "76 86.627588\n", + "41 148.154755\n", + "1 160.234296\n", + "120 326.382010\n", + "12 2793.266143\n", + "Name: damage, Length: 133, dtype: float64" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "HealthCenters['damage'].sort_values(ascending=True)" + ] + }, + { + "cell_type": "markdown", + "id": "437b207f-7c43-4846-b9ba-63e416ff92cf", + "metadata": { + "id": "160dafed-34d4-44c8-8d9d-aa44c8dfb621" + }, + "source": [ + "# 7. Your Final Task" + ] + }, + { + "cell_type": "markdown", + "id": "c412b628-014e-41ba-8c41-0722098ad006", + "metadata": { + "id": "c412b628-014e-41ba-8c41-0722098ad006" + }, + "source": [ + "As you saw, due to a flood with a 1000-year return period, some hospitals may be out of service. Therefore, we need to estimate the post-flood urban/rural demand for services from the hospitals that remain operational.\n", + "\n", + "### Your task here will be: \n", + "- Create new clusters of populations and assign them to the remaining hospitals, then determine the post-disaster demand for these hospitals.\n", + "- Calcuate the urban, rural and total demand (population in need of services) for each hospital.\n", + "- Plot the remaining hospitals vs their total population in need of service.\n", + "- Let us know how many hospitals were affected by the flood and the total number of rural residents who need to find an alternative hospital." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f9203aa-b2e2-4207-ad93-bf71e1ff5636", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [ + "dc39cef6-eac9-40a0-a737-bac3cd5bc2a1", + "zotYyVnD4Jt2", + "_tY_sxXy4SPA", + "c9NfiE5dFr19", + "44mziLGt8j4C", + "uS-6YzaM9P5-", + "BnOR_Ouk-CFv", + "573f10a4-d0bb-4675-903f-71ffbe45358f", + "5299738d-567d-4473-aaa2-095dede18b92" + ], + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/TAA4/tutorial1.ipynb b/TAA4/tutorial1.ipynb deleted file mode 100644 index a614ac0..0000000 --- a/TAA4/tutorial1.ipynb +++ /dev/null @@ -1,178145 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tutorial: Visualizing your results\n", - "\n", - "In this tutorial, we will be exploring different forms of visualization in Python using various packages. We will start by learning how to use **Matplotlib**, a popular plotting library in Python. We will then move on to **Seaborn**, which provides more advanced statistical visualizations. Next, we will explore **Plotly**, a powerful tool for creating interactive plots. Finally, we will introduce **Folium**, a library for creating geospatial visualizations.\n", - "\n", - "Throughout this tutorial, we will cover different types of visualizations, including spatial and non-spatial, single and multi-panel, static and interactive plots. We will also explore the use of different colormaps to enhance our visualizations.\n", - "\n", - "By the end of this tutorial, you will have a solid understanding of different visualization techniques and be able to create a wide range of visualizations to communicate your data effectively\n", - "\n", - "
\n", - "Important: This tutorial is not part of your final grade. And also no assignment is attached to this tutorial. You can just enjoy and explore some of the possibilities of visualizing data. \n", - "
\n", - "\n", - "### Important before we start\n", - "---\n", - "Make sure that you save this file before you continue, else you will lose everything. To do so, go to **Bestand/File** and click on **Een kopie opslaan in Drive/Save a Copy on Drive**!\n", - "\n", - "Now, rename the file into Week7_Tutorial1.ipynb. You can do so by clicking on the name in the top of this screen." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Learning Objectives\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Know which packages are available in Python to visualize your data.\n", - "- Gain a basic understanding of plotting static figures, both spatial and non-spatial.\n", - "- Understand the impact of scaling and color choices.\n", - "- Know how to create a basic interactive visualisation with Plotly or Folium." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Tutorial Outline

\n", - "
\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1.Introducing the packages\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Within this tutorial, we are going to make use of the following packages: \n", - "\n", - "[**GeoPandas**](https://geopandas.org/) is a Python package that extends the datatypes used by pandas to allow spatial operations on geometric types.\n", - "\n", - "[**NumPy**](https://numpy.org/doc/stable/) is a Python library that provides a multidimensional array object, various derived objects, and an assortment of routines for fast operations on arrays.\n", - "\n", - "[**Pandas**](https://pandas.pydata.org/docs/) is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.\n", - "\n", - "[**Matplotlib**](https://matplotlib.org/) is a comprehensive Python package for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible.\n", - "\n", - "[**seaborn**](https://seaborn.pydata.org/index.html) is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.\n", - "\n", - "[**Plotly**](https://plotly.com/python/) is a Python data visualization library that supports over 40 unique chart types covering a wide range of statistical, financial, geographic, scientific, and 3-dimensional use-cases. It is built on top of the Plotly JavaScript library (plotly.js), and enables Python users to create interactive web-based visualizations that can be displayed in Jupyter notebooks, or saved to standalone HTML files. The plotly Python library is sometimes referred to as \"plotly.py\" to differentiate it from the JavaScript library.\n", - "\n", - "[**folium**](https://python-visualization.github.io/folium/#) is a Python package to visualize data on an interactive map. It enables both the binding of data to a map for choropleth visualizations as well as passing rich vector/raster/HTML visualizations as markers on the map. It is build on top of the leaflet.js library.\n", - "\n", - "*We will first need to install these packages in the cell below. Uncomment them to make sure we can pip install them*" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install geopandas\n", - "!pip install rasterio\n", - "!pip install plotly\n", - "!pip install folium\n", - "!pip install branca\n", - "!pip install mapclassify " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we will import these packages in the cell below:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import seaborn as sns\n", - "import numpy as np\n", - "import geopandas as gpd\n", - "import rasterio\n", - "import matplotlib.pyplot as plt\n", - "import folium\n", - "import branca.colormap as cm\n", - "import plotly.io as pio\n", - "import plotly.express as px\n", - "import plotly.offline as py\n", - "\n", - "from urllib.request import urlopen\n", - "from zipfile import ZipFile\n", - "from io import BytesIO" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "### Import the data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## this is the link to the 1/100 flood map for Europe\n", - "zipurl = 'https://github.com/ElcoK/BigData_AED/raw/main/week7/Data_Week7.zip'\n", - "\n", - "# and now we open and extract the data\n", - "with urlopen(zipurl) as zipresp:\n", - " with ZipFile(BytesIO(zipresp.read())) as zfile:\n", - " zfile.extractall()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Better understanding matplotlib\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As you have already been made aware, Matplotlib is a popular data visualization library in Python that enables you to create various types of plots such as line plots, scatter plots, histograms, bar plots, etc. It is widely used in scientific computing, data analysis, and machine learning (and within our course). It is, however, not a very friendly and intuitive package to use. Let's distentangle some of the steps that we can take to create a simple plot.\n", - "\n", - "First we create a DataFrame with some sample data:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "data = {'x': [1, 2, 3, 4, 5], 'y1': [2, 6, 12, 20, 30], 'y2': [1, 4, 9, 16, 25]}\n", - "df = pd.DataFrame(data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To create a line plot with two lines, we can use the code in the cell below. The first argument of the function is the x-axis values, and the second argument is the y-axis values. We have also added labels to each line using the `label` argument." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(df['x'], df['y1'], label='Line 1')\n", - "plt.plot(df['x'], df['y2'], label='Line 2')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "While this provides us with a basic plot, it does not contain any information about what we see. We need units, a title, a legend. So let's add all these things. Here, we use the `plt.legend()` function to add a legend to the plot. This function will automatically use the labels we defined earlier for each line. To add labels to the x and y axes, we can use the `plt.xlabel()` and `plt.ylabel()` functions. \n", - "\n", - "Moreover, we often want to add some specific font characteristics to our labels. Such as the fontsize or the to make it **bold**. We do so in the `plt.title()` function with the `fontweight` and `fontsize` arguments. " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(df['x'], df['y1'], label='Output 1')\n", - "plt.plot(df['x'], df['y2'], label='Output 2')\n", - "\n", - "plt.legend()\n", - "plt.xlabel('X-axis')\n", - "plt.ylabel('Y-axis')\n", - "plt.title('Non-linear data', fontweight='bold', fontsize=16)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Sometimes we want to make some changes to the way the x and y-axis ticks (the numbers) are represented. Maybe you want to add more ticks, or maybe less. Luckily we can specify this through the use of the `plt.yticks()` to change the ticks on the y-axis or `plt.xticks()` to change the ticks on the x-axis. Let's change some of the ticks on the y-axis! " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(df['x'], df['y1'], label='Output 1')\n", - "plt.plot(df['x'], df['y2'], label='Output 2')\n", - "\n", - "plt.legend()\n", - "plt.yticks([0, 10, 20, 30])\n", - "plt.xlabel('X-axis')\n", - "plt.ylabel('Y-axis')\n", - "plt.title('Non-linear data', fontweight='bold', fontsize=16)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And this is just a tiny start of all the things you can do with matplotlib. Feel free to explore many of other options on their (improved) [website](https://matplotlib.org/stable/plot_types/index.html)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Creating static non-spatial plots\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will use the [Natural Earth](https://www.naturalearthdata.com/) data as a starting point for some visualization. Natural Earth is a public domain map dataset that provides geographic data at various scales, including world, regional, and country-level detail. The dataset is designed for use in cartography, GIS, and other mapping applications, and includes a wide range of physical and cultural features such as land cover, water bodies, cities, transportation networks, and more.\n", - "\n", - "The Natural Earth dataset is created and maintained by a community of volunteers, who work to ensure that the data is accurate, up-to-date, and free from copyright restrictions. The dataset is available in a variety of formats, including shapefiles, GeoJSON, and raster tiles, and can be easily integrated into mapping applications and analysis tools.\n", - "\n", - "Natural Earth data is useful for a wide range of applications, including environmental monitoring, urban planning, disaster response, and tourism. It provides a consistent and reliable source of geographic information that can be used to create high-quality maps and visualizations." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "global_data = gpd.read_file('ne_10m_admin_0_countries.shp')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "167 -99\n", - "257 0\n", - "204 0\n", - "174 0\n", - "173 0\n", - " ... \n", - "8 2868929\n", - "49 3861123\n", - "189 5081769\n", - "9 14342903\n", - "154 21433226\n", - "Name: GDP_MD, Length: 258, dtype: int64" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "global_data.GDP_MD.sort_values()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Single plot" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "We will start by exploring the relation between *POP_EST* (Population Estimates) and *GDP_MD* (Gross Domestic Product (GDP) at market prices in millions of US dollars). We will first make a simple scatter plot, which is a common way to look for a potential correlation." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.05, 'Country-level comparison between Population and GDP')" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig,ax = plt.subplots(figsize=(8,6))\n", - "\n", - "ax.scatter(x=global_data.POP_EST,y=global_data.GDP_MD)\n", - "ax.set_xlabel('Population')\n", - "ax.set_ylabel('GDP')\n", - "\n", - "ax.set_title('Country-level comparison between Population and GDP', fontweight='bold', fontsize=12, y=1.05)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That plot doesnt show much yet. We have a few very large countries (either in population, or in GDP). All the other countries are clustered together. Maybe we can already see a bit more when we use log-scales." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.05, 'Country-level comparison between Population and GDP')" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAArYAAAI9CAYAAADPdDv3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAACD+UlEQVR4nO3deXwU9fkH8M/mJoEEQiQJEiByWNYImBAEORQKCGgErf15VIWqVDBakGKFUkW0Sj3q0bKgoBUrVVGrgJViUVTuEo6IMVgOgyAmYBJIIJgQdr+/P9IZdzczuzO7szt7fN6vV166s7Oz392dhGe/83yfxyKEECAiIiIiCnMxZg+AiIiIiMgIDGyJiIiIKCIwsCUiIiKiiMDAloiIiIgiAgNbIiIiIooIDGyJiIiIKCIwsCUiIiKiiMDAloiIiIgiAgNbIiIiIooIDGwjUE1NDR577DFcdtllSE9PR2JiInJycjBy5Ej85S9/QUNDg9lDVPTwww/j4YcfxnPPPWfaGJYtWwaLxQKLxYKHH37YtHEoOXTokDy2K664wuzhhK3JkyfL7+Onn35q9nC8CvTn/umnn8q/e6WlpYYfP5Q5v7fOP4mJiejZsyeKi4tRWVlp9jBVSePt3r27z8dYtmyZ/PmfPHnS5b5o/5vz6aefyq9/8uTJmh9XUVGB+++/H/n5+Wjfvj0SEhKQnZ2N/Px83Hvvvfj4449d9nc+92JiYpCUlIROnTphwIABuPfee7Fnz55WzxHu525ACYoo69evF+edd54AoPqze/dus4epSBpft27dTBvDK6+8Io9j3rx5po1DSUVFhTy2yy+/3OzhhK1JkybJ7+Mnn3xi9nC8CvTnPm/ePPn4r7zyiuHHD2XO763aT2Zmpjh06JDZQ1VkxN/Myy+/XD5ORUWFy33R/jfnk08+kV//pEmTND3mxRdfFImJiR7PqYsuusjlMd7OwZiYGPHQQw+5PCbcz91A4oxtBNm3bx+uueYafP/99wCAcePGYdeuXWhqakJNTQ1WrVqFkSNHmjxK4/3www9mD4HCwJkzZwC0zFAJISCEiMpZKFInhIDD4cCePXtwwQUXAACOHTuGRx991OSRmaN79+7y70o4XN0w2z/+8Q/cddddaGpqAgD86le/QllZGZqamnDq1Cls374djzzyCHJzc1WPUVFRgbNnz2L//v34/e9/j7i4ODgcDjzyyCN4+umnVR/Hc9eJmVE1Gevmm2+Wv6nl5eWJc+fOKe7X3Nws/399fb34/e9/Ly666CLRpk0bkZSUJKxWq5g7d66oq6tzeZx0bPfZAaVv/O7f9NeuXSsGDRokkpKSRLdu3cScOXPE2bNnhRCuM0buP9JzOc+kPvTQQ+KJJ54QPXr0ELGxseKvf/2riI2NFQDEwIEDXcZ27NgxERcXJwCI/v37e30PPc3YlpaWiptvvlmcf/75Ij4+XnTo0EFceeWV4qOPPpL3WbVqlfz4KVOmuDx+06ZN8n0/+9nPXMY4c+ZMceGFF4qkpCSRnJwsBgwYIF544QXhcDjk/XyZPSkvLxe333676N69u0hISBCpqamiX79+wmazuey3fv16UVRUJM477zwRFxcnMjIyxFVXXSU+/vhj1ffnoYceEgsWLBA5OTmiTZs2YtSoUeK///2vOHnypPjVr34l0tPTRUZGhvi///s/cezYMZfjOH++JSUlYuTIkSI5OVl06NBBTJ48WVRXV7vsP336dFFYWCg6deokEhISRJs2bUTv3r3F9OnTxffff++yb7du3eTjl5eXi6uuukq0a9dOdO/eXQihPmP7/vvvi+HDh4v27duL2NhYkZ6eLvr16yduv/12UVtbK+/ncDjE0qVLxWWXXSZSU1NFfHy86Nq1q/jlL38p9u/f7zIW5+datWqVuPfee0VWVpZo27atGDp0qNixY4fXz9D9c//000/F4MGDRVJSksjMzBTTp08XZ86ccXnM2bNnxbPPPisKCwtF27ZtRUJCgujdu7d44IEHXH6v1X7v8L/Z29mzZ8u3//nPf8qP69KliwAgBgwYIG9bunSpvO+f//xnebvW81t6b1955RUxbNgwkZaWJuLj40W3bt3E3XffLSorK132df67s2XLFnHbbbeJ9PR0kZaWJsaOHSsOHDig6711/+fw6aefVpxh8/XzX716tbj33ntFZmamSExMFJdeemmrKwbO567acZwfo/Q3+bvvvhM333yzsFqtIj09XcTFxYl27dqJgoIC8ac//Un+++88G6n0U1FR4fFvTmVlpZg+fbro2bOnSExMFCkpKeKSSy4RTz75pGhqanLZ13mc27dvF6NGjRLJycmic+fO4q677hKnTp3y+lmVlZWJ66+/XvTu3Vv+HU1LSxNDhgwRL7/8ssu55D7Tunz5ctG3b1+RlJQkevbsKZ555plW596GDRvk36usrCwxc+ZM8cEHH2iesXU4HKJr167y/jNmzPD6mtzfH+d/QyU2m02+r23btuLEiRNCCN/O3WjBwDZC2O12kZqaKp/Mf/vb37w+5vvvvxcXXnih6h+2Cy+80CXA8DWwTU1NFTExMa2O//jjjwsh9Ae2GRkZrf4B/r//+z/59n/+8x95bM8//7y8/YUXXvD6nqgFtqtWrRLx8fGKY7RYLGLx4sVCCCHOnTsnzj//fAFAtG/fXjQ2NsrHuOuuu+THfPjhh0IIIQ4ePCiys7NVX/+NN94oP15vYPvBBx+oXhKbMGGCvN+f//xnYbFYVF/bX/7yF8X3RynlpVu3bmLQoEGtto8ZM8ZlbNL2lJQUkZyc3Gr//v37u7x3aWlpqu/RRRddJH9JEsI1OHA+V6RzSSlI2LFjh/wFSOlHClgcDof4+c9/rrpf27ZtxbZt2+SxOD9Xhw4dWu2fkZHR6gukO+fP/bzzzlM8D8ePHy/v39jY6PI76f7Tp08fOVBX20f6vfroo4/k2/fff78QQogDBw7I22JjY+Xx/+IXv5C3l5eXCyH0nd8Oh0PceOONqvtmZ2e7/KPv/BqV3ts+ffqofrlXem8B138On3rqKZdzzN/PX+n3JT4+XmzcuFHe34jAdvfu3R4/11/96ldCCP8C2wMHDojMzEzVxw4dOlT88MMPrcbZpk0bxb9J0pg8ee+99zyOV/r3xP21KZ0bAMTrr78u779lyxaRkJDQah/pbzngPbDdsWOHy2Pdv4h54v6+O2tubnb5+/fOO+8IIfSfu9GEgW2EOH78uMtJvnPnTq+Pufvuu+X9x4wZI7799ltx9OhRMWrUKHn73XffLe+v9EdUCO+BLQDxm9/8RtTW1oqVK1fK2y688EKX46gdXwjXgAqAeOqpp0Rtba2orKwU3377rSgpKZHvu/XWW+XHDRw4UAAQ7dq10zQroBTYnjlzRv5HqXv37qKkpEQ0NTWJ//73v/IXgzZt2sgzhw8++KB8jLffflsIIURTU5P8BzY3N1eeLbj66qsFABEXFyfefvttcebMGXHs2DGXfzylmTI9ge0PP/wgOnXqJO9/++23i0OHDolTp06JTZs2iRdffFEIIcSRI0fkP+hxcXHinXfeEadOnRLvvPOOPAuekJAgvv3221bvT1JSkvjwww/FiRMn5PcZaAlWP/vsM1FZWSlycnIU/9C7/6NWU1MjysrKRK9eveTtzl9EXn/9dfHVV1+JkydPiubmZvHtt9+KsWPHyvuuXr1a3tc5OLj44ovF7t27xZkzZ8SePXuEEMpBwp/+9Cd524oVK8TZs2fF8ePHxZYtW8RDDz0kj/2tt95yOU937twpTp48KR544AHFf0icn+v8888X27dvF9XV1eKKK66Qt7/xxhseP0v336X58+eLuro6sWXLFpfAfe3atUII19maOXPmiJqaGtHQ0CCeeOIJefusWbPk43vKsW1sbBRt2rQRwI9XQ15++WUBQP6yKp2f0ixu586d5cfrOb/feecdedvkyZNFZWWlaGxsFK+//rq8/frrr5eP7fx3Jy8vT3z11Vfi22+/FX369JG3b926Vdd7K0RL8PrFF1+ICy64QN5+5513+v359+jRQ+zZs0fU1taKadOmydsHDRok729EYHvs2DHxj3/8Q3zzzTeioaFBNDU1iT179sifT2xsrDzr5/4+as2xHT9+vLz9tttuE9XV1WLfvn2iX79+8vYnn3yy1TiBli8z33//vdi6dasc5CYlJbWaQXV34MAB8cEHH4ijR4+KxsZG8cMPP4gtW7bIX4w7duwoH8M9aH/mmWdEXV2d+Mtf/iJvu/LKK+VjDx8+3OWzrq2tbfX3yFtg63xudOrUyeU+5y990s+//vUvxffH/TMQQrj8fZXeV73nbjRhYBshjh075nKS79q1y+tjnL+Nfv755/J252/8Xbp0kbcr/REVwntge95557mkP3Ts2FEAEImJiS7HUTu+EK4B1ciRIxVfjxQsJCYmiuPHj4t9+/bJj5k6darX98P9eaTAdt26da3+KCn9SN+kv/nmG/kf/aKiIiGE6z/ajz32mBCiJfj0NEso/dxzzz2t3lNvga3zTFuPHj1UZ66cLx9fe+21LvdNmDBBvu+ll15q9f7ccMMN8r6//e1v5e0333yzvN15Bs45yJC2xcXFidOnT8vbX3zxRfm+6667Tt7+3nvviVGjRomMjAw54Hb++eMf/yjv6xwcbNiwodVrVgoSnL9wDR8+XDz66KPirbfeEvv27XN5rPM/UM8//7y8vbm5WT6vAciXwZ2fy3l/539gFyxYoPjZSJw/986dO7sEAHPmzJHvmzlzphBCiCFDhng9p/Ly8uRjeFs8Jn3RjYuLE6dOnRK33XabACBfJZk1a5bYv3+/fIzbbrtNCKH//Fb6x9/9p23btvK4nP/urFq1St7+m9/8Rt6u90uD0k+nTp3kv2v+fP7Sl0khhDh9+rT83lgsFnHy5EkhhDGBbXNzs3j66adFYWGhSE1NVbwa4zyrrDewPXPmjMvYnYNk51nVoUOHthpnTEyMS1pPQUGBfJ+3Gc7Tp0+LBx98UPTt21ekpKQovq6qqiohhGtge8kll8jHOHXqlLxdmlhpaGiQ/6ZYLBaXKyjOf4+8BbYrVqyQ901NTXW5z8jA9qmnnhJC6D93owkXj0WIjIwMpKamyre//PJLr485duyY/P/dunWT/9+5dIzzPhIhhMvtc+fOeXyeXr16IS4uTr6dkpICAHKCvV4FBQWK22fNmiUfd+nSpfj73/8u3zd16lT5/6+44opWJVKWLVum+nxK74GS6upqAEDXrl1x5ZVXAgDWrl2L6upqvPbaawCAuLg43H777QBayrJ5e++cj6tHVVWV/P9WqxWxsbGK+6mdA4D386Bnz57y/7dp00b+f+eFEYmJifL/NzY2tjpGRkaGfD64j+H48eMAgHfeeQfXXnstPvroI1RXV8Nut7c6jtoCQrVzxd2ECRPwm9/8BsnJydiwYQMefPBB/N///R969+6NgoICfPfddwDU36+4uDh06dJFvq30fvXp00f+f+fXrPS+qOnatSssFoviGKT3S8v5quecGjVqFICW3/PNmzfjs88+Q1xcHObOnQugpSSS88IiaX+957eWcZ8+fVrx74YR762z+Ph45ObmYurUqdi5c6f8u+DP5++8f0pKCjIyMgC0/D1V+jyc/85qeR8l9913H2bNmoWSkhLU19e3+nsN+Lfgtra2Vh5PWloa2rdvL9/n7W9GVlYWOnToIN/W81ndeOONePTRR7Fnzx40NDRofl3ezo0TJ07If1PS0tJc/h11/5voSY8ePeT/r6+vx9GjR+Xby5cvhxACl19+uebjSZqbm/HVV1/Jt6WFYWrUzt1owsA2QsTExKCoqEi+/dRTTykGAMCPfyQzMzPlbd988438/4cOHZL/33kfKUiRVpcDgMPhwNdff+1xbPHx8S63nf9h9kVycrLi9vHjx8NqtQIAXnjhBTmwHTx4MPr16+fz8zm/B1deeaW8Stj5x+Fw4K677pL3mzJlCoCWP0o2mw1r1qwBAFxzzTXIysoCAHTs2FEO+Nu1a4empibFY7/++uu6xyw9BwDs3bsXDofD62tzPgcA9fNA4vxlRct2JdXV1S51lZ3H0KlTJwBw+YJy//33y/9Yz5w50+vx1c4VJU8//TRqa2tRUlKCt956C8XFxQCAXbt24ZFHHgGg/n7Z7XZ8++238m2l98v598DX34EjR464/IOu9H45P/fWrVsVzykpUNcyFilQBYBXX30V33zzDfLz89G3b1/k5ORg9+7dWL16tbzPT3/6UwD6z2/ncb/xxhuqv2fOX5YkRry3AOTnOXv2LL7++mssXrzYJWD15/N33r+hoUEOZi0WixzkJiUlyfs4/509cOCA5tewfPly+f/fffdd+X3Pz89X3F/v+5Weni5/rnV1dairq5Pv8/Y3w9d/C06ePIl//vOfAFr+Hdq0aROam5shhEB6errHx3o7Nzp06ICYmJZQqK6uDvX19fJ97n8TPbnkkkuQk5Mj33788cc1P9aTF154QR5Tu3bt5N8vd97O3WjCwDaCzJs3D23btgUA7NmzBxMnTkRpaSnOnj2L2tparF69GiNHjkRZWRmAliBL8sADD+C7775DZWUlHnjgAXm78z7SN7/q6mps27YNQgg8++yzhhWB7tixo3x852+7WlksFnnW9siRI/I/Bs6ztUDLDJP7P5ieim8PGTIE5513HgDg3//+N55++mnU1NSgqakJX331FZ544gmX2UsAKCoqQnZ2NgDgD3/4A5qbmwG0lH+RJCUlYezYsQCAU6dO4fbbb8ehQ4fQ3NyMI0eO4NVXX8WQIUOwYcMG3e/FkCFD5EDnwIEDuOuuu3D48GE0NDTgP//5D5YsWQIAGDt2LBISEgAA//znP7Fy5Uo0NDTgvffewwcffAAASEhIkGegjXbu3DnMmjULJ06cQHl5uUs5mzFjxgBwDZSTk5MRHx+PjRs34tVXXzVsHJ999hkef/xxfPnll+jevTsmTpyIiRMnyvcfPnwYgOvvw7PPPovS0lLU19fjwQcfRE1NDYCWGXLn2RsjHT16FI899hjq6+uxbds2LF26VL5Per+uvfZaeVtxcTF27twpl/xbs2YNfv7zn2PBggXyPtLvHQCUlZW1mh285JJL5H1WrFgBAPLM0+WXXw673S4HHVarFZ07dwag//x2HvecOXPw2WefobGxEXV1dfj0009x++23y182zOLP5//000/jyy+/xMmTJ3H//ffL7/Oll16KtLQ0AK4zntJ7+s477+A///mP5jE6/760a9cO586dw5IlS7B7927F/Z0//88//1xxJtRZmzZtMHr0aAAtwdR9992HmpoaHDx4UP4CCLi+V/6Ki4uTg9KYmBi0a9cOP/zwA+bNm4fa2lq/jp2cnIyhQ4cCaHk9an+PvImJicFTTz0l3168eDFmzZqFgwcP4ty5c/juu+9aNcBQc+7cORw4cAC///3vXb7Az5s3Tz5XyIMApDeQidavX9+qaoD7j9Sg4fjx4y7J8e4/vXr1ciml9Nhjj8n3WSwW0bZtWwHAZVW7WrkvZ2p5ZEVFRa3GIOU1aW2c0NTUJDp37izvm56e7rI61xu151m9erXiqlnnH3e/+93vXO7v3r27sNvtLvt8/fXXLrnOSj9STl2gqiI8++yzHp//ueee8/r+OOdpOm/3lhvYtm1bl2oe0o9zVYQ333xTcVy9e/dWfE6188vTmF577TWP74FUGcLhcIjrrrtOdb/k5GSxefNmr69fTyMQLVURxo0bJ+feNjY2uixOU/pxfk731dzuv8tCCHH99de73PfBBx8IIYR46aWXXLb/+te/dhm7nvPb4XC4lCxU+nHOc1TLDdXTcMLTynIl/nz+Su+De1WEv//97y73t2vXTgAtCzI9/R4559hOnTpVcVzS4jH3YzgvnHQ/ntrfnH379nlsBDR48GDFqgha1meocV4sKv106tRJtG/fvtUxPDVWUBqLWlUE59eotUHDn//8Z9UKOs4/ajm2Sj9aGjTQjzhjG2FGjBiBvXv34tFHH8Wll16K9u3bIz4+Hueffz5GjBiB559/Hr169QIAnHfeeSgpKcHvfvc7WK1WJCUlITExEX369MGcOXNQUlIiXyIDWi4D/+53v0O3bt2QkJCAn/zkJ1i5ciUKCwsNGftf/vIXFBUVucwg6JWQkIB7771Xvj1p0iSXy3u+Kioqws6dO3Hbbbeha9euiI+PR1paGvr06YPbbrtNnslyNmXKFJdLX3feead8yUuSm5uL0tJS/Pa3v5U/gzZt2uCCCy5AUVERFi9erHoJ0Zvx48dj9+7d+OUvf4nu3bsjISEB7dq1Q79+/eTZPQCYMWMG1q1bh6uuugoZGRmIjY1Fx44dMX78ePz73//G9OnTfXp+LTp27IiNGzdi1KhRSE5ORvv27TFp0iSsW7dOvuR8ww034IUXXkDv3r2RmJiICy+8EEuXLsVNN91k2DgKCgpw55134uKLL0Z6ejpiY2PRrl07DBo0CEuWLME999wDoOWqwNtvv40XXngBgwYNQrt27RAXF4ecnBxMmjQJu3fvxmWXXWbYuNxZrVb8+9//xuDBg5GYmIhOnTph+vTpeOedd+RzLTExEevWrcNf/vIXDB48GKmpqUhISECXLl0wfPhw/OEPf8CkSZNcXvuiRYvQq1cvefbenTRDBwCxsbHyDJd7zqBz2gKg7/y2WCxYvnw5XnvtNYwYMQIdOnRAXFwcsrKycOmll2Lu3LnyFRmz+PP5L1myBPfddx+ysrKQmJiIgQMH4sMPP5TfSwC46aab8Mwzz6Bnz55ITExE165dsXTpUlx//fWax/inP/0JM2bMQOfOnZGUlITBgwdj3bp1qlcR7r77bhQXF+P8889v9fdJTa9evVBaWop77rkHPXr0QEJCApKTk9G/f38sWLAAn3zyiSF/d5299tprmDRpEs477zwkJydj9OjR+PTTTw2ZwZTeo0GDBsm/V/fccw9efvll3ce699578cUXX+Dee+/FRRddhLZt28qf5aBBg/Cb3/wGn332mcvvlDOLxYKEhAScd955KCgowL333ovPP/8c8+fP9/dlRg2LEF6uOxCFmRkzZuD5559HbGwsvvrqq1ZpAmQ+KQjr1q2bS14eUSSZPHmynDLzySefsNMdURBoX+VBFOJ++tOfYu/evXLO7y9/+UsGtURERFGEgS1FjIMHD6KyshLnnXcerrvuOjz33HNmD4mIiIiCiKkIRERERBQRuHiMiIiIiCICA1siIiIiiggMbImIiIgoIjCwJSIiIqKIwMCWiIiIiCICA1siIiIiiggMbImIiIgoIjCwJSIiIqKIwMCWiIiIiCICA1siIiIiiggMbImIiIgoIjCwJSIiIqKIwMCWiIiIiCICA1siIiIiiggMbImIiIgoIjCwJSIiIqKIwMCWiIiIiCICA1siIiIiiggMbImIiIgoIjCwJSIiIqKIwMCWiIiIiCICA1siIiIiiggMbImIiIgoIjCwJSIiIqKIwMCWiIiIiCICA1siIiIiighxZg/AbA6HA9999x3atWsHi8Vi9nCIiIiIyI0QAqdOnULnzp0RE6M+Lxv1ge13332HnJwcs4dBRERERF4cOXIEXbp0Ub0/6gPbdu3aAWh5o1JTU00eDRERERG5q6+vR05Ojhy3qYn6wFZKP0hNTWVgS0RERBTCvKWNRu3iMZvNBqvVisLCQrOHQkREREQGsAghhNmDMFN9fT3S0tJQV1fHGVsiIiKiEKQ1XovaGVsiIiIiiiwMbImIiIgoIjCwJSIiIqKIwMCWiIiIiCICA1siIiIiiggMbImIiIgoIkRtYMs6tkRERESRhXVsWceWiIiIKKSxji0RERERRRUGtkREREQUERjYEhEREVFEiDN7AEREREQE2B0C2ytqcfxUIzq1S8LA3HTExljMHlZYYWBLREREZLK1ZZWY/345Kusa5W3ZaUmYV2TF2LxsE0cWXpiKQERERGSitWWVmLZ8l0tQCwBVdY2YtnwX1pZVmjSy8MPAloiIiMgkdofA/PfLoVR7Vdo2//1y2B1RXZ1VMwa2RERERCbZXlHbaqbWmQBQWdeI7RW1wRtUGGNgS0RERGSS46fUg1pf9ot2DGyJiIiITNKpXZKh+0W7qA1sbTYbrFYrCgsLzR4KERERRamBuenITkuCWlEvC1qqIwzMTQ/msMJW1Aa2xcXFKC8vR0lJidlDISIioigVG2PBvCIrALQKbqXb84qsrGerUdQGtkREREShYGxeNhbfko+sNNd0g6y0JCy+JZ91bHVggwYiIiIik43Ny8ZoaxY7j/mJgS0RERFRCIiNsWBwj45mDyOsMbAlIiIiIk3sDhHSs8oMbImIiIjIq7VllZj/frlLQ4nstCTMK7KGTB4wF48RERERkUdryyoxbfmuVl3SquoaMW35LqwtqzRpZK4Y2BIRERGRKrtDYP775RAK90nb5r9fDrtDaY/gYmBLRERERKq2V9S2mql1JgBU1jVie0Vt8AalgoEtEREREak6fko9qPVlv0BiYEtEREREqjq1S/K+k479AomBLRERERGpGpibjuy0pFYtfyUWtFRHGJibHsxhKWJgS0RERESqYmMsmFdkBYBWwa10e16RNSTq2YZ9YPvf//4X/fv3l3/atGmDlStXmj0sIiIioogxNi8bi2/JR1aaa7pBVloSFt+SHzJ1bC1CCPNrMxjk9OnT6N69O7755hukpKRoekx9fT3S0tJQV1eH1NTUAI+QiIiIKHyZ1XlMa7wWUZ3HVq9ejZ/+9Keag1oiIiIi0i42xoLBPTqaPQxVpqcibNiwAUVFRejcuTMsFotiGsGiRYuQm5uLpKQkFBQUYOPGjYrHeuutt3DDDTcEeMREREREFIpMD2wbGhrQr18/LFy4UPH+FStWYMaMGZg7dy52796NYcOGYdy4cTh8+LDLfvX19di8eTPGjx8fjGETERERUYgJqRxbi8WC9957DxMnTpS3XXrppcjPz8fixYvlbX369MHEiROxYMECedtrr72GDz/8EMuXL/f4HE1NTWhqapJv19fXIycnhzm2RERERCFKa46t6TO2npw9exY7d+7EmDFjXLaPGTMGW7ZscdmmNQ1hwYIFSEtLk39ycnIMHTMRERERmSOkA9vq6mrY7XZkZma6bM/MzERVVZV8u66uDtu3b8eVV17p9Zhz5sxBXV2d/HPkyBHDx01EREREwRcWVREsFtcyEkIIl21paWk4duyYpmMlJiYiMTHR0PERERERkflCesY2IyMDsbGxLrOzAHD8+PFWs7h62Ww2WK1WFBYW+nUcIiIiIgoNIR3YJiQkoKCgAOvWrXPZvm7dOlx22WV+Hbu4uBjl5eUoKSnx6zhEREREFBpMT0U4ffo0Dhw4IN+uqKhAaWkp0tPT0bVrV8ycORO33norBgwYgMGDB2PJkiU4fPgwpk6dauKoiYiIiCjUmB7Y7tixAyNGjJBvz5w5EwAwadIkLFu2DDfccANqamrwyCOPoLKyEnl5eVizZg26detm1pCJiIiIKASFVB3bYLLZbLDZbLDb7di3bx/r2BIRERGFKK11bKM2sJVofaOIiIiIyBwR0aCBiIiIiEirqA1sWe6LiIiIKLIwFYGpCEREREQhjakIRERERBRVGNgSERERUURgYEtEREREEcH0Bg1mca5jS0RERKTE7hDYXlGL46ca0aldEgbmpiM2xmL2sEgFF49x8RgREREpWFtWifnvl6OyrlHelp2WhHlFVozNyzZxZNGHi8eIiIiIfLS2rBLTlu9yCWoBoKquEdOW78LaskqTRkaeMLAlIiIicmJ3CMx/vxxKl7SlbfPfL4fdEdUXvUMSA1siIiIiJ9sralvN1DoTACrrGrG9ojZ4gyJNojawZecxIiIiUnL8lHpQ68t+FDxRG9gWFxejvLwcJSUlZg+FiIiIQkindkmG7kfBE7WBLREREZGSgbnpyE5LglpRLwtaqiMMzE0P5rBIAwa2REREFDXsDoGtB2uwqvQoth6sUVwAFhtjwbwiKwC0Cm6l2/OKrKxnG4KitkEDERERRRc9dWnH5mVj8S35rfbPYh3bkMYGDWzQQEREFPGkurTuQY8057r4lnzFYJWdx0KD1ngtamds2VKXiIgoOnirS2tBS13a0dasVkFrbIwFg3t0DMYwyQBRm2PLqghERETRgXVpo0fUBrZEREQUHViXNnowsCUiIqKIxrq00YOBLREREUU01qWNHgxsiYiIKKK416oFwLq0BtFSB9hMUVsVgYiIiCKPp1q1rEvrHz11gM3COrasY0tERBQRtNSqHW3NYl1aH/haB9gorGNLREREUcNbrVrgx1q1rEurjz91gIMtanNsbTYbrFYrCgsLzR4KERER+clbrVqAtWp9FU51gKM2sGWDBiIioshRVa+tBq3W/ehH4VQHOGoDWyIiIooctaebDN2PfhROdYAZ2BIREVHYS09JMHQ/+lE41QFmYEtERERhLyutjaH70Y9iYyxhUweYgS0RERGFPWlW0ZNQmVUMR2PzsrH4lnxkub3HWWlJAS/1pQfLfREREVFEuLEwB89+tF/xPgtCZ1YxXI3Nyw75OsAMbImIiCisKXXEchZq3bHCWWyMJaTrADOwJSIiorCl1hFLct+o3rhnZM+QmlWkwGGOLREREYUlTx2xgJb0gzdLDgdzSDK7Q2DrwRqsKj2KrQdrYHeojZKMxBlbIiIiCkt6OmIF8/K5UmoE0yGCI2pnbNlSl4iIKDRpne0MdkcsLeOSUiPcA+6qukZMW74La8sqDRkLKYvaGdvi4mIUFxejvr4eaWlpZg+HiIiIoH220+4QqD6lrYuYER2xtIzLU2qEQEtqxPz3yzHamsWc3wCJ2hlbIiIiCi1aZzvXllVi6BPr8egHez0ez6iOWFrHpSc1ggIjamdsiYiIKHRone10OASKX9+tumBMYlRHLD2zsMFOjaDWGNgSERGR6bTOdv5+VZnXoBZo6YhlxGItPbOwWlMejEiNIGUMbImIiMh0Wmcxaxuave7z4FV9MHlIriF5rHpmYa/u2xnZaUmoqmtUDL4taAm42dY3cJhjS0RERAHnraKAkbOYGe0SDVucpWcWNjbGgnlFVgA/pkJIjEqNIM84Y0tEREQBpaWiwMDcdI+znQBgsQBCQx6CkUGyt3G5z8KOzcvG4lvyW71eo1IjyDOLEFpOkcgllfuqq6tDamqq2cMhIiKKKGotb6U5y8W35MvBnrQvAE15tO6kIHPTAyMNnRVVG5fSa5DYHQLbK2px/FQjOrVrCXw5U+s7rfEaUxGIiIgoILxVFABaKgpIaQnSbGdWmuuMq5540OhL/XaHQFqbBNw+pDs6pMS73JeVlqQY1AJAbIwFg3t0xIT+52Nwj44MaoOEqQhEREQUEL60vB2bl43R1ix5trP6VJPXerUAkJ4Sj8evvdjQS/1KKRTpKQmY2L8zRluzOAsbghjYEhERUUD4WtdVmu0EgFWlRzUd48GrLzI8qFVKoTjRcBavbD7EoDZEMRWBiIiIAiIjJVHTfp4We2ldCJaVatyCMb0pFL4c31OFCPIdZ2yJiIjIcGvLKvHw6nKP+2ip66q3KoERfEmh0EpLhQjyHWdsiYiIyFDSZfyqevXgUGtdVzNqwwaqNa70vrgHzVV1jZi2fBfWllXqOh61FhGBbUVFBUaMGAGr1YqLL74YDQ0NZg+JiIgoKnm6jO8sMzVRtaKAO7VqCZ6qEvgjEK1xA53eQC0iIhVh8uTJ+MMf/oBhw4ahtrYWiYnacnqIiIjIWN4u40v+9H/9MaRnhubjuldLCGRt2ECkPwQyvYF+FPaB7Zdffon4+HgMGzYMAJCezv7LREREZtF6eb76dJPuYztXSwgkKf1h2vJdsEC5KYPe9IdApTeQK9NTETZs2ICioiJ07twZFosFK1eubLXPokWLkJubi6SkJBQUFGDjxo3yffv370fbtm1xzTXXID8/H48//ngQR09ERBS9lFb3B+IyvhmMTn+IlPcl1Jk+Y9vQ0IB+/frhl7/8JX72s5+1un/FihWYMWMGFi1ahCFDhuDFF1/EuHHjUF5ejq5du6K5uRkbN25EaWkpOnXqhLFjx6KwsBCjR49WfL6mpiY0Nf34LbG+vj5gr42IiChSqa3uf/CqPkGvYhAoRqY/mFHdIRqZPmM7btw4/OEPf8B1112neP8zzzyDO+64A3feeSf69OmD5557Djk5OVi8eDEAoEuXLigsLEROTg4SExMxfvx4lJaWqj7fggULkJaWJv/k5OQE4mURERFFLE+r+4tf341r+rXMZgarikEgGdUa14zqDtHI9MDWk7Nnz2Lnzp0YM2aMy/YxY8Zgy5YtAIDCwkIcO3YMJ06cgMPhwIYNG9CnTx/VY86ZMwd1dXXyz5EjRwL6GoiIiCKJltX9qz+vhO3m4FUxCBfBru4QjUxPRfCkuroadrsdmZmZLtszMzNRVVUFAIiLi8Pjjz+O4cOHQwiBMWPG4Oqrr1Y9ZmJiIqsmEBER+Ujr6v4OKQnY9MBIzZfx7Q4RlIoHZgtmdYdoFNKBrcRicf2whRAu28aNG4dx48bpOqbNZoPNZoPdbjdkjERERNFAz+p+rVUMgtmNKxQC6GBVd4hGIR3YZmRkIDY2Vp6dlRw/frzVLK5excXFKC4uRn19PdLS0vw6FhERUbQwenW/lK/rntogdeMy8hI929lGvpDOsU1ISEBBQQHWrVvnsn3dunW47LLLTBoVERFR9JJW96vNcVrQEixqWd0fzG5cbGcbHUwPbE+fPo3S0lK5kkFFRQVKS0tx+PBhAMDMmTPx0ksv4a9//Sv27t2L++67D4cPH8bUqVNNHDUREVH4Uao7q5eRq/v1dOPyB9vZRg/TUxF27NiBESNGyLdnzpwJAJg0aRKWLVuGG264ATU1NXjkkUdQWVmJvLw8rFmzBt26dfPreZljS0RE0cTIy/DS6n7342XpPF6wunGxnW30sAghovrriZRjW1dXh9TUVLOHQ0REZDi1PFZpTtXXPFZ/F2JtPViDm5Zu87rfG1MG+RVwrio9iulvlnrd7/kb+2NC//N9fh4KHK3xmukztkRERBQ43i7DW9ByGX60NUt3dQB/V/cHqxsX29lGD9NzbImIiChwgpXH6gtP+bpAy9huLOzq9/MYueCNQlvUBrY2mw1WqxWFhYVmD4WIiChggpXH6iu1blySZz/ah6FPrPeragHb2UaPqA1si4uLUV5ejpKSErOHQkREFDDhcBl+bF42Nj0wEveN6qV4vxEludjONjowx5aIiCiCBSuP1QhvlhxR3O5rLrD74rbR1iy2s41wDGyJiIgimHQZftryXbAALsGtFM7dWNgV/9zzHTJSEgELUH26KehBn9EludhlLDpFbWDLOrZERBQt1OrOtk+Oh0BLHquSYAaCRuYCB7NNL4UW1rFlHVsiIooSzpfmD1U34NmP9nvc3986t1rGIc0Mb6+oNaSmrd0hMPSJ9aqzv1LqxaYHRjIFIYywji0RERG5kOrOSsGfN/7WuVWiliLw4FVWQ3KB2WUsukVtVQQiIqJo5S34c2ZknVspRcD9uavqGlH8+i5c069lVtifklxaUxr+VVaJrQdrYHdE9YXriMPAloiIKIrYHQKbD3yv+3H+1rn11gENAFZ/XgnbzZcoluSy3ZyPtDYJWFV61GNAqrVs2d+2foOblm7zu0auErtDYOvBGq9jJeNFbSoCF48REVG0UUoD0MrfOrdaUwQ6pCRi0wMjXXJwTzQ04dEPtFU48FbezJ3RC8pYjcFcUTtjywYNREQUTdTSALwxqt2snqoHUi7whP7no+6Hsyh+fbdi+oJS0wZvbXrdScHv/PfL/Z5Z9ZRq4W+DCdImagNbIiKicKX3UrenNABPjGw360sHNC3pC0oBqbc2vUrH8jeP2NexkrGiNhWBiIgo3NgdAgvX78crmw/h5A/N8nZvl7r1LBZzlmXgJXRfOqD5U+FgbF623GXsX2WV+NvWb7yO0Z88YlZjCA0MbImIiMLA2rJKzH73C5w809zqvsq6RkxdvguLbr4E4/t2lrdL9WL/pfMS+D0jemJIzwxDO4956oCG/92+sbCryzZ/mzZIKQ0ANAW2GSmJ2Hqwxqd2u0Y2mCDfMbAlIiIKcWqdtNzd88ZuLIQF4/tm+7VQrFdm24DMKqp1QJM8+9E+vFlyWJ4l9iV9QYmW2eK05Hj85u3PUVXv26Ivo8ZK/mGOLRERUQjTkx/rEMDdr+/CgjXlPi0UkwQy+Bqbl41ND4zEfaN6Kd7vvNBKCkjV5kydF7Z5yjv2tKBMmj0+eabZJah1H4s3esZKgRO1ga3NZoPVakVhYaHZQyEiIlLlS37s0o0VuheKSYIVfL1ZckRxu/NCKwAeA1Lp/nXlVRj6xHrctHQbpr9ZqlifVm1BWWZqItonx3sdi7dFX96CZ2msbOMbWBYhRFQvz9Pae5iIiMgMq0qPYvqbpUF7vhcMqufqydaDNbhp6Tav+70xZRAG9+iomFaRnhKPa/ufj9Q28Xj2o/2tHiuFj+71aaW8YymP1iEEfvHSfzSPxRvWsQ0MrfEac2yJiIhCWLByMmMswMKbAh/UAvoXWjlXOFhXXoWVpd+htuEsXt58SPWxAi3B7fz3yzHamiXPlDovKANavjgYOWbnsfqyCI38w8CWiIgohOntpOWrhTddgvF9gzOj6MtCq9gYC+p+OItXNh/S/D5oKbHlz6Iv99lfKYB1D54peBjYEhERhTBvZbL8ZcZlcl9q2vraZALwPNt6oqEJMZaWhXdKlMYCMOUgVDGwJSIiCnHeymSpcQ+EpYvhM0b1RveMZMMuk6vNXKrxFKyrLbTytckEoD4ru7asEsWv7/YaLLuPRa38mlRFQcrr1fu+kP8Y2BIREYUBKXdz29c1KP77LpfOY+6y05Lw4FVWPPqBayBsZCcxia8zl2rButoYfWlsoDbbCmibAVbKO/bWOlfK63U4BB79YC9ndIOMgS0REVGYiI2xYEjPDNxQ2AUvbqhQ3McCyMHTlXmBXcSkdeZSjZ6FVnoX0XkrsaVlBtghgA4pCboeJ+X13v367lb3aX1fyHdRG9jabDbYbDbY7Xazh0JERKTZ2rJKLFEJagHgV8Nz5aBJ6yImXy6Ze5u5BFpXJFCidYx6F9F5m532tQWuPy1x1So1kHGiNrAtLi5GcXGxXBeNiIgo1Gm5fL7680r8dmwfzUGTr6kEWmY8vVUk0ENLXq6e3OFD1Q2antd9ptjf8mtaKjWQ76K28xgREVG40RNMarG2rBJTFVrvamklq3Xmcl15lab93Cm1yFXrHpaVloTFt+Rj+qhemND/fAzu0dFjUGt3CLyx/bDXMSh1YfPWOlcrf2Z+SV3UztgSERGFG18vnyuxOwRmv/uF4n1aLplrnblcVfod5l7luZWseyrEiYazrRa+Oc8i+9sAYXtFLarqm7zud2Nh11bH9TZzrLUcWbAab0QbBrZERERhwp9mAu4Wrj+Ak2fUKyt4u2Q+MDcd6SnxqG1QPwYA1DSc9XjZXSkVQon7wit/LuNr/YLQPSNZcbunig5SNQo9NXrJOAxsiYiIwoQvjQ2U2B0Cr2xWX4DmTC0IjI2x4Nr+53tsa+vtGGpVFZQYufDKiC8InmaOY2Kgq0YvGYc5tkRERGHkxsIc1aAW0BY0ba+o9VgH15mn4G6UNcvnY/jSScx5Ftkf3vJkLVDOr3UnVXRwz+v1lgvMUl+BwxlbIiKiMODtkr2e5gtaL8UnJ8R6DO78mUH2p5OYvwuvfOl8ppcRucCkH2dsiYiIQpx0yV4tELxvVG9semCk5plArZfivcVgUoAIoNXsp7cA0Z/g1IiFV8GYVVWb0aXA4YwtERFRCPN2yd4C4M2Sw7hnZE/Nx2xZ+JWA2oazHvc73WT3Wm9Vb2tciS/BqdELrzirGnkY2BIREYUwrS1c9RT8j42xYGL/zvirHwu/nI22ZqFdYjy2fl0NoGWWctAFnmco9XYSC9TCK62dzyg8MLAlIiIKEl9a1xpZu9bZaGuWpsDW28yqUu7vP3Z96zXf11Oeq5IOKfG4tv/5SGuTALtDcFaVFEVtYGuz2WCz2WC3280eChERRQFfW9caWbvWmTRjqjYbrOWyv1q5Lveas2rU0hiy05Lw4FV90CElEevKq7Cy9DvUNpzFy5sP4eXNhzS9bxSdLEIIPZU2Ik59fT3S0tJQV1eH1NRUs4dDREQRSC0AlOYcPQWAdofA0CfWe608sOmBkbpmMe0OgYXrD+DZj/YpHlPruLwFxlrGpTaT7c/7RpFFa7zGqghEREQB5Gnxl7Rt/vvlsDuU55n8qTygZm1ZJYY+sV4xqAW0VQbQk/vrjVL1AH/fN4pODGyJiIgCyIgA0MjSVN5Lh/XSVDosULm/EiMDZ4oeUZtjS0REFAxGBYBGlKbSVjrsCO4Z2cvrsQKV+yvR+r59VF7FqgYk44wtERFRABkZAPpb8N/IWVCj2tKq0fq+vVd6lOkIJGNgS0REFEC+BoB2h8DWgzVYVXoUWw/WGBK8GZk+EIjcX2dSEwlvahuamY5AMga2REREAeRLACgt7rpp6TZMf7MUNy3dhqFPrMfaskq/xmJ0+kAg29JKTSS08Kc9L0UW5tgSEREFmJ62s/7WhvXEW7cvX1rWBrItrVFNJCh6MLAlIiIKAi0BoLcSVxa0lLgabc3yKXD01O3Ln/SBQLWlNaKJBEUXpiIQEREFibfFX8EocRXI9AEjOOcWb6+oxYNXWWFBYPJ4KfJwxpaIiChEBLo2rCSQ6QP+UGs7/KvhuVj9eaXXNA4iBrZEREQhItC1YZ0FKn3AV55yi5dsqIDt5kvQISUxpAJxCj0MbImIiExgd4hWM6Z6FncpPT7YgZ5RY9CSW/zoB3ux6YGRDGbJo4gIbOPi4pCXlwcAGDBgAF566SWTR0RERKRO7ZL7vCKrpsVd68qrVB8frEvznl6D3jHoyS0OpVlmCj0RsXisffv2KC0tRWlpKYNaIiIKadIld/dATirnBcDj4i4AHh/vT61brU0hvL0GvWMIVm4xRb6ImLElIiIKB1rLeW16YKTi4i4AGPrE+oCUA9M6A2t3CMx+9wtDx+BLbnGgUjFCIcWDfGf6jO2GDRtQVFSEzp07w2KxYOXKla32WbRoEXJzc5GUlISCggJs3LjR5f76+noUFBRg6NCh+Oyzz4I0ciIiIn30XHJXKg0WqHJgemZgF67fj5Nnmg0dg962w4HqzBao41LwmB7YNjQ0oF+/fli4cKHi/StWrMCMGTMwd+5c7N69G8OGDcO4ceNw+PBheZ9Dhw5h586deOGFF3Dbbbehvr4+WMMnIiLSzN9L7oG4ZG93CDy8Wn0WGWiZgbU7BOwOgVc0dALTMwZphnR8Xpbqgjngx3q1RqdBSAJ1XAou01MRxo0bh3Hjxqne/8wzz+COO+7AnXfeCQB47rnn8OGHH2Lx4sVYsGABAKBz55Ze0nl5ebBardi3bx8GDBigeLympiY0NTXJtxkEExFRsPhbzisQ5cAWrt+Pqnrts8Anf1CfrdU7BqX0hxgL4Jza61yvNlCd2QLd8Y2Cx/QZW0/Onj2LnTt3YsyYMS7bx4wZgy1btgAATpw4IQeq3377LcrLy3HBBReoHnPBggVIS0uTf3JycgL3AoiIiJzoveRu9OPdrS2rxLMf7de07/FTjZpnYdPaxMEhhMdFaGozpOJ/u94+pDvemDIImx4YKef4BioVIxgd3yg4Qjqwra6uht1uR2Zmpsv2zMxMVFVVAQD27t2LAQMGoF+/frj66qvx/PPPIz1d/Rd6zpw5qKurk3+OHDkS0NdAREQkiY2xYF6RFYBvLWL9fbwzaZZSq07tkjTPBNsdwC9e+o9qnqqWGdJ/lVW1WrgVqOoJrMoQOUI6sJVYLK6/oEIIedtll12GL774Ap9//jlKS0sxceJEj8dKTExEamqqyw8REVGwjM3L9ljOy1sNWH8fL/E2S+lMmgWWZoy9Od10zuW2e56qrzOkgerMFsyObxRYpufYepKRkYHY2Fh5dlZy/PjxVrO4etlsNthsNtjtdr+OQ0REpNfYvGzFcl7eZlqlhVZN5xx4+vp+gAWoPt3kU1kqPbOPzrPA1/TLxosbKjQ/Fmidp+rrDKm3zmyAvlQMrcd17vhGoS2kA9uEhAQUFBRg3bp1uPbaa+Xt69atw4QJE/w6dnFxMYqLi1FfX4+0tDR/h0pERKSLVM5LK091Zn3pxqV19vG+Ub3lWeC1ZZVYojOolTjPwvo6QyqlYkiNLJRc0y9b9wIv5+N66vjGhWOhz/RUhNOnT8tdwwCgoqICpaWlcjmvmTNn4qWXXsJf//pX7N27F/fddx8OHz6MqVOnmjhqIiIKBVo7ZYW7QJSi8rYQDQCyUhNxz8ieADznxepx/FSjX4vgxuZl41fDc1WPv2RDhU/vh1EpHmQu02dsd+zYgREjRsi3Z86cCQCYNGkSli1bhhtuuAE1NTV45JFHUFlZiby8PKxZswbdunXz63mZikBEFN60dsoKV1LaQVV9Ix7955e6SlFp6Z6lZZby4Wsukh+nJyfXk07tkuTnnqow8+pthtTuEFj9uefA1dfSXL6miFDosAghIvPrrUZSKkJdXR0XkhERhQlpBtP9HzAp/Aj3GTaloN2bN6YMwuAeHXUH/Fr3X1V6FNPfLPXtBeHHPNVND4yUGy3MfveLVl3M2ifH44/XXaz6+W09WIOblm7z+nzS+0GRQWu8ZvqMLRERkR6RXkxfLWj35vipRtXHSikLUsDvPqP72f0jsPObEx5nKfVUBPCWp+rpNdZ5aNcrvU4tWJorOjGwJSKisKKnVFS4zdj5k8eakZKIWe987jXgdzgEHv1gr+IM7YT+56seX2vlgAevsuLRD1xngLV2D5N4+mKiNcA+VN2gaT+KLFEb2DLHlogoPEXyjJ2veazZaUmABZoC/rtf393qPvcZXSVaKweMzcvGlXmt81SBljSCzQe+1zTObV/XYEjPjFb3ayn5BQDPfrQfF2a1C+uUFNLP9KoIZikuLkZ5eTlKSkrMHgoREekQicX0peoOL3x2wKfHX9MvG9Wnm3x+filAnP9+ucfKElorB0ilzCb0Px+De3TEuvIqDH1iPW5aug0LPzmoaUzFf1eu9uDcfc0TaYY6UitlkLKonbElIqLwFKrF9L1VIlC735eFYu5Wf16J4b07+TV+rSkceisH+JozfPKHZtVZ5LF52Zgxqjee/Wif36+HIgsDWyIiCiueLonjf7eDXUzfW2UBtfuv6ZeNJRsq/K4NW1nXCAhoukTvjZYUDq3NJYyofauWb9s9I1nT48MxJYV8F7WpCDabDVarFYWFhWYPhYiIdJIuiaclx7e6r73CtkDy1jxhwZpyxfsr6xrxogFBraS6oUn1Er2eEN/IFA5/a986z7q6i8SUFPJf1Aa2zLElIgp/SqWh6s40+9yNSy9vpccAYOlG44JXTzq1S8I/dn2r+FwJcTFYdHO+z92+fGXUbKnScfzpXhYtHeuiEVMRiIgo7IRKLVstpccC3QZJyil+edPX+GjvccV9ms458F7pt15TOB68qo+h75dRs6VKx9FapcH99UR6x7poF7UztkREFL701LINJLPzN6WQbc6VP1ENaiXryo/j8t6dFKsaSB79YK+hM91aZlU9xdHeZpG1VmmQeEsbCcYsPwUWA1siIgo7oVLLNlj5m9lpSbhreG5LvVonUgBXclhbAP/4mnKMzcvGg1cp5+JWGhzgOZfmco9fpdtThuXC4uF+bwsBx+ZlY9MDI/HGlEF4/sb+eGPKIGx6YGSroFZL2gjLg4U/piIQEVHYCZWFQ1pKj1ksgLdYSSk1AADuGNIdo6xZcjmt347to1hm6+//OaxpvIdqzsDuEHj0g3LVfQSMTeOQZlXdL/87dyO7pGsHj/d7o6VKQyR3rKMfRW1gy85jREThK1Rq2WrJ85wyLBdLNlQAKvf/anguVn9eqSnnUy2A694xGRv3ex9v947JmioVGB3geat9q7c2ri9CZZafAitqA9vi4mIUFxejvr4eaWlpZg+HiIh08HXhUCAYMSOpNhOrhd0hMKpPJl7b5n3W9nfjrZrTDDbu/97w4NITrbVxfRUqs/wUWFEb2BIRUXjTElAaTa17mL8zkr4GdXq6lo22dkKbhFjUNpzVdOxFnx7Eu7u+xcPXXOT3e7m2rBIPr/4SVfU/tv3NSk005NhahcosPwWWRYhAFyIJbdKMbV1dHVJTU80eDhER6eStla1RQq1MlJ5WtaP6nIeXJg0EALy3+yjuW1Gq67leUKgwoNXaskpMXb4rIMf2ZSzT/jcWpVl+pUoKFBq0xmusikBERGFNmu2c0P98DO7RMWBBbSiVidLbqvbL707JY8xK1X+pffa7X/hULcDuEJj97hcBObYv9JYHo/DDVAQiIiIPQqUZhDO9rWqlAHzxLfkYbc1CdlqSrsefPNOMbQdrMKRXhq5xbjtYg5MK3eF8ObZRM/PBWKhG5onawJZVEYiISAutZaKeXfdfDOl5XlCCJL0r990DcGnhnXSfFlu/rtYd2G79utqQYxudBhLohWpknqhNRSguLkZ5eTlKSkrMHgoREYUwrUHkwk8O4qal2zD0ifUBT03wZeW+c51WtUvynvkSrGt9jPp+oZYGQqEtagNbIiIiNXaHwNaDNVhVehTVp5q8P8BJMAIub61qPZECdalj19zxfTQ9zpcZTq2PUduP3cJILwa2RERETtaWVWLoE+tx09JtmP5mKR79YC/0ZBYEI+Dy1KrWG+fZ3tgYC24fmov2yfEeH9MhOR6DLtAf2A66oKPXY7dNjENhd+USW3q6hREBDGyJiIhkape99canwQi49KYTWNCSl+pepzU2xoI/Xnexx8cuuO5in/KGtRz7dNM5XP7UJ4oz3OwWRnoxsCUiIoK2Elp6Y7tAB1xSOsEbUwbh+Rv7475RvQC0nsX11o1tbF42Xrglv1UpsOy0JL/rzKod25la+sah6gZNz8FuYSSJ2qoIREREzrSU0HII4MGr+uDEmWYs/OSA12MGI+CSVvhL5bBuH9KMlaXfuXQY09KNTU8ZLL2lt8bmZWPkTzIxaMFHqG1oXf5LqWya3SHwxnbvbYKVZqEpejGwJSIigvbZ1Yx2iZg8JBf/2PVtyLRnVSqHlZ4Sj2v7n49R1izNJci0lMHytfTWzm9OKAa1Euf0jcE9OmJ7Ra1LC141NxZ2ZQ1akkVtKoLNZoPVakVhYaHZQyEiohCgdXa1U7skj4u3vF32N5paXvCJhmb8dfMh1P1w1rBx+FN6S2++rNb9u2cka9qPokPUBrasY0tERM4KunVAekqC6v3ui69CoT2r3nJYzmXMNh+oxub91VhVehRbD9Z4reDgb+ktPV8cfNmfCGAqAhERmcyoVqn+kC6vO+eluhNoPQtrdntWPeWw6n442yqFwJm3dAI9z6WUziDV3tWavqF3fyKAgS0REZnI6Fapvo5h2vJdXlvLqtVjNbM9q9bL9evKq/DK5kMeX6OUTqA226z1uarqflDcLqVvTFu+Cxa4tvJVSt/Quz8REMWpCEREZK5QaJWqpcSXpO5Mc8i1cNV6GX5l6XdeX6O3dAKtz/XoB3tV3yO19I0OKfGw3XxJq4A6FNI9KLxwxpaIiILOW76me+mnQNFS4suMcWml5XJ9h5R4jykWzjylE/TPad9q5lRJbcNZjzO/Y/Oy4XAAv19VJo+rtqG5pcNbjEUxuDUz3YPCC2dsiYgo6EKlVareBgqh1sJVS3WGa/ufr/u47u/L2rJKDHlivaaZbYnazO/askoUv76rVbDtaaZeSveY0P98DO7RkUEtqWJgS0REQRcqrVJ9XVFv1LicqxRoqUygxNvl+lHWLN3HdH5fpJQRrbO+gPoXAH8rKxB5ozsV4ZtvvsG///1vNDc34/LLL8dFF10UiHEREVEEC5VSTt4u5asxYlxGLpzzdLne7hCaX6N7pQE9OchK3L8AaJ2p3/Z1DWIsFqYekG66AtsNGzZg/PjxOHPmTMuD4+Lw6quv4qabbgrI4IiIKDKFSiknTyvvlRg1LrVKDN4qE3iiVp1B62tUqjSgJwdZifsXAK0z3cV/34WTP/zYpSzYlTIofOlKRXjwwQcxYsQIfPvtt6ipqcHtt9+O3/72t4EaGxERRahQ6tyldinfnZ5xeUox8PdyvJb0Bfd9RluzvL5GpUoDvqZcuDezkGid6XYOaoHgVsqg8GYRQmi+wpCeno4NGzYgLy8PANDQ0IDU1FRUV1ejQ4cOARtkINXX1yMtLQ11dXVITU01ezhERFElFOrYSpwbRRyqPoM3th9GVb3+cXl7TVsP1uCmpdu8jufBq/pg8pBclyBay/vlaR/ndIWMtomAAKobmlQv92sdq5JFN1+C8X07u2yzOwSGPrFed+oH8ONs+aYHRjItIQppjdd0BbYxMTGoqqpCp06d5G3t2rXDnj17kJub69+Ig8xms8Fms8Fut2Pfvn0MbImITBIKnceMGteaPd/h7td3t9ouPWrxLfloOufA9DdLNY3BOWhVS19wPjYAr/vo+cKgJRC1WAClSELti4D0OgDvqR9K3pgyyLSGGGSegAW269evR3r6j5cWLrvsMrz11lvo0qWLvK1v374+Djv4OGNLRERGWLOnEve8sQtqC/qlGccnf9YXt/51u6ZjSgHpn2+6BPNWf6lamcACIDM1EYDFZZZZ6fn1zniqBaLSEe4cloulGytUx64UTCvNKrdPjsfJM64pCEqev7E/JvhQwozCW8ACW4vFAqWHSNstFgvsdrtvozYBA1siIvLX2rJKTP1f8OdNekqCrtJZgPqsqC98mfFUS2948CorHv2g3OMCs2yVYNp9RtzhEPjFy/8JyPgp/GmN13RVRaioaP2NjIiIKNBCNV0B+HExmFZ6g1rAuKAW8G1BmFo5MS1VE9Q6mcXGWORjHD/ViIyURGSlJuFYvbmVMii86Qpsu3XrFqhxEBERKQqlBWZK/C2JFWy+1uBVKiemNUheV17V6rFq6QhS62KltIdgVcqg8KW7QQMA7N+/H6tWrcKhQ4dgsViQm5uLiRMn4oILLjB6fEREFMUCUe/VG72zw4HujqaFc46trzOe0uuuqm9E7ekmpKckICutjcfXrzVIXlX6HeZe1VLebXtFLT4qr8LLmw+12q/ufzm2aW75tlkh9EWGQpvuwHbBggV46KGH4HA40KlTJwgh8P3332P27Nl4/PHHMWvWrECMk4iIIpx7QFnQrYPHeq8WtNR7HW3NMmwWz5fZ4UB3R9Pq4WtaOoEqNWLwNuOp9Lolnl7/wNx0pKfEo7bB86KvmoazWLj+AN4sOey185gFQFJcDP5+56WoPq1eioxIia7FY5988glGjRqFBx98ENOnT5dr19bW1uK5557D448/jvXr12P48OEBG7DRuHiMiMh8SoFVu6Q4nGo85/WxRi0m0lJOSym486c2qxE6piTgsWvzNNWxVRq/2ut2ZoH663/0/S8VZ1/9xUVi5CwgVRFuuOEGtG/fHi+++KLi/b/61a9w6tQpvPHGG/pHbBIGtkRE5tISWHliRPknKThVm030VirL39qsamIsUC0fBgDpKfHYNmcUEuJcG4lqTafw9rqdqVU38KeJgycs60XOtMZrulrqbt++Hbfeeqvq/bfeeiu2bTP+5CYiosjkqb2sVkakAnhbACbw4+p+JWPzsmG7OR8dUhJctmenJeH6fN+Cswev6oOFN10CC1q3HZZc2/987PzmhNxWV2ql+8893wEAru7bGYN7dFS9jK9n4Zva6x+Ym47stCTVMfoqVFI8KLzoyrE9duwYunfvrnp/bm4uqqqq/B0TERFFCX8qChhZ/knrAjC1/daWVeLRD8pdSnmlJMbizNlzeGfXUZ/GlNEuEeP7dsbiGEur1AJpJvflzYfw8uZDyE5LwjX9srH680pd+cF6F759pFDdIDbGgnlFVsXcXl+wrBf5Q9eMbWNjIxISElTvj4+Px9mz+uvzERFRdPK1ooDR5Z+0zg4q7SelIbgH6A1NdtT94D1H2Ntzjc3LxqYHRuKNKYNw+5DuAFqnJ1TWNeLFDRWtxiBVj1hbVunxObR6efMhxWONzcvG4lvykZXm3ywry3qRv3RXRXjppZfQtm1bxftOnTrl94CIiCh6+Hq52ejyT9LldE8LwLIVZhGNSKVwpzRjKTUzmPlWqa5jeaseMTA3XXMrW8nDq79Eu6T4VhULpCYOyzZX4NEP9uoap4RlvchfugLbrl27YunSpV73ISIicqa2mElLQOnuwav6YPKQXENn9LRcTv+h2Y515VUuQVegmjMozVj6+lzO+cFGVBmoqm/CL176sfWtc7pDbIwFGe0SNR9Leq9vH9Ido61ZLOtFftMV2B46dChAw/DfmTNn0KdPH/z85z/H008/bfZwiIjof7yVn9KanynNZBod1Eqky+mz3/1CcQaz7kxzq6YQRjdn8JQT6+9zKT1+e0WtrtlaJe7NMvTMwnOGloymK7BtbGzERx99hKuvvhoAMGfOHDQ1Nf14sLg4PPLII0hKCv5KxsceewyXXnpp0J+XiIjUae0ctviWfNUGAUDwci9HW7Pw8OovFe9TuqxvxMr9tomx+L8BOV5nLP19LqXHGxGYu78vWmbh27eJh+0X+Rh0gXrFBiJf6Fo89uqrr7rUsF24cCG2bNmC3bt3Y/fu3XjttdewaNEiwwfpzf79+/HVV19h/PjxQX9uIiJS5in/VNo2//1y2B3CZYHUHUO6Iz0l3mX/rLSkgLTPddfSUrZJ9X73sl9GlLp68dYBeKjoIo9lufx5LguU84MB40pqOb8vUlqH9NzuY7EA+OPPLsaQnhkMaslwugLbv//977j99ttdtr3++uv45JNP8Mknn+Cpp57C22+/rWsAGzZsQFFRETp37gyLxYKVK1e22mfRokXIzc1FUlISCgoKsHHjRpf7Z82ahQULFuh6XiIiCiy9tWFjYywY3KMjHiy6CCVzR+ONKYPw/I398caUQdj0wMigXK7WW/bLUxDnjRRwDrpAW96rL8/lbabb6Bq00vuiViUhWF9QKHrpCmz37duH3r17y7eTkpIQE/PjIQYOHIjy8nJdA2hoaEC/fv2wcOFCxftXrFiBGTNmYO7cudi9ezeGDRuGcePG4fDhwwCAVatWoXfv3i7jIiIi8/lTG1YKcif0P9/rTKaRfCn7pRbEdUiOR3JCrOLjfU2tUHuu7LQk3DU8F9k6A0l/AnMl7u+LNAsf7C8oFL105djW1dUhLu7Hh3z//fcu9zscDpecWy3GjRuHcePGqd7/zDPP4I477sCdd94JAHjuuefw4YcfYvHixViwYAG2bduGN998E2+//TZOnz6N5uZmpKam4qGHHlI8XlNTk8sY6+vrdY2XiIi08ac2rFm85YeqNQ+QSl25V34AgIXrD+CVzRU4+cOPi7T8WTSl9lyxMRb8dmwfTa103Y/nLcfZG7X3RfqCQhQsugLbLl26oKysDBdeeKHi/Xv27EGXLl0MGRgAnD17Fjt37sTs2bNdto8ZMwZbtmwBACxYsEBOQ1i2bBnKyspUg1pp//nz5xs2RiIiUuZrkGgmT2W/vM2yqgVx00f1wj0je+oOOL2N0/251EqqaeFPDVo2VaBQoisVYfz48XjooYfQ2Nj6G90PP/yA+fPn46qrrjJscNXV1bDb7cjMzHTZnpmZ6XPr3jlz5qCurk7+OXLkiBFDJSIiN94WEQGhGQwFIj800KkVa8sqMfSJ9bhp6TZMf7MUNy3dhqFPrFftOGZ3CGw9WINVpUex9WAN7A6B2BgLJg/J9Zpz6z505s1SKNE1Y/u73/0Ob731Fi688ELcc8896N27NywWC7766issXLgQ586dw+9+9zvDB2mxuP4WCSFabQOAyZMnez1WYmIiEhO1F48mIiLfqV3mDvX6pZ4u90v8mSE1ktaSas77+1JXWHplC2/KR4eUBNNfN5ESXYFtZmYmtmzZgmnTpmH27NkQouWUt1gsGD16NBYtWtRqdtUfGRkZiI2NbTU7e/z4cb+fx2azwWazwW63+3UcIiKzeAusQiXw0hIkhiJP+aHegkMjefocvZVUc6+7609dYU9fRkLlXCOyCCk61am2thYHDhwAAPTs2RPp6f7nSFksFrz33nuYOHGivO3SSy9FQUGBS31cq9WKCRMmGFLiq76+Hmlpaairq0NqaqrfxyMiCgZvgVUwA69ooxYcSmGckZflvX2OWw/W4Kal27we540pgzAwNx1Dn1jvsQlGVloSNj0wErExFs3BKs81Cgat8ZquGVtn6enpGDhwoK8Pl50+fVoOkAGgoqICpaWlSE9PR9euXTFz5kzceuutGDBgAAYPHowlS5bg8OHDmDp1qt/PTUQUCvTOdnmbdfvV8Fws2VCh+dI0aad3htQfWmZXm845NB3r+KlGXXWFpTxgbxUN9KZBEAWaz4GtUXbs2IERI0bIt2fOnAkAmDRpEpYtW4YbbrgBNTU1eOSRR1BZWYm8vDysWbMG3bp1M2vIRESGWbOnEr9fVYbahrPyNk+zXVq6eS3d2Dqole43MvCKRnqDQ19pDaCfvr6fpuN1apfkV11hf8bIc42CSVdVhEC44oorIIRo9bNs2TJ5n7vvvhuHDh1CU1MTdu7cieHDh/v9vDabDVarFYWFhX4fi4jIFwvWlOPu13e5BLVAS2A0bfkuxRXt3gIrAHB4SDBz7/ZF+hgdHKrRGkDDAo9VDJzb6RpdV1hvZzmiYDA9sDVLcXExysvLUVJSYvZQiCgKrdnzHV7cUKF6v0DLbJfdLUr1N2Ay+jjRJlhNJ7R+PtWnmzSXVPPWPtc5CDZyjDzXKJiiNrAlIjKL3SHw+1VlXvdTmu0yqktXKHX7CidGB4dq9ATQWuvuGl1XOBw7y1HkMz3H1iws90VEZtleUYvahmbvO6L1bJe3bl5ASwF9IWB4ty+WdPKvM5keeru2aS2pZmRd4XDsLEeRz+dyX5GC5b6IKNhWlR7F9DdLNe37xpRBrRYhSSvRAeXASqqKoHa/LyvVWdLJVTDeD2+fsz8VB4z6khLIMRI50xqvMbBlYEtEQaa19mjHlARsnztKc+3QjikJeHRCHsb3NbaOrb91W42c6XU/VkG3Dtj5zQlNxzZ6xjkYM9hGfY6BHCu/9FAwMLDViIEtEQWb3SE8FsqXLLo5H+P7qgcGa/Z8979SYT+mNTgHFEYEM97G6l7U353RAbb7sWIsrlUg1I4dzsGXv59jMF4701Qo0BjYasTAlojMoDYLKrlreC7mjLfqfrzRl4D1dLZSS5lQe40v6Bijt2NJnF+/lHO6rrwKf918yOO+oR7c+iqYXdKIAklrvBa1VRFYx5aIzCQt4sl2W8menhKPRTdf4jGo1dKkQalUmPsxth6swarSo9h6sEZ1X19LOnkao2T2u194HKOeY0mkfea8+wWG/PFj3LR0m2JQ67yvt/cqXBlxnhCFm6itilBcXIzi4mL5GwARUbBpXcnuzt/uV3ouTfta0klLI4mTZ5qxcP0BTB/Vy+N+Wo7lTAA4cUZb1QmjOoWFomB1SSMKJVE7Y0tEFApiYywY3KMjJvQ/H4N7dNSUl+hPYXzp0rR7wFOl0u3M17qtWsf4ypYKrzOGwSjwH4lNBLS+ps0Hqr3O3BOFi6idsSUiCle+zqJ6uzRtQcul6dHWLDnA9rVuq9YxnjzT7HXGMBgF/jPaJmLrwZqIWvyk9X1b+MkB+f/DZUEdkRrO2BIRhRlfZ1H1XJp2prWzlfsY27eJ1/JyvM4senu9/rAA6JAcj9+8VYqblm7D9Ddb/jv0ifWtZq/DjS/vm9rMPVG4iNrAlovHiChc+doa1Z8UhrF52dj0wEi8MWUQnr+xP96YMgibHhipOrMXG2PBL4d01/R83mYWPb1ef0gz0CfONKOqvsnlvkgI8Hx537iojMJd1Aa2xcXFKC8vR0lJidlDISLSzZdZVF9TGCR684HvGdkL7ZPVZ23VZpaVqL1e9yFkpyWhfXK8pkAu63/7KomUAG9sXjZsN+ejQ0qC5seozdwThQPm2BIRhSm9VRWkS9NVdY2KebZSswUtgaYWsTEW/PG6iz3WUVWaWVaj9HqVOo+tK69SzAmW3DGkO0ZZs+BwCPzi5f+oPl8kVA1YW1aJRz8oR23DWXlbSkIsGs7avT42EhfUUeRjYEtEFMakWVSt+/qyEMwf0kyre3mxLB8XKSm9Xvfbas/pvjBqVelRTc8ZrgGeWnMGLUEtEJxFe0RGY2BLRBRFxuZl41fDc7F0YwWc+05aLMCUYbkBWQ3va71eT7y1cNXynP6mZoQyPU0t3Bk9c08UTAxsiYiiyNqySizZUNEq4HEIYMmGClzStUNAglv3mVap85kvga7WBhPeZrODnZoRTHqbWkgCNXNPFCwMbImIwpS3WUul/b3N4rnXsQ0EPZ3PlB6rdHldqmKgtnBOiRmpGcGiNX2ifZt4nPzhxy5tvqaIEIWKqA1sbTYbbDYb7HZtuUZERKHEl+AwFFqs+hOY+tJgQu04zl8IbDfn49EPjMkBDhVa0ydsv8hHjMUSUY0pKLpFbWBbXFyM4uJi1NfXIy0tzezhEBFp5i04lMo7uQcr/tSxNYK/gakRgbnaF4IHr+qDDimJERPgaU2zGHSBtjbOROEiagNbIqJQ5z6zWNCtA0oqajH7H1+oBocAcM8bu+BcelWayfWnFa8RC7/8DUz9Dcw9fSEofn03Ft+Sjwn9z9f0HGbR+llEcpoFkScMbImIQpDSzGKMBdDSK8B9nx9nci9BdlqSx+DSvWGCP/mw7vwNTP2pYmBUGoOZ9H4WvpRaM+pLDJFZGNgSEYUYtZlFXxtgSYHbox/sxdV9s7F0Y4Xqvtf0y5YDGSMXagH+l9fyp4pBKOQX+8PXz0JPqTUjv8QQmSVqW+oSEYUif+qPeiIFbv/Y5bkpwerPK2F3CK8znID+drNSYKo2/+etxa50eV3a1/2xgPrldbPzi/3h72ehpRWyFDi7B/9S4Ly2rNLPV0EUHAxsiYhCiK/1R7Vybq2qRJq11DPDqZU/galEuryeleY6q5uVluRxBjmcmzEE4rNwFogvMURmYSoCEVEICYUZQz1j0DteI1rs+tLJLJybMQR6tjnc0zSInEVtYMs6tkQUivyZMfS0uMwCoENKPGobmpV38HEMvozXiBa73rqKKS2CCtcqAYGebQ7nNA0id1Eb2LKOLRGFIm8zi0rat4mH7Rf5qDtzFsWv7wagHLj9YUIeHv1gr+ZZy0DOcHoLTP3haRGUv7PFZgj0bHM4p2kQuWOOLRFRCPGUh+rO8r+fP/7sYgzpmYHxfTt7zD8d37ez5hxXI/JhzeBtERQAbHpgJN6YMgjP39gfb0wZhE0PjDQsqLU7BLYerMGq0qPYerDGkLzUQH8W/i7qIwolFiFEVGeDSzO2dXV1SE1NNXs4REQAtNWxVSvF5K0WqZ6yTuFUAsruEBj6xHrVfFFpZnPTAyMDEpAH+r1as+c7/H5VmUs6iVHHl74QAMqz/XpLuxEZTWu8xsCWgS0RhSilzmM7vzlhSPF8PYX4w6Vo/9aDNbhp6Tav+70xZZDhaRBqdWaNCgyVgub0lAT8YUIexvc1JuAMpy8xFH20xmtRm2NLRBTqlPJQjQrI9OS4uu8rXW4PtUDXrEVQge5qphY0n2g4i+LXd2FxjDGzqUYs6iMyGwNbIqIgC5cZUCWhPKtn1CIovZ9PIMtlaQ2aR/4k05DZ/EAu6iMKBga2RERBFMqBoTdGt9j1lVrgaUT1AF8+n0DOFGsNmgct+Nil+Ua4nFNERmNVBCKiIAnntqXB6E6lpaLA2rJKDH1iPW5aug3T3yzFTUu3YegT67G2rNLv6gG+fj6BLJelNRh27ygXDucUUSAwsCUiCoJwb1sa6LaungJW5328BZ6+ttz15/PxVi4LALJSE30ql+Vr7dhwOKeIAoGBLRFREAQ6MAy0QF5u1xKw6gk8x+Zl665V68/no6X2cOM5B9aVV6keX42WoFlNqJ9TRIEQtYGtzWaD1WpFYWGh2UMhoigQCm1L/WkeEKjL7VoD1m1f1+gKPKVFUBP6n4/BPTp6XUjl7+cjzRSnJccr3l93ptmn1AA9DTvUsBUuRZOoDWyLi4tRXl6OkpISs4dCRFHA7Lala8sqMeSPH7tc6h/yx481B1qB6k6ldaZ068EaTcfzNYgz4vMZbc1CUlys4n3+pAaopVekpygH0e7YCpeiSdQGtkREwSQFhp4Eqm3p2rJKTF2+C1X1TS7bq+qbMFXjLGKg2rpqD0S1BYP7j532qZWtEYH79opaVNUHJt1EKb1i25xRbIVL5IaBLRFRgEnlqazZ7Tzud02/bMPr2dodArPf/cLjPrPf/UJTIOjrwixPtM4mDr4gQ1Ou6cJPDiguPPPGiMA90Okm7ukVCXExAfmyQRTOGNgSEQWQ82r/j7/63uO+qz+vNHwF+7aDNTh5ptnjPifPNGObxkv9vizM8kTrTOmgHh115Zr6Uu7K38DdjHSTQHzZIApnbNBARBQgag0N1PjancqTrV9Xa95vSK8MTfsa2Z1KmimdtnwXLHBNOHCfdZSCOPcGCkp8bWXrT1tZIxpE+IKtcIl+xMCWiCgAPK3298T4FexagxvzgiC1gDVLoXuWcxC3+cD3WPjJQdXj+trK1tfAXU+QbjS2wiVqwcCWiCgAvK32V+PtMrVaO1k1g3t0xMJPDnh9XrODIj2zjlIQFwol1IDWn4nt5nw8+oH3IJ2IjMfAlogoAPQGU1ouU68tq2w1q5ntJWAadEFHtE+O95hn2yE5HoMuMH+2T++so9kl1AD1z+TBq/qgQ0oiUwOIgoyLx4iIAkBPMKXlMrWW7lxKYmMs+ON1F3t8/gXXXRyWQVegautq5ekzKX59N+p+OKu5QQQRGYOBLRFRAOhphdohJR63D+mOtDYJilUR9LSTVTI2Lxsv3JKPrFTXYDs7LQn3jeqNpnMOn2q/mi1QtXW18PczIaLAsAghovq3rr6+Hmlpaairq0NqaqrZwyGiCCLN6AGtFxIJACN/ch5Kj9ShtuGsfF/7NvH45ZBc3DOypxyQbT1Yg5uWbvP6fG9MGeTxUr5zLuih6ga8sf2wS9MGb2kNocpbiobevGQtjPpMiEgbrfEac2yJiALE02r/a/plY8mGilYzfid/aMazH+3DK1sq8MfrLsbYvGzDFklJOaxryyrx3Ef7Wz23lNYQbvVPPS088yUvWYtQWbhGRK4Y2BIRBZBS0FXQrQMuf+oTj6XATp5pxtTlu/DCLfmGLpLydgndl9qvoUBp4ZlaHWEjAvhQWLhGRK0xx5aIKMDcW6Hu/OaE5lJg898vR0G3Dq3yY53pWSTlrQyZc+3XcBboHFizF64RkbKwD2xPnTqFwsJC9O/fHxdffDGWLl1q9pCIiDzSc3m6sq4Riz89iMZzdsX79S6SipZL6IEO4M1cuEZE6sI+FSE5ORmfffYZkpOTcebMGeTl5eG6665Dx45M1iei0KT38vSzH+1Tva99cjwW/C8X18jnDvdL6MEI4PV0TCOi4Aj7wDY2NhbJyckAgMbGRtjtdkR5oQciCnHSZWxfOpO5S4yLwWhrlu7nrqprVLxMr6VRhK8CUZ1ATbACeD0d04go8ExPRdiwYQOKiorQuXNnWCwWrFy5stU+ixYtQm5uLpKSklBQUICNGze63H/y5En069cPXbp0wW9/+1tkZGQEafRERPpJl7GNCH2q6pt0XU436xL62rJKDH1iPW5aug3T3yzFTUu3YegT61UbS2hhdwhsPViDVaVHW9XhDWYOrHsONYNaIvOYHtg2NDSgX79+WLhwoeL9K1aswIwZMzB37lzs3r0bw4YNw7hx43D48GF5n/bt2+Pzzz9HRUUFXn/9dRw7dkz1+ZqamlBfX+/yQ0ShwVOgEmmky9jtk+MV79cTGum9nC49d1aa62xlVlpSQEp9+do1zdsxPQXKzIElik4h1aDBYrHgvffew8SJE+Vtl156KfLz87F48WJ5W58+fTBx4kQsWLCg1TGmTZuGkSNH4uc//7niczz88MOYP39+q+1s0EBkrkDVGw11dofAwvX78crmQzj5Q7O8PTstCTcW5uDZj/Z7PYavTQD8TQ3Q8ni7Q2DoE+tV0y6k1IdND4zU/NxqZbykRzsH59F6XhFFmoho0HD27Fns3LkTs2fPdtk+ZswYbNmyBQBw7NgxtGnTBqmpqaivr8eGDRswbdo01WPOmTMHM2fOlG/X19cjJycnMC+AiDQJZL3RUBcbY8H0Ub1xz8herYJEAHiz5Igp+bDeaA0Y9VQn0BKc663DyxxYougS0oFtdXU17HY7MjMzXbZnZmaiqqoKAPDtt9/ijjvugBACQgjcc8896Nu3r+oxExMTkZiYGNBxE5F2kdowQC+lBgNAy+Xyact3yW14Jf5eTvdnJlPPFxGjqxP4EiirvbdEFHlMz7HVwmJx/aMthJC3FRQUoLS0FJ9//jn27NnjcbbWmc1mg9VqRWFhoeHjJSLtoqVhgK8CkQ/rT86r3sYHRlcniJY6vETkm5Cesc3IyEBsbKw8Oys5fvx4q1lcvYqLi1FcXCznbBCRORioeGfk5XR/Z8j1zpgaXV4sWurwEpFvQnrGNiEhAQUFBVi3bp3L9nXr1uGyyy4zaVREZCQGKtoYVVLK3xlyvV9EjK5OwFa2ROSJ6YHt6dOnUVpaitLSUgBARUUFSktL5XJeM2fOxEsvvYS//vWv2Lt3L+677z4cPnwYU6dONXHURGQUBirB5e8MuS9fRIxMp2AZLyLyxPRUhB07dmDEiBHybaliwaRJk7Bs2TLccMMNqKmpwSOPPILKykrk5eVhzZo16Natm1/Pa7PZYLPZYLcr918nouCQApVALJCi1vydIfc1tcDIdAq2siUiNSFVx9YMWuuiEVFgsd6od0a0pJXqynoLTD3VlZUWnwHKX0SCVZ4tmC16ichcWuM1BrYMbIlCBgMVdUYG/kYEpvwiQkTBxMBWIwa2RGQWrYG8nk5bWhkRmPKLCBEFCwNbL5xzbPft28fAloiCSmtgqbUl7Wf3j8DOb07oCjIZmBJRuGBgqxFnbIko2NRmYIGWQNV5BnbrwRrctHSb12Omp8SjtqFZvs20ACKKJFrjNdPLfRERRRNPDRKAlpzXOe9+IXfu0lqeyzmoBbR1ESMiijQMbImIgshbgwQAOHGmGQvX7wfge2MKpfa2RESRLmoDW5vNBqvVisLCQrOHQkRRwO4Q2HqwBv/SOIP6yuZDsDuE1wYWnqh1EZPGsqr0KLYerGHgS0QRgzm2zLElogBTWiimxRtTBmFwj46q5bm0ev7G/pjQ/3zVsTAfl4hCHXNsiYhCgBSU6g1qgR/za9Va0nZMSdB0HCmdQW0szMclokhhektdIqJI5W2hmDfO+bVKLWkLunXA5U99oqm9raexiP/tO//9coy2ZrHkFxGFLc7YEhEFiJaFYkosaEkPGJib7rI9NsaCwT06YkL/8zG4R0ckxMVgXpFVfoz7MQBgXpEVsTEWr2NRy8clIgonURvYcvEYEQWa1lJdztwDUm/U0hSy0pJc6uFqHYsvYyYiChVRm4pQXFyM4uJiORmZiMhovpTqyvJhIZdSmoJ7FzGtY/G1vBi7mBFRKIjawJaIKNCkUl3ecmCfvr4fqhua/AoIpTQFX8cCtHQvK+jWQfdzs9ICEYWKqE1FICIKtNgYi6Yc2CG9MuS82UDNcnoai6S2oRmXP/WJruoIrLRARKGEgS0RUQBpzYE1cyzO9ASk3iotAOx8RkTBxVQEIqIA05IDG8yxjPxJJgYt+Bi1DWdb3a+n9JeeSgue0iSIiIzCwJaIKAi85cAG085vTigGtRKtASkrLRBRqInaVASW+yKiaGVUQBroSgtERHpFbWBbXFyM8vJylJSUmD0UIqKgMioglSotqCUrqDWaICIKlKgNbIko8pw958DLG7/GQ6vK8PLGr3H2nMPsIYUkowJSrVUfWM+WiILFIoSI6uWqUoOGuro6pKammj0cIvLRgjXlWLqxAs4L8GMswJRhuZgz3mrewEKUVKYLgEtVAykE1VOxgXVsiSjQtMZrDGwZ2BKFvQVryvHihgrV++8azuBWiZEBKTuPEVEgMbDViIEtUfAEIvg5e86Bnzz4L3gqlRpjAb6cPxalR04y8HLDgJSIwoHWeI3lvogoKAJ1ufq1rYc8BrUA4BDAwMc/wqnGc349dyQGgaFUhoyIyF8MbIko4KR8Tvf4U+py5U8Hrm9qz2jazzmo9eW5mUdKRBT6orYqAuvYEgVHoNuudktP9ulxep5bCszdu2zpaT9LRESBF7WBLevYEgWHnrarvrh1cHf4mg2g5bkDHZgTEZFxojawJaLgMLLtqt0hsPVgDVaVHsXWgzWwOwQS4mIwZVhuwMYY6MCciIiMwxxbIgooo7pcecpxlUp5udex1SojJVH1vo/KqzQdQ2sAT0REgcPAlogCSupyVVXXqHg53wIgy0uXKy2Lz+aMt+I3Y36C17Yewje1Z9AtPRk9O7XFpFc0pBuppDLYHQLvlR71/nhoD+CNEInVGYiIjMDAlogCSmq7Om35Llig3OXKU9tVbzmuFrTkuI62ZiEhLgZ3DLtAvn+VxqC0+nST4vbtFbWobWj2+viOKQle288ahdUZiIjUMceWiAJubF42Ft+Sj6w011nNrLQkr+W2/Mlx9TcNQmt6wYT+nYMyY8rqDEREnnHGloiCYmxeNkZbs3RfQvdn8Zm/aRBaA+PR1ixN+/lDz8w10xKIKFpxxpaIgkbqcjWh//kY3KOjpgDM08Iub/tJaRBA6zRaLWkQUmDsaZTZXvKDjRLI6gxK1SaIiMIRZ2yJKLRpnXxU2U9Kg3DPS83SkJfqb36wkYwsm+aMObtEFEkY2BJRSFNb2KVnP1/TIKTH+hoYG8mosmnOAtnqmIjIDAxsiSikGRXQSWkQWriX0xptzfI5MDaKEWXTnDFnl4giUdQGtjabDTabDXa73eyhEJEHRgd03oTqpXmj0yL05Oxq/UJARGS2qF08VlxcjPLycpSUaCjeTkSm8XcBmB6hXk7Ln7Jp7gKVs0tEZKaonbElovARjDzXcLk070++sLNA5OwSEZmNgS0RhQWjAjo14XRpXk++sJpgp3gQEQUDA1siChtGBHRqou3SfCiVMiMiMkrU5tgSETmLxkvzRubsEhGFAs7YEhEhei/NBzrFg4gomBjYEhEhui/NBzLFg4gomJiKQET0P7w0T0QU3jhjS0TkhJfmiYjCFwNbIgoo9/a0oRgkKo2Rl+aJiMIPA1siCphQbU/rLBzGSERE2jDHlogCItTb0wLhMUYiItKOgS0RGc5be1qgpT2t3aG0R3CEwxiJiEifsA9sjxw5giuuuAJWqxV9+/bF22+/bfaQiKKenva0ZgmHMRIRkT5hn2MbFxeH5557Dv3798fx48eRn5+P8ePHIyUlxeyhEUWtcGhPGw5jJCIifcI+sM3OzkZ2dssCj06dOiE9PR21tbUMbIlMFA7tacNhjEREpI/pqQgbNmxAUVEROnfuDIvFgpUrV7baZ9GiRcjNzUVSUhIKCgqwceNGxWPt2LEDDocDOTk5AR41EXkitadVK+plQUvlATPb04bDGImISB/TA9uGhgb069cPCxcuVLx/xYoVmDFjBubOnYvdu3dj2LBhGDduHA4fPuyyX01NDW677TYsWbIkGMMmIg+k9rQAWgWOodKeNhzGSERE+liEECGz5NdiseC9997DxIkT5W2XXnop8vPzsXjxYnlbnz59MHHiRCxYsAAA0NTUhNGjR2PKlCm49dZbPT5HU1MTmpqa5Nv19fXIyclBXV0dUlNTjX1BRFEuHGrEhsMYiYiiXX19PdLS0rzGayGdY3v27Fns3LkTs2fPdtk+ZswYbNmyBQAghMDkyZMxcuRIr0EtACxYsADz588PyHiJyFU4tKcNhzESEZE2IR3YVldXw263IzMz02V7ZmYmqqqqAACbN2/GihUr0LdvXzk/97XXXsPFF1+seMw5c+Zg5syZ8m1pxpaIAiM2xhLy7WnDYYxERORdSAe2EovFdeZECCFvGzp0KBwOh+ZjJSYmIjEx0dDxEREREZH5TF885klGRgZiY2Pl2VnJ8ePHW83i6mWz2WC1WlFYWOjXcYiIiIgoNIR0YJuQkICCggKsW7fOZfu6detw2WWX+XXs4uJilJeXo6SkxK/jEBEREVFoMD0V4fTp0zhw4IB8u6KiAqWlpUhPT0fXrl0xc+ZM3HrrrRgwYAAGDx6MJUuW4PDhw5g6daqJoyYiIiKiUGN6YLtjxw6MGDFCvi0t7Jo0aRKWLVuGG264ATU1NXjkkUdQWVmJvLw8rFmzBt26dfPreW02G2w2G+x2u1/HISIiIqLQEFJ1bM2gtS4aEREREZlDa7wW0jm2RERERERaMbAlIiIioohgeo6tWZhjSxTd7A7BbmNERBGGObbMsSWKOmvLKjH//XJU1jXK27LTkjCvyIqxedkmjoyIiJQwx5aISMHaskpMW77LJagFgKq6RkxbvgtryypNGhkREfmLgS0RRQ27Q2D+++VQukwlbZv/fjnsjqi+kEVEFLYY2BJR1NheUdtqptaZAFBZ14jtFbXBGxQRERkmagNbm80Gq9WKwsJCs4dCREFy/JR6UOvLfkREFFqiNrAtLi5GeXk5SkpKzB4KEQVJp3ZJhu5HREShJWoDWyKKPgNz05GdlgS1ol4WtFRHGJibHsxhERGRQRjYElFIsTsEth6swarSo9h6sMbQhVyxMRbMK7IqLh4DWnJs5xVZWc+WiChMRW2DBiIKPawvS0RE/ojaGVsuHiMKLcGoLyuV+1JjAct9ERGFs6gNbLl4jCh0BKu+LMt9ERFFtqgNbIkodAQr4GS5LyKiyMbAlohMF6yAk+W+iIgiGwNbIjJdsAJOlvsiIopsDGyJyG/+lugKVsAplfuSjun+HADLfRERhbOoLfdls9lgs9lgt9vNHgpRWDOiRJcUcE5bvgsWwGURmdEB59i8bCy+Jb/VmLNYVoyIKOxZhBBRXdemvr4eaWlpqKurQ2pqqtnDIQorUoku9z8iUvi5+JZ8XYFiMOvY2h0C2ytqcfxUIzq1a5kN5kwtEVFo0hqvMbBlYEvkk7PnHBi04CPUNjQr3m9ByyzopgdG6goYGXASEZE7rfEac2yJSLe1ZZUYtOBj1aAW8K1EF4NaIiLyR9Tm2BKRb9TSD9RoLdHFdrpEROQvztgSkWaeOoSp0VKiKxjtdImIKPIxsCUizbx1CHOmtURXsNrpEhFR5GNgS0Sa6e38paVEV7Da6RIRUeSL2sDWZrPBarWisLDQ7KEQhQ2tnb/SU+I1l/oKVjtdIiKKfFEb2BYXF6O8vBwlJSVmD4UobHjrEAYAHVMSsG3OKM0LvoLVTpeIiCJf1Aa2RKSft5a0FgCPXZuHhDjtf1qC1U6XiIgiHwNbItJFakmbleY6g5qVlqS70xjgPVgGjGunS0REkY2dx9h5jMgnRjdTYB1bIiJSw5a6GjGwJQod7DxGRERKtMZr7DxGRCEjNsaCwT06mj0MIiIKU8yxJSIiIqKIwMCWiIiIiCICA1siIiIiiggMbImIiIgoIjCwJSIiIqKIELWBrc1mg9VqRWFhodlDISIiIiIDsI4t69gSERERhTTWsSXSiE0BiIiIIgMDW4pqbOMaXvglhIiIPGFgS1FrbVklpi3fBfdcnKq6RkxbvguLb8lncBtC+CWEiIi8idrFYxTd7A6B+e+XtwpqAcjb5r9fDrsjqlPQQ4b0JcQ5qAV+/BKytqzSpJEREVEoYWBLUWl7RW2rIMmZAFBZ14jtFbXBGxQp4pcQIiLSioEtRaXjp9SDWl/2o8DhlxAiItKKgS1FpU7tkgzdjwKHX0KIiEgrBrYUlQbmpiM7LQlq6+ktaFmYNDA3PZjDIgX8EkJERFoxsKWoFBtjwbwiKwC0Cm6l2/OKrCwlFQL4JYSIiLRiYEtRa2xeNhbfko+sNNeZvqy0JJb6CiH8EkJERFqxpS5b6kY9Fv0PD6xjS0QUvbTGawxsGdgShQ1+CSEiik5a47WISEW49tpr0aFDB1x//fVmD4WIAig2xoLBPTpiQv/zMbhHRwa1RETkIiIC21//+tf429/+ZvYwiCKK3SGw9WANVpUexdaDNWyAQEREIS/O7AEYYcSIEfj000/NHgZRxGA+KxERhSPTZ2w3bNiAoqIidO7cGRaLBStXrmy1z6JFi5Cbm4ukpCQUFBRg48aNwR8oUZRYW1aJact3ter2VVXXiGnLd2FtWaVJIyMiIvLM9MC2oaEB/fr1w8KFCxXvX7FiBWbMmIG5c+di9+7dGDZsGMaNG4fDhw8HeaREkc/uEJj/fjmUkg6kbfPfL2daAhERhSTTUxHGjRuHcePGqd7/zDPP4I477sCdd94JAHjuuefw4YcfYvHixViwYIHu52tqakJTU5N8u76+Xv+giSLU9oraVjO1zgSAyrpGbK+oxeAeHYM3MCIiIg1Mn7H15OzZs9i5cyfGjBnjsn3MmDHYsmWLT8dcsGAB0tLS5J+cnBwjhkoUEY6fUg9qfdmPiIgomEI6sK2urobdbkdmZqbL9szMTFRVVcm3r7zySvz85z/HmjVr0KVLF5SUlKgec86cOairq5N/jhw5ErDxE4WbTu2SvO+kYz8iIqJgMj0VQQuLxbVWpRDCZduHH36o+ViJiYlITEw0bGxEkWRgbjqy05JQVdeomGdrQUvL4YG56cEeGhERkVchPWObkZGB2NhYl9lZADh+/HirWVy9bDYbrFYrCgsL/ToOUSSJjbFgXpEVQEsQ60y6Pa/IysYIREQUkkI6sE1ISEBBQQHWrVvnsn3dunW47LLL/Dp2cXExysvLPaYtEEWjsXnZWHxLPrLSXNMNstKSsPiWfNaxJSKikGV6KsLp06dx4MAB+XZFRQVKS0uRnp6Orl27YubMmbj11lsxYMAADB48GEuWLMHhw4cxdepUE0dNFNnG5mVjtDUL2ytqcfxUIzq1a0k/4EwtERGFMtMD2x07dmDEiBHy7ZkzZwIAJk2ahGXLluGGG25ATU0NHnnkEVRWViIvLw9r1qxBt27dzBoyUVSIjbGwpBcREYUVixAiKiut22w22Gw22O127Nu3D3V1dUhNTTV7WERERETkpr6+HmlpaV7jtagNbCVa3ygiIiIiMofWeC2kF48REREREWkVtYEty30RERERRRamIjAVgYiIiCikMRWBiIiIiKIKA1siIiIiiggMbImIiIgoIkRtYMvFY0RERESRhYvHuHiMiIiIKKRx8RgRERERRRUGtkREREQUERjYEhEREVFEiDN7AGax2Wyw2Ww4d+4cgJbcDSIiIiIKPVKc5m1pWNQvHvv222+Rk5Nj9jCIiIiIyIsjR46gS5cuqvdHfWDrcDjw3XffoV27drBYLGYPhwxWX1+PnJwcHDlyhFUviOcDueD5QM54PoQ2IQROnTqFzp07IyZGPZM2alMRJDExMR4jf4oMqamp/ENFMp4P5IznAznj+RC60tLSvO7DxWNEREREFBEY2BIRERFRRGBgSxEtMTER8+bNQ2JiotlDoRDA84Gc8XwgZzwfIkPULx4jIiIiosjAGVsiIiIiiggMbImIiIgoIjCwJSIiIqKIwMCWiIiIiCICA1siIiIiiggMbClqHTlyBFdccQWsViv69u2Lt99+2+whkcnOnDmDbt26YdasWWYPhUxWUVGBESNGwGq14uKLL0ZDQ4PZQyITPfvss7joootgtVrx61//GiwoFbqivqUuRa+4uDg899xz6N+/P44fP478/HyMHz8eKSkpZg+NTPLYY4/h0ksvNXsYFAImT56MP/zhDxg2bBhqa2tZ2zSKff/991i4cCG+/PJLxMfHY/jw4di2bRsGDx5s9tBIAWdsKWplZ2ejf//+AIBOnTohPT0dtbW15g6KTLN//3589dVXGD9+vNlDIZNJAcywYcMAAOnp6YiL4zxQNDt37hwaGxvR3NyM5uZmdOrUyewhkQoGthS2NmzYgKKiInTu3BkWiwUrV65stc+iRYuQm5uLpKQkFBQUYOPGjYrH2rFjBxwOB3JycgI8agoEI86FWbNmYcGCBUEaMQWSv+fD/v370bZtW1xzzTXIz8/H448/HsTRk9H8PR/OO+88zJo1C127dkXnzp0xatQo9OjRI4ivgPRgYEthq6GhAf369cPChQsV71+xYgVmzJiBuXPnYvfu3Rg2bBjGjRuHw4cPu+xXU1OD2267DUuWLAnGsCkA/D0XVq1ahd69e6N3797BHDYFiL/nQ3NzMzZu3AibzYatW7di3bp1WLduXTBfAhnI3/PhxIkT+Oc//4lDhw7h6NGj2LJlCzZs2BDMl0B6CKIIAEC89957LtsGDhwopk6d6rLtJz/5iZg9e7Z8u7GxUQwbNkz87W9/C8YwKQh8ORdmz54tunTpIrp16yY6duwoUlNTxfz584M1ZAogX86HLVu2iCuvvFK+78knnxRPPvlkwMdKgefL+fDWW2+Ju+++W77vySefFE888UTAx0q+4YwtRaSzZ89i586dGDNmjMv2MWPGYMuWLQAAIQQmT56MkSNH4tZbbzVjmBQEWs6FBQsW4MiRIzh06BCefvppTJkyBQ899JAZw6UA03I+FBYW4tixYzhx4gQcDgc2bNiAPn36mDFcCjAt50NOTg62bNmCxsZG2O12fPrpp7jwwgvNGC5pwMCWIlJ1dTXsdjsyMzNdtmdmZqKqqgoAsHnzZqxYsQIrV65E//790b9/f3zxxRdmDJcCSMu5QNFDy/kQFxeHxx9/HMOHD0ffvn3Rq1cvXH311WYMlwJMy/kwaNAgjB8/Hpdccgn69u2LHj164JprrjFjuKQBl3lSRLNYLC63hRDytqFDh8LhcJgxLDKBp3PB2eTJk4M0IjKTt/Nh3LhxGDduXLCHRSbxdj489thjeOyxx4I9LPIBZ2wpImVkZCA2NrbVjNzx48dbfTOnyMZzgZzxfCBnPB8iDwNbikgJCQkoKChotZJ53bp1uOyyy0waFZmB5wI54/lAzng+RB6mIlDYOn36NA4cOCDfrqioQGlpKdLT09G1a1fMnDkTt956KwYMGIDBgwdjyZIlOHz4MKZOnWriqCkQeC6QM54P5IznQ5QxtSYDkR8++eQTAaDVz6RJk+R9bDab6Natm0hISBD5+fnis88+M2/AFDA8F8gZzwdyxvMhuliEECK4oTQRERERkfGYY0tEREREEYGBLRERERFFBAa2RERERBQRGNgSERERUURgYEtEREREEYGBLRERERFFBAa2RERERBQRGNgSERERUURgYEtEFCEmT56MiRMnhsxxiIiCjYEtEZEBJk+eDIvFAovFgvj4eFxwwQWYNWsWGhoazB6aqkOHDsFisaC0tNRl+/PPP49ly5aZMiYiIn/EmT0AIqJIMXbsWLzyyitobm7Gxo0bceedd6KhoQGLFy82e2i6pKWlmT0EIiKfcMaWiMggiYmJyMrKQk5ODm6++Wb84he/wMqVK9HU1IRf//rX6NSpE5KSkjB06FCUlJTIj/v0009hsVjwwQcfoF+/fkhKSsKll16KL774Qt7n4YcfRv/+/V2e77nnnkP37t1Vx7N27VoMHToU7du3R8eOHXH11Vfj4MGD8v25ubkAgEsuuQQWiwVXXHEFgNapCFrH//HHH2PAgAFITk7GZZddhv/+978+vItERL5jYEtEFCBt2rRBc3Mzfvvb3+If//gHXn31VezatQs9e/bElVdeidraWpf977//fjz99NMoKSlBp06dcM0116C5udnn529oaMDMmTNRUlKCjz/+GDExMbj22mvhcDgAANu3bwcAfPTRR6isrMS7776reByt4587dy7+9Kc/YceOHYiLi8Ptt9/u89iJiHzBwJaIKAC2b9+O119/HSNGjMDixYvx1FNPYdy4cbBarVi6dCnatGmDl19+2eUx8+bNw+jRo3HxxRfj1VdfxbFjx/Dee+/5PIaf/exnuO6669CrVy/0798fL7/8Mr744guUl5cDAM477zwAQMeOHZGVlYX09PRWx5BSKbSM/7HHHsPll18Oq9WK2bNnY8uWLWhsbPR5/EREejGwJSIyyD//+U+0bdsWSUlJGDx4MIYPH457770Xzc3NGDJkiLxffHw8Bg4ciL1797o8fvDgwfL/p6en48ILL2y1jx4HDx7EzTffjAsuuACpqaly6sHhw4d1HUPr+Pv27Sv/f3Z2NgDg+PHjPo+fiEgvLh4jIjKINDsbHx+Pzp07Iz4+Hp9//jkAwGKxuOwrhGi1TYm0T0xMDIQQLvd5S1MoKipCTk4Oli5dis6dO8PhcCAvLw9nz57V/Jqk59Qy/vj4+FbjltIeiIiCgTO2REQGSUlJQc+ePdGtWzc5yOvZsycSEhKwadMmeb/m5mbs2LEDffr0cXn8tm3b5P8/ceIE9u3bh5/85CcAWtIGqqqqXIJb9zJdzmpqarB37178/ve/x09/+lP06dMHJ06ccNknISEBAGC321WPo2f8RERm44wtEVEApaSkYNq0abj//vuRnp6Orl274sknn8SZM2dwxx13uOz7yCOPoGPHjsjMzMTcuXORkZEhVye44oor8P333+PJJ5/E9ddfj7Vr1+Jf//oXUlNTFZ+3Q4cO6NixI5YsWYLs7GwcPnwYs2fPdtmnU6dOaNOmDdauXYsuXbogKSmpVakvPeMnIjIbZ2yJiALsj3/8I372s5/h1ltvRX5+Pg4cOIAPP/wQHTp0aLXf9OnTUVBQgMrKSqxevVqeVe3Tpw8WLVoEm82Gfv36Yfv27Zg1a5bqc8bExODNN9/Ezp07kZeXh/vuuw9PPfWUyz5xcXH485//jBdffBGdO3fGhAkT/Bo/EZHZLMI9aYuIiILq008/xYgRI3DixAm0b9/e7OEQEYUtztgSERERUURgYEtEREREEYGpCEREREQUEThjS0REREQRgYEtEREREUUEBrZEREREFBEY2BIRERFRRGBgS0REREQRgYEtEREREUUEBrZEREREFBEY2BIRERFRRGBgS0REREQR4f8BYRGil+NFkOkAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig,ax = plt.subplots(figsize=(8,6))\n", - "\n", - "ax.scatter(x=global_data.POP_EST,y=global_data.GDP_MD)\n", - "ax.set_xlabel('Population')\n", - "ax.set_ylabel('GDP')\n", - "ax.set_xscale('log')\n", - "ax.set_yscale('log')\n", - "\n", - "ax.set_title('Country-level comparison between Population and GDP', fontweight='bold', fontsize=12, y=1.05)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Much better. However, it does not say much more than just the relation between GDP and Population. It would be interesting, for example, to add some more info about the different points.\n", - "\n", - "While we can use matplotlib again, a more convenient package to use is the [seaborn package](https://seaborn.pydata.org/). Seaborn, which is built on top of Matplotlib and provides a higher-level interface for creating statistical graphics. Seaborn simplifies many of the complexities of Matplotlib and provides a wide range of visualizations for exploring relationships between variables. By learning how to use Seaborn, you will be able to create more advanced and sophisticated visualizations in a shorter amount of time.\n", - "\n", - "One way to include the amount of data per location in the plot is to change the size of the dot. We do this through using the `size` option. Let's compute the size of the country to use that to change the size of the dots. We do that through the `area` function within **GeoPandas**." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "global_data['Country_Size'] = np.log(global_data.to_crs(3857).area)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.05, 'Country-level comparison between Population and GDP')" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig,ax = plt.subplots(figsize=(8,6))\n", - "\n", - "ax = sns.scatterplot(x=\"POP_EST\", y=\"GDP_MD\", size=\"Country_Size\", \n", - " data=global_data)\n", - "ax.set_xlabel('Population')\n", - "ax.set_ylabel('GDP')\n", - "ax.set_xscale('log')\n", - "ax.set_yscale('log')\n", - "\n", - "ax.set_title('Country-level comparison between Population and GDP', fontweight='bold', fontsize=12, y=1.05)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Hmm that does not work great yet. Another option would be to change the color! Through *seaborn*, we do this by specifying which column should be coloured through the `hue` argument." - ] - }, - { - "cell_type": "code", - "execution_count": 210, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.05, 'Country-level comparison between Population and GDP')" - ] - }, - "execution_count": 210, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig,ax = plt.subplots()\n", - "\n", - "ax = sns.scatterplot(x=\"POP_EST\", y=\"GDP_MD\", hue=\"Country_Size\", \n", - " data=global_data)\n", - "ax.set_xlabel('Population')\n", - "ax.set_ylabel('GDP')\n", - "ax.set_xscale('log')\n", - "ax.set_yscale('log')\n", - "\n", - "ax.set_title('Country-level comparison between Population and GDP', fontweight='bold', fontsize=12, y=1.05)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Colormaps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's stop for a moment here and let's take some time to explore colormaps a little bit more. Choosing the right colormap is a critical aspect of creating effective visualizations. A colormap is a mapping between a range of values and a set of colors, and it determines how the data is represented in the visualization. Selecting the appropriate colormap can help communicate the data accurately and highlight important features, while choosing the wrong colormap can obscure or even misrepresent the data.\n", - "\n", - "Different colormaps are appropriate for different types of data and different visualization goals. For example, *sequential* colormaps are useful for showing variations in magnitude, while *diverging* colormaps are appropriate for highlighting differences between two groups of values. Selecting the appropriate colormap can enhance the clarity and interpretability of your visualizations, while choosing an inappropriate one can lead to misinterpretation or confusion.\n", - "\n", - "And the amount of colors, and colormaps is endless! Let's have a look below which colormaps are available in matplotlib through the `plt.colormaps()` function." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['magma',\n", - " 'inferno',\n", - " 'plasma',\n", - " 'viridis',\n", - " 'cividis',\n", - " 'twilight',\n", - " 'twilight_shifted',\n", - " 'turbo',\n", - " 'Blues',\n", - " 'BrBG',\n", - " 'BuGn',\n", - " 'BuPu',\n", - " 'CMRmap',\n", - " 'GnBu',\n", - " 'Greens',\n", - " 'Greys',\n", - " 'OrRd',\n", - " 'Oranges',\n", - " 'PRGn',\n", - " 'PiYG',\n", - " 'PuBu',\n", - " 'PuBuGn',\n", - " 'PuOr',\n", - " 'PuRd',\n", - " 'Purples',\n", - " 'RdBu',\n", - " 'RdGy',\n", - " 'RdPu',\n", - " 'RdYlBu',\n", - " 'RdYlGn',\n", - " 'Reds',\n", - " 'Spectral',\n", - " 'Wistia',\n", - " 'YlGn',\n", - " 'YlGnBu',\n", - " 'YlOrBr',\n", - " 'YlOrRd',\n", - " 'afmhot',\n", - " 'autumn',\n", - " 'binary',\n", - " 'bone',\n", - " 'brg',\n", - " 'bwr',\n", - " 'cool',\n", - " 'coolwarm',\n", - " 'copper',\n", - " 'cubehelix',\n", - " 'flag',\n", - " 'gist_earth',\n", - " 'gist_gray',\n", - " 'gist_heat',\n", - " 'gist_ncar',\n", - " 'gist_rainbow',\n", - " 'gist_stern',\n", - " 'gist_yarg',\n", - " 'gnuplot',\n", - " 'gnuplot2',\n", - " 'gray',\n", - " 'hot',\n", - " 'hsv',\n", - " 'jet',\n", - " 'nipy_spectral',\n", - " 'ocean',\n", - " 'pink',\n", - " 'prism',\n", - " 'rainbow',\n", - " 'seismic',\n", - " 'spring',\n", - " 'summer',\n", - " 'terrain',\n", - " 'winter',\n", - " 'Accent',\n", - " 'Dark2',\n", - " 'Paired',\n", - " 'Pastel1',\n", - " 'Pastel2',\n", - " 'Set1',\n", - " 'Set2',\n", - " 'Set3',\n", - " 'tab10',\n", - " 'tab20',\n", - " 'tab20b',\n", - " 'tab20c',\n", - " 'magma_r',\n", - " 'inferno_r',\n", - " 'plasma_r',\n", - " 'viridis_r',\n", - " 'cividis_r',\n", - " 'twilight_r',\n", - " 'twilight_shifted_r',\n", - " 'turbo_r',\n", - " 'Blues_r',\n", - " 'BrBG_r',\n", - " 'BuGn_r',\n", - " 'BuPu_r',\n", - " 'CMRmap_r',\n", - " 'GnBu_r',\n", - " 'Greens_r',\n", - " 'Greys_r',\n", - " 'OrRd_r',\n", - " 'Oranges_r',\n", - " 'PRGn_r',\n", - " 'PiYG_r',\n", - " 'PuBu_r',\n", - " 'PuBuGn_r',\n", - " 'PuOr_r',\n", - " 'PuRd_r',\n", - " 'Purples_r',\n", - " 'RdBu_r',\n", - " 'RdGy_r',\n", - " 'RdPu_r',\n", - " 'RdYlBu_r',\n", - " 'RdYlGn_r',\n", - " 'Reds_r',\n", - " 'Spectral_r',\n", - " 'Wistia_r',\n", - " 'YlGn_r',\n", - " 'YlGnBu_r',\n", - " 'YlOrBr_r',\n", - " 'YlOrRd_r',\n", - " 'afmhot_r',\n", - " 'autumn_r',\n", - " 'binary_r',\n", - " 'bone_r',\n", - " 'brg_r',\n", - " 'bwr_r',\n", - " 'cool_r',\n", - " 'coolwarm_r',\n", - " 'copper_r',\n", - " 'cubehelix_r',\n", - " 'flag_r',\n", - " 'gist_earth_r',\n", - " 'gist_gray_r',\n", - " 'gist_heat_r',\n", - " 'gist_ncar_r',\n", - " 'gist_rainbow_r',\n", - " 'gist_stern_r',\n", - " 'gist_yarg_r',\n", - " 'gnuplot_r',\n", - " 'gnuplot2_r',\n", - " 'gray_r',\n", - " 'hot_r',\n", - " 'hsv_r',\n", - " 'jet_r',\n", - " 'nipy_spectral_r',\n", - " 'ocean_r',\n", - " 'pink_r',\n", - " 'prism_r',\n", - " 'rainbow_r',\n", - " 'seismic_r',\n", - " 'spring_r',\n", - " 'summer_r',\n", - " 'terrain_r',\n", - " 'winter_r',\n", - " 'Accent_r',\n", - " 'Dark2_r',\n", - " 'Paired_r',\n", - " 'Pastel1_r',\n", - " 'Pastel2_r',\n", - " 'Set1_r',\n", - " 'Set2_r',\n", - " 'Set3_r',\n", - " 'tab10_r',\n", - " 'tab20_r',\n", - " 'tab20b_r',\n", - " 'tab20c_r',\n", - " 'rocket',\n", - " 'rocket_r',\n", - " 'mako',\n", - " 'mako_r',\n", - " 'icefire',\n", - " 'icefire_r',\n", - " 'vlag',\n", - " 'vlag_r',\n", - " 'flare',\n", - " 'flare_r',\n", - " 'crest',\n", - " 'crest_r']" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plt.colormaps()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Ok, so that is indeed a massive list. But we can also conveniently explore them. Just change the number in the cell below:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/html": [ - "
magma
\"magma
under
bad
over
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cmaps = plt.colormaps()\n", - "plt.get_cmap(cmaps[0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "we generally abbreviate colormap as `cmap`. This is also the argument we generally use to specify which colormap we want to use. Seaborn, however, generally uses the term `palette`. We can add any colormap through the `palette` argument to our plot. It will change which colormap will be used to represent the `hue`. If you try different colormaps, you will already discover that some colormaps work, and others don't work at all!" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.05, 'Country-level comparison between Population and GDP')" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig,ax = plt.subplots(figsize=(8,6))\n", - "\n", - "ax = sns.scatterplot(x=\"POP_EST\", y=\"GDP_MD\", hue=\"Country_Size\", palette='magma_r',\n", - " data=global_data)\n", - "ax.set_xlabel('Population')\n", - "ax.set_ylabel('GDP')\n", - "ax.set_xscale('log')\n", - "ax.set_yscale('log')\n", - "\n", - "ax.set_title('Country-level comparison between Population and GDP', fontweight='bold', fontsize=12, y=1.05)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can generally distinguish three types of colormaps: \n", - "\n", - "- **diverging** \n", - "- **sequential** \n", - "- **qualitative**\n", - "\n", - "The [Colorbrewer website](https://colorbrewer2.org/) can be very helpful to play around with different color schemes. But let's explore the three different types first. We start with **diverging** colormaps. They generally use *three* colors. The third colour is generally located in the middle. We can have a look at some **diverging** colormaps below:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/html": [ - "
spring
\"spring
under
bad
over
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plt.get_cmap('spring')" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/html": [ - "
RdYlGn
\"RdYlGn
under
bad
over
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plt.get_cmap('RdYlGn')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The second type are the **sequential** colormaps. They generally use *one* color, and move from white to the \"purest form\" of that color. We can have a look at some **sequential** colormaps below:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/html": [ - "
Greens
\"Greens
under
bad
over
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plt.get_cmap('Greens')" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/html": [ - "
Purples
\"Purples
under
bad
over
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plt.get_cmap('Purples')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The third type are the **qualitative** colormaps. They use unique colors for each category we want to plot. We can have a look at some **qualitative** colormaps below:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/html": [ - "
tab10
\"tab10
under
bad
over
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plt.get_cmap('tab10')" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/html": [ - "
Pastel2
\"Pastel2
under
bad
over
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plt.get_cmap('Pastel2')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "### Multi-panel figures" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Multi-panel plots, also known as small multiples, are a powerful tool for visualizing big data. As the name suggests, multi-panel plots display multiple panels, each containing a subset of the data, side-by-side or stacked vertically. This approach allows you to compare and contrast different aspects of the data, identify patterns and trends, and explore relationships between variables.\n", - "\n", - "Visualizing big data can be challenging, especially when the data is complex or contains many variables. Multi-panel plots can help overcome this challenge by allowing you to break down the data into smaller, more manageable subsets. By visualizing these subsets side-by-side or stacked vertically, you can quickly identify patterns and trends that might not be apparent when looking at the data as a whole.\n", - "\n", - "Multi-panel plots are also useful when exploring relationships between variables. By displaying multiple panels with different combinations of variables, you can quickly identify correlations and relationships between different aspects of the data. This can help you develop a deeper understanding of the data and uncover insights that might not be apparent when looking at the data as a whole.\n", - "\n", - "Let's continue with our global data. One simple way to create multi-panel figures is to, for example, create a panel for each continent. Ofcourse we can manually split our data, but one convenient way would be to loop over the differen continents. Remember the `groupby` function? We can also use that too loop over subsets within our data!" - ] - }, - { - "cell_type": "code", - "execution_count": 227, - "metadata": {}, - "outputs": [], - "source": [ - "for continent in global_data.groupby('CONTINENT'):\n", - " continent" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `continent` object is a tuple that contains the name of the continent as the first element and the dataframe with all the countries within that continent in the other element:" - ] - }, - { - "cell_type": "code", - "execution_count": 283, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('South America',\n", - " featurecla scalerank LABELRANK SOVEREIGNT \\\n", - " 2 Admin-0 country 0 2 Chile \n", - " 3 Admin-0 country 0 3 Bolivia \n", - " 4 Admin-0 country 0 2 Peru \n", - " 5 Admin-0 country 0 2 Argentina \n", - " 22 Admin-0 country 0 4 Suriname \n", - " 23 Admin-0 country 0 4 Guyana \n", - " 44 Admin-0 country 0 2 Brazil \n", - " 45 Admin-0 country 0 4 Uruguay \n", - " 136 Admin-0 country 0 3 Ecuador \n", - " 137 Admin-0 country 0 2 Colombia \n", - " 138 Admin-0 country 0 4 Paraguay \n", - " 139 Admin-0 country 0 9 Brazilian Island \n", - " 159 Admin-0 country 0 3 Venezuela \n", - " 173 Admin-0 country 0 9 Southern Patagonian Ice Field \n", - " 242 Admin-0 country 1 5 United Kingdom \n", - " \n", - " SOV_A3 ADM0_DIF LEVEL TYPE TLC \\\n", - " 2 CHL 0 2 Sovereign country 1 \n", - " 3 BOL 0 2 Sovereign country 1 \n", - " 4 PER 0 2 Sovereign country 1 \n", - " 5 ARG 0 2 Sovereign country 1 \n", - " 22 SUR 0 2 Sovereign country 1 \n", - " 23 GUY 0 2 Sovereign country 1 \n", - " 44 BRA 0 2 Sovereign country 1 \n", - " 45 URY 0 2 Sovereign country 1 \n", - " 136 ECU 0 2 Sovereign country 1 \n", - " 137 COL 0 2 Sovereign country 1 \n", - " 138 PRY 0 2 Sovereign country 1 \n", - " 139 BRI 0 2 Indeterminate None \n", - " 159 VEN 0 2 Sovereign country 1 \n", - " 173 SPI 0 2 Indeterminate None \n", - " 242 GB1 1 2 Disputed 1 \n", - " \n", - " ADMIN ... FCLASS_ID FCLASS_PL \\\n", - " 2 Chile ... None None \n", - " 3 Bolivia ... None None \n", - " 4 Peru ... None None \n", - " 5 Argentina ... None None \n", - " 22 Suriname ... None None \n", - " 23 Guyana ... None None \n", - " 44 Brazil ... None None \n", - " 45 Uruguay ... None None \n", - " 136 Ecuador ... None None \n", - " 137 Colombia ... None None \n", - " 138 Paraguay ... None None \n", - " 139 Brazilian Island ... None None \n", - " 159 Venezuela ... None None \n", - " 173 Southern Patagonian Ice Field ... Unrecognized Unrecognized \n", - " 242 Falkland Islands ... None None \n", - " \n", - " FCLASS_GR FCLASS_IT FCLASS_NL FCLASS_SE FCLASS_BD FCLASS_UA \\\n", - " 2 None None None None None None \n", - " 3 None None None None None None \n", - " 4 None None None None None None \n", - " 5 None None None None None None \n", - " 22 None None None None None None \n", - " 23 None None None None None None \n", - " 44 None None None None None None \n", - " 45 None None None None None None \n", - " 136 None None None None None None \n", - " 137 None None None None None None \n", - " 138 None None None None None None \n", - " 139 None None None None None None \n", - " 159 None None None None None None \n", - " 173 Unrecognized None None None Unrecognized Unrecognized \n", - " 242 None None None None None None \n", - " \n", - " geometry Country_Size \n", - " 2 MULTIPOLYGON (((-69.51009 -17.50659, -69.50611... 27.858902 \n", - " 3 POLYGON ((-69.51009 -17.50659, -69.51009 -17.5... 27.809022 \n", - " 4 MULTIPOLYGON (((-69.51009 -17.50659, -69.63832... 27.923672 \n", - " 5 MULTIPOLYGON (((-67.19390 -22.82222, -67.14269... 29.093419 \n", - " 22 POLYGON ((-54.08080 3.30931, -54.11429 3.28538... 25.713018 \n", - " 23 MULTIPOLYGON (((-56.48182 1.94161, -56.52851 1... 26.090793 \n", - " 44 MULTIPOLYGON (((-57.60279 -30.19052, -57.61170... 29.830299 \n", - " 45 POLYGON ((-57.60279 -30.19052, -57.58684 -30.2... 26.252015 \n", - " 136 MULTIPOLYGON (((-78.82868 1.43431, -78.76997 1... 26.272481 \n", - " 137 MULTIPOLYGON (((-78.82868 1.43431, -78.81286 1... 27.772363 \n", - " 138 POLYGON ((-62.65036 -22.23446, -62.62752 -22.1... 26.889734 \n", - " 139 POLYGON ((-57.64247 -30.19309, -57.63397 -30.1... 15.148693 \n", - " 159 MULTIPOLYGON (((-60.02099 8.55801, -59.95969 8... 27.563589 \n", - " 173 POLYGON ((-73.46510 -49.75996, -73.49262 -49.7... 21.928842 \n", - " 242 MULTIPOLYGON (((-59.69445 -52.20810, -59.67687... 24.130787 \n", - " \n", - " [15 rows x 170 columns])" - ] - }, - "execution_count": 283, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "continent" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The code below is creating a scatter plot of population versus GDP for different continents using the **seaborn** library for data visualization. The plot is divided into 2 rows and 3 columns using the `subplots()` function. The size of the plot is defined as `12 by 6` inches.\n", - "\n", - "Then, the code loops through each continent's data grouped by the `'CONTINENT'` column in a pandas DataFrame named `'global_data'`. The loop skips the `'Antarctica'` and `'Seven seas (open ocean)'` continents using a `continue` statement.\n", - "\n", - "For each continent, the code defines a new variable `continent_data` containing the data for that continent. The plot axes are defined using the `scatterplot()` function from seaborn for each subplot. The plot markers are colored by `'Country_Size'` column using the `'magma_r'` color palette. The subplot axes are retrieved using the `flatten()` method of the axes object, and the legend is turned off.\n", - "\n", - "The code then sets the axis labels, scaling to logarithmic for both `x` and `y-axes`, and sets the subplot title to the name of the continent. \n", - "\n", - "Finally, the code adjusts the subplot spacing using the `subplots_adjust()` function from matplotlib." - ] - }, - { - "cell_type": "code", - "execution_count": 266, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(2,3,figsize=(12,6))\n", - "\n", - "iter_ = 0\n", - "for continent in global_data.groupby('CONTINENT'):\n", - " continent\n", - " \n", - " if continent[0] in ['Antarctica','Seven seas (open ocean)']:\n", - " continue\n", - " \n", - " continent_data = continent[1]\n", - " \n", - " ax = sns.scatterplot(x=\"POP_EST\", y=\"GDP_MD\", hue=\"Country_Size\", palette='magma_r',\n", - " data=continent_data,ax=axes.flatten()[iter_], legend=False)\n", - " ax.set_xlabel('Population')\n", - " ax.set_ylabel('GDP')\n", - " ax.set_xscale('log')\n", - " ax.set_yscale('log')\n", - " ax.set_title(continent[0])\n", - " \n", - " iter_ += 1\n", - " \n", - "plt.subplots_adjust(hspace=0.5, wspace=0.3)\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is nice, but let's improve the plot to make it a bit more friendly to read. We could, for example, give it shared axis, using the `sharex` and `sharey` parameters in the `plt.subplots()` function. And we also want a legend! But not six. So let's add a legend to one of the outermost figures.\n", - "\n", - "We have to make sure that the legend is plotted outside the figure, so we use `bbox_to_anchor` argument to make sure we can locate it outside the plot, and we add a title so people understand what we are looking at." - ] - }, - { - "cell_type": "code", - "execution_count": 284, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(2, 3,figsize=(12,6),sharex=True,sharey=True)\n", - "\n", - "iter_ = 0\n", - "for continent in global_data.groupby('CONTINENT'):\n", - " continent\n", - " \n", - " if continent[0] in ['Antarctica','Seven seas (open ocean)']:\n", - " continue\n", - " \n", - " continent_data = continent[1]\n", - " \n", - " if iter_ != 2: \n", - " ax = sns.scatterplot(x=\"POP_EST\", y=\"GDP_MD\", hue=\"Country_Size\", palette='magma_r',\n", - " data=continent_data,ax=axes.flatten()[iter_], legend=False)\n", - " else:\n", - " ax = sns.scatterplot(x=\"POP_EST\", y=\"GDP_MD\", hue=\"Country_Size\", palette='magma_r',\n", - " data=continent_data,ax=axes.flatten()[iter_], legend=True) \n", - " ax.legend(title='Log (Country Size)',bbox_to_anchor=(1.1, 1.05))\n", - "\n", - " ax.set_xlabel('Population')\n", - " ax.set_ylabel('GDP')\n", - " ax.set_xscale('log')\n", - " ax.set_yscale('log')\n", - " ax.set_title(continent[0])\n", - " \n", - " iter_ += 1\n", - " \n", - "plt.subplots_adjust(hspace=0.3, wspace=0.1)\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Creating static spatial plots\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When visualizing raster data, the display is made up of a grid of pixels that are colored according to the attribute being represented. For example, a raster map of temperature might be displayed with cooler temperatures shown in shades of blue and warmer temperatures shown in shades of red.\n", - "\n", - "Vector data, on the other hand, represents geographic features as discrete objects, such as points, lines, and polygons. Each object has a set of attributes, such as a name or a population value, that describe the feature being mapped. Vector data is commonly used to represent discrete features, such as cities, roads, and land parcels. When visualizing vector data, the display is made up of the individual objects, each with its own shape, size, and color.\n", - "\n", - "As such, raster data is visualized as a grid of pixels that represent a continuous surface, while vector data is visualized as discrete objects that represent distinct geographic features. Raster data is well-suited for representing continuous phenomena, while vector data is better suited for representing discrete objects and features." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Vector Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The code below creates a chloropleth map of the Netherlands, using geospatial information of the municipality boundaries within the Netherlands. The `gpd.read_file()` function is used to load the dataset, and the `plot()` function is used to create a chloropleth map. \n", - "\n", - "As you can see, the `.plot()` function alreaedy contains multiple arguments:\n", - "- The `column` parameter is used to specify the data column that will be used to create the map, in this case 'bevolkings' (Bevolkingsdichtheid per gemeente). \n", - "- The `cmap` parameter is used to set the colormap, in this case *'RdPu'*. \n", - "- The `legend` parameter is set to 'True' to show the legend of the map. \n", - "- The `edgecolor` parameter is used to specify the color of the lines around each of the polygons.\n", - "- The `linewidth` parameter is used to set the width of the edges around the polygons.\n", - "- The `ax` parameter is used to specify to which axis the plot belongs (especially important within multipanels).\n", - "\n", - "The `figsize` parameter is used to set the size of the map. The `set_title()`, `set_xlabel()`, and `set_ylabel()` functions are used to add the title, x-axis label and y-axis label to the map. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gemeentedata = gpd.read_file(\"gemeentedata.shp\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(304.1396387160643, 0.5, 'Lattitude (in meters)')" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize = (12,8))\n", - "\n", - "gemeentedata.plot(column='bevolkings',edgecolor='black',linewidth=0.05, \n", - " legend=True,cmap='RdPu',ax=ax)\n", - "ax.set_title(\"Population Density in The Netherlands, 2017\")\n", - "ax.set_xlabel('Longitude (in meters)')\n", - "ax.set_ylabel('Lattitude (in meters)')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But this actually mainly highlights the largest and most densily populated cities. We do not really see much spatial variation within the remainder of the Netherlands. One way to put less emphasizes on the extremes, would be to use the `vmax` argument. But we will already play around with `vmax` when we create the raster. So let's play around with something else: turn the legend into quantiles. To do so, we can use the `scheme` argument.\n", - "\n", - "Moreover, let's also remove the axis lines, as that will look nicer." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(344.0296500000022, 291214.17734999995, 291224.70168500004, 634913.196615)" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize = (12,8))\n", - "\n", - "gemeentedata.plot(column='bevolkings',edgecolor='black',linewidth=0.05,\n", - " cmap='RdPu',ax=ax, scheme=\"quantiles\", \n", - " legend=True, legend_kwds={'loc': 'upper left'})\n", - "ax.set_title(\"Population Density in The Netherlands, 2017\")\n", - "plt.axis('off')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But these quantiles look a bit ugly and slightly arbitrary. I am sure you have seen this before when doing some automated categorization in QGIS. Luckily we can simply specify our own bins, by setting the `scheme` argument to *\"User_Defined\"*. And through the `classification_kwds` argument, we can specify the bins. Moreover, as you may have already gathered in the previous plot, we also set the `legend_kwds` in which we specify the location of the legend within the plot." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(344.0296500000022, 291214.17734999995, 291224.70168500004, 634913.196615)" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh0AAAKQCAYAAAA7TsjhAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydd5wU9f3/X7O9973d2+tHrwKHoYUAoljAEk1ETYgkhmiMBdFf1BRBTSyEmNj9mqjY0agkFkRABUUPBTyQ3q7f7d72Xmfn8/vjuPX2bvdud2/vFmGej8cqO/OeT5nZm3nP+/MuFCGEgIWFhYWFhYVlkOEUegAsLCwsLCwsZwas0sHCwsLCwsIyJLBKBwsLCwsLC8uQwCodLCwsLCwsLEMCq3SwsLCwsLCwDAms0sHCwsLCwsIyJLBKBwsLCwsLC8uQwCodLCwsLCwsLEMCq3SwsLCwsLCwDAms0jEIrF27FhRFJT48Hg+lpaX45S9/iba2tkIPDwCwdOlSVFZW5nTsa6+9hn/+858p91EUhVWrVuU8rlzpec5FIhGMRiPmzZuHBx98EFardcjHlI6e5+jgwYNYtWoVGhsb89rPqlWrQFFUXtpaunRp0vlN91m6dCkAoLKyEosWLcpL330xd+5cUBSFCy64oNe+xsZGUBSFNWvWZN1uMBjEqlWrsHXr1l77us6r3W7PZchZM3fuXMydO3dI+upJ199Vvn+bXXzyySf41a9+hdGjR0MqlaKkpASXXnopdu/enVL+m2++wbnnnguZTAaVSoXLL78c9fX1veT++c9/4vLLL0dVVRUoikp7/rp+P+k+Fosln9NlAcAr9ABOZ1544QWMHj0aoVAIn332GR588EFs27YN+/btg1QqLfTwcua1117D/v37sXz58l77amtrUVpaOvSDOknXOY/FYrBardi+fTsefvhhrFmzBm+88QbOPffcgo2ti57n6ODBg7j33nsxd+7cnBXBVPz6179O+TDOhT//+c+44YYbEt+/+eYb/O53v8MDDzyAefPmJbbr9fq89JctH330ET755BOcc845eWkvGAzi3nvvBYCCPfDPBJ5++mk4HA7ceuutGDt2LGw2G/7+979j+vTp+Oijj5Ku5+HDhzF37lxMmjQJb775JsLhMO655x7Mnj0be/bsSfrtPfPMM5BKpTjnnHPw3nvvpe3/qaeegtfrTdoWDAZxwQUXoKamBkajMf+TPsNhlY5BZPz48Zg6dSoAYN68eYjH47j//vvx3//+Fz/72c8KPLrBYfr06QXtv/s5B4ArrrgCt912G374wx/i8ssvx7Fjx2AwGAo4wqE7R6WlpXlTAIcNG4Zhw4YlvofDYQDAiBEjCn7NR44cCZqm8fvf/x47d+7Mm3Wn0ASDQUgkkkIPY1B58sknUVRUlLTtggsuwPDhw/HAAw8kKR333HMPhEIh3n//fSgUCgBATU0NRowYgTVr1uDhhx9OyB48eBAcTqchf/z48Wn7Hzt2bK9tL774ImKxGH79618PaG4sqWGXV4aQrptzU1MTgM4b9913342qqioIBAKUlJTgd7/7Hdxud9JxXabq9evXY+LEiRCJRKiursZjjz2WJJfOFLp161ZQFJXSVNydJ598Ej/60Y9QVFQEqVSKCRMmYPXq1YjFYgmZuXPn4oMPPkBTU1OSGbKLVMsr+/fvx6WXXgq1Wg2RSIRJkybhxRdfTDnG119/HX/84x9hMpmgUChw7rnn4siRI32Ouz/Ky8vx97//HT6fD//3f/+XtG/Xrl245JJLoNFoIBKJMHnyZLz55ptJMl3n9dNPP8Vvf/tb6HQ6aLVaXH755Whvb0+S/eSTTzB37lxotVqIxWKUl5fjiiuuQDAYTHmO1q5di5/+9KcAOhXTrvO5du1a3H///eDxeGhpaek1p1/96lfQarWJh38qUi2vdP2WNm7ciClTpkAsFmP06NF4/vnn+z+ROZBJPxaLBddffz1KS0shEAhQVVWFe++9FzRNZ9QHn8/HX//6V+zevRtvvPFGv/L99dfY2Jh4a7733nt7LR110dHRgauvvhpKpRIGgwG/+tWv4PF4kmQIIXjqqacwadIkiMViqNVq/OQnP+m1JDB37lyMHz8en332GWbOnAmJRIJf/epXaedw7733Ytq0adBoNFAoFJgyZQqee+459Kzfmc313rFjB2bNmgWRSASTyYS777476W+/i0x+45nSU+EAAJlMhrFjxyb97mmaxvvvv48rrrgioXAAQEVFBebNm4f169cntdGlcOTCc889B5lMhsWLF+fcBkt6WKVjCDl+/DiAThM0IQSXXXYZ1qxZgyVLluCDDz7AihUr8OKLL+Kcc85BJBJJOnbPnj1Yvnw5brvtNqxfvx4zZ87ErbfemtN6dTpOnDiBa665Bi+//DLef/99XHfddfjb3/6G66+/PiHz1FNPYdasWTAajaitrU180nHkyBHMnDkTBw4cwGOPPYZ33nkHY8eOxdKlS7F69epe8n/4wx/Q1NSEf//733j22Wdx7NgxXHzxxYjH4wOa20UXXQQul4vPPvssse3TTz/FrFmz4Ha78cwzz+B///sfJk2ahMWLF2Pt2rW92vj1r38NPp+P1157DatXr8bWrVvx85//PLG/sbERCxcuhEAgwPPPP4+NGzfioYceglQqRTQaTTmuhQsX4oEHHgDQqfR1nc+FCxfi+uuvB4/H66UoOZ1OrFu3Dtdddx1EIlHW52Lv3r24/fbbcdttt+F///sfJk6ciOuuuy7p3OSDTPqxWCz4wQ9+gI8++gj33HMPPvzwQ1x33XV48MEHsWzZsoz7Wrx4MWpqavCnP/0p5YMym/6Ki4uxceNGAMB1112XuCZ//vOfk9q64oorMHLkSLz99tu466678Nprr+G2225Lkrn++uuxfPlynHvuufjvf/+Lp556CgcOHMDMmTPR0dGRJGs2m/Hzn/8c11xzDTZs2IAbb7wx7TwaGxtx/fXX480338Q777yDyy+/HDfffDPuv//+XrKZXIeDBw9i/vz5cLvdWLt2LZ555hnU1dXhL3/5S69+s/2NZ4vH48E333yDcePGJbadOHECoVAIEydO7CU/ceJEHD9+vE8FPFOOHTuGzz//HFdddRVkMtmA22NJAWHJOy+88AIBQHbs2EFisRjx+Xzk/fffJ3q9nsjlcmKxWMjGjRsJALJ69eqkY9944w0CgDz77LOJbRUVFYSiKLJnz54k2fPOO48oFAoSCASS+m1oaEiS+/TTTwkA8umnnya2XXvttaSioiLtHOLxOInFYuSll14iXC6XOJ3OxL6FCxemPRYAWblyZeL7VVddRYRCIWlubk6Su/DCC4lEIiFutztpjBdddFGS3JtvvkkAkNra2rRjJeS7ue/cuTOtjMFgIGPGjEl8Hz16NJk8eTKJxWJJcosWLSLFxcUkHo8ntX3jjTcmya1evZoAIGazmRBCyFtvvUUA9LpOPel5jv7zn//0uj5dXHvttaSoqIhEIpHEtocffphwOJxe17knK1euJD3/xCsqKohIJCJNTU2JbaFQiGg0GnL99df32V53uq7Xf/7zn5T7M+3n+uuvJzKZLEmOEELWrFlDAJADBw70OY45c+aQcePGEUII2bJlCwFAHn/8cUIIIQ0NDQQA+dvf/pZ1fzabrdd16qLrvPb8273xxhuJSCQiDMMQQgipra0lAMjf//73JLmWlhYiFovJ73//+6R5ACAff/xxyjnOmTMn7Tno+lu97777iFarTfRPSObXYfHixUQsFhOLxZLYRtM0GT16dNI9JdPf+ED42c9+Rng8Htm1a1di2xdffEEAkNdff72X/AMPPEAAkPb29pTtjRs3rs/z150777wzo/sNS+6wlo5BZPr06eDz+ZDL5Vi0aBGMRiM+/PBDGAwGfPLJJwDQy2T705/+FFKpFB9//HHS9nHjxuGss85K2nbNNdfA6/Xim2++yct46+rqcMkll0Cr1YLL5YLP5+MXv/gF4vE4jh49mlObn3zyCebPn4+ysrKk7UuXLkUwGOxlJbnkkkuSvne92XQtSQ0E0s30fPz4cRw+fDjhW0PTdOJz0UUXwWw291rW6W9skyZNgkAgwG9+8xu8+OKLKb3qs+XWW2+F1WrFf/7zHwAAwzB4+umnsXDhwpydTidNmoTy8vLEd5FIhJEjR+blHGfbz/vvv4958+bBZDIlXYMLL7wQALBt27aM+5s/fz4WLFiA++67Dz6fL6VMPvtL9XsIh8OJSKn3338fFEXh5z//eVJfRqMRZ511Vq/lTrVanbEj7CeffIJzzz0XSqUy8bd6zz33wOFw9IrUyuQ6fPrpp5g/f36SvxOXy+21xDAYv/Hu/PnPf8arr76Kf/zjH6ipqem1vy9/nYH68tA0jRdffBHjxo0ruJ/S6QyrdAwiL730Enbu3Im6ujq0t7fj22+/xaxZswAADocDPB6vl7c/RVEwGo1wOBxJ21N5UXdt6ymbC83NzZg9ezba2trw6KOP4vPPP8fOnTvx5JNPAgBCoVBO7TocDhQXF/fabjKZEvu7o9Vqk74LhcIB9d9FIBCAw+FI9Ntl2r7jjjvA5/OTPl1m7Z4hkf2NbdiwYdiyZQuKiorwu9/9LuF8+eijj+Y87smTJ2P27NmJ6/D++++jsbERN910U85t9pwH0DmXgZ7jXPrp6OjAe++91+sadJnWsw1Lffjhh2G329MuO+azv/5+Dx0dHSCEwGAw9Opvx44dvfpK9XeSiq+//hoLFiwAAPzrX//CF198gZ07d+KPf/xjUv/pxtk11u5yDoejz3tMF4PxG+/i3nvvxV/+8hf89a9/7fX77ppDqnud0+kERVFQqVQD6n/Dhg2wWCysA+kgw0avDCJjxoxJiqTojlarBU3TsNlsSYoHIQQWiwVnn312knyqePGubV1/kF3r+z39QTK5kf73v/9FIBDAO++8g4qKisT2PXv29HtsX2i1WpjN5l7buxwwdTrdgNrPlA8++ADxeDwR/tjV7913343LL7885TGjRo3Kup/Zs2dj9uzZiMfj2LVrFx5//HEsX74cBoMBV111VU5jv+WWW/DTn/4U33zzDZ544gmMHDkS5513Xk5tnWrodDpMnDgRf/3rX1Pu71ISM2XSpEm4+uqr8cgjj+Ciiy4a9P76QqfTgaIofP755wmFpDs9t2X6pr5u3Trw+Xy8//77ST49//3vf3Meq1ar7fMe053B+I3fe++9WLVqFVatWoU//OEPvfYPGzYMYrEY+/bt67Vv3759GD58eE7+Td157rnnIBAIsGTJkgG1w9I3rKWjQMyfPx8A8MorryRtf/vttxEIBBL7uzhw4AD27t2btO21116DXC7HlClTACBhbv/222+T5N59991+x9N1w+t+IySE4F//+lcv2WzeiufPn49PPvmkV5THSy+9BIlEMiRmzObmZtxxxx1QKpUJp9hRo0ZhxIgR2Lt3L6ZOnZryI5fLc+6Ty+Vi2rRpCQtFX0tg/VlzfvzjH6O8vBy33347tmzZghtvvPG0CQtdtGgR9u/fj2HDhqW8BrkoAX/5y18QjUYTeTZy6S8fFrZFixaBEIK2traUfU2YMCGndrsSDnK53MS2UCiEl19+Oeexzps3Dx9//HGSc2s8Hu8zGiib33hf3H///Vi1ahX+9Kc/YeXKlSlleDweLr74YrzzzjtJS2fNzc349NNP0744ZIrFYsGGDRtw2WWXpbQMseQP1tJRIM477zycf/75uPPOO+H1ejFr1ix8++23WLlyJSZPntxL2zaZTLjkkkuwatUqFBcX45VXXsHmzZvx8MMPJ2L5zz77bIwaNQp33HEHaJqGWq3G+vXrsX379ozGIxAIcPXVV+P3v/89wuEwnn76abhcrl6yEyZMwDvvvIOnn34aNTU14HA4aS06K1euTKyj33PPPdBoNHj11VfxwQcfYPXq1VAqlTmcvfTs378/sXZutVrx+eef44UXXgCXy8X69euTrEr/93//hwsvvBDnn38+li5dipKSEjidThw6dAjffPNNwo8iU5555hl88sknWLhwIcrLyxEOhxOhiX0lJevKI/Dss89CLpdDJBKhqqoqcfPjcrn43e9+hzvvvBNSqbSXH9D3mfvuuw+bN2/GzJkzccstt2DUqFEIh8NobGzEhg0b8Mwzz2Sda6Sqqgq//e1vU5r8M+1PLpejoqIC//vf/zB//nxoNBrodLqs/GhmzZqF3/zmN/jlL3+JXbt24Uc/+hGkUinMZjO2b9+OCRMm4Le//W1WcwM6I54eeeQRXHPNNfjNb34Dh8OBNWvWpLSmZMqf/vQnvPvuuzjnnHNwzz33QCKR4Mknn0QgEEiSy/Q3vnTpUrz44otoaGjo85z9/e9/xz333IMLLrgACxcuxI4dO5L2d38puffee3H22Wdj0aJFuOuuuxLJwXQ6HW6//fak43bt2pVIHeD1ekEIwVtvvQWg8z7Z3ZoLdObmoGmaXVoZCgrpxXq6kkkkBSGdXuR33nknqaioIHw+nxQXF5Pf/va3xOVyJclVVFSQhQsXkrfeeouMGzeOCAQCUllZSR555JFebR49epQsWLCAKBQKotfryc0330w++OCDjKJX3nvvPXLWWWcRkUhESkpKyP/7f/+PfPjhh72OdTqd5Cc/+QlRqVSEoqikCAmk8Pjft28fufjii4lSqSQCgYCcddZZ5IUXXkiSSRcN0RWB0FO+J13nvOsjEAhIUVERmTNnDnnggQeI1WpNedzevXvJlVdeSYqKigifzydGo5Gcc8455JlnnunVds/r2TMqqLa2lvz4xz8mFRUVRCgUEq1WS+bMmUPefffdpONSnaN//vOfpKqqinC53JTzbWxsJADIDTfc0Od56E666JWFCxf2ku0vQqInmUSvZNqPzWYjt9xyC6mqqiJ8Pp9oNBpSU1ND/vjHPxK/39/nOLpHr/RsU6FQ9Ipeyaa/LVu2kMmTJxOhUEgAkGuvvZYQ8t15tdlsSe2mix57/vnnybRp04hUKiVisZgMGzaM/OIXv0iKzkg3j3Tn7PnnnyejRo0iQqGQVFdXkwcffJA899xzvfrP5jp88cUXZPr06UQoFBKj0Uj+3//7f+TZZ59NajPT3/gVV1xBxGJxr3tZqnF0/7vt+enJrl27yPz584lEIiEKhYJcdtll5Pjx473krr322rRtprqXjBw5klRWViZF/rAMDhQhPbLJsJxyVFZWYvz48Xj//fcLPRSWAvH444/jlltuwf79+5PyF7CwnIoYjUYsWbIEf/vb3wo9FJZTDHZ5hYXlFKaurg4NDQ247777cOmll7IKB8spz4EDBxAMBnHnnXcWeigspyCs0sHCcgrz4x//GBaLBbNnz8YzzzxT6OGwsPTLuHHjehVRY2Hpgl1eYWFhYWFhYRkS2JBZFhYWFhYWliGBVTpYWFhYWFhYhgRW6WBhYWFhYWEZElilg4WFhYWFhWVIYJUOFhYWFhYWliGBVTpYWFhYWFhYhgRW6WBhYWFhYWEZElilg4WFhYWFhWVIYJUOFhYWFhYWliGBVTpYWFhYWFhYhoS81l6Jx+OIxWL5bJKF5ZSAz+eDy+UWehgsLCws32vyonQQQmCxWOB2u/PRHAvLKYlKpYLRaARFUYUeCgsLC8v3krwoHV0KR1FRESQSCXtTZjmtIIQgGAzCarUCAIqLiws8IhYWFpbvJwNWOuLxeELh0Gq1+RgTC8sph1gsBgBYrVYUFRWxSy0sLCwsOTBgR9IuHw6JRDLgwbCwnMp0/cZZvyUWFhaW3Mhb9Aq7pMJyusP+xllYWFgGRl6jVwYEYYB4HAABQAFcLkCxEb0sLCwsLCynC4VXOpg4QEc7/98dGgCHC/AEnf9nYWFhYWFh+V5TWFNCnAaiod4KRxdMvHN/nB7acaXhX//6F2bPng21Wg21Wo1zzz0XX3/9dZLMqlWrQFFU0sdoNPbb9rZt21BTUwORSITq6mo888wzvWTefvttjB07FkKhEGPHjsX69evzNrcu9u7di6uvvhplZWUQi8UYM2YMHn300SSZxsbGXnOkKAobN27Mek49aW5uxsUXXwypVAqdTodbbrkF0Wg0SWbfvn2YM2cOxGIxSkpKcN9994EQMvDJs7CwsLAMKoWzdDBxIBbOTDYWBihxwS0eW7duxdVXX42ZM2dCJBJh9erVWLBgAQ4cOICSkpKE3Lhx47Bly5bE9/4iHRoaGnDRRRdh2bJleOWVV/DFF1/gxhtvhF6vxxVXXAEAqK2txeLFi3H//ffjxz/+MdavX48rr7wS27dvx7Rp0/I2x927d0Ov1+OVV15BWVkZvvzyS/zmN78Bl8vFTTfdlCS7ZcsWjBs3LvFdo9FkNaeexONxLFy4EHq9Htu3b4fD4cC1114LQggef/xxAIDX68V5552HefPmYefOnTh69CiWLl0KqVSK22+/PW/ngYWFhYVlECADJBQKkYMHD5JQKJTdgZEgISFf5p9IcKBDzTs0TRO5XE5efPHFxLaVK1eSs846K6t2fv/735PRo0cnbbv++uvJ9OnTE9+vvPJKcsEFFyTJnH/++eSqq67KfuBZcuONN5J58+Ylvjc0NBAApK6uLu0xmcypJxs2bCAcDoe0tbUltr3++utEKBQSj8dDCCHkqaeeIkqlkoTD4YTMgw8+SEwmE2EYJtupZUXOv3UWFhYWFkIIIYVZXiFM+iWVdDDxzuNOIYLBIGKxWNIbPgAcO3YMJpMJVVVVuOqqq1BfX99nO7W1tViwYEHStvPPPx+7du1KhGemk/nyyy/zMJO+8Xg8veYIAJdccgmKioowa9YsvPXWW0n7MplTT2prazF+/HiYTKakYyKRCHbv3p2QmTNnDoRCYZJMe3s7Ghsbc50iCwsLC8sQUBilI56lwtFFtorKIHPXXXehpKQE5557bmLbtGnT8NJLL+Gjjz7Cv/71L1gsFsycORMOhyNtOxaLBQaDIWmbwWAATdOw2+19ylgsljzOqDe1tbV48803cf311ye2yWQyPPLII3jrrbewYcMGzJ8/H4sXL8Yrr7ySkMlkTj1JdYxarYZAIEjMM127XftYWFhYWE5dCuTTkaPT3ynkLLh69Wq8/vrr2Lp1K0QiUWL7hRdemPj3hAkTMGPGDAwbNgwvvvgiVqxYkba9njkgyMm5dt+eSmYwc0ccOHAAl156Ke655x6cd955ie06nQ633XZb4vvUqVPhcrmwevVq/PznP+9zvKm2dyfVvp7zzKVdFhYWFpbCU6DolRwfDqfIQ2XNmjV44IEHsGnTJkycOLFPWalUigkTJuDYsWNpZYxGY6+3dKvVCh6Pl0gtn06m51t/vjh48CDOOeccLFu2DH/605/6lZ8+fXrSHDOZU09SHeNyuRCLxRLzTNcugEE7FywsLCws+aEwSkeudStOgXwdf/vb33D//fdj48aNmDp1ar/ykUgEhw4d6rNI2IwZM7B58+akbZs2bcLUqVPB5/P7lJk5c2YOs+ibAwcOYN68ebj22mvx17/+NaNj6urqkuaYyZx6MmPGDOzfvx9msznpGKFQiJqamoTMZ599lhRGu2nTJphMJlRWVmY6RRYWFhaWQjBQT9QzKXrl4YcfJgKBgLz11lvEbDYnPj6fLyFz++23k61bt5L6+nqyY8cOsmjRIiKXy0ljY2NC5q677iJLlixJfK+vrycSiYTcdttt5ODBg+S5554jfD6fvPXWWwmZL774gnC5XPLQQw+RQ4cOkYceeojweDyyY8eOvM5x//79RK/Xk5/97GdJc7RarQmZtWvXkldffZUcPHiQHD58mPztb38jfD6fPPLII1nN6Z133iGjRo1KfKdpmowfP57Mnz+ffPPNN2TLli2ktLSU3HTTTQkZt9tNDAYDufrqq8m+ffvIO++8QxQKBVmzZk1ez0Mq2OgVFhYWloFROKUjTmendMTpgQ51wFRUVBB0OqQkfVauXJmQWbx4MSkuLiZ8Pp+YTCZy+eWXkwMHDiS1c+2115I5c+Ykbdu6dSuZPHkyEQgEpLKykjz99NO9+v/Pf/5DRo0aRfh8Phk9ejR5++23k/a/8MILZKB65MqVK1POsaKiIiGzdu1aMmbMGCKRSIhcLic1NTXk5Zdf7tVWf3NKNd6mpiaycOFCIhaLiUajITfddFNSeCwhhHz77bdk9uzZRCgUEqPRSFatWjXo4bKEsEoHCwsLy0ChCBmYd2Y4HEZDQwOqqqqSHCozIk5nliCMLwK4hc/YfqqzatUqbN26FVu3bi30UE5LBvRbZ2FhYWEpcO0VLq8z02iq2isAW3slSz766KNeKctZWFhYWFhOFQpvPuBwAYH4u4RhhHRGqXDYKrPZUltbW+ghsLCwsLCwpKXwSkcXFAfgskoGCwsLy5kETdPgcrlsnp0zhFNH6WBhYWFhOWPw+7wIeVygYzG0dthQc/YPwMk1nQLL9wbWtMDCwsLCMqQEAwHEQwEwDINANAaRgI8dX2wHYdLX1yIMA4/bDYZhEI1EhnC0LPmEtXSwsLCwsAwpIb8XWqUcSoUcRYTA0mFFjI7hxPFjGD5yVMpjbB1mKCUieDraEA5HAQ4HCq0OUpl8iEfPMhBYSwcLCwsLy5BCcXmIx+MIh8Noam5BlKYhFAjh83jApCkIyqUoCIVCxOIENJeHGKHgsFrh83qHePQsA4G1dLCwsLCwDCkanR4Ocyu0ahUIARRKNZQaPeJxOq1fR5whaGkzw1RRBS6PfXR9XzllrhyJRUF8XiDOAFwOKLkCFF9Q6GGxsLCwsAwC1MmEjyUmI8KEC4VS2ad8kakEhBD4vV6EQ0HojenrWbGcuhRc6SDBABirBfC4k7cDgFIFTpERlERaiKGxsLCwsAyApvrjiEWjUOv00Or0yTtPJn30+QPQFJdm1F5XWG3Q7wPAKh3fRwrq00E8LjDHj/RSOBJ43GCOHwFJt38QcTqduPnmmzFq1ChIJBKUl5fjlltugcfjSZKrrKwERVFJn7vuuitJprm5GRdffDGkUil0Oh1uueWWpCqpqYhEIrj55puh0+kglUpxySWXoLW1NUnG5XJhyZIlUCqVUCqVWLJkCdxud17m351Vq1b1mqPRaEySIYRg1apVMJlMEIvFmDt3Lg4cOJD1nFLx1FNPJVKP19TU4PPPP8+6bxYWlsGHEAKvxwOX0wE6FkM0Fkf1yNHweFL4XVAcWO1OgCfIKkeHXKlExfCReRw1y1BSMKWDBANgmho6M5D2KUjANNWDBANDM7CTtLe3o729HWvWrMG+ffuwdu1abNy4Edddd10v2fvuuw9msznx+dOf/pTYF4/HsXDhQgQCAWzfvh3r1q3D22+/jdtvv73P/pcvX47169dj3bp12L59O/x+PxYtWoR4Nyera665Bnv27MHGjRuxceNG7NmzB0uWLMnfSejGuHHjkua4b9++pP2rV6/GI488gieeeAI7d+6E0WjEeeedB5/Pl9WcevLGG29g+fLl+OMf/4i6ujrMnj0bF154IZqbm7Pqm4WFJT9EIxG4nE44rBY0HDuC9tZW2MxtcJpbYWmsR8jnht/XqWSMGDUK0UgE/BQ+GHK5HMEoDW2RYainwFJIBloxLtfKm3TDcULv2ZX5p+H4QIc6YN58800iEAhILBZLbKuoqCD/+Mc/0h6zYcMGwuFwSFtbW2Lb66+/ToRCIfF4PCmPcbvdhM/nk3Xr1iW2tbW1EQ6HQzZu3EgIIeTgwYMEQFJp+9raWgKAHD58ONcppmTlypXkrLPOSrufYRhiNBrJQw89lNgWDoeJUqkkzzzzTMZzSsUPfvADcsMNNyRtGz16NLnrrrsy7jtfsFVmWc4kfF4vcdhsxGGzkvaWZuKwtBNHewvxWtuJraWB2FoaiaOtmTjamomttZFYzW3Em+KeFgmHSTAQSNmHy+kc7GmwnGIUxNJBYtH0Syrp8Lg7jysgHo8HCoUCvB5a+8MPPwytVotJkybhr3/9a9LSSW1tLcaPHw+TyZTYdv755yMSiWD37t0p+9m9ezdisRgWLFiQ2GYymTB+/Hh8+eWXiXaVSiWmTZuWkJk+fTqUSmVCJp8cO3YMJpMJVVVVuOqqq1BfX5/Y19DQAIvFkjReoVCIOXPmJMaSyZx6Eo1GsXv37qRjAGDBggWJYzLpm4WF5TtCwSCcdjucNiuc1g44LO2wtrWg+cQxOC1tMDfVw2VuhYDEoJGJoJGJUaxTQ6OUQ6NWQS6XQ61Ww+H2QGMqg8ZUBl1JBfRGE+QKRa/+BEIhxBJJyrGo1OrBni7LKUZBHEmJL7e4auLzgdJo8zyazHA4HLj//vtx/fXXJ22/9dZbMWXKFKjVanz99de4++670dDQgH//+98AAIvFAoMh2XyoVqshEAhgsVhS9mWxWCAQCKDu8QdpMBgSx1gsFhQVFfU6tqioKG27uTJt2jS89NJLGDlyJDo6OvCXv/wFM2fOxIEDB6DVahP99ZynwWBAU1NTxnPqid1uRzweT9lu9/PQX98sLGc6VnM7eBQBCAORUAiNLLUS0B80TcPp9oAjEGHEmHF5HiXLmUBholfi6VPd9n1c+rX/wcTr9WLhwoUYO3YsVq5cmbTvtttuS/x74sSJUKvV+MlPfpKwfgBI6SRFCMm6wFHPY/LVbn9ceOGFiX9PmDABM2bMwLBhw/Diiy9ixYoVaceTyVgykcmk3Vz6ZmE53YnTNOwdFiilIohEopzaIITA6XKBAQcCiRRFpRV5HiXLmURhHElzrSZbgGJAPp8PF1xwAWQyGdavXw8+n9+n/PTp0wEAx48fBwAYjcZeb/IulwuxWKzX23kXRqMR0WgULpcrabvVak0cYzQa0dHR0etYm82Wtt18IZVKMWHCBBw7diwxFgC95tlzvP3NqSc6nQ5cLrffdvvrm4XlTMVuaYdBq8pJ4QgEAnC63HAFItAUl0FvKoVSxS6HsAyMgigdlLz3ul9mxw1tjn2v14sFCxZAIBDg3XffzegPt66uDgBQXNwZQz5jxgzs378fZrM5IbNp0yYIhULU1NSkbKOmpgZ8Ph+bN29ObDObzdi/fz9mzpyZaNfj8eDrr79OyHz11VfweDwJmcEiEong0KFDiTlWVVXBaDQmjTcajWLbtm2JsWQyp54IBALU1NQkHQMAmzdvThyTSd8sLGciPq8XKnnmOY7C4XAiJQBN07A6XBDIFNDodKzVkCV/DNQT9XSNXvF6vWTatGlkwoQJ5Pjx48RsNic+NE0TQgj58ssvySOPPELq6upIfX09eeONN4jJZCKXXHLJd/OkaTJ+/Hgyf/588s0335AtW7aQ0tJSctNNNyVkWltbyahRo8hXX32V2HbDDTeQ0tJSsmXLFvLNN9+Qc845h5x11lmJvgkh5IILLiATJ04ktbW1pLa2lkyYMIEsWrQo7+fi9ttvJ1u3biX19fVkx44dZNGiRUQul5PGxsaEzEMPPUSUSiV55513yL59+8jVV19NiouLidfrzWpO55xzDnn88ccT39etW0f4fD557rnnyMGDB8ny5cuJVCrNuu98wEavsHyfcFg7CAn5Mvr47Rayb/fXZM/XtcTa2kTamxsJ3S1Kj4UlXxRM6WACfkLv3Z2ZwrF3N2EC/oEONSs+/fRTgs7EqL0+DQ0NhBBCdu/eTaZNm0aUSiURiURk1KhRZOXKlSTQIzysqamJLFy4kIjFYqLRaMhNN91EwuFwYn9DQwMBQD799NPEtlAoRG666Sai0WiIWCwmixYtIs3NzUntOhwO8rOf/YzI5XIil8vJz372M+JyuZJkKioqyMqVKwd0LhYvXkyKi4sJn88nJpOJXH755eTAgQNJMgzDkJUrVxKj0UiEQiH50Y9+RPbt25ckk8mcUo33ySefJBUVFUQgEJApU6aQbdu2Zd13PmCVDpZC43G7ib3DTGztreTIoYN9yto7LEmKRdhtJ56Otu/CXFsaid3cRuwdFuL3+YZoBixnOhQh/WXn6ptwOIyGhoZExsisrCweN5im+r4ThFEUOBXVoJSqgQzzjCQUCkGj0WDDhg2YN29eoYfzvWcgv3WWU4s4TcPtdkEmk0NYoGtJCEE0EkE4HIZcoQCHk361mxCC+qOHIZFIIBSJQUAhRsdgLDalPYaOxWA3t6FIp4HL5QYlkkIsFifCVzssFkilUsiGeNm6UPi8XtCxKAgTBxgGFADCxEFRFBgA4PAgkcmTwnt9Hg+ikTCkcgUCgQC0Ol2hhn/aUNDaK5RSBc7wUSlrrwBga68MkG3btuGcc85hFQ4WlpM4rB0AHQWXQ0GjVGLfgf2QSCWgOFwMGzlqwO37fT5Eo5HOFynSGaJKGAYAOfmQ63zYdUIgFAohFQrh6WjrVCQYAkNJWa92mXgcKo0WWn3vMPl08Ph8GMsr0dZYj1icQWVJedJ+Q49SBqcThBA47TYgToMCARgGcrkMfGnfCmYg4IPd44JEoUI0EgaPoaFVSHHsxDFI5ErUn3CjetjwIZrF6UlBLR3d6awy6+sMi+VyQcnlbJVZllMK1tLx/cZh7YBSIkxK7heNRuELBAEOD+BwTjpMnlQLurQDcvI/iVslAWFI58Osa/vJj1QqgVAozHmMVrsTRaXl/Qtmgd3aAaVKDb7g9L+fhoJB+D0ucAgDjVqVswNsIBAAh8MBn8+Hw+0BQ9NgODwIeSetUVw+tEUG1sE2BwpeZbYLii8oWOIvFhaW0xuP2wURF72yCQsEAmiH6GFM0zScHh+4AuF3DytCTpr4OWAAqIuSrQ8Omw0cLgfqHvdGp90OwsQzqluiOwNqmwQDAQS9Lgh5XOjVygG3J5V2WtcJIYjHaHgDAYwYPRbtjfUoLemM2utobYbOaAKvnzQKLMmcMkoHCwsLy2ARC/ig1GqGvF+v1ws6zoBQHFA8PopSLJ30hVavT72DMOAUIG/RqYbP60EkGAAXBDq1Ku/tUxQFtUoBoUgEt8MGsVyO443NKNZrYdRrYbdZIFaoIJWdGX4x+YBVOlhYWL43+LxeRMOhhKWAy+f3m7CKicfB5QyNGTwWi8Hn9wMcLgg4kMhVUKSpOzIQNFn4dpxuRCMR+DxuxGNRKKRiyFW55X3KFLFYDLFYnPiuU36nYOg0aji8flbpyAJW6WBhYTll8ft8iIQCwMmIA7lMCrniO8fyWCwGe3tLn2+bHC4XcZKd0hEMBhGMdVoTOqMd4p0uHoRAJpVA0G1JJhaLwevzg3C44IvE0BRnZ81g6RsmHofb5QLIyagTEodWpQJwigQYUIVJ7P19hVU6WFhYTikCfj/CQT8QpyEViyBTyJL20zSd5JvR3t6O4cq+l074YinC4XBGDsA0TcPu9qK8OnWUwv69e1BsKjlpbSHg8QXQmlhFYyAQhoHT4QCHQ4EwDBiGAYcCSDwOm82GUSOGnZJOmx6PF0pNmiUwlpSwSgcLC0vBCQYCCAV8QJyGRCSEtoei4ff7IZVKQVEUDh05gmKDAVwuFww4KKsaBom077depVoNl8OOiNcLZYry6124XG4wPEFahQMAho8cBVE3cztL9vh9PkTCIVAnw4rjdAw6jRoURcHj9SIUCkEhlSAYi4HD4Z4yCoelwwatVg3+SaU3Dop1JM2SU0bpIP4QmBYbEKUBAQ+cMj0oGfuHzcJyuuJxu0BHI2kVDQBoaWlFWVkpAoEgrDYbykpLIZUpIFHrIRKL+0yo1RO1VodIOAybywEhl4LipPLhcrkRJ50RdAptUb+hpazC0T+EEHg9HsTpGCKhEARCwUkFg4CJ05DLpJDJU/u6+IMhlBgNcLvdMBTpEIlFh3j0vQkEguiw2WAyGtDU3IryshII+HxQWfz+WDopuNLBWN2I7z4K0tgBMN+lDIlzKFCVBnBrRoJTpCrcAFlYWPJCKBhEwO8DddJHQiGXgZ9C0QA6H1pdb70qnw8ajRrROAN/lEH1qDE5j0EoEkFfXNKpfLhdsLS3o7isDLoz2DEzX8SiUXg9biBOAwwNlVIJrkgMZPHyGAyGEKdjAACVSgUA4HEL95gihKDN0gEuRaG6sgIAMLy6Eo3NzdBpNABODQvM94mCqmlMvRn0O9tB6i1JCkfnTgJSbwH9znYw9ebUDQwyc+fOBUVRSZ+rrroqScblcmHJkiVQKpVQKpVYsmQJ3G53kkxzczMuvvhiSKVS6HQ63HLLLYhG+9beI5EIbr75Zuh0OkilUlxyySVobW3Nuu9c+Otf/4qZM2dCIpEk/vB7ksmc9u3bhzlz5kAsFqOkpAT33Xcfeuai27ZtG2pqaiASiVBdXY1nnnmm3/Hlq2+WwSUYCMBh7YCjwwyHuRVMyAedQgqtSgGtRg1+H2bp4/WNaGptR1VlBdo67PBFaJRVD4dam59cPkKRCHpjMSZMqTmjFA5zWxuOHj6E5sZG0LEYCMMgdvJvx+VwwGm1wGluhaO9BXZLO6KRSL9t0rEY7B0W+BxWaOWSk9dXA24OIb02hwPlpaVJ27jcoXtMWTpsOHa8Hj6/H16fDw2NTTAZilBsTM51UllejnA4DKfTcTLjLEumFEyFZKxu0Jt2A/F+LlicAb1pN3iX/7AgFo9ly5bhvvvuS3wX9zCtXnPNNWhtbcXGjRsBAL/5zW+wZMkSvPfeewCAeDyOhQsXQq/XY/v27XA4HLj22mtBCMHjjz+ett/ly5fjvffew7p166DVanH77bdj0aJF2L17d+KPub++cyUajeKnP/0pZsyYgeeee67X/kzm5PV6cd5552HevHnYuXMnjh49iqVLl0IqleL2228HADQ0NOCiiy7CsmXL8Morr+CLL77AjTfeCL1ejyuuuCLl2PLVN0t+idN0jwgDBiKhAFpFbhEGI4ZVwe/3w+X1QyAU9BsWe6bSZRGiYzG4XS5IpNKU/i0uhx3NTY2oqKpGcUkJPG43vtz+GUaOHAmX3Y4ivQ5qlRIcTnI2VY/HAS/hQKPT98oLEotG4XE5QcU7/THygd/vQ3NLC8rLOh1zj9c3oNgwNMnN3B4PCGEwrLoSBw4egaFIi+qqyrTyOp0OWi2BrcOCoj5q4LAkU7A06LEPv+60cGQIVV0M/oVn5zLEnJk7dy4mTZqEf/7znyn3Hzp0CGPHjsWOHTswbdo0AMCOHTswY8YMHD58GKNGjcKHH36IRYsWoaWlBSZT5w9z3bp1WLp0KaxWa2JduTsejwd6vR4vv/wyFi9eDKDTQ7+srAwbNmzA+eefn1HfA2Xt2rVYvnx5L+tJJnN6+umncffdd6OjoyORFvqhhx7C448/jtbWVlAUhTvvvBPvvvsuDh06lGj7hhtuwN69e1FbW5tyTPnqOxfYNOjJeD0exCIhEDoGLtVpDs/23BJC4Ha7E1YosVgMgUAAj8dzshAXBYbiQGcoPmWcCQuB2+VCnKYBkM7wYcKcLFpGQCgOaJqGQa9DKBRCMBQCOFwAFEBxQOI0lHIpvOFYonaLw9rRmSHV5YJBo4Kmn8RaDqcLPLEUSnVnlFAoGITPaUORLr9ZpOsbm1BZXpbw1bE7HNDlybqVjng8jpZ2MxRSCSRSKVrb2jG8uirj430+P3hSRVKhOJb0FGR5hfhDIA0d2R3TaAHxhwZpROl59dVXodPpMG7cONxxxx3w+XyJfbW1tVAqlYmHPgBMnz4dSqUSX375ZUJm/PjxiQckAJx//vmIRCLYvXt3yj53796NWCyGBQsWJLaZTCaMHz8+qd3++h4sMplTbW0t5syZk1SH4vzzz0d7ezsaGxsTMt3n2CWza9cuxGKxQe2bJXuikQgcNisclnY42lsgouLQKmTQadRQq9UZKwXxeBx2pwsObwCuYATKIhM0pnKoi8sAoRS+KAN1cRkU+mJoi0uhN5rOGIWjS/nqOtfODjMc7S2Q8Slo5eLO5QulHFqVElqNGhqNBlq1CgZ9Z/VTsVgMrUbTuV+lgFYpg06jgs/nh0bXGdppt1mhlAhh0CihUSrAcPmwWG1g+lgm0GrU4DEx7Kv7BlZzG+JBX94UDofThfqGRhw4fAQKmRQcDgf7DhxCm6UD0kEu9skwDOobmlBZVgqNRgORUAgqSz8NigJoOvX9iqU3BVleYVpsfZezT3kQAdNqA3d0fosh9cXPfvYzVFVVwWg0Yv/+/bj77ruxd+9ebN68GQBgsVhQVNR7PbioqAgWiyUhY+hhHlSr1RAIBAmZnlgsFggEAqjVySZLg8GQ1G5/fQ8WmczJYrGgsrIySabrGIvFgqqqqpTtGAyGzjwJdjuKi4sHrW+WzOherZPPoaBVDCzzYiQSgTcUgT5FXguKoiCWSBJvjFxewf3c84LX4wGXy+kza2VXnopw0A8BjwshnwdtH6G92UKAhOLGoThwe31QKxUw6DTosDsgU2vRbLZCJRVDpUpdu0QqlWLCmJF5GxMAdFhtCIXDvZYx5HIZSoyDv6zS1NKKEcOrk7YpFPKMc7oAgEwmg8PjBxQDr/lyJlCYv+ooPbTH5ciyZcsS/x4/fjxGjBiBqVOn4ptvvsGUKVMAIOUbWNc6axeZyGTCYLWbC7nMu+stLluZweqbpW/8Pi/CPg+0A6jW2Z1YLAZfOAa98cxa/5YrFNi3dw9GjByFaDSKeLzzPkZI51sy4jQQp6FRKUHJByccl8flgI7FwOPzodHp4PN60eF0w6TvtIq0tLeDw+X0Kog32Ph8fgwf1vslIJEgbBBDUm12B/Qp6vHodVo0NregsjyLhG/sfSVjChO9Isjxh53rcXliypQp4PP5OHbsGADAaDSio6P3MpHNZku8WRuNxl6WB5fLhVgs1uuNvQuj0YhoNAqXy5W03Wq1JrXbX9+DRSZzSiVjtVoBoF8ZHo8HbZp13Hz1zdI3hBAEPa5EwqaB4vP54QlGoDMY+xc+zfD7vNCqFIj5PZBwCTRSETRSEbQyEdQSITQK2YDKsGeCQCBAc1Nj4rulvRUCoRDt5g7weDxUlZeiosQEmWxoU4vz+Knv6YYiPU7UN8DpciPST6RfLjicThCGgUyWOmRbr9Wio8OacXskPrQvxN9nCqJ0cMr0QLYFmDgUOKWFTTd74MABxGKxhNl/xowZ8Hg8+PrrrxMyX331FTweD2bOnJmQ2b9/P8zm78J+N23aBKFQiJqampT91NTUgM/nJ5ZxAMBsNmP//v1J7fbX92CRyZxmzJiBzz77LCmUddOmTTCZTImljxkzZiTNsUtm6tSpacMp89U3S99Yze0DikhwuVyIx+OwOZywu33gSuRnnMLhcbvg6LCAEwujxGiAQiHv9bseKstbLBaDt9tLjFwqBZeJwVRcWCVcKBCm3C6RSDBi+DDEojGY281wuz1569Nmd4ACUFSU/nkilUoQykLZ4VKdocMs/VMQpYOSiUFVZvdjpyqNQ5qh9MSJE7jvvvuwa9cuNDY2YsOGDfjpT3+KyZMnY9asWQCAMWPG4IILLsCyZcuwY8cO7NixA8uWLcOiRYsS0SMLFizA2LFjsWTJEtTV1eHjjz/GHXfcgWXLliUiV9ra2jB69OiEAqFUKnHdddfh9ttvx8cff4y6ujr8/Oc/x4QJE3Duuedm3HeuNDc3Y8+ePWhubkY8HseePXuwZ88e+P3+jOd0zTXXQCgUYunSpdi/fz/Wr1+PBx54ACtWrEjcaG+44QY0NTVhxYoVOHToEJ5//nk899xzuOOOOxJjWb9+PUaPHp34nq++WdLjtFmhkopyMm37/H7YnC5I1Hp4IzT0JeXQGYv7TVN+OuHzenDw2zpIuIBWKYP0FJg7l8uFQd/NesjlIRCOorHNAqvbj2MNTTh2ogF+vx/hcHjIxmUo0qGhD+dug0GPysoKuD25Kx3dAzTbLR3gcihoNH3X6gEApVwGu92eUR8qpQIejzvXIZ5RFGy9glszEnSTtf88HQDA5YBbM2LwB9UNgUCAjz/+GI8++ij8fj/KysqwcOFCrFy5MinpzauvvopbbrklEYVxySWX4Iknnvhu6FwuPvjgA9x4442YNWsWxGIxrrnmGqxZsyYhE4vFcOTIEQSDwcS2f/zjH+DxeLjyyisRCoUwf/58rF27Nqu+AaCyshJLly7FqlWrMp77PffcgxdffDHxffLkyQCATz/9FHPnzs1oTkqlEps3b8bvfvc7TJ06FWq1GitWrMCKFSsSMlVVVdiwYQNuu+02PPnkkzCZTHjssceScnR4PB4cOXIkq/OZSd8sqfG4nBDzqKTIn1QwDAOrwwW+QJhwUqQIA4rHh97UadEQnmFhxU67DXQ4CJlEjKqyMrRbOlBRVtr/gUOAVCpFJNa5BMAwDII+Lww6DWKxGFw+D4ZXliMUCqHD5oDX54NBp4PROPhJ0zoV2/6VWx6/79T0QGehPnOHFXQs1hluHY+DAQUuRcAQCgwhKCspzthBVCGXo76xGQKBACKRKKmycE8oiso+OOIMpWB5OoCTGUn7SxDG5YC3oAac6t6RDCx9EwqFoNFosGHDBsybN6/Qw/nec7rn6YjTNDw2S9qcDR6vF3EGiDMMCMWB3nhm587ogo7F4LLbIBXyIOmWq8Hv98PudEMmEUGn0xVwhJ04XW7QHD6KTvo12TssIHEaQokMdJyGSCRGKOBHxO+FXq/rM2NsvrA5HBDy+SnzFXWnobkFlWWlff7e9ny7DxPHj8ur8ykhBD6fDw6XG1UVfUdOOn0BaPSsz1h/FNQzk1NdDN7lP0R89zGQxh6p0DkUqEojuDUj2NorObJt2zacc845rMLBkhEuhw26NApHNBoFJZBAkyYt/plIKBiE3+sGDwz0Kc6bTCaDTCZDa7sZ0Wi0zzflwaahsRH+QAAKjR7RSAQURaX0sQkF/BDKFDjW0IxRwypzSmWeDTKJFGaLpV+lQ6tS4sSJegjEYpSXpI5+UshkeY92oajOwoChcP/p4Fln0swoqKWjO8QfAtParcpsKVtlluXU4nS3dFham6FXK3s9aBwuN2JxAmNpFiGEpylMPA6X0wFCRyEW8DP212hoakJVRcUgjy49XSHlDl8QdDgEkZCPKAPwBULEoxFwBUIoVWrYrR2gmDi0auWQWbHi8Tiam5szyp/T2t4OnVYLkVCISDSKDqsNQqEQMZqGRCjIyFcjF5wuN7w+L0pNpqSwYq/XB7/HB2NJZ8ShPxSBRKE6o3yYsuWUyb5DycRDmviLhYUlGWNpOewdFgg5ncmZCCGw2BwwlJT1qrtxJuJxuRAN+qHXqgFkl/I64A8Oet6JvkgoEAwDikNBqVDA4XCCjhDo1EocOV6POE2Dw8TA4XDR1GaGkC+AUMDrN0V6fsaX2XmJRKJoaW1HsaEIx07UY/JZEwZ5ZJ1o1CqEw2Gc+PcGUHIRuGo5+CUaRDtc0DW44LtKCqVSCZFIBK/PC080kkgZz5LMKaN0sLCwFB6dwYhIOAxXMAhCCIxlFWe830bA70fI54FcIoJSm1sY8bDqKtgdTgRCIagV8rTVmwcbLhi02uwQCwQIhk5GqaiVUCoUcDrsoCgKQr4AlaUm0DQNt9uD+sZmiMViqBSyXgUvB0o4EsHx4/WorsrMCkTiDEYMr0a72YxRI4ahqbUNFaUleR1TOgR8Hiq4EnDCHMAcAsxtAIDmYjmGK7/LRqqQyzudctvboCsynDaZdfNFQUvbs7CwnHoIRSKoNRpotNozWuGIRaOwmduASAA6tbLfiJ6+EItFKNLrUFVehkAonJRDZihRKRVQy+Vwev2Qy6TQqDsflga9FgI+D2KBAGUlnb4ePB4POp0W1ZXliMai2LHrG9B0/vwW7A4nmltaMX7cmCQH3L6gTuZ3MhUX48ChI9Ao85cqvj80Gg1aSnuPkyvu7XArFotBkTgajh9FrEDX+lSFVTpYWFhYemAxm+FzWKFXK/OeZ0MmEaO+gIUHORwO9GoFotFoYm4URYGhaYiEAhw7caJXfgoOh8KsaWejqaUVodDA83g4XS7QsRhGDh+W8THhSARi8Xe+VGdNGIcOmwOuPCYO60k0FoPH40Hr8Sa0HWuC0N177iQWT3ksh+KAy+Xi8KGDaGttwQDdJ08bWLsPCwsLy0lcDgfikRB8DgeGpagJkg+USiUCwTAOHz2G0SOHNv8Q0FkmRCKR9LIulJeVgsvlQpuiHgnDEAgEAgyrqkRDQyPKykozrtNidzoRCAYRi9AwGvSg4zRiURrGLAu6+Xw+mLoVgRQIBBheXYnG5hao0xSpGyjWFjN4H+5BkSh1unQAiFpcKbfrNCpQhIFULEIkFsXOr3ZAqVRBqVLCWHxm1R/qzimjdNC2ACK7WsEEYuBI+RBOLQVPz3oAs7CwDC50LAa3ywnQUagUcvCkKmhUCjidLujyVL69J6ZiAxgQHDl2HKNGDAfQmbTL7/enDB9lGAYNTZ2JqigOBwzDdFonmDjCoTCEQiHoaAxiqRjFBkOfDquRcDilU2tf4bFMt9LtVVWVsNrsKNIn5x6J0TTsDickIiEUCgUoikIwGILf50flyRwXTa1tUCvkoKjs3/rj8dTH8LgcRGMxCAYhr0jMG+hT4egk/Vy6K3ClxiI4XS64PW7sbm09qYCowOVyoUlTa+p0pOBKR/SwDd5X6hD6sgno/qPiUhDPrIDi55MhGF3YmissLCynF16PG7FwCGDi4FKATqUC8N1LDofDATPI5vDSYiOO1zeCYRg0NjVDKpVBLBKgrd2MElNyMkRLRwfKSkwpc30cOX4cleVliESjiEQiaDWbEafjKDYUJYV2Hzt+AhweH4IsfVNomu6lkPj9fkglEnh8PkQjESgUcgRCYZSZiuH3+9F4cvlIKpUmFI4Oqx1Bvx8cCigryd75MxpL7RtRWlKCEw2NkIgloCgKRkNmzwtCCDqsdkjEwrR5QoQaBRimrU8lrigMuFxuqPuJ8qEoClqNBlqNBpFIBG6fH4iFQdD/9YjTNJwOB/SnQcHKgiodwc8a4Lj/EyCaYk0sThD6vBGhr1qgvWc+JLMrh3x8LCwspxehYBB+txNKmQQKpbxPWYfTiXAkAh6PD5VCDokkv5Ebbe1mcLkcWO0OVFV2RglFYzG43O5essFAEILi1FmZBdzO27hQIIBQIDgZPRFGa7sFErEQpuJiNLe2ory8DMIcEpQFg0HQdDwpwVl1VSWcTieMRXpwOBzY7XZIRZ0Pz66kaD2JxWmMGZ17Xai+fCKGVVUCAOx2O1rNFlAAjEX6XsqSx+uFx+eHUi5DMBSGQa/DwSNHMT6N0lFcZsJh6X4MC6VXOmRCCdqbLP0qHd0RCoUwnFT+LDYH3E4nxBIJQsEgVD1yjTQ3NkDE4wDcgtsI8kLBHEmjh23pFY4kwTgc932M6GHboI4nHA5j6dKlmDBhAng8Hi677LKUctu2bUNNTQ1EIhGqq6vxzDPP9JJ5++23MXbsWAiFQowdOxbr16/vJfPUU08lkkzV1NTg888/73eM+eo7W/71r39h9uzZUKvVUKvVOPfcc5Oq2wLAqlWrOutvdPsYjckZDwkhWLVqFUwmE8RiMebOnYsDBw4kyUQiEdx8883Q6XSQSqW45JJL0Nra2u8Y+zufmfTNcvri9bjhtHUgFvBAr1FllB10zKiRKC8tgbFIB6/Pn7exRKNR1Dc2QavVoKqiHMYifSJKiM/jIRKNoqGxCS3tZrSaLWhsaoHH6+ujxd4PY7FYhOHVlYiffFBHo3ROCgcAKBQKVFdVoqm5LWm7RqNJWAB0Ol2/ibni8X7u9f1A+ljG6EKn06G02Agel4fWtja0tJvhcDoBAIFAEIFgEOUlJlAA4nEaHA4HUokk7dgikSh4kf4jdjL1b0mFUa+FTMBB1OeCkIrD1t4Kp9UCZ4cZdnMr1FIRCMVBUfHQhAYPNgVTOryv1PWvcHQRjcP7at2gjicej0MsFuOWW25JVHLtSUNDAy666CLMnj0bdXV1+MMf/oBbbrkFb7/9dkKmtrYWixcvxpIlS7B3714sWbIEV155Jb766quEzBtvvIHly5fjj3/8I+rq6jB79mxceOGFaG5uTju+fPWdC1u3bsXVV1+NTz/9FLW1tSgvL8eCBQvQ1pZ8Exo3bhzMZnPis2/fvqT9q1evxiOPPIInnngCO3fuhNFoxHnnnQef77sb6vLly7F+/XqsW7cO27dvh9/vx6JFi/q8YWVyPjPp+3TC63HDYWmHo70FTnMrHOY2OK0WuJ2OQg9tSPF63LC1t0BEMdDIpVDI+7ZupKKxqRmGovzUTunosMHh8qC6sgKiFMscFEWh1FSCqsoKlJmKUVpsRGVFGWqmTErZHk3TafNAEEIQCgZxvL4R1ZW5J160O5xoaGxCWWnu9a/cHk/CEpIzTGbLXc0tLdBqVKgoL0eZqRh8Hg/1jU2w2e0wnXwRUigUKDV1OnNWVZTjyLHjaDd39LrPCAR8uEcVoVUGOOnOyBWaplFfKsJR2otmJYWWiUUICQYWWs7j8SCXyyEWi6HXqKBRyKBRyqFTqxCL0TCUnD7ZgAuSBp22BWBe/FqyD0d/cCkUv3HNkDiXLl26FG63G//973+Ttt9555149913cejQocS2G264AXv37kVtbS0AYPHixfB6vfjwww8TMhdccAHUajVef/11AMC0adMwZcoUPP300wmZMWPG4LLLLsODDz6Yckz56jsfxONxqNVqPPHEE/jFL34BoNPS8d///hd79uxJeQwhBCaTCcuXL8edd94JoNOqYTAY8PDDD+P666+Hx+OBXq/Hyy+/jMWLFwMA2tvbUVZWhg0bNuD8889P2XZ/5zOTvjPh+5AG3Wm3gYlGoJBJUr7N250u6Ezf3cBi0Si8Xi9UKtVplcQoHArB67RDIRUP6FoxDAOH0wV9nhxKm1pa81p51uF0Qi6TpbXcOF0uqFWqAeVbOVHfiGHVlTkfDwAOpwt8HrffGit90dDYiKrK/sfR0m5GmSl7BcntdsPp8aL6pA+K2dIBhhAUG4pACMGhLV+Bd7QdEYMCZ125IJFavlPWiuJBqsrrcDihMZWdNjlzCmLpiOxqzU7hAIA4QWR3W/9yg0htbW2ijHwX559/Pnbt2oVYLNanzJdffgmg07S6e/fuXjILFixIyAxW3/kiGAwiFov1MqceO3YMJpMJVVVVuOqqq1BfX5/Y19DQAIvFkjQ+oVCIOXPmJMa3e/duxGKxJBmTyYTx48ennUMm5zOTvr/v+LxeWNtaoBQLoOu2fOD3++FweeDyBdBitkIg+e5N3+WwI+CyQSMVor2xHocP7D8tcgkEAwGEPE4UadUDVg6/2fstCCGwORyw2e3w+wMDao+O0Xk7xwzDdNb+8Kdf+tGo1Tk/rPz+AFra2lFRPnAlSatRd0bgNDbl3AbJwNIRDochyfGaq1QqMPHOCKKG5hYoFQqUFBvB4XTm2xh//kyMuukKjLv8HABIOq9ymRQnTjTk1G9/qNWqzuiq04SCvNowgVj/QimPK2xmN4vFAkMP72GDwQCapmG321FcXJxWxmKxAOh0dIrH433KDFbf+eKuu+5CSUlJ0jLUtGnT8NJLL2HkyJHo6OjAX/7yF8ycORMHDhyAVqtNjCHV+JqamhJzFAgEUKvVvWTSzSGT85lJ399XmHgctg4zJHwuinqk6Pb6fOCIpNDqOt8u1d2c+l0OO8RcQChRwOF0QW0wokw+dNkdB4tIOIyw15WXeiEtrW2YMG5ski+E3W6Hw+1GkVablKgqFcFgCGKxKOnhVFFeCofTBV2KXBjZ4PF44HS6UFFRPij1XNxuD2g6BrFQMCB/he5EYzT4OfqVdFoV+pczWzpQVZl7Yb1QMIhINIqq8tTLGRRFpTwfbp8PxcWDE1ni9fkg1wyOFaUQFETp4Ehzi6fmSAtXGrqLnm8NXW8t3benkum5LROZwep7IKxevRqvv/46tm7dmvQWeeGFFyb+PWHCBMyYMQPDhg3Diy++iBUrVgxofLmem3yc81OZzgJkPhRpkt9mI5EIvIEQZCoNxGnSSzN0DHFwYHW6UVRcCqpAhcjyCR2Lwee0QafJrT5KT+IM08v5UqfTQYfOh5vD5UKRXpcyP0RTaxv4XC7aLR0oKTYmFJTG5laUlQ48MVQ0FkPVyYiNwSAUiaI4w9DTTCCEwGLpgEarRmNLCwjDIBgMoaKsNGWkS088Hg+K00TvdEeQo5XDarUhQscwdsyoPvOVpEOrUiEYDCAcDsPlcmPYsOqcxpEKBhzwBiEHSaEoiNIhnFoKcKmsfTqENYX13jUajb3euK1WK3g8HrQnk7ukk+l6y9bpdOByuX3KDFbfA2XNmjV44IEHsGXLFkycOLFPWalUigkTJuDYsWOJsQGdVofuN4/u4zMaO8tDu1yuJGuH1WrFzJkzU/aTyfnMpO/vE0w8Dqu5DWq5FMpub8zBYBChSAw8kQR6U98mcYlciUg4BEPJ92/+6fC4nHlTOABAJEz/ACs+mU2zrd2MaCyKYqMx4Rhqtdlh0GkTSnlDYyNUKhVcLjeqK/NjmRjsVTBCmLy2R1EURo0aASbOJBQwQgja2s0QiUT9WlNidByqfhQKu90OVZaOwuFwGBarDXqtFkXS7CoHd0csFiXm5XS6c24nFd//Bc9kCvJ6w9NLIZ6ZnQlMPKui4BlKZ8yYgc2bNydt27RpE6ZOnQr+SU00nUzXQ1MgEKCmpqaXzObNm9M+WPPV90D429/+hvvvvx8bN27E1KlT+5WPRCI4dOhQ4iFfVVUFo9GYNL5oNIpt27YlxldTUwM+n58kYzabsX///rRzyOR8ZtL394VIOAy7uQ0GrRpCoRDRaBQOlxtOXwCUSAZtcQmU6v4fvGKJBCrN6ZMFMRqJgIlF8tYewzDgcvq3gpWYiqFWKmG12RMhroFgMMkKWFVZiZaWNpSVleZtKWSwH0Q8LjfvRemEAkHSkhRFUSgtMaGppRUnTjQgGAziRH1Dyki1aKz/JflAOAxpFopDU0srvD4/KsvLsjquP3j8vhWoYDCIxuYWmC1WWG12HDte36c8mDjoDOb/faFg7uqKn09G6KuWjMJmKQEXip9NHvQxHTx4ENFoFE6nEz6fLxGJMWnSJACd0SJPPPEEVqxYgWXLlqG2thbPPfdcUmTIrbfeih/96Ed4+OGHcemll+J///sftmzZgu3btydkVqxYgSVLlmDq1KmYMWMGnn32WTQ3N+OGG25IyNx9991oa2vDSy+9lNe+c2H16tX485//jNdeew2VlZUJq0L3JEB33HEHLr74YpSXl8NqteIvf/kLvF4vrr32WgCdN5jly5fjgQcewIgRIzBixAg88MADkEgkuOaaawB01qS47rrrcPvtt0Or1UKj0eCOO+7AhAkTkvxH5s+fjx//+Me46aabMjqfmfT9fcDn8SAeDqBIp0EoFII/HIVQIoW2OH/REN9HwqEQAi479AP0k+gOh8OBx+eHTtd/pV2VSpUoVX+ioTGRqKo7EyeMQ31DI0pKTDnny+jOQC0dbrcH9uYWCOUyCKVS6PW6xDy9Ph/8gQAU8v6XPfLBsKpKBAKdzunVVZWor29M1L2JRKJwuzzwWd3wSaUAQyBXpfY9YjIMqfX7/bC73DAZDYOSOj0eT20lslg6EI3HIZdIUNnNZ4SOxxNp6esbGqFSKpKc9LVqFTo62mEozd1X5VSiYEqHYLQe2nvmw3Hfx30rHgIuNPfMH5JU6BdddFGSY+HkyZ2KTpfvRFVVFTZs2IDbbrsNTz75JEwmEx577DFcccUViWNmzpyJdevW4U9/+hP+/Oc/Y9iwYXjjjTcwbdq0hMzixYvhcDhw3333wWw2Y/z48diwYQMqKr77UZnN5qQ8E/nqe+3atfjlL3+ZlQf9U089hWg0ip/85CdJ21euXIlVq1YBAFpbW3H11VfDbrdDr9dj+vTp2LFjR9Kcfv/73yMUCuHGG2+Ey+XCtGnTsGnTJsi7mUT/8Y9/gMfj4corr0QoFML8+fOxdu3apHXWEz2qYGZyPjPp+1TG5/GAioWhUirg9njBFUuhL2bLAxCGQUvDcYyozn9xturKchw9fiJRGyUTxOL0WUurqyrR2NSMIr1+wNlNQ+FQvzLhSARulxuEEGi0GpjbzeAAMJaYEI1GIQz4YBLyQQcDOOF0YfjokYjGYnA6XagegDNmLnS3NHB5nX/rhBA0Hm2AaMVOEBUHrcGvEDdJwJHw4SQB/PCxnyW1UVykh8PphLaPJGVNzS2QSCSozGPYck9EKa5tfUMjjAZDyutebCjC4aPHIBKLUV5WCqvdgWAwmCjI53a7EYlEYG1tArh8KFRqCEWi760/WkHydHQnetgG76t1CH2RovbKrAoofsbWXsknq1atwtatW7F169ZCD+V7R6HydHjdblB0GPKTViWH2wOt8fTITjhQOtpaIeRR8Pr8UMikCYtDvmhua0d5SeaOn+2WDpj6qZ7a0toGAV8AvV6b83JLU2srKkp7PzhpmobFbEHU7oAwGoZRpYLT70eQIShVdCrYrW4PJBwONIrvFO6OQBDEYEAoHIZMKoFIJAIhBIQQCARCyGRDt7T9zeavEf3PUfB4POiP0SnPUUDPBWYXg9EJUXXhRIhPPszbLB0oSXH+XWY7Wp/fCc5MI8bNG1yrec88IW3tZuh02qwsXA3NLZBLxNDpdDh6vB4jh3/nmOrxeBGjYwDFQZwAMqUaUpkM0Ugk67o6haDg2YAEo/XQ3b+gs8rs7jYwgSg4UgGENSUF9+E4Hfnoo4/w6KOPFnoYLBnitNsgpBhITyocDMMg4PMhGm9DcQ5Fs/KFy2EHwzAQiSWJsQ0lcZqGw2qBWt6ZBE2lVKK5rT2t0tHRYUMgGEB5FiXZAWS9jsHNQIkoKy3pLPLW3JK7RaHHuOLxOCyWDkTa21GhkAMSUecHgEYmQ/d3/9IUZeANUgng96HeH4DiZG4KDocDHo+HppbWlEpHMBiCPxBAPM6Az+OCL+BDmUPyr87IFiuKiw2IRKLgfmVDSRMHAAOkOZ9SWxx4pxWEEBzd2AhSJAJfIkRERqHk1u+SCDIMg8P/2wnqjXqoXAT+r21oLdOhdPjgZfjs/h4fDocRZ0jWS2pV5WWw2R04cPgwqsqTfyNKZfI59vv9MDusEAuFcNAMFCp14m8yEg4jFApCpc7f0uNAKbjS0QVPLwXvgpGFHsZpT1f2UpZTGyYeh83SDrVcCoGg8y0uHA7DE4ygbPiogptWY5EwuCBwB/zgcDhpQ3MHA5/Xg4jfi6IekSqlxUbUNzaiOkXWSl8ggGFVFbB02BCL0wgGAuBxOKiururT2uBwOFBemrlyF2cyi/rgcDhQKeRoamlFLEZjeBYZP8ORCIT8zoeY2+OBz+1BtKMDlUoFoMh9uTAWo0ELhQmzfvexer1ehCJRxONxxBkGsUgEKqUCRXod/H4/JBIJ6huac1I6gsEQrEvehWPFJPjXH0ZJc+YhqxRFQdMcB5oDAALwKQhCy0IQS8SwNppheeZrKL/xgUN1XmNZmAf7qq1QPv1jyAfJZyUYCuHI8RMoMxWjw+7IOqKmC71Om1Em3O6+dd7mVkS5QMjrAsXlweV2QyQSs0oHCwtLegJ+H0JeNwzdkn25XG6EYzSKyysLN7BuFPUTkjsYdFk3FBIR5ClCYzkcDnRaLex2O3S65FopfD4PoVA4KVU1IQR79u3H5IkTAHSuu1MUBSbOQCSRgM/jorysFO1mM0wZ5IgAOqMsfD4fIpEIBAJBn2m/NRoNNBoN/H4/3B4PVMreFojuY/V6vYjFaDidToi4PBz/ehcMAj5MYhGgHFhiN3coDK9QiLIUS0kGvQ40TcOgT11/puuBx+dRqG9sgrGoKCufFb/HDw1XCjx6DEpknyOjO3IvhZbthwFQIC8chtrJAFSyUkmpReByBy9wUyGTodhQBEuHLW2SscHCZDImWfKkQj5aLdYhHUN/sEoHC8sphL3DAhGXSso34XK7wRVLUVycvxwU3zc8LifocBBFGlWfcgq5HI1uDzQnowG6qCgrxdHjJ1BdWZG4KVMUhQljx8Bqs4OOx6FSqXplMiWE4PD+w4jb/CAiHkqGladNHhWPx+H8cj9kNhoyoQj2H1ZnVGtEJpN1Lg2lUTpsdgdcbg+YliYMMxqh5nAAOgoo8+cEHYrFQAuFkKUYb6b+SxUVFfD7/TjR0AijQQ+lUplRdEiozTtAVSOZyIuHIbXT4KdpVXnz2b2sOfmEoPO3NVi1WPqi59KhSCRK+IKdKrBKBwvLKUAkHIbHYYNWpUh6qPn9fnDFcij6eAs+3XE57BCAgTLD1OYVpSU4Ud+I4cO+i2hpaW2DWqXqdVPm8XgoSvMGDwAHvvwGww+6wOFwwDAMTsQiKKksg1Ta28eh+cAxjPcLALEADMOAr+682cdiMRCgzwcwHY0mwia78Hi9cDhdMBkN0Ou0aIzT4AxShg4vw2S1jJQOmUyGCePGAAAaW1ozihIJ7m1HPmPIVHYCpFE4GIaBRJl/X8ETJxpQVVUBDocDu9UGn8+H4VWVeUshPxB4GeSbGUoKf0ZYWM5wXA47OPFYr9opQGdmV6c/XIBRnRp43W6IOARiceZvphRFwWgoQkeHDcFgAAKRCEVF+pzyY8iKNOAc9gDoXL6p3NGO9iAN6dnjk+TcLjfktY0Ar7MPZzSIaIsVJ+pOgGqyQXbZDBQZ00fhVVaU40RDE5QKORhCEI5EoJLLkhxNtSUmNNU3Qk1HoUih9AyEUSolWhqbIJswvn/hDFFIJXC6XNCcTFZH03Svh3A8HkfgkwbIMTRpvgMSBvI8KgJt7WbQTBxl5aWwO5yIxxmMHj0SAj4fh44cwdjRo/PWV64oFXK4Xc5Txq+DVTpYWApEMBBAwOOEWiEHj9fbhB2NRuHyBVBUfGaGx7qdDnCZGMQ5mIdlMikCwRCkMhnC0WjOCbn46P2WyDnWgYZQFHylHBy5CIRDIXqgCSW87/rQiWTAtyfX0vky1B9tRjQWhcFYlMggnNQmh4MRw/rONSKXySCfOB5utwetdgcohx0lKSJRckXOxOG026HRpbf8ZINGo0F9QyMUcjlaGpsQstlQMm5skrOp3eqA0TF0dUU4y8+CJoVyny1OpxOBcAR6rSaR/r6nxUwiOTWiL3k8HuLBgVVHzienjNIRaPOidfNxxLwR8BVClJ43HNKS73/VSxaWVPi8HlDREPRpaoWEQiH4IzEYSobWEe1UwWW3QcLnQCjJfT3aUNT5EPh234Gscm10h+L1NtMbQxRwzA3AnXE71cc82BPyQ6VWplQ6skGlUkKlUiJcWgKzzYaQxYJyiXjApnwRlwtXKISBvg8zDIOGY8cBAJxYDIe/2okxOg2gUaOx3ZykdITM+fXnSIeHhMFcVoHqs4flpb2Wtnac1Y9ViGQYyTQUFDrarTsFVzpsu9pQ9+A2NL13GIT+7iJRPA4qLh6NyXfPgX7qmfmmx3L6wuXyEDuZLjkUCiEQocHh8RL5F7g8HvR9mONPZ8KhEHgkDqFwYFk7u1Ao5AgEgjnV15Co5LBOKwf99TGYyMASLxkavXBXWMEp5+TFkVEkEqK0rBSktARff7YdUwfouNhBx1FdlruS63Y64W1rB6FplIuEnf4pfB6g+06NKQqHUV/fiKIiHWQyGUKHrBhsN8cgHQXviR9i9JiBp2RgGAYHjxzFuDF9L5sEg6G81dnJC4NdITALCqp0NKw/iE+ueRPxCN1rH6EZNK4/iJYNRzH/9StRednYAoyQhWVwkEilaLdZQEAAvgg6Y2YhmWcCrcePY/iI/KU1r6woR1NLKwRCAfhZWgMIgIjdDX2UwkDdDgjNIFzfgZBOmdfoCULIgJ0FQ9EohH2kD0+Hpb0dYYcTvDgDAUVQIhIBgvQnSiIWoSIWgfvQIXTQcQQcdsgARBkaAk7+H0c0Q0PCE4B2D8zqQNM0WlrbwBPwMW50/3lyotEotHmseDxQmDxXDR4IBVPFbLva0ioc3YlHaHx89Zuw7Wob1PFs3boVl156KYqLiyGVSjFp0iS8+uqrvWQoiur1OXz4cJLc22+/jbFjx0IoFGLs2LFYv359r/6eeuqpRDrtmpoafP755/2Ocdu2baipqYFIJEJ1dTWeeeaZXjKZ9J0ta9euTTnvcDjZwbG/ORFCsGrVKphMJojFYsydOxcHDhxIkolEIrj55puh0+kglUpxySWXoLW1td8x5qPvocZQWgGZpui0qvY6ULxWO+SfHsaJjbUI+P15abOxqRl8Pj+rekNdSKQScJvsEPGz9wmxiAnMZxlgnzcC7kUTULzsQgy/aAa0GSR8ygYOhwNZDuPrIhKjYeXxUZJFTomGxkacqG9EtKEBZQI+isVCaLMoDaBSKCDn8RBTi0H+PhPK1y6C55oyODQ0nCM7o38GinMMB4LLNXAvEsAb9Q6orW/3H0RVZQXKTKaMliqcHk/eq/TmisPlhlJ96txjCqZ01D24rV+Fo4t4hEbdQ9sGdTxffvklJk6ciLfffhvffvstfvWrX+EXv/gF3nvvvV6yR44cgdlsTnxGjBiR2FdbW4vFixdjyZIl2Lt3L5YsWYIrr7wSX331VULmjTfewPLly/HHP/4RdXV1mD17Ni688MKkAm89aWhowEUXXYTZs2ejrq4Of/jDH3DLLbfg7bffzqrvXFEoFElzNpvNSfH7mcxp9erVeOSRR/DEE09g586dMBqNOO+88+Dz+RIyy5cvx/r167Fu3Tps374dfr8fixYtSlnuOt99DzVcHg+8Qahy+X0mtO8E1FwhytpCsL/3FU7s2ocTdftR/9W3aNp/NKc2KS4354qiQoEAnFm5RSDEyzSomDoOpuoyFBlyi57JFFEOVgoAsATDsIlEqO5nuaA70WgUkRiN6qoKeENhNNidWffbyBBIxozGjCXnoXx8JXR6HUZcNQ2jX1iMUX+7GOHHfgDy95lwKftWPjzXFSF6cznspu/uD34SgeMHfFSN1UIvkGCURIMRh91oOtaQ9Ti7kGZZe0YulSIYPnWizmg6s2ftUFCQgm+BNi9eq1wDkqYEcCooHgfXNNwxpM6lCxcuhMFgwPPPPw+g09Ixb948uFyutDUeFi9eDK/Xiw8//DCx7YILLoBarU6UoZ82bRqmTJmCp59+OiEzZswYXHbZZXjwwQdTtnvnnXfi3XffxaFDhxLbbrjhBuzduzeR2jyTvnNh7dq1WL58Odxud1qZ/uZECIHJZMLy5ctx5513Aui0ahgMBjz88MO4/vrr4fF4oNfr8fLLL2Px4sUAgPb2dpSVlWHDhg04//zzB63vTChUwbczBZqm0f6v/0LJcCFO4V7YbBBg+KIfZt1ua3s7JGJxInQzWzosVijf+zbrNfpjEQ+G/3rhoCobXTidLsTqT0CXRcpthmHQHGMwbOK4fmXNlg5EYzHw+XwIBXxo1GpQFAWn04Wg243iWOZv9U6vD5KxY/v0sWkzd6Ck2IC9D30IzaeulGO3zxSh8tJhUEkliMRi6Gjzwv2/RvhlMdQMN4HXI4mbl44CPz4buqLs3votHVbw+bw+q9f2pKGxCVVDXKm3L3w+P6LgQKsf+oRlPSmIpaN18/GsFA6gcz20bcvxQRpRajweDzQpfmiTJ09GcXEx5s+fj08//TRpX21tLRYsWJC07fzzz8eXX34JoPMtYffu3b1kFixYkJBJRbp2d+3ahVgsllHfA8Hv96OiogKlpaVYtGgR6urqEvsymVNDQwMsFkuSjFAoxJw5cxIyu3fvRiwWS5IxmUwYP3582jnkq2+WwuM62oSQgItoCv+EEENDdlZufh6lJhPsjuzfxrsI2z1ZKxztET+Kr5o7JAoHAGg0anAqqmALZB4a2REKZ6RwAJ0Kd0VZKUxGA7QaTWKJQaNRgyfO3OHX7PcjZjL169Qr4HHh9fkg/WE5gE4lo0XgQVtFDO2UG+H/V4azrh4P1cl2hHw+yiu1GPW7s6D6gREefu/ni4InQHDjN/D7+l+2CwZDMJstaKw7hMDmPeh4+RMcPni43+OAzno4p4o/RygUgtPjRZQAMvmpEQ1aEEfSmDeS03HRHI/Lhbfeegs7d+7E//3f/yW2FRcX49lnn0VNTQ0ikQhefvllzJ8/H1u3bsWPfvQjAIDFYoHBkFxa2WAwwGKxAADsdjvi8XifMqlI1y5N07Db7SguLu6371wZPXo01q5diwkTJsDr9eLRRx/FrFmzsHfvXowYMSKjOXX9P5VMU1NTQkYgEEDd4420rznkq++hJBIOw+d2IRAMoqI6PyF833ci4TDaDh2FNBqDkultRfKoBCgvyy2K7fiJBpRkWDslFZRUAH8kCJkwc+dPk1AGRzCMAcefZoFer0VrOATG78tISYplqCxEo1F4vL5eGVO7CAVD/bbR4vGA0uhQNHYcxOL+rYR6vQ5tlg6U/2Ak9v/cBhh4mFqqAYfDQTweTxseLOTxMHZCJcKhEFoO21AWSLZ2GGNctHxSB/4F0yAU9lYID9XWgTpshiJId+ZaAQDwAJkG7gx9cSKRKORZLscMFoFIDDpjbuHig0VBlA6+IrfQM0GOx2XL1q1bsXTpUvzrX//CuHHfvQmMGjUKo0aNSnyfMWMGWlpasGbNmoTSAfSOiSaE9NqWiUxPUh3Tc3su7fbH9OnTMX369MT3WbNmYcqUKXj88cfx2GOPZdV3LuPL9dzko+/BwOdygNBRGDRKWNtbC1I87VTj8w2bMKY9Aj0n9QOJkWb/t+/1euHyeBGORvp9s25pN4OJx1GcwveDS6isFI4uIu0OoHRoo5JMJSbs++prjFbI+8zb4fAHUTI+fUQgwzBoam4GTyCEWCjEpInpc1IIBfzOWjBpaKA4qKqpSVuzJh08ioNYLIYpS+bC4/XCdfQodPK+59WFSCxGXMkD8TO9/sbL7DG0fLEXsrEV0Om1SeMShWiUMCKgx8/QGgtCk2GSukg0CoNEj2AwCJFIVLDQWb/fD6lCVZC++6IgZ6P0vOGgeNl1TfE4KDl3+CCN6Du2bduGiy++GI888gh+8Ytf9Cs/ffp0HDt2LPHdaDT2eiu3Wq2Jt2ydTgcul9unTCrStcvj8aDVajPqO19wOBycffbZiXlnMiej0QgA/cpEo1G4XK60Mj3JV99DhcflhFwigl6vh0gkgkomgcthH9IxnGrs/KIWZR1h6Kj0SxHE6UfT2o9wdE/mEUcevx8VZaUYOawaB48cRfTkMmR32s0WHDh0GGqFHBVlpWhoaoalwwa325OQibbYOxNeRbxo52RubaUPt+QlCiMbOBwOJkz7ARr8wT7lokIBBMLUilxTczNsdgcqystRZiqGTpveXBMORxDw9B0ZotJoslY4AMBg0MPu7LwXKBUKhFTZmY0qKwxoVDEIMb2dKMsafFB/sB+Naz9Ew+d1aNl3DE17DsPu9eAI7eklH6nWZ1Q5Nx6PIxKJoMNmRywWw6Gjx3CioTGrcWcLISRlZFY4Fod4EAvb5UpBlA5piQIVF2fnEV5xyehBdyLdunUrFi5ciIceegi/+c1vMjqmrq4Oxd1MtzNmzMDmzZuTZDZt2oSZM2cCAAQCAWpqanrJbN68OSGTinTtTp06NZHhsL++8wUhBHv27EnMO5M5VVVVwWg0JslEo1Fs27YtIVNTUwM+n58kYzabsX///rRzyFffQ0E4FAJiYQi73ewFAgFIFk54pyNyvhCxaKxPq1NxiEJQJ8HISZn5IHR02KA4+WYqEAhQXmKCxWpFh9WGppZWBAJB+P1+EEIwZtTIRHn2UcOHoUivhT8YhN3h7HxgVOoQuHwKRv72UpDhxoznVRrh49h7n6Nx+x40fXMIrQdPoO1YE8xNbTC3mmFpt6D5RP6X92I0DWmKB20X4WgMlCK1w6nX64VYLIGhSJ/RG7rdbkcpSR9Z1hgIQqHM/b4dj0UTD1SJQgGGYcAwDJze/qPOuFwuhk8oh3uYGH6S+nyUQYKSoy4Yvm5B8e52nOXgYBSvd2p5vi6zOXC5XIwYVg2DXgelUolxo0dBJBLDYunI6PhcMFusOFHfiFAoOVqGwzs1I+MKlhxs8t1z0LLhaEZhs1wRD5PvmjOo4+lSOG699VZcccUVibdigUCQcCb95z//icrKSowbNw7RaBSvvPIK3n777aSw1VtvvRU/+tGP8PDDD+PSSy/F//73P2zZsgXbt29PyKxYsQJLlizB1KlTMWPGDDz77LNobm7GDTfckJC5++670dbWhpdeeglAZ6TKE088gRUrVmDZsmWora3Fc889lxSVkknfuXDvvfdi+vTpGDFiBLxeLx577DHs2bMHTz75ZMZzoigKy5cvxwMPPIARI0ZgxIgReOCBByCRSHDNNdcAAJRKJa677jrcfvvt0Gq10Gg0uOOOOzBhwgSce+65ib7mz5+PH//4x7jpppvy1vdgQwiB12lDUYq3RplEhObGRpRXVg7JWE41Rv9gCqwhAuxPn4vHHYug+rzZGbcZJ0xSum2ZTAaRSIRwOAxDkR42mw0MA5SYei9/cDgclJqK0dTcgrISEwTdnEGpLJKLcTgcVNnjQB8hpZbxRUCe3XrcLjdMfURa+KMRlJSXp9xntztQmUXURWlpCY61tqI6hcNsUyCA0vHjcwpV7oLL4yJG0xDw+dDpNNixZw/Kx4wGzRdCE+9tuUo5xpIiHPW3QGodwHLqAJZhS4oNsFhtOR+fjkAgCI/PD4oChg+rQmt7O4QBIXg8HhwOBySn4NIKUEClQz+1BPNfvxIfX913gjCukIf5r1056KnQ165di2AwiAcffDApbHXOnDnYunUrgM634zvuuANtbW0Qi8UYN24cPvjgA1x00UUJ+ZkzZ2LdunX405/+hD//+c8YNmwY3njjDUybNi0hs3jxYjgcDtx3330wm80YP348NmzYgIqK7/7YzWZzUp6JqqoqbNiwAbfddhuefPJJmEwmPPbYY7jiiiuy6nvt2rX45S9/mVWiJLfbjd/85jewWCxQKpWYPHkyPvvsM/zgBz/Iak6///3vEQqFcOONN8LlcmHatGnYtGkT5N3C/P7xj3+Ax+PhyiuvRCgUwvz587F27dok8+yJEydgt9vz3vdgYrOY09ZZEQgEEGXgXHc6Q8J9W3uicmFWNUuikQisdgeKujn/8Xi8hEVDr+8/xXxFimRZHHn+rhPDMODoc7cCEEIQjUYRCoXg9flgMBggFAgQ65EfgmEYtNvtKC0qgjcQQLvbi7SLihxO1j4IfEXvOdgCAairqwccvSMUCBNLVBwOB9PPPQccDgdWqx3RDnOSQtgXw0eUwOxqhpHOTQEifeQJygQORaV1xM16LISgpa0dIgEfpm6p70tNJgSDQVg6rKiqrIAndGpaUAuSp6M7tl1tqHtoG5reTVF75ZLRmHwXW3sln6xatQpbt25NKFIsmZPrbz0cCiHscUIqlaR9cNqdLoDLg85wZqZDt7y6EdFWGyRcPhQpyti3jFZj2KzJWbXZ3NqG8tL83juavz0K487+M+RmQoOBh5ELZ/f79u3z++EwW0DCYZBIGLFgEBwCECYOqUAAlUwGHo+H434/xBot6EAQw8SdS3jBUBjWUBgCHhehcAQamQQ0AYxTks8lwzCob2yGyWjIyHehO63NLSgOJfuQtPAEqOynam5Gbbe1Qa/vnVgtGAzBs38/ijJcuolGIrDtboeRyV4JauPFEBihx/iZU7I+tguaptHW3o6KNBamvojGYmi3dAAE4PN5iEYiqCgv61OBcThd0JpOzWKRBS/4pp9aggVvXYNAmxdtW44j6o1AoBCi5Fy2yuxg8NFHH+HRRx8t9DDOKHwuB8DQ8Hh9aZ3ydBp1Z2iiywVljkmsvs90lEsx/ILJYBgGVpsLwePtKG/yd77V8miYpmSfFZTH5aChqRlVFdnf6NMR9+SnRHgHNwbjD2v6VTg6LB0g7W0ok0o7a5oI+EAa69xolQoMHYMlGMBxrwcqsQg0xUWlRoV4PA6OXAaKomALJCsIdrsd4WgMw6oqclp+4AkEQA+lg8vPz6OltKQEHVZ7omJwFxKJGB1aLer9flQK+P1aEARCIQifC2SZdYGmabiLhJg8AIUD6LSy5fJ+H45EYDZbsk40FitcsvF+KbjS0YW0RIGR1w7swrL0T1f2UpahweNyQSEVo6G5FeDxEI3GYCpObdwWCATwuTwAziylIxQMYuT40RCfzBshl8tBqspg7bDhxAfboZ4wEqJAILG/661cKBKCicchFYuhS5FDwVRcjGg0CpvdDr1O12t/TuShWmcz7UfRT+dBkcaZs4u21jbIHHbIpJnnfOBwODBpNYjRNMI0Dd1Ji1z35UmC7xSLE/WN0Ou10A3g/AiEAkSj0cRSh9Prg7okfxamOJN6aaNqWDUYhsGRvd9ieFdV2z4gcgEQyXyZJByNom2yAZOmTcpmuOmhslMEHE4nAsFQTplNs1mKHGpOXXWIheV7DhOPo6n+BOpb2hAOh1FWpEurcHShlEvPuBDaUCiUUCi6oCgKBmMRZl53OUZPHgeLzY6Dh4+gvqERHA4HcpkUZaZiVJSVwhcMpgxNbWxqRofNDqWydzRCLhBCELb0TsmdLaUcCVwdfV/jpoZGqBx2yHJMt8/n8SBPcyxFUWhqbkGb2YKqynIoBujXpFap0Noty2dAJE74zuSH9NYXDoeDUWdNREOkf/+FbFdWRAIBFMX6vOXyEQozv5bmDhv4PF7Oy4MkfurUWunJKWPpYGE53XBYO6DVqhGJxjB6WFVGNy8ejwfGnx8T/veGfspuUxSF8tIS8HmdlWKP1zeirPS7LIuVZaXYvWcvJGIxRo/sjLAKhyMQCIUwGfOXh8VmtaPCFQeyiGBJBYfDAW/7UfiL9b0ezsFgCB2tbSgKBQatvo+Mx0HQ70fF2DE5HU/TdGetl0gEsWAQjM+Lom6O0II+8npkAsMwONHQBIVcDg6HgqofixCHw4Ghugr2k8nD0hHj0CCkd5LAvgjUnUBQr4G9wwqajkOmVACgwOFQoKjOqKhYLAar1Q447ACXA45IDJ5IBK5IBA6XB61WDR6PBx4nM2dSh9MJIZ8LRQoH3UyhI2HQsdgpWVCSVTpYWAYBr9sNuVgAkTq7GwdN03D7AtBmng7i+08/SgeApLfx4dWVSfsoioJSoYRWo8KRY8chlyugUsjgzbPyFmqzQzVAhaMLPcNH26d7EPrBaOgNOsRoGu0treDbbShXKIBBLCgo4vMRdbrR0taGshyWQVqaW1DePbdMt2vT4fVCXZ57oTOGYeB0OqHXaaDKwkKlVCjg0ugQDfjSRrSUGXXwNLZBxcv83JbbYti/aRsmj6nu3OD6LvyZYRhYDx9GnOKiQqsGuitHsWjnB0BrYwMotQYxDoVgKISK8jJYrTYUFfWOoAoEgohGYygeoLJsNBTBYbOAJ5adcj5irNLBwpJn6FgMsaAPihyKPgWCQZSW5c/x8XvBALN22uwOCPhcaNTqpEqy+TXxA7Sj78yb2VJijSK8fhe+naCHQsxBmVQCDODtNhN8kQjsFBcjp/8Abo8XTqczZVHLdHi9XkQj0bQL81GJNKPaKj1paGyCWCxBjI5CIZcn5VjJlMrqShz7shbVPSwtFp8PcaUKAoMR3MtL0HkVKTDxOJg4A/fxVlQedae0QATpGAza1NYTDocDYwa+MKUqJUDiaLU6EDOY0HC8HgqPCw0dHZ1WF4YBYQhidAxhvhATz0qfcj4btBo1wuEwPG43lGmqohcCVulgYckzTlsHinKsMhlnCISD+JZ7qhGnaVAZWDr6IhAMojKHUMRs4allQHN+rScivgD6b1ognT3w8NL+8EaiCGu0GH7ST0CjVsFmd8DtdkOVwUPpyIFDMMWiGN6HUsEVZRdu2wWHx4XR0H/ulH7b0WrhD4cgE4nQ7vWC6IugL6/oUxEymAw4xq1D5RF3r30ygRDOYH78I0q1WuxoacQktRoimQyqHvvjcR6amIE7KneHy+WCYoa+vlRfnDJKh73NjbrNhxD0hiFRiDD5vDHQlagKPSwWlqxwOx1QSnO78XZyat0gBpM4TcNqbkOxPrPqnT1hGAb7DhxCkT5PkSl9EA6HYXW7oaQjkPDyW3hSK1HAHqUxoJ9NPwSjUQSVCpT0cEzU67TweDyw2jodWxmGIE4YcE9GWjCEQTQSQWVFOeRqJSS+vtOPc3jZ11gBAIbOz4O9euQI1H78CcpGDIe+ohIiUf/XiqIoVE6bgObQNyhPoVRy4vn7mxwuV6T11eFyuTDGo6g/cBDV49IX48uGYDAIqXZo60v1R8GVjqO7mvDmAx/hq/f2Id4tORiXx8G0iyfgyj+cj5FTc18jZGEZKqKRCEg0DKFqANESZ4jO4XW7EQ36clY4gM4S4lqNasDr3/3h9XrhD4Ywee401HV8hLPC/R+TiraAG3GNGJSYD46g89ZLxxnoLWFEA2IgP0E2vXCHQgjIFSivSH0f7S+6Jx6P43h9I9RKBaIOR1qfieZQBEp9Ucp9/SGVSBAKhXNamukOwzCYMvuHEKUpZpcOAZ+P8rlT0LT1G1T0UDw4mWVbz4x+Qq4lAgH4oVDeuovH4xlV5R1KCjqaL9fvwcNXv4BYijTocZrBl+v3YueGA7hz3a8w87KzCjBCFpbMcTtsKNKoBtjKmaF1RIL+tGnhMyUcCSf5cAwGVqsVFIebiIIZueiHOPTaxxjGkUDAyzwG8wjPh5HzR6V8ANhKA2B8+XvQdBGJxdARjUFZXobyAeTh4HK5qK4sR0NTM1z+AKo0vefNMAwkRiM0OV7ToqIitFs6Bqx0cLncnCraAp2KR+W8GjR99g3KG74LAeaGBpYCvYtWux0aWd+RONEYDZJXv55T735SsDwdR3c1pVU4uhOL0Hj4qudxdFf+qzF2p7GxERRF9fps3LgxSW7btm2oqamBSCRCdXU1nnnmmV5tvf322xg7diyEQiHGjh2L9evX95J56qmnEum0a2pq8Pnnn/c7xnz13R/PPvss5s6dC4VCAYqi4Ha7e8m4XC4sWbIESqUSSqUSS5Ys6SXX3NyMiy++GFKpFDqdDrfccgui0eR4+n379mHOnDkQi8UoKSnBfffd1ytzXybz7km++s4Uj8sFhWTgvhgDrErwvSEfNSjUKhW8vgDaLR1oaTfj+ImGPIzsO8yWDggEQui7JR5TKpWYcP1lsM8ZATeTeW0LQZikfePUy6WoMOV/icgaJ6iaMhmaPCRG43A4MBmN4BUXo8EfRLhHfZdmjw/6ooH1E41EEMvTMkuu+H1+UJVFaKz4zgmZExx4DZNgKAyRSAJJP8s9NppGeXX1gPtLcOrpHIVTOt584KN+FY4uYhEabz64aZBH1MmWLVtgNpsTn3POOSexr6GhARdddBFmz56Nuro6/OEPf8Att9ySVGW2trYWixcvxpIlS7B3714sWbIEV155Jb766quEzBtvvIHly5fjj3/8I+rq6jB79mxceOGFSQXeepKvvjMhGAziggsuwB/+8Ie0Mtdccw327NmDjRs3YuPGjdizZw+WLFmS2B+Px7Fw4UIEAgFs374d69atw9tvv43bb789IeP1enHeeefBZDJh586dePzxx7FmzRo88sgjWc27J/nqO1OYeBzRoC8veRXylIfolCYSDoNL5Ue5Mhr0MBkNKDMVQ6nMX/E+s6UDIpEIqhRLZRwOB2K5FFFh5m/UfAIEwzmuy+RAKBqFKM++LmKxCJWVFRheMxmNfAFauXy0uD0AAL5eP2BFsrKiHOYOaz6GmjUutweNTc3g83kYVl2JqrlT0HRS8ZDFKFj7qBScCbZwGDpZ75pC3YnEYqAGsjSbglPxHaYgBd/sbW4srfgzmHjmXutcHgcvNN4/aM6ljY2NqKqqQl1dHSZNmpRS5s4778S7776LQ4cOJbbdcMMN2Lt3byK9+OLFi+H1evHhhx8mZC644AKo1epEGfpp06ZhypQpePrppxMyY8aMwWWXXZZU4XYw+s6GrVu3Yt68eXC5XEne7YcOHcLYsWOxY8eORAXbHTt2YMaMGTh8+DBGjRqFDz/8EIsWLUJLSwtMps5ETuvWrcPSpUthtVqhUCjw9NNP4+6770ZHRweEJ9dgH3roITz++ONobW0FRVEZzbsn+eq7J+l+6zaLGTqlLC+ZCx0uN7TFpQNu51QlEg6j8cQxFGk1UOf5Buv2eMChqAElVQI6l1REYnHaTJ3Nxxqh+ORQVg6ldXI/pp41akDjyob2SBRlEycMStt2ux0ikRgymRQ0TcNms0Mqlfab1j3TtgVC4YCzpIYjETicLqgUCkilfT/sAeDA4SMYNzr5+kRjsYSPR3MpB1WVuRVjbHe6oZSKIe3Hz6Q9FEXZpPxeM6fTCXVxWd6yquaDglg66jYfykrhADp9PPZsOTxII/qOSy65BEVFRZg1axbeeuutpH21tbVYsGBB0rbzzz8fu3btQiwW61Pmyy+/BABEo1Hs3r27l8yCBQsSMqnIR9/5ora2FkqlMqFwAMD06dOhVCoTfdXW1mL8+PGJh37XWCKRCHbv3p2QmTNnTuKh3yXT3t6OxsbGPufUfd6pxpePvjMhGolAwCF5+6M+lW4O+SYcCuHEsaMYNnI0GCq3dfe+UCmVcLjcaGlrR1NrK6xWW07thCKxPh96JEZnpHAwDAMPieGElsHoUYMfEttFOBoDJ0+p31Nhd7kgEHb6dfB4PBQXG/OicACATqeD0+UecDuxWAx+vx+BgB8NTc0IBlP7zMTjcTS3tcOQonaPgM9HxdwpaCyTgEfn9qi0e70QiwR9KhyEELh8fnAH7A/WG5VKBbdr4Kn780lBlI6gNzczY67HZYJMJsMjjzyCt956Cxs2bMD8+fOxePFivPLKKwkZi8UCgyHZU95gMICmadjt9j5lLBYLgE5NPh6P9ymTinz0nS8sFguKinp7qRcVFSX6SjUWtVoNgUDQp0zX9/5kus871fjy0XcmeN3OnBIZpYM5jcoh2W22xA0vFAyi/vgxjB47Djw+H1KFEr5u9TryRVVFOcpKTKgoLUU0nr0DYEeHFRJx+gdEe7sZ2Jd6GTQcp9HKjaJdSaFJC7RUcCCYrIe8RAWZqPMhbfEFYPEHYPUHYA0EsM/hRAuANoaBOUbDEo7AGgzB4vGiIUajBRSawlG4QyHEM5gPIQTt0ShK8lhZtyejR4yAzWZHc1s7zBZr3v2QdBr1gBUPuUyG8tJSeH0BVFWUw+l2weFMfvg6nE40t7SivMSUtuBdl3OpS5KbkhyME6glfVtaLD4/2p0uROx2OHvc09wuV9qXq0zgcDggaQrmFYqCRK9IFLmtfed6XCbodDrcdtttie9Tp06Fy+XC6tWr8fOf/zyxveebaNcfXPftqWR6bstEpif56jsfpGqzZ1+5yGQ6p3Tt57vvviAMAypPhZUIIeiwO6A/jZZWHA477FYrSk3FiNJxjBk3Hk6HHRKpDGKJBAHP4L6BdXccPn6iARwOheqqyiSZtnYzaKZT1QuHIyg2GiCTpa/qajAUoUHVBPiTS7l7qTgCwyWoKOmd4Mpis6PVRyEei0Ep4EPVLVNqIBhCcVUVhGnCULtwu1xoammFKBYHhwIohgHFEFAgEFCAUiJBMBJBB6FQPWlwI/0i0SgoDgdMJAKxVILmlhbwBEKIBAJoBxiRBHS+ALa0m6FRqwbUjlgsglQqRTAYRKnJBK/XC0uHDQxhEAgEUGwoyqiCq4DPx9hZZ6Pp0BFUiLKrGifi9/2IZRgGwVAY4yrKAACu1la0tJvBZRhQdAxiHh/2OI2YSo3yYdWJY7Lynxlg8r18UxClY/J5Y8DlcZLycvQHl8fBpHNHD+KoejN9+nT8+9//Tnw3Go293oKtVit4PB60Wm2fMl1v0TqdDlwut0+ZVOSj73xhNBrR0dHRa7vNZkv0ZTQaezmwuk5q7d1lUo0XQL8y3eedanz56Ls/HHYbtAO8MXZhtjthNJWBk2O436lCwO9He1srKIoDAZ8PsUSKUJTG6LEnkx0xcUT9HrjtVnC4g3v7USnkOHLsOIRCIUpMxaA4FFrNFvAoCsFgEGKJBCqlElKpBDa7A6Ulpn4VTi6XC9mYcqD1u6XeOBOHu4SHqhQKBwAM78OhkxZLIMigKJdKrYYqTXhwOBRCY2sbpHo9hhfn5nfQH4QQ2B0OBENhcCgKZd2SjHX5e0WjUdTt+RZnTRw/YKfSVFWDc0GtVqK5pQUjhw+HQqFANGqHSqUFL8vcLkKBACVjRqHp4BFUiDNTPLx+PyT9/MbNPj+quuWqUYvF+O4qd75kywEc9fnwzZ690Op0iIQjGF5dmdE5bm1rQyTGQKM/dRKEFcSWqytRYdrF2TnMTLtk4pBnKK2rq0Nxtz/iGTNmYPPmzUkymzZtwtSpU8E/eeNIJzNz5kwAgEAgQE1NTS+ZzZs3J2RSkY++88WMGTPg8Xjw9ddfJ7Z99dVX8Hg8ib5mzJiB/fv3w2w2J41FKBSipqYmIfPZZ58lvZFu2rQJJpMJlZWVfc6p+7xTjS8fffcLHc2LFcnmdKHIaPreKxwnjh2Fw27H8JGjMHzkSFQNG45JkyejqnsIIIcLuUyK4iIdQqFg+sbygEatxqgRw1FZXgaxWASRUIjSYiP4fD5KSktOvgl3mr71Om1G15IQAl97sq9Is4KgapgpzRF9M0wiwvH9B2G3O3I6HgBEYjGGjRgO4yApHADQ0tYOtUqFirLSJIWjOwKBACqlPC/h0KnCi71eH9rrW+CwO9F0uB7NxxtxonYvzG3mFC104nR5MHL48MR3nU6Xc7IsoUCAkrGj0BSKZCTP4/Lg6CNiKRCJgEMys1ootDpMmXQWKkpLMGJYFRpbWuF0po6ocTqdaGpphdVmh6GoKO8O2wOlYAvIV/7hfPCFmV18gYiPK+9e0L/gAHjxxRfx2muv4dChQzhy5AjWrFmDxx57DDfffHNC5oYbbkBTUxNWrFiBQ4cO4fnnn8dzzz2HO+64IyFz6623YtOmTXj44Ydx+PBhPPzww9iyZQuWL1+ekFmxYgX+/e9/4/nnn8ehQ4dw2223obm5GTfccENC5u6778YvfvGLvPedCRaLBXv27MHx48cBdOaz2LNnT+JHPmbMGFxwwQVYtmwZduzYgR07dmDZsmVYtGgRRo3q9ABfsGABxo4diyVLlqCurg4ff/wx7rjjDixbtiwRXXDNNddAKBRi6dKl2L9/P9avX48HHngAK1asSDwAMpn3+vXrMXr0d1awfPXdF6FgEGJhdqbWdHB5glOyBHWmMPE4Du77FgZjMcorK5POH4fLTaolo9Hp4Ti5Xi8UCODtJ632YKDVaiAUCLKqYtrFgc92omzfd0rHEWEYFeNyUziAzjX3agEP9ADW7Qcbt8cDQtLnGekOxcmP4tyVhr075v3Hofv0GJo/q4N+23EYt9Wj7KAD4RO9lY4YTePIseMQ8PL7iBMKBDCNHoWmYP/+ha5gAB6PB7FY6iVYVySK4gxq3hxjCLTdrGUURaG6ohzBcHL+EKvViqbWNggEQlSUlaJIr+t8MTvFllcKpnSMnFqBO9f9ql/Fgy/k4fev/3JIUqH/5S9/wdSpU3H22Wdj3bp1eP7555P8PKqqqrBhwwZs3boVkyZNwv3334/HHnsMV1xxRUJm5syZWLduHV544QVMnDgRa9euxRtvvJEU6bF48WL885//xH333YdJkybhs88+w4YNG1DRLU2x2WxOytuRr77Xrl3b70P1mWeeweTJk7Fs2TIAwI9+9CNMnjwZ7777bkLm1VdfxYQJE7BgwQIsWLAAEydOxMsvv5zYz+Vy8cEHH0AkEmHWrFm48sorcdlll2HNmjUJGaVSic2bN6O1tRVTp07FjTfeiBUrVmDFihVZzdvj8eDIkSN577svgn4fpNL0a/+ZEovFQH2PLRyxaBQH9+/DsBEjIcsgzJGiKPhCETS1tSMcDuN4QzPoAieEygaZXJZ4M20croBgYmlai1umRKNReDwetJvz6/A9UCLRKBqaWwBCUFGWoa9RHtzHmltaoNMmLyURQsAcawcAjHNRSQpQ5HhvpaOltQ2jRgxP6yA6EEQiIUxjRqOxW0TMMUsH/CfTlwdDYTR02MATiTGxsgL8FH4dMZoGiWWWdIzQcVhb2+D2eJK2Fxv0aGxqRkeHFc1t7ZBKZagoLenlk0SBnFJJBwuSp6M7R3c14c0HN+Grd7/tXXvlkom48u4FbO2VPLJq1Sps3boVW7duLfRQvnd0/60H3E5oVQOLWmEYBnaPD0XFqc3VpzrBQAANJ45j9Nhx4GZpsra3NcHhdMHvD2DMqBGQ9OPhf6rg9Xjhe/tL+EcWYcTMSWhpakZpNDNze180xOIwVpZD1s3JtFAwDIP2DiuYOI3y0uwcm5taWjNXUNJwaO8BKGRyxDx+qKtLoFQpEY1GYX5xE4o5vX8nDSSIUb9elPgeDIbg9/tSRtjlk1AojMPf1EHJ5aBMqUCTy40KtQrHLRaMKC7u8+Wu1etDaZZhxhaZHCUlyVY1r88HHpcHiSR9tUCGYeAJ01BrNFn1N1gUvBLMyKkV+NPby2Bvc2PPlsOJKrOTzh3NVpkdBD766CM8+uijhR7G958BhqHRNI3de/fhBzN/mKcBDS1Ohx02qxVjJ0zM2q/FbrWiw+ZEKBjA1CmTcLy+HsPzmfr5/7P332GO7fldJ/46QTlnVU6dw00zd3I0zjM2zjaw2GvABK+f348fi2EXFszC7o9dlrA2YDBgWDAYg208OOHx2J7x5Dtz872dK5dKOcejoxP2j+qurupSVUkqqbq6r17PM8/0lY6OjlQ65/s+n/D+jABFUUhlczgdDpofOs+F84sIgsDM3CzryyuEmg3cjsHGxK5VqsSvXD4TgiOdzdGoN1iYnx2oNmPQuScPME0T89U1IsaOuNhYzWD75AcQBIG2VYIuQTHRt/97t1gtNFujd391OOxceuF5krduIooi88EAq/kChiiRKFeZCXRP3ymdDlKfXW/1ZhNPl5qdXkzUzlrb7GMXHQ8IT/n5xh953+M+jKeew1w8x/SO2m5jPaYV7ji2k2lm5heeSDOwdDKJorS4eHmw8dvhaJRQOExyY41qtYrnmCFYZ4FUJsvCfe+L2J78uiiKLF44T6FQYHNji9kB5tNHrBZyG5swd/qRjraqktxOIttsaKrKzPQU8Wj3LpxeOGkQXxAEZKsF7muGuYrJ+r/4TYzLkyxq3f1T9Oj+xd0iy8jHtCAPC4fDzuTlK2zeusWsy8mkb2delaIopGt14p6Df89cU2Gmz0GFuabC/AmigWdJdDw9TkRjxpwSjUYDzwlsmlutFm5/gMmpJ8+TY311FYD5xaUT7UcQRWwuD8VShVx+MOfQ0yKZSjFxTItlKBRi6vpVNtT+C0LdDgezskT+5k221jdoncJd+gNSiSSSbjAzOcHC/NyJx6B32u2eTMwOI7G2hUfZL13mXQEWNw+fwjt5M8+93/oS2xuJ3fogeUgFrb3gcNiJXbrERqOJw2rFbrHg93iwY5J+xACvobSxDVDYORf0c/fzXyCxvT3YQZ6hmo6x6Bgzpk+EAe7n6vUGxWqdYrWBJlkJRc9O33yv3Ll1E5/fT3xy8G6NvQSCQVTdYGnh9CzCB0IQsR8zNwN2TKRmLl9ipT3YVNIZn4/JtkLh7bfY3k4OtI9+SKXSBKrloUbbFhfmSZygINbisBEw+hM+dquVuXSbyO/d5aVf/h1WVtdotw8XKaPA6XQQv3SJrcbDNvAHwiNRru4+VlTaRAdoYRVFkYtTk/hyeUpdpn4fy5B8T4bB0ETHWaqOHTNmFOz+xvs8gdvtNh1BIhiNE4zG8HjPVt/8ceiaxo0332Bmdm6oxWiSLBPw+3AMWAtxGhiG0VcqzSLLLF29wsohbZK9MOH1oqysnChicBSGYbC+to41ndqpQxGHm+Jz2W0Dt0JHYxE23YMvkMGyxtLiAnOzo7OBPwyn00H00kW26vuFR9RpZzVXYKNUxiMN/l03Wwo5p3OwsQumgTGi31O/nFh0PGgXazZHa/QzZszj5sFvXO5jETJNk0pDIRAafuveaVAqFvnql77I7Pw8ziG0CD+Kyxcgnes+Q+cssLW1RaAHL4W9SJLEuSuXWVMGi3gASKHQiYsy92KaJplMjs3lVda+9nWmlBb++zUCw64rCofDZLK5gW5EBUEg8A0vsBHvvyajqqkEvvPx1gU6nU4ily6Q2BPxsFqtLEZCzAX8+AdMy+ZqDZrxOIsXzg9U4BsM+CmckTTmiQtJJUnC7/fvWkg7nc4nsjhuzJjDME2TZrNJNpvF7/cjdDEuOoxcsURkcmaERzc66rUaqe0EcwuLI4vOOF0uGtXySPY9DKw2G41ms+9R66IoEpqdQdncGMhKwDKkKbHNZotcKo2ayzLv8+4sWI9a9w850gGwtDDPVjLJ7FR/7eCapqEqCvS5higdlfpHzjE/Fe/rdaPA5XLBxQskbt9l2n3yVvBspYrr0qUTTfLNF8uEJ89GDdlQulfi8Z0/9APhMWbM04jf7ycej1OrVlFVFeueCvlcvoAgW7DL4m4HQrlSwReKPpEiPJ/LUioWuXL9mZG/V6vZhEPaCx83E/E4mUyOdlsl0mX8+VEEAn5WV1aY61NzrNcbTMycXKg2my0St2+z5HIeFBp76EdE94ooithkC/V648gBeg+oVqtsJ5JYXt9ittV/hCc/7WHxyrnjNzwlXC4XXLpA4tZtprt0sPRDvd1m4gSCA0C0WM/MdWgookMQBCYmJohGoycawztmzFnFYrHshru9Ph+FVILQHtHh9biptFQkp5dsqYTZUel0NKxu/2M64sFJbG5imibnL45+wGImlSJ8RgXHA0zTQJIGcx2VgyHo9GceZprGietc8vkCrfU1lnpxiR3CrJRuxGJRllfXWXIdHv3O5/M0Wm38Pg+hUAhvIzFQ0t966ewZ7LlcLoxLF9m4eZO5E0SuTioWms0mDtfj94B5wFB9OiRJGmoecsyYs4rwyCJks9kIyTLlcgG704MnfvzE0rPInds3CQZDRE6ru8bQcDrPpk9HIplCFEVkSRp4zLrb76O6sY63D4+FOZeLZDJ1wH2yV1LJFPZMmskeU0Kj/J3Oz06znc4wPbE/7bGV2AZBIOj371qVG26DrzRzvOju/7dn9Z6dRVXXdQrFEh1dR223keNT5HMZwj1EfEZBq60SCo7WnbUfzow52JgxTxJ77w7r9QZt3QAEsNiw2mxPnOAwDYNbN28wOzff0wyVYWGxnI6JU79sJbYJh0I4HP3XY+wlGAywVirhVts9FwDWm00c8cFqE/L5AvZMui+RM8rfqizLSIJAs9nC6XSwvrGBbLURjUawPWLgJYoi8U+8F+Ozq30VS2qahiQ9XveHarVKs9VG1TqYus7c7P70WEIw0aqVwXxQTvrnOUXPkl4Yi44xYwbgQWV+qVSmrRvEp0+/RW9YdFSV27ducunyFSyn5OS4yxkSZ+VyebceR5TlEwuOB8wvLnDvrbdZ6nEqcd5i5VyfHTMPUNU2sT4Ex1q1xuQQ6keOYiIe4+2btwgGg8xMTx8ZDRcbvYuzB6y4da4F+3P4PCmaplEollA1DU1tEwmHiccOd3Kdnpnm3lsVBjP7P+E5MhYdY8Y8GbSaTeqlPKZp4o/Ese4xiHK43BRqdXzhGIEneCx9o15nY32Nq9euIz6G1KjFZqfdbmPrwXxr1NSaLZpKm3q9zoVzJ3Nc3YsgCMxeuEDixttMH2Nznm40mLp0ue/og6K0SSe2sVcr4DpadBiGwUatgWNygvmlpRO7kPbChXNLFEuVY9PvnWL9yOcfJeE0OP/d33BqkcVGo0muUEASRWam+6sjmTl/jsSNmwN0tAzugVWv13F7/AO/fhSMRceYMYfQKBeJhHbMsEqlPBUTAuEossWCw+nE8YRMRj2MYqFAsVDgyrXrj+0YPF4v+e3NMyE6REFgMh4DhlfPous6iY0tjEYdZw++FW2748iJod1QlDbbN28y73IeKTgMw2CzpeCIx1m8eHEgv4dBsVqt6IZGq6UcGkFKJVJM3i2A3Hu0zQi5sdtH/9tR2m0yuTx2i4X52cEiQ3a7Dc/CPOWN9V2PlJ44ge9mu6PhHqBle5SMRceYMYcgCg/P9oDfh2maJBJbzCyc7YmovZDcTqBrOucuXHjch4Ipno3L0IO5HcNEFEU6yW0WImGwHh0RUxQF1Wo70I59FOVymcr6xo7gOGbfaYeLxecvPbZ6o6mJCRKpNNOO7vUqTreT9ISL2dzxHZCappFY8hF/z2BDB3tF7XTIZHNgmsz1GdnoRjAYYKNSwau0Tkf0nbHUCoxFx5gxh7L3xlRRFGqKyvTc/GM7nmGxuryM2+M+MwPnXF4frVYT0zQpFktMD+HiPgijGOQgCALS/cJcwzCOXGjsdjvngcTLL9OxWrF4vMgOB7LVhsVmIeD37wqGcrlCOZ3G06gfm7LRNI2c18fSwvywPtZAbCYSxKKHd1G4PW6yPY4Y2L4c4sJH3jWsQ9tHs9miWq+j6RqGrjM7PdzzZHZ+juW33maxxxqfkyBIZ2+JP3tHNGbMGcGUZFRVxWKxUG21iU7sXwx1XSfxm6+ir5YxVQ2z2aGzXUGxmtj/2HWsDQMh22Txhz/8mD7BfkzT5PbNG0xNz+AdkuPlMHC6XLxx8y3sdjsXzz9Gg6cBpn/2gi0SJi9KZNJp3C2FuaD/yO2nQw9MyExoNaHVRFVVVm7exnQ6QFEIWy3MuN1wjOAA2NIMlubnTv5BTogoyQc6VvZSq9Wx1TscZ9SRljtMvfvy0I6rVqvRbLXp6Bpap4Pb6SAePbwo9KQIgsD0+fMkbt44VjACtAd0jC2XK3hDZ6dV9gFj0TFmzCGEYxNUSiUqzRrh6MGQ8Oavfh3pn7yBZU+42gZ03uMlEAjgeTbMjZ/4xTMhOrROh9u3bnL+wkVsZyzHW8znmZ2ZITDA9M2hMqKZlZP3p/JGohFarRaZmzeJ9Tm0y2q1shC+P2yvj5qPfL1B9ML5x97CvZlIEI8dXStjtVpA0+kmOu45NWYqBnaLFeOZWZxDqqfaTqXxuJzEoqc7G8nhsOOam6e6tXlse3NgwHSuboJ8Bovcx6JjzJgj8AUCwMF2vOp2Ae3fvInc5WLueL3MLemzxEtWfCH/6A/yGFrNJivL97h85SrSKXQq9IthGPgfk3HSXoQRez2IoojL5SIXivTtUtoviqKQ6WjYJyf6nhszCiRJxnrMArh1e5WOV6btcTG1+tDTwjAMQh97loaus/WZV5g7N7wWX6tFxjvI1NYhEA6HWKtU8WpHDwaUBqz9MEdgbz8Mzt4VaMyYJ4Dip29gP2Swsl0VibylUJKqLP2NT5zugT1CuVQik05x7ZlnH+txHIUgcCptm8dxWm7KsYk4qbffYmJEi12iVsc+M8Ni7GyE1jc2t5ianDh2u4svXMN8/ioAqa0knS/dZqIpsN6pcj4aRpIkXN/roVKv4T7hPJMHuJwu8vn8rivqaTO3MMfyG2+ydGT0qv8oVblSwRs4m5Otz6YUGjPmjNN+NXnk8766RPjqLLEXH1+NQjqVpFwqcfHyaCv8T0KlXKJZqw5lX+sbm2wlk2Qyg43wHmAS+0A4HHb04PAXBMMwWO3oxK5dI9aj4NhMJEgkU0M/lr3IVmvPolIQBARBYHJ2Cv3a1E7xrc/1cO6R14N5YovOh9QaDdrq0ZGGUSKKIlMXzpOuNw7dpt/MmGmadExxn6/QWWIsOsaMGQCjcfyFyrrU31TSk9Ko18ln0gAktzbpqB3mF892e2+rXmdm8mTjyIvFIpuJbaamJpmZnEQbsCBUPMW6B3kE3hLLzRbnrl4+1rdC0zRWVtdJpNLEolHa7dGletY3NolHBhNYC9cukvnYORr+/TVI0XCIzURiGIeH2ukwNTnYjJth4XK5sM7M0mwpAGQaTTJOFzmfn2IwhL/PuT+FUpnwac1OGoDHH9McM+YJRM/WOe700WrKSI+hmM9hah0QBARBRDA0OorCf/vNX+f5d73I5MTxIe3HTTASpVopDJRXNwyD9c1NQsEgs3vabMPBAOvr68zPz3d9XTKVQhCknZZI08DUdXTdoKPrg36MvonGomxubzPrO/pzb9cbdGQLkyJHends1OrYIxHa7Tb2QwqFDcNgbX0Tt9vN4sLcbnHpwtwsq+vrLB7yfQ1KIpnE6/UMnLYSBIG58/PMLO0fMWC1WJCG1Ap6VgaURqJh1ut1zHIJYjEmBxz21+l0kO3OkU0OHgZj0TFmTJ/oug41DYSjTx/1K1vo/x99aBc20zDIpZPIws6//X4fkvRoLthHud6krYxW8AwLq81G3eg/r5HNZmlrGvOzswe8L2xWK5YjOnTUtsr8Y24htcgylok4NB8WBimKQrKjIUoShtLGEg4Tv3oVu93G9nYSSzpN6JGC22ZLIe9wMHntGna7jbX1DRa6fLaVtXXcrv1i4wGiKGK3O1hdXWNycmJXtGxsJZAtFgzDwDRMDH3HPM1mszJxzEC61bV14rFY3+6q3ejmbRKLhNnY3DowWK1vTiml1gvzi/NsbEqYukYylRropqFcqxOZHO0snZMyFh1jxvRJs97AbkrH1ne50xrpL95m6qNXh/K+uXSSSMB3ZPujaZr4AwFcPfT/nxX0Pi/8yVQKt9tN9IiujEhoJwTfzdhJOCN3t9F4nK3XXmfG58UwDHJeH+cPMfCampqk6vGwubzCrGtnIU/UmzhnZ3bcTu8zMREnkUwyfT9lUK1WKZYrzM/OHCl+J+MxTNOkWq1Sq9dJp7NcuHCuq69Gq6WwsZVgbubgd1utVllZ22BxYW4oguMwZFnG6XSyuZ1EEHYsViyyxES8v7TCaabUeuGBiMrl8pTLZfx9DP5rNps4zticlW6MRceYMX3SrrWwHhPlgJ3wcO3Xb8KQRIdoGsf6LQiCgFMW6DRqGG7PYxni1i9iH6HybDaL0+E8tg10bwhe0zQ2NreQrRYssgWf9/G3kML9Y4zHoNViXVE5d+nSkdt7vR7s16+ytbEJCExcv3pAFNhtNmTJshM1SafxeX09zwoRBAGfz0cqnWFqauJQIy+Hw040HGZzK8HsHuGRTKWwWK08/+x1stksaUUlPkL/i0h4f81Uo9FkfWMTAF3TiMWiu1ODD+OMaY5dFFUl0mctTFPVCAfPxm/7KATTPK2a7TFjng6yq9u0f/S3jt2u6tYxVZ3Cn5jho//9Hz3Re+bSKYIeZ8+pGtM0KVQbhGMnK9I8DQqZNCHf8ZGZarVKW+0cWGwOQ9M0kpkMLruDYDDw2A2yuqF2Otz6+suce/YZXK7heZUcFok4jlQ6g9vlxNODt0erpZDJZZmdnt4pGN2TTkmlM9hsVoKB0x05v5dKpUJb7dBUFMIBf1cBki8U8Pt8Z6Jl+wG6rrOdSvVlv16t1bB7g2e2Y2UvZ+ebHjPmSUHrrTvC/hdfIPrcLPOiSDa1TTgaHyjyUC2XcdvkvmpDBEFA0DsYun7mox0PagWOQtM0yrUas1O9z2WRZbmv7R8HpmninZwcquAABhIcmqahm2ZPggN2Ih6xSJREKs3C/MNaEUVRMOCxCg4A3x6r/43EdlfREQwE2NzcPLTo+HGQzRf7EhymadLWwfsECA4Yi44xY/rGVI/vcqjYO8w/N7drYuRymhQySSwO932X0x7exzQp5LJYBQPHAK6SwYCfQj53JqMdartNpZhHAHzu4y2tN7cSLD7mgWXDplqrUSyWuhZ+Pg42E9sszM0ev+EeHA47s3s6LRRFIZPLDyR6Rond2n1BFkXxLNWSAtBu91cEXiiVCcUeb9tvP4xFx5gxfWJUj78oSH/y0j7XREEQCAcDqKpKdnuTlqozt7Bw6OsL2QxoKqGAf+C0wFmMdhi6TiGbwSoJRAK9z1rpp+7jrNNWVXL5Anarhfk+F/lRkcnkiEUiJ05BbW2nOL90+O/6cREI+NhKJJjpEkGQDxEkj4NypUI42HuESNM0JJvjzJzfvXB2m3nHjDmj6Jn6kc+rpkb4w0tdn7NarURDQSI+F9nUNuYjo7wr5RLZ7S18ThuhIdQhBAN+sunROk72immaZLa3iAS8fRdz+r1ucvnCiI7sdNjcTrK+tUU+n2d6cuKxWW8/iqqqaKaBy3XyIWoTsQjrm1s0my0qlcoQjm44WC0W9ENas2Xx7CzY1Xrj2OLXvZRrdQKhs/E76pWn5/ZhzJhTQsvUOeoyVfuWKEuxoy8ETqcTh8OkkN5GkC07Hty6htfjxhcaXi5cEAT8Ljv1Wg33Yx78VchmiEcGc2n1+/3cW17puYj0rJEvFLFIEhMDmj6NimQqhSRZmOqz1fQw3G43uXyeQqmEz+NmbX0Di82KLMpEI6GunhunxWHmao/zmPaidjqYfRjUtVot7O7HM6zuJIxFx5gxfaLEDczvCdNOVFFWKwQyEk7p4QUt/Ed7a5F9kHIZNTabjUat+dhFRyadJuBeGNgsTRRF1E7n2GmlZ41KtQqm0beHxCjZaanNMBGL4XAcbqQ2CLLVitJsMTM1ues0axgGr7/xFi88//gGD8qH/O7q9TpOhx3PkIbIDUKhWCSfzWGXJDbuLQMCc+e7R0sf0Gh3CAcip3OAQ2QsOsaM6ZPQhJ3gwsPiv0q9gdLR0dsm6UyZFxbO1t2sIAigqY+9tmNmbo5OpzOw6FhaXODNt29w/eqVM9n+2o2OplEqlc9M7Qbs2JPLkoXFERWwioKI55EUgSiKnFtaIJcvPLZoVSgYIJXO0lYVouEwdrud9c0tpibjbCS2mZGkkRqaPUqrpVAsFFCyWSYtMuesVjB2Ih35ep1Op4PlEIFdrdbw+IOndqzDZOzTMWZMH5QLBexry1gs3fX6FgLzFy+c8lEdz1nx7chubxE9JH2kqiqbiW1kScLtch6oedA0jVQmy8wZS1EchmmarK5vsHRGum40TWN9M8HURHzo0Y1eyefzCKJE6BQifEdRrlTI5ouEAz6CwZ3Fe21zi4WT2qofQ0fTSCW2MVUVS6VM7JCZQ6Zpspwvsvjiuw54bxiGQaHaIBI/+7OVujGOdIwZ0wdarXKo4ACwnlH78bPSyeILRciVi7isOzbWe7FarWi6zrnFBZrNFtlcHtOEptICwGGzMT355Fxo84XCmWkdLRaL1FsK5xbnH+txhMNhkunMYz0GAL/Ph9/n497yyq7ocFitKO029hH6XSQ3tpjW7k+oPmLIoSAInI+EWL9xk6UXnt/3XL5UPvPzVY7ibFTQjBnzhGCUy4c+pygKdsfphWf7JRQMUMznHusx2Ox2IvFJNNFKrXawC2gyFqVWq+F0OohGwsSiYRZmZ1iYnSEeiz4xaRWAeqN5JpwuNxPbIIj7/DQeJ2cptG612zHud5DF4zG2kyk62vFmdYOiZvsTXJNWK2+9/Oruf1eqVTyB8BN1HjzKWHSMGdMHwp6poI+SbCkEH3PY+Disokm9Vnvch4HX78eQbTQajf2Pe73Um62Rva+maeSLZbIjbr8tFIvE+pydMWwMw2BldZ1IKEQw4H+sx/IATdPodNTd/zaM3tx9R4XTbqdare7+99LCPLfv3GMUVQflcgXvEVHSblgtMhccNl7/w8+TTWfo6CYO58lbmx8nY9ExZkyPlAt5PNbDOyesgZ0wrWEYaCO8WzoJXo+HYiaFfgaOzxcIoIlWiqXyvscNw6DValEolWm1hidAGo0GlWab8OQ0ofgU6dzohEe1WjuQPjpNUukMt5eXCQb9j61+oxuyLNNW2iTTWbZTaW7cvE1zhCLzMJLpDNvpDJIoHJjkOjM1wVYyOTTh0VZV1lfW6KwsEx6gg0yWZcI+P9F4DBEDo4+22rPI44/9jRnzhNAplfAccafSqpZZe/1NjHoN2WbHPjNN7Ay1ST7A7bTTaDTw+np3BB0VvkCAjuoiX8yDroEooiOiIhGamKbZaFCo1hFMA8HUCfQ46ltVVWpNBUQJBAEBsNjshEI7n1mSZYT7LqfVahWn0zm0VEirpeA6xS6IvTSbTdLZHXfRiXiMrWSy5+/stLh4/tzuv7WOis3W3T9jlGiazux093ST3+/H6/Wyur7B1OTEiWo80ukM7USCGY8bTlDv5RBh6+0baLpOx5SITZ6NVNkgjLtXxozpkdzrrxDsI5VaVxSU2ASxeHR0BzUghVqLUOTJ6vHXNY1SIQ9651h7+GK5QjB+9LC3XDoFeodSuYo/4EeSZDS1jd0qI8syVquVXL5APBbty0AqnckRjw3+3a6vrw80gGxrexunw0Eo+LCVUmm3yebyzE6fzcF39XqdQqmMx+061QFxW8kUMz0UJW8ltvF6PfiOKPrsRqPRILm8wqwsDbWuJ9/RiL37vUPb3+NgHOkYM6YHFEXB0lbA3nuo2m23o2wnKFrl3Qr5s4IgPnmFaJIsE47FdwfhmZ024S5W8YZh9DQIOBKfwDRNHN76rnFaq9nktZe/htvt4frlCyhKi63ENiYQDYeP9XEwTZN6owH0JzoS9wVDU2kTn5hgY3OLuWPaN1PpDAY7Nt6GYeD3+Q74Y9htNjxuN/l8/szYru/F7XbjdrvZTiapW6y43cOdttuNtqr2nKKYmZ6iWCySSKZ67pxKpzNo2wkWR9DJJoaerBuFboxFx5gxPVDPpAn0ITgeEHa7WNvYPFOiQ1EUbLazk+fvF0EQCEdjmKZJMZ/D1FRE00AURXTDxBAlopO93dkLgoDL7aZcKmEaOqZhcP78eSRM1tc3cLvcRKM7F/qNrQRRIXxkjUQqk2VxvjcjMMMwWF3fxOtxEw6H2dhMcOHcIoIg0Et5paIoPU2oDfh95PIFyuXygfqFs4JmmKciOOr1Osl0lgvnFnt+TTAYxKtprKyuMzMzdagjrtrpkFhdI9Jq4RyB4CgpCv4rT26r7APG6ZUxY3og9/ZbBHX1+A27oKoqlUiM+BlJsxSLRYKTZ8chcxiYpomh60h9hrKzyW1kDPx+X08plPXNTWKR6KHCY21js+fx8MvLKywtLXZNE9XrDZrNBtHo4b8ZRVGoVOvEor1FMNY2NolFwo+1wLUb5XIZQRT7TmEMQq9plcNYW98g3sU6PpVKoyYSTPc5yLAfCnYn0YuXR7b/02LcvTJmTA+Y9cHbTK1WK/Xk9kja8AZCePpOe0EQ+hYcxXyOoNdFMBjouWZjfnaWTC5Hq6UceC6ZzhIJ9R7RkmUZtdPp+pzb7aJab6IoO++jaRpr6+vk83lgJ0qynUrj8/W+yC3MzVKqVlHVwcTzqChVa6ciOADsVuuJOssW5uf2/f1N02RjfQN3JjNSwdFQVFyTZ8No7qQ8fVefMWOGTKNexymcTDDMOeykzoATIwBPsLHQsOioKoKmDlTkNz87QyafQ2m39z3eajV7HkueSmcIhoLYDpl8CnBucZ5kOsPaxgalcoX5uTmUjkYimSSZybI4P9d3Z8VUPE4ilaZeP2jM9jjI5/NEQ6c3i8Vus9I8wmunF+ZnZ0hnMui6ztbmFpOtJs4RtyUrkojrMQ9sHBZj0TFmzDEo5TKOAeo59iKKIq3t7cduhrTDWHSUCzkC/sFbhudnZiiXd0yl1E6H9c0tQGBtfePYO+nNRAJJkvH2sIgszs+xMDdHJBxCEARikTDhUIjpifjArpSLc7PU6s0zITyaShuX63TSPflCkVy+sDv59iQszM9xb3UVM5vtq7NpUITHOLpg2IwLSceMOQajMZyL86zLSTqdYfJxzw95h0c6qpUybvvJvSFUrUOj0SSXz++bIru8ukY0HOq6uG1sbhEKBgcumrTIMpYhtGBOxKOsbW7hdDpPZdE8jH5TYoOgdjrIkkS5UuHc4sLQ9mvT9JGmVPbxFJ2zY9ExZswxmPUqyCe/0xBFEekxXuB3GeACZhjGztC4J/ziZxoG7XoVbx+1F0fsjLsrKzx3/dq+h6PhEEpbpZXNoek6rUYTr9eNqhvEYtGRDhTrh/mZaVbWNoYyBK5SqaBpGsFgsK/fSCmbxyKKRCKjmSeSzuRoNevoJniH3B0jKwqckgmc8BTVYY1Fx5gxR1Arl3EZOjCc8ObZCJP2f3EvbmW4+Ttf5fw3vouJpSe38yWfzRAe0nyc2elppicnSWdyTOzpTPJ6vTwa46hWq0RPqViyVwRBYGoiTiKZZLpPh8tqrUar1UbTNdR2ezf9U6lUem7LLRSKLJga1kKe1dU1LOEwDp+PSGQ4NR7NZgvTNFhYGF504wFb6xuE+pyjciKecLG/l7HoGDPmCFrZDKEjiv36wTAMpLNgyjXABSw8N8F7f/jbTiUcPipazSZ2abjRmp3UhEmlWj2yA2MYdQSjwOGwo2a6d9AcRqPRpNFoMrHH4j+fz6N0NKYn4oe+zjB2vFS20xlUVUXWdfz3a6XmAz7QO2TXVmm6nMeasB2HaZqkszvFtqPArNWwHTGHaeg8RaLj6YnZjBkzAsxifmj7qjebZ8OUa8ALmM1hRz7Nu7shUy8X8XiGb9o0EY/RbLaonoHpvf2SyxeIhvuLLFRq9X2CA3YMtDY3NnYLpZvN1r5BbtvpDMur6yjtNpVyhZnJCUrp7IF9R31eqtWTf4/b6TTzxzi6Dkq1UsFzykPXzKdIdDy5V5AxY0ZMKZfFP8RFdltVufKYBoHt4ym6gPVKuVjA7xmd4+VEPMb65halSgVTN5AsMiAQDgTO1JTXvVRrNTRNw+3uXXR0NA210z7wuCiKnFtc4MatO7jdTnTNwGaz0tE81OoN/F4PVkkin89z5dIFALyug99LtV7HdcJhZvV6A4soj6xANpvOMG8/3bqcJ72Wai9j0TFmzCF0ctmhDmtyB4KPtVMAdtxRLZbTn+r5ONE6HYx2C4vTP9L36XZnvZ1MIVXlEw2AGwX1ep1qrdZ3LUe9Xj/UVyMajR5wUC2Xy4SDAex2+z4Pk1q9jqWjwSNFtcWOztIJ/ChM0ySdyXJuafh1HA+Ynp2hePMmoRFYnR/KWShAHxJPzycZM2aINOt1bCdwId2LqqpsNlt4Io9/4FY6naG0sUHjDHg0nBalfI5gwP9Y3ntqcgK/z8Py6voZ8WjZqa1YX9noW3AABPx+srneU45+vx/7Ho8b0zRJptKU79wl3qWbxGGR2Li3QiKxvftYq6XQ7tFFdSuZZHFhNHUcDyiXSjiHVOfVM+NIx5gxTzf1xCYh28kvLIZhsGV34vf5CJ2i8+IDms0W5VIJXVXRNY1Qo0G6WqXUaeN64d2nfjynTb1Ww2l9vB1Ddrudc4vzLK+sMTc3MxSfjUFROx3W37yN/VYS7erFviN5+UJxIG85TdNYu3UHs1phIRhAPKR9NeZ2g6HRzOdZ1zWEjo5UzNOWLUgWC6ZuUNd1rr/r+QOvrdZq2C2WkUcT45OT3EhnuHSawuMpinSMRceYMY9gmiaUSzCEXLwoikgdlWiPQ7mGiWmaJO/dY2Fv/tlhZ9FhJ1+vU8hmCR0xUOxpoN2oEfKfjc6Rc0sL3FtZY252+tBJpaOiraoUs3lab64zn1IAC7l0jonpHse1Z3M06g1mpicJD+BxIssykqYx12PRqtNhx1qv74gi337n2KTcfbHP5wssLsz3fWyD4HSccm3WONIxZszTS2Y7gUca3p1FvKOSSWeJnfKU2bWVVeYOaeuziSKVUvGpFx3iGbtYn19aYHNrC90we55GexLymTyl1+5h2SoyKe5fKJt3E3CE6EgkU2iajmloTE1OEo+erC7FOzNNYmOTaXdvtueHRWHkLsXYyXSG2ZnTG4hmGgaIpxdBE07xvUbNWHSMGfMIlk6HVL3BQnA44VO73U5xe4uWzztQJ0M+X6CUSuEOhxEFEeF+qFUQwGq14Ha7kSQJ0zSp1Wq0mgr1bJZpSUA85MLtcTpptJqYpnloZXxxaxOjWtkJ7dpsiB4vJgIYOugGYBKcmHyqKutPg9mZGVRVZX1za2RtnXC/bfVXv8yczQ3iwYXaczdH7cUani6Fm/lCEafdRjA4BOfW+4TDIcql0s7vZ0DK1SruR2pRlHYbw9CHWvR9HDZFgSE7nB6F+RSdYmPRMWbMI5j1GhNOB1ulMjNDKkCcdLtZX1ll6dqVvl63eucunnqdRbcLqpV9z+WqVUSLla1KhQ6AaRD2eAi73YRtx4fvYxgkXnmZ6Xe9+4BwUFUVM5kg8CA1oyqY1fK+7UzTZDOVZO5dL/b1mU6Xk00HHhVWq5VwMECpXCbQo4Nnv1isFjpOGxyyxvstdjLbeTyXdkRHaiuJxWolHAtTrVZHkqqQT1hMayBQKZVxOBy7IiOdzTF/ilGOaqWC7ZRN/p4mG/Sn55OMGTMkzFYTu92Oz2ohUxtel8ekqZPN9l75b5omejpN8JA7KkmUUDsqM/EYi/EYixMTePto4xMEgQlZ4MYX/pBqtbrvudzd2/gf8SJ4VJh8+tdf4savvsKbf//XaVUbPb/vmB3cbjfVIf6+HsUiy1jfvXTkNq03ljFNk46m0fjCDfjUy7z18htMT53MK6Mb2Vwe6yPCuV+CXg8TrSZbr77Kxto6mWyeoO90a3a8Ph+l057U/BRFE8eRjjFj9tBqtbDdvxvzulyIzSZ301kuDKEew2q10izkocei0lQyxfwReXRJOPmUTkEQWHLaufH1rxEOBjBMEysQNDQ4pthxIusg8pYGpNnM/g4X/973nuhYRoFpns1IxwNsFgutljIyA7HQ4iSZL90mJnevo5itCmTSWTqNFrMtCWQJxe/DOoLODK/Hw7D8fWd9Pgylxd1SmasvPDekvfbG6o2bxIZY89UTT5HoGEc6xozZQ7NYxLVnAXA7ncQ9LjaKFdSO1tM+NO3w7TzNBvl84dh9qJ0OnWTqyPY/n8tFrqX0dExHYbVYeD4cYFowmZMEJiQBWw/dFRb/w+9JfClDOXP85xqzn3g8RmWE0Q6n04l+5fDUgyiKFG+uoX31HgAJu050bmrox9FqKeQymaEWRCZbCpefe2Zo++sV3TTHPh0nYCw6xozZg9FuHXjM63IxF/SR6nFxePMP7pHOV7s+53e7qa2v0TlCmACkt7eZ9h6fKvHa7TSHIDygf6tl0fVwAVFjNixD8DUZNk9CkWun05vx1aDYl+JHGpPNLpeZau/8La3vuzCSdt6O1sGez3U1BBsUw9DZvnmLlRs3h7bPXli6cplc/XTTiYL09CQlxqJjzJi9HDHIKeSwUWg2j3x5U2lj/b0itVLj0NC+0zRZu7VCtdJdmDQaDaw9REMAQi4nK/nhDaXrB8n9UGQIdhmXf3D76ncyoiCgdvqb9NoP0XiUhPvwNJP1vu/FpkMnPoIoB+ykVhqe4dZezHo8TFpkrNrpDl8TRRHttCMdp53OGSFPj3waM2YICPrhd4Ruh4NioYjXaj3UVbLaaBNU7Cg/vc5Xg3e49j+/D4/DTqvdJp2uYhY0yndyBL/UYvOKm8hf+RCxqf31IrnENjPHTEM1DAPDMNiuNZjx+47cdhjk8kUqlTqhkI/A/feT3A/viC2pFpXNLL7Zs+X7cdZrOmDHKj2dyY1sPosoitheOAdfXDtyO2kmPLK202azCYoCI4iGiYbOxq07mMKOWaphmixcvjT099mLftrTlsc+HWPGPJ1o7aNTFbOhIBvFCnPB7gu9phiIgF20Mle2sv6zb2P4LXCnhhhwQLJBXHCBKGO9rVD8c79D52e+genFnbx7sVgi0GrCIY6HakcjVavTaDZw2OwsREZnrV6r1km8nMV4uYxjq4MDiaK5TcoBuCw0wgIP7ovtqkjp9c0zJzp4AkQH7KQfSuXKrqAbNsHpKEXtFkH58IJVwRjdd1VIZZgZUfot/ohZWKY92nQVgN3vR8nnsZ9CxMM0zbEN+pgxTyuSIBxr7aC3mxiGp2uRp1Zos/cyFNoQYEMjNWVjYhsQ9ue03R2Z9p//A17/xgjBT16m3SqxeIRjoyyJ6IbOlenRhMEfcOfWBsK/y+Jr3P+Mws6lwiFYcbSBNhg+C/AwLaD941e4U2viuzZN/NnFkR7f08bM1CQbm5t43K6RRBtcLheZa5MEbxcP3cbUNEzTZHt9C6Ot0drO4b04C5qOKYlMzgzeRtsRBNabLdyGTtsXwOpyUms0iNaquJ29OZT2ymnIzPjEBHc3NpiAkReVNhUFh+v0jMhGzVh0jBmzB7OtwCHW4Q+Yj8UoNhqEuzg5HhbOd+cM6pqKu8udplWQCf1+CX7/y3j/zoUj31sURXxOJ9VGA+8ILkSJXB7TYsUb91K25uCoerlHPqtNFWiV29h8w11E3inEolG2kkkWZkdjj+5ciMMRosO+XuTeL/8+sxUeCur1WwDkZI3Sdzjw+X0DDVRbXJxH13Vq9Tqx+7NUYqbJ8uc/P3zRcUrBrQvvex+Jt28w6l97W9Pw2EfTUv04eHpiNmPGDIMeOglEUaS5p/ajoShspypsLhdofi3X9TUeVaZkax+536pFJVetk6w1SBbLh24XcjqG0iq7l/VcnmS1RsTvY8bvZSIQgGeODvWLe7yZy3KbwjeGiH/HcwTm40M9tncKdrudSDBIuXIyA63DCEZCJI2D3VkPCGFlviZ2FRWdRovtl26Qyw3eFi1JEv49w9vK5TJyOMJKS2GjrbKldsg1ji7U7ul9TjGlpp1Gd5QoPRFdWL0yjnSMGXOfWq2Gu8d2Qa8ssbyZRV9tov92Er9iQwYCR7xG8tqg1P25iqVN9G89Q8jj3qnbOKK2RNM0bObJ7KRhx+o8UanhsFmZDQUPLDbyRTvG58qIh1gwm3vaMC0/fI3rP/Jx9CO6f8Ycj9VqpT2idkyrxULhcgTLvRIRo7+2WLdoo1ls4HQO7447EAgQCOw/Y9bffOvE+3WJUMznCYZHP9nZkE6hwPMpqueAcaRjzJhd2s1Gzx4FfrcbuyYg/04Bv2I7/gUAh1gnl65b8PzkFUIeNw2lzXa5cuQI8HKrxXTkZJ0Oa7kCFbXDYiTEhLd7fcrSlSmqk0dfVGtug/oHw6TtLd7+6lf44m/8V2698Trb6+snOr53IoqikMrmCAWPkq6Do2ka4dkJhG9+Zp+BnaZprDaLrMUsVNXuYleTDPwfvd51ONww6cgyxfrJzNJcdjvlUnk4B3QMosVypAfKMDCforkrMI50jBmzi2gYZMpl4oHeLvrTixHe/rYq7a9UMFs6/qJwTBi0+3Oeb5hiOu4HwG6RsdmsrGTzLIQPRh8A9D7nPjQUhc2tMpfOxUiXyuiSxFwocGxuXhRFxGf91MQazDsRAjJ4RbCJCKE43kkfU9fP0Wg2MFdXMbUOrlCYS888S7Vc5tYbb2CxWFi4eBHpNO4IjyGTTFNOFBAFgejCBL7waBb3QdhOpZBFmbkRFQgbhsH6ZoJzi/OsvHKDsCyzHrXgStdQ3j3HxRe+CUEQ2F5PsHknwWxif5qj+uI83lP4G56/cpnVr3+d4c22HS2zC/PkXn21a33XsBCeIo8OGIuOMWN2MZqNngXHA65+ZJHl2DZzGYPVfBmjImJbUXHUDuaVJYela3pF5+GdkiRJTHo9mB432XqDZlsl5nLidNgxDIPVXIFqq0nIYT+2y0HTNO7dy/Ol//kl9HWN2r99nmuXpnDaeo3MgOOSHW3RytKlh1bahWaLyPveuyuw/FY/zmvXWF9e5vxzzyMIAr5AAF8ggKqqLN+6iWGYLJw/j/2QVuBRUStX2fzHX0R6o4i9ZuIULJimSfp/1c+M6MjlcjgdzpG1yzabLdLZDOcW5wEIn5smKYrUSiVc3/YcMb939285NT+NEo+QzxVo3Ekwt7ETdXA4HMQnYiM5vkdxzsxxc20Vl80GuoZoGMwcMYk3W67QtFqxeH3oSgt7vYZ7fuFUjlUURXR5xG2zZ0CwD5Ox6Bgz5gFK/8WZgiAwvRglm1nnfCwEMbirl3G8tbOvjqlTnrMgmmDJtID9C342qnJ9/uB9nSAIxDxu8ECp2WQzmUaWZZai4Z070koVSZZptFosPZKK0XWdUr3BdrLMy3/8FQRAFkRu/N1bXP+3/Y0An1vs0iZpsx2I6FitVi5cuXJgU6vVysVr1zFNk7s3bxKKRAhHT8/Lo/B/foXImg7Iu4EmQRBQv5SC918/teM4CqWjMXPCdNlh1Ot1Mtk8S/cFB4DP58P3vI/5Q15jt9uYnJkkLQqspd6gJZksxIMYhrEvOtZoNCnXakRCwaFap8fjUeJ7BixmMjkoP+y6SRaL6C43steLxeHEPzvHxJ55SaVymWqtTj6fJ3wKdR26PFpRIDxlNR1j0TFmzH0MtQ1i/1Xium5i0wAJGu0Wwnpj5z+A7KzA1OaDSMbBCIPjQxPHph4CTieBR9oKp+6P81asFtYKJaY8LqxWK7fvpPmDv/RFLNvCgQJQZblJXVFwnNSkydF/q64gCFy8epXVu3eQJIlAaHSmZnuxH1KT6fpMhrvPvMaFb37+VI7jKCQBbt6+jcfjRZZknA4bPt/Joh7pZIpWvkDbauHSpYsD7SM+NUH8T06wmdimWKmQzmaZiMdptdq0VRVZEpiamGArmWJmcuJEx3sk97tREvUmtqlJogsL2I7wxgj4/QT8fur1BhuJbWRRZGqEx6ePWhQ8RW6kMBYdY8Y8pN2GPkaMV1oK1VITrVBn1razEIuI6DYRatA0VdzrGkiHd/J7r54sxG+3WFgIBcjWG5gdjVqliS0pdS0fseoyX/i/3+Rb/uqLuOy9p1j2Ypomon/wY168cJG7N24gSRLeI0Lmw8A0TYyWygMBuBfdJqJVjm5hPi0mJyaYnHi4KNbrdTYS233VdxiGwdqNWzg1jWpbIe5wUlVVli4PJjj2MrvnOLaTKWRJZnZ6JwKmqurJReyxmGw0W8QvX8Lp7D0953a7cLtdKO02iVQaTW0zPzc39KPT79dcaJo2Ghv5cSHpmDFPH+12G4vRe7unaZps3klwpe5g76JW76g4CjodBOrP2LG8eXglfnEBzvuHYy0UvT+901gK8+YR25U+lWPlO3M883x/aZYHlBtNAidMj1y4epWbb7zBnCzjch8/SXdQBEHA8RfeTetnX8PxyJ+h/i1Rnv/+943svU+C2+1GlmXWthKE/b5jO0bKxSKVjU3mHHYEq0zUaedupc7Fdz8/kJHXUTwaMdB1g2QyjaYZxO6n/oZNpV5n7uqVI6MbR2G32Zie2Jm0m87k6GgdREE4Mvphmib5YgnJYkPXdUQBDF0j6Pehqiq1Vnvnu9U1RLuD7XoD19QcRrWMIFvQ2wrWShlvHzcxhx7LU+TRAWPRMWYMALVSiUAfDp/lZoto2TxwBuXrLaxemc7zNiyaTkA4wtL8egDbkMeI93Kj9fWfepXJXwgQ9vWXJtF1nU4gOJROlCvPPsubr7zMxWvXsfVR2NovS594kaTNTv1nXsJReViw6/p0htz3ZonEz9ismPvIsozXH8Tq8ZIrF3FaZFyug7+l9Tt38SgK049EAOwB79AFRzdq9TrPXL+6U+S8scni3OzQhIdpmtxbWWN+dgbrEKzGRVHcHaqndjrcvnuPSxfO7z5fq9VpqZ3ddEZkYnpfPYVpmlTKZWTZTnTyYQ3OvqqR+ENjPFVVyd6+SdTcfzNjmiYtpU3V6cLickMug6BpuO22rrUxxgAp37PMWHSMGQMIPTiRPiBVqiK/liFsPbgI+OwWLB+QkUWR4pcORjkUU8OKuFNv8fkclQ9M4OsjZHwcSvtozwAtYHDu+5ZoKR3oo2ygrHYgNsHE7PDC09dfeBdvvvwyz7744tD22Y3Jb7xO8XyEjd99BUdTwDRMlHabc9HRFxkOSrFSJTq1Y4cuihK5UhlJErHb7azfuYutXkM0TSYcDuQuqbJRe0c8QLtvBieKIguzM6xvbOLxeggHT970ms8XOLc4PxLxVK1UCe7pFqrV6ogOF9HI4VElQRDw99HdZrVa8czOUUynQDcwmzvvYdjtuC9MMelwIAgC+vwCoihS2FinlkoQsNt3P3OxoxG4fP6Yd3qyEMwnYfbzmDEjJreyTLB+vP20ruvceWWFi+rRUYI7G2X8Lx3shqm8305H1wm81EYSRIqXJZb+9BUcQ7iTK9Sa/PZf+iLtr++Inb13nJrVRGzD3I8v8M1/tvfiyU5Ho+RwETp/YSh3m4/SqNdJbW5yrkvny7ApZDP4XfYz4RlyFNVqDdnlxXk/8larVqiWitgdTgprK0xJIvYjImS1Vgvm5vpaIAdlM7G9r+YDYHVtncWF+RPvu9lsoSgtgkMQMI+itNuUSmUm4jFUVaXRMQiEHr8INU2TUj6H0WqhNxs4Y3E8gSfFtaQ3nq4KlTFjBkTQOsdvBJQbDc4rx0cmWoZCeUlG42Fo1TANRLvO1fkQzW+wUj5vIXhLp1I7eUHj8mqe//I9n6b19RrP//w387E//GN0XDqdoMnlf/hh/sjn/zhX/v6HWfv9jb6syis2OxNXr41EcAA7NR2mSeOELpS9EIrGKJRHM9dkGNRqtZ3jszp2BQeAx+vD7fZSL1cIG/qRggOgJIinIjhM00TvHDxvpqcmSWe6zyDqB6fTQXlEf69GvUEoGKBcqVJTOmdCcMDOjUIwEiU8O0fs0pWnTnDAOL0yZgwAZpeLZ9ft4IBfQTeeW4jDAqzMldDbAhjQVjWuR3ZaRc9FwpScDbZjBrZ8Bb/Xfuxichj1lsJn/twfcvV/+ACXf+QF4vOTNOoNLv70h1m6fp6FF3fCsxffcxXrFFSbLXRJIuSwd82/lzUNMxAGpwvviG2vAc5dvcpnfvM3+aZPfnLk7+UJhKnVqng8oytg7YdGo4GiaiDJuL2BrtNEK7ks2so9wqKAvYeJrMIpTSQtFkvMzBwsSLZarTvTZA8pLK3Varjd7p5qP8QRRaVESSKTL+Ly+gmdgpfHmIeM0ytjxgDZV75OSD4+8NfudNi+sc1cfbgFoNsLdmanBvOuUNQOK1mVZz7xkd62VxSsVivZ114m8oixUV438F2+durOoaVCgWajwdSIxrrvJZdOEfb1tuiNgmazSavdAUnG6fbgOERIqKpKMZHAWcjisvb+e7tbqTN39dK+aMkoSGdyu4WZj1Iul3ecaR/xG2m1FLL5PBZZZnJip+iy2WyRKxaJRyMHOlQ2EgnmpgfrtOqFSrWGxeXB6TobIvSdwDjSMWYM9wtJ5cPvEN/61G2sWNCnRDpfKXM7IrMU92AZUveJXh98VL3damHqWu/j5O3374QFjw+zWaPcUjBsNjRBJHL9uaF9pn4IhEKkt7eJTU6OxutgD+FYnHwyQSR0ejboiqLQaCkgyThcHkLBg4Kg0+mQeeM1bHY7iCLtWg1TbePy9hdtuuBzs7m5ycLly8M6/K7oR0w6drvd5PJFfL6dNEy1VqNYLGGasLgwx0Zie3fbUqXK3PQUK2vr2B0ORAQsFplwKIg4YmMsn9dDvlgei45TZCw6xrzjabVaXbxCH7KdzOH5gyrWvSZTyyrLC2Umr7qH0n1iC58wjTFAwNLuD1B2uQjEj3dFPQ0uXL3Knbff5sqzz470fQRBwOnz02g0cI0wGtBut6k3W2jmTkFuo1ahVaszHQphTEzh3pO6yq+tQjbNhN0Gegd0wG4luWcabD+IWu91O4PSrZ7jAbIso7QVMtkchWIRp8PBwvzDzqeA18P65hayxbJrAry0p/hUVVXWNjb7HG04GE+ZDcaZZyw6xrzjaVbK+I8w8aks1/B3cbUMrulsR5v45k4uOiqFCoamI1lkRAksokjQs7MglpU2TcNg8hBxo2ka5XSaUJ9DrnynOAOlFyRJwhcIUMznCY44z14sFmk3mwTUTk8Lm6HrCKL4MCVzzDThSqPJ1to6TllENk08kkDcYsHtdEKrQenN16j7g7TbCrZOB78kYnmk9dUwDHR9MNHR26canFZLwXGMq+3C3CyGYRCLHkzBeL1evF7voa+1Wq0szI0+1QYgPGU242edsegYM0Y93KOjUW8hfrXa9TlF0PD7hmNsdSErQnZnnPhWSKYWdKFRB0nGMhnHIQg00+kDE2JN0+SV332VpWfOxvCykzI1O8vbr78+MtFh6DqJzXVsDhczfXiOFPJ57HZ7VwdVXdeplUoIkoRar+ONRinfusUHI4enbwJuF2jtncErUvfOoPVSifkBu1D66VAahFq9TrSHIXWnYVB2UsxTiaeMecBYdIwZc4QxWOJrabyJ7hdwuynT6AzvgtXU2hTfv8jclSVkWSaZSOAPBHC6XBTzedq6zqMlh612m0WLF7N8yGSzJ5CFc+dYuXOHpYsnnxsCO0KjWCggmDroGrMTMbLFcs+vL+bzJBNbRCKRrqIju3wPSW3z5uYWDtnC+VaDuaD/RMecr1SIOt0DL9pO0yCbShOd6L3Wpx80XaetqnTUDm73aAtWR40gjZfB02T8bY95x2MeNdK+eoyzozmcOznDMCh+cInFaxd2H5u8X7VvGAb1e8tMBQN0Ohq1VgsDMEwoV+ssOTxUinVajQaOEXcsnAYut3vHKrrVwjFgF021UqHTboGhI5omIb9vX7eK3+0kk9wmHI0hHVO4WshnuXTlKsu3bhCbnDrQ9WINBChvbGC225y7fp1OJgV9dJt0o4VI2DF4FG3S6aCezbHZaDJ7bvFEx1KtVmm2ds4R9X6NSbNRx9A1NN18okVHq9XC7hjO/KMxvTEWHWPe8ZiHjLQ3DINmtclRJZ76qsqdZgkpYnDONfi49q2AxNKVc12fE0WR1KvrNI0tbBrEnL7dDo8H76g5LT15ODwpnLt0iRtvvM7VZ5/r+TWGYZBbWaaSTDD33LN4vYd3JFitVmJBK+V8Bt0ERBFECZ8/gPxI947L6eLuzRvYbVaKhTyh8E5aoZBOUy4UKOWyeH1+rr74Ikq5hMNmgyM6O3pBHkIAzW23Uq+UB3ptpVKh1e7Q6exMkY1GdtJdj0Zestksy2vrePdEgAzD3BVmJmAaBo1mk3OL8wMdyyiRJIlKrTLy9uIxDxmLjjFjlDY49xeSbm2lMTMqSqOJaUqHejpYEwrZCY2IK3aiQ7BfmT0ylB6fnmYiv5MGqnXaeB45dYMtk9yNe0T3REqedKwWS8/jwg3DIP3Ga0QFk6DXw/bWNvM93OH7/ft9JCrFLJphgihhCiL+QJDY5BSGIKKpbez2h5EXQRSxOR1cfc97SayuMjE9gzAzS/qtN04sOrRHZqcYhoGqadj7dIYN2mxsrW8wMz9HdiNF5VYGPVXHSDUwKm2E58LYroWwul1IDgu6rqOpKtFImHj0+OE80WgULZ3ZFSWHUa83KBaLI7E0PwlWq5WgJJHe2iAyMXVs1GvMyRl/w2Pe0TTqdRx71vpkJo9e1fFkVTymRPRSjBu+HK6XOwghO+1aC6ti4tFtCIJAe9rO5SkvakuFAZtYcmIH/8wxosVjp5ZtULwQInBplq3XVwlvVXEIO6ewIAgYNzbhKRIdM4tLJNZWmT9/9GcyDIPUm68TE3busAVB6GuA314eNbMqF3YiIQ6rDHYbzj3RpGA0SpCdDqDz166xdfcuTqcDMRAit7VG5ASRJ59Fpqa0ccgS2WYLtd1GslqZ6VN0WC0yRrFIolpFaCmo2TLeX9pjUf56HcNcpflnznHpB9430LGq7eNt/N1uF2sbhTMnOmAn2hGPhMhlkniCkVM3xnunMRYdY97RKNUqtXwFWhXksoq/aWCTZLjfImuxWHhudpJSuI5mmETcUVrtNtu1CoYhYpEkGjaRmDyYuVDC0sH9Tc8fa8vtOD+JeWWG8/EdcRL+ligrv/tVZraau9uYhe5dNk8qdrsdVT3cC6KQz5O8d5eg00EMY180Sj6iI6kf/H7/7r9N02Rz5R5uz303011rFBPdhPWNDXwWmUWX/VCX0V7xulysZPPIAsxFwoCHTKlMs90+0MF0HNOu+8dis2K7KlEwtnGKD8WLKIjoy4PPOAmHglSqVXxHtMAC2G2927Mvr6whyxLzp9Q2CxAJBSlXSnQ6Kh5vHyOYx/TFWHSMeUfT3MoSXWkjSxIggtQ9xRFwPhQFDpuNc7aHHhfbogYDdCgmnSaT3/0x7Mf4HQBEZyYPPGYa+w3B3KpJrVLF4zv64v8kcdSUhlA4TObmDWKm9UD6y2HoNBuNoebqBUFgbmbq0Ofb5xf5+le+hvfqFaaNzolt1pei+1MWsYCfrUqtb9Gxl4DbRfLbQjg/Xdv/xOt5Ws0WjgGM7txuN4lU+ljREQz62UxsY7FYMAwTTdcQBYG9f2FBAFGUWFyYo1qtnXpKxu/z7qSC8irB8PEtwWP6Zyw6xryjsbe0+4JjcJr1Otj8fb9OuDTVk+DoxsqXXiO+VQPx4SncFHSCT1lo2GK1oijKrnX7XpJvv8V5j7Pr4u5zOtnaTjJ/4fxpHCYAU7Eoxnvfg9Vmp5jYIDTg3/YoBE2l3elgu1/sWmspqB2dkPehuDJNk3s3U0wtBnF1Mb3zvRiFR0SHpwzrr93j8gefGei42q0WpmkeKbRsViuz04eLtkfx+31sbG0zas1RrdbQEAETo6MiiSLtU3B0fady9p1bxowZIWalefxGxxC3edgSVdYaJQyj9wJCyTdYCH7ti68Rv5nHKu6/ZwjIdvK/9jnarcHnuJwVatUqt996C1EUuwuOG28TVltHFt8KQ0qx9IogCEzHI2AaGM7RdENMh0LkFYVSrcHdr2yR+KnXSPzy8r7fXbpQRf5nGzS71FrUW20q/y1x4PHqjIWL77828HHNzc6QLxQGfv1hzExNsLq2PvT97qVjmASjMYLROKHJGbyROBMzvRvHjemPcaRjzDsao3JyUy0PEh5DwnRaKNChaejMikdHHDYmbMzPHkyZ9IIlVcNyiHVzKN+i/Podou8f7fySUVIqFChks1y63t1lNXXrJuF289h5MRKnP0BbFEXCfg95QSC7nSBqG97wPMMwuPP1TZSXi7juKLixAVaM11Q+732Z93/X81hlmcLXsgRFK+mfX6bxiVlsESvxoJdCrUnm524T2Dq4b+kjkydyD5VlmaZyfEFpv4iiiKXP4tmjSGeyWO0OMA1ETFwuF6Ll4f4FQRh3sIyY8bc75h2LaZpQaYDY30XtXi3Hec/BfK8gCISxkrBocMTIjIyhMPnR92IZ8OJmHLM2GLc20d9z7UwMcRuE9PY2l5/ZCfNXq1U6rRampiEYOlq5RFBVelsYTl9zADsLcNDjJOnx0KyWcXaJ1PTLyusJ2p8v4run4hME2DOiUBRFLnwB7qy8hvCeAIHfLoMoEtoAfnYT3TB440MObK/XCTQPiqCWVSf6jSfvejK0DrquD/1353Y5aTab+zqHBsXrcWNanbjcbnRNo1IpEwyNds7PmP2M0ytj3rGUttP4+9DdVaXJHb1KxOLiRjVDXW113U47poVQ/9DFE003FWtHp0+CLZPCa7cG3v/jpFIq4bs/b2T99VeR79zAn04QyKfxF3OEjd58OwDQT+aVcRKsVisen5eWdTh1HcrXyviXjy5ODSclQp+qHohYiKJI9MttfF0EB0Dn3WFCUycf/rcwP082N/wUS8DvJ5PNDmVfTqeTVm2nU0eS5bHgeAyMRceYdyxGodJzSLmpKtR1jYuSF5/NwbwzgC7LrJpNtEfGj+utg7UEKRSKnSZrEZnJc4O3AW68fRfP4V2kuxh3tgd+j8dJenubyZkZAJxqG9sJQutdTGZPFb/PS2VIKYfJ75uh8kk/9cBwP5RpmkgfnBja/toD+qMchyAMb6lyWGVazZPXco0ZjHF6Zcw7FrNQO36j+zitdprSzgVfEARc8s5i6MNC0qrSUmtYkZiRnQfcKMvtJtYXJwCT6LklrJbB8vxbN5fxvLSOQzz+tJXaHdqtFrYnqJtlb3tsKZ3GL58sTC8Yjym/ch9JksDlxhxC+2wg6CPwzT4q76mR/IV1fPeGs7hXvDpXPj68CcUWSaKtqicSi90Qhjit1uVykS+XT+ylMmYwxpGOMe9YMm8nWE5VWU5XWM6UWc7u/P/dTJG7yTLL2TKZ6kPDLcPafRGcNKwsyR6igpWs2cZ4pJhA1Trk8iUiXg+FVHqgYzUMA/OVddw93if4WgblV28P9F6Pi1KhQCgWI5fNkrnx5olrA4QTWpEPA4vTcaTXSL/4/B4E7/AWdHdVYPPW2tD2NzU5QbE0uNHYYXjcLnK53PEb9ohNElFa3dOjY0bLONIx5h2JaZrwBzk8paO3U9C4M6nQiQhMXDzapdAmyUSRUTwPF5lNZ4fIuxaZuO+X4FZVsukM0Xh/s1re+uIbbP9hkvZ5N3Nh/+7jt1IFZAHOxw8Om5NubKK8cBl7F6+Gs0huaxPJNBErJc6HBx+et8sQF/tBqVRrxAwd6xDu1NvtNitf20YoD69LpH7ByfVr3QcNDkIylaJabzARG66xVjAQYDuVGdr+PB43+XJ5bHn+GBiLjjHvSIqbadxFA47JFduRsScNCjGJEN3vMEvNJoZhELo/aVO2WtCaGtvnnUxGIvuGdEUcdprZDJntBG3JgjUaIT55fE69udUk8YtJNp0Q+XiEi8956WgGL/3sOqZicPOby3z8I3G8e/whvB2BSiKN/fx8D9/I40e2O7CKIj51OD4jZyHS0VFVqp02SqNJ0GbDeQIBuPrSNv5fOUYl90EdFdf3D2YG9ii3797D4XAQDgaZnBhejche/F4P2WwWRe2g6xoLcyfz0hAfd9HPO5Sx6BjzjqR1O4vUR3Gac+Lw2Si5tIrljQZ5X22nTdMwKX0izDNT3e/2nDbbrpV1q5Anmc2hyxKGLGMKAv5IGP8jNoy1lZ2QtdiEwm/l+MJnsoh2EbFmAgLtsobbfvCuTfjiDUoeJ4H4ybsTRs304iI2m43VL6S6Omn2iwg9T6kdFTOzs9SKBWbnZsikMxiFPO5B766HqKEUQUP/a8+w+JHBDcH24vf5iQ85uvEoLpeTZqtFu92gVCqeWHQcd8MxZjSMRceYdySde/m+fvzNUhPcDlpaB4e8vxDUsGs4VBFn/sGdk4AW6m3+icNqfWQ4rUl5a4t7yRTOgJ+tV7ZorNRY/xc39m0lqQKo5v33hxc+Hu3aieNt6mS+8jb+7/r4iYsZR43NZsM0TZxDCnnr4k7e3u3xDGV//aKqKg6XG9nUsVoszMxMs6ooOLXOQEZc8++Z4N5qk9Bryon/lnZTRvnUJsaHnjuRKdgDOvoRxjRDJBIOEQmHUDvTrKyuMzM9iXXQotUzfj48rYyl3ph3JOrt/orSpJdr3ForcKdU3Pe4YRi068aBRSD3BxsDH5vfbmfRIpP/+h1e+eHfZ+WvvwLFw2dBTHwyxrlY4NDnI4kqxY2D1tdnDaXVIv3Gq4RP2LXyAFOQHpvgAKjV6ui6RiDg331s4dwSa0dMzj0MwzBIF0q4Px6g8z0+iu+yoBknW+h9bzdY/oM3T7SPB+j66c4qsVosLC3Oky+WSKX7r/UwTXMc6XhMjL/1Me84qtkS4o3igceNPTUAqqBTDjwsRPR37HhfajHt3x/BeHulQOy1gxdc1xsN6ieojtd1nc/8+OewqEcvwOKMhfe+6+iwtiiKdFLDN20aJvVymcaNN4kKDC0iIxlaX7Nwho0JpJPJfZEEQRCYuXCBZL3e837aisLGa5tMrWnENzrMWJxcXgqhf0+Imq1/AbP3WNSvpAZ+/T4e0/c8GY/h9XhYW9844JfzKJ3Ow++qWq3iPWYq7pjRMBYdY95x5L54m7xPIxc1KEUhNy+RiWhsi1U29CIbrhpll4ZY7lCeeHiKyKJM5u0KqU6L5VyOG8sFwq9292BwY0dRB78TrbdVbIc4SD7ANE0WPxkl6D7ab6AiGQSfvzzwsYyazXv36Ny7hd8y3GxvwOlkY2VlqPvsB1MQ8XsOOs86HHakicmeBJGmaWzb7AiXZtANHdue1N6s1YH9E5ETCSvXF/O8/Q8/QyV/sgLVYMBPrda7780wcbmcLMzPsb6Z6PpdqO02y6++Rv7NN1l+6wadTgfdMJAH9MsZczLGNR1j3nFoX08xXdpbqKizcyr4QQL2zICrZVpk5mxITYNQVsC6rJKrl4gmBTyCcGiINjOrs3CCXHmp2MKmHn1RjP3pKeIvhjFqxqF5edXQ6Tw7i3UEY9aHQTG5jb+Ux2UbrpkU3B8WVquPZB7IcRiGQb3ZZH6qeyfHxESclWSSBc/hBcqGYbApyZy/dBGAZcNg9o39duCTopW3pnXiycF+azIS1s9lKH+ihC98eIruOLxeL5lsHs8Q01m3v/wVfHYHpiSiI2CIAsgypijij0bwBx4e7wMvlG7nQeLWbRZczt3t0m+9RUu24gpEsA1hLs6Y/hDMYTrXjBlzxqlny9z9sf+A4LchJloEtd4W47LeIBPUcPk9TG/0aJ2Oivmn5pi/Fu1ruFtDUbj12TukX22S/mwW0yEgJvancHSfyQ/81rfhc9rZLlYxb+ewaKDOeJHdNiSbRGIrx+J730343PzOfhuNE818GRamaZJbWUaolHCaBvYR3nGapsmGbrB09crI3qMbiWQKu9NN2H/4IpzcThKrHx4dWO3onLt6eTeSZhgGd//Zp1i0+/dtt9luIf1aAbkHp9puVP/4DFd/5MMDvXYvW9tJZqYGm5z8KM1Gg/LNW8S83b+/ekuhboJmtaBbZES7g9npqQOio9PpkHrrbaZcB6OB5baKGZ8kPDseY3+ajCMdY95R5P/rG0TKFigbVP1Wig4BQTMx3DKdZhtnpoOXg3c/fskFiopU7r3ewIkV/nWK5e9S8F8P43LJeI+xXq4pbUqvbvO8JwwfhfK7w2yJKsufK1D57Yd1GS/+n+/G79rp8pgO+Wi96EDXTdyOhyJKmJzcFRwA5VLpsYuOZr1G9c5twpKAIEvshJZGR0fT0VrD8f3oFU3TKNcbRI4Z9haORkhsbzPt86KqKinZgmi3E6pUSAsCi9eu7kvdiaKI77veT/Ird5isPEwjzNocrHzCj/bZAn6l94hW3QfGhAP35f6M6g7jpMWk9VqNTCqFrqi4mg1i/sPN+NwOOw9iRKZhUMhmSWgdZhcX9223ubrK1CFRNL/NSjW5xVqrSSASxR8cgiHdmGMZi44x7xjqmRLqr9/dbVF1lgzkByIipwESdYtBIq7jSmsEOvsv4PqEE/96/3Uavk+VMD9VIm0oVP/mVaaj/n3PG4bBZqqA2IFypsBV4+Hdnd/lpBF1M/k+667ocHzQw5UX9oftHV3aBuVHak1cLhe5bJZI9PF4duQ31pGzaSLW0efSTdMkVW9g+PxcvHZ15O+3l3K1zpWr1ylnk0duZ7VYEIIhEqUSut/P4vklAO7cus3cwkLXlNDE1ASlb7BT/09fwW19KI6XPG4an7SRrCjYP13BekTUo2UzUD8WI/4DzxCeHo7ggO6pjV4wDIOVl18lYJGZdzrAZgGbv+fXC4JA2OMmUd7xslm7ew9aLTTTQESAI2qFiogszc2gqirZZIJgJHaqtR6maZJLJxEEgUh8OFGis85YdIx5qqlXaiiNJuHJGNs/+3k0Q8fygxdI2Vvk31hj8baBT3l4kXF3LLi3oBKRKZWVXeFhGAZC+2R3cm7RTvEzCVzfKeN3uzAMg0yljrJZZq6+cypqpgSPBFMEq4XIVT8vSwpWw8I3/y/vwtbDhdFazFELBPHcNxrzBwLcvXnz1EVHW1Eo3r1NsNNGPgXBUW4plCSZuWefwXLKxYLtdhuLw0WpWCDo9x+7fTAexTI7vW9A2sXLl458TSAY4N6EC3dh/+/RJVo4H7Cw/AmQf6t2MNWATu0DQTzfdQF3zENDbWOv13G7D68r6Y/+u47u3r6DWSyyGPCf2C9kwm7j5c99nivhILYeapjKrRaR+XkArFYr0aCVQi6N7HDjCwxe39IPxXyOiN9LtVqlo6pYhjwo7ywyFh1jnmraTYVOpcnNWy/j+4Hr1D8+TfS5C6T//R8wJwXxtmpdr5W+nElySiawDZqhkZqBqQQn7u0PvtQm8bXXSXynD6ffxXzLxoPTsGZ08HWAR6+XhsH5xShTX/xuWmqH0CF5boByowmyjO72IpgCUrMBe9xNDbVNW1FOrYCunEmjba4RtVrhFJxB250OZYeDcxcvjPy9upFMZ5haOMfy175Ke2qSyYn4kdsPsuCbpolZbcEhtvyLLhdvxYvEs/t/SKVnnLzrpz4BQLPZQrbsjHjP5wvohkG90UASRQRRxDTBNDRmpqd7d3QdoNO5lEzxXCwyFIMySZJ4fqL3yE1NlJl/RBiGggFUVaWY3sYURARJ3klx3S993CmBfFAGufOBVVUlPjU92EHrGoIg4PP5yBXz74hox1h0jHlqUVoK9Uad8loSSwe2f/El7G4nX/zVrzG1IeCriIe6ErbEDmJGBVxk5iRmtoSBLqrdkGc9XLAF4REbj5pNYFI8WPPRyddhMYrTbsd5jFjIShYmz11A0jr4owcvwEHToHjrBtbYBHRUBFkmOOgF8wg0TSN39zbeRg3PKd692SwW5Gbz1N5vL81Wi1K5intzg4seF81MhtVqlamF+aGOeleUNs6yAq7u+xRFEfu0B7LqvsfDb7S4+Vtf58onXsTp3EkyWn0P6yZi7Pd7MU2TYqlES9kRqnOzM0cKkH7tVZrNFkarSVXtEDxlq3pN0xC6tDPDTtQj2Mffq1hSj9+oC416HYf14ecWTsnV9XEz9ukY89SiKSqRiRgXPvwcyq/cIvKFMq7fSRJa10A7umnLYVhwu1ysTLWJbQ3X+MiYOpir1w2dTqW7YZRZqPVcpDcpQmttmXaXMeCqqiJ22kRF8GWTdPJZzBGYOtVKJfKvvUK009437O60iFktbNw7XX8OTdPY2trGo7SwlHeM55wOO3OYJN96i2p1eB4WlVKZqKu7sZVhGCiqitV7ML0gCiLGnd7HzguCQCgYZHpygsWFeTYS20dub5Nl1jc2d//36htHu53W63Xec26JYr1BL02Uuq4fMAAzTZPtSpXVfH/md0W1Qyx+dBSqV2RJopBKUEhvU8imqVZ6+46btQrOPYXlAb+PUuFsm/gNg3GkY8xTizvw8MLsCnqRN8sAxKq9dUy4K9BpQj1uwZ8ezuLcRsPd5Q7VNEHUu194hbivZ58Jt9WC0WrhvHpwemhp+S5hm428AVIoQnR6Zihh7b1kV5axF/JEbI/PeMkiy4jV8qn6c5RrdaZm51lPpXD79qe/Zt1ubt2+zbX3vDiU92qupXmQMNM0jYQXrJNhxIAbeyxAq96g+dV1nHQROlNOVtfWWVyY7+s9BUHAe0wqKPpIrZB0TPSi1dgxxFkMB9ksV5jbYxf/KHWlTaZawyaYaKKEVZTomAa6YTAfDJDoUdS1VJUyApZYdGhj7b2PpDs7nQ6F5BbIFhwuD84uHWPlYgG3ff91QJIkDO3xROlOk7HoGPOOwHYlCq+X+35doGOjmmmRn7IT3j65pY0NmVJZgEfq1GRJwvB0T50IfVrpaIbZtSCtpnaQgmGCE5NDL7BsNRpU7t4mhIH4GAXHAyZcLlZv3eb8KXSumKaJIFuxOByEnPauDrWOIRUmVipV3G8nqRpQuRzFc2mO87Hw/veMRWh89WDnTEVWmPmWq6i6hqIo2Pus6/F6PaQzOdrtnVTLcdRrNZR2G7vtYNRF7XQwCnnweBBFEY8kkW20iLq6C4GSqrIUDe/+t2E8YojXQ8Su2W5TcrmZXVo8dtuTYLFYCAV3/t7NZoNCtQSihCBZCIRC5DJpPDYZRzfRY5zuDJvHwVh0jHlncAK94DVtKIk26VkrsU3zxLNBHK+12PaLTAX2+xDIsgxd0rp6tT+fCbss0Wo2cT1yZ2ra7SMxQiokthCSCSI2K8PI2DYUBdcJC11TLYWZ+06eoyZXKBGZmqGU3CbgOFiTU282cQwhlJ/J5mk26rj+6ItYHDaWfIfPDol+40XWilUi75rHaHVo5CrMfOt1fPe9L9bWN1iY7++3YLNaiccirG9s9rT95UsXWdvYZGFudt/jlUqV/NoG83vcS4NeD9VGg7VsHovdhiAISIKAed/ZNfaIc+tewaGqKloPwrzRVpm+Nt/TsZ+UcrlMsVRGFEUESUSSZAzDILm1gcvpIPrId/IASQBd046NEj3JPL2fbMyYPbSsNcwPODErBkayia8o9CUe7IKV6IZBel7An9RwaIOfOnZNpFLiQLRDURSQD0YJbC0NtdPB2mN0QhZF6qvLNJxOMAwklwfJbsfQh1u/oaoqxbt38CkNrEOyMU83W3Q8XjqNGv4BhUe+qWCLx4cWPj+KWr2Ow+uj0+lgJhNYurQEu51O6skUTZ9/t4CzX6q1GgIGCz2mRcKxEMr3XCcYCHZ9z2AwQLlcxt9DW++j2B0OVtfXWZyfR9O0I4tL93ZJFYslqpkMPqXFfBeTPK/Lhbebed0R3VqGYZCo1lgMH2/sFXK72FhZYeH8+WO37ZdkKoWqdpAsFiySjMtp757Cmjp6Pz6fj3QqSXx6ZmiDD88aYxv0MU89pmmS+We/RtDcuTjqus69TAnhro4/2//Pf326jf/ZKGJOR71XxVs0sAr9iZCaz8T3QTdhl3v34pKUNSa7iJlivYb7Y/M4BlyETdPkq6ks7/umb+69/fEYMi8vs/X5V3n24wtDqwspNFswMUE0HiOVSGAvlvAOIGZW2ioXnrk+lGM6ilK5gt0bwGK1knvzdaLS0YvEWrvD0vWrfS8mbVUlnckyN9N/l1EimWJ6svv8l+10BlEQabdbzM92v/M+jHq9TktREBCo1Gq70QxRFFlfX8cwds6raqOJz+2GRo2wpuN0DLdVeyVfYDEU7Pk7XW2rnB/yb2N1dY3JqcmuaaRBMAyDfLlKdHL4XWVngXGkY8xTT3EjQcB4aLolSRKXJsO0I23Wsy3M5Ra+tIkoiJimya/bciz4ApyryzhbDy9m9bCI/ryHZz8+hcvtoN3p8Me/5d8iqCbPXY0T9TqJuBxMWSy4yxo+xXLoxVBuGfzkP/oi7/3YHN/3vgU8yEiW7ou3YnTwSw9P1abSxmGz9nyh3UTig9/6bUO5c2rkKiT/xReQfneLGBKr01nOXTxZ6sA0TTbrDYKLC/jve4pMTE+TMsEsFfH1eTEPGQbVchnvAHfxvWCaJtlCEV8oimyx9CQ4AGYkgc31Deb6KOI0TZPEdpKlPgs/HyBLh1/ip+I7LdWVapVMNke+WMRpt2EaJvPzc0eKSbfbvesxEgoFWV5dx+VyonY6hMPhff4jlUoVpZjHOTQTsh02Mxmmgr0Ljnpbxb3Hs2ZYiKI4NMHxYH8Oi3yqfjqnyVh0jHnq0ZPFrhcmm8XGxSkbTPm5+fkUoTSkwia/+WvbiKQIx2T++tVLODoijYhE8M8vEN5jYZ6vtVCLOxfml17a36JqoDG34OL5cxMsBDyEVQFvScd+P9pS8QiUMyKf/k9bfPo/bSE5Na6+O8SPftezLFj3h8OrsklpK4vNYqNaVfiFf/gVzr83zKu/l+Cbvv8qn/jkZVwWuWsUo9JsMv2u955YcGiaRrlQYvP/+2tEUvBgZor4T9e486d1Zi9FcAwQlehoGgm1w8Lzzx3oNJmYmSYtSZRzOfx9TMn1O+xsbSZGIjo0TSNfrhKb3LHOzvcoOGCnZsdbLpHJOInFenOF3dpOHqiJ6JVUOk2ghwJWn9eLzwux6I5PRy6fR1XVngtNBUHg/NLC4fv3eanHJ1CLBaxDaqE2DIO62sHVVnseGFjrdAiHhi86ELoUtp4Qj8dNvlLCZu8epXqSGadXxjz1pH7htwlVjzbeubtcxvNKk3/TTvG1r+V3H3cHDP7st17i4g9eZHJ+v3nSq7fT/NQP/XbPxyFaOzx/Pc5zsxGanTb/8TfXDmxjovF9P3qZ73x+Bp+4czH99Xtp/vVPv3Hoft1xifCsgx/6C+8hEHAwF/chSxLVZoumbGXxAx/s+Ri7cff1m9RLVSyf2cL/pVLXbQoRDc8PLBCd9+Hs8a6v0W6Tl2QWj5kAm02lETMZAn2E5hW1Q9HjZua+zfUgdDodqtUadrsNl8tFo9FE0U1C0Rj1chll5S4Buf+W3HKzSdXrZ35x/kgxWCyVEQUGqrsABmqNBdhIbDM3fUzxwQDc+9rLLPoOr884Dk3T2CgUsTmd2AWBkMtJolpj5oiC2kdZbzRYeuGFgY/hMFbW1pmdme5rmvRxlMoVPPejaU8TY9Ex5qmmuL6F/ddfRj7Gr+FGMsdbbxT5xd9aRbzfgWFgcPXjEf7yT32cqP/ghe0//vIb/OL//spIjjs8Y+Ev/sR7KNRU/tU/f5Va/vhWOh0VCSv2EHzgT1xl8cISz3zkIueuzw90DPVMidS//CLCZ7aQTRGpBwv4UlDD8f1zxJb8R3agZJsKhENM9FinkNlOYsnn8fUY8TBNk616nYV3v7un7feiaRqlSg3Z7iQQCqG0WtQqJZxuDy63h+J2AjG5dSKnVcMwuGfAlauXuz6vtNtkc3lmT7D4DyI6Op0OyVS6p5bYfmirKtlXXmGyz/SGqqokylVsTgc2IORy7hNq6VIJn8vVdeBhN3RdZ8uEpWOE7iDk8gUiPRS09kMmX0Cy2EAQCEWiT0Vx6Vh0jHmqSf/2FwmuFY/d7jduJ/j5f3Jj32P2EPzzX/sBQt7uuei/+Bf+CytfKQ/jMIfKpY9N8/c+85MDF4121A6JX/462n+4gb0x2OWh5Ong/MF5IucDuB0PhYLS6ZDVTWLnFruaJh3F9uYm7nIF9zFpHKXTIaHrWL07QlGQJERRRlVVzi3OH/o6Xdcpliv3xUa46zZbN24QatV6Grh3FLcqVa6++O6ui4jSbpNKpftuaX2U9Y1NIuEwLtfBTpHDME2TlbV1zi0eni4ZhHQ6S6TSPUp2FKv5wrGdKdu1BlOHWJp3o66otCJh4ocU2A5KLp8nEu7+uzkppmlSbCiEwpHjNz7jjGs6xjy1tOoNSptJmk2Faaf/yG3fuHvQuvjD33Xu0IES1UaT9FaJoQ1kGQaCwQ/9b9/E9//FbxtYcKS+cofKP38J17pyootDoGaBf7XNVngL758/z2TER7alYAQDLMwNtphOzc6S0NcR6zWch9zZtlSVjCRx8dq1A89tbG11fY1hGBTLFUSrnchU9/qJjqpSLuTQBBNLl6jZnXwBu64z12OtxoLNSjabO1DbsZHYRhbFEwsOgPm5WTa3EpSrVaaOGTz3AEEQ8Ho8A5mHHYV63320X2w9pOo6rRaGy3GgpiJfbVJJVWnUWlx+bgaLLFNpNPC5XNQzGZSAf6ht1aO8fRcEAUNtj+4NTpGx6Bjz1FJ9e4Ul3U7HLpMRNVTBQK4rTDgOpkrefuWgg+Nnfn6Zd31ojg8+f/Auyuty8gu/9iNkaw0y2Rb/+Z+/zI3PZUfyOXrl43/qXfzwX/uegV+/8Ssvof/Mq7iE4VmH+/Ii2t++w5f/uzDv+6E/gvWEVf7TC/Ns3FtGUFoHQupKp0POYmHpUvfR8EKX9FCxVMaUrYQnD08nNOo1WtUKkaCfsN/L8q3byJUqlnicRqWCKxDg4rlz5PNF6skE7i4eFI9it9vJJ7ZQfD7sdhv1eoNcsYDTZu+5yLQXZmemWd/sLrYOIxwKsrK6xvlzS0M7DrVUBE9/3SuapmHrwdVvPhomXasTv79/0zTZ2CzQ+rl7+OpWHMDdjsDcczFu/Nzr2K8EseR1zB8LMz07vLbUUacM7BbpqehoGYuOMU8lpmli3N5xTrSIMrH7vliKRWTLbOFRDPwOF6uFPE6LFaXa4UFHxl6cLgm1o2G1HDxVLBaZqaCPqaCPxLeee+yi48VvP3h33yuVjSzKf76Jc4iCwzAMSt87Qfw7r/HeUGBoBmJz58+xubyKv9nAdX+fO4LDysIRI+3FPRGKTqdDqdYgGIntK9RLbm8zOfWwjiKbSuK0SISDfmDnjvPc5Uvoun4/mvRQrMRiEe4u3+tJdABMu93cunMHbyiM025lYXaWre0k1VoNr2fwgstHsdn6W6RaLWXfILKTUq5UiA0QeZNlGb3HSGKr3SZviqTeSCE0Dfy/UcYnPvy9+f5DjtVf2mRWdyCu78w30b6jCYM1BnVnxKrD4/FQqFbGomPMmLNIYWWDQE2DR0KudlnGKpr4HRa+msjybz+1QiFVg073xfZv/sDv8qf+j3fz3d/6cIBauVyhWGggAJG4D7fLhc3xeE+lyWcCPPvR7nf4R9FutEj+4tfQfvkWzvbwWv5K0wK+v/whnr00N5Lit9lzi6QSCbRiEZsokrNYmT9CcABI938LO4Kj2dV86UGIvpjPobUVgj7PgVSVIAiHpq9sU1OoteqxraGaprEliCxduIBjT1fOzNQk+XyezUqVmanJ4Xx3PcT9S+UyuXwBm92OLIpMDbHeoVGrEx/QFKzcxQK9GzGXizf/5WvMLt//3h8570VRJGy69rn0F3/lLWbetTS0oYCDlEfeubfC9ORE73U3T8FslrHoGPNUot/cPLRvXr1/S/Jv/ss9Mne7j5N/gIHBwlKE9c00UltArKo4ax3m5J00QSWZ416ggGPEosPE4NwHJ1j5UubAc1e+aYbnPnmeQMjf1z7TX7pN+We+givdQR7CzBSAqtfA8ZMvcu7azFDvlrsxMT1NVrJQbzaYP2aIl2EYiIJIvd6gbUB0sntXSCgUIrW1STToQ3L3n++fm59j+fYd5o7xbdgyYOnapa6iIhwOYxgGq2sbRCKhE0U9stksTufxC36hUCIcDBAcgnmWYRjk8wU0tY1aq0OzCe7+iobzlSpN02Q+2NugPKfDTvxji7Cc6Pk9Im+0uflbL3PlE+8eivDoV3JomobdZqVcrdBoNolGuhehGoZBvlBE03Uq1Rqh+OSJj/VxMhYdY546GuUqts08dLEmb2odbJoKdiuiePRdpIHBj/3la8QzCiFDvr+IiCA/rEvwCRa8JZPfeenusD/GPgQB3vMd1/jEj3+Ef/Zjv8rExRCL75ngsz/3OoLd4KOffH9f+6tkCtT+1h/iUocbhRBbBp650MgFxwOiE7GetttKJJiIx6m1dULRgx0AN3/va8Suz2B0VKJB/4kWoaWLF1h++yaL1u6iI1FvMn31ypFRDFEUWVqcJ5/Ps1WrEQmFena9rNfr5AtFJIsFn8d9rGjZ2k4iiOJQBEe1WiNz+zbzHvfO+SIKfQsOgBYw6/cdu91ePPNuaoaGVex9WfP+43vk37NILD6ErpA+Ix2FYnm3NbnZbLK2sUEktNNtlC8UUTsauq5haBqzszOIonjirqmzwFh0jHnqaLy1jP+QWShlu8CkthOuvXQtROp27dD9xBetfHJuGgHhyOGpgiAQ9/ZuUDQQpsgv/k+/xyf/xw/wk//ph7n2/gv4Qz7mr/wW4Rk/M4v93f1Y7DYMmwDqcA/T3ZFJ/v0v4Pk73469DxfRUWMCpVqT2NTBlMrmr30d4WdfRv0nASbmTp5WEASB+csXWb9xi3n7/jRLudnEszDf83cTvt+CubW9DYJALBI5dPBfLpejoxu4nQ7m+3AxrZQquAaI6jxKtVqjubzMYh9mXcMk5HWx8Y1u4n/Q+1RmURRRMjXoUXQ0m00y2RySLKN3Ong8brxeL7Is09E7fR2vpj9MlTidThbm5shms+TyOWamp7um8J4Gn47hJXHHjDkD6LqOcevw0dvtPW1nV6dsmEcERaslFVU/2sn0AYXiYC2BvWL1ifzg3/4G3vriXUSXQTKdZGtzi7nnY0xd6L/bwelzY/3B4RskAQTfbLHy6TdHsu/jaDQaO9N6H0GW5F3vjbbSJvXVO9TSRVZ/4Yt0fvpl3KpM7p++RLs9HBVmkWWmLl4g0WjuPmYYBnV/gGCPKYO9zExNMTM5SSaTZWMrQb1+8PfW1nQm4zG8fQpgu93KwgmcWx9QK5eI9OEJchRW0yDZaJCsVKm1ehcR1z5xkbSrefyGe8j/1xtHPp9MZ9hOZ0hncijtNgvzc8xOTzE/P4fL5aLZbJLKZJmZ7E/4Wy0Smrb/+hKNRlmYnz+i5f3Jt9UaRzrGPFUUb64Q7HTX0lVDJdQR4cFNZl3kOy5P0nFLNE2dbKPBnVvV3e2VkkgBnUmOD2m+/vrBlttholYMKvkGH/zha/zOv/4iE+dD/OhP/hAzJ3CODH37NbL/4g1s4vBDtvZ/eovt65NMLQ7fTvswGo0GumTDlCQKlRoCJtuvr9IqNnBfjmHPlKgVK9z7B7/D0paNikPA2RQQ7nfsBN5osvL5t7n8jc8P5Y7SbrfhX1pi7e0beOxWKrKF84e08/bKzH2H0rX1DVpKC8Pcierrhk6hUEAQBEIBf38DyIY0M0QtlsExnOhWbI/1+3a9Qa9VLaqmIdb0vm6n1ddSmKZ56N/c5OFwvL0IgoDD4cDhcDCIU304FGJtfaMv11jdMPp/ozPGONIx5qlCv7Vx6HN1m4jX9jCMXL6rIN9WcLzcIPSKQqRLHUJROb5aXNN1Wq3+QquD8Omf+Rq/+fe+iCxYWPn6Nn/lG/8Bme2dOTGNen93dwDekB/FO5pwrVWQKf3/v0ijyx35KFAUhY4g4/X7cXs8hGITBGOTXP+WD/GuH/hGzl+5ytd/4dPUf/ltLm85sSLhaokHFhrX37vJxq31oR2Xz+dl6oXnqfmDzFy4MLTw+ML8HKlUFp/HTTwaZioe49lrVwl4PWRy+eN3sAe/z4uqnizCU6vV8GrDy9UpisKNRJJcvYG/S7v6YaTezhMV+yu8tS4EMQ5ZzKvVKq4BO2+OI18oIvXRSmwYxr56sieVsegY89RQSWZwpw+v0VCard1/Z6s1yq/st2X210wMHl58QjMWJl3He0vIksRP/+WP860/OLfv9cPmGz4ywV+/epn22znaikoqmeKvfPQf8I//6v/D1r3BIi1SYHQFn74tneVf+tpArYT90Ol0aHYM/MHudtmSJNGq1Gm8mqD20sEhe3sRRRHZOtwAcL3RwO/z7GuNHQbPXL+yzzV0bWODtqr2PawtHAqxnc6wvrFJsXj8yIBuKEob95DcPVOFIlVN5+r0JBG3C1ePUZuOptH68sHurqOo2TXm/urHuxYOb2xu0Wgq+H39FbT2QqFYQhIF5nqcPQSQL5WJxJ/8qbNj0THmqUF5exWr1H3BKBptJs2HF6/l7Tpya/9dp3hXwZB28sff9H2z/MOf/DBBobfUg0eQ+XMfusjf/rsfxBsZnsHWAyx2g2/1RgnUJX40MMVCx8q1j54nMO9k8+0k1lcTbP7Gq8fup17dL8pM22gzrP7/tM36mysjfY9KvUEwcnRdS/1OivcWokx2jr8LPi7H3y9Wq2WoY88Pw253EhhwIu3C7Azzc7NYrTZWNzb6jnyEwyE2a0e3n/dKXTOIDtDx8vIvv0m435+aKJL+7B3Wb62QTmV2BfLm1hYTE3Em4sNzh91LtVrtq1vINE0k2/As2x8nY9Ex5qlAaTaR76UOfb5pFbHvMWwqrB+cY6DbTWYuuPkb/9t7+Qsfubg7Wr5XBEHgOY+Xn/6bH+XFb+itlbNXfvjbLuO7X25i00S+2wxyoaRzKWzjuy9OY/l3dzH+r6+RuXN4ES2AUm5QK+zMmWnVGhiF/tMy/SCKIo3//ctUSgdn2wyL4xIWaqtN7Rd3Clt7mZQr/W6CRmN434vP66W2J8o2KoYRUXK7XSzOzbGZ6C9yJggC9mOEXy8ki0Wmukx07oXIc/37V3iaIkIqx+SXN/D/5lvc/o3P02opaJp+aJdQrxiGwdZ6Yjd1oyhtttYSpDK5vtNspVKZQODkLc1ngXEh6ZingupbK/gP+TkbhkGn3gC7f/ex0u2Hi6DpFZj+5ATT8xb+TPzk46MDgoUf/+6r/MRrORqlAdMtokZo2oGhmXzzt83y/L39F0AZiQ+V3VAGUgplUcH4/gtItRbNSp3iV1YQbRJKrUXn0yvIXjvSs3HmfvC9u5/P4XFhXgnAl7pP/6xJKvaOhEU8WeTGV5FZ/7kvc+UvfxOWAQfRHYXDZqVRr+NyH3SubNearP+t38J5o9rlld3x4+Tmr77Eu//kx4ZWgzEZj7GxtcXczHBHxu/lON+ZfrD0UUex+xqXEyqD13WoqoposeIc0C5/8WKUO39SxfbvtrELx+8jf0XAuejZbWsWRZGlnM76l99Es4hUggF8J0itZNM5jB/7HK/MCAghO2LITuCjS8y8v/95L4Yo77Pxf5IZi44xTzymaaK8uUy32SkAOUljzvrw7mktV6Cz0kQSJAw3fOgnFrkwcfT47H4JCBb+0f/6MX7mP77B218p7D5uoCNYDUxVQjwk0Dhz2cH/8KfexaLNRsc0SJRbWO49vFOuCypu8+FFtejuYJFsyH+YJH+vSPPmZ3EqIjqQ9DWYq7iAKsaX0txLlpn9sx/B7t6p5bjwv3wHq//jf8F9s45hGjSXnDg+MIf9xRnOXZ6mlimy/TOfo/12BpdgwdMc7JIR/GyJ9fff5vxHB58PcxhOp5PVjS2sTjfTexb14lqK3P/9BZyvl/vep/ob9xB++ONDO8aO2hmJ4NpOpdB1A93Qe5750gvGAFGTSCTMRiLBXJ+D3R6wXi5zITp4tEQURS6/OMs9mwz/6vCoJ4BmaPguBJm2HvzO5td30kTrwjLeD70wkPCsVqrk/vWr+EWR+Daw3QbaVJ6rU61U8fbhZWKaJsIIfjuPC8EcdZXXmDEjJndjmc5nX6cpm7jrHYKO/Re9TanNrP6wnqOutLibKnPrd4sEzrn49j8yP7JjU3SNT91I8MUvpAn5nbzgkHmP6uenlTS3X851fc3/9Q8+wvn7+dtSR+HVP9ziWtFDLmiwZrb4l//1Fj/+Pdd5Pr/zmTKzApVwhblAiJYqYiQ0nJsqdl2iY+gHIhXNsIQw6cHz3z1HQ2tR++W3CVybwf7MBLEXzx24yCq1JuVEltDSFPnX1mh9eQP1s2u4+syY1C0a4Z//diKx7nbPJyGdyeIORnDfd99c/dWvYvzs69i0h8JOkQ0aboNASUQ8Is3SlnTK/79zXP3Q80Mr/kwkU0wPcZ7JA7bTGabiMdbXN3ZdK4dBLp8nEu7/75TJZPFk0vtG0quqynaxjGy3IckykgAmAppuoLZaxL0eSq0WNlkCUcIQRDTTwDRNZFFE0zQq9TpXpo5PnxiGwY1fukXoq8d7e9QtKq7viBKXu/+NDcNg+30zTJ2fQ5blI7wz9lPIFNj4u58lcqt71Kd4Tmbmb38jwVBv6ZJ6vY7o8OB09V/nchZ5euTTmHcs+hsrhAUr6FC1CSTkDnK9TdzupqV3kFvtXf8AwzBw2x28sODgwo/4We8M2ZLzEeySzMeuTfLx2/cvbBogQtRjwfK+OIIAkiggSyL1lsrtt1I49nTAfCVR4t9+epmPvHea3/3UJgIyIhb+/R/c4fILV7F1RDYbNa5dDjPru38Rm4DUhQrJt6r4qhKWR+r7nHkd8mUaf/X3aDoMJv/xHyV27vCwv93jJH55fmfX770A771A+88pZD93i8an72J/rdRTrcQo3UrDoSD1zk7bcn5jnWZpG6ss7HzfQNtqov9QjOvvnuWt37iD/3erXY/ZNE3q3+Lj+dkgmbffIusP4AoECIcHj4S1WkqPs1L758EQO00/etbLcbRaCrmVLSSLjCBJlJv1gURHLBbljdU1fC1lV2BYDZiPRbpHDLxuKo0mLrsdv/PwQsmA3cZKOk1L7XDtCG+arVSRwJebx3qP1GwdpI+G8RxSeA47kZOZr22T/YObdCYDiOfiFmIU9QABAABJREFU2CaCxI8oLq1WqiR+58ahggNAulfHYu29XqRQrTMXjve8/VlnLDrGPNHk767hL7R2LzJe0YpXg5ZF4I5WQ+m0edYRptVuc2+jijVvIBRVCNgwp2VcM6MfE91pw6MZ5j+hRw4+6IBbPxbDY324ILvsVtotkc98Lomw53Stl01WgyblTpN/82vL/NxHD16IVR8EXvCT2WzhuKlg1/ZHPGymjK0Jtd+5Q+wn+qs1sDntzHz78/Dtz5P60m0q/+rruFaPL5Z84FZ69Y++2Nf7HYckSWjNNjdfe5WpjsLlF+cpLPjJ3Sij1BX8Cz7OX9mJNFz/jovcsN7D81vlA4PuCi/YufbN5wGIeb1g6KipJCubm3imponG+pvRYRgGie0U588tDOeD7mFza4up+y6YoiRw4/Ydrl662Nc+suksRkejvppidvlh6KouKaiXz/ddTLm5ssqCy9FzmyuArwcXU6fNxmwojCDAVj7PTBdBZJomlVIdSxR8x1iVCB8NsuDvLR0VdfugatD6yho3PvN5qj/6LlxXY0wt7a/NqFZqrP+l3yKYODp54BPs1KsNPD0M8lMUhWB4CHNhzhBj0THmiabz6nLXOzyHJOOQrJwTnaRljVS6yuTrD4y+LNA0MBIKNTsQG+1wsla5dUBfHMaFcxF89+fGVHSVn/9Xh7fB/j+fu0kpryMiojxynZvw+Zi4vlMEF7jkJDNZJX+3RWjFOJg++cN11t57k4UXj7ZFr9dq1ApVlIpC5m6S933/h3fe64OXiLznHIlPvYL679/CUX4YqSn8wATiV7MENne++2xYw1Zr02g0cA0xXFwoFAlNzdJcXcZ9PyUSCvsJfdTfdfur33KeW/ZV7P+1sJuCqcVFFr5v4cDvyWq1Mm+1kmzUgf4WgPXNTZYW5/r+PD0hSrv+Eovz85TLZbK5/KHTSh+l2WyRf/k25zIajwb653U7W7/+ZQIfu95zGmB7YwNfo9GX4OiH3eJWS/ezSRAEJqcDrLy/jvQbVdx0P47it4QIf991SlaZ2u1N4nfyWOWjz9C76QLCss5M0w3/9A7b11eZ+Hvfu++3svnLrxI4RnAA5K5aebbHWS+1VpvIxPDTkY+TsegY88RSWNnAl61Dl6ruNaPBrGFHEiXCbZ3CRodHO8RLl6xciY22DS2jtZG+VOFgWKM7qqLBfUOyT726STVzePdLJW/uFqM2VJ2j3NpjXi+tBZP2ag37I6e9VRfZ/Ee/j+PHReIf2rHpzr6+RuOr6xiZBp10DW2zTFNr8+rvZqlPaXiCXqauzRJdiGGz25EtMvPf/15qHz7P9v/xe7heK1P872d45gc/iPLHFArrWWSvnWdiISwjmJQpSDKtRgN/HymGyx9dZNljRfnPaexNAeN9Pjyew4WQaO29q8IwDNY3E0zG4yPx6DAMA9sji6/f7yeVyR76mmq1SqNYpZ0sotzewlvvcM51eEHjTFmn+itfY/0DC0xdXjqyEHbtzh1C7fbIBMdejqqtCPt9hL/JR+G9DVL/+i7B1Yfnj4KG+T89z7WPXNkVa7F4lNXQHeJf3jh0v5Vmi1q+zdS2sDPuGQj9wPUDf9emqVJ1N5iqOo78m/t/4FpPU4wVRcHmfDrqOPYyFh1jnlg6ry3jOURwxLAh3S+g3FRbhDf2XwQUScc/NzqzHcMwWKk3qHw2Tdjqgh5mVpWmDKadDxeSDz8zya9y79AuFwBnSORP/c0P0pHNY2dBqS3wdTnlHQWNRWzUf+pz3L2842ch3iruRgB2jkjClEQ0NNzbMkJS4Tev/ByEJC79xHv48F//VmSLBYvbhv1bJ7g10eQj3/MeRFHE6XTivDJ//BdwAgRZppFOEeyz3fLcC9NsuWXSn0ly/t2H5+oVRcER6T3KkcpmWZzvfdJrv2xsbjI3e3D/9VqDrJin0WgwNTWJoRsUklmaNzcIbVUIWR/85h3gOv7375WteL+2zeZyGnkqCCYIggimiaEbmE0FrdzAPePENaAxWb9IPQw98zltbJWbwE7UK3deYuInP951ivDi9YssdzRmXzvoZtpoK6Rv1JleE/cZwqh/6yXu/KU2notRQtEQNpuV9/zYN2H+GZO1t1cpf2ENraQQ/lz5gACp3kzB+46fwdNoKYQm+m+vPeuMu1fGPJGUN7ax/PpLWMT9i2hKbeCTbTjvP142O+R/LYlH238HVrls48L1/qd99kraUBF+ZcdSOjMnENs4+jQzDIPUB0VemHlYMGaaJj/3pbv8zi+tH/q6v/bv/gjvf2aORLLIxDE1FelqjexGi8hN7cjujQeUvQavbeeRLAJun4PES1kstYOixTRN3N8e4xv+/rfTfnuNhltm7oPvRmnV8TntI4ls7KXdbqMKMq2Ve4SG6FWxl9VCkfMfeH/P2+fyeXxeL9Y+oiP9sLWdZOaIbo5CoUDmN7+Ku6ETtw3WwtoPxWe8RLz9zTwZlM1yhVn/0f4Zm9ki9ZfreN43i2M+QDgaOjK6oOs6977wKvP3Krsi4e3tNLY3dLz1o6MS2fe5eOZvfOJAJMg0TbZublD9O1/EV9rZZ/J5K8/+re/oqZC6UCo/laJjHOkY80SivHp3V1jsPqaqGHYLTmPnccMw2LxVJKZ1OcGDo+on2KGSaOK//2/HRou6x4778LEw1L7NzfOPXLQFQeC7P7DAf/ulFYQ9HiQGBiIizpDItXO9+xo4ZAmpbfYkOFR0btarKK/vhGgatLB0uVyYpknge4PMLTgI/v4NMpeiXP6G95J4+U3U1RTmx57D5zRHtvgCVOsNZLsLS7MJA9hnj4JIOMzK6hpLi8MvIAUQjknZBINBcm4Hce3IzYaGcYZuXQ3DQJqd4vqHe480SZLExY++m7vW19E/v4q+phFY05GF49Mg0a82SK9sM3Nxp3ZH13WS60lEU6DxZopiFBQXyBYZs6ySWU0x10Pkz2Gz0mw0nppW2QeMRceYJ45yIoVrqwR72t0UVSVjN/h/2fvvMMnyu74Xf51YOVd3Vec4OW3elVZaBSSEECKDSTbG8WL7wu9e459xAGPjhMO1wYANtgnG4AsmK2dpV9LmndkJO6mnc6yuHE8+94+ame6eruquTrOzq349jx7tdJ+qOlV96nzf3094f4actW6UZcdAaWG2WQm79MQPbhGcNxv4v16F26JI90FId2ln2F2RDZLhRMuWwpSo8gN/7TS//9+uAuCLifzsf/8IXQkfgiASut1mKAgCV2tZxjxhqrZJ1S9jmxYj4toNK+jxIFpVts3DACoS8YiPEu2Vknzcx8j7kzx1tBtJknBdl8CpUURRZPCJhygO9xPtTpLLrBB03Q3eDfuJIKtYpQL5RgNwCbdwJt0rfkmkWq0S3MFzd5K3345sNkc4Et7URWJo2pbj2AVBIPLkCfjM1T2fQyccVLy8omlUBBEkCcF1ERyHhiiRr1aJt/lbTGs648e3T1/ciyAIHHnqHOc/OUP3tA4dCA6AhXf5eWhoLUK5PLMMf+s5HCAIBO+mR5sK0PrJZ7ny/89z8gMPb2k8pmk6sejbq4gUDmevHPIWRHvtJp51giOv1cj52CA4HMehcLNMeN5kfsDZMJfCOuYl5Nv/jhXbsZmq19H/JIMqrkVbhLgPr9H+5iJ9c7LlYLmFWhHHcfjQ2R5k1cGRDP7F//wop0dTpCJhusNrN918rsiAL0pOBdOr4vpl5u01wbBkVikbDdwjCiuDAsWu5vk4rsNkd+s5I24LcaLLJma3y5H/Y4C/8DeOc/pIHOf2cYIgYLy+NnEr2t28YSa6U9RMh0Zj/+eP1Go1guEIdj7HeFcSQZJYKFeYy+W2f/AOSEejFK7foFrtfKiZ6vFgWbsPNeRyOXwL8xSLm13YhocGyWwzwl44oFRTKxoLJWra5nlGu6VQbzBnmDA4yMDZMwycOkn/6VP0nT3DyccfpRqJtnxcvlqla2R41/b1oijS9f3nOjrWdGxuddVAhLkvXGXu0hRz12fI/86FLR8nizLBf/MGlz/x8pbzckKhIMX8/l7HDwKHkY5D3lKUl1fxz2TvRhHm7AZBj0qfvXHRnmzUSV5yQJTpnXFYHHLonRVZPSNzvH//d8IrjkH29TzJm8C6Vr3lPoee+bWq93txHIeAd7P2n3ArDL//KNlqHdeCn/vdj9DbHSDZJm+eMFUCgktdgJJkcvTMKHGti+Vi08MkFkviOi7Wa3N0fa6BIVisDHtwHAi4mwsKl5MO0y83HVOFtIKa8oAq8G3fNUDyts11w7EwjkepOxZWw0YyRZyVDDOX32Do9Mb223iyi2I+h1uv499Hu27NMPF5HXy2BYpMyOcj5GuOOV8sV7ChWfzo2PTHont6ragkoesGnQY7VEXGsqyOnSzvpbKapVKp0BMOkVnNYhkGrm2jGyYjYyPYztZzfWKJOHNJhYGsuavX3wmDBYGlV+apnO4mHd19bUehoVGVZZLjYyS38LHoHRxg6vWLjIRDFMtliraLoEiIsQTDeyxoVeI+OpGKiigR9YTwfKWI99k37v68k1cXRZHwL97gqiJx8kOPtn5+RcGt1nAdZ9t02luJQ9FxyFuK+qvXid8WHLfMMn2iH6+w+TK2Cms3WlEUSU/bzLzD4fHBxL62MK66JkuvrhKbcEjeU2NSRiNU82y56xJFEbvF+US1Zm64J9YsmOvt2rpwLl8sUokHGD89QM/tsH7U79vk8vjQO8dZPVWl/JVlUl8oAyK5ARHYuIBFywKx4Qj5uSKnfzDN2cEEy3qNZGBddCUuMdi9ueW41iiTuXIZPCrF5Txj73gcSZKIxhOUCnlqtTqBDgyhtqJYLOEIAqLqJT85QY93o8mbIsv0rhNodV0nU67giAKGZRFVFMI7zJVn/H7GbntW3JvasG2b7GoO09AJhkNEo1FM08brbW8+l88XaNTrmMUiZqmE7PMheD0gybiGQdK2IRhi8eVXGFznvTFdKiOMj257vh6PSvoDj7Dye18mJR78WPQevCy/NMfN43HGB1I7jjZkqjUCY6MdiQaPqjJw7iy5Yhl/Xx9j+5hSE6XO7w+JeYelXpng1qNeWr+OKFL/1C1oIzoAYtEI+VyWxD5M8H1QOBQdh7xlqKzm8E1mQJTJ6HWiXj9ed/MlvOwYBL5auxsNASi5Dc707t9sirpjsVIzML+4Skr3tLRdrg156ZnZ/rkkd/OO1Yx5cdzObtqLK6v04aXeHdi2jkAQBLqjIax3OPCF5uRVwdz8+l5D4FjAz0sfrBHuksmqIHgDd23F646J2t3a4yGgKgQsnVq1RPDlWVYvLSKkYji4qCeGEHoSVKs1gjso+nRdl0KxiCtIIMlEEt0Iosjq/Dzheh28W9fo+D0e/OtqSjLVGlqpTPcOBm/VVle5lcvhmBaubTYXVVEC10ERBPq6uhBFkWo+x5TqoW4YRCPhu/NbarU65XIZq1ZHz2Xp8XqIeL2gyLDBZt0FVeGO8crgPfNfen1ezr/8KkdPntj2nAOBAMt9cVja/9RWK9L+CM60yYS+yOhouqO6Ftd1ma/VCQ8NEt5BlMKjqnR373/NQzQe5Ypfp6u+fQ2S4zpYy2U6i29sRrlWpl5v4G9jAS8IAq6p49j24ZTZQw6531Sfe52EKDNn1Yl4VMItBEfDsSi8lLsbDblDXA5Sw2Y/Avs1x2T62WWSGQXauB5O+svEZz1tf3+HbJ9F+p6BbKt2g8DJPoK+zgovtZpJSvGSKTagw5likZCHGUVDD8skloS7RXO265AfktEbGtEVl/d+10kGVA+CI6CbFpNCnVHRTz6mMNQiyrGexelVRtVAM4iy1Kwv0RcuUvimU8T6U1QqVUJbTCS9KzREGUQJ13RwKyVcvUFhfg7LcdDLJbriO2997g4GKDY0VktlujoUHj0+H35J3DJ6ARD0+QgC+LxkikW83hTzc/MomQzdd97vNi2fW6GqKiHL7jha5D83Qn32NfzKwVv+Q3MHP7oCt/KTdJ/p33KmSq5ap6TIjD50bl8Kb/cDj0dFPNcFz5e3PdYSXYSghy3qrbck7vqplmttRQdAMh5jZWmBrp6+t4XwOBQdh7wlyL5xi+hCCUSJhugw0OLSrekab0znGZzdHHXQHIPoPtRNl7GZv5C7LThas9xt0VsJYQQcMkkRaa5Gwm59U4k/1U3iniLSSlQmvcVN6F5kSwBcQjXI5gtEQsFtvTECPi/JnznL5NenyV0xUJEgq9GIifTOOoBKNqbTK0t3w+QeSSbtwIRWJHx0e3UT6A5jLOc3WEx7RIngFy+jv9tB6I1ving4jkO+WEKQmkLDXixTXVhB7AuRdp2mFfad8Lcowi4Exx2iPi8rjk22XCYZ3l54xAN+ZvJ5hrYRHevRl5a4ns0xJouIuxz5fi+O46B2aHUOYORKJO6T4FjPmOknoxstRYdumqyYFj0nj9PtO/jUz3ZMvX6Lwv+6iOhVkIIKysT2gqOcEtE0g/7S3s7f0rYfOplKxt82wuNQdBzywGOaJvbzV5BEiaxWJeHzbur6LNTrZC7X6F6AlaRFKrvx0s5GDAaVvbVsuq7L7NUsXROtf193DCojHuKzAqor4EUiXHVoqCrZAQl3qUaXvnbzX3gIgl0iZljBMR1czcIT9dMb21nIWNCa6ZFQw6V+o8xcV4PRse3HgHdHQ2iP9CJ/ehqwsRyBWGPthuZ/uguftFG8+EUZOeyjZ5soR7ZSw6i1vpkGBZnGV6+iP30cZ7iHfKncrJEQZUzDQsxUcKsaTrGKZz5PUNNQu1WUA1icUoEAJU1jsVCkt4NCU9Wzs8V7YI+1K61YKJboP3Kk4+OlgJfpkEN/wdl1Uetu0coaxDZGdVzXZbZcYfTRR1Dvg216J2R++1V6r1hAZ2moYlpEXGnQ7e7t/EsBi+FkZ5G2VDJOZmWJ7t63tmHYoeg45IEn98oV4rdtxOOqn0VHh3XFo7lqheyFBtFFBxCpqJsjGv6RyK7b6KB5o5xs1EhctNuOza6MeEnNOMDGnYjPkPBN22iqykKPRd+STOmHkpx9tBfPPrh1Sg0LkPELMn4LDKfznZDfr3BHGsjrUlJVVyfu2fx5ua4Lia1vtDVNw315kQHVD20GafmQUJ+7RmFyGeHMEMFknNrLV5EmV4jY6z9fEb/Hz0rNIHRAO+KI10vDsrc9rlSvU6vVcXzeA5mn0in1YHBH4qF/bAh3dJCVxRU0Q8f/ideJ+g7epXTBY+P29TFXreIRJFxRRHId8qbJsaeefFM/w/U0GhrS4ym4srDpd7brYAkOnnuWSl12SO1RcACof/1Mx94vjuMgSm/9Jfut/w4OeVuj1erIr09zx1JGFEX8dYsln0aP4KVUb5C7dEdwNIks2SwqdXrNte4Fb8/uXP10xyJj2mTPr5Celre8UdYLZRzX39bx02uIpBdh9jGJM+dS+yI4TNNEqpvNYsQ7P6t2MOjlNrFAgIsDFqm5jbeCoOBhaaFOeFC9O8MGYEWxGB4e2PI564ZFXN1+hy+JEoG5PJVbiziqh7is0s46SF8qQXL3dRDbEfd4mFlZZWjd6PqqplMwdAQEjIZGdyjA+AEULu4U/y7EsyAIROJRqs9fPDDB4TgOM0MBoufGkSSB3lAYT6tZOJnVB0ZwAJQLZby/N8W9Qxlt12F5AJQVA7dXRtEgvGwjIWKs1mhaf+0NNdHZfSlfKOLKKonu1J5f883mUHQc8kBTfPEScXvjDSruD5HVqtySylhXdKJzG7svagGXQEW5awBqOBae4O7yoDOFOpEv1OlF3dJKLxMxSIpBtvJjchyHxackjnzHGAHf/uTY55YyDMobn2ugInLj0gxHz2w/Ul2SJJw2d4HESyZLfRb960SHnlC2LfjzKzJZvUqyg5kfFRW65e19HaIZg5VSBb8iEdpHn487qIqMLsDk8jKiKIMAQVWhNxhkIZdjNPXgtCwGdI1SqUxkB503ALn5ZQan1szNHMchq9Xo9u/PzJTZ4SBHP/DkthHFWr2GZcXue6qnHUauRsTYLI5cXChodJsBuN2FtuTTacREhhf8+2KtWfmtixSPpIjGImTzBQRRQpAkbMNAEgUQRBxBJNaVRnpAPq+98vZ4F4e8Lanli3iuLnJvugIgIChkLpVJTG0Oi8eqMuW4zIps41s1CHxvml5pZ6FQx3G4ZJaJfq6MIYjof6WP2EgYECgVa3j+3czddMR8j0kyK+PdwoMpkzSI/dhRnuiP7ynNcwfbtrl0dRLVEhGENdHhui4Zj4Ma3MH71dubTOWv5Ok7l0YQBFYFg/7B7WtFTNdl1qgQV/zb7mgtRYQOjCzDHh/aq1lKgknpbIqpG9M88djJfYkW3cGnqgy06Cqp2Q7FapXoAdir74Z4MMhsJrNj0eGNhqhaGkHZy5U4RB8aJ5lMMPXF15BMB7lQox72IvlURMMmtlLDEcArypQUF8Gw0dMhlLEetIkFhgtrhVWO48AWtuzrGR0eZmF5hb70g7FrL52fp1UMzZIFFGWjGOnR/LDEvnl5R28ZzP70Z8j93ccZOnEc+YCHIz4IHIqOQx5Yys9fItFCcADcXCiRmmi/WIYLoKoCM4MOT4s7m7OyoBqIY3FOhFIs9uRREj6GU2u3JVF0MUSZumNQGlbomZGQbqdUKn8lRWQgTOZmlnCoGTpVujycjAVRlf35uk1PLaOuGpzUPBtSHwCzQoPhx8d2Fr7eIjzTdc0lc8YgJXlodHk6Kvwrles8FEiRx6ToWowL7UPIlm4AndVqeFUVLypcrdJDkqmby4yf3DrVsxMcu7UP5YmeNHOl8i6dGA4Gy7axbXtHbabJrgSr3/k4C7cWqVULxBUF27Y59h3vAcAwTfLZPOmephioVJp9oKZhEvd6sCyb/ttCpzyUJrtaRJRFUCRUn4e+HRTZWqaJYZqbZsrcb6YvThL4nakNnj7Q9N/Ip1x6Fw/+/GLTFsKcjXz27S844FB0HPKAMvf6JPXr8yQ8rRcs0bP9hCmvITI+4+N8OsPxdISg189CrUjK07oQL2/VaYzHSaW67+6gx06mNx1nO5BXdYh76JkFBAHLsaj9aJojp9PIsozumvTHovvuPZDNF7DzGilDabnbEnoCHQkOw7S4cXEe+1KZyHz7z1IURUrLGqk+DytTizi5GqJHRlBkXElAVCVEj4KgSiCA47jEwj4m3BWOShEcUbzX7PQupm0haToEdlcgOpBxWEiV6EvsvtZDMwxWGxqWYZLawqxMfsDaFHt1jZvnLzD+0LkdpSm6Ul10pbpY+tyzDPX3sbKapVwuEw43h8rdERwAoS1syMORMOEdRlrWMzTQz62pafr7evEc4ATi7ahdWiYsbv786qpNcN6C++DkqgsWyTPbRxDfLhyKjkMeOFzX5fl//FlWPzHD2F8ZQA2LnBlJEhFViqbGVEbDc8mkk8tXEkTSTw0j9PrImjZpf4qyrtMoNLBWq4gOOKqIHA8QTSbo6sCDQRRAURVCy5CJaoR+YIxgOsBgfO0mPZRMMFMoMRTb3+JHba7OcKP9jqjTYZ+aZcFLRbqu2txbQLeJrxeY+ajAGSWB11Zhw3w4h/X5EcdxmHFWOCo337clCW1Fh4DAzEoFMSngOCJ9O5zZIcsy3ktZFs9A7y6ER7GhUa7XGEwk2h9TrZKt1ggoCuzAQfWg8Xq9HPFCLl+gu7tr+wfcw5mnHgYg1ZVkZm6BcAc+JfvN2Mgwk9Mz9Pf1vmkRj/6PnGL+TycJlzcK9aCpsNgnEt6FvflOsU7GiA4+ODVDB82h6DjkgePyn75C/uNzKILE7G8sAjD/cJbj39bHyz9/jfT7kjxqhtpNit9EvVhjaDzBndtqUpGbC8jA7joRPKrC7JhOOu/j6E88tsFeez3KPqVT7mDbNkpJB7H169VsA0+gs7qDsM/Lkb92iok/myD2bOsps3eIuF6mFyoMjW2/MImiyIjYPM51XeYvrVANBFADDsPxjd4eoiCQeN0hG6kQrknceNzmaDra0fnfISp7Wbq6Sv1xb9u/w724rstcqUxYlrYUHACRQIBirU5PYmtfkjcL09jeWKoV66MY8j5fpzthdHiImfkFhvr73pTXj0QjTP+NY5R/c4JwbqNkD2kSZTTCHJyxmu06BL//zIE9/4PIoeg45IFCb2i88o+/iHhPQZp2XuPC+VsoKEiygGSJZIImiiiiOCLBansFYl0uYj3Uu+dqecO0cFyHhuPyrr/4BDXN2nKhc7YYW70bZuZXGBTaRyVUQUJrdD5e3KMo9H14iIlXX6G3tvUuPqHuvMOhbplELupEkLBcm2uxBcQuL3RLiB6bWqNGt6EgZpu7TOnZGpcetTg+GNnWUXU9PfiYWy4xPNR+t2jbNvlaHduGfLnC8YF0R2koQRBwH+DiPquxt5kqhWIRd5tptQeNKss7nsWzX6xkVhl7xwmkp08z89lLWJ+fJ3ijjiSIhAqwGIFw6eBeX380wfB7Tx/cCzyAHIqOQx4oXvrFL2Fe3XqQwa3zS5QG6tSerWPhcuLbBzlO64VhPl6DfaqtyDQaSJZJ3XJIdSfxbZOLNnWdKV1nOLE/HSuSLSIIrYXMlFZC7Anh7FDnRAN+/O/phU+2v7Nqsk3DWwM6C8HfsCvYDYeA6MWKSFACWZCIFYGiCTebbT6KK2zwNFGQ6HrF5OXsPO98cmRH7yMyUWZWlZBlGce0cQ0H1zBxNQu3YeCWNfo9YURRxOgSd1RoOxAOcWs1y9gOrMfvF1Y+3zSN2sH7KZXKlKpVbMsiEYvS39vhwJ4DoiedIpPJcH1igkcfOncgr1Gr1SmUSncjQ4IoIYoiQb/3rjnX8e98Ar7zCW4+d5Hq5+fwvJxHkA/WTyTwbdsP7Xu7cSg6DnlgWLm5yBu/8Py2WRPfqkI96CALMjLQTk/UbYNjf+9RVFne86KvmyYYOj3JzheekWQCx3GYyKwymkzsWvhMTy9B3iRcsqCNlfuIN8Jq2cQM77zwzTcUBtqLjsIxifEOcv55rUYtqBC3FEqfWMEjePBsMfDOK2wWioIg0BXc+TyVsMdH+NZGUzTXdVnFxFEkUt6mI23FNQluY+F+L7Is0xsJM18o0B+LYVkW85lVkBWQRDyiQE9s9zNg9kJcVcgXCiS3SROtp1StMtj3YBUudnd302hoOxZQW1EoFtE0A8MyCHi9HYurI+8+C+8+y+LEHMUvXoM/Wt2X87mX6rCXsac7t7N/u3AoOg55IHAchy/+xJ8glLYP9VrDMp4ps9k14lp01Vpv70vHFcYDew/ZLleqFGs1ju/CV0AURY6kupnOFRhO7G5hkkyR3roEytaipctWmNJbt322o1RvUP+jGTxtjAfqPgd/r0LQ096Qq6jXyXsg4VEZsBQW0Kh7LILG7myiXbuzcH/JNbEsi4SyWWjd8Gl4kj6G+ocQBIHJqSUqmRKRZITR2M6LJn2qSth1WalUkV2HwXTq7uJY1XSWCkV6Opjdst8UBInRHQgOAK/qYWp6hq5komML7vvB0NAg80vL9Pds7hjbKZquUyiWiIZD9KR3V6TZOz5A13APV93niP3x/laUNiSL+E88jep9MGbP3E8ORcchDwQrXzvP8afDPP/pOWSh9WVpuBZmD4RiEZyZGgCyIJMLuOTDNl5XILG6NichcHZ/wuGm6+xKcKynNxRgoVyhL7yz2gjTNDHKDWiTPlqPblt4g51FOnTTZHmpTO03bhHLtxcz+hkfI5EQi40yvb7Ni/WsXSfsURhlzQG2T/aS/OgQk7Ml4i9v4ZjWji1C2nnRohZrtgsn+7pQgfKFVcK3Px/Xdcm6OkgCo0Nru/nxsT4Y21uxYtjjIdyihifo9aA7NuVajfA+iNydoER23rXT3ZWArgQLyyvNsQIH4PC6WyRBYHlllVR3EkEQMAyDufkFFEWmv69vUxSkXC6TLxRBFIiGQkSjUQC8Hg+jw0OsZLKsrKySSu28wwdAkWWO/eWnuVb9MvHPZnf1HIZgYx2LIKeCiN0B1OPd9D46RHAX4vftwKHoOORNpzgzT+D1WU7FIrg/f5IXfuaNTcKjGK0jDXgJXBRwlpuCgwEVU3V5/dI8n5woIKHw3vckef9gP6ooEzsV3ZfzszWj03KGtqiqSsAwWCiW6QkHOw4hz1xbYmSLFtn1WKJLdboMHezsphcziP92hoigtu0CqkRcero92LaD6m4+aMatk8KDt4WBm0eS8aS9lJ6WEV8sErJ2sKNr83YX/A7R8QRd4Y2786l4kXC++d8lo4H/iTTpLTwmDgQXrA4jNPuJVcij9/XsyuuiL51iOZMlm8szOPBgTC7tSadwHIfXL18lFg0T8PsYGx3Bsizy+QKm7eA4No7joCgqAZ+H4aFBAFZWs+TzeeLruqRS3Ulm5ub3dE4ej8rRv/VurvMVEp/N7/jxwvcd49jffv+ezuHtxKHoOORNRavXMT5/nqjQXLhOx6Lwz07yws9uFB7Roh+n4GAnBcQcuMMevlJZJT9r0jvoQbq9UoXUAKkZFzCx63tfBKoNjah3f8yLosEgYcdhdjmD6/UwFI1sKT5mZlcYyNPKBb4lAUGhr+GQyebpTm6sW3Ach6mZJRRdxMhWCVUNvN+eZLVWQxRkcCVcV8A1wTFcBBuEkEvE72fJqlOWbdbHjaadGmnXg3eLOpVRXwD6YPYjHoqfXiGqdyY8hDZdoE5MJhzenA5IDERYLGcJ6i5a3EtupUj4PouOTLHIiTehTmIw4Gf21iQjJ47v6vHp7iSNhsby8grpB8SWXBRFQgEfQ+uEkCzLJJNbp5FSXUluTU3fFR03JiabM3UMk+m5eeKR8K79SHw+Hyf/z2/ievpF3BtFEn/pYSRVQl+tUfnqNHxuruX8lkZYIPHM2K5e8+3Koeg45E0l/4WXSWobazJOx6Po//Ao1z6+gHlprQXUGpcQFZXpXpNsuUQxayMisjy7FsI/HQvD8u3n/swcPT8W3lOrbN226d5H4yRRFBnubeasb2Wy9ERCbdtuxby5YydMv+IhU2nQnWymZmZmV/A0RNSCzhAKguDgOF6mJYOUL0DEv306oEf2E3M8LFQK+CUFTRLoUlW8LZwcWzHo8XLjySg821l7p2W2rktp14IcDgXxPqxgWBZ99zm9cYfAm/S6AP5alVqttutz0Gp1GvWtvVruN7sdbiYA9Xqd5UyW8dFhyuUykUiziLhQKDI1PcPI8PaDEFuhqgqnfvCdGIaJ904txgDwyBi1v1qjuJin8uo89fOL6LMlvK7M4L/6KIkT+2fV/3bgUHQc8qaRee0Ksek8iJsX1kd7k/i/38OXrl3Abza/4FbF5FqgzuzV9qPb13eUdl2ymLixTEPTOXN2aFfio1HTqcgyoQMo+BrrTjKfz9OwbBKBtby6aZpMTS3TU7JB2rlHhJ7XmGss4snrDLtKs812nb/HrGwyvMN8kVeUWKpWcVYEXFXgyZM7q48wlut06ubmnzS4Hi4i+EEQbcDCjXgYHzja9jGqp7O5MAdFUFbQNA1vB462+00yFCJTKu9adMz9txep+WzKxTfo+UuP0D3y5rbQAnjU3f0th4cGKRSKjA43Uy53ajwAYrEogtAUJbutYxFFcU1wrCMQCBA4EoAjA/ADUCqXsUSVxA663b5ROBQdh7wplBdWUJ+/sWlg2XpOREP0/ZvHuTpT4ervT5FPKkxfLCFuMeKx6tqsH0oS/i8r2EqD+ok+wi1ER0PXqegmlmFjlzSsmo7g3u6gcFw8dYuyVMB+uJeof61IM1etUy80CMZ9xAK7L8Trj8eZW1qhWKji83opFytEV01GLRlhF4ID4FjDCw0X2FyvsSxbpOsCorrztsTgso/YTQtNtFjpL5EKd1bEWDF1PDc0Oh3sVlV1gi9ZeG8P6iuGbbQP+XZkGHa/8SsS5VrtTREdALaxfcFutVqj3mhgWCa6pqMoCsWVPL4vZ4k6IoIgsHLp80i/+CESfW+OLbdlWUxMTjE2ujOfljuIokhiC/fYar1O+j507JiOQLL7UHC04lB0HHLf0Rsa2udeJdpBsUJY9vLkmJfhv+vjb//Mc1sKDoD/8smL/NtHz+FdVw8SM33M/cktxr/vKJWGhmmBVdOxVit4czrdvvX5f2nj/4squLD02gLK4wMEPB7ytTr6y/P0KgEWJWFPomPijTniOZteQQFMNKNMlxru2OJ9JxREG2/DxKvuboiV6JMBC68jU6q4pDoMlly7usJgB4Ijf1QgcDzKqJqk4NpUPp4hpCtEyxKNT9ZZOZIjld5Ze+j9QhIlDHMXnTr7RIs6X6DpVVHXdExdpzFXwPqli2C78M40gz/6OFpmBa8r3b3eohWJyV98jpXeKKH3DTNwbvt6hFKuiKzKBEK7X8wty2J2foFwKMixI+P7YqbXClVR980HZCvEPbofv505/GQOue9kP/cCXbWd+Ul0KX4Gx0PculjloXemuHoph1GxNx1n1GVKMQFvcePPYy9ovCS8wFN9w+tuOir4OisS7cHHwkqZWsSHcGGZlNIMZZuTWexUZNfGX8GyQ2SdSdZ+W6dvwHHJC9buR7Sv/6iWLOgwwyItmmw3VC7bY3H0bA/K7ciXD5nsd/RQWK7jaA61uRJx9cGY9Oo4DtPZPKIk4jouoiTgOi5lzcApFAGhOXHXtgnLMvE9TGPtFE82RykaJRIJY1oW2WyeRr1OOt1N7HaK4dpX5oiWbn+Gn82y8OWP3Xbc3LgIp1/X4fUVajEv3BYduq5z8/deQEbG9YuIjoA5V4abJaTlBsKPHeP4dz+56/OXZZlGo0Ffb8+BCQ6AZCLOzMwMIyO7i6R0QqPRwOt7cNqQHzQORcch95Wlr58nMVOEHe42TNehdyCCx+Pj/xBT/Fy6zkqluuEYB4ef+KVnkP0q/I/cht/pjslIKLanXU52OcvILQ/hdWZUQwRYrdRJbzMh1XEcLNPcVHfgihtvsKIrUHJNdAm6nf1NJ8SQCYt+5kQdT8OmewvDr1asT8moUw1Wj5fpCm69oNZMnUCBbTtw/I/E7wqOOySRSaabz68NBCnM1chlKowfv3+FedPZLJKiIkkigiDiui64DiNdic2LY4vuipqmMb2aZfiALdQTQT/XJycpp1K4js1g/+YW2MCZFKXxZQSfgnQkijtRJHSx2uLZmrifmuXSp6ahooMoEm3RndFEpjLb/nk65dSJ483uk1iMWHR/pzPfQdP0Ay/6bWg68djufEG+ETgUHYfcN7JXJgi9Oo24RR1HK1zX5dZymSfrHhK+ICIiwaCHFTbe6N71g/186Jmj2LbN8lAUp+pQ/PgMiZsO9W6Zox3WILTjnBFu6SFh5OvQRnRkc0XquQaUTSzXQU576O9bc7PkHtHRJXmwDAsZl6JPIOq0/4qWHROPK+CROv8aS6JElwk5aWeRJgCUtSiMz1Uo1LVtRUdVEohKW9/ks/0249u0JXslmZ4KLFYMbNvel1k6neDzeEjtof024PWS76DeYj9QGw361jml3svAw+MM/Mr43X9Pfv0NtOvn8eqtIwvBgksz77J9nUq1WGZycorBwYE9dYuNjQwzNT1DIOA/kHH3y5kMo7vsXukU9wAjNW8HDkXHIfeF8vwy0lcuo+5QcABMNerEntNIiGsLXFfYy611x3z4x4/xl//yYwBIkkRfVxS6IP9klmK+DEc8B5bLlWdKNPpi+DybF8769Tx9jofmVl9Cn9BZWpjFDXkQdItIw4F1Q8/862arTOsVwlJg03m7rsuNSpXnf+kq7/yJUxwNb/81th37btHuimIy5Ox8t+ewls5yXAdZ2P7zrOZ07mT6M2qVbmNj3r8i66Te2XfXRXY7elwPmRfn0FIeRsb2t8ui3tDINjRMQ0MABEkhuYc6hTvcjxoCADca3VFqYvSdJ3nlmWm8nyvu+jUrXgvlb53j9LuP4PV6KZVKWLZNrVa/a9oFzZqNTsXIyPAQ80tL9Pfs7983m80RPuBJtrVaHVHeH1+ftyuHouOQA2dxepnSJ7/KmLvzL2MRE+dTOURx7bE1S2Ol2kyfSKrDj/+7Z/jA02Obdr8VTaO/LmE+Gdh2R74XUkqArKa3FB2qs3ER8CgKaQsoOIC4QXDcy7ASYkkyiRoOq4pF0pIRXbhZqvDyz15HFERe/YMJRn/s9JZ+HnnH5OO/fZF3/8AJFFmgnClD3266LNbM1kRBxNK2/3uKt+fFZM+IjBwZZFWzKJxfQRZl/KNRQqk4yR3chgRBIGRCrbH76IGmacxnswiyjKR4kORm6qSh6RzpTuK67r7WFYj3Yec7WanSf+rUjs87+uFx3M++vOPH5cYVwj9wir4zfUTWpUJitwffVetzQLM9dX5xCdMyOXW8cwMzs41Xy15IJhMUSyXmFpdwbBvLMBka2ltkZj31eh1bUu/W0BzSmkPRcciBMje5yM9+66/gkyz++Y8/SbDNXJVW1FyL5QsFYvba4uY4DsVhhb9cGuVfL1zjX//udzKabt0iVylppGQvBA+2jXHJqNDl39himMkWKGYqDDrSvXV6O8LVDIouDEoB5lWTa19fZv53lu6OhNdf13lpapV3jKUQBIGaa/H5L93i3e8bJeAKLOkNXv7DWZzXbL7w6kUM2cTn87L6/QbvPbd5lsVWbDr0psZsoEDE6yXib92dYhk2+R6b4eMJAoJCMKgy8u7dF/HNeUy8Q0HGd9CO6DgOq+UypigjCOATRMZb1Dxwey7OfhcyShxccXCxXqcajTH68PiuUk5+j49qSMDXYUlGRTWQf/IRTrz7OJ4WIvsO/b09zC0u4fN4GLk9A2UnCAdUUB2NRIium1eTzeWo1uq4tsWdlxREAQEBx3XvGos1f7F2Xbh3DxZoFg6LqF4f4V3MwvlGQ3DdgyyXP+QbmTdeu8k/+ZZfpbba9LV+5qN9/J0PnugoxWLZNjdvFYhf2LjjWRxw6JkVKPgMYj99mr546whGrlpn+cYCx+sHX0U+3yczNLJmIX19cYnaUoUzNd++htZX9Rp/8suvo05vFG6O63Dy747w5HA3X31jhVv/ZRZ6RSKnghQ+W7wrUNbjui6+J8IMPRlltMffkefGxOoKwS86GxZlzbXQ3xng6MDmCbpzpkbxUwv0fXSI+A7E5lYsJEUGj7cPu68US1TqdURRQFBUfF4vsmMT8Hjw7WI+yX5g2zazhRIjyfb+EbtlQfUwODK8p+e4/Df+N5GZziJH1r97ipEzozt+jUZDo1QqdWy1PjO/wFD/3gb07YRWdUL5Yol4+v6dwzcK9yfZeMg3HDM35/nn3/Ff7woOgGc/tsDHbyzSic69kS9uFhwxncRCM/Ttt2Tckt7m0WAY9n0RHEs0SPZEAVjI5bmVzXEkneLhR46weMRP2Wx/jjulyxPgqR85tennoiDyxr+f4nMXZrj6X6eaP1x0KH2u3FJwQHM3r71c4fovz/HH/+YylrV9ONu2HUx3Y5uyV5BxJ3Vse+PPF22D/IVVer99cF8Ex4xQJ6NXsBYKvPa1N3CczXN1yrUasqIQDQUZSaUYScRJB/wkQ6E3TXBAs8YoFvBRrFT29Xln6hrpfViYhXf1UBpUKMYdbHftc13os9Ccte/vaq9L79HdDYZbWV2lu7uzjo7p2dl9GW+/E1pFiQ734wfDYaTjkH0nu5znH3zwP7BwefNERgeHf/av3slDofY760uTq6iTFg3BpCfXXCxW0i6RVRevvbaIFs54OPXXT7d8juVCha4r5T2+k60xLIPC2TheWaRsmnQHA5vmqOSqdepXVug192fRazgWf/BLr+Pe2uxRslssyeFDP3uEgXh746035nIEXtLw2q1vzstnBHy2itDlEEoEWLya5+SZFL59inDkzoRRFYmVYgm7pHH0yMZcfLlWo2hYDMYe3PD2TKHE0D6dX7lex+wboHufXS8v/MaXiPyvOWp/5zgnvu1RSqUyWr6ObduEUhGiu2xldRyHuYVFYvcMXXMch6mpGRzXJRDwEY1EyGRzDA+++fNK8qUK8dSbbwn/duMw0nHIvlEulfn4H36an/2uX2wpOO6wWmwOl9Idi9+/NMWivRYNuDKTJfGySTQv0JWVWBkUmA9UCdaEDYIDIHyxwdUXp1u+xkEaDN3hmldDUiVURWE4EW85uC0R9BM5k2ZZ37uPAUDJtbFX97cFU7ZFvvhrs7x6K9P+GFlqKTig+Vn3XIboVYPwV0xyzxXoGgrsWnA4jsOkWGW5Xrr7M1FwKVc1klfLeG2FYmPjKFrLsoluUWPwINDl9zKZzZHJF/b8XAVB2nfBAXDk+x5n5hGZ09/xBJIkEY/H6B3vY+DY4K4FBzQ7eIYG+jEti/nFRQBWVlaZmZ9nZGSI8bER4vE4C4vL+1bYuWcOW18PhAfkr3vI2wFTt3ntY9chriOoLq6x+Uv7yPsSnExH+Womz3PPz/Pi55Z44cwyP/tjD7MwUyX+moF0OyUgCxKpWRfL9SG3+P5Lggify1M7nSIQuKeQ8QDvFytmDWMozGhqiEgHFuimbZP27M+8h4mJArrXRBEU5NL+7RmcBQNJbh30zFWrNAo6nSw5giDQlRVYfaXGxFMO44Ht6xhupcCXN+k1m74MMyGb8YeOohkGU7M5nHyNIV83+dcm6POEiRZhvrpEcSCAIIkoHg+Veh1VVQi3KWh9EPB7PIx6PDR0ncVKBV0zEJzmZ+64FpLj4vN66Y5vro+5F/mA5ocEQkHs/gCmaR7IrJtEPE693uDKtWsM9PWRSq2lXLweD+NjI296WsN1XTK5At7A7v1ZDmnPoeg4ZF8wTZN/+5d/g4ufmmwrOABe+1KOH//SFxHXXXpTl8r82Rdv8eFyomULqSy0LzyNZFwWZ7KkR5IEvd4DjXDMqRrqWBexYFfL9th2uO7m+oPd8tR4F0//4xSXMxVe/efX9u157RD0xFoLqNWCQfpyZwtBIeUiPhknfSpCMBZkamKFvqyN2sa7wHEc4qkovkGVyWtLuJpJ+sQggiDg83gYG+/BcRwkSUJQRO40gvRbXqybOrIscyOpcer4IFP7EEG4H/g8HnweD9yzptm2zUK5s7oPwzS2P2gHZLNZqtUqgigSf/fwgQ7Xc12XeDRGuIXpmiAI9yVKuRWapuENhIjEthd/h+ycQ9FxyL7w6//g97n4qUmAtoIDuD2wbU1YODh86L39vE/b3Rfccm1WJgp89Zcvcey7x3n8vaMoBxSelUo6Yb+6I8EBICFiWEbbhXcn3PHj6IormJgorSxSd/O8VYHP/votQmNhbMEmMeTnvQ83ixSFQuc7T1/eRS/bqKpMwOth/PQg+UqNkuVg1Q2sQh17ucSgGkEURWaNEqOhfgRB4Mi54U3PJwgCkiSxUqrgFWXW+ZPdDcP7bnusq/fJpfSgkCQJW98+dTZdqzNy7uyeX69YLFJtaEiCSDgUJB6PY5gm9Vpjz8/dikZDYzWfxzZNRg7YFXQv+Hw+aoUicCg6DoLDQtJD9sz//qVP8hs/+XGEHeY0/CGBv/mhExxbkXe1u/myu0L+Uhlfaa2WovtHe3nv33yIfKlC/8T+dY7cIXMyRE+bNt12uK7LxKcuMRre3wmpBUvnk799FfPC/u56AYRhlUd/sIuecJjcRY3I/PaFq0WhQdRtpjdqQRfzPVGOvH9o067ZcRwK1SoOApbl0BNvn7hxXZebU0sMzOotRdtUzCE8EKM7HGS1UiXm8z44NQE7xDQtMsUCfV3tuzzy1SrK2BjRdQZUc5dv4r42jW4a9P/g+/F1mGKanptneGB33Si7YSWTJfUWGPderlQQvQGCoYMf1PeNyFvz23nIA8MX//B5fuv/3rngOPdQjB/q7yeaEe/WX6zXv2WfTUTbfHlWIpDzuKxmK5Sfr+MzNhZvZn57kT+6kOOhj/bQn+re9Pi9YpZ12KHdgiAIEPGwU4+o9dblrYjJHvSKjrhPBSyO6951z3SnDfSbIvnVPBFnc4HsveSe8dP7zcdY+uMp4q8ZBKoCfKLElcgsyVMpHMvCNkwSAR/hYJBEuLMb+rUb84yv0NZaulyrMh5udjokgwGm8gVGE/vvh3E/KDXq9CQ2ClPLsjBMC7+vaXBXjyYYui04atUay1+/RNdMBa8oAT6yf/hVVnoi2MkAw2ePb2kYJt0ne/Y7hENBlpZX6OnQq+N+UqvV0HS9afLlDx0KjgPkUHQcsmte/uJFfvFHfw/X3tmi5+BwvC+Cd10UV1Nc/sRcIexVWC42uHG1yD9+4hShavO5HddhJuVw6RNzKFrzRqq0GV1qv64zd7zOmW3ubTfsKhHFS8XWEGomI/5IWzOvBZ+JNBgjEtx+AW5F3SdAvbNjy1iUkgr4FZyCSX+JluIjbxsYUwZedndO9+L5ngixYBjHdGksNqh2QazugW3KDArP+Dny7eN4VQX/Dx3jVmSS+JdqGK5FcjhB/7r5JZVGg0y1hmbZ+HGIh8MYhoHX29o1Vg15YGVjxCrv6tTHwwiiyPHoWlTgTh3IWxHTsli9VSSnFQifSeDGEih+P4oqI0kSC9k8hcUFTjzVHB9vmiZLn3mZwaID666NpCnBbBV3psLkxAq9H36CQIui05m5eeKx6P16ewD4fF5yxeJ9fc2tMAyDYqWGpHrwB0IkEg+eGHo7cpheOWRXXD9/i5/50K9QXd19CqOrR+H/fugIPlviN1fmef31jYWA730mxQ+4KRo+uNgoU/hqqc0zbUboVXnsR/o5NdA+nLus2qSN5g3bdmzmy3mGohtD23VTo+pYSI/30R3efceAZhgsPTfBoKd1KqFhGVRFh2pUoXskTnjdQj1xfZ50xsF3T0HtjK7zpZ96fdfntB63V+YHfv8DBIMbQ/PZTJGVP5ghdqO1eVj+XT7Gv+sI3nUpFMO0uPHcDJ60nyMn2/sc5Ku1pq29rhNVFWq2iyiLKC6kI82dpuu6LBXLGNdX8WkuedUhdCRFf1e05XPOlSsMhN9aXQflhsbcn9wi9oLG6kM+jv+TD+H3by7qXVheoS+doloqs/rZV+krb3/rnumSOfLtz2z42cTkNAP9vXjeBMO0g06xOI7DravXkVWVZLqbYDCIYZqYhkHwHvFl2zYVwyF6WDB6XzkUHYfsmFtXpvmZb/kVivMdbt1b4A1b/MQ3P0TclPhvb9xi4uZmHwsHh7/1XUdZfj4HKzt/DScgcPQv9vOuUxsXvtlqAYJ+TCzGWJs6eUUr4BelZmdAMoAa9uHm6vQ9NIgiS3sqUC1Ua+iWgyQ4CLc7dFaKJby2gCBCMOCjewub7OuvTeJXvQi2i1xoYIc9TEyucv1fTCK1ifjsBDcs8sHfemqDnfsddE3n+p9PEn2usqGTqPieIKPfPrpBcNzBcZyOOhEWS2VKtRoD8ThBbzNKMVcqMXDPDAvDtDBti7Lt0LNFm/J8pbohsvIg47ous/N56r92k0hZoeK16P71j5BMta79ufjcS0TLNmq+TnyLYu31WLbNrQEvsSMD9I8Pc2NikpHhwQMrtt4Ox3GYnZtjeOhgCknr9TqNK1eIhsOcn53jzHueYe7mBKFalRXH5cyTT9y9JnOFIome+1fTckiTw/TKIR2hVxtIHoXZ67P83Ed/fdeCw8XivY/14b/VoKRXmbNMbtws3+5q2YiiuFydLhLbheAAEGsuN/9sjqAh4D/jJ+j3I2sWvb4wMhJzkrChG+KUt7njqdBAPNlHyOvlvDWJ37v3kH1s3Uht13VZLFewBJACHoY72Pkde2Rt3oXjOIiiSN8jA/Se6ebV35+g9iet20WFUz5EScJYaSCttC8GtR2LaLz12G+P10PkTBTzpQry7ZrVwkMeBr51qKXggM7HuSf8PhJ+H551z2Obm89TVWRURaZW2+a6ewtsoUzT4pU/uUTQ4yP2uQoRsfne5R8/01ZwAHiXyqQrIjsxoZEliaMLBvrcDV7P5jnx6Jk3TXBA87oQpYN7fcMw8N9O1fUnEkxcusLxoB8iEeKOQzaXoyuZpF6vo/rfWhGxtwuHouOQjlj65EX0l+Ywl8pY1d25awZi8J5AN+qrBiDx+RsZbl2vbxAcvQMeetI+EsEgk4s5PBWTvRjnPvyOfgLX66RPRQkZMohrl7xrWCBtDjGHVB+Xn7/B2fed5ZETY7t+7XsxLYvFchXH0Bnq7qI3HKJhmMzn8/THOy9+vLOgm6ZJOOLjSDDIhZNFeGPjiuv2KxiXy6iCgu2amOc8eF63cR7yIt4yEKprx3vOBQmF2kcQlLCM8SNpqjpYdYv4QwnCvr1P7/XcI1pm8iWS9xq90RRaM9k8mm1TrVQYTCZadqm4zv7Zwx8UiiIjnS+TqDkbRveG70lFGaZJKV/CcR1M0ySa00Dd+Twh0zZZPprg+MOnUQ/Qf6NTDlIXFgpF+m9fF10BP+uTpdV6nXqtTs1Xw+PxUDG2nzd0yP5zKDoO6Qi1J4T0ctPa/NveMcbvfmyi48fGuiTODHURuaYjLazdcsZdHzep3hUdp06H+Ws9Q3g1EG0RUglMx6Z6xMUMyOiCwPnPTaKUtk4nGK5N/zd14QoOiYxDLiUSUjcvkHXLAKn1TXyYANOvTRM+2kViH8L1Vd1gtVxhpGvjTtbvUXHcINcWlwgGg1imyXAH3RdvfHYCLlRQVgzShsT742k+7pvC31h7n0raizDf9H1QBQXrdYvGURH5QpVaxObEX+/GL0Wp101OPNm1ZXSit68L1s0WW6jsj637vYiiQNC3JjrqDY2VWh1FkRnuSiAIQjNSVCgQUlXC9+TpLfPBWUgqDY38QhUcUGMqiiqgSAKm40BQhtrasTm/wfHujbUFtmWj/flLpMTbf9NdCI4iBua3nObo0IOTRohFwtyYmODo+Pi+Pm+xVCJeLCC2qIcBCAeDhLUGq1evkY0nUKOHtRxvBoei45BtcRyH8hdu3q1+eFTz8oc+B72xfQTi5MkoR5YlhFc3F5wuqPZdwTE6HuCv9Q3jbwgboseKKBGrcfsG7VJ8Rw/zn24/I8SOODz1gRFSdw4RwDew+SZkOvaWi2xQ9WHkKtQaGmGfd08hacdxWgqOu6/lVTne29zlljSNbKlMMtK+Za9ULCN/IU+wIQEyCOBB4YlvHeO1P5xBbTPzRBZEjBs6quBBLslYdZWnn2pO87QtmwuXbhKJBjAti5H+ni1dKaV9dFldT8Ln4cbyCr3hEAvlCkGvb9NIeEEQ6I3FuL6weFd0/Nl/fJHa9Tp44Y3jIT7yNx87kPPrlKXVMoVfuU4037zGXEBzHMpYeEWVfjaKYHskiPeeNJ7P58U+NwiX2l/v29F4xzhDD5DgAAiHQtTrGplMhu7urdva84UC2aVlkuk08W3s4TNzC4y1ERzr6QoFwdSZyufJKzK4zXbxcDSG+hbtfnorcTjw7ZBtufWrX8D/xeW7/w7oEt/5/iMA+IMCP/RtgzisLUIODojNHaeiiAiF1gvUsLt24/X7FKqyST6+dfB10OPj6Hf10ffhzXUQdszh6fcOrwmO21iNjS6PpmMzi8ZRZete/Lg/RN8NnfkXp5idzDC1kKPW0LZ8TCsWKtW2guNeJG7vhNtg2za3np+7LTg2MpCRGP3IWojedTZ/ln6heVMVBYHe3rUaDkmUOFPyMzjjMjovsvziLNmtbMVb2NXvB36Ph6PpFBXd4Fg6RV+09d9IEAR6kgkWcgVefvYGmT9epPFCkcaXi+gr+2+WthNK9QaFX7p6V3DcQRRFvGLrjhEz07ovOTjcg2XtPnojBzaKm9nrU1z57NdxHIdarUajcTDuo9uRTnVhWDZOm2vddV2mJqcpTE0xJonUpiap1WotjzVMk4k3rjLgdp5aK9frpFNJ4qEA8XCAZCRIo5gju7xIo777AvlDtudQdByyJVO/81XUP7i1qQuhT5H4me85w88/foJ3lyI8/khTBASjAv/ge8/yYx89ioPD65dWsaKtC9/c6QYuTUEwsVDhH33iEr91cwJni110JO8ylpUY1z0YvubN2HEdDNnkyfcOEWsx3Na6J88/I2iMia2LJluRtj1Mfn6Wz3/ky/zp33+Oht55m3BJ01B3EBVQJBF9C9FRLlcJfq69ccaw48GQm5+ps0VjmpUQON4Tbfk7QRDwOgINrf37NAyD2g4+h53S04GHRMTrZeqVDBd+4hLiuo8k94UMH/u3L3P9jfkDO7922LbNzMcmiZZ2FhlTVgz0Fp9nsjvBUnr3Q+zsr11n+vokywvLTF+4hv/L1zkyp3HjNz9B6fe+TLXaeiG/H3g9KuVyedPPF5eWufjyK/TrDYYDze9pbzDI0vUbGObaBkLTdZaWlpm7fIURSUTtsAXYdV3Kpk3wntkvkUiYZDSMXS+TXVo4FB8HxGF65ZC2ZF69hf2bl1oOXDuWv3MjbDqKfs9gP+GAykdC3YQyAiNukOK3DPEnn57DHPEgn98cIVDqIo8/0o2NSPRmA/OZON/lTyPWt9fCjW6Fpx7vxoMELsiIBLOtxU3y1MaoSL/jZUqoMiJuXavhui5Lls6LH5+h9MUSoiCiPVelqukdmVAVq1WqjQb9W9ha34tHUUiFgszm8wy2KC6NxSIsDwVwJ/SW7aihmsjxb+vn2sfnUe324iV2KopHaf8e4qKH8vUac/YyAwPpTb8fTMS5lc3RKwj43gS/B2jWTFz5hcvI90RdxLJL5vfmWfzEHMKvwdFj9y+9MHMrS/JrO4+GJQlQqVTxtLiuvKcG4Su3dnU+6YaA+9wU04LGCD647ew6SgDNNmhscY0cNMlEgqvXb2ywdM/lcviWlzjdIr04EvBz69oNQsk4erGEXC7RE43CDicLz9UaxI60LxAPBoMEgXKlhGkYhNed3yF75zDScUhLGqUa+X/7LB67Mw+IRF7gB4TUXQdRQRB4px6kK63w6lIWW9x4c3NcB+GRAF2XLIbOO0SqHrpnHKRtIqSGYJMdllAWG6TcIFHXRxQfwS1cOZV7rnKvJNHj+phxW+9kNMNgTqvwsU/d5JP/1wVKX9xoSnb9hSUmZ/JoxtbDuVxRQtzCxrzV684XipR1466XRyvUb4ox+e1rz1uXLex10ZRjOQ9PfecYjtj+/CqXy7wxn9vyfMKKF8Nq/wcZSyaYWNlcb+C6Lrn7sIO+eSGDXGj/OfkGg/dVcJQbGvpvTe7qsaIoYrf5rL2xEHVj50LmDoIgEGxT5/Nm09eTZjmTZXZhkZu3JqlNzxDeoi5jTJXpLpcZEIWm4Nghqw2NyNBAR4Zg4VAIW6tRLhV3/DqHtOfBvBIPeVOxLIuZf/0ZAkvbT7zcirCt8r5H0szm6/ge8vLQD53ipX/yKm5VxEkIiK/V8Ky7BJ1Zh5XHYHh1i3MTXZTJKmGxs92N4zgolgX37Ma9kkTK9jAj1hkS1m5yt9w6Ea9KVXd49AP9fH6ujHt5o2C6/tNvcB0QTih8x395H4nwxlRNvaExmc3TGw7Qu4M5IF5VxazU2ppb3VnMQyMJIgEof5uAdDRK6pFh5v/Zl4jeWAvPp1ZgfLCbyQuLrV8sb5MpVjnZv3WtibNFOYFhGCRbuH/OFStEVYm5YhlFEgnKMsF9aK9dT13XOf+vLmx5jNkw0XW9ZfRgNyxmsmgVo1mA7LiIFk1xKAANk7mLSwxXdx/1cYzNoiO7mqP8+Qv0t+i+2gmWLMA9X2dVkqm+yd0+4XCYO2N4rn/tefq2MMjbK6u1OqRTxJOdO6LGohF0XSezuEBXT++uBlMespFD0XHIBlzXZeI/fJbA17P78nzvroSpvKeXwe8YJeL30fgpg1f+/qt4rrW+9K69vsRATw9Sm52+35aZj9tEip29voOD2KKgEtaEx5RYJ2WJLEk2Q64XWZDIIXL+uYVNgmM97lWT1780xdln+knGouSLJcqWjd/r4VR/D+cnp7Fsm6rj3l34DF0nFQjcHeB1Lwm/j0KlQux2vrncaFAVJBxVwZZlBs6dRVEUegC+Ze1xC+/thRtTG59LlGi377awGe3dvhXYUzCZnFwkHPWRvKd7QFVV6sWNOflcvU5UlQgHAtwJkOdLZWZ1g8E2RaG7YW65hBRUsWlfCGlP6dx4Y5EzD4/s+fXmFzJEJuqkJIUNjnK3C6jLhkbyBQPE3YsDR98sOlb+8FmOqO2n8AJk9RplbPolb8tJvMDtWoi179xiRER94hg9/e1t6u83amzr97lXGpLMSG/vjh/n8XjoUlVWF+eIJlOHHS575DC9csgGJn/rWfwfm9uX56p4DJy/PczJ7z9OxO+jrmm89B9fJf6OZNti0ZEziS2nppa6BeL5zncbq90WMU/7cK1XkhgR/GiS0BQct6dyNko62T/donvjNjf+6RsYjkO2XMHj8TCcjNMdDCAIAg+PDhMOBBhNJugLBekLBRlJJlht0wFjWhYN12EqX2RRM5h1XKSREQbOnWHoxHFGj4y3bWMd+eazVEIbP9NY1kUYaf0Vl0d9DMa376jpMxSGFl3UKyUmrm2+LgaiYSazOeqaRk3X0TSdcGBj5CceCdMb9HMrm8PYh521ZhgEgzI//NvfxNF/dhwr2Ppa8j4U2hfBAeAUTPxS+xZiXRQJ7kFwACxd3CgaHcdB2abmYt5p4FE9jHrCFBTIG5tThppto9wjaOQzQ/QO9j1QO3dri893PxCU3e+xBUGgOxGnXsxRKXc+A+qQzRyKjkPu8tX//DlKf351X25E+RMyyZ89x+jx1N3x2n6vlx/5jW/lW3/uSfwf2byrsVyH7srW8zoapo6/TdthK4LH4x29n7jouSs4VswGz//Lq22PTf1YH+/9o2cY/OlRBEeikrVIhkME7oleCIKA955dkeM4aNpG0eG6LovVGiuql96HH+bxb/4AA+fOMHLiOOFIZ7u/QCiA+0Q3VXkthi4JImPnNheBAsTGdhZ1CAgKgxmX6amlDT9XVRWBZkopUyzT18ZLQZZlxpIJlkolints08xqOn2JOIqi8O6PnuDsPz6NE9r8NxYFgetzSy2eYWcUS2W8ja1buRt7y0QC4P+tW+RW19qvMsurbQcEQjMNqiIQul2vkXJVqvcUMFVFh1XBoNezFtW6QQV/8mCjCrshFAlT3qXb8XY4joOwDym+aCSMaGoUcvsTCf5G5DC98g3C1Es36T07iKfNHJEXf/WLXP4/v0z48QBJNbEn4ZH/3ijj7xhsOZcjGgzw2oVpqp8obeo6kAURI+qBNl5IDdGkXq4Cned9/d07v9GkFB8/8AuP89rrq8z9dnPRslyLYz9/ivmvLiCmREZH4oyOxFn+wDDmNrvRlUoVWxCwTBNH1zmSXjNEytQaaH4fQw8/1PG8knakf/gh+NFHWPwXzxK93hQ2wdsOnsqpAOlzMRzHpThbIziw812lJEoElk2KsTJer8p8sYRXVZoTPcMhOsmUDyUT5MsV5owykiThuC4xVSHQYcjaMC0wN/pwvONDx5A9Eq/9/CWCj8VofCaPeibEh/75Y9Tk3ZtuO47D9ZtzILj0V11Q2ovdsFek/NEY5ckC0rU6cXvn7qEhQ2Xuc5fR3zGC3TBwXp9ue004jsMt6hwVNtbU2K6zwVyvLDkMOGv1T5NujaDqo5DJEd9BvdH9ILuwyLEOzL12Q13XiaRbC/CdEggEKK+skrctZNVD+NDZdEccTpl9m+M4DotX5vjYw7+O1KUy+J3HGP6eUxx538nmTd9xeO4XPsUb/+jrSAg4rkP/h7sYd3wEOnAcbUX+fQFiT6fp645u+t1qscZX/9N58n/culp09Nt7OFZovSCu9kJs3kIWO9PKxYTFwPvS+Ncdbzs2WdsgpWxfiGo4FldrRfzjKaIJP+l4GNd1dyzIZgpFBiLhDQtIrt6gqqoMHD2ypfPnbshMLZH7yc8R0CRWexyyMZt3Hd/oMDpdLSBFg8TMnXc2XPbX6RtPkQyHydXrgEBih22LG863WMYUoG8LF1YA3bSYL5YYa2O0Njefoa83yed+9wKn3ztI30CSumGQrVYpfHYZJe3h+DMjG2bXiKKIIAh3f7a0uEplso49reHerCAM+zh6JLqj91O3DCqCC4KA3nAoT5foutLZbXa51+D0O/uQWnQ9lXWNSkBGEMB1HHotZdO1WMSkWq8hixJmwINj2fhFGdF1sV2XpKsgiiLZ9x+hd2RgR+9rPYVCkVgsyvLSCkamiJqMkO7b26K+srKKMT9Lb/BgpgTPiDKjW7TK7obDSbU751B0vE34/L/4M4oLORpXS0iyTPREkvwrSxSvZPGlgtgTa7le13WRx3wMffdJyjMFVv9gctPNyx53+Eh6aNcRj5KgY/ylNKMnewitm6Vx4bV5Xv2rL7d9nPecl/eFN1sjZ/sE1HmNsNB5EVf2CJx6eO1GmLMNPvs712gsa/zg33sY3zY55KKlYT+Soju8t5ug67rcyuYY70pSbGiUJIn06Aj+QOcGZTvl2qdfpfjVG5wbTN5Nb7XillZiRA3tOMpy0cpx5l0nWw5d2ykz+TzdweCWfh91TSfb0Oj2efB6dxa90gyDhcUCtd+cxvGISH4FOeXFvVrBVlyEmo39SATJFgi8UMbjrr2n8jNejqSju31rACzZGuIfFTs+vvbREKO+zdfGvGzSb20vUHXLxAF8cvtjlx/rY/Dcsba/1zQdx3Hw+300GhqWZRK6XdycWc5Q+NOv446nGbxVRpZlqpaG9q0P0Tuw80LN9ZRKJarXrpE+AG+MWUFk5OiRfX3OXD5PondwX5/z7c5heuVtwCt/9HUiRxN84B99B8XlPKWFApd+/XnqL+RREbErG4vLBEHAntSY/Hev3f33vQSDfqoRgdBmw8COiLge+O0CNz9Y58gHxgndzqeeOdfD9PenyP1B63n1xUtlas/ECNjNG6bruiwNuMTnLLw7EBwAxlIN+5x9d9dYtxz0VzWsmNXRqMvaqRiDexQc0Px8+6MRLmYyjJ09y2iHdRq7RdN0JNnmkZHUtseOqCHyjkFyh0WQIWVv+fHJzCqK14sApEOhDdNmi9UaiiwRWCcuZvJ5QoqC17vzz86rqowNp3jZN0nfogzYMFljraRNgq/eqTNZuyXqWAS8e9+TSY6L6XQeoXM/mWPygwLpgLKheFW1OzsXzxZi4w7iNkWVVz79VdR4iDPPPMHNVy7iu7ZC/h3HsWsa/ssLjChhmKnDbdEZlL0sv3R9S9FhWha6phHcIpJRzObpPyAzLuGA5gUdsjMORcdbnPxclt6TA/Qcb4b4ouk4+YlV5v7bG0h7qBNuXNB4Rctw4mQX3RkXcZezNtSsS61u3hUdkiRx9NtHeO73lxBOSCi6hDPV7GgwBZPQM1EmAjrnys0bZyFsE591NsysyHYJFF2D0VVpy/PqrQZYskz6VYm8Y/L6swsAjHxz35a7QMdxmHDLjEQ6dxLdDq+iEPMHNlkv7zeNhsbip15kqNDZHApRFLFlGXZ4Px5xA8y9NI04FmcwtfPaAFlRGbjH48NxHKbqDfqPH0PTNGZWV5FcEFwXMRLBURVKmkZkh5GOO/hPxmGx80LFRhDi+9BR0a34uDDi0jPT2fFBW4VPl5mNmUS/KU1aVHhjdYnuUIi66OJXm+JbdyxyajNtAhA3ICC1jhZlzRr1hBcdF3/dpba4imNaCKqMqCrguljVBlLIh+BC75OnWf6jr3Dedhg4fQTrWpb4i3e6lza/hmVZxJ46s+nnruuSz+eZ/sp5Yg0XUZUQvvVJAi2ifNVqleL8HMl4DG8HLrc7SXW6rou5jZnfbohGIhRyOWKJzmYrHXIoOt7SaLUGH//R3+F9//67Nnz5Zj59Dcnde2OSdk3j/LU5fOe8nBvpJra1geVdciEd7ZEg5Ykc7/qRd23YxQKMHU2S/PIHSIZDvPqVGV74Zy/iL3oI/3AXP/R338en/uer1M/buLLARKNOIKpw6nbExXVdrkxnqL+mUflgnFOGH9Vp/V7Lok5Ibv5OdFwWZzKoSBgVk4Zr4WtRyzDt1Qke6eJYqG/PhZ330h/wc+38BY7vQ9HozNw8kiyjNzRGhgfvPt/05AyiLLAqW3RZnX29NSw6uRXojsWybOG6LilHZcDxMavvrgU2LIssFEt0BwPIkkS+VqcsKxx99BEA/IEA8RY38nw2y8zMLIMB/45Tf73vH2DptSubBrG1w1sFawdDxNqh2ya6YAE7EzCxgkLmj6dZfmeETK7KcDRGXddZUm3G8LOs2AxZTQFyVSuQVloL2mVBI/jUICmfl5VSheSlcjNKMdN+tkjWqHHCE0ecrJO5/jzdyvYFntWXbjAVmAFRxNYMiksZRr/nvSQSCepnx0l9ZRI0uPn5l4k9dhRREnFciEWjVCpVtMV5TqdTLOXz6K6AT1UIq8qGkQOO47BYqWIaJrahoXj9uLhIsozruMiKjOS6BGV5QzfZZKXG+KMPd/rRd4xpmijqoW/HTjis6XgL89KvfZnGUpX3/Ny3AXD5k69x+lsf4TN/538z86uv7+trGZLF0Y+kOFHYvg6h5Dfo/dmH8CsKyjZh3LqmYzoOtbpBKhZEkiRml/O8+B/PU3ylhJgTsFyHR793iN6MQCFs84VPTBCgWScSeMSHEHU55+8mfLt9vu5xWAg63Hx1gTM/NE49ZzD1tUXcqzaW6jD+wwM8/FA3EXFtEZjqkwjFAyQCvn2pU2iH4zhM1xsMnz2z6wLS6ZlZent7UBUF13WZnpsnFg6h6waBQIBgMMD8xAzdHc7rMCyDJdUhYAmEHLFteH5BNum7XVMw7VQx0gGOjvVsWTOynlKthu26xAJNH5Or84uo4TBSOMjA8HDHpkuO4zB99RoewyAsifh3YNZ06/ISvl9v49J6D4U0HH1XN6IoUtRrWLgkPe1TAytOHXs8gepXmL+4SHDVJZBQkHDJXiyRmOjsHIuqgdnrRam5VOpVIo9HGE3E7n7OGb2GHlCI1W2CavN7kLV1ktLmz2FB0ZHHE/Qmmmkpw7RYeXaCXs/BRtwAllUbcyCGkoigFcsM32h+QRfH/Qyk1zo+5nM5ukOhlgPbsoUiFd1EUmUs10Vw3I4mNueLJcqGCbKMKAr0nD2D17f7Yud7qdVq6JaDZTt09/bt2/N+I3AY6XgLs/riPMPffQqAy3/2Kl/+a3/M0fnT+M5EsLwusrZ/xj+qLaO0Cd2up6RoyH9hBFWWthUcAH6vh9VylVrR4EtfvoLdsNCWdcqfKd81CZMFkdc+NcPS03HmP54lIKzdPGqvNXPxL44s8tTJXiwFXr2cwZwwEBC4/AtrC6+blDj3N0d4PN06bdIdDh64WZIoiowGA8xcvcbo2c3h6O3IZrPEYlHU24JFEARGBgeoVCoEg0ECgeaOND3cx+wrE/TXtn8/qqwy5AAiLDaKVEWZAbz41tUg1FwTuWaAp/m6w2KQhZzGSrJKTyy85efmui5T1Ro9x46iqipzs7M4jsvgY4907EOyHlEUGT11EoDM8gq5XI6YZRP0bn19NnSd2p/P4aMzkSSqEtcKeSJPDhCLJPAgMDWdYWDF2SBMNcMgM+wj1TeE39M8h7LgI/CVZoeWC3QafC+LBmLIQ9d0M98lyAr9kdAGYdftCYDFxiyH43Dnbd2U68RPpHFcSAV9eNadq6rI2MNRWNp7BGc70oYEt8rN/91mya7RFd9YKN6/RWoiGYvebcWuNjSK1fYTltcTj0buNtZPIuyr4FjO5glGYsQTBy/c3o4cio63MMnH+6gvVajmy7z4k5+i5+khvvD3/pjpX76I5ApsYey5K6aurDKcSiHfc9MuSzruR9JEziYYCHl2PGfjhd98g8xvLSA87Mc93zrkK9VFMp8rorZp77SmLL68PIUclODebtwBBTciYczVuPIbUxz76Rjhe4r6huZMFkIl+pPRHZ37bgnZNlqjseXNcHpmFr/fj2nbaPUGkggen59keHNraeieWhHDMBBtBzpcYO8QVbyokkTO1JBklS5TooFNQbIZ9GyMcnXpItl4glnBRjQtZMdBNE1SgY2h+NlqndGHH7q7cI4c2b8Ogu50CtIpJq+8gd9xtkxb+TwehNEALLd2hHUch5WUSeCJNP6xCNGwgi8gE133fsaO97OUKtO4nqFfV5n16kTO9jKyruC4VNcwv7bFAKEWLMYbpLMetCEv3TNrBTYhU2auVORod/ui4EytTNizdh1JxQbdkfYLYrI3xtLSHD3s30LcKdZYDK+68whfTdfJ1+sM7mBiM8BKuULPieM7fr126LpOMBI78NqstzOHouMtzMN/5Wn+7Mf/B/EzKcwVjfg3D3D9Z55vFpAewIbdumHxWk+Nc2IQj7V2c/f/9FEGU50PUbrDUq7E/MU82a833cAkW2QvJtlyQ6LVKA4xqeKcr+FBghI896VpPvy+0Q0LlCiKVG+twn0SHXG/j+mbk4ydPdXy99lslmQiQTC4ttBbltVx6mfp/HUGtJ0JDoCM6jDsekFWcCyHpXqJgOJhUNqc019+uI/xYxt9D0zTZOqNq8RsC0EQKCHQe+pExymY3TJ66iQTr18kLQpbtt5G39kDX5/a9PPcU166PzzAw5HglsJFEAR64xHcp8IUqlXGAoFNx9caOtHOMjh36Xl/H6szJaSsyPqq3qpsMhDZOk5SC6p0u2sLubjNdz/o82A90sfslSUG9ftXjzCdgJHe3RVcFg2TwV2YmWmx2L76ftRqNWI9D5ap2luNQ9HxFkb1qMSGkmSmljj7b97Loz/2LuZ/8yqNV/PbP3iX5L5S5MWHdB7rTiJrDitxm2Tegu27M++yUqzwwn+/zMzXFvFPrtmea9fKlOJ1Evn9NQey5zRMr4WqNS/31T/NM/+uXgbXzWSpanW6zt3f4Vc+y2j7u9mFJR45tzH9spNak64zY+SHapi5CuZ8Fv98kbi8dQSqrDeIqMpdwSqKIn2h1m6LZV0jPLz581IUhfFzZ6nXmqPt4wfoRXIv4+fOsry4SLZcRrZszEqFdDBwt1ZAM0yqqxUiQFnQkP6vUTyql9XVAqdPDmwqeN4KQRCIt9vt7iBzkR13Ubv9pB3oGopzLVaiNmUQuJ07kWWZqqlvKKbchGVSVGSiTvP6aDPfcANRv4/gw4NMzawSmqmQVA/277SKRnq0f8cF1LplkdcNXGt3nSd2fvv5STvBdVtbDBzSOYei4y3O2IdP8tkf/QM+8qd/iS/+gz87UMFxh9qFBl8bXiQw6qPwbAV5JUv3v/9mQr6td01VTefqKwu89rPnkQsiPhc44oGJ5uIrGyIRKYTuN/HU9+/SFLI24sN+OL+2yE/dKDJw2nf3BpILioy2GSl/UIRliczySjNFsI6ZuXmOjY8xOz/PYP/u3A6bI8PD0N8D545SrVaZvDRB7Px82wF4BZ/AkLP9wjsfkwg9/QTd3e2jWwdpfLYV6d5e6O1FazRYvnqdgm5gVquEfX6u/sEVBl5tHidbInXHRFBkHj03uq/n4Dguy94q6bofQ7DwCq0jL4uxOqcfGkRZl+o7FY0z8SEFPtMUbQ3BYsi/9XWZclTa96G0R5Zlxsd6KPREKb6ySBiVGWpYqkRwII7ruGDYOIaFq1m4roNWquHvjYMsUTcNxjN0JCTMY4mOre7XU9J1egIBCOzOHl1w9tebQ9jnjrZvRA5Fx1uc4cePoARkvvrjf07+2UXkbbw5TMlG6fNi6Rbiso0oiHjfHUENqVSul7AmGkjC9qFwS5bJfaGILMg4z1t86dcu8K1/59G2u3HDtPj8L79K7ndX7p6jKIi4vnvmr6y6KMdDGB6LqlYjdr3z4W7tsB/2YF6oIK+rvLv1a7NE/4mfc11RAJS+6J5fZ8tzsG1WdRNXkcF1ES2TqKJQKxWph0MsLa+gqB4UWaYrkcDv95ErFvft9YPBIP6uKJInwy2ryojo37BYFI0GMUXuKC1nOhaR+IMzMOz1518gFgwiCgKYBqaiIlsmA4FmzUJdN1hYKOAP+LCcCrIoU3tnCFdsXoM3F5c40rt/Ua7+dJT0v3yaQrVOQBLRGg56TqPyiVkSs2sfcOjR7g2C4w4xv8ySUEM8E8WbVLZd1BdkiyPO2oKuuAKaYXTkddF8PR+rD6d55dItHn9ia5dZ27bvpspytTqvmLMMlOQt60OmVY3h+O4s0nVrbwWviqKgaTreNjOndoJhGDgdGrwd0p7Dltm3AZ/5J3/Mu3/qQ/zBO38Z48rm6m7b4xJ9poved3dx5IkuUqkEjuPw2gu3eOUfXORb/8e7GRxqVpRPTMwze6FA5rlVyl8rItmtVyFh2IPe0FBX1n7/0K8+zKPvGN50rOM4PPexa0z83PVNvyv6aoTr/uaCsQ7pkSD2a/szcVJ+OIR1fvPnYmLz2P9zkv6HBwj7PHc7D/Yb27aZNkyOPnRuw88XFxYwTYtoLEqkRRfH4nKG3vRmS/jd4LouNz/zPMMLGo7jsKRYVKo1/H4/Uc2l4BcYsju/MV/rkTn+TU923OZ6kCzNLyCtrm47/8V1XeaWitRez5F+dx+xoI9SQyNfrd1tw8zkC3S3mZS7Vxq6QXa1Rvm1DJUXlzj5oWEC4ubIkuu6zIk6g+72BdmGZVARHBLSxmNnYi49R7q3rG+5F900USSpo8iFbppky2X6EgnqmsbSconh+dYVWVNJl/HjO4/Y1TSNSqNBOra3v8dstYq3tw9d1xkaGtx1emRpeYX00OhhemWPHIqOtwGFxRz1QpU/P/trd03BbMEh+GSc6DtjHHsmzdhQ653Gn/6/X+M7f+DpDT8r1xtUzy9RXq7y2qcyNF5t06aWktGrGp5aU/2bis27f+Npjp/ovrsbcl2Xr336Otf/YetR8XXZQLFFFHfjDsIdU7EntE2TaHeDMyAjzm2+IVquxYl/f5Znvqn9DIq94rouk7X6XdOrnZBZzVJr1An4/HR37bxQ9w6O47C8sEz0k5c37GLvODrOODW6BS++DiJc0CxoXTkeJBjyoUnN53MDAQbH9zdNsRPqtRq3Ll/hZHRnEZhMpYJWrePc7qjQDIOgJNLfvT9irxXZSg1eWCDWYr7KHUzHYsnUGNzCF8RxHCaoc1RsfcyCohM6nSa2y9TEViyWK/Suc5RtGAbZ1xfo1TeKnOmIQ8/R1K4E/VyhRF9k53OB2jGxuMT4M+/eVVFzvV7HElXCB2TR/o3EYazobUCsN0EtU0EISoj9HuLv72HwW0YYOjNGvK8fvdFg+Y3LpD2bd1X3Cg6AsN9H9UScMVuh/B6DC6+UkFot/isW3ofC2OeriIKIYkq88Bdf4NL7Iwy8p5/k0SjLk6vc+IfX2tqVix4BWVahtDH3Ktwy4JwHLu7dutgR1qZsGK6N+mgIoeqgeETe+PsXGf7zbgZ79393a5gW87rO+MMP7fixruuSyaxy7Og4y6vZXZ/D6kqW1c+8hF9WN4XNhdtj7yuqi+9olIVsmdFld8NNPmvV8LoiwdtTeYuWRu1YlMH0xi6E5couh/TsE/5AgGCLVuLtSPj94PdT1XUKxTLHe/dn/PlW5KdXGdtCcECzLqQubN3L5TgOHrn9Atpnesi/ssj8qeS+t4Jb9sbvq09ViZ/pIVPXECUZq2Fiuw4jqdiuRYModxZ16ZTRdIr5uXmGhod29DjTNGlYLokWU7MP2TmHkY63EdOXb6FZFZJ9fSTv6Wdfmp9Hnb5FtINiyYqmsbyQZ3SpeWncXMny6qdW0VpEPFzXxXnEh/BafYOwcF0X+1Ev4quNbee2yGcCWJdqm36uPBbGfGV3i5k7pqJEPbgCNG4UUcsSZp+AjISwsPFm7nQLfNcffJBkZO/Fj47jkK3XMVUPQjDAwMjIjp+jXm+wtLxy1958emaW4aGdT7I0LYu5P/8a/VvMYVn0WPQ+0o8kSbx44SrJuoSciuBJ+JEEF7+qMjuxjNcEtTdCMOIjco8Pyx0L87Gzp3d8jvdi2zY3nrsEuk2ukMEXlAl2x5C9XjRdJ5JMQKmCKYsI60SUY9tYwIgo7GqhylWr+CSZfK2KKsl0x6J7fi/tWCqUCJ/Pb1tz0XAs8rZGn7KFC6pgknK3Lv61LIu5foWBoa4ddehsheM4rNbqIArUNJ3RXbSzboXrukxmc4ztIcLXignDINydRvX5sS0LWXCJt/lba5pGtaEjqd7D2Sr7yKHoeBtSrVY3THLUNI3F188ztI1jIzRTK7UXZulWN97oSrU6X/naCqsfW9k0SM51XcTTfvSKhjrrNgXHI17E17YXHADyYyGsVzYKGqFXQTMNPKu7y5/aD3sxrlSQTwZQLhjoqoXvSBjnSus6f/Wcl+/8z+/ftgOnFZZlsWJaOKqCqygMjY3taYd2by3H8vIKqkclvsPc9q3zbzDw2nLb3zuOw2yvwNh4M9++vkhwPdv5g8zWG4zsIpqz6Xw/f5Han14nesPYkDe3/+EoA+kYeU1DFkXCbRbrxUqVpNeL2oETbiuy1RqKAKWGxmDy4BaZhVyJ9NXO6pUyoolSN/DJHkoBASt0u6W5YZHJ5jgiRzAcG8unIIjgWg5hU8Tb4u+14DNRxxKko/tnbGVZFgvlCkP7XAcztZplKBHf10hHUTdwutMkh4bvXl+lQh5HrxNrkTZZzeXp6jscW7/fHIqObwBe+MLnOR0OdFRUVmo0sF5YIKq0Lsp76eYyV357HqG0uRXNFh3kh0NotoZy3ux4Mq37kB/hwkYxYMcETNPAW93lzuyYh7qrY6oWYSHYPN/ZZsusLpgojkQ9ahA9EiX1SIrex1OMnU5sGKfeCTVdJyNKjJ9ubfK1G8rlMqIobhCO5XKZWr1BT7ozQ5T5uQWCH38dv9r+/UwFLdJHkgT32N5qmBYZj5ehI2PbH9yG13/pc/i/sIJX23zN1P92H+PHDj7tcYeqplPXNLp3WB/SKfOZIj03Nkf22jEp1ZH6/IwObp7xMbe4TLorcXeOj2maZHJFhKk6KXuz8ChaGrk+L109UWLBvUf25kplEqqKf4cuxO1wXZc3FpYYS8bx7nKa8L0UTQs30UWkf6DlvKNCLotXdPHddgculspYCATD0X21Tz+kyWFNx9uc0uoqJ4K+jqvYIz4fsycSRCdaRwSeOJIm9LcUXvofc7gLG82tJEfEfbWGp0/BPKHCVaMj4eHq1uZOzbyN2yvALhtYnGsNhIRLvD+C5JExJBP5jMLIY4P0PpQgHvcSCfj35JSZqTVwknHGB/d3NxQOh5mamt4gOsLhMJZlkcsXSHSwqzRyJbxy+795zTUJ90X2LDigOc/DW61QrVR2ZQ99/SsXCX58aZPFveFYFM95iPn2JyXQKUGvh6LR3rhtt7z29cvEfSGUhgV0vqCO2n4yGYc5YZmBgY3ia+CeGhRFUehLdzGzOEMr8w5VlIgTYClXIezz7tkpVhYFvPvY9SUIAqFgYE+Cw3VdCrqBGwojAL7hcYJbzPiJJZKUCnnsag1BAFdWSSb2N61zyBqHouNtjK5pmNO3iO3wC9zfHWE6W2Oo2Dq1caI3ge+vSjz736dxFlrcnBdMZNeFIQ9ilwej2qA+XyVY9SAKIo7roPc6hPpi2FULxzJRHg0iuiJmSUcOq1gNDeVqY9d27qIg4suDna9h07wRdSd9HHm6l56unRccrqekaRQEib4Txw5sJ9TK0igej7OazVIslcjlC4yNDLd9vNiwtgxNF2MKg137FxJP+n3cvHqd+EA/3T2dRyUqxTLOf7rccqZO8X1BHvqeziNIhVoNA3AREADRcQh51I79KtYjb+clvgv8gQADFZnd3Ha7NRFzSmcyO0Pv8e5trzu1L0RhsoqsW1RHu1EKVeRjgwSODRGNhglkcty6coV0UCW8i1kod0gHg0zm8oQkad/qYFRRwNlmjs69uK5LXtMRIlHESIxkT8+OWlsjsTi1ahVBEIi9ScZ23ygcplfexmRuXidR312oIF+t43lpZcsb9rMXl5j8r3MdPZ/jOthdIkrYg1U1EFfsjtMv+4EVh2853kM9IuD762Mk0gFC29y4XddltVrDUlVsWcYVBATXJZpOEY0f3PyFldUsoYAfv39zq+PVa9cZ6O8jk1llZGS47Y11eWmF6Mcvtb1xT/fB2Mj+j+QuaTp5UaJ3dHhbV9K5CxMUfv8NYm38WEqKRv8/f2TLv5PruixXqjQ0nXQouCnMv5zLUTNNRAQEBBzXBlFClGUkWUaSROTbIsVxXUSai17dtAjIEuE9zO0wTJNyvcHqcomAIKNmG3Q5e/c1mYvB8Knt/3ZXr07Tfewk3Q+faHtMtVikeuMaCWVvnSINw2A6l+fEDgRnO4qVKl5F7jjaUTYMzHgXXaNjhx4abwEORcfbmOz0NOF8Ztch1InrC4xsMSyzoen871+5hjOt7/IM7x++R8O81x+9+++SoOM+E8f/dBo15sdRFLAsJEBwXRwXDEVi5PjxfS1m245qtUqt1iCV2nqa5vzCAulUqmWBp+u6LC0soX3iVfrlzQt/1jWIPJY+0Hz1QrVG8vixtsLjjT96AeW3b+HVt14kjL89wPCxzZ4ZpXqduiCRt0yCqkrENIgGg2iaxpJmYLsO4zvceS8XK3RdLlPTG8z5LKLHeuiJ7dwnolSv84c//2UqXywQ0L18+D8+TI+8fyZqedcg1MHfr1it4YgSriwhxJPEh0da3gtc12XxpedJ78G1s1yvY5gmyS3SGJ1SbWhYlkm0TarOtm0Kpk1YEiiLEuHjp/C1EOiHPJgcio63OcsXXqNL6PxPrJsmxWoDLW+Sm17mnCe65fGf+uIsK3/SvkPiQaH7m7p41Nh8k86d8XHyX30b6j61Eu6WXD6PYVjYjk1/h5bcmdXsBtMwy7LILKxQv3CL3mVtgyCZe7SHQFcMBCgVivSa+r4V/7XCcRymbYcj9xTYNmp1rv/qs0Q/l23t/XKbUsjC++MPIw4EQHBwXKdpm+/YCKJEdzq1oX5kNZPFdmwURSYWizH5ymuMbDHevRVzCzl6p9bG3juOw6zfxDsYR/GIhH3ejlM1Vy8v8/W/+DwAVr+Nhc33/dTjJKT9ER9z/SLDLQbubcVSQ6P3iXdsElGzr75M0rHw7CINtZ6pTJaRLebx7ITZfJHBeHTTzx3HYVWU6T33MCs3bxAdGMSzTwWnh9wfDms63uZEj52gePl1oh3kbQ3T4vJ/v0jPG2C7FqFv8sM298iR4wGWfCC2GCn/IOC4DtK4j0DLKglIXGrw6m9+gePf9xTFmVW6xns2FHDW63VK2RK+kI/oAXg32LZNoVgkVyhybLzz7g/DMBBv1x3U6w2WFpZwX77FYOP2Tnad4DAsg9hgmvjtAtR0b5qJ8xfYuYNI51zJrBJNJpl+4yrpkWG8Ph+ZqUVW/p/nSdzQYQvBUTzppf9n3k+sxaLTjq51i935l17i9C4KWo3FIusLPEVRZFjzwO1Ok8XGPM5QFLUrSNTv2XLyq7VuZog8LyEjkatZJMK7Ex1ZR0MfiSB6JOzJPEJp58+T9nrIz8+RHNxojhUdGsGcmtjuq74t5j4OVxNa1NQYpkkhGKHn2HEAUkeO7tvrHXL/OBQdb3O8Ph/a4DDa7NS2u7T5hTw9b9x+nCBT3Cb0DRD2K5z57n6uv7SCdXPv7qHisIdQf4B6SccxbPTJOrK5S0fDox5G+gMcN4OwRTNC+As5rn/6D+mr+ZlWDfiOEQJHu3Bsm9pvXUZsmIz85vfs8h21Z3lllUajxtDgIMkdmg/Nzy+Q7Eoyt7CIR5ERFwv0NTaHzsuGRv6Rfo6u63ixLAu3UoVtZpXshTPrWnsvPH8BzysV5FdyREtbR91KAxJD//SDhMO785Ko1WqEAsEdp0QKtRrpighbfEV6fRHIuJCpkDGWWIp7URIB5IAHn1ciui7EP/fS4qbHZ+YqHD21lm7SDINFo4KgSnQ5KkFv6xRB3mogP9ZLKtj8vRYNMje7yuuv3uTMw517wgiCALXN9TPhZJIyUJiaILbF5kQzTWqBEK6uE7FMlHv8UOwtREdD0yirXuK2hSCwpe9Ltlonsu6567pBI55EjUTp7do67XjIg89heuUbhOUrl+my2tdeLOfKVP/pFfzr7rqNABhpGXpleqIqId/aTdE0TSaWyvjP63h1EUOwWe4SufjKLLXpOn58O56bIvQqvHMkTlhc23MtO1UWvAILL60il4WNplGug//hMMGoFxBwcXEdF9t1CXhhvCJhpbxE89uLp+vJGseyresP9L97mvFvPruj97IVmq6zsLjEQF8v6i5D2vV6HcMwiN42Nbrxu59hWGsuGEVLw/zWc3h8XlRVaY64v4c3zr9OKBjENHSGtrDS3iu1aoObv3SZ7uXtrwXHcXD/w9MMn95dDEbTdOZv3GBkFy2c01MrDCxsbTu+FVW9TrbXh6Uo3PjUNEt/tIBU2njdOaMuP/qTj1FyDbQjMQJhL7GAH0EQKNRqVN/IbJpdArAw7mMwvblwOVepUdJ0RByGO3TuzBsm4TMPtUxJ6JpGcXoSikVEnw/B58O1LBBFBMOAYIjkaDMaV1haoj43g23oOI6L6zr4BYFkpHVnWK6h0/3UO8mtLCNKEubiAl5dI6AquK6LaVmoSvO/r8/Nc3xwoPm4uobv+ElCexz6dsiDw6Ho+AZB13Wqr79GtM0NeXYhj/wLUy1/57ouMyctnjzTvBEslsuUr+tEJ61N1eKu62K4Fg3RAlkAScQVBRAFXAFcSWAJg7mPb65Qjb8vzpNW626Bmqkz59Mp2iK5iRK2btF1xs877K13PssDkO6gwWY56TBTqvGkuXGHXfr+PsZ/+Kl9GY0NkM3nqVaqu7I134qJL77E4FRzF7sg6qT+wntadr/ci2GaLF66zMABDAWzbZvL/+saXS9p2x8M5L8pzpmf+uZdF+4uL6/QVSpue1yxVKI4V0VSZRzBwXAtfNMVevx7L4Kcz5f47H+eQFxubT0/+JM9PPK9Z0kEN3/exXoD8+UF4tJaBGrJrJJ8emTLKKVt20yXq/QFfB3VnBR1AyueIDkytiefjuz8PJHVpY7+XjnTInbukQ3mXJnpKdTMMg1/ECUWx7c0x2qtQU/QjyzLNAyDWjBM6sT+Ge8d8uZzmF75BsHj8VDqSuGW8giC0KwlqNXQNYfy6zmET2cIt4kt5xI2J8fj2LbN9YUi/gsGMU2AFu1pgiDgERQ8KGDR/N89BBQPM0kXMbvx8T5ZbHn8nccct24v/GNNYbIQN2GL7hoA/0yDWthDoLr1jbF7FWZVhzfUKieN5vM7jkPyw8f3RXAYhsn05Ay9/WmSHQqOpaUVCp++SfhdQ+SvLhI/2Uv3YBpVVZhfWEAQRfp6enAch+CxQZhq5sb6HA/Lf/gc4W9/iug2rpqqouDp6cEpFva9S+fWxDzJFxsbrpNS0EL+4RM0Xl0g+cpG96rYR/bWKeT1esi4YSRJplgoMEbr/VQ0EkG/lGctNiAyH/aRrdVIevbm0dAfj6AmVKzl1kVOSl+wpeAAiPp9zJ/uwriUR71t7GYOhbcVEpIkER8axvB6MeZn2trE330dj4pbLbMyNUnv+JEO3lVrEn19rM5OkehAsCYUmdz5V/AdPUHodnSua2iYRlc30dsdTlldQy9OUHP92IoH3/A4qX3ohjnkweJQdLxNmLw1gaVpJONRSuUq0a5uYvd4SXQNj5B9cZm4z8PlL96i62MVBKD5tW5/o0rmZa5dK1LT6oxkg3i1vfXCe02RJ58Z4uufnELR1nZaTptiz3YoXpWyoBN22597WPQxH3OQ6zYeZ/OuTlMcKlGBZEbA61WYu1ZhLO3BIymIooik7D31kMvkmPulr+F/tJfg8fGOHxcM+in+r5u4/+8sMcDlFm8MCPT9iw+iKgqCIJJZzSIIICgSxY+eRRDAMkxk2yHSJtR9L4qidLTYz5ZKmLqB5POBZeEYBpLfjxgMYusGYqlI/+0w/4xpo548gvMvR7ANEyXsR/RIDPfECAaD1L/lFBP/6VliX8wDUFINhof21vkQjUbvppsi0QjTV68z3GbekBlQYJ0Teb+lMumx2GvvxfRqEX2ijtTG1e7iv7/Cez5wqq2fRH8iwuwJB2upjBT1ke6Nbvuac4ZNT28fHo+HejBE7sY1EvLWf09BEFD34EFy5zmEQADXdajUatg0pxYHPGrLTpiER6V+/QqrPf10DQ4hCMKGlurk6BieRBJZlvEdGnS9bTkUHW9BHNumkMsi4oLrsrC4QLVaI9WdIpsv0jfU2pjJqGlMfWmamakqkesGW1bN3cPgTYkyKmZcxNvYe0YuuQrv/pYRXru5hHGlWYDqcXcmZrrnXGbiNuH81sf1zkBhSMEztyZqqmGYtBssXsqhZ3R6nkmy+okCoiByYaTBk4aCaZu4lQbssnat0Wgw/8IE5n88T0KTyYmL8F2dPz4UClHulwkurP0sMeeSuTxL16kBDMukr39nbZOt2CrDmimWqCsKciRK/yOPIEtS2wXTsixWszkalQpjx49vadTk9/s59v97P6vfl0Hxe7CuzW7oGtorHlUF26LVNZ7J5glXLRA2Fk16ZbVtpG07FgslJuar5OYbSFsUYCceTm5rYDWYikGqsxqGkq4TSjcFB4A/FEI99zD5mWncfBbB0JEAv8e7qfCTxt5bztTeAUqmSbirC+V2TUalVKK6vESgWsJ7Tyu64UIk3f6aDR1GNt72HIqOtxCu65JfzSDYJonb7ZvTMzNEQyFOHD2CYRj4fD6u35ri2KkzGx6buzrHzJ+8xNC3vYPCxy7jvb60o9c2HAt9wEPXwv6VAMVy8N5Yisz3CBQ0E8Fns+oR7+669VyV3srWPfiBcBDy7Ue3A5Q8JlbNJjviRZqrEbO83NBrrHw+B4CKQu4LpbsOqavPFph4RsY2dKa+74949698mOGHhvF6PbeNt1aYfX6a0x9ujnIvZUvIXpXGYgljsYJ/PIFVMVj4ny8zeM3Fe/trJr66Sr3ewN9h10i9XkdI+2FhYwFw+N9co+G8gfELj+P2pffswujzebmmG83ImNh07nQdB0GSiAwM0tPh+cqy3BxI1+FQOo9HpX+0H8dxcHbgJdMpUhvvFb/PQ97jErino2m1UqDX17nCXCjk6YvFub6Y4+XfX8Ca3L52ZewjQ9sesx2GaVKxHFxJJHTyLP57xJosy3SNjcNYM6pm2za1Wo2apkGjjlMpo9QqENh94ewdIvd0kwiCQDgaJRyNsvLGFbzmxs/EVj27Lp4+5O3BYSHpW4RCLoutN0jEolsuMrZtMz07T8/gWrRDa2hcf/USZ59+DEEQMA2TW//wzwi8vE2IYB0zvgoD9cD9tS53bQoDCk6uTqrRWnws9Fj0LbXXznXbQO9RiWWa/26oNpUeha996Sbehfbtgfa4hEdUsW40d4PCKR8j33+cpa/OUvvsKqIg0vNXehlZhkil9evPeWsMaBsjTsp/fz/p/vZW0ZVKhXqtQaNSp/Tr54lfbL8bLVLHfqYH/8kUaipE4kwfodDuogUzc/MMDfTv6rF75dbkNKMjQ/tuYT0/PUOPrt2d4+E4DpNTS3hqECoYhKSNi98COoIiNbspdB2rrjHoC9+tr1jPxZkML/3qJOkPpVn93CpCtbPbqDgic/YfnebRR3fZoWMYaP1DhBLNiEmrqakdPU+jgV6vETnAwWbZuVli+Y1FV6V6HcaOEe/e7DJ7yDcGh6LjAcV1XcqlEpah41om0XBwy972eylXKtyanqO3r5eV1Rxnz53b8HutXOPWT/8p+kSOmKYgCxK267R0iVxMWcRKEr491nJ0giHYaLJN2Gze6Jf7XbwzDfQhP4orIJrNlJLQ70cc8KMe8aP6PFSuFTA+u0iy3AwzO46DgUW+V6J3eXNdRt3ncoM6c59ZQW4xbKzR7+Kbb/9+DZ/FB98xhN9sX/OxEGrQV9kYJbD/5RMMPzpOrVYjcKeALpOjfG2F0hcnUL++SljwMR+o01/bWUeJ9398M12p3S0i07NzDN9uU7yfTE5NEwmHSOzQp6QT6vUG9UaderVG9totkismKaN9euheXNelZOnoqoQpgdHQiFsiUV+A3/+f12i8WN7VeZmKzXd++oOk461TCZphUFI8hA1tgwFZSdOxu7rpGu28LujNRKvXKS/MI/n9uIaBkFtFsEyCjzxxGO34BuZQdDxAuI5DPpfDtQwExyYcDu16JwNN4ZEvlPD4A/T0b15QHMehuLCK67rc/F9fRX92mtHqWvFh3mtgpjzEZywUDs7L4Q7VqEBNMInkHDJ+A1GV6S7KqGJTFNg/NcLAYPtBa5phkC82ECQBRQFRkJj/vVskr7Q3LcsmXF6fyGBc1jF8FoPvSuH3S0zNVXFfazEb/Da9H+riXH3rtMNiTKO3sDFCk4tYuOfi2NfyKI/1YM6WiF2s332Pd1hO2aRXOv/M89Q48ic/0nHq5l6mpmcYGd576H8nLCwsEl5dJefzMnzs2LbH5zOr5JdWGTl9rKNWT8dxsG0bRVGYunCVvld3llJsRcUyuFEu8/rP3kLcZWTGcR2G/944Zz48Sjq+sUXbtm3ywQjpo8eolsvo+Rxuo46jqkT6B+/WbrwV0RoNSrcm8Pb0EjkAkXnIW4PDmo4HhFq1Sr2YI5mIIwj74xQZDoVwXZdCucbywhzpvo3CQxRF4gPN/Hvxw2eoeLzUF3UC33sKsWLivjBB92cyCMLBC47iCS/ZlSzj+SCIMKipoAHrAi+yb+vz8Koqvd0bd1Crj0XgSrbtY5I5gffEkmS/WyRUdQk0RNyGSzllkmVNdBiKBX4RoeQguSK9Xg+01yQACC2iRomSDM+Wgf+Pvf8OmiRP7/vAT/ry3r3etJvunpkesw5YAGtAAARhCBAHGtHoKB6dGLw4UWcUClF3EnkXFwrxGBQjFEdKxwgpGEeJxBGECEALgFgAC2B3sW52fNvXm/LepM/7o942b79Vb5m3uqd3tj4REzFdb1VmVlVW/r75mO8jw6+fhJ5PBEctbKH97DKB9TBLKoCAUbcxDtp03i8Tes8gIA6+QzQ3Q2cEh+u6FApFXNNEEEX8oRDxIVbu6nOeX1Gt1vAVCwQCAcx2m2KxTOacuR357X3kL3/Iiidx8PYBwR+9RWrpdO1Icf+I7ne3ES0HwXKQmjoCUNHA39BhBj4cIUnh4GuVqQUHgJsWCKeDhAJnv8uypLB4tS/AQpEIoQHGbt+r+Px+fC+/MvqJcz7WzCMdLwCu41DJH5JOPptx6Z7nYds2dx5ss7K2QXRMdz/P87j/T76M7188mOnxNNYUpBtJhPUQXr6H1dW58td/mA/+P18h82vD60yE/+wKC5nJLsKtdof8f/keQX18fd2KQ0EzadUdJDx8psWCoRJW/BStJhXJIBQOICkSol8jYApEKme3U0y7dMp14moIJAFPFBB0m5DVT2dB/zPuSBb8+RWWX00TOOdO9s5RgbDoo3fYwWmYdEpt/DUBt9TD/XSGW3/7x4C+2Mgf5+keH7MWDDwqzNV1nYKikF1be5TaeUi5UiEWjU6UwoN+x0qtVqfTbEGnja0biD4NNR5naXlpYCrD8zzuv/VdNoMBup0epb0aLRlu/fQXhu5n50tfZ/Hwsco7DgmkfupTBEOP38fu//w7LAyprWhiUzA7XJLDmLY99uC2p3Fdl7Kt03Xht/+Ld5BG+L88zfX/+mVufXaF4BCRV7EdQteuEwhNZwM/Z86Lzlx0vABUy2XiQW3mhXRP43ke1VodT1ZJZcbrMLBtm/v/0S8RfGe6/PXD/TbjIF6L422GufxnP4U2wHCrcFzE/N/+u4HbaLs6zt9Y4/rLkxc7vv+/3iP2642xPt96ToRij5g7fhi765o01xRUWUUUBSzHwWh0USoGWfG0SHJdl2OviRuQETSZZsTk1b/2CTKx0YvMVqHIZvZxAZ7jOOw1mmwk4uweFxAiEQRNxeu0WT2n9bDQbmMnU4QiEYLBAM1mC8syqZbLRMMRFH+AbDY98PPqdnvUqlWcbhe73ULSdZaTSbYrNTaSj8Ws67ps2S5XX75xZjuHh0dkmg1EUWT7w0NWK2C5DsU3F9l446z7pGEYlP/V75M2Ti/wddfECCg4uQh2z2DxqId8TtrF8zyqRt9Do67BoiMPLBIdl99764jtf3Yw0Ws+9c8+xSuvLw39e9F2cP1Blm7MXTjnfDyZi44XgGqlTMyvjmXQ5HkepWody7IwTZPFbJp2u4OiKGMPyXIch1qjiaT5iY9Rvd48KHPwX3wJr9QlWB1cbPoQ13NpahZRQ+13yuDQ/DPL3PyLPzSyPqVwUMT8K6dFx55UZeM//zQ+VSI6pWGQ67rc/so2/GEdX97CJww+jlYc7LZB3Ho+efPqNZnV/+AakTFHzG/li2zmTlf9lzpdwpKIb4r0iK7r1DsdMvH4qXPPdV122x182RzaybE5joNer6M1m2QGmI49KJS4lD3dPum6LsfBEKtPFKgapsnRO++yGg6RL1bx320TOqkXaksetUsx/GtZQrEIlaMCQktH2CoPjWBchG23w4Y4vQnVganz7/7jd0Y+z/Z7+NYCeKKHcEXmF/+THyEwxOW2rJuImSzJtfWZO8TOmfMiMBcdLwCe51E9PiCZOJv2sG2bRrMJgoTjOkiqj0QqTb1aoV6vk0yliUSjGLpOo1IiHgmNXXxqWRa1ZptYKoP6RFj/rW9+A3/Az9WXbiA+cefoeR7H72zR+pfvoP1+4Yz4sDyH9t+8xPWf/iQ7b9/D3mog+GRe+tlPjjwWx3H44Le+Q+wf3jv1+GGixyf+r5+dWRSoUq1TPmjg3jUQ32oQbom4nkt1RUYs9kiYz0dwVH7Ez+ZPXyI4psV6rdtDEwQCAwTKUauF5DhkT9w4Pwp2q3XWBoyiL7damLkcy0tLdDodCls7rGsK9XoT83adpH02MmE5NoZtE1SfbfTvSLZZtKcva3Nch9/+owOO/r+FM38TL/tY+zPLRBYCbLyUJJmM4boulXab9Ig6Dc/zKDoemVuvT5zumjPnRWcuOl4QqsU8ichjj4V8qYKiKJiWxcLK+F0F5aMDUgMu/ufRaDaxPJF4MoU05kVu99e+jfUPvoHmPF40ap+N8up//lMT7fshpWKZ7p//0pm7u8qnVF75C8+m+KzT6fH+723j/bsCK/bsCvYaPhN+OI0UkjGP2nTvVElVFUREyuuQ/NkNVi8lR3ZgVLs9HNclHQqyV2+wes4clY5hUDctXNNg5SPoDHhnb59Xh7Tcuq7LbqVKVNNInETjdt85YLn57Fuwz0N3bCqOzpI6vRNqz7X5pf/2HZw7j4221M9E+Km//0kSybPn1EGjyfKY9vRFxyP1yq0LdbDNmfOiMZfRLwqijOd5j+7sRFkhmVt89OdGrUqjWmZpbfNcYSD7/BiGMVFrXfTkzmtnZwtF1VhaHS1y1n7qTR7Uu/BPPwSgE4XFv/KJsff5NGajNzCcrF19diOtg0E/i7cWEb90cTtogPKKR/RPrrOyEibkf/z5e55Ho93BA9ZCwbHv3hvdLiuxKPfzRXKR8xfGoKYR1DRMy8d2pcrGMypKHkSp3SZ6ztAvURTZeGL0+v5+gXTNAemjvfz4JJmg4qPY65CZctCbX5T58b9xk9/4Zx/gvmuhvBriR/7PrwwUHACCbdMzjFP+G8MImgbNapVkdrz6qzlzvheYJw1fEBzXpd5oYhgGnU4HUXy8MBm6jmfqrC4tUi0c0esO79WMJZLU271z52kMYzGTwrVMivs7lAv5kdtY/7M/iPM3X6FzK4r1pzfOtDBOhCJi/93XqaycXpAjlx8vtq7rslWtcdxun3qOaZrslMoUG5MXu/rCMrZ7cTvo+s/HuPZ/uMXa1dQpwQF9a+hYOEQ8HBpbcFiWjeJ5yLLM5VyG0Bhj6gFURWY1FmW3PKCd5hnw4VGeqKqyNmZ0xTQMpCMd7SMWHA+JuTI+VeVYsTlSbY6szugXPYHneXQiIj/x//gU6b+yzJ/8b36AtY3hbpsL8Rhd3Rj69yfpCuLcuXPOx455euUFoVzI4zgO/mC/JsN/ssj0ul16jSrxWJR6vd4fqNTu4g9HSGWyp2ouHuK5LpX8IakBNSLj4nkexUqNSCL16FgGcXywj+e5xEMB/P6L+4uUjssc/y/vEP2lI5p/aY3Y5zcwymWUYBAtEiWTSbH3YIsV18G2bXTT5FiSeenVVyiXKti7O6QnsAL3PI9v/4NvsLg3/SJY/Ykw1//4pbEMq8beZrNJJBCYOqdfaLVJ+n3IstxPb9TrqIqK6rqkxwzvj8N+s8XKmAXMAFv3Dlk7WwLxwtBzbEqYrErni7yGbSB7AvmUyOWbK2MXfbquS73TIREe/Zk1dAM7GkPL5ojGn1/kas6cZ8mLcbsxh0q5xNUbL5+6Ey4W8uitJoFggGpbJ55ZRJQk4p5Ho16j0+kQHlCUJogignixBVAQBLKpBJ1Oi0qzhiDJtLs9VtfWTz1PkkTK+QKL6ZcutL+HpBdSpP7aF3jntXe48eZNFFmGp2aCpBcX+PBb36aHwNXXXuWlk/bQVDpJ269RbLUxDw5ZHpGSePg+1WwA9syRzx1EJedw+cfWZyo4AOLhMFtHR1xaGt5eeR7ZcIi7hRIhAQrdLq+trSIIAm3dYL9aZSUxm0Vs0qoMteNN8arnh1+S8QsupmWeaqfVbQtZEKkpLnbMh5SM4ioiV6cQ9m3bZZxPP+rTsDstKgVhLjrmfGyYi44XgGq5RCwePxN6TySSuLH4qc4SOAnXj7gIyT4/7Xb7wuPCg8EgD7PdvVYL27KQnyhsS2cXENzzp7xOiiAIRLKxvuAYckwvf+5HODjOE33KjyIUChEKhWiGguzt7ePqOl6vx0I4NLSt1GlNJzgAwr+wjv8ZzJGotzssXbAg9Go2jWGaGPCoXijk0xAE2Ks3UD2X3JhGcYOYNEhqWRZaVQd1No67z4q0p7Kn6nT1Kn4k/D4/zRWRgF9lZXH4sL7zOK43cCUJDcAyaeg60THanE3bJrL00QzimzPnWTAXHR8hpmHwwbvvsLyQwbBcoH8hd2wbWVFOLe6TIgjizKvelxZzFApHpBeWH6V1BFEknEize7DP8kJ2Jnf8tVqdbGb0iHGjN3yUeCQcJnLzxqN/73z7OwwbZ2blO8DkhYS2axPMPZsFVFMVyp0Oy2P6bxzWm4iiQDrgP5WS0VQVSVVOhf8fFp32TJOdSo315GjhYVk2tV4Pi35hqG07mHqXoCwD46VXDgolluXvjdkhnipx5c2ryIrCQb1BVlWIXUDA26LIysO0XzjEg1KZiDa6JdivaRzdfp/gJz79zM0D58x5HsxFx0dEo1Zj695dlpcXMQ2ToKaSP9jHEwR8Pj/xCe5yG/Uatmlg2g6hUJhwJEI4EuF4Z4vFhdlWvmdTSfJHB2SXVhBOFjKf38/a5avUKmUcs43oucTjsakvkq1ud+iMkCeZROD0ZJmdag3VdVlMPf5sPc/D601XSFq6LvHGEymcZqfDXqXKyzOY1hrQNIqN1ljPdV0Xo9dhc2GB/XIFVxAQZbkvDiyLuDY4EuNXVUSpd2ZbhXYHQRRpdbrIHriujabILKefFoKT1YZ4DiNrHzzPo2dbBJRnP4X04ch7gLd9PV7paoiiSEUwWbyZexRhXE3E2as3iF1kX9bpc2wjmWC/3mB1xHkuCAKyMP5k3DlzXnTmouMjoFGrUS0VsFyX9OIK+zvbuAjYps7SxqWJLjCddhtP75KIRjiqNNA0jXqtiqt3yGVHRwumIZtKUDw6IJ7Onkr9PHQ3dR2HWq1GqVgg6FPJZdJjF0QapokypphwXOfUwnEe12+92t++YVBptvBcl9b2DhvxKOpqBO6N2MBTuK5L+uf631W93abWM/D7NRIzqpUAcJzh03GfpGsYJE7cWldSk6VkrF4PnvD/qHW6+EWBWChILjS9W+cgvKeycF3HoiOC7YDestDLPbo7DeRFP7duLsx0309SwsT7gSs4Phn5926TdhVWvvgJdrYOCX17F30jSuapouhsMMBeucLqBJ9vvlyma9tkIhG8p+pYRFEkoalUO10SQ9qNu6ZJxx8ifWM29VJz5rwIzEXHM0Tv9eh2u3ieRygUQjsJlVuGTjSZYv1Kf5rkyvrGWNuzTJNmo44gCEiyTDQWJxgKUe60cRwHn6aiahqmaSL7+34ds+goeZqHRaa1WhnDHyJ8UldRKRZwzR6iKOEJIqlUilAkSkfv0alWCKgysXMMrgCq1ToLufGiMxtrq+zt7yMpKulkAt8Y3geapqGpBpXDA5aC/c8m9HoW7hXH2udDar8Q5+XFOIV6HVFR2Uj3F6OGrlPodBE9D08A03Exul0EBJSAD9OwWIuPHqzmeR6NZgsWRi++QZ+Pe9UasSkmknpPHUcyHCLfbGGaJuoMa1Vq1QZi2QBUPM9j3zLo/k6JeENBBAIn/yUIUK93aV8zCV1gLsogDkUD4eUV5GSE5c1VAAo+H1u//IesRiPkPpulfnOTxu07Z8SspigkQmEelMsshU7XBxVrdXTPQ1ZVBEACBNchFg6T0zSa3S6SJFBuNkk98R2FAgHyncHt73XTQtm8Qm6MMQVz5nwvMW+ZnRGe54HnUa2UwbHBsfH7fY8W/U6ng2HZIEp0mk3CyTSxeJx6rUY8kaDdamH0uuA6tFstfOEImWzuVNSjUiyQjASp1Rt4kkIi/biHv3R0gCiKjwzFdu/dodvtcf3a5Wf6vrvdLl3bI5XJ0qzXsHsdEk+EjNvtNobl4IkS4UiUVrMBtkkiHhsYodjZ22d9ivTE9u4euUwG/4g5JuVKBWFn51R+fn+7ivQPt8feV/Vnolz7wjqKLHPYaLI0QQuq4zjsFYpsLI4WE4ZlUdcNbMADRNdBkhVAoNyoEw2FeXh6uKbJyoSGYJ7nsVWpcmnA3fugOS/TkD+qUP9GHemPqpifDhII+mjcr5F6f0SE6hfiZKXZ1H/srwQJ3lwlmU0PLE4uFstkMo8X91qthvFga2jrdbXdwaAvvkXPwzRNlsfoYql0ugiO88iVFeC4XscWRaKKQuTkWpFHJHn1JXzP4IZhzpyPmnmk4wL0ul069SqC555c/AUSQ2oZQqEQDy9h8XAQy7KoHu1hmBZWp0E0EiEU6YezE9EwnudRLuQBEF0bJBlF81Pt6ERT2TOupKKiongOpmGgahorm5eplUvP8N33CQQCdEplOu02kVgcOxiiWCoQCfjw+Xyn3ne1VgFRJpLKUm3UkVyb+BORj0azObW3yEI2w+2793jt1fMt05OJBPeO8qfz82Nms2zXpvOXcrz0xhKyLPOgWGZljNqTJyl2uqyNGcnRFIXskGLgVqsxVkvwMCzbZrtc5XJm8J20KF3MN7B4XKPy5QLad1pELBEQCX65B/TIAIxIifV6Dlyg8arq6nSXYsgLCRaurRI4x2sm89RnEI/HeeC6DEtOJp5IOz0oltlIjSf2ksEA26XKqXbZhZN5OduVKhG/n55pImYW54JjzseWeaRjSpr1Op7ZfWQh/izodLoUqnUE+neloWAAQQDpxM3R8zxE1Uc8kcAyTbq1Ej3TflTkaZkmzXJh4CC5WVMsV0g90dVSr1YQbGPg59NoNLE9D0HWcCwDnywRDofYPzxiZWnxzPNH0Ww26fYMUsn4WLUjlm1zfHhEvFYj4Pex/V4B7Z+eP6K8pRhof/sK6+v9pWi7XGU1Hp24W+e43WFhBrUSHx7nWYqEiUw5ebfYapEJh6nW6jTqXWRTROo5eLYLmkzHM1HDKr2WgflHTdSWB6t+cp/PEk89FoqFQoVGtU06FyMe7z9+5+u7iL9aJji5QewjKmGTjR9bwC8P78CybZs9ukiiyAqBU5GTo0sR1j8/nS1/o9HEvn9vrG6VZqdD1bBYH3Pe0X6jycqAyNh+uULbMolpPkjn8Cfi4HrguXhAIJEkMOV3PWfOi8Q80jEF1XIJTfAIPkPBARAMBvA1miwsZAdGT4rFEvsHh0SjURRVxXT7RZ7lYoF0bgFFVWm0u89FdKSTCUqFPJnFvplVLJGk1+1SqpSJhkOn6gOi0QitVot2r8PCyhq9bpdyrYZlTueXEQqFMExr7GJVRZZZXVtlx3Gh2aB7u8p5gfzqZZHsX3qVdCzEca2OI0mkQ6Gp2oN1w4QZiI7rCzm2i+WpRMfBYRGranDYrhAyPFZlFXhY5SmC7gIyNF1A4V3bIroD7LQo3O3Q+bNLhJN+9n/9gMDXmoSQqfpL5N+I4LVtQt9tIwsXa52ON2SqOCxxWnSUrQ691QhqzI9PlbkcCuB5HqVWh852lZWm2O/aaQwfFTAKVVUp+ANjdatEgkFK7RI90xzLr0Uecs74fNrjImDHgFL+1N9b+UO66QVS6+tjHNWcOS8u80jHhDRqNTTBGWo09bwpFkt4io/swgKObVOvVQmGwo/Cs7VKmaAizrQocBiGYdDSLVLZ0wZKtUoZxzIRAdd1cBGIJlKPCmsfUi2XEGzzVMplXLZ299hcW538dQ+2Kf8ff4uF7tm72mbUQfn3Vlm6nMSnKGyXKqTDIUJjjqMfxF61zuqEU4AHoZsm5UZjQBvrcHZ3C8hlk2TbRZlg9smRbdD5N8dE3P73pQs2rioQMGbrwvo0+c8IXF5OEBQV9oUu8uUU8aif4JDfnud5HFabeB0LUzeRri5y6dq1qfbdaDTZvnefuG2yPMJArdho4tdUwmNcE0rtNukBEZSDZovlEXbyjuNQQ0DJLZJYnM6pds6cj5q56JgA13GoFo4uNNPkWXBcrrKwPHzBLR8fkopPvpBPg2VZNNpdbE8gtzh5qsQ0DLbu3GZ5MTuRm6ppmuwfHrGQzRIIjJcPN02Tg51D6n/ny/hUlXCnv4g2BR3jzTCBL2S4spLrD5qrVFiJxdAuaLi2Xao86nS5CMMWr0F0Oz0K98ssNFwUcbrgZt7sUt7rkP7ObN1nR1F6XcTzPJI/vc5KZrJC2Uq3h7q2SmyKNmbLtvnWH32TlwI+IiOG7T3IF7g0Zp2OYVkUmi1Wnyj6bRoGpmGSGnOGjeM4VD2I33z1jHCfM+dFZy46JqBSKpII+V84ox7DMGj2DNK5wYt8pXBMyKdONO7+otRqNRwPkJRTkY+9nW2yuYWhF0vHttna2mJlZYVOq4nguXiORbvVwe/3kU6nhn7+h8fHxCJRgueMWX9IvdHAkzXslkk3X6erOLR+/31yN9JEIhphv5/DRhNZlhEch/QEE2KHUet20QSRgN9HR9dpG/Yje3JJ8PpGZYKIoVtgONj1Lna9i6TISBEfUiJAMKwRCwSGdp08TbFco3SvSKIDGd/FLPHzronwS9ULbWNaql8I8vLPT+5XsW9arL/y8sSv2zs4ZHV5iXq9ztGDbWILOaxyiZ4gEUkmQBAwGk2kaoWarnMplRxrXD30/TfK3R6urrOcSrJfb7Axob8KwAPTZvn6TQIXHHUwZ87zZC46JqBazJO4QMfAs8Q0TdqmQyJ1Ntyu93q02y2wTQTPQ5ZENE1DkiTa7TZ+v/+ZpYscx6FUa5BdXH7kYHoeH77/Pi9dv37qua7j9FuODaNfVOs64Fgoknhq9srB0THhUHBocW+z2cQwLQTVRyQaO2Vs9v6772BXSrw8YTfKJHx4XCCq+jGKTUJHXRLa4HqMgmiTdQdHJLqmzkHIwR8NI4dFlhcH32FblsXuVpFkySaCTNexKEg2G0zfFXFgmij/5qMRHQ3BYPn/+TrhCbs6yp0u8Zs3zswvGsX2zi4b62unHuv1dDRNPVWw2uvpVO7fZ0GdPALmOA6NbhcbgXpPJ+j3EVHksdI0D6fVWrEUiy9PLqrmzPmomIuOCXiRRQf0PTMcSXtk1jUMQ9dpNhtomo9QOEy306HXadNtNlhbnf1wKc/zKFZqxNNZFEWhVquSODE9uvPB+1y7cRMA27K48/573Hj11lgCxbYsysU8Mh7BgB+/30+xWARRIvPEnWO90cT0BOKJJMpTtS3tVhO91SQc9NPrdND39kmP8PqYlL1CFeewga+kkwmOLj4+UmwWrTG6cBybmgpeSMNRAVHA81xEW0BqGKTt0/bZPcemJNqsCtO9v2PXpFipE/jtNhFxdDRp1tS+GMJ/LcrqldTYRcMAO47DpRs3Rj/xBMM0KZVKLI8x4dc0DA7efZ+18PTFwV3DoNjusJ5MUG+16AniyA6noiiTe/nVFy7qOmfOKObdK5Mw5Q+8Vm/gCiIIArZpkk4MNsa6KIFAgEq9CSNEh+bzkX7ibioUDvfFRzhCudFEdB3isejMLmgPHUwbjQoNu29+dri3h+d5LC3mqBaO8QBch2wqQa/XIxAM4nkelmkOvUuVFYXcUt9IrNVsUK7VkRWFaq2OLMt4gogHROMpYmrfCbNWreDYDqlMhnIhj+TapE4KOzVNY6dUpm/FNTskSWKpq0JwvGJex3EY56epSDIZB2gMqrOQz/iP+CWZgOeObR3/NAuiihEK4xfdiV87C7QvV7HiPowNe2zR4bou3e7wwYAD96Oq/dTgGAiiCKEgh80WS2PWZDxJSzfo6jrrJzUesXCYdmv0zB1F880Fx5zvSeaRjgmolIokw5Pd4bXbbfaLFSRJZmlpiWAoRPFwj8yE7pHjUq3VSCxcbOCY6zjkDw9YSCee6YXta3/0TTRNJRDwY5gWqWQSVZHJVxqsrK5y7+4dVpaXcVyXbG5hrKm7jm1jmib+QIDDg33KxSJ+TcNxHSRJJp5IoGo+9G6bRCR0ZhLv3u4u2XZ7ojvpURQbTeJvN8Ze6B+0q2wG4zP/7G3H4f1OmeuBOOqUFuMNbCotE/vrNaK16bpXqgkL4WoY46hDZkdA92zqCy7a5RhKOoB1u07yzmlhU/9MgNgfW2QxM3lB9Hf3CiQkP26ji3c5y+VXRteGWLZNvlBgZYxoB8D27dsolo3neiRlEW2M86dpGNQ7HVafKnTdq9ZYHVGsXrIdcm9+aqxjmzPnRWIuOibAtixalQLxExfBUXQ6HXqWSzgWR/P5cGyb27dvk4yGyc2gg2EQpmmiexKREdGOUbiOQ61w9Ew9PoqlMq6k4PP5kGQZAYGvf/WrxONRBEHi0vryo/qMSrWGEgwTicbG2vb9e3dxTINUMklXNwkE/Ah4eK6DIopEhtyVuq7L8VvfnemwM8dx2P3D+6yJ46XmbNum7lmklNm7Ut5vlrkcudg8j45jcv83Dsi1JxPgB4EWqb92g/RCjLBfxXVdys02miwRfeLz7ugGO//yAfFv9SfgtkWTzP/9FrExCoQHsbNXZGWvPzzvMKWQ+/FPjrTLB6hUq/h9vnPdTAex9d23WRuxfcdx2K3W2Eyf/S4OiiVyifON7nq6jvjK6xMf25w5HzWzj/F/jJEVBdsbo9bAtilX66AFSD3RqSHJMjdu3kT1BynXGvR6vfM3NAWqquLqbYqH+5TzR1TyRzRqtYm3U8jnyZcq1Go1npUuzaRTuKZOLJEkHIkSDIdZ29hAEiVeuX7lVEFoMhFHw6F8fEi5WBhqJOZ5HqZhkIjFSKfT4Dpk4mGSkSCJSIhkLDpUcEB/+qepaZQ6XVon308/3TE9kiQhXxp/oZdlGdt3sdbcYShDxtyPw51OjTv3qxz8m8OJBQfAcjeMGlQJ+/vHIIoimVjklOAACPo0gm+kaLs6O5Em+s+kphYctm3jHj1OVyyVLY7+3bfQdWPka5OJBIVyZeJ95q5cpjRkkNtD9hvNgYIDYDmTpjbi+HouM43GzZnzvJiftROSzGQpHh+SGTBvod1uYzsunqySWhxckCkIAolU/2LTajY4KJTQVA2/IhGa0d117KkoR6/Xo3R8RDgWPzPTwTLN/qA2x0EQRAQ8bNui1+lw89Zr2JZFoVxGEkU8s0csGpmp0ZiiPN7W4cE+miyycf3qwAuqpmmP2n6b9TINx8N2XJST6Z6e63B4eMhiNkNqihbEh2Q31hFFkXq9TvnwiJ5usBIJEb5Ay3E2E+HtO/tEXJU1NTwy1WIJz0boCcr0P/mI5mOvnGfZUtAxcV3QZYsE45+39f91n+xfCA0cvPYk4dUQD/5qhk/d3MC6gOjbO6ywZp+OOqwUTfa+/B1WfvRNtBEibCGb4eDoiOUJPGcCwSDlaBS928U3pKvFHSHkzRF/14R+EevzMP2bM2eWzEXHhIiSRCydJV84JptOIggC5WoNQVYJhqNInod/zJBnOBIlHOkLhFazQavVJjxksuVF8Pv7nR3NVp297QdcvdFvset2Ohzv7SJJEuFoBEEUcF3odHu4bj+nLisK5VKJVCpFbmWdRq1Gs94koMoXDu16nofwxGwNv99PPDlekW1kSFtsLBycyp78SR7OuMj5/bjZLHqvR/72nQuJDkkQEDMh1ms+HnhdLo9YqF3TwhNkTMdGO2f+yKR4kjhVnaxpm/gcj099ep3qmzqSBwICiiBStGz0b1SJF4dfTppRB+XnlpGOO3x494BXb6yfu79kJEB3I4csy1Pd0fdMk0K+TvZBFwYszKvHPfZ+9zusfvFN1HNqhXyahiwpmKY50QK/emmTt7/2dV4ekJ4stNr4R5zjD39/w9Bdh0x48sLVOXM+auY1HVPieR6VUol6pczapctnWjGnoVTIk44+25Zcx3HIF0v0LAfHttm8fIXd3V0uX7ly6nn5oyNEUSAWT6BqGsVCnnt37/LZH/4RAMr5Y1Kx6S96nueRL5XJLa89ao8t549IxZ7tPJtpuX/nLqledyKfCM/zqHa6tMsdhP0GS0JfpDmuQ0n16JomquXiyiKu52CLAgHNh1FvYfpkVMvBkUQ2pCCSeFpI7csGCVMkKI4WJJ7nUXENOkERp9Zh0z95nc5Wo8xGJDm0uLWBzfFvHBNvDD6e6qbI5b/1Mj5FoavrBMbwoii02mSnEOGe53H7G/e4ao2OwOyuhVj//BvnRl5c12Vv/4D1MW32Xddle3ePbDrF0b37XAr4MSybfLuDbZosRcMjfXF2K1XWzik2r3V7dNJZ1i5fGfqcOXNeROai44I8uH+fS5cvz2RbjXqNgMSZjopnga7rNHWLTG7h1OO1apVKpYJrWzTrVbq6xcraGhubm6df3+uhN2vEBkzMPA/Hcag1miCrJFPpR4Kj2+ng9FqEXzB3xWq5TLNQJGgaJMaM7DR6Ou16D2O3ypI+/E7dcR16nkNIOi1YW5ZBzzLJBPqi7ki2WbQfb8PzPA7WRUJBP+1iD1/VJOXIpwRBXbDohhQEWcRUXdY2c5TbXRJv14dGkjzPQ7dMeq6NJ4m4osCR02WZ/gKZVM9//4e2Dr9UpiNYHF9yWPupl9D/5T7xgkAtabPwd14mMaafRb5aJRYK4ZtCzOfrLZLvDH+fT7O9EeLy5948FSGr1ep0Kw3MwzKdD/ewP7XJa2/cGmt7d+494OrlTQRBwHEc7nzwIf7WaevzUYwSHdu6yfrN6zRabZBkguHomdTpnDkvIvP0ygXotNtoYn+gWTx5sY4AgGgszvHeDguZi29rFD6fD8M06XW7p9JBerdDPBKmVCqSSmexHIfOAN8An9+PbVu8f/sOL125PDSlUa/XcRwX23X73gKSTGrxdEtvr9vFaNWnGvT2rKk82GIzFgV5PMHxYLdAbKtFTvEDvnN/YZIoEeLs5xZWNBp+Aez+vy3H5skN1R2DpdwqiqKQSsT77qN7RdSGS7LlokkyvaDMyq3T7Z6CIJy7EB/GZSI/8iqp+OPOCe2wgP3v3iVhj17AFyWNnZ+P0Yl7/OD1NXx+P8d/3cfRb+/w0p98meAYg/J0XeeoWsWRVXJTCA7P82htl0mL4792Y7vNtvdtgleWMMtNjPuHZComafUkGqFE+U65MXI7R/kChmmSzTy26pckiVAkwtKEjqWj7gTDroPe65E8cdBttxtUGjVcBEKR6Ngp3jlznjdz0XEBAsEgO50OkcTsREIqt0ijVjpl7/2sKJSqbMRSmIZBq9lAlGT8wSCuZaGqGqlYBN1lqD9GKBzhpZdv0Wo1cW0Dz7HBdfpFnYIAkkIslUM6J3RdKRVpNhtsLC0Mfc5HSXxjncZxnuiYk2WllklsBq2uquXhen0TL7fdheDjcLztk09FwxRF4dKlvsA4OCrgtlyEwFkxc15QM6+5pL7wOpGnahBSS1mqf1xg5+u3WStZ53qHCILAguxDS2uP7roXUlHSv3Bz7LqMfKfL5uLiyJqGYTQ6HTBtYDLBsrrThp07J/8SQX38eTddCzk4PELjui5bO3sk4lEWBwx+E0SRertNbIIoniQPFvHlbo+qKKIoCuknjikUChGi/x03GlWa1TIe/XMjEJ6LkDkvDnPRMQXdTod6vUa322VxaYXImL4d49BfGB5f2BuNBsVyGb/mY3l5duOsPc/DsG0cx6FdLZFKJnBdl1q9jifKpHM5uu0WIZ/K/uEhsfjgUK8ky0P/Nmr/5UKegCJR7rRptzsz696ZJaIo0nYcxpWA2mIM6s0L71dyPUzHxqeqCPbj7o2mpdP0CeSGvG7YLBYA6RyvMTcZPCM4HpLIZoj+dJLtf/uHrFbsc4/bJ8lIOx14Ipg1juAo1OtYloU/0D8HpnXsjYVC6FczuO82Z+b6a+OwtLnO0fExiwtnxfHO7h6XNtaGCrKVlWWqwSCHO9ssnSNeTu3TOt2xo1sWRcsmvLjA9ezw71gQBGJPXY8KpSL+wPpY+50z51kz9+mYgsP9PXBsQup0lfXnoWoa9onoKBZLNBpNrly6NFPB0W63KVbrLCwu0qoUSZ3kjkVRJJlIkIpFEEwdEDA8ibVLl3nnu29Nvb9apcLe9haH+3tUK2UqxTzlo31S0RDBYIBrV69QbzapN0aHsJ83jVqN5ARtptGgStHsXHi/usSjeoaYFmAfne1ODdFxkYfcBY/Cp8g0e4P9I4QR25QkCW85znFYwHTPFx4pT+Hee3tjH1ehXscTRWKhMKbtsF0ojf3aQdS7XaoM9nGZhoToo313n0bprN9Nt9slEAiOdI9NJOI4wfEjHXFNoX7iE1PXDSr+ABuvv0bqHMExCMuyEKRnXyM2Z864zEXHhHQ7HTKJGIuZFN5JR8H9u3dnug8BuPdgC8/zWF3t3zK6rjsTM7Fmq4XoD5NdWsE19aF1FKFQEEVwsbotWs0Gyyur7O7sUK2UMfTzZ1m4jkOlWGDnwT1Kh/tEfDLpWJjFVJx4QCMZCZFOnrZYX15cQBQEjvLFC7/HWbJx5Qp56/Qi2+7pVFvtgc8PaBrm6sU6cCzXwbUfpxdiviAr+NgIxgn5AsjFNoZlTbzdkN9PmcGmU1Jt9Ll15ROvsPanv0BhxPuTRImQfv4i/MHBIXu1OrvVOj6fn1wkQsjvYyUaZjWV4LjZYrtcpd0932TrSbo9nZ1qjeVkAuXNBfaF2Znvrd1v4q+cPpaDoyPanS657NnJzoOolUpsOy5bhsn+CMO+WDhMu6dzt1JFWFpk9dLmuc8fRrPZIj5g8vScOR8V8/TKBHieR6deQRIEiuUq5XKFZqtNb8QiPCmWC/5gfy6I67rU6w0cDxwE/BeoUNd1HUdUiJyEeEd5IMTj/XC7ZVkcF0tsbe+wspCFRJK2pODRL3hLZ/p3X61GA6PTolGvc2lznYAiPjrecY47EonQ7nReqFSLaVm0exbHvSZGUaf13RLSH9XweRKFX1wg8UqCTDT8SEB5nkfbM7jIT2tfNNg8Z4rrUiBGuauTjU5+B6vEAjzUHY1ul0LTQA6I2LpH1rLG6pzS/RKmY6NKw9/jefUjb+/scSWXGdo2K0kSCyeusfVuj61yhc0RZm+75QpBn5/1J1JE0msLVL92SMI3m3NJbPR/591ul0KxxMJCDt8E3i2vf/YHH/2/aVns7eyS6HYIDfltLCfimKbJ0XEe1ecjNEVnl6CoM2nnnzNnVsxbZsfANAx2d3YI+jU0WSIYT7H14D7XXrpOMX9Mbmn5mQxGcx2HSrlM6MQESNM06sVjEicV66PQdZ1Wz8BDQBJA8QVO1Z80alXMbod0cjzfht/+nd/j6uXLLC0tsL2zhz/gR1MVBFGi0WpjGQaXN9cvnEvfPzxEVVSCgcBzEx+WbVMtVTArLUzLJpRL4Fg2pf/u2yS+MXzqp+u61H4xziufu0yrp/Ph79xjIRhEdRwiooI6obHXgWiQM6WRabvCS2EWU5NFVCzL4q1f/oBQWUBsuUh1i2BHpB0VyP2jHyO+MF5BdHH/CPVL7xEQhx/jgd1h+YcvDexq2qpUCYoi2THPY8dx2Ks3WIlGkGWZYq2BLIkkToTJdqGMKwlcekqY2LbNg29vs2n4ZlLfUVIcnC9eJ+APPEpJXpRyuUJnd4/l0PmFnoV2G9/GBonEZPuttnsk5pGOOS8Qc9ExBrZlUatV2b5/l1Qmh2VZXLl6DfGCzpfTcPfObdLR8LntpY1GE8sD9SmRMQjLNKmeuKuOotVq0e50cT2PxVz2lNB6sLWNpmosL49vFz2K3b09lpeWLuQwurezi11vIEgiwUyWTDaN53kcHxyjaCrpTArTsnjwa1/lUqVfvLfT7OL/0vjFoA1PR/5bm1j/0z6xav9Ybdem4nbwbkW4fCNFUBhjQq7rsK83WQ+MFoEHOYm1y8PKSQfz3q/fIzHkfdU/F+eV//Qnx9pOce+I3tduk226Axfz70QMkqkA4WiE9ICZKfeOjtD8QVbjk3Vo7dUbxBSZmmGRCPiomxaObbMQDqMNqbsxLYvtvSJKQMUpddhsnv0ePM9DEARcd/D7eUix1yL673+R8IydQA3T5Pi7b7MywvOm3O4gra2NbfHfbDbxx1LzSMecF4q56JiA+3fvICsqa2uPXTQ/CrqdDp1aifTDGS6tNpbrgge26xJLplEnCPsauo7RrJ07CA2gUCjhD/iIPEf75b2DQ1anLKI9zheIlYqP7KtN0+TIhU67y5X3mxieTe3VJdyOzspuv0bDdhxuv1cifee8LU9G6abAtespFPF88bRvthEtm6VgbOQ2t7wmV374+kTH8cEv3yX2O4OjNhYO/N032Pyhm2Nv7+5X3yL9QYmg0F/wXdclr9jw6Uso9TI+VcUUBDIDOja28gVysehYzqRPUmx3iGgqvikM9BzHYXunQOLQxAjJeIKAEQTP178EWpaDIiiIroBkgmA4yD0Lv+ERUvq/p/pPvUImN/vIwd79LZac0XU64woPz/Oo1pskF2ZXgD5nziyY13RMgiCyvrHxUR8FgWB/vkil1UQQBHyBMOERrXiGrtPptMHzkBWVyBM+IJrPR71iY59MlBUFAUEQMW2HYqXK9csbSJKE7bnPVXAAKKLA9s4u62urjyIr5UqFxuERnu2wcO0KwSfeu+d5lEpl9G4XtVpBfeJvqqqyDtQtk4bZJeEPE/igfGp/ReyZCg6A9Pse98Qq69fi56YkluQAH7gNxlkmFi0f9XaH2ATpJzE1/I5XQaL4L9+n98Ym/sB4dUNXf/B19uP3qX79Ht5mCqttcP2nfgiAB99qkguFqLdafHCU58bi6ahMyOej57hM6h6RuUC6TZIklldS7Gllrk4Qkas3GuzVu4gIyMbsJ0MDBGJR9MODkfboqVCQ/fv3iEQj586MqVar+KLTDz2cM+dZMRcdzwHbsqhVSriuSyyRwrIsjF6XUCSKqmlT1YNoPh/amHeJlWIBx9QfdYxYlkX1eB/L8UjlFpFkmezSY2MFx7axbZuoptFoNJAkif2DQ7LZzMTHeRHarRb1/QM2ggEe/MEfIqgqruOwGAwQ9/lAg60H21x+5eaj8Pi9995jU1H6YfIhQiwWDrH/ag7unW1tlT0PZ0SYfRqS7zoUVkw2QucUX+LhJTS2RJP1pvzoGO4WS6SDfuJPtFz6VJW2PVmQsny7SGSAA+pD/AUbs2eMLToAVq5fxr22eerzqlQqyCfndCwcpm6dnRKbiUXZrtZITjmyflp8qkogFMJxnLHTdrFo9NHk5kPr/HbhaUmlkmwVi6yN8dyVeJxSpUYuN/z3KAjCo8GFc+a8SMxFxxg8NLLS2y0qpSKWaZLJLQyt6SgXC4TCERrVMooogOeRPqmqbzZraJpKKBLkvQ/eRVF9JDMZkunMzItRS/ljcB3ikRBy5PEFSFEUEvE4tm2zt7PN6voGkixj6Dq63uPo6AhZFEnEoo+KVh3PO/fO6lmgqioZWUJRFDYyg0Pa64rEgw9vE0mmaFbKXB4zrRSP+imaBTLq6Y6AlKjy3nWB7IyjHQC+wPk/N0mUSOJj+dYy5XYHvWlQz9fxfcOkZugUb1poGY/1k2JCs2NA7PTx735wjH6/h+13ICbiT2l03usgmwJL759/fKImsfOvv8Otv/rFid7Xk4Kj2+3R29pi+WQK8FalytKQtJ3gfjSZ3aVomAflMpfTk6dJnO7FPViGsXT5Evdv32FTkUeK3pFXCkF8JsXtc+ZclO8r0WEaBvVqGVmSwLH7BWSiCIKIJwhEYolHRVeGrtNuNcF18GyLVCJOOnYVAM/zUykcISga/kAQQ9dxLAPXMpFliVg4jN5pkI5Hz1w8nqybePnG9ZPteRSPDk5FG6bBdRw6nQ6tRh1ZFEjGIufezcmyzMbyAn/0tT9EFEWWFxcJBv3k4pFH7bIPmfWd/ziomoYz4vIqiiIbAPUqSWn8Y2zrBrEB8zlEUSR+PUYp0CL91nRW3EP32TIgen5Rn9owEASBdDgE4RArS0mOM1Eqv3JI/G0dB5c74YP+kyWBDwJ7CEG5/19IwT3qEt3u1wa4nksh0GOh91Bwnn9nHyw42LXJ7+TrjQaBQABJFDl6sMVG5HFBpCxJyAPOHdd16XQ7dHoBgv7J6jouiiAIJINBuj2dwIT7tputR4Wns6bT7iDaNoxlRne+YFNkmXq1QiwxT7HMebH4vikkrRQLiK59ftdHs4njehzmC8iixEtXNs+9uHQ6HSzLIhwOX6jDAvrtjG3TmWpwXLNewzYNuq0W0Uhoqur6URfS7Z1dNtbHCf7OlsJb3yU1Qbh/HHTTovidPZbs4QtOy7M5fNAk/p3ZOFvWMg4rP5IZOYr+ntLj0htrZ/wyHMfhzt//DrHKsxV/tVtB0n/7TdqNBldePr+otNvtUjwqEG3VKfd0RFlm/akOjHqrxU6lhihAMpHAPTnPDo6P+cTmxswdfSfhQaHEpTGNvR5iGAb6ygqJ+Hht5uPSaDRoffghC2NutxAKs7h0fl2KYRjU2z0yC4sfaeH7nDlP8rGPdHQ7Hdr1KoloGFk+P8cZjUSoVGtcu36TVnO0JXdwhjlTRVFwO+O7Lz7EMk1co0siGiURmX4s/Kg7t2wmTa1eJz7DOTPj4I4OJE9MPl9j5RzBARAWZBKbIbzvVGez05ZNxbYJDpk2ajk2+ahAZi090KBLkiTab/qI/ebs7L0H4b/dpv7NOyTXYhzs77O8Mjj6ZpgmxQ9vsxIKQiBAZMhAsVg4zGsDRLChxz9SwQGgTtg5A/3v4fgoj2FYWI5NuVjilZdvjGWqNoxGo0l5Z4/1CYRMp1TCW1w493eraRoZVaV8fIg/EiUUvphT7pw5s+BjK38t06R0fAhGh0xy/AucIEqomkYimaJWf76zQII+jWa9PtFrWq3moyK3Z0kgEOj7f9jPppBuGPaM79A8z8O8XRjruWFXoCpNLgQHEe9puEMaHwqSTWHdx/prqyTO8a6ILzzb77kXAOfPZbh6c5lEMEi4WuO9b32HcuH052WYJge37/YFx5RoeBRqH+2sHXmMdJxpmmzV6hyIEoVQmFY2y40bL7GQy7C6tMgbr9+iWKlMvO9Op0un02H77n3srQes+ybz0ljXVHa3d0c+TxAE0sk4vWb9XJfYOXOeFx+7SIfnupSLBRTBIz2h+VD/9Q6e6yKI4jO5yz4Pn89Hs1zBsm2SqfHSLNFojEL+EE2WiEajz7R4bH1tle3dPTbWVp/ZPp7GmnK42TAEQUAKjFds6pcV3CtBuD2bi3W31MENa4/qY9o4VJISy5u5sXxVnM5sa0yeRA94iH9xkUs3+yF7QRAI+TRe0lSOj47Zr9XBdRE8j3q9zvUxDaqG7s9xiYWfby3H08hPLcKu63JULmO6HiSS+KMRAuk0V0a4gNqmhW3bY9/YNJpNah9+iAKsxmIwxdh5URQJ1iq026mx7NFTiTjVcolk+vl2oM2Z8zQfm5oOvdej22njmTqJeGzqxdfzPPLlKgsra3Q7HbrNGuGAH20Cs62L4Hke97b3uHpjfJMm6Le5VkpFIn51ZK//RdB1nWq9wWJusmmX09LtdGjduUtqRq2Vtm1z8Nu3WQmOF8rernUJ/NbFR9U/pPKGRGIzRtvtkXxlgVQyNvZr7/zKA8K/XZ/ZsTxEVzy8v5Dj0uvLYz2/0OmSncH38fbuHq+urnxkXRaWZfOhYbK0soIggiSKhMPhqVIlB0fHLC+eHXv/NEdHx1hHRyyHp0+FPkklniSTGe8GpdlqI/mDBEPP12tnzpwn+VikV2qVMnqzSliTSSbiF7qICYJANhmnVMgTCAZJLSxTbevUTsauW5bFg63tWR36wP2nErGJXyfJMpmFRUxkSpXaMwul+nw+VFmi0Xy8EBuGycHWHq3W8Bkl0xIIBunO0Ma51umy5B8/AqZFZtsmnPyOg/BLFSI3liYSHL1eD+ft8yeTTkv3i9GxBQdAUJKoNi/+Xcd8fg5aLbrG4Mm3zxpFkYnKMplMinQqRSKRmLo2I+DTOMqfn7YrlyvY+/szExyu6yJMcAWPhEPY3Tad9uAJyXPmPA++50VH4fAAv9Q38LlIMdeTiKKI2Wmxv7ONbVmIkki9rVOqVHnvgw/Z3FifyX6G4jo4zllDpXGIxGKkFpYoV+uzPaYnSKVS1BtNDLNf1Lj/7Q/I/M59ev/897h/b2vqYx+GrTtsv39ItzO4KMK0LO4VSxy3OxSbrXPHvluON1H7b8Bz0d3ZF28e//J9dHP87cqyDMZsP1eAVshl43PjCw6AkE9jFm4Va9k0nmXDRxRsdRwHa0b7TiQSJGJRtnZ20YeIqPbxMUvndM+dR7ens1OpcVh9LDxFURzVOXuGaCSM0+v7Dc2Z81HwPVvT0W416TbrpGLRC7erDmLpJFSazx+RWVymqTUBj8XFpXMjKZVqDUQJz3UIBwOP0jL1eh1FUdANE0QJ17FJPzGp0rIsWp0uiBKCFrjQexJEEUF8tiHrtZVl9g6PSESjRL69B4EQMTVA7Cs7bH/tLvKr64SW02MPpzoPRXdZrMG+VcIMibi2QyThJ5NIIEkSt4/zvLy8hCiK6LpOraeTGyJAW9tlsoyffvIsG/EZaHOx644dket1e9z5zR3STWkMV6jJsN4MEwxN3pKc1DSK9QaZKRfRhwiyPPH8lVnQ0g1KrRa+KVrUh+Hz+dhcXyOfL6AbBr12i0g4gqSqKEO6lkbRbLepmTYhTWU9GadjGOxUqqyfXDu6rTakJ3sPkXAY0zRpNupEorGpjmvOnGn5nhQd+YN9okEfmRmNlz6PbCrBB+++zbWbr1ArHmPYFq12m/BTxVu1egNHkDC6BnK1zHvHedauv0QsHEbwHCzT5qBUY3V1lXAkgue65A/3yZ3kY2uNFpnl2RVoKr4g1UYL17ZmNob7aRzLpNVskQyc/ixWLRW+fcTudoHUz3/uQvt48Nb7yPk6ACttCdoAMlahR83doay5vPSJ9UfRi0K3x1ridL1Gs9uj7bpYpoVvJcZuy2Tl0B4r4hFSNfblLll3tu2G3hsRtDEic51uj/v/1TtkqjI8g9oHaXm6BT+gqVR0/cL794sChVab7IxSDq1ej67rYbsulmkiKTKiIBKQJOInfi8NXafb09lMp8gbs49i5U7qnfbu3iPX60Kvi2matFstCj4/luPgwyMVGXxOmabJQb2J6tMIShJrT6Rbg5qGJIocVKsEZAW9dcSHnRbJdIbMBHVWqqrSrDVgLjrmPGe+50RHpVggHT/faXOWCIKAX1Np1KqIiopq25jO6ZhmoVxBVf0Yx4dI3TbFrs71198gvbCAoih4nsfuB++T67bQ338bA/CCIeTsIvliCVkU6A2Yo9FuNlE0baoi1uhJz79tWRTzB2SmsHwexdrqKm/9ypdJDokERAsdHvz/fhdlJUVX11GjAbKX1gie02ppWRZ779xBbBsY9TbrRWugOFAkmYQk40jOoymyANLJ513tdNE9D1M3SPo1Fp8QiV7a4yBch6aJku+QZvjnq0gyVkA8ETsXo/KZENHPb+LbTBLdvT/Wa4IBP4r97KJWsjJ9FCcd8LNfqbCSnD6alQqHKbQu9uHeLRRR6I8bSIYCZAd0c1SbLbZKFaJ+ja5psnLSkaLaNqZhTDSVeVwkTQO9nxJUVZXlJz6nWrfHYbXGUiJOuVanaegIkoLm9+ETBDbPKQ71KQohv59qq8NiMkFAVekWC9zN57n62q2xjy+gKXQ7nfmMljnPle+57pVK/pDkBUO601CtN0FWsUyDdqtJPBwkGAzS6PRANwhUivhPFj/XdekZBrptgz+IIEDIdc7MLqm4Htr6JTrdLu3iMYlAEM/QIRDCrtUIeQ6251K3bDZ+aPqIgaHrdOvlmRt7ua5L4f/9ayS14Retpt6jHhSJmgJRSaVhG5gRDScawI35kXNRsitL7Lx9G6XYRjtukPCUsVMPx2GP5VuPaxKOa3UMy2Y5ERurhXF7v8jq7vkjxX/39+6QenOT9K9P7scAfbEh38py9WfeQFUUCvkC8uEBsTFaHQHe/V/uIO9ZaNs9fNZsUz2lnwxx6yevTf3641qdqk8jJEoEDf3R3Xu13aYTDOH1dFYD50dTip0emeD0rrP1bg/Rc4mMsXjqun6mu2tfEFi/enXq/Q8jny+QbtSH/r1rGHRNk4CizCTF5LouB7oBJ5FU0fNwXZeVS5tD693K9Sap3PgTd+fMuSjfc6KjtL9DesIc5jAajQa1d7ax8lW6AYlbPzV8YT/YOyS5uIw/FKRytI8oCKD6CEdj1N76Fokpc7aWZaNbJuFzevULjSaJT/3AhVphK8cHJE+Gt82Knffvkvvq3rlpCt00yftcMrY8cKy75di0XIu47Juq66jnOTRCArrkIfglNq+MMxj+MY7j8ODuIaG8TlY5vWjppklxM0byk9eIRCPc/o23CPzDD5DPGU//JC3VRP0/fZK1H7yGciKA9vf2idSqhKb4Lvfv52n/T0dEixf7ydajDtF6fyBY+ZJI4CdTXL46+dwf27ZpWDYVx+H6m2/Q7fao1+tY3R7JXIZQKES73eb49l0Ew0AUIOLTiAeDuK5LsdnGrBk02y1efmXzQu9pt1Znbcrz+8gwWXn1lQvtfxClUplEdTqhehHcJ6Yke57Hrm5y6bVXBz632WyhReJjT6yeM+eifM+lV3ozHC3t8/lxDsqsdUX2643+hMxel1gsdip9U61UEX/rXZpv6vg/cwsPARsBxRWo7e1OLTig37anjBjwlI1GKO/t4Lv6EgCtZpPwkHzwMGRfgE6nS3CWo8SP6iPrInyqyroLR24XUxaIuafTYg/TJNPiFyT8J60UhSm8syRJ4ur1VXbNPXiiC7Tq6ng/+zqXlx57L9z8E59gKxeh/Z99jZBz/ndeez3I4n/0E6Sy/ZB6rVansrPDiqog+3wD77hHoUU1juMO+BTkY4OgJWO4FrrqEbXHays2JRff33mdxn/7HrGCi7jkn0pw1HWDiuOQWF7ieqZvOBUI+Ak8NScnFApx5RNvAJA/yuP88je451mIeKxqMURRJGtL3L13yNp65kydS7Pbo9018WwXR/BYzQ6uT7pIAirkOtSrVWIjTMAm3m4oRPHBAzIzFvujePI3KQgCfm9411MkEua4VCC7uDx0avacObPke050pHOLVKoVkonx5xQMwvM8Wq0WTkCFrs0SAYr/3ZfIhaJUfvoV0pkU5VIFRZFp/9o3yaFS/2Af87WXCMYSqKrKg+++j9TtYJtdMunZDoB6mkCjxvH9ewjVCmFc3q/WiSwuEV9bJzSGDXo0nqBcyOM0m0iSRCAQuJCfieu6yAd1xj2FFuUAB6JN7NmZauJq0180HcPi4RTWpmXCz7zBwlLuzPN8mTDt//QWR79yj8V3zqZlGhEb7S+/ytoPbhKNPRaG7UaDJB77nS5qKo0ajdM6OiQ9gQg0jru8easfCm90uxTaHcKqRlzTyP9hk8jJmJjmgoh7RUYSJJw7OrF8PzLieR71H47z5mde5vbbVRq/s8/aT0w32bilKFy9NfjueRi5xRz3r2bZ3DvdcCvLMpcKcF8ssrCQQBCg1THp5ZvECj2yJ+m7PZ8JQ0SHT5Ie1UhMSsTv52D/cOaiw+/3ceB6fNQeoDFF4XBvn6XVwd/1QjpJuXBEIJqY13fMeeZ8z4kOfyCAZZk0Gg2iF5g5cnR0TPxX3yNyEvYWRZFcqL+97q9+g3uSwLIhcygarIn9H2Ks51L6za+x9LOfp7Z/TPiDQ3z1Hp4kcK/W4crVyfwOJiGgqgQ6TdD6d4KhgJ9Fz6Zz+30KgSBCNEZicencOoZUNker2URSFCrNej9F5HngeXj0/S7S8Si1egNJEolFo0MjGdtvf8iyM1kLZ6Brsu9ziDgiUWG2plsAjjx92sHVLRB96LaF8eM3WBogOAAWlxdYXF4gv7mA9Zd/+9HjLZ+N/DdfYf2zVwg91YnRarXQy2VMSeLypz756PGaplLa3iI9xoX+8LhMuu6A2BdG0UCA6BMpucIrFo2yC7KAoVq8kusvoO80TBqXFAQJmn6J1//DHwUg9SeusmXUCYcnW2RKrRYBRSWQndyRttVqYSeDsDfY5SO+1aa80yKORFYLACI8US8UaFu0ezqhp8bRd3QD44MS0qXpC1ozksj2h7fZuP7S1NsYhHSB+TSzQlMUlGqFQ89jacgIg1QiTrPVpO04hCaMos6ZMwnfc6IDIBKN0fA8ms0Wkch0lr6t/SLpIQv0In5wABnWnvqIkrt1vvE//hL2UZU3Q1kEqb94CocmR7Eyi2NaEl8U7UQMBH0aQdemd3zA7bu38SIxbr75iaGh0odpGZ9/cOFepVwitbSKIAjk93fJpQdfyOWj5sSRkoQaIOFCye7CjIzcHlIRLcKp2MSvK7XaGNEolm5SClnIP/saSwujF9RYIsb7YZNMS6XxcoCF/+RHSKYH3ymHQiGSlzbPCMJ4PEbVW6e0tUN6xOJfrnfJicMjOZezURhw2K9efdy1tH0pgnRyDKmVHO4vfppqMU9c08b6Lru6Tjcao9nTuTqhDb7neez/y9/lMsPfZ9Ifoul1CDP43EzJQe69s4+8FOHycn//umVx+71t0oaIXe9BerobEVWR2dvamb3o8AfAfT5DEgudLnYkitFqIgkiTquJJsssJeIk/X7a9QYH7g7LQ8wNI+EwpUqVYDj8kVnTz/n48z0pOgCisTiddotKrT5xgWSz2UQoTzdPQxRFUiWdmBqlJFq4UR+2X0RQRVafk+AAzng2+H0+rqkqB45LqVgguzBdRXoy1V+katUq/nCUQqlM9qnC3U6ng/+4CVNGK9qOie1TWBhRFzEOnudxrNmYEYVLE54H7V4PM51mZWWZby5tsfi5T5AY09PE59NorciEPnOJ7I9sDhUc0M+rJ4aMLU8kElThXOFxr1rj2o/9IIWDIu2tQzYO9IlcVR+ixoKPCloBMgtZyiLcOTxiQZKI+s5vG61aNptXLk+8XwDTtNC6FozIJq15fo4UiyVncI3KFSvAvVqPezu3EQQRxfZ4TQpz26qhHVbwLmenXjDXF2ffxaH4fdB5PrbjdijM6uXHBbmO41AslTFLRVRVJeRTKeYL7IgC62trA7eRSsSplookM89nttKc7z++Z0UHQDAURtN8FEsF4uHg2Dbo4XCYw67FtOVnScXPh1qHzRsrxILB5+YZ8hBd13EHNB1VOx38S8vEAhrFowMS6SzylBEFv9/P++++w+W1symj4v4xSxdIj2z4YuQlpx9NuiAlwWTxzbWpvoMKIpsn4eZP/6kfn/j16hs5rv2ZT0/8uqd5WniYpsmxrqMkUqihEJubm6iKQvDaBo1cgvv/w29yNThZKqFudomunF1IUtksqWyWw709epUauSHtrfuNJoG11VOdEZOQ39pjJRAb+TxRFPF3bRo+kag3+PJ0paEASt8C/ORrj4bCZE2RSqtNasroZ1YW2b53j40rV6Z6/SAkefZpxGEIT/0GJEliIZfl0HFw9vZoShKZzU3iyTgHx3lEPBYXTg+pEwQB7Nkbps2Z85DvadEBICsKmcVlquUSqmESGiOHKggC0U9dg9++O9U+6wmNz/xvfgLXddnf3cUzLUTHQbBsJNdFcGzSoeAzCVHuVaoENN9AB0d7cYmlhX4tQkbTKJfyCLKK43lksoNrFIbh8/u59fobtKtnZzR4lo3neRd6f/V2i5wam/r1DzHjvrEEx1axhH9zA1mUcEwT2zSJLE/WXvskum7gfGmb7i90CUwxmvxpHgqPQqeLL51mc0hRYzQaxfxzX8D8199ClcfrWDmOiLhLK6TO6bJaWl2lm0yyc+cuUc8jFgxQ6nTxJBlTEknfuE6j1eb9D+4Qi0dJxuNnOlXOQ57AfCuhBdgTDaLO+Jcn07YADWuAyR7025815Xz/F0WWwZ7tfBtJlqYWapPimoNnviwtLdIMh1h9olZjeSFHu92hUCiRzZ42Dqwd5gklM89tsvac7y++50XHQxKpNI16jVarRTg8+k7HucA1wF7pLwiiKLK2sXHm767rsn3vHmq3x+IEF+ZR7FRqLEQiaENabJ82H0udVPK/f/f+xKID+oJODoQp1xr4FInQiZnVykuXKH57j/QEi8LThNXZ+AKMO2JmM5OmgEBuYfLPYRCNSp31cpCtX36L3E+8hCAruI5DOjl9F1MikYAxOiisnkF0TMFxkNPIfeH1sYRRIBjk0huvUy6V2KtWWXvt1ilBVypXeOXl6xweHSPJk0WWJG2yc0WVlbEiYbbjcKTYxHsCoiZi3Sujv+HHd/JbcF2Xg8M61X9xj5W/fp1k9PHC63ked+8es7yWpGfamLrNwbeOkfCR2VjANyLdNA6yIrNfLrOWefY9LFKzQa+n4/ef/W1FBhSHhkJBjncOML51H6HZRfTAlURSTZPWwhLa8tw0bM7s+diIDujXeRwf7KEooz0QzLuHU+/H85//sYmiyKVr1zANg+1332MtGLjQnc52pYrP5yMTDg4VHDB84GTmAvnZUDhCKByh1+1SrtdRBIhGI1jrSXjQmHq7puvMZMaxrY0fbSlubbO4NJsLaW+/gSM4BEQ/kWQO1a9h6DqlSgkPD5+iTF3kPHLf94/Gep7rujib6YkjMal0mtQA2/xMJk2xWKRULj8aiDgukXiMstkhpY6ORLqui9y1QBq+6Lc9m5ri4jgm626Ih072S5bG3u086kIYASj9+j7pb5lkkCltN4i+HECWZXTL4u6vPyDxWy12b5SobHqs/arBuijDl77Kd1ccbvy//uTAxXoUjWaTdqOJWakSc21k/wy9cc5hIRbjuFxmeWX8LjrV8sjlezxONXt4gkxnxoXec+Y85GMlOgCS6Sz14vFI0aFdWcTdvnNGDBiWgaYMv9hZlsXx/QMuv3pj5LGomsblT7zJg3ffJ2j1SAUCNLtdeoLYnwRr99MxiXBoYNjXNE32qjUS4TCJcfwchqgOzz7f5nsc/IEA/kAA0zAolgs4S3Fa98qExekuTkuuwq6kk3Fk/GM6fD5N0zVJjulOu+O4XH3zjan2M4j2d48Q14Pc+Ms/8ugxzecjvdT3QjB0nUqzjk8SZ2vIBshrad46LvC6Pny7tm1z+MYCl65NV/g5iKN8kZWlhX5EZkICAT/717KktkcXVR6068g+H3c7Xar5Dr6wQq9pIgoSru1i2w46Jp+7uYgqnU0zLtfgW7/6LiuHPtJP/L5D/32eb0q3Wfo7b9L6ZoHUV7ogisRu28RuA0+ch5GKQG23TOSV8URHsVjG7HYxq1WSgkcuEAC/BmjUSiWcgP+51H7Z+uAUyzC82tnvo23qhOPPf9TEnO8PPnaiQ9U0XFHCcZyBP/K3/4evoC2E6DyoUPpmkU7Kxq/6UFWFnqmj7DjE34iwGD17l3rnuAq3DSS/C39q/GO69MpNLMtid3ePhWvXSDzhy+A4Drtb23itJpooIQKi4yB7HqZtkYtGz/gSDGNYvnqWFztV0+gZJmsbqxzcyRMuTDdpVJVV1jy4bdR4yT9dSqIdVlgZI5pgmiaR3MJENQjnYds29W/ssvqnhxeRaj4fmi9H5fhg5qJj7dI6umlgfmV7aF1HE5v05ZWZ1hK4rnuhPP/GD7/OYfwe9lvbrHlnvwvDNPjG3Sq7Xy1jvtNBFIYfu3JLQXp5cE2OKIpkP7mAeNw48/ial4B/sM2oMy7cFan90ge01jOEB9RP1RsNet0eVqeL3mywosj9wYMDZsispdMUWi2yY6R9J+Ww08GNxMHUMZpNsi9N1vLrNM56poRUH/UPt8jcmm378Jw58DEUHQDxZJp2s0r0idDog+/cQVMUxLerqP98l6AoAQKpQ4V+8tghBoBE+xtdDj/hsRTvvz7falN7oBO7Y2IqwF+8Sa3eoN3usDJm3lNRFC5dvnTmcUmSBrYhmobB/e++TXZMwdFncKhj1gWtkWh/mJp4OYt1/ABlykgFgH/KMK7h2BjK4/fleR7NbhdHlpEEkaiqsG05hFJJmpUqV1LTG0c9zVGhzGv/6M8TSsdGPtfxBCzLGruzahzKpQrh37+PKj9e4Go+gfblDMvv5tm5HieUitOsNojNyIL74PCQtdWLmd+pisLG6zewX7lKuVimeWef+pcfYFoOhgE7Xy7h3OuL2PMEh+25/OCfvYR0jm/JoqpwZ8UhvT+94I5/vcn9f/R7JP/iK0iShGdbGNUqbqtNLhol8/C3OYaodM851mmpt9tELl0idoFBjtJaGqr5U48JgoD99Q/xXr029+uYM3M+lqJD8/kolxxCjsPeu1u0vraP71f26YQVYk0euToOI1Tz6H69y93LLrig3emS0EUQRNygQPutA5Qvvo5mPzvTH0EUSUy4ULnuYNER8Kk0arVH4+4vTv9CtPrSJXbf3mOhPZ0T6IHXI6T4hhejDMDzPCoLYVyfgpKSqQUjCKoKmo9oMoksy7QbdUrvfJf4pU2SiQS5CY2szqNSrbG8vjH2nIrM4hLNRoNmq0e9XEBVVXyaiuN4eHiEg0HAe1SkOw6BYIDGF29g/vYdVFmlmgqQ+LkfRri/hyAUiK4vkhviqDottuOiqeMVr45ClmVyizk80+adO29T/dXyZMciWaRHRK0UUSbxyRT6bgGfOP1xJ75So5l9n+s/cuJ/EQz2/5sQ152d//9+vYGYTOFfWSN5wcnRmZfWyb+7T84+fa1JWiLVnX2SG4MdTOfMmZaPpegAWFpZ4a1/9WWi/80d4qIEKDCBH1igBbzVv+uyPI+u38XyC2h//BIZv8KD//o3uP4ff/GZHPtD9EkHAA95vs/nQ280MXT/TKZJyory6O7dWo3DB9WptiOoCklrvFPQ8zzqjoG5lmbpZz93btogFI1RSKa5NONZGqZpImr+iQdjRaJRXMdBtHXi8fipFspqtf/Z7R0esZTLjpUKCwT8XLq8wVbPpH57l0s//AqqphG/sk71W/eQRgwQnIbz7PWnRhCwOpMvxj5Xo2M7xEcMWlyUNQp/Okfx2yUy29NFGkRBRPvVCvuLQVYuTy9enQvcoNS7XdqBEGo4hKwoLF+6NLPvIxAIwKeuwFd3Tj0uCAJutQVnm/PmzLkQH1vRAfD6L36R/WSa3j/6I/z1yS9uut9D+NwK4htZcq+soXd7FP7HbxD/vQryZ+IIgkC3OxufhqdRFIXU1Svs3X9AVpHPTN80LIum45H2Pb6L884JGcSiEYqVEpml6QZ8PUkoHKZ6tEcymWTtEzcp3vky6QHuovsLfpyoxvrtOtD3SvA9cbfsOaO/k1pARHx1AymbIJlLj3WxdR2H6ICanGnxPI9qrYbpeCysDHZyHEWtWiFxclf6pGB6WJSZALZ3dkkk4qfSguex+co1ymvLRE+6kxRVQfvcq9TNHlKzn6s3DJ1MInahup5yuUxgiG3+Rdh/94j675WRpmhjMsc4dwCyoor8epLDep5sbXLB7Xke7UUZ+53yxUSHMbnh1mGzCck0scuLrD2DepCHKJHB361wgenZc+YM42MtOgBWvvgKtbUUh3/vN/Dv9FC90xffRkYkWhx8Aau9Hmb9z90is77E8R98SOfv/i7CZzN0f2YZ4dU4uwfHLDxD6/NQOEzo9dfY/c5bLCsKrutSNUyQFdTFVdLZLOW3vkVKkdnpdEnlzg+pJ6Jh8gd7pHOLj2ZwTEOr2XzkhaIoCsZqArZbZ56nrqRYvLHJ7fbXUYJ+wpuLHN4/QKp1SRy2sewuXiA+MG9c14DXNkm+9tLEd3XVcunCU4ifpNlsEU5mUS9QRCmMYaa2sb5GqVSiXKmSGsOOvV6vE0ueXghj68sntUmPqRztT/15mKaJaTkspiY/z3XD4O79B0QjUaKREJ2ejqUbKKJC7bDGN//Mb6F40xW6/tE/v0/6f3eDsDD83CjbOuXjLvIftci600X49j4v89pPXsN/wSLkbDRCo9MhOiI1Y5omR7aDP5Mhu7Exs5TWeVit3uA/PIOI2Zw53xdnVfzSAsF/8u9R/IO7GH/v908JD+sHMvArpwupejmV8P/lh3jz1gZ7Hzzodyu8tYciCvi+Xqb71y+z8sYVfKL7TKIcp46l1wPboWNY6MkUmc1LpxYv39omdx/c5cqrr5AvlPD7/f0q+gHIskwulaBSrVxotoJlGkTCj9937OV1Wg++c6Z9VpBEJEni5k989tFjC4t9YdRqtQjkK+y+s0PAcIl0HHyiRF328F7bJPHGS1MVX7qOA7aJIIxfIzEK0/WIXtCd0XJcqrU6iRGFnel0mnq9zv7RMZlUkl63h26YeCepM9t1kCUJURSxLYueU2Rh6XxnVU+a/md+eHjExpABYYPo9XRqjSaO6yDgkYhFWV5aotlsIgHf/Bffpvj37uB6Lso5xaKjMN7R+a1/dY8f+YVLpKTB53unbWJ8vULcmz5KELMDFxYcAAG/j0KrzbBG1P1aDSGVJriQ4tIMi55H0W634ev3gNO/NcuxURLzabNzZs/3hegAUDWV5R99mfu7FZr/+jaeKiBkgnjLIRpZEVGRkQIKngChn7vJwhv9jpKNV64CcP1v/wT5T9zl8J9+BTIaRqdJz+OZi45ut0v8lVuIokgmevaSFU2nUQIBOp0G/oCfVrtDMjHi7si5mG+H8FQ9RSKTZicXIlw87RHgSsMXlXA43I+WXFnHcRw+/MbbJOUgyU+9fKGIwqyjHLVanXDsYotAt9NBdAyCY4bIY7EYsViMw6MjJFEkmx0+xKzcGO17EQxHabebExWrPkQZswao1WrR7em02h0ub66f+XskEuH9X3uP/H95G1EQzu1OGZfm77f411/9Nj/9X73GonpaGHSMHuY7OkHZx3HGI7xnEJL670V3TaoLIprfRzcGK//+p3BEaP/9rxItnd6HeK9Do9GaSbrOeeorLDSbmOEIWjQ60zqNcfE8j4M/fIfNAanRlm2SHTJhes6ci/B9Izoecvk/+Bz2X+rfecuyjG3bSL/4hbFaw3I/cBUtFeTOr32dhU+/9oyP9ARRJjKi6yQQDFJqt2k1G0QGeAo8TTwaoXK0jytIJDPZcwsj9V4Pw9CJxh4fg6b50PXTrq/C5SzW8RbKyV31VlJkbXU818pGs8nCzWtofv+FBAeA4LkzafOrN5rYiIRiSXwXqGcwDYNuo0p6wvREs9lCUjTqjQaRSG+guK3U6sRSoyNW/kCAcqPGpJLDNE3854T3DdOk2Wxi2g6aLBGPRc8VfI5pI864BVN1JN77RpHUDy6jPtGVdlAwiOVdQEEuOjRyMjVHRwv70eoKiwUBsFF+MM7KjXUA9v9vIuZ/+LuoT6RsQgWHow/LRD9zcdFhWTb1dpumqqElk8RX1wZalj8vdu9ssb7XN0h7GluT6Ha6BMeYZTVnziR834kOOF2JP+ndRfzKEpd+8bM0Wy0iz7C46yGRUIBSsUh6xOyGdDaL5vOhdztUqjVURR46g0YUxUeLQyl/iD8SIxQ+HUo1dJ1GtYJPFggGAlSP9ymUq6RzCyRTaapHlVOiY+36Zd7eOiLi99PzHK59/pNjFy8+HPteLFfOHMekPB2FmYZipUo8nUOZQT7d87xzresHPf/dDz4kk05j6Aari7mh0TTH9cafIizJEw8eG5Za6fV0StUqguexMsHQvPjLz+bO+eh/LlD7VIbsSbSj0GyhffextbffkvCXAGSoeo8edz0X4Y3HYnDl8irv/+9voP7jx4Mg20mRxodV+MzF2zjiikwjlWZj7aNvQ+10Oki/8yGiOvjc8n3+9bngmPNMePajDz+GpJdy7N87olKczF9gGpqt9ti205FolMzCIvHsIoYrUK3VHtUCDCOdTCBaOvmDPaC/6JXyxxjNGplElEgkgizLJOJxrl+5RDLkZ+fOBwOPKXxjlY0vfpIbP/qZqbolPM/r12RchBncScuyMhPBAX3PGMsav13S8zw6PYN6q0MuO3xuSrFcIZ4evy4nmc5QrdXHfj4MT61IsgSuM5HgACj8wRGu52J6sx+dXu/1z5tSq03jvolfH30eFP5Eghs/88lTj1358VvkPxOgGXZp3PDT+qSPy79wlXK7PfK3NIpIKIRdqV54O7Og1WyTHSI4AHiGHkRzvr/5vox0zIKbP/gm5fx4g7cuhKJN3GkiShKpbA7XcTjc3WJ5xHCuQCCAoihUj/fB80jGYkPviAVBYGN9cNvo8tIipXJ54lTCQ3q6cfFIxVO1Ao7jUG808QARj3h8cLfMaaZbFPRevwvg6XTMJFvrdrtsbG6SXVhk7/5d1lZOL+yO41CuNUjlliY6LwRBwJvAOfZhamVvawvHcdk4cc01TJNiuYJPmyItsFfm2p9aJGUrHAkGIUXhu1/eI/N6jNLX6si96dt6v/KP38H6S1cJvmMR7Y4WHLpns/anz87iUVWFhb/xGdKJOJqmPTL18jyP3fv3kU2TOBCYUpSu+TUODw4nGsr2LJBk6dzIlzdDM7M5c55kLjouQG+K3vtJODg4Iru6PvXrRUlCHXNxUBTlUZpjWlRVpd3tcXY+6Wh0XSeWTF+8HuPk9c1mC8v1EBWN5GLfm8TzPGrVCrgOuC6eY+PTVIInbYymaaKq6sQXXL3Xo3x8QCIWxbRsOq1m31E2lT51TOMQCoXQLIvCwR7Rp+bK6LpOSzfJLk8Xnm91urRqFXx+H7Ik0ev1MC0LAYF2t0cqncLo9RBFkeOjIxb9frKKgu06HOzts7y6QrlcYWXCCbP3/+B9qn+4xcZ7NjL9xTpG/7xMv7KMz5R58MeD+ESZ7/zbXRRzMvGx8tMp0qqf1Nessc8fPSlzZXFwi7kqy49mzDy5KG9euwbA/vY2tFoEpqg/EkURL5+nl0p9pPUcyWSC+3aHdXVwClaIzlMrc54Nc9FxAbK5BYqlPOlkgk6ni27ZBDRlZh0tij9w4TC/PxSh3e4Qeg752e3dXRYXJluQHlJtdVic0njrSQKBIOVGg0gsQeSpRUEQBBLJ01EYvdejVKuC6yD5AnhGFzU4WV2Jz+/H7/cTCAR4+M07jkPl+AAHEWtCcaooCrmnOgdarTaOpJDOjTfr52kc2yYa0Egu5zBNE9d1SSaTZ9JgDwclCobBykmMRkWmXSqy22widbvcKRa59tqtc/fXabU5encHt6xjfeWQhXc7wFkx4fcUEOByuX+ef7jpw749fnfV0k8ludzx4WsIj6ezj4G4MPz3EImEyecLQ+3zVzY2ePDdt1mybZQpOk4WI2H2Dg4eRY+eNQ/TOU8KMlEUEZeTUDx7bnYsk2BumluHOXNG87ESHY5tc7izRSgU7P/QBAEPEctxCEeihGZc+KlqGumlVWqVCv5AmFQgQKvRoNrsgGON9GQ4j06nQyR+cRvvcDRKq9EvjgwH+ovjsyLgD05tZqQqs6mh8Pn9E3Wb9J8/WW3CQLzT0RFJkkiefP/H+R7v377LS1cuTVXr0m53QPMTi0w/brxaLpI+KR4e5uMCjycSC6IAT8zyyTxst41GsGybe++8y5VXXxm6nXtfeovkf78/8XEGwhpNxhcdCX8Q34jxBoZn0UtICEshhIUg4mIIMzu8ANenafT0IYZZJ1x67Rb3v/MWi553xi14HGLtVt/c7YKzUwbx/u/+Eb6GiRhQcT1wDsoon7rCxstXHz2n0+mgldrA2XPBDCrEn7EVwJzvXz4WoqNcLOA6DjIuqwOmvjaaTQ73dllZ30AQBPwz/EEJgkDiiRqGcDQKRMnv707cLfAkPdMilRx+nHqvR6fVBEEgGAqfu9CGo1HC0Si1SplOtYZ2TmfLRXAvUiB3Qe+QjxoH6UxHk67rdHs9VE0jl8tRqzcQRJFIKIiuG/RMC08QET0XPGdgLYxhGFiCRPwCgsM0DNQJT0NvyPBAAEWWWcHm7re/w6XXbg0UUsqdsyPTx0GQJ0yvCQLDqmYMyUH/bIrIz7/E5Runu08O84VzNyuNIYIvv/E6D97/YKqIRyQQYGtnl+it6MwnufpqOitVBx6JNz/NP9ji3u1DrIUoNz77BsX3tlnyBr9HLzoXHHOeHd8zosM0jP44dUnCsW3qtX7LnuecpDTOmVcRjUSIRiJs72zzYO+QT336M0QGGG3NktzKGqX8MRG/+ig/PC6e5yHI51/0WrUK6UQMgEazTqdRA1HCFwgSHGICFT9JLfS6XSqNBrahEwz4h5pGeZ7X92FwPERZwbXNR3fLg7Dt6YVDPBalXMiTys52OurzIrO4RK/bpVSrIUoyCAKaz09i4ayQqJaKKKqfTLr/Xh3bZnvrAVqzReSkjqNarRGLRWl0dTILF4vENGsVUrHJUkauKPZrX4agyDKbQYnd776NL5dl8SlXVOWHFnG+UkY6Kew1XRtJEB/9exiRmJ8GZy31hx7nAL1h4dD9dJzIL7zE5VuDUxijOkj8mjbWXKVLN2+wdfsOaqdLxqdNFMla11Ty+QILC7M75w3DRGrpPO0wGlFUIg2PHbc/YNDJDx/SKMRm5+Y7Z87TvPCiw7FtapUyWAauICBJMiIeydjkomFjfZ2N9XVKlSpuKDTxtNBJSecWqJZLWNbwmgrP8yhV60iKiufYaLJIu9Mlu7I+dLv7u7tk44/vqJ8sODw4ziNJS+dGPvyBAP5AgGKhgByMUGm1EDwXnyw+ush2Oh06pk08mSZ6Eo5vNupU6k0Ezx2YOnKc6dvsJEnCJwl02i2CoWfvf/IsePi5jiKRPu25Iskyl69eo1I4fvRYtd7Acr2pi0Yf0ut28cmTn+e5xQUad+6cOytEEATWQkFqxRL3G03QdQKAHYly9fOv8u6DKlJQRVoNE1qKU/qNO8R+6Xio8HA8l1ZHJ/KFCKFE//z1PBfXBdtyMNsW7UoH/Y5J4icX0UI+jLhE03NwG8aj9ImxoPDGX/1jE7/nJ0mnkuwdHpJitOvw5kv9AtPtu/cQ220yqoo6hjeLKIqYh4eYqSTqFCmaQVSOiyxaw7clFpo4joOciEB5sPBww35qlQqR2MUGBc6ZMwjBexGaxgeg93q0GjWkIQvcRfA8j3K1jqRqIIhEotHxTZamoFYp4xO9R/UUhmHQ6fbwBBEXSGUXHoVYDV1HkqRzj6dSKpIMD78Q7u4fsrC6PrG7Z7vVwjR0bNMgFI0TGLLgPBSCAUU8dUHe3T9g7YKtgOVqjXhm4UID6b5XMXSdwuE+uC6BYIBYOnfh87KcP5o4yvGQ429/h8wUBcj5VpvMrVcHzs75zj/+TZL/tjjQBr3uN/D/WIpFpd9dM4zvWk0+9Td+7tG/O+0OqqZONKvnMF9gaUih6JPsHRySSSVPGeGNYndrC7HRZHHMmS0HisbaAOv4aTjaPiD15btD/+66Lu2few2j3ib9la2Bz6nbOiFBYT8I1/7Kz8/kuObMecgLZw5WLhY43LqP3WmQjkVmLjigf4eWTsZJhAMkQj7qpTyt5ohqtAsQT6YwPJFqq0Oto2OJKomFZZK5RdK5xVM5Xc3nG7nQ+ANBut3u0L8vZNMYxuk5KJ12i3br/LB1KBwmkUqTWVweKjigf2eeyuZoG9apMPUsctOpRJxKMT/6iR9DNJ8Px4OuaRKMJS8sODrtNgF1evFmTts5JclDBcBrf+uP0Voa8r7ejLHqC54rOACuolKvNx79OxgKTjUccBxWl5c4LpYwzfE7kNY2N0levcJhTx/r+Vq1QqczXQ3MQ1rNJjsffIheOGI3LWDag49XFEUqX/oW1m+9M3RbMdlHTxNZ+pOfu9AxzZkziBdKdFSKBcKazNJibqrhVNOSSsQRrR6V4wMqhTzVcnnm5jixRJJEOks8mbpwF0233Tr3zktVVdrN+qnHLNOkWatw5/13cWbkNpjOLlCu1h79u6fr2DPYdiwU6KfUvg/ZuHyFl26+MpNiZ1VVL+QlY6vjL+Su61JstynoBp1ztKcoigibZ1OjrusSSo8ncgKKj26lMfqJ5yBM0F+7sbrCwfFkQjgQDKJmMnSfEv+DSIdDlA6nMxrc29pm9533sB5ssSKJrMWjbF5b5Pja8JuGDVNlUTv/+upcyhFKXbx7bs6cp3mhYtieY6NpH40pTTAY5OHNved51AtHeIKA43r9egsEYvH4CxH2F/FGdsUEFLnftXCSYoklksQSSUzDwLKsmbwPQRRPVflfu3yJ/aPjic2jnkZVVXSjRa/bnWmn0fcbiqoSjCWpN2rEopOnWATLhjHnxjQME2V1lVR6tL9D5GevoH/76/h6jxd+3bNRJ4iUGftFuDR9vYt8zgTkQYSDgYm70XKLC+yUK4xzBhtHR+wYBqgKsXSa2DmjD9qtFsW9fTTTIq3IqJrCk4Wjnufh6BcbJ+CVLybq5swZxke/gj6J+GIULQmCQHxAWqdcKZHKXmxBvSiWafZbLEcQjUYo16tnjneSOo9up0MgGOR4b4dSqUhuYYlEOnMq7O+4HtXGSdpGEHA8oe9TAidmTY8Xkk6nw3I2NVYaJhIOUyiV8Acubhj2/YzP78e2LVqtNuExJhA/RO/1MDsdGLNgu6VpbIwhOABWbl3i/b9YQvkn96n/bBZZUYj82Ca9bhf3awdjLezBeyWabzzu9pmUWCzK4dERS4vjma3FolHyhcLE5nex5UWa+/tEzolMHtXqpOMxoieRpdrePg/yhf6Mm0fZSw/Z9cAwCXgOa4EADCkQ3jkus75vD5weOy5qqUWv08UfnIv+ObPlhRIdkqI+ckR8EdE7HVrNBuELeCZMS6VcYm9nC8fxeO3mS2O9xi9LlAp5orH42GLDc11KhWNKxSKqquIikEtEefXmDWzb5v7d27x087EpVDp3+iKcOGcYbizhUC8eDxR0g3AnsZicM5RQOELdsuj1emOZwxULRY7ff5+Xl8ZbkKudLgvXxzsnH7Lxk6+yfyXOzRuXHnlc7G7tnCs4LMemgQOeh6DI7H3rA17+4qcn2u9DVEXBmSCDqigKhj46VfI0sUSC/f1DhsWZ9qpVUqHQqVkucb+POMDTNf6iAP7Rv+NsMsr9nQqX7eDUPkFBUaGxdYD/laujnzxnzgS8UDUdnuu+sIIDQFFkJNugUjzfWGgW2JZFuZCnWjiienxAQILXX77JYjaDPGZqJBgMkI6GMJpVqsf7/ZqV/CH16uNWuXIhz+HOFoXDfTzPo9VqEVAkNleXicdiOEaPg6N+K+eDrS18sjz1lExRkvDGDKG32228C7TgzjlNLJGka7ljFURmshlCqXPU41MYijK2C6xpmtx/sEWlXufaq9dOmWq13t1mW+hhOw6241AyuxRFi2PNYV8wKJldUpJGDBmv02P1bo1SYfran0mLTy3boVarjX7iUwji4HN+u1wh/ZTgmAUBTeWlH7jGbuBihnvu4fdnXdWcZ8sLJTpkRT7TdfFR0mq1KJTKHOeLfHj3HodHRwQCAcI+hcLhwbPbb7NBo5QnFQ2RiPY7eB7eoZrW5IWB4XCYRDxOMh4jGYsSkOFobwfbsjg8OECVRAKqzPHeDpFolFqzjc/nY3dvD9cTyKTTNJtNrl29iqrIHGw/mPq9eSOiF71er+9bEogQTkw3rXbOYJKZLLVW99Hk1PMQfSq6Nd6i5YwpgguFEuVqjcuXNlkZkNYIxaJseH52vC5HvSZJ2UfGVVgwJFY8jUV/P14gyzKpQJi6X8DUx+sQGYTfp57bBfY0V69cotFuT7wfZ4DQ3ipXWIpG8c9YcDxEEAT868nRTzwH76A0o6OZM+cxL1R6JRKNUTg8IKOqM7cGngTTNHn/w9ssLy6STfcXvoXc4zs/VVVJx2VKR/t990mE/kh2QSCRSlM4PECRJRzPI5XJjT2u3bYsmo06PtEjOcT5U5Uv3hqoqiqLmRQHeztsXr6C5vPx7lvfYnlhgaPDA5KZHNVGE78/QCoR5+DoCFWRUQoF1tfW2Nnbn9rEyxcKU2n1Ttyr+46yDyMagiSjBoKkF+fDpp4V2aVlSkf75zrLAsiKgtHp4BsjGjBO9Gpre4dMOn3u4EG70xcQl+UwnFN+UnV19E9tkr26fqFJrbFolJ3dPdbXxi9IDfj82LY9drQRwHvi92/bNrv1BhuJ+NSpj3GJh33c1Xe56ptcfNiOg70x3XDBOXPO44USHQCpbI5mpUD0GduUn0e32yOTTpNOD7/TFkXxzIW7dNI+qkgCiWi47zZ6dICoqCQzWQRB6FucD7hIl/PHOKZONBLG5xseqlYv4LvwNMtLCzSaTdq2hc/nQ1UVmt0eZcui226j6z0sI4AkCmTTKVIns0GuXr5ErdFEFKWJu0uCofD3rOPoxwVZ82NZ1rnpBdu2iI5pbiWPiIjsHxyytLQ4chignIrA8fmRi720QubznyQ3ZQHp00wS6YC+U+nO7h4b6+MXOAuKSr7XRW/UERSVzWTiudxUBTSNq1+4ydaDPJvF8V9nuQ6NN9dY/uwbz+7g5nzf8kKlVwB0vfeR13UIooDfN5mbJ4AkijRqNR52bAiCQCaVQHJtyqUiH7zzNt/95jfotE+bdJUKefyKRDaTHsv5cJzw+LhEIxFco0c8maajm/S6XRTPxTJNXn7pKqVKmVdfvvlIcDzEu8AwuzkfLfFkikqjdW5tTjAUpjamudUo0eE4zkjDLwAlMVpI2I3O1B0rT+K6Lm999euE2uPPeYH+b1o756bgaXa3tvFMA8H1yMZirMVjzzWKK0kSKxsZ7ovjmY8ZjkP7M1dYnAuOOc+IF2rV6HW7WJ3WczUGG0Q0EqHeanN4dDz6yU+QiEXxSx6+p6IR4XCIYv6YTCLK66/exOm1qeSPqBbzVPJHqIJHcMzWtFg0ysHh4UTHNYpUIk6r0cDodXjpyiVUVcbv03j/g9vcHNCV0G53UIJhtAmsoee8WGSXVqjU6kP/nlnIYY05wC+qqhzsDx9jLysypjl6W3Lo/MW8a5vI1y9ms/+Qrdt3eCURI+X3czTh7ymZiA29Npimydb2DvuHR7z91a+xoPdYkSWyfu2Z1W+MwqcoJF9eoGmcH9XRPQf98zfIfvLl53Rkc74feWHSK4au02tUn4nt+TRsrq2ydzD54q6qKupTFxdZlokGgyST/dxq5AKOpKIo4joXM/4ZxOpSjp29A+5vbSOJItFwkEsba2fuykzTxEQk8RG0Dc+ZHYIgIIyY+OqMmHT8EFWRsTvDFzTXdfueEyN4WNMxiKLmof30J7icnU1xcbNaQ0gn0RQFqzVZcaimqjwZa2w2m1RrdRRVxaeqbG6sUzzOsxEKfuRR24c0j1usaeff2LQzIRZfvfacjmjO9ysvhOiwTJNWtURqRHHb8ybg91Eul0kkEpTLZTKZ8dsIn2Z5aTamYqVyldXVi00eHYTf7+f6tSvnPsfzPKrNDrnllZnvf85HwIgov6WNV7TseR6OObi9+Tifx0PAsR0YUZQ6bPSAbdtIf/wV0jMSHAC+WIyjRpPFaARxCqt4vdvlwfYOfr+fcDBwphg1FAnTLBT4aPyVzyJJInD2ZqXt2djXFhFiIeRkeGjN2Zw5s+KFEB2GoaM9wymv05JKJimXK1RrNSKRCPliCduxcR2H1eXZhHknxXLsj6yWolipkl2aveCZ89EwKtIRz+Vo7e0RHpFG2+90WX31lVOP6brOUT7P4sICvjGN6QKJCHXXJCaejRSKU0YM9F6Po61tVMvGVmUs28FEINJpsXhiNe6zbfReb6TXyN7+PggSqqKwsrJ87vsKBIM0kkk6tSrBCac9PwtERWaQ6NAzYRZ/7DPP/4DmfN/yQoiOUDhC03HpdDoEz5lu+lGQSj1uN8v5fHiex70H2x/Z8djWR2OYVa3Viadz87ugjxGjTN6CoRB5yyZ8juYodnXSVy6f6YQ5Os6zsrJ8yvxrFIlkgv0/dgPzNz9AfSq10/pwl2xu/Ehjs16nvHdA0LZYCz5hGS5J6KaJ74nZJolQgAcffMjVN08XT5qmycHhEYqmocoKS4uLE6VLFlaWuVersf4CFF1blRZw+jPtuTbhL8wLRuc8X14I0QEQicU42t/D7/c/lx9oo9GYqi233W4Ti3x0ha7OmMV9s6TVaqOFoxPNbZnzPcAQAanrOp2ejm5aKJEolmmgDBj8Vu/p5PHolSsUSmVkRUYQJRRFZWlpcWzBYVoWR/uHLK0uI0nSGcEBkLtbYSt3n83rl8/dlmVZ7H74IXHXZc3vhwEpIt+Ags6A1D/WQfUZF+HSyzd58N77+F0P1bFJBAPPXbgfVRssNcUzV/teJkwufTEDsTlzJuWFER3lQp5Y0PdcBEer1cZVfJTrTTRJnGgQFnCmUPR54bourjudBfm0GIaBLcrE594aHztEWaFSrREOBVFVFcuyqLe7+EMRkguP6ycq+3twfEDsKdHZUhVev3ED6EdN3CnGGDQaDYrfuM3aXoeDT+g4xcHTTVVZJfW796nlksTjg2u/yoUC3cMj1gP+iRd2y7Y4yhcG1mdcBFEUufLqK5imye7uPnXbwuc4BCyTuN9Pq9cjPKaF/DSYlo11p4wsDwhXrWef2X7nzBnGCyM6VJ8PnMkLusal0+liOzbRSATdNEnm0oiShN7rUapViQb9Y4kJ07RIJoePnX6WiKLI8tIie4dHWIZJPBYhcc4I7Iviui71To/s4kdTvzLn2RJP9oVFs9Gg1WwjCCLphaUzz0uurKKn0pTu3SFmGtQsC1tRCCQfCxNBEKbq1Ch/9QPWjnoABL+5g+oAQ7pmZFHEsAd3bm3fvkPU0FmaYipqzzSRkwkWc89uET7M57ly5dKjf3c7HXa3dyg326QUBVU3yAZmH+U9OK6w5pwVHF3HJHRjY6b7mjNnHARv2uldz4BWs4FndC/UUvoQXdfx+Xw0mi0sx8FDJByL06mVEUURNRQl+IQfSKVYwHMsYuEQoiietBSevVtqtzu4rkMkMmxu5PNje2eHjfX1Z7b9QrlKZmllXscx5xH57S0cx2RpRoXUd/75l9gwxosc7oZdLv/ij546Hy3LYvudd1n1aRPVjzzE8zy2dIOrr92a+LXjUiwWCYXCBM5xeHVdl937D5C6XRKSOBNPj71ijewHzYGW7ZWNJAt/4rMX3secOZPywkQ6AMKRKOXjyRwCB7GfLxJLpKjlC8i+AOmFxxdI143T63ROCQ7oD8MCqJZLuJ6HKErYpk5AkU6ZlRmmMbS173kzqvvgIhTLFRKZhbngmHMKJRQiF548mjAIy7JQq10Ijl5gHdfB2VygXm8Qi0URBAHTMNh/5z0uXaDG6qjb49KtV6d+/Th0dZNM5vwUiiiKbFztt6znj44oF0ssqspU0SPbttkt1kh82ETWzkY5WpJH9LPP9j3PmTOMF0p0lIsFwmPOeziPgD9AOBLBHwic+dEGgiECweEXqUTq9LCxTrtNoVgiHAoiKwq1ao3Lly8NefX3PqZpUmt1SGYXkV/ANuY5Hy2CNzvBbdsOwoAC1UHUF6O89EOfwbFtqrUauA71B/dZv4DgaOg6gaXJOlKmQVUn+x3lFhdhcZHt23eI9HRiEwy1K3d7dNpt4n6VwpKMUrQRXA/fyaBIw3UQfvwNAtF5jdacj4YXSnTE4gkatQqWZZ87jXIkJxfGSSZBDiMYCqFpGqZp8uHt97l188aFtzkzxNlGIeqNJp6skl2am3/NGYI7OzdcVVXopIMwuHb0EbrnoN7oD1iTZJlkun9j4DTq0BtvpsjTuK7LfqfHreyzLaY8PD4mEZvO9HDjpWvUq1X2dnZZ8mlDxZFl2xy3O3iuhyaJrGX6n08iHmVLPUJWBeQji7Qu0HpjjcUr4w+rmzNn1rxQokNWFJKZHOXC8XmTrUczw7sx6B+XrChceekmlWYLTgzCRElClSXCM6hBmQZhlKXkmNi2TbXRIppMz+epzBlKp90mMMUgxP9/e3cWI9eZnnf8f2rfq7qqq3plN5ubKFGipJFG1siascexYQO2gTiOLxznIvYAAZJc5Ma5yIYEAQLfGAgQIHEcG4Edw4hhDGw4MWY0dqyZyPasWjkSKXFtkr1W176fOlsumiLZe1dvbIrPDxCErjrn1FeCuvqp73zf+27lxruXOVvf+v/hhmVipyIEX3ue3PlTG54fmjlN+dJ75HZRYn29uUaLyXiUUrHI8LpKw67rMnvjJp7n4gHTMzPbduTdSq/XIxAIEh1gpmK9TDZLJpvl1pWPSfV6ZNb9frquy91anVPDm299PXVqtT39QrDE4mybk6+9uOexiByEYxU6PpXJDrNSKpJJxPb0y2543qGU843F48TWFS8zez3KzQaubRMOGAeyCHa3DuL9NZst+p5BQbMbsgOz2yF7QOs5AEafOsny5UVG7LUfQ03HxP25V0hPjm27oywYDJI49zT1Tz4iPcDnRL3bI2Z4ZCIRVhYWuV4qM332DOWVFXqlCuG+yVQijmEYeJ5H+dIlOrEYM08/PdD7uzu/wNnTG8PSXsw8fZ5apcLtW7NMPzQLfLta2zJwPGx8ZJiPfWGt0ZJH7lh1mf1UIBgkPzZBsVxhYXmFfn+wrbTpdIp6rXY4g1snHImQyxfIj43Ts1fDTqlc3rHa44HYxweI53kUSxUC8dT9RbQi2/GcvVfDXVgqUq3W1vxepFJJIj/zIg3rwe93I2zgfOlZ8qemd7WFPZZMEjpznsYuuth+qthoMnyvMGA+GuGkz6B66RLxUokT4SCFZOL+H2fDMBhOJBi2LJYWB+s6HT7gYnqZbJbR809R76w211toNDmR3t0uup5lMfGsmrnJo3csQ8enJk6eZnx6hnq7O9B5fr8fdx8fkHs1XBih3OyQzI1QrNTodgcb96D2mjna7Q7lRpv8xAmisYP75iqfXZ7n4Tl7q4ZbbzTIj0+SLoxRaXUp15uUq6u/H/mRPM0fO4PrupiOg+/LFym8MNiMQiKTIXD6HCVz5/FZlk183e+NYRjkEgli2/VSCYfplcoDjcs4hEKHsXicvuth2zamZe1q3Zpl2XRyBQXhKB0AAB7YSURBVDKjB9N0UmQ/juXtlfV8gT3songE21p9fj/D92YNRiZOsLy4SDBoH8iC1oNSqlSJpbMMH7MeN3K8GYaBgx/btun3+/T6Np7PB54HrkMkFLzfN8myLBqNBrC6mLtnu6TvzVrk8g/WT7RbLcqNNomRHFdOLDKayjF2em+LHJPZLNHPvUzp0nsM+7f+Y9/sdRndxe2IzaQ8l0qpRHZ4d91ufUDPNHfd8G43VlZKlFZW6MWizOxiEazrulTjCcZmDuY2j8h+HZ+/htuIJ5KUGw082yJ1b51Hp9Oh1+sBxuo3CsMAjNV/GwaG/9G/tYDvYHbQbMWxHWx7d6Gm1+vR6PTIFcbwH6MQJI+PkYlJKqUVorEE2ezaGbJup0O52cIwDALBELmJ1fBg2zbpLf5/iycS9+vl+F+Pkspk9jW+QCCALxwF29zyGBf2XPUzHQ5TmZ9ntlLh5LlzOx4/MT7GwtLygVY6bTeaTAwPk9pl64ai4Wfi6QsH9voi+3WsKpLuRqNex3EcYrEYofDxXhhVXrhLLru37XK70Wq1mV9c5Kmzq02wOp0OwWCQYDC4JozU6g184di+P9RFjrvSD99naJttvSutFvnE/ho22rZNsW/hy2WZmNq+T8vi0jJd0+TUAfVzWV4q0vo/32fq9dNrwtPs3UVOnlh7+6RoOxReeOnQ65CIDOKx+8qb2kNn2EflsANRIhFnYmyUK1evEY5EGZ04Qa1Rx/M6OI5NOBjEMwwi8SRxNWyTJ8EO36EOol9iIBBgPBCgXatx2+wzfXbrzrdjoyPcmr29/xe9JxQOUnAC3H7vDuHJBI5j0y2bTFbgavMOZ85P4vP5WDYtchdfUOCQY+exCx2Pl8OfRLJsh5GxCbL37pWv39Ir8iTxLAu2qdvhHeDizng4jN1pU6tUyGzSeHFhaQnwYRzgH37LtIgHQ0x1oX2lScjwEfQHwA+nyh533r1D8OUzTLykwCHHk0LHYTrkzFEsVRgqjBI8gOZQIp8Fq2u5tv7Fcw54gXk6EuHGjZu0ul36fYt4PI7f78N1PdLJFPE9dL3dTq/yoDdVfF03XsMwONELUu35FDjk2FLoeAz1ej2arRbR1JACh8hDjHAY+r0tn3cPYVdbIBanUCgQOuReRe12G/e9m8D2gcK9chvvlWeP9Xo3eXId6zodTwrHcShVaiysVLCstbUGWq3Wmp/r9QZ9I0h+8iTJ1OOzvkXkKHjbbE+ttttg763WyHai2cyhBg7P85ibvUvpa99jvLfzDEa25VC+Nnto4xHZD810HCLTduj1erTbHXK51Xu+/X4f27YJBlfbVrc7Hfqej+HxSWqVyv2KjZ/2QzECIXydDn3LwsXAxbhfTVFE1jJCq6HDdV2KrRY2Prq9Hn5gNJlgKLe3Gh1bma/WGZmZOdBrrjf70TXGvnNnV1t9O4ZLLxXGvrsM5w53XCJ78dhtmX3clIrLJJIpGrUKfgN8wTDhSHQ1fPRNYokksXiccnGZkOGRvLf/3vM8qh2TbG6Y8kqRWDyh6qEiO7Asi/lPrlC5PcuzY6OHurah0mpjFgpMnpg8tNdoNJp0/tdfkw3srmlc7VyBwk+9emjjEdkvhY5jYvHubcbyORzHwTRNGp0emVyeSDT6qIcm8liqLC7glkuEO61tS5wPdM1WC7MwgmEYGIbB+MT4wNe48dY7GAtVyKcofP48idTW/VNu/PW7nLha2/W1a1NDFH7+iwOPSeSo6PbKMTEyPsnCwhzReIJwOM5otrDzSSKypezYOIyN02m1qC4t4JVLDIVD+1pgudTvc2FifM9VTW/87XuMfVwh4PdDu8XH5e+SvnCS6QsbK5ze/OHHjF0uwQAVhL3O1otoRY4DzXSIyBPBdV2q83N4lTIxyyS8h8WfrutSLxQY3sPaENM0Kf3hN8l7D3acNRyTgM9PbSIJsdXZGCOXxOx0mHxveeA2CrWYn8Kv/uzAYxM5KprpEJEngs/nI3diCk5M0axVKS/ME201iYV3v+18ttXmzPmNhcB2487la5xwAmv2DKb8q0EjttAFutiOww3vDk8FUgPNcHzKHbAjt8hR05ZZEXniJDNDFJ55lvqAjSFjY2N7vj3jVTrbnmu6NncNczVw7JG/72zYdr/pWDyP8kqRlfm71KuVPb+eyKAUOkTkiRWK7H6hdqffx91j4HBdF9fwMJ3Nm9G5rstdr8eMb3871FKhCI3y1iHC8zxKxWXKi3NkE1HyuSHChsvS/Ny+Xldkt7SmQ0SeWL1ul/rtWXyhIGZxmXAshmNZBGybTDhE2XHxJ1M4hkFsuEAkHqdaKuLDI5NK7mrNRaNSZeXND5isOVvOdMz7+0w4e68uvGB38P/4BXBcAtkMYyenNxxTKa3g9nvkhjL3x1Gt1XAMP5nsMIFDrqgqAlrTISJPsEg0SuT806s/nHrQLdY0TVaWFhmdmt4QFArjq3U5FufuMJLNbLuT5eaHH+N+7zpTxGCbWZJuvw/+zUOH67o77pYxfJCfHicSCbOyyUxHcWGObCpBILE6s1Or17HxkcmNKGzIkVLoEBFZJxwOMzZ9cttjDMPHVhPFrXqD5bcuMbrUpW66sMNdnFAoCJvcebFtm7mXxzAMH9PvLW95/uKZIYb6JpFImNxQhvm7dyiMjGJZFp1mnUwidn9WZnFpmWSuQCaZ3H5QIodAazpERPbAc2w6nc6Gx/v9PsU33uZEsU/Q52c4mtj2OhWzi+t5FM2117Jtm8VXT3DupefIn5rgA6Oxen27v+Ean7tpsvTme8DqLp2JfJZurYTXazGcSRF6qDHk2OgITrdJaXlp4Pcssl9a0yEisgfzt2dJRIKk7/VC6na7+P1+Gt0+/WsLZN/bXb+UT1V9DlbXpBCOYds2S1+Y5tTFp+4/X6vWKL9/neQz01jtLs0rt8nerFKxTRiKE3n9aaZPn9z17hrXdSlX68TSQ8QT2wcjkYOi0CEiskelhbvkhjJUKlW8YJh2q8n0qTN4nsed//G/GesNttul5dlUvT72y6d46sVntj3W8zza7Q7xeGxfVVbnF5cYnz6FsccqqyKDUOgQEdmDeq2G12vRczxGxic3/OFf/O4HZN+eHSgQ2LbN8mvTzDz31M4HH4CVcpVkdlg9nuTIKHSIiOxRu9Xa8taE53ksf+cDYu/eImLs3O129ZbKFKcunj/oYd7X6XTodHvgD+Dz+/EHQqSHhg7t9UTWU+gQETkknudx8+tvceJWfdvj+naf4msznDqkGQ7XdVkolsjmR4jF44fyGiK7odAhInJIlr79Hpl3bm+7oNS2bZZemz6UwNFqtTFtB3x+svnCvtZ+iBwE1ekQETkkRrFxP3BUrA69p4exeiaRZAzbdjAbHcx0hucPIXAslyqkc3kSWq8hx4hCh4jIAWpXarTeeh+j2SVe64Jv9WO2czrD9Oi69RMjQ9y1N+/Hsh+1Wo1EZkgLROXYUegQETkgjuNQ/9q3Ga7bqw/cCxxFu8NwIbfpOf7+xmJf+9HtdinVW5wemTjQ64ocBG3MFhE5IItf+5sHgeMh1ukh4pHIpufkggFuX7ux4fHrl6/QqNf56P0PWPjB29z8wTv0TZMP3nkX13U3HG+aJuVaHdPzceap81q/IceSFpKKiBwAy7Ko/Zc/JR1cGy5WnC7JHzlBPBLe8tyaadLxB+h0uoSTCexWi+l4jGqnSyYawe9f3XI7X60znklxp9Fi/LkLa26fFKt1CmOa3ZDjTaFDROSA3P3q/yXY6RNom6RcPzWrS/fiMJO5zIG+juM4XGq0ePnVV4DVXSqEYyTUxE2OOYUOEZED1qk1mPvjrxM7V2BsKHUor1FqtRl69gJm38INhEgPZQ/ldUQOktZ0iIgcsGg6SeyZ0UMLHADDiTjXLn0I4agChzw2NNMhIrJPrVqN9o2rGI4D2RxWt8uIYx3KYk7TsmiEIvgTCZJjE9oWK48VbZkVEdmnzvwcwwE/BPzQbq4+eAiBo9q3CEyfYrRQOPBrixwF3V4REdmn+Ikp6n3rUF+jYtkknn2ejAKHPMYUOkRE9imeSuGk0od2/V6/T2jmtG6lyGNPoUNEZJ/qKyuEGjUA5u+u0O10D+za7b5Fe2iYdG74wK4p8qgodIiI7JPV6xEPBrEsi/ofzDG/tLLrc/vW9rdl2oaPwpmz+x2iyLGg0CEisgedSoPSlTsAZCcmqHa6XP/eHOlFB7e3u02BK5ZNc2Qcc4v+K62+RfzkqQMbs8ijpt0rIiJ7UPzT9/F+/yPKL+Xwn8pSby8R+6C/uk22uovzXY/cxRcJhUKsrCyzWZH0fjxBfli3VeSzQ6FDRGQPDAz8hp/AuzV4t8bDe0q86saGbJ7n0ep2sTywQhEKL7xIILD6EWyk0g+22j78GqHQIY1e5NFQ6BAR2YP+bIXN+8ZCb7HJ5b++yfkfPYnPt3oXe9EzGHnpRwhtEiRC2WF6tQqRYHDtEwGFDvls0ZoOEZEBtVZq8N3FLZ/P/9Ai9cdlrvybH/DhN1fb1sdsC2+TlvQAqWyWRiLNpwWiPc9jxbKJ6daKfMYodIiIDMDzPOb+8zeJ9Lf/+PQZPoZaAdw7PQAyoSDlKx9tefzoU+cp90zKnQ7NbpfwzBliicSBjl3kUdPtFRGRAax8cpfo/1sGY3ff2UIjDwp6dcuVLY8zDIPo+Qsk02ka1QopNXGTzyCFDhGRAQzNjNL+tQt0v3mL+Gxvx+P9b9e56t3CWejijSTgJ7c+NpXJAJDO5g5otCLHi7rMiojsQbfRZvYXf59Ef/ff3drPJDn/W798iKMSOd60pkNEZA8W375GdyJMLbv7723G1Srtcv0QRyVyvCl0iIgMyLIszN95j+Bsm3B796EjZgeY+w/f4O43PsBxnEMcocjxpNAhIjKg6s1FEgsW/ekYUXOwj9HY+zW8//hdVi7fOaTRiRxfCh0iIgMKDSXoOCb9nkmfwWcsDMOg8acfUr+1fAijEzm+FDpERAbVd/D5/UwuBVka39ta/OhfLdJ49+4BD0zkeFPoEBEZQLfeZunff4PIvYoDqcBWxdB35ktu1uZN5LNLoUNEZADzv/s3xK91AGg4HYLz3T1dx/M8fKm1vVVM06S8sMDStav7HqfIcaTiYCIiu9RcquD+xSxgAFAP9jnhxPZ0rc5PjHLulbMA2LbNyseXiTRqZOJxLMtm+fpVCqfPUrp9G8N1wHPBtsG28WwLJxxh9PwzB/TORI6GQoeIyC7c+K9vYv/VLWI94/5jI3aCWsYl0xhs0rjns8n/ysv4fD4qt+5S/Phjzo5nIR4HIBgMkKlXKX7v2+QiYQzD2HCNChsfEznuFDpERHZQnV2CP/6EmOdf83jIF6CZDUDDHuh65lSE6te+TT8ZIdyxCIwHNxwTCAQYDmz+Ed23LEKThYFeU+Q40JoOEZFttEt1lv711wmtCxyfMrrbB45G0qHnWfd/7sYhei7IVCBMrutRDtjkR9IDjanp85POqT+LPH400yEiso27/+lNEnOrocHzvA23Ogy/D3A3PbeV95H9fIK+Y1Nu2dhtj1w+wkQmef+YaSfCzdtlzl6Irzm32+ky+0mRp54/gc/34Puh53kYGXWglceTQoeIyDacW1UAmhdT5P7Jq9R++/vE3q/df94NbR06nLMhcokEAGNbTGZ4nkfbMTc8fv0bt4l9v82HbxQJmOA7kcDIhQj+5AynL87s6z2JPCrqMisiso3GnRXqVxYY+bHzhCJhqnNFKr/8VUK+1e9s5ZMB0rdMAoaf8lk/vp7L0N3Vj9X6j4U4N7LzrMSK36KR9zMzPUrftLn5jVmGvtXCZ2y8A96LQ/zXX2P8J5492DcqcgQ00yEiso3UVJ7UVB6AypW7rPze94kaD9Z3tPxd/D8ewVzxOH02Scjn59pEA9+NPm2fhe04+H2+TXegfCrvBBle9Lh88xOM73TJlX2wSeAAiLTBaWycGRF5HCh0iIjs0vIfvU3iu2W4FyDKKYvpM0lG0hkYeXDcM1M53EkX27W5azaIJOKMuaHNL3pPy7MJ/FWDTHfnKqVOpbOftyHyyGj3iojILvQ6HZpuec1j/nx0NXBswufzEQqEmIkNEdxi1uJhFcvE1918bch6znxjV8eJHDea6RAR2YHrupS+9Q4nEjGWn+6RumISxA/DO3+EVnptoqEg+FYXjS4HbboBj6Ttw0qGsCIepUtFQh9ZhDHouzaVkwFCRoB+uc1oa+PMR/3WMtf+7Z8R+fwkmS+cIpkfOoy3LXLgtJBURGQHi2+9TfbS/P11GdeLNVpFk5MzCTLx+Lbn3qJL3vJhBHysjAU5OTNKu90hEg7RNG0qs3X8v3ObiBGiOmrQD3iMzK2ea/ocOuMhCPpwPQ/Xceh2ukQ6HoVuFICe38Y8mWDol19k4qeeO9T/DiL7pdAhIrKNbrtD9/e/QWJdcbCG28ftW2QiW4eOBbo0cwEKhTStdo+TU2P3n7u9WMb6javE2H6tx3rlKR+5Oxtvw7R/ZpzMl8/Rfm+OYC7OyM9epN81SQ5nBrq+yGHS7RURkW2YnQ4RG1hXkLTqd5neJHC4rstcr4799DD53AQnYqszEsPrCoiaxR7mdJDY7d2PpRv18Mo92CSoxN9YwHpj4f4zH/3ZJc785t/b/cVFjoBCh4jIJtr1JvF0kmAwhOU6BPwPUkfJZ5HrerhBl9lOlVA6SSfjJzuexW94TMXH8fs3L5v+qXPPTzA7HKb+W9dJNzb2XlmvF3RpRl0KlZ1nRmxcRn7tdVJjKpUux4tur4iIrPPRv/oqwe8s4/3jp0mFILfUWvP8LbcNhkHKgmwwSrXfgVcmyCe3X9+xmXavx+yf32Lore23wS5OeYzd2bmzbMvp0friMJ//jX8w8FhEDpu2zIqIrGPMNom6QULF0obAsUKfgO0x5oXJhWIYhkHKH8bqD9Zp9lNL9QaRLxWonNr+4zjibNFx1rWxXZu5eJelE0DET/7UxJ7GInLYFDpERB7iOA5Yqws1i385T9fur3k+4PeTikSJ+B7cPmm4fcql6sCvdatUZiSd5nQhR+5XTtH3tgkuvc2fWxmyaUVccp0Ao3chYQWJPD8+8FhEjoJCh4jIPZ7ncfMP/5bE8uof+NF6lNlye80xQ46ftLt2vUbVNXn23PSax+4Wi9u+1kKlRiYaIRFZrcMxlk/T+fv5LY+P1lyWstbGMQcNEn0/UW91XUjftYmd3vo6Io+SQoeIyD23fu8twr975X49Dp/PR//KztU/c0aI64tL3Fop0+v1uLZcxPZvvU5/drlIOhZhaF2Nj5OvTlA+sfk5EcdPpuVnZWht8PBFglgPrUMNGn7aN7YPPCKPikKHiAirW107X728oTFbtOhxu9ul5zgbzrkVt7gxZNMciTKcSjGTz1Hq9jhTyOO4LrfLFW6Vq9yuVPh4aZm5ZovZSo1QLEo4sDGUxMJhCl85R4f+hucAIn0f/lT0/s9lOmTnXKLWg5kXwzBo/tEl2iv1vf6nEDk02r0iIk+0ytV5ln/zW3jlLpGiRdC3catryzPJ/+IoqcCDkuRL/RZDP3qSWHjzBm22bRN4KFjcLJU5tb5YxxauvjtH4veWN32uPmwQXukTMYIsjNiML28+o+L86jOc/Eev7+r1RI6K6nSIyBOpU21y57+/hfHGLAn33v2JTQIHgBOCSt8l4LMJY+C4DstDBhNbBA5gTeAAyCXiFCtVCtmd+6RMPzfC1Ytlcpc2Lh5Nlzyuj9kk8BMIBoHNvzc6LXPH1xE5agodIvJEMuttePMOMXfnwlxpKwx/Xud20qQa75N6LcvnXrw40OulIxFutXbXkj4cDDL6S6dpXvohkU2qj/otj3wJ/MbmgcPzPMIXRgYan8hR0JoOEXkirbzxEfHe9lVD18s2w0wvRImN7213iM+3+4/cfDpB7+c3vs7isMVYLYLf2Ppa7dfzTPz4hT2NUeQwKXSIyBPJrexu1uFhfdem9U/HOXtubOeD1/E8j+WVlYHOccf8NOje/3ku3iFph4g4az+6Xc+lPv4gQA39wnMbFsSKHAcKHSLyRPIVewOfE/IFKP3ZDRaaTWbLNe6Uy7s+1zAMXjg1w43Szue0eia3Ls8xsezgfPHBGpBCM4QVeXBc33BoTgTh118m/y++TC/o4nouwdHUQO9L5KhoTYeIPHEsy8JabhLd+dANIl0f48kkAG3TZKHVBs+jVq/zzOT25cdb3S6G38cPF5d4bmyUxXodO50hlEjgLC0xnlit21FqtJiqGBCOEhoJcfMLDkN/axLyBWi0TVphP4GXR0n/3ec4/cqZ+9eP/sEvMf8n7zA1WdjDOxM5fNoyKyJPpI//2R8R/3Dnwl8Pc12XuVd9vPoPX9rwXKtr0jF7FDLpLc9fbDQYfu4irVaLdq1OfnyMWGw1+tydvU2kXKKdyeIYMPaX1+7vgPE8j3nHpPWtEkOVAPPn/bz6218ZaOwix4FmOkTkiRT+8gx8+MGujy8nTYJfmeaVk5uv50hEw3iGx2y5wngyQSAQWLNw9E7XpG74GA8FGR7OMbyuZkd+dBQrlyWfTOK6Llf773AqkAVWb81MBiI0/s4oC3+xSMpI7OEdizx6Ch0i8kSa+oWXufL1T0hf393ajlwzTL8b2nYHSjISwbVt+rbNcqWCEwzheVBu1Hj2i19iJhHf8txIJEzkXh8Wz/MIpOOwrtVKygiw8FKK/qxqcMjjSQtJReSJ5Pf78ca3DgGbsayNDdfWSycS9D0IxuJMZ4eYGc7y0swMy/NzA40t+CPncV13w3Pnh9P4xn18/N/+cqCxixwHCh0i8sQ68+s/TSu5+2VtkVxk54OAbDzGaCp5f9tqo9NhizpeW5o6d5I7L4zQtzf2YUmPxun+YPchRuS4UOgQkSeWLxQg9FOnaI0HaUQ3lhyH1cWj1Z9L4/t3Zxkf33qR6FZW2h2Ynmbm/FMDnWcYBkMnx6j6NjaaG/OFiH5pd31cRI4T7V4REWG1QduHX/mfBGsWqdrqDMVKssfwP7/AeD69p2JbtU6XXjbLxPTUnsfV7faY/dq3OV178FjftWm+eJKx1z+35+uKPAoKHSIiD+k1O3zyu2/gmj3M9yt8/l9+YeBrVNodTJ8fK+Bn5tn9lSO3LItipYnv5hKpj+YJ4KN8cZLxL23ctity3Gn3iojIQyLJGGM//RSRZp3Fs5mBz290OngTE0yO7K9AV7fbo1Kp0O10OfPc8zA9RWn4Gv3lMiM/+sK+ri3yqGimQ0TkIbWlRYJ3Z4mENnZ33Q3LsimmkkxN7e2WSs80MQyD+Y8uMx2LUrYd4ufOE0+qtLk8/rSQVETkIbZlEQrsfRI4GAxgVap7Pn9x9jZ3fvA20/cqleYCfpzLH7L44SUcZ+OiUpHHiUKHiMhDcpMnKNsb62MMYiwY4PIP3tnTuT4MTq2rVhqPhMnbfYq3bu5rXCKPmkKHiMhDDMPAPza+7TF9y6LsuNT7mxcLCwcCpHCplEoDv34gtnkbOsMwGKpVWPzoQ2x78+29IsedQoeIyDq+bSqPep5HLZ6i8LnP4+byWx43lkrRnFs40HEFgwEKtkn1ne+zfO2qbrfIY0ehQ0RkHadW2fxxx2EpEGb0/NMABNNpLGvrWYe059Co13d8vWKxxCdvvsn1T65h1Wo7Hp+NhInUypi93fWNETkuFDpERB5SvHGdjLf5mo5yMMzks8/dLxSWzg1TCoVZ9Ic2nXVIRKOsFFfu/+x5HsVSheVSec1x4XCQbDrDDC4nQsFdjbOXSBOLD9Y7RuRRU+gQEbmntrhAolrC7/cDq9tf53urHV1b3S6Z02c3nDN58QUmn32Oqn/tjpeeZVGKJUim05imyUq1TrVjkp84QTC8dt1GOp2mnRmi012duTD7fZZNi2q7w2ZVDTzPwz+UPZD3LHKUFDpERO7pVisE/X56/T4r/iCtwhgjz3+OEj7qGIQjWzd8S54+x3KjyUrPpBKN0x+bZPTceRwMWqZNfmyCbG4YwzBwPQ/v3j+tVguAialJblkWpWgC56kLjL/yKplXvsCKt7H8et+ywNStFXn8qDiYiMhDKktLGHgMjY4NfK7neRt6tNRrNdKZzNrjXJdScQnbtgmEItDv4I/EyQ5vXJjabjboVSq4/T6G3w+hEOFUmkAgQDQWG3iMIo+SQoeIyCP06UfwXhrKiTxu1HtFROQRUtiQJ4nWdIiIiMiRUOgQERGRI6HQISIiIkdCoUNERESOhEKHiIiIHAmFDhERETkSCh0iIiJyJBQ6RERE5EgodIiIiMiRUOgQERGRI6HQISIiIkdCoUNERESOhEKHiIiIHAmFDhERETkSCh0iIiJyJBQ6RERE5EgodIiIiMiRUOgQERGRI6HQISIiIkdCoUNERESOhEKHiIiIHAmFDhERETkSCh0iIiJyJBQ6RERE5EgodIiIiMiRUOgQERGRI6HQISIiIkdCoUNERESOhEKHiIiIHAmFDhERETkSCh0iIiJyJBQ6RERE5EgodIiIiMiRUOgQERGRI6HQISIiIkdCoUNERESOxP8Hdbn5sQ4km8YAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize = (12,8))\n", - "\n", - "gemeentedata.plot(column='bevolkings', \n", - " cmap='RdPu',ax=ax, scheme=\"User_Defined\",edgecolor='black',linewidth=0.05,\n", - " legend=True, classification_kwds=dict(bins=[250,500,1000,2500,5000,10000]),\n", - " legend_kwds={'loc': 'upper left'})\n", - "ax.set_title(\"Population Density in The Netherlands, 2017\")\n", - "plt.axis('off')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Raster data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And we can do similar things with raster data, ofcourse. Let's plot a map of population density for Kenya. This data is downloaded from the [WorldPop](www.worldpop.org) website.\n", - "\n", - "We first load the data, using the **rasterio** package." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "src = rasterio.open(\"ken_pd_2020_1km_UNadj.tif\")\n", - "kenya_pd = src.read(1) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And let's try to plot this data:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(-0.5, 956.5, 1222.5, -0.5)" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(8,6))\n", - "\n", - "ken = ax.imshow(kenya_pd, cmap='viridis')\n", - "ax.axis('off')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That does not really tell us anything! It seems we need to do something about the values we plotting. Let's first add a colorbar so we at least know what we are looking at:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(8,6))\n", - "\n", - "ken = ax.imshow(kenya_pd, cmap='viridis')\n", - "ax.axis('off')\n", - "\n", - "fig.colorbar(ken,ax=ax)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So it seems we may have quite some extreme values, which remove all the detail. Let's use the `vmin` and `vmax` arguments to see if we can get a better image if we cap the minimum (`vmin`) and maximum (`vmax`) values that we want to visualize." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(8,6))\n", - "\n", - "ken = ax.imshow(kenya_pd, cmap='viridis',vmin=0,vmax=1000)\n", - "ax.axis('off')\n", - "fig.colorbar(ken,ax=ax)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That already looks much better, but we seem to have a lot of nodata values around Kenya, which all turn purple as well. They seem to have some large negative value. Let's explore:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-99999.0" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "kenya_pd.min()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We may want to change this large negative value to actual nodata. To do so, we change all these large negative values into NaN (Not A Number) values. This will make sure that these cells will be ignored when plotted." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "kenya_pd = np.where(kenya_pd==kenya_pd.min(), np.nan, kenya_pd)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we plot the results again:" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(-0.5, 956.5, 1222.5, -0.5)" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(8,6))\n", - "\n", - "ken = ax.imshow(kenya_pd, cmap='viridis',vmin=0,vmax=1000)\n", - "ax.axis('off')\n", - "#fig.colorbar(ken,ax=ax)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "Now we need to make sure that we know what we are looking at. So let's add a description of the colorbar values as well. And add a title." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.05, 'Population Density for Kenya in 2020')" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(8,6))\n", - "\n", - "ken = ax.imshow(kenya_pd, cmap='viridis',vmin=0,vmax=1000)\n", - "ax.axis('off')\n", - "cbar= fig.colorbar(ken,ax=ax)\n", - "cbar.set_label('people/km2')\n", - "cbar.ax.set_yticks([0,200,400,600,800,1000]) \n", - "cbar.ax.set_yticklabels(['0', '200', '400', '600', '800', '>1000']) \n", - "\n", - "plt.title('Population Density for Kenya in 2020',fontweight='bold',y=1.05)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Using Plotly for interactive visualization\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plotly is an open-source Python graphing library that allows users to create interactive visualizations, such as line charts, scatter plots, bar charts, heatmaps, and more.\n", - "\n", - "The library offers an easy-to-use interface for creating visualizations with a wide range of customization options. Plotly allows users to create interactive plots and charts that can be embedded in web pages, Jupyter notebooks, and other applications." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's explore how easy Plotly can be applied through using one of their dummy datasets. " - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - " \n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "hovertemplate": "%{hovertext}

continent=Asia
year=1952
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Afghanistan", - "Bahrain", - "Bangladesh", - "Cambodia", - "China", - "Hong Kong, China", - "India", - "Indonesia", - "Iran", - "Iraq", - "Israel", - "Japan", - "Jordan", - "Korea, Dem. Rep.", - "Korea, Rep.", - "Kuwait", - "Lebanon", - "Malaysia", - "Mongolia", - "Myanmar", - "Nepal", - "Oman", - "Pakistan", - "Philippines", - "Saudi Arabia", - "Singapore", - "Sri Lanka", - "Syria", - "Taiwan", - "Thailand", - "Vietnam", - "West Bank and Gaza", - "Yemen, Rep." - ], - "ids": [ - "Afghanistan", - "Bahrain", - "Bangladesh", - "Cambodia", - "China", - "Hong Kong, China", - "India", - "Indonesia", - "Iran", - "Iraq", - "Israel", - "Japan", - "Jordan", - "Korea, Dem. Rep.", - "Korea, Rep.", - "Kuwait", - "Lebanon", - "Malaysia", - "Mongolia", - "Myanmar", - "Nepal", - "Oman", - "Pakistan", - "Philippines", - "Saudi Arabia", - "Singapore", - "Sri Lanka", - "Syria", - "Taiwan", - "Thailand", - "Vietnam", - "West Bank and Gaza", - "Yemen, Rep." - ], - "legendgroup": "Asia", - "marker": { - "color": "#636efa", - "size": [ - 8425333, - 120447, - 46886859, - 4693836, - 556263527, - 2125900, - 372000000, - 82052000, - 17272000, - 5441766, - 1620914, - 86459025, - 607914, - 8865488, - 20947571, - 160000, - 1439529, - 6748378, - 800663, - 20092996, - 9182536, - 507833, - 41346560, - 22438691, - 4005677, - 1127000, - 7982342, - 3661549, - 8550362, - 21289402, - 26246839, - 1030585, - 4963829 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Asia", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 779.4453145, - 9867.084765, - 684.2441716, - 368.4692856, - 400.448611, - 3054.421209, - 546.5657493, - 749.6816546, - 3035.326002, - 4129.766056, - 4086.522128, - 3216.956347, - 1546.907807, - 1088.277758, - 1030.592226, - 108382.3529, - 4834.804067, - 1831.132894, - 786.5668575, - 331, - 545.8657228999998, - 1828.230307, - 684.5971437999998, - 1272.880995, - 6459.554823, - 2315.138227, - 1083.53203, - 1643.485354, - 1206.947913, - 757.7974177, - 605.0664917, - 1515.5923289999996, - 781.7175761 - ], - "xaxis": "x", - "y": [ - 28.801, - 50.93899999999999, - 37.484, - 39.417, - 44, - 60.96, - 37.37300000000001, - 37.468, - 44.869, - 45.32, - 65.39, - 63.03, - 43.158, - 50.056, - 47.453, - 55.565, - 55.928, - 48.463, - 42.244, - 36.319, - 36.157, - 37.578, - 43.43600000000001, - 47.752, - 39.875, - 60.396, - 57.593, - 45.883, - 58.5, - 50.848, - 40.412, - 43.16, - 32.548 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Europe
year=1952
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Albania", - "Austria", - "Belgium", - "Bosnia and Herzegovina", - "Bulgaria", - "Croatia", - "Czech Republic", - "Denmark", - "Finland", - "France", - "Germany", - "Greece", - "Hungary", - "Iceland", - "Ireland", - "Italy", - "Montenegro", - "Netherlands", - "Norway", - "Poland", - "Portugal", - "Romania", - "Serbia", - "Slovak Republic", - "Slovenia", - "Spain", - "Sweden", - "Switzerland", - "Turkey", - "United Kingdom" - ], - "ids": [ - "Albania", - "Austria", - "Belgium", - "Bosnia and Herzegovina", - "Bulgaria", - "Croatia", - "Czech Republic", - "Denmark", - "Finland", - "France", - "Germany", - "Greece", - "Hungary", - "Iceland", - "Ireland", - "Italy", - "Montenegro", - "Netherlands", - "Norway", - "Poland", - "Portugal", - "Romania", - "Serbia", - "Slovak Republic", - "Slovenia", - "Spain", - "Sweden", - "Switzerland", - "Turkey", - "United Kingdom" - ], - "legendgroup": "Europe", - "marker": { - "color": "#EF553B", - "size": [ - 1282697, - 6927772, - 8730405, - 2791000, - 7274900, - 3882229, - 9125183, - 4334000, - 4090500, - 42459667, - 69145952, - 7733250, - 9504000, - 147962, - 2952156, - 47666000, - 413834, - 10381988, - 3327728, - 25730551, - 8526050, - 16630000, - 6860147, - 3558137, - 1489518, - 28549870, - 7124673, - 4815000, - 22235677, - 50430000 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Europe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 1601.056136, - 6137.076492, - 8343.105126999999, - 973.5331948, - 2444.286648, - 3119.23652, - 6876.14025, - 9692.385245, - 6424.519071, - 7029.809327, - 7144.114393000002, - 3530.690067, - 5263.673816, - 7267.688428, - 5210.280328, - 4931.404154999998, - 2647.585601, - 8941.571858, - 10095.42172, - 4029.329699, - 3068.319867, - 3144.613186, - 3581.459448, - 5074.659104, - 4215.041741, - 3834.034742, - 8527.844662000001, - 14734.23275, - 1969.10098, - 9979.508487 - ], - "xaxis": "x", - "y": [ - 55.23, - 66.8, - 68, - 53.82, - 59.6, - 61.21, - 66.87, - 70.78, - 66.55, - 67.41, - 67.5, - 65.86, - 64.03, - 72.49, - 66.91, - 65.94, - 59.164, - 72.13, - 72.67, - 61.31, - 59.82, - 61.05, - 57.996, - 64.36, - 65.57, - 64.94, - 71.86, - 69.62, - 43.585, - 69.18 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Africa
year=1952
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Algeria", - "Angola", - "Benin", - "Botswana", - "Burkina Faso", - "Burundi", - "Cameroon", - "Central African Republic", - "Chad", - "Comoros", - "Congo, Dem. Rep.", - "Congo, Rep.", - "Cote d'Ivoire", - "Djibouti", - "Egypt", - "Equatorial Guinea", - "Eritrea", - "Ethiopia", - "Gabon", - "Gambia", - "Ghana", - "Guinea", - "Guinea-Bissau", - "Kenya", - "Lesotho", - "Liberia", - "Libya", - "Madagascar", - "Malawi", - "Mali", - "Mauritania", - "Mauritius", - "Morocco", - "Mozambique", - "Namibia", - "Niger", - "Nigeria", - "Reunion", - "Rwanda", - "Sao Tome and Principe", - "Senegal", - "Sierra Leone", - "Somalia", - "South Africa", - "Sudan", - "Swaziland", - "Tanzania", - "Togo", - "Tunisia", - "Uganda", - "Zambia", - "Zimbabwe" - ], - "ids": [ - "Algeria", - "Angola", - "Benin", - "Botswana", - "Burkina Faso", - "Burundi", - "Cameroon", - "Central African Republic", - "Chad", - "Comoros", - "Congo, Dem. Rep.", - "Congo, Rep.", - "Cote d'Ivoire", - "Djibouti", - "Egypt", - "Equatorial Guinea", - "Eritrea", - "Ethiopia", - "Gabon", - "Gambia", - "Ghana", - "Guinea", - "Guinea-Bissau", - "Kenya", - "Lesotho", - "Liberia", - "Libya", - "Madagascar", - "Malawi", - "Mali", - "Mauritania", - "Mauritius", - "Morocco", - "Mozambique", - "Namibia", - "Niger", - "Nigeria", - "Reunion", - "Rwanda", - "Sao Tome and Principe", - "Senegal", - "Sierra Leone", - "Somalia", - "South Africa", - "Sudan", - "Swaziland", - "Tanzania", - "Togo", - "Tunisia", - "Uganda", - "Zambia", - "Zimbabwe" - ], - "legendgroup": "Africa", - "marker": { - "color": "#00cc96", - "size": [ - 9279525, - 4232095, - 1738315, - 442308, - 4469979, - 2445618, - 5009067, - 1291695, - 2682462, - 153936, - 14100005, - 854885, - 2977019, - 63149, - 22223309, - 216964, - 1438760, - 20860941, - 420702, - 284320, - 5581001, - 2664249, - 580653, - 6464046, - 748747, - 863308, - 1019729, - 4762912, - 2917802, - 3838168, - 1022556, - 516556, - 9939217, - 6446316, - 485831, - 3379468, - 33119096, - 257700, - 2534927, - 60011, - 2755589, - 2143249, - 2526994, - 14264935, - 8504667, - 290243, - 8322925, - 1219113, - 3647735, - 5824797, - 2672000, - 3080907 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Africa", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 2449.008185, - 3520.610273, - 1062.7522, - 851.2411407, - 543.2552413, - 339.2964587, - 1172.667655, - 1071.310713, - 1178.665927, - 1102.990936, - 780.5423257, - 2125.621418, - 1388.594732, - 2669.529475, - 1418.822445, - 375.6431231, - 328.9405571000001, - 362.1462796, - 4293.476475, - 485.2306591, - 911.2989371, - 510.1964923000001, - 299.850319, - 853.5409189999998, - 298.8462121, - 575.5729961000002, - 2387.54806, - 1443.011715, - 369.1650802, - 452.3369807, - 743.1159097, - 1967.955707, - 1688.20357, - 468.5260381, - 2423.780443, - 761.879376, - 1077.281856, - 2718.885295, - 493.3238752, - 879.5835855, - 1450.356983, - 879.7877358, - 1135.749842, - 4725.295531000002, - 1615.991129, - 1148.376626, - 716.6500721, - 859.8086567, - 1468.475631, - 734.753484, - 1147.388831, - 406.8841148 - ], - "xaxis": "x", - "y": [ - 43.077, - 30.015, - 38.223, - 47.622, - 31.975, - 39.031, - 38.523, - 35.463, - 38.092, - 40.715, - 39.143, - 42.111, - 40.477, - 34.812, - 41.893, - 34.482, - 35.92800000000001, - 34.078, - 37.003, - 30, - 43.149, - 33.609, - 32.5, - 42.27, - 42.13800000000001, - 38.48, - 42.723, - 36.681, - 36.256, - 33.685, - 40.543, - 50.986, - 42.87300000000001, - 31.286, - 41.725, - 37.444, - 36.324, - 52.724, - 40, - 46.471, - 37.278, - 30.331, - 32.978, - 45.00899999999999, - 38.635, - 41.407, - 41.215, - 38.596, - 44.6, - 39.978, - 42.038, - 48.451 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Americas
year=1952
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Argentina", - "Bolivia", - "Brazil", - "Canada", - "Chile", - "Colombia", - "Costa Rica", - "Cuba", - "Dominican Republic", - "Ecuador", - "El Salvador", - "Guatemala", - "Haiti", - "Honduras", - "Jamaica", - "Mexico", - "Nicaragua", - "Panama", - "Paraguay", - "Peru", - "Puerto Rico", - "Trinidad and Tobago", - "United States", - "Uruguay", - "Venezuela" - ], - "ids": [ - "Argentina", - "Bolivia", - "Brazil", - "Canada", - "Chile", - "Colombia", - "Costa Rica", - "Cuba", - "Dominican Republic", - "Ecuador", - "El Salvador", - "Guatemala", - "Haiti", - "Honduras", - "Jamaica", - "Mexico", - "Nicaragua", - "Panama", - "Paraguay", - "Peru", - "Puerto Rico", - "Trinidad and Tobago", - "United States", - "Uruguay", - "Venezuela" - ], - "legendgroup": "Americas", - "marker": { - "color": "#ab63fa", - "size": [ - 17876956, - 2883315, - 56602560, - 14785584, - 6377619, - 12350771, - 926317, - 6007797, - 2491346, - 3548753, - 2042865, - 3146381, - 3201488, - 1517453, - 1426095, - 30144317, - 1165790, - 940080, - 1555876, - 8025700, - 2227000, - 662850, - 157553000, - 2252965, - 5439568 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Americas", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5911.315053, - 2677.326347, - 2108.944355, - 11367.16112, - 3939.978789, - 2144.115096, - 2627.0094710000008, - 5586.53878, - 1397.717137, - 3522.110717, - 3048.3029, - 2428.2377690000008, - 1840.366939, - 2194.926204, - 2898.530881, - 3478.125529, - 3112.363948, - 2480.380334, - 1952.308701, - 3758.523437, - 3081.959785, - 3023.271928, - 13990.482080000002, - 5716.766744, - 7689.799761 - ], - "xaxis": "x", - "y": [ - 62.485, - 40.414, - 50.917, - 68.75, - 54.745, - 50.643, - 57.206, - 59.42100000000001, - 45.928, - 48.357, - 45.262, - 42.023, - 37.579, - 41.912, - 58.53, - 50.789, - 42.31399999999999, - 55.191, - 62.649, - 43.902, - 64.28, - 59.1, - 68.44, - 66.071, - 55.088 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Oceania
year=1952
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Australia", - "New Zealand" - ], - "ids": [ - "Australia", - "New Zealand" - ], - "legendgroup": "Oceania", - "marker": { - "color": "#FFA15A", - "size": [ - 8691212, - 1994794 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Oceania", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 10039.59564, - 10556.57566 - ], - "xaxis": "x", - "y": [ - 69.12, - 69.39 - ], - "yaxis": "y" - } - ], - "frames": [ - { - "data": [ - { - "hovertemplate": "%{hovertext}

continent=Asia
year=1952
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Afghanistan", - "Bahrain", - "Bangladesh", - "Cambodia", - "China", - "Hong Kong, China", - "India", - "Indonesia", - "Iran", - "Iraq", - "Israel", - "Japan", - "Jordan", - "Korea, Dem. Rep.", - "Korea, Rep.", - "Kuwait", - "Lebanon", - "Malaysia", - "Mongolia", - "Myanmar", - "Nepal", - "Oman", - "Pakistan", - "Philippines", - "Saudi Arabia", - "Singapore", - "Sri Lanka", - "Syria", - "Taiwan", - "Thailand", - "Vietnam", - "West Bank and Gaza", - "Yemen, Rep." - ], - "ids": [ - "Afghanistan", - "Bahrain", - "Bangladesh", - "Cambodia", - "China", - "Hong Kong, China", - "India", - "Indonesia", - "Iran", - "Iraq", - "Israel", - "Japan", - "Jordan", - "Korea, Dem. Rep.", - "Korea, Rep.", - "Kuwait", - "Lebanon", - "Malaysia", - "Mongolia", - "Myanmar", - "Nepal", - "Oman", - "Pakistan", - "Philippines", - "Saudi Arabia", - "Singapore", - "Sri Lanka", - "Syria", - "Taiwan", - "Thailand", - "Vietnam", - "West Bank and Gaza", - "Yemen, Rep." - ], - "legendgroup": "Asia", - "marker": { - "color": "#636efa", - "size": [ - 8425333, - 120447, - 46886859, - 4693836, - 556263527, - 2125900, - 372000000, - 82052000, - 17272000, - 5441766, - 1620914, - 86459025, - 607914, - 8865488, - 20947571, - 160000, - 1439529, - 6748378, - 800663, - 20092996, - 9182536, - 507833, - 41346560, - 22438691, - 4005677, - 1127000, - 7982342, - 3661549, - 8550362, - 21289402, - 26246839, - 1030585, - 4963829 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Asia", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 779.4453145, - 9867.084765, - 684.2441716, - 368.4692856, - 400.448611, - 3054.421209, - 546.5657493, - 749.6816546, - 3035.326002, - 4129.766056, - 4086.522128, - 3216.956347, - 1546.907807, - 1088.277758, - 1030.592226, - 108382.3529, - 4834.804067, - 1831.132894, - 786.5668575, - 331, - 545.8657228999998, - 1828.230307, - 684.5971437999998, - 1272.880995, - 6459.554823, - 2315.138227, - 1083.53203, - 1643.485354, - 1206.947913, - 757.7974177, - 605.0664917, - 1515.5923289999996, - 781.7175761 - ], - "xaxis": "x", - "y": [ - 28.801, - 50.93899999999999, - 37.484, - 39.417, - 44, - 60.96, - 37.37300000000001, - 37.468, - 44.869, - 45.32, - 65.39, - 63.03, - 43.158, - 50.056, - 47.453, - 55.565, - 55.928, - 48.463, - 42.244, - 36.319, - 36.157, - 37.578, - 43.43600000000001, - 47.752, - 39.875, - 60.396, - 57.593, - 45.883, - 58.5, - 50.848, - 40.412, - 43.16, - 32.548 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Europe
year=1952
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Albania", - "Austria", - "Belgium", - "Bosnia and Herzegovina", - "Bulgaria", - "Croatia", - "Czech Republic", - "Denmark", - "Finland", - "France", - "Germany", - "Greece", - "Hungary", - "Iceland", - "Ireland", - "Italy", - "Montenegro", - "Netherlands", - "Norway", - "Poland", - "Portugal", - "Romania", - "Serbia", - "Slovak Republic", - "Slovenia", - "Spain", - "Sweden", - "Switzerland", - "Turkey", - "United Kingdom" - ], - "ids": [ - "Albania", - "Austria", - "Belgium", - "Bosnia and Herzegovina", - "Bulgaria", - "Croatia", - "Czech Republic", - "Denmark", - "Finland", - "France", - "Germany", - "Greece", - "Hungary", - "Iceland", - "Ireland", - "Italy", - "Montenegro", - "Netherlands", - "Norway", - "Poland", - "Portugal", - "Romania", - "Serbia", - "Slovak Republic", - "Slovenia", - "Spain", - "Sweden", - "Switzerland", - "Turkey", - "United Kingdom" - ], - "legendgroup": "Europe", - "marker": { - "color": "#EF553B", - "size": [ - 1282697, - 6927772, - 8730405, - 2791000, - 7274900, - 3882229, - 9125183, - 4334000, - 4090500, - 42459667, - 69145952, - 7733250, - 9504000, - 147962, - 2952156, - 47666000, - 413834, - 10381988, - 3327728, - 25730551, - 8526050, - 16630000, - 6860147, - 3558137, - 1489518, - 28549870, - 7124673, - 4815000, - 22235677, - 50430000 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Europe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 1601.056136, - 6137.076492, - 8343.105126999999, - 973.5331948, - 2444.286648, - 3119.23652, - 6876.14025, - 9692.385245, - 6424.519071, - 7029.809327, - 7144.114393000002, - 3530.690067, - 5263.673816, - 7267.688428, - 5210.280328, - 4931.404154999998, - 2647.585601, - 8941.571858, - 10095.42172, - 4029.329699, - 3068.319867, - 3144.613186, - 3581.459448, - 5074.659104, - 4215.041741, - 3834.034742, - 8527.844662000001, - 14734.23275, - 1969.10098, - 9979.508487 - ], - "xaxis": "x", - "y": [ - 55.23, - 66.8, - 68, - 53.82, - 59.6, - 61.21, - 66.87, - 70.78, - 66.55, - 67.41, - 67.5, - 65.86, - 64.03, - 72.49, - 66.91, - 65.94, - 59.164, - 72.13, - 72.67, - 61.31, - 59.82, - 61.05, - 57.996, - 64.36, - 65.57, - 64.94, - 71.86, - 69.62, - 43.585, - 69.18 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Africa
year=1952
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Algeria", - "Angola", - "Benin", - "Botswana", - "Burkina Faso", - "Burundi", - "Cameroon", - "Central African Republic", - "Chad", - "Comoros", - "Congo, Dem. Rep.", - "Congo, Rep.", - "Cote d'Ivoire", - "Djibouti", - "Egypt", - "Equatorial Guinea", - "Eritrea", - "Ethiopia", - "Gabon", - "Gambia", - "Ghana", - "Guinea", - "Guinea-Bissau", - "Kenya", - "Lesotho", - "Liberia", - "Libya", - "Madagascar", - "Malawi", - "Mali", - "Mauritania", - "Mauritius", - "Morocco", - "Mozambique", - "Namibia", - "Niger", - "Nigeria", - "Reunion", - "Rwanda", - "Sao Tome and Principe", - "Senegal", - "Sierra Leone", - "Somalia", - "South Africa", - "Sudan", - "Swaziland", - "Tanzania", - "Togo", - "Tunisia", - "Uganda", - "Zambia", - "Zimbabwe" - ], - "ids": [ - "Algeria", - "Angola", - "Benin", - "Botswana", - "Burkina Faso", - "Burundi", - "Cameroon", - "Central African Republic", - "Chad", - "Comoros", - "Congo, Dem. Rep.", - "Congo, Rep.", - "Cote d'Ivoire", - "Djibouti", - "Egypt", - "Equatorial Guinea", - "Eritrea", - "Ethiopia", - "Gabon", - "Gambia", - "Ghana", - "Guinea", - "Guinea-Bissau", - "Kenya", - "Lesotho", - "Liberia", - "Libya", - "Madagascar", - "Malawi", - "Mali", - "Mauritania", - "Mauritius", - "Morocco", - "Mozambique", - "Namibia", - "Niger", - "Nigeria", - "Reunion", - "Rwanda", - "Sao Tome and Principe", - "Senegal", - "Sierra Leone", - "Somalia", - "South Africa", - "Sudan", - "Swaziland", - "Tanzania", - "Togo", - "Tunisia", - "Uganda", - "Zambia", - "Zimbabwe" - ], - "legendgroup": "Africa", - "marker": { - "color": "#00cc96", - "size": [ - 9279525, - 4232095, - 1738315, - 442308, - 4469979, - 2445618, - 5009067, - 1291695, - 2682462, - 153936, - 14100005, - 854885, - 2977019, - 63149, - 22223309, - 216964, - 1438760, - 20860941, - 420702, - 284320, - 5581001, - 2664249, - 580653, - 6464046, - 748747, - 863308, - 1019729, - 4762912, - 2917802, - 3838168, - 1022556, - 516556, - 9939217, - 6446316, - 485831, - 3379468, - 33119096, - 257700, - 2534927, - 60011, - 2755589, - 2143249, - 2526994, - 14264935, - 8504667, - 290243, - 8322925, - 1219113, - 3647735, - 5824797, - 2672000, - 3080907 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Africa", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 2449.008185, - 3520.610273, - 1062.7522, - 851.2411407, - 543.2552413, - 339.2964587, - 1172.667655, - 1071.310713, - 1178.665927, - 1102.990936, - 780.5423257, - 2125.621418, - 1388.594732, - 2669.529475, - 1418.822445, - 375.6431231, - 328.9405571000001, - 362.1462796, - 4293.476475, - 485.2306591, - 911.2989371, - 510.1964923000001, - 299.850319, - 853.5409189999998, - 298.8462121, - 575.5729961000002, - 2387.54806, - 1443.011715, - 369.1650802, - 452.3369807, - 743.1159097, - 1967.955707, - 1688.20357, - 468.5260381, - 2423.780443, - 761.879376, - 1077.281856, - 2718.885295, - 493.3238752, - 879.5835855, - 1450.356983, - 879.7877358, - 1135.749842, - 4725.295531000002, - 1615.991129, - 1148.376626, - 716.6500721, - 859.8086567, - 1468.475631, - 734.753484, - 1147.388831, - 406.8841148 - ], - "xaxis": "x", - "y": [ - 43.077, - 30.015, - 38.223, - 47.622, - 31.975, - 39.031, - 38.523, - 35.463, - 38.092, - 40.715, - 39.143, - 42.111, - 40.477, - 34.812, - 41.893, - 34.482, - 35.92800000000001, - 34.078, - 37.003, - 30, - 43.149, - 33.609, - 32.5, - 42.27, - 42.13800000000001, - 38.48, - 42.723, - 36.681, - 36.256, - 33.685, - 40.543, - 50.986, - 42.87300000000001, - 31.286, - 41.725, - 37.444, - 36.324, - 52.724, - 40, - 46.471, - 37.278, - 30.331, - 32.978, - 45.00899999999999, - 38.635, - 41.407, - 41.215, - 38.596, - 44.6, - 39.978, - 42.038, - 48.451 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Americas
year=1952
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Argentina", - "Bolivia", - "Brazil", - "Canada", - "Chile", - "Colombia", - "Costa Rica", - "Cuba", - "Dominican Republic", - "Ecuador", - "El Salvador", - "Guatemala", - "Haiti", - "Honduras", - "Jamaica", - "Mexico", - "Nicaragua", - "Panama", - "Paraguay", - "Peru", - "Puerto Rico", - "Trinidad and Tobago", - "United States", - "Uruguay", - "Venezuela" - ], - "ids": [ - "Argentina", - "Bolivia", - "Brazil", - "Canada", - "Chile", - "Colombia", - "Costa Rica", - "Cuba", - "Dominican Republic", - "Ecuador", - "El Salvador", - "Guatemala", - "Haiti", - "Honduras", - "Jamaica", - "Mexico", - "Nicaragua", - "Panama", - "Paraguay", - "Peru", - "Puerto Rico", - "Trinidad and Tobago", - "United States", - "Uruguay", - "Venezuela" - ], - "legendgroup": "Americas", - "marker": { - "color": "#ab63fa", - "size": [ - 17876956, - 2883315, - 56602560, - 14785584, - 6377619, - 12350771, - 926317, - 6007797, - 2491346, - 3548753, - 2042865, - 3146381, - 3201488, - 1517453, - 1426095, - 30144317, - 1165790, - 940080, - 1555876, - 8025700, - 2227000, - 662850, - 157553000, - 2252965, - 5439568 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Americas", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5911.315053, - 2677.326347, - 2108.944355, - 11367.16112, - 3939.978789, - 2144.115096, - 2627.0094710000008, - 5586.53878, - 1397.717137, - 3522.110717, - 3048.3029, - 2428.2377690000008, - 1840.366939, - 2194.926204, - 2898.530881, - 3478.125529, - 3112.363948, - 2480.380334, - 1952.308701, - 3758.523437, - 3081.959785, - 3023.271928, - 13990.482080000002, - 5716.766744, - 7689.799761 - ], - "xaxis": "x", - "y": [ - 62.485, - 40.414, - 50.917, - 68.75, - 54.745, - 50.643, - 57.206, - 59.42100000000001, - 45.928, - 48.357, - 45.262, - 42.023, - 37.579, - 41.912, - 58.53, - 50.789, - 42.31399999999999, - 55.191, - 62.649, - 43.902, - 64.28, - 59.1, - 68.44, - 66.071, - 55.088 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Oceania
year=1952
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Australia", - "New Zealand" - ], - "ids": [ - "Australia", - "New Zealand" - ], - "legendgroup": "Oceania", - "marker": { - "color": "#FFA15A", - "size": [ - 8691212, - 1994794 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Oceania", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 10039.59564, - 10556.57566 - ], - "xaxis": "x", - "y": [ - 69.12, - 69.39 - ], - "yaxis": "y" - } - ], - "name": "1952" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

continent=Asia
year=1957
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Afghanistan", - "Bahrain", - "Bangladesh", - "Cambodia", - "China", - "Hong Kong, China", - "India", - "Indonesia", - "Iran", - "Iraq", - "Israel", - "Japan", - "Jordan", - "Korea, Dem. Rep.", - "Korea, Rep.", - "Kuwait", - "Lebanon", - "Malaysia", - "Mongolia", - "Myanmar", - "Nepal", - "Oman", - "Pakistan", - "Philippines", - "Saudi Arabia", - "Singapore", - "Sri Lanka", - "Syria", - "Taiwan", - "Thailand", - "Vietnam", - "West Bank and Gaza", - "Yemen, Rep." - ], - "ids": [ - "Afghanistan", - "Bahrain", - "Bangladesh", - "Cambodia", - "China", - "Hong Kong, China", - "India", - "Indonesia", - "Iran", - "Iraq", - "Israel", - "Japan", - "Jordan", - "Korea, Dem. Rep.", - "Korea, Rep.", - "Kuwait", - "Lebanon", - "Malaysia", - "Mongolia", - "Myanmar", - "Nepal", - "Oman", - "Pakistan", - "Philippines", - "Saudi Arabia", - "Singapore", - "Sri Lanka", - "Syria", - "Taiwan", - "Thailand", - "Vietnam", - "West Bank and Gaza", - "Yemen, Rep." - ], - "legendgroup": "Asia", - "marker": { - "color": "#636efa", - "size": [ - 9240934, - 138655, - 51365468, - 5322536, - 637408000, - 2736300, - 409000000, - 90124000, - 19792000, - 6248643, - 1944401, - 91563009, - 746559, - 9411381, - 22611552, - 212846, - 1647412, - 7739235, - 882134, - 21731844, - 9682338, - 561977, - 46679944, - 26072194, - 4419650, - 1445929, - 9128546, - 4149908, - 10164215, - 25041917, - 28998543, - 1070439, - 5498090 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Asia", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 820.8530296, - 11635.79945, - 661.6374577, - 434.0383364, - 575.9870009, - 3629.076457, - 590.061996, - 858.9002707000002, - 3290.257643, - 6229.333562, - 5385.278451, - 4317.694365, - 1886.080591, - 1571.134655, - 1487.593537, - 113523.1329, - 6089.786934000002, - 1810.0669920000007, - 912.6626085, - 350, - 597.9363557999999, - 2242.746551, - 747.0835292, - 1547.944844, - 8157.5912480000015, - 2843.104409, - 1072.546602, - 2117.234893, - 1507.86129, - 793.5774147999998, - 676.2854477999998, - 1827.067742, - 804.8304547 - ], - "xaxis": "x", - "y": [ - 30.332, - 53.832, - 39.348, - 41.36600000000001, - 50.54896, - 64.75, - 40.249, - 39.918, - 47.181, - 48.437, - 67.84, - 65.5, - 45.669, - 54.081, - 52.681, - 58.033, - 59.489, - 52.102, - 45.24800000000001, - 41.905, - 37.686, - 40.08, - 45.557, - 51.334, - 42.868, - 63.179, - 61.456, - 48.284, - 62.4, - 53.63, - 42.887, - 45.67100000000001, - 33.97 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Europe
year=1957
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Albania", - "Austria", - "Belgium", - "Bosnia and Herzegovina", - "Bulgaria", - "Croatia", - "Czech Republic", - "Denmark", - "Finland", - "France", - "Germany", - "Greece", - "Hungary", - "Iceland", - "Ireland", - "Italy", - "Montenegro", - "Netherlands", - "Norway", - "Poland", - "Portugal", - "Romania", - "Serbia", - "Slovak Republic", - "Slovenia", - "Spain", - "Sweden", - "Switzerland", - "Turkey", - "United Kingdom" - ], - "ids": [ - "Albania", - "Austria", - "Belgium", - "Bosnia and Herzegovina", - "Bulgaria", - "Croatia", - "Czech Republic", - "Denmark", - "Finland", - "France", - "Germany", - "Greece", - "Hungary", - "Iceland", - "Ireland", - "Italy", - "Montenegro", - "Netherlands", - "Norway", - "Poland", - "Portugal", - "Romania", - "Serbia", - "Slovak Republic", - "Slovenia", - "Spain", - "Sweden", - "Switzerland", - "Turkey", - "United Kingdom" - ], - "legendgroup": "Europe", - "marker": { - "color": "#EF553B", - "size": [ - 1476505, - 6965860, - 8989111, - 3076000, - 7651254, - 3991242, - 9513758, - 4487831, - 4324000, - 44310863, - 71019069, - 8096218, - 9839000, - 165110, - 2878220, - 49182000, - 442829, - 11026383, - 3491938, - 28235346, - 8817650, - 17829327, - 7271135, - 3844277, - 1533070, - 29841614, - 7363802, - 5126000, - 25670939, - 51430000 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Europe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 1942.284244, - 8842.59803, - 9714.960623, - 1353.989176, - 3008.670727, - 4338.231617, - 8256.343918, - 11099.65935, - 7545.415386, - 8662.834898000001, - 10187.82665, - 4916.299889, - 6040.180011, - 9244.001412, - 5599.077872, - 6248.656232, - 3682.259903, - 11276.19344, - 11653.97304, - 4734.253019, - 3774.571743, - 3943.370225, - 4981.090891, - 6093.26298, - 5862.276629, - 4564.80241, - 9911.878226, - 17909.48973, - 2218.754257, - 11283.17795 - ], - "xaxis": "x", - "y": [ - 59.28, - 67.48, - 69.24, - 58.45, - 66.61, - 64.77, - 69.03, - 71.81, - 67.49, - 68.93, - 69.1, - 67.86, - 66.41, - 73.47, - 68.9, - 67.81, - 61.448, - 72.99, - 73.44, - 65.77, - 61.51, - 64.1, - 61.685, - 67.45, - 67.85, - 66.66, - 72.49, - 70.56, - 48.07899999999999, - 70.42 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Africa
year=1957
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Algeria", - "Angola", - "Benin", - "Botswana", - "Burkina Faso", - "Burundi", - "Cameroon", - "Central African Republic", - "Chad", - "Comoros", - "Congo, Dem. Rep.", - "Congo, Rep.", - "Cote d'Ivoire", - "Djibouti", - "Egypt", - "Equatorial Guinea", - "Eritrea", - "Ethiopia", - "Gabon", - "Gambia", - "Ghana", - "Guinea", - "Guinea-Bissau", - "Kenya", - "Lesotho", - "Liberia", - "Libya", - "Madagascar", - "Malawi", - "Mali", - "Mauritania", - "Mauritius", - "Morocco", - "Mozambique", - "Namibia", - "Niger", - "Nigeria", - "Reunion", - "Rwanda", - "Sao Tome and Principe", - "Senegal", - "Sierra Leone", - "Somalia", - "South Africa", - "Sudan", - "Swaziland", - "Tanzania", - "Togo", - "Tunisia", - "Uganda", - "Zambia", - "Zimbabwe" - ], - "ids": [ - "Algeria", - "Angola", - "Benin", - "Botswana", - "Burkina Faso", - "Burundi", - "Cameroon", - "Central African Republic", - "Chad", - "Comoros", - "Congo, Dem. Rep.", - "Congo, Rep.", - "Cote d'Ivoire", - "Djibouti", - "Egypt", - "Equatorial Guinea", - "Eritrea", - "Ethiopia", - "Gabon", - "Gambia", - "Ghana", - "Guinea", - "Guinea-Bissau", - "Kenya", - "Lesotho", - "Liberia", - "Libya", - "Madagascar", - "Malawi", - "Mali", - "Mauritania", - "Mauritius", - "Morocco", - "Mozambique", - "Namibia", - "Niger", - "Nigeria", - "Reunion", - "Rwanda", - "Sao Tome and Principe", - "Senegal", - "Sierra Leone", - "Somalia", - "South Africa", - "Sudan", - "Swaziland", - "Tanzania", - "Togo", - "Tunisia", - "Uganda", - "Zambia", - "Zimbabwe" - ], - "legendgroup": "Africa", - "marker": { - "color": "#00cc96", - "size": [ - 10270856, - 4561361, - 1925173, - 474639, - 4713416, - 2667518, - 5359923, - 1392284, - 2894855, - 170928, - 15577932, - 940458, - 3300000, - 71851, - 25009741, - 232922, - 1542611, - 22815614, - 434904, - 323150, - 6391288, - 2876726, - 601095, - 7454779, - 813338, - 975950, - 1201578, - 5181679, - 3221238, - 4241884, - 1076852, - 609816, - 11406350, - 7038035, - 548080, - 3692184, - 37173340, - 308700, - 2822082, - 61325, - 3054547, - 2295678, - 2780415, - 16151549, - 9753392, - 326741, - 9452826, - 1357445, - 3950849, - 6675501, - 3016000, - 3646340 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Africa", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 3013.976023, - 3827.940465, - 959.6010805, - 918.2325349, - 617.1834647999998, - 379.5646281000001, - 1313.048099, - 1190.844328, - 1308.495577, - 1211.148548, - 905.8602303, - 2315.056572, - 1500.895925, - 2864.9690760000008, - 1458.915272, - 426.0964081, - 344.1618859, - 378.9041632, - 4976.198099, - 520.9267111, - 1043.5615369999996, - 576.2670245, - 431.79045660000014, - 944.4383152, - 335.9971151000001, - 620.9699901, - 3448.284395, - 1589.20275, - 416.3698064, - 490.3821867, - 846.1202613, - 2034.037981, - 1642.002314, - 495.58683330000014, - 2621.448058, - 835.5234025000002, - 1100.5925630000004, - 2769.451844, - 540.2893982999999, - 860.7369026, - 1567.653006, - 1004.484437, - 1258.147413, - 5487.104219, - 1770.3370739999998, - 1244.708364, - 698.5356073, - 925.9083202, - 1395.232468, - 774.3710692000002, - 1311.956766, - 518.7642681 - ], - "xaxis": "x", - "y": [ - 45.685, - 31.999, - 40.358, - 49.618, - 34.906, - 40.533, - 40.428, - 37.464, - 39.881, - 42.46, - 40.652, - 45.053, - 42.469, - 37.328, - 44.444, - 35.98300000000001, - 38.047, - 36.667, - 38.999, - 32.065, - 44.779, - 34.558, - 33.489000000000004, - 44.68600000000001, - 45.047, - 39.486, - 45.289, - 38.865, - 37.207, - 35.30699999999999, - 42.338, - 58.089, - 45.423, - 33.779, - 45.226000000000006, - 38.598, - 37.802, - 55.09, - 41.5, - 48.945, - 39.329, - 31.57, - 34.977, - 47.985, - 39.624, - 43.424, - 42.974, - 41.208, - 47.1, - 42.57100000000001, - 44.077, - 50.469 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Americas
year=1957
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Argentina", - "Bolivia", - "Brazil", - "Canada", - "Chile", - "Colombia", - "Costa Rica", - "Cuba", - "Dominican Republic", - "Ecuador", - "El Salvador", - "Guatemala", - "Haiti", - "Honduras", - "Jamaica", - "Mexico", - "Nicaragua", - "Panama", - "Paraguay", - "Peru", - "Puerto Rico", - "Trinidad and Tobago", - "United States", - "Uruguay", - "Venezuela" - ], - "ids": [ - "Argentina", - "Bolivia", - "Brazil", - "Canada", - "Chile", - "Colombia", - "Costa Rica", - "Cuba", - "Dominican Republic", - "Ecuador", - "El Salvador", - "Guatemala", - "Haiti", - "Honduras", - "Jamaica", - "Mexico", - "Nicaragua", - "Panama", - "Paraguay", - "Peru", - "Puerto Rico", - "Trinidad and Tobago", - "United States", - "Uruguay", - "Venezuela" - ], - "legendgroup": "Americas", - "marker": { - "color": "#ab63fa", - "size": [ - 19610538, - 3211738, - 65551171, - 17010154, - 7048426, - 14485993, - 1112300, - 6640752, - 2923186, - 4058385, - 2355805, - 3640876, - 3507701, - 1770390, - 1535090, - 35015548, - 1358828, - 1063506, - 1770902, - 9146100, - 2260000, - 764900, - 171984000, - 2424959, - 6702668 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Americas", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 6856.8562120000015, - 2127.686326, - 2487.365989, - 12489.95006, - 4315.622723, - 2323.805581, - 2990.010802, - 6092.1743590000015, - 1544.402995, - 3780.546651, - 3421.523218, - 2617.155967, - 1726.887882, - 2220.487682, - 4756.525781, - 4131.546641, - 3457.415947, - 2961.800905, - 2046.154706, - 4245.256697999999, - 3907.156189, - 4100.3934, - 14847.12712, - 6150.772969, - 9802.466526 - ], - "xaxis": "x", - "y": [ - 64.399, - 41.89, - 53.285, - 69.96, - 56.074, - 55.118, - 60.026, - 62.325, - 49.828, - 51.356, - 48.57, - 44.142, - 40.696, - 44.665, - 62.61, - 55.19, - 45.432, - 59.201, - 63.19600000000001, - 46.26300000000001, - 68.54, - 61.8, - 69.49, - 67.044, - 57.907 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Oceania
year=1957
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Australia", - "New Zealand" - ], - "ids": [ - "Australia", - "New Zealand" - ], - "legendgroup": "Oceania", - "marker": { - "color": "#FFA15A", - "size": [ - 9712569, - 2229407 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Oceania", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 10949.64959, - 12247.39532 - ], - "xaxis": "x", - "y": [ - 70.33, - 70.26 - ], - "yaxis": "y" - } - ], - "name": "1957" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

continent=Asia
year=1962
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Afghanistan", - "Bahrain", - "Bangladesh", - "Cambodia", - "China", - "Hong Kong, China", - "India", - "Indonesia", - "Iran", - "Iraq", - "Israel", - "Japan", - "Jordan", - "Korea, Dem. Rep.", - "Korea, Rep.", - "Kuwait", - "Lebanon", - "Malaysia", - "Mongolia", - "Myanmar", - "Nepal", - "Oman", - "Pakistan", - "Philippines", - "Saudi Arabia", - "Singapore", - "Sri Lanka", - "Syria", - "Taiwan", - "Thailand", - "Vietnam", - "West Bank and Gaza", - "Yemen, Rep." - ], - "ids": [ - "Afghanistan", - "Bahrain", - "Bangladesh", - "Cambodia", - "China", - "Hong Kong, China", - "India", - "Indonesia", - "Iran", - "Iraq", - "Israel", - "Japan", - "Jordan", - "Korea, Dem. Rep.", - "Korea, Rep.", - "Kuwait", - "Lebanon", - "Malaysia", - "Mongolia", - "Myanmar", - "Nepal", - "Oman", - "Pakistan", - "Philippines", - "Saudi Arabia", - "Singapore", - "Sri Lanka", - "Syria", - "Taiwan", - "Thailand", - "Vietnam", - "West Bank and Gaza", - "Yemen, Rep." - ], - "legendgroup": "Asia", - "marker": { - "color": "#636efa", - "size": [ - 10267083, - 171863, - 56839289, - 6083619, - 665770000, - 3305200, - 454000000, - 99028000, - 22874000, - 7240260, - 2310904, - 95831757, - 933559, - 10917494, - 26420307, - 358266, - 1886848, - 8906385, - 1010280, - 23634436, - 10332057, - 628164, - 53100671, - 30325264, - 4943029, - 1750200, - 10421936, - 4834621, - 11918938, - 29263397, - 33796140, - 1133134, - 6120081 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Asia", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 853.1007099999998, - 12753.27514, - 686.3415537999998, - 496.9136476, - 487.6740183, - 4692.648271999999, - 658.3471509, - 849.2897700999998, - 4187.329802, - 8341.737815, - 7105.630706, - 6576.649461, - 2348.009158, - 1621.693598, - 1536.344387, - 95458.11176, - 5714.560611, - 2036.884944, - 1056.353958, - 388, - 652.3968593, - 2924.638113, - 803.3427418, - 1649.552153, - 11626.41975, - 3674.735572, - 1074.47196, - 2193.037133, - 1822.879028, - 1002.199172, - 772.0491602000002, - 2198.9563120000007, - 825.6232006 - ], - "xaxis": "x", - "y": [ - 31.997, - 56.923, - 41.216, - 43.415, - 44.50136, - 67.65, - 43.605, - 42.518, - 49.325, - 51.457, - 69.39, - 68.73, - 48.12600000000001, - 56.65600000000001, - 55.292, - 60.47, - 62.094, - 55.737, - 48.25100000000001, - 45.108, - 39.393, - 43.165, - 47.67, - 54.757, - 45.914, - 65.798, - 62.192, - 50.305, - 65.2, - 56.06100000000001, - 45.363, - 48.127, - 35.18 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Europe
year=1962
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Albania", - "Austria", - "Belgium", - "Bosnia and Herzegovina", - "Bulgaria", - "Croatia", - "Czech Republic", - "Denmark", - "Finland", - "France", - "Germany", - "Greece", - "Hungary", - "Iceland", - "Ireland", - "Italy", - "Montenegro", - "Netherlands", - "Norway", - "Poland", - "Portugal", - "Romania", - "Serbia", - "Slovak Republic", - "Slovenia", - "Spain", - "Sweden", - "Switzerland", - "Turkey", - "United Kingdom" - ], - "ids": [ - "Albania", - "Austria", - "Belgium", - "Bosnia and Herzegovina", - "Bulgaria", - "Croatia", - "Czech Republic", - "Denmark", - "Finland", - "France", - "Germany", - "Greece", - "Hungary", - "Iceland", - "Ireland", - "Italy", - "Montenegro", - "Netherlands", - "Norway", - "Poland", - "Portugal", - "Romania", - "Serbia", - "Slovak Republic", - "Slovenia", - "Spain", - "Sweden", - "Switzerland", - "Turkey", - "United Kingdom" - ], - "legendgroup": "Europe", - "marker": { - "color": "#EF553B", - "size": [ - 1728137, - 7129864, - 9218400, - 3349000, - 8012946, - 4076557, - 9620282, - 4646899, - 4491443, - 47124000, - 73739117, - 8448233, - 10063000, - 182053, - 2830000, - 50843200, - 474528, - 11805689, - 3638919, - 30329617, - 9019800, - 18680721, - 7616060, - 4237384, - 1582962, - 31158061, - 7561588, - 5666000, - 29788695, - 53292000 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Europe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 2312.888958, - 10750.72111, - 10991.20676, - 1709.683679, - 4254.337839, - 5477.890018, - 10136.86713, - 13583.31351, - 9371.842561, - 10560.48553, - 12902.46291, - 6017.190732999999, - 7550.359877, - 10350.15906, - 6631.597314, - 8243.58234, - 4649.593785, - 12790.84956, - 13450.40151, - 5338.752143, - 4727.954889, - 4734.997586, - 6289.629157, - 7481.107598, - 7402.303395, - 5693.843879, - 12329.44192, - 20431.0927, - 2322.869908, - 12477.17707 - ], - "xaxis": "x", - "y": [ - 64.82, - 69.54, - 70.25, - 61.93, - 69.51, - 67.13, - 69.9, - 72.35, - 68.75, - 70.51, - 70.3, - 69.51, - 67.96, - 73.68, - 70.29, - 69.24, - 63.728, - 73.23, - 73.47, - 67.64, - 64.39, - 66.8, - 64.531, - 70.33, - 69.15, - 69.69, - 73.37, - 71.32, - 52.098, - 70.76 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Africa
year=1962
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Algeria", - "Angola", - "Benin", - "Botswana", - "Burkina Faso", - "Burundi", - "Cameroon", - "Central African Republic", - "Chad", - "Comoros", - "Congo, Dem. Rep.", - "Congo, Rep.", - "Cote d'Ivoire", - "Djibouti", - "Egypt", - "Equatorial Guinea", - "Eritrea", - "Ethiopia", - "Gabon", - "Gambia", - "Ghana", - "Guinea", - "Guinea-Bissau", - "Kenya", - "Lesotho", - "Liberia", - "Libya", - "Madagascar", - "Malawi", - "Mali", - "Mauritania", - "Mauritius", - "Morocco", - "Mozambique", - "Namibia", - "Niger", - "Nigeria", - "Reunion", - "Rwanda", - "Sao Tome and Principe", - "Senegal", - "Sierra Leone", - "Somalia", - "South Africa", - "Sudan", - "Swaziland", - "Tanzania", - "Togo", - "Tunisia", - "Uganda", - "Zambia", - "Zimbabwe" - ], - "ids": [ - "Algeria", - "Angola", - "Benin", - "Botswana", - "Burkina Faso", - "Burundi", - "Cameroon", - "Central African Republic", - "Chad", - "Comoros", - "Congo, Dem. Rep.", - "Congo, Rep.", - "Cote d'Ivoire", - "Djibouti", - "Egypt", - "Equatorial Guinea", - "Eritrea", - "Ethiopia", - "Gabon", - "Gambia", - "Ghana", - "Guinea", - "Guinea-Bissau", - "Kenya", - "Lesotho", - "Liberia", - "Libya", - "Madagascar", - "Malawi", - "Mali", - "Mauritania", - "Mauritius", - "Morocco", - "Mozambique", - "Namibia", - "Niger", - "Nigeria", - "Reunion", - "Rwanda", - "Sao Tome and Principe", - "Senegal", - "Sierra Leone", - "Somalia", - "South Africa", - "Sudan", - "Swaziland", - "Tanzania", - "Togo", - "Tunisia", - "Uganda", - "Zambia", - "Zimbabwe" - ], - "legendgroup": "Africa", - "marker": { - "color": "#00cc96", - "size": [ - 11000948, - 4826015, - 2151895, - 512764, - 4919632, - 2961915, - 5793633, - 1523478, - 3150417, - 191689, - 17486434, - 1047924, - 3832408, - 89898, - 28173309, - 249220, - 1666618, - 25145372, - 455661, - 374020, - 7355248, - 3140003, - 627820, - 8678557, - 893143, - 1112796, - 1441863, - 5703324, - 3628608, - 4690372, - 1146757, - 701016, - 13056604, - 7788944, - 621392, - 4076008, - 41871351, - 358900, - 3051242, - 65345, - 3430243, - 2467895, - 3080153, - 18356657, - 11183227, - 370006, - 10863958, - 1528098, - 4286552, - 7688797, - 3421000, - 4277736 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Africa", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 2550.81688, - 4269.276742, - 949.4990641, - 983.6539764, - 722.5120206, - 355.2032273, - 1399.607441, - 1193.068753, - 1389.817618, - 1406.648278, - 896.3146335000001, - 2464.783157, - 1728.8694280000002, - 3020.989263, - 1693.335853, - 582.8419713999998, - 380.9958433000001, - 419.4564161, - 6631.459222, - 599.650276, - 1190.041118, - 686.3736739, - 522.0343725, - 896.9663732, - 411.8006266, - 634.1951625, - 6757.030816, - 1643.38711, - 427.9010856, - 496.1743428, - 1055.896036, - 2529.0674870000007, - 1566.353493, - 556.6863539, - 3173.215595, - 997.7661127, - 1150.9274779999996, - 3173.72334, - 597.4730727000001, - 1071.551119, - 1654.988723, - 1116.6398769999996, - 1369.488336, - 5768.729717, - 1959.593767, - 1856.182125, - 722.0038073, - 1067.53481, - 1660.30321, - 767.2717397999999, - 1452.725766, - 527.2721818 - ], - "xaxis": "x", - "y": [ - 48.303, - 34, - 42.618, - 51.52, - 37.814, - 42.045, - 42.643, - 39.475, - 41.716, - 44.467, - 42.122, - 48.435, - 44.93, - 39.69300000000001, - 46.992, - 37.485, - 40.158, - 40.059, - 40.489, - 33.896, - 46.452, - 35.753, - 34.488, - 47.949, - 47.747, - 40.502, - 47.808, - 40.848, - 38.41, - 36.936, - 44.24800000000001, - 60.246, - 47.924, - 36.161, - 48.386, - 39.487, - 39.36, - 57.666, - 43, - 51.893, - 41.45399999999999, - 32.767, - 36.981, - 49.951, - 40.87, - 44.992, - 44.246, - 43.922, - 49.57899999999999, - 45.344, - 46.023, - 52.358 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Americas
year=1962
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Argentina", - "Bolivia", - "Brazil", - "Canada", - "Chile", - "Colombia", - "Costa Rica", - "Cuba", - "Dominican Republic", - "Ecuador", - "El Salvador", - "Guatemala", - "Haiti", - "Honduras", - "Jamaica", - "Mexico", - "Nicaragua", - "Panama", - "Paraguay", - "Peru", - "Puerto Rico", - "Trinidad and Tobago", - "United States", - "Uruguay", - "Venezuela" - ], - "ids": [ - "Argentina", - "Bolivia", - "Brazil", - "Canada", - "Chile", - "Colombia", - "Costa Rica", - "Cuba", - "Dominican Republic", - "Ecuador", - "El Salvador", - "Guatemala", - "Haiti", - "Honduras", - "Jamaica", - "Mexico", - "Nicaragua", - "Panama", - "Paraguay", - "Peru", - "Puerto Rico", - "Trinidad and Tobago", - "United States", - "Uruguay", - "Venezuela" - ], - "legendgroup": "Americas", - "marker": { - "color": "#ab63fa", - "size": [ - 21283783, - 3593918, - 76039390, - 18985849, - 7961258, - 17009885, - 1345187, - 7254373, - 3453434, - 4681707, - 2747687, - 4208858, - 3880130, - 2090162, - 1665128, - 41121485, - 1590597, - 1215725, - 2009813, - 10516500, - 2448046, - 887498, - 186538000, - 2598466, - 8143375 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Americas", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 7133.166023000002, - 2180.972546, - 3336.585802, - 13462.48555, - 4519.094331, - 2492.351109, - 3460.937025, - 5180.75591, - 1662.137359, - 4086.114078, - 3776.803627, - 2750.364446, - 1796.589032, - 2291.156835, - 5246.107524, - 4581.609385, - 3634.364406, - 3536.540301, - 2148.027146, - 4957.037982, - 5108.34463, - 4997.523971000001, - 16173.14586, - 5603.357717, - 8422.974165000001 - ], - "xaxis": "x", - "y": [ - 65.142, - 43.428, - 55.665, - 71.3, - 57.924, - 57.863, - 62.842, - 65.24600000000001, - 53.459, - 54.64, - 52.307, - 46.95399999999999, - 43.59, - 48.041, - 65.61, - 58.299, - 48.632, - 61.817, - 64.361, - 49.096, - 69.62, - 64.9, - 70.21, - 68.253, - 60.77 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Oceania
year=1962
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Australia", - "New Zealand" - ], - "ids": [ - "Australia", - "New Zealand" - ], - "legendgroup": "Oceania", - "marker": { - "color": "#FFA15A", - "size": [ - 10794968, - 2488550 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Oceania", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 12217.22686, - 13175.678 - ], - "xaxis": "x", - "y": [ - 70.93, - 71.24 - ], - "yaxis": "y" - } - ], - "name": "1962" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

continent=Asia
year=1967
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Afghanistan", - "Bahrain", - "Bangladesh", - "Cambodia", - "China", - "Hong Kong, China", - "India", - "Indonesia", - "Iran", - "Iraq", - "Israel", - "Japan", - "Jordan", - "Korea, Dem. Rep.", - "Korea, Rep.", - "Kuwait", - "Lebanon", - "Malaysia", - "Mongolia", - "Myanmar", - "Nepal", - "Oman", - "Pakistan", - "Philippines", - "Saudi Arabia", - "Singapore", - "Sri Lanka", - "Syria", - "Taiwan", - "Thailand", - "Vietnam", - "West Bank and Gaza", - "Yemen, Rep." - ], - "ids": [ - "Afghanistan", - "Bahrain", - "Bangladesh", - "Cambodia", - "China", - "Hong Kong, China", - "India", - "Indonesia", - "Iran", - "Iraq", - "Israel", - "Japan", - "Jordan", - "Korea, Dem. Rep.", - "Korea, Rep.", - "Kuwait", - "Lebanon", - "Malaysia", - "Mongolia", - "Myanmar", - "Nepal", - "Oman", - "Pakistan", - "Philippines", - "Saudi Arabia", - "Singapore", - "Sri Lanka", - "Syria", - "Taiwan", - "Thailand", - "Vietnam", - "West Bank and Gaza", - "Yemen, Rep." - ], - "legendgroup": "Asia", - "marker": { - "color": "#636efa", - "size": [ - 11537966, - 202182, - 62821884, - 6960067, - 754550000, - 3722800, - 506000000, - 109343000, - 26538000, - 8519282, - 2693585, - 100825279, - 1255058, - 12617009, - 30131000, - 575003, - 2186894, - 10154878, - 1149500, - 25870271, - 11261690, - 714775, - 60641899, - 35356600, - 5618198, - 1977600, - 11737396, - 5680812, - 13648692, - 34024249, - 39463910, - 1142636, - 6740785 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Asia", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 836.1971382, - 14804.6727, - 721.1860862000002, - 523.4323142, - 612.7056934, - 6197.962814, - 700.7706107000001, - 762.4317721, - 5906.731804999999, - 8931.459811, - 8393.741404, - 9847.788607, - 2741.796252, - 2143.540609, - 2029.228142, - 80894.88326, - 6006.983042, - 2277.742396, - 1226.04113, - 349, - 676.4422254, - 4720.942687, - 942.4082588, - 1814.12743, - 16903.04886, - 4977.41854, - 1135.514326, - 1881.923632, - 2643.858681, - 1295.46066, - 637.1232887, - 2649.715007, - 862.4421463 - ], - "xaxis": "x", - "y": [ - 34.02, - 59.923, - 43.453, - 45.415, - 58.38112, - 70, - 47.19300000000001, - 45.964, - 52.469, - 54.459, - 70.75, - 71.43, - 51.629, - 59.942, - 57.716, - 64.624, - 63.87, - 59.371, - 51.253, - 49.379, - 41.472, - 46.988, - 49.8, - 56.393, - 49.901, - 67.946, - 64.266, - 53.655, - 67.5, - 58.285, - 47.838, - 51.631, - 36.984 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Europe
year=1967
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Albania", - "Austria", - "Belgium", - "Bosnia and Herzegovina", - "Bulgaria", - "Croatia", - "Czech Republic", - "Denmark", - "Finland", - "France", - "Germany", - "Greece", - "Hungary", - "Iceland", - "Ireland", - "Italy", - "Montenegro", - "Netherlands", - "Norway", - "Poland", - "Portugal", - "Romania", - "Serbia", - "Slovak Republic", - "Slovenia", - "Spain", - "Sweden", - "Switzerland", - "Turkey", - "United Kingdom" - ], - "ids": [ - "Albania", - "Austria", - "Belgium", - "Bosnia and Herzegovina", - "Bulgaria", - "Croatia", - "Czech Republic", - "Denmark", - "Finland", - "France", - "Germany", - "Greece", - "Hungary", - "Iceland", - "Ireland", - "Italy", - "Montenegro", - "Netherlands", - "Norway", - "Poland", - "Portugal", - "Romania", - "Serbia", - "Slovak Republic", - "Slovenia", - "Spain", - "Sweden", - "Switzerland", - "Turkey", - "United Kingdom" - ], - "legendgroup": "Europe", - "marker": { - "color": "#EF553B", - "size": [ - 1984060, - 7376998, - 9556500, - 3585000, - 8310226, - 4174366, - 9835109, - 4838800, - 4605744, - 49569000, - 76368453, - 8716441, - 10223422, - 198676, - 2900100, - 52667100, - 501035, - 12596822, - 3786019, - 31785378, - 9103000, - 19284814, - 7971222, - 4442238, - 1646912, - 32850275, - 7867931, - 6063000, - 33411317, - 54959000 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Europe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 2760.196931, - 12834.6024, - 13149.04119, - 2172.3524230000007, - 5577.0028, - 6960.297861, - 11399.44489, - 15937.21123, - 10921.63626, - 12999.91766, - 14745.62561, - 8513.097016, - 9326.64467, - 13319.89568, - 7655.568963, - 10022.40131, - 5907.850937, - 15363.25136, - 16361.87647, - 6557.152776, - 6361.517993, - 6470.866545, - 7991.707066, - 8412.902397, - 9405.489397, - 7993.512294, - 15258.29697, - 22966.14432, - 2826.3563870000007, - 14142.85089 - ], - "xaxis": "x", - "y": [ - 66.22, - 70.14, - 70.94, - 64.79, - 70.42, - 68.5, - 70.38, - 72.96, - 69.83, - 71.55, - 70.8, - 71, - 69.5, - 73.73, - 71.08, - 71.06, - 67.178, - 73.82, - 74.08, - 69.61, - 66.6, - 66.8, - 66.914, - 70.98, - 69.18, - 71.44, - 74.16, - 72.77, - 54.33600000000001, - 71.36 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Africa
year=1967
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Algeria", - "Angola", - "Benin", - "Botswana", - "Burkina Faso", - "Burundi", - "Cameroon", - "Central African Republic", - "Chad", - "Comoros", - "Congo, Dem. Rep.", - "Congo, Rep.", - "Cote d'Ivoire", - "Djibouti", - "Egypt", - "Equatorial Guinea", - "Eritrea", - "Ethiopia", - "Gabon", - "Gambia", - "Ghana", - "Guinea", - "Guinea-Bissau", - "Kenya", - "Lesotho", - "Liberia", - "Libya", - "Madagascar", - "Malawi", - "Mali", - "Mauritania", - "Mauritius", - "Morocco", - "Mozambique", - "Namibia", - "Niger", - "Nigeria", - "Reunion", - "Rwanda", - "Sao Tome and Principe", - "Senegal", - "Sierra Leone", - "Somalia", - "South Africa", - "Sudan", - "Swaziland", - "Tanzania", - "Togo", - "Tunisia", - "Uganda", - "Zambia", - "Zimbabwe" - ], - "ids": [ - "Algeria", - "Angola", - "Benin", - "Botswana", - "Burkina Faso", - "Burundi", - "Cameroon", - "Central African Republic", - "Chad", - "Comoros", - "Congo, Dem. Rep.", - "Congo, Rep.", - "Cote d'Ivoire", - "Djibouti", - "Egypt", - "Equatorial Guinea", - "Eritrea", - "Ethiopia", - "Gabon", - "Gambia", - "Ghana", - "Guinea", - "Guinea-Bissau", - "Kenya", - "Lesotho", - "Liberia", - "Libya", - "Madagascar", - "Malawi", - "Mali", - "Mauritania", - "Mauritius", - "Morocco", - "Mozambique", - "Namibia", - "Niger", - "Nigeria", - "Reunion", - "Rwanda", - "Sao Tome and Principe", - "Senegal", - "Sierra Leone", - "Somalia", - "South Africa", - "Sudan", - "Swaziland", - "Tanzania", - "Togo", - "Tunisia", - "Uganda", - "Zambia", - "Zimbabwe" - ], - "legendgroup": "Africa", - "marker": { - "color": "#00cc96", - "size": [ - 12760499, - 5247469, - 2427334, - 553541, - 5127935, - 3330989, - 6335506, - 1733638, - 3495967, - 217378, - 19941073, - 1179760, - 4744870, - 127617, - 31681188, - 259864, - 1820319, - 27860297, - 489004, - 439593, - 8490213, - 3451418, - 601287, - 10191512, - 996380, - 1279406, - 1759224, - 6334556, - 4147252, - 5212416, - 1230542, - 789309, - 14770296, - 8680909, - 706640, - 4534062, - 47287752, - 414024, - 3451079, - 70787, - 3965841, - 2662190, - 3428839, - 20997321, - 12716129, - 420690, - 12607312, - 1735550, - 4786986, - 8900294, - 3900000, - 4995432 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Africa", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 3246.991771, - 5522.776375, - 1035.831411, - 1214.709294, - 794.8265597, - 412.97751360000007, - 1508.453148, - 1136.056615, - 1196.810565, - 1876.029643, - 861.5932424, - 2677.9396420000007, - 2052.050473, - 3020.050513, - 1814.880728, - 915.5960025, - 468.7949699, - 516.1186438, - 8358.761987, - 734.7829124, - 1125.69716, - 708.7595409, - 715.5806402000002, - 1056.736457, - 498.6390265, - 713.6036482999998, - 18772.75169, - 1634.047282, - 495.5147806, - 545.0098873, - 1421.145193, - 2475.387562, - 1711.04477, - 566.6691539, - 3793.694753, - 1054.384891, - 1014.514104, - 4021.175739, - 510.9637142, - 1384.840593, - 1612.404632, - 1206.043465, - 1284.7331800000004, - 7114.477970999998, - 1687.997641, - 2613.101665, - 848.2186575, - 1477.59676, - 1932.3601670000005, - 908.9185217, - 1777.077318, - 569.7950712 - ], - "xaxis": "x", - "y": [ - 51.407, - 35.985, - 44.885, - 53.298, - 40.697, - 43.548, - 44.799, - 41.478, - 43.601000000000006, - 46.472, - 44.056, - 52.04, - 47.35, - 42.074, - 49.293, - 38.987, - 42.18899999999999, - 42.115, - 44.598, - 35.857, - 48.072, - 37.197, - 35.492, - 50.654, - 48.492, - 41.536, - 50.227, - 42.881, - 39.487, - 38.487, - 46.289, - 61.557, - 50.335, - 38.113, - 51.159, - 40.118, - 41.04, - 60.542, - 44.1, - 54.425, - 43.563, - 34.113, - 38.977, - 51.927, - 42.858, - 46.633, - 45.757, - 46.769, - 52.053, - 48.051, - 47.768, - 53.995 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Americas
year=1967
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Argentina", - "Bolivia", - "Brazil", - "Canada", - "Chile", - "Colombia", - "Costa Rica", - "Cuba", - "Dominican Republic", - "Ecuador", - "El Salvador", - "Guatemala", - "Haiti", - "Honduras", - "Jamaica", - "Mexico", - "Nicaragua", - "Panama", - "Paraguay", - "Peru", - "Puerto Rico", - "Trinidad and Tobago", - "United States", - "Uruguay", - "Venezuela" - ], - "ids": [ - "Argentina", - "Bolivia", - "Brazil", - "Canada", - "Chile", - "Colombia", - "Costa Rica", - "Cuba", - "Dominican Republic", - "Ecuador", - "El Salvador", - "Guatemala", - "Haiti", - "Honduras", - "Jamaica", - "Mexico", - "Nicaragua", - "Panama", - "Paraguay", - "Peru", - "Puerto Rico", - "Trinidad and Tobago", - "United States", - "Uruguay", - "Venezuela" - ], - "legendgroup": "Americas", - "marker": { - "color": "#ab63fa", - "size": [ - 22934225, - 4040665, - 88049823, - 20819767, - 8858908, - 19764027, - 1588717, - 8139332, - 4049146, - 5432424, - 3232927, - 4690773, - 4318137, - 2500689, - 1861096, - 47995559, - 1865490, - 1405486, - 2287985, - 12132200, - 2648961, - 960155, - 198712000, - 2748579, - 9709552 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Americas", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 8052.953020999998, - 2586.886053, - 3429.864357, - 16076.58803, - 5106.654313, - 2678.729839, - 4161.727834, - 5690.268015, - 1653.7230029999996, - 4579.074215, - 4358.595393, - 3242.531147, - 1452.057666, - 2538.269358, - 6124.703450999999, - 5754.733883, - 4643.393534000002, - 4421.009084, - 2299.376311, - 5788.09333, - 6929.277714, - 5621.368472, - 19530.36557, - 5444.61962, - 9541.474188 - ], - "xaxis": "x", - "y": [ - 65.634, - 45.032, - 57.632, - 72.13, - 60.523, - 59.963, - 65.42399999999999, - 68.29, - 56.75100000000001, - 56.678, - 55.855, - 50.01600000000001, - 46.243, - 50.924, - 67.51, - 60.11, - 51.88399999999999, - 64.071, - 64.95100000000001, - 51.445, - 71.1, - 65.4, - 70.76, - 68.468, - 63.479 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Oceania
year=1967
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Australia", - "New Zealand" - ], - "ids": [ - "Australia", - "New Zealand" - ], - "legendgroup": "Oceania", - "marker": { - "color": "#FFA15A", - "size": [ - 11872264, - 2728150 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Oceania", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 14526.12465, - 14463.918930000002 - ], - "xaxis": "x", - "y": [ - 71.1, - 71.52 - ], - "yaxis": "y" - } - ], - "name": "1967" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

continent=Asia
year=1972
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Afghanistan", - "Bahrain", - "Bangladesh", - "Cambodia", - "China", - "Hong Kong, China", - "India", - "Indonesia", - "Iran", - "Iraq", - "Israel", - "Japan", - "Jordan", - "Korea, Dem. Rep.", - "Korea, Rep.", - "Kuwait", - "Lebanon", - "Malaysia", - "Mongolia", - "Myanmar", - "Nepal", - "Oman", - "Pakistan", - "Philippines", - "Saudi Arabia", - "Singapore", - "Sri Lanka", - "Syria", - "Taiwan", - "Thailand", - "Vietnam", - "West Bank and Gaza", - "Yemen, Rep." - ], - "ids": [ - "Afghanistan", - "Bahrain", - "Bangladesh", - "Cambodia", - "China", - "Hong Kong, China", - "India", - "Indonesia", - "Iran", - "Iraq", - "Israel", - "Japan", - "Jordan", - "Korea, Dem. Rep.", - "Korea, Rep.", - "Kuwait", - "Lebanon", - "Malaysia", - "Mongolia", - "Myanmar", - "Nepal", - "Oman", - "Pakistan", - "Philippines", - "Saudi Arabia", - "Singapore", - "Sri Lanka", - "Syria", - "Taiwan", - "Thailand", - "Vietnam", - "West Bank and Gaza", - "Yemen, Rep." - ], - "legendgroup": "Asia", - "marker": { - "color": "#636efa", - "size": [ - 13079460, - 230800, - 70759295, - 7450606, - 862030000, - 4115700, - 567000000, - 121282000, - 30614000, - 10061506, - 3095893, - 107188273, - 1613551, - 14781241, - 33505000, - 841934, - 2680018, - 11441462, - 1320500, - 28466390, - 12412593, - 829050, - 69325921, - 40850141, - 6472756, - 2152400, - 13016733, - 6701172, - 15226039, - 39276153, - 44655014, - 1089572, - 7407075 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Asia", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 739.9811057999998, - 18268.65839, - 630.2336265, - 421.6240257, - 676.9000921, - 8315.928145, - 724.032527, - 1111.107907, - 9613.818607, - 9576.037596, - 12786.93223, - 14778.78636, - 2110.856309, - 3701.621503, - 3030.87665, - 109347.867, - 7486.384341, - 2849.09478, - 1421.741975, - 357, - 674.7881296, - 10618.03855, - 1049.938981, - 1989.37407, - 24837.42865, - 8597.756202, - 1213.39553, - 2571.423014, - 4062.523897, - 1524.358936, - 699.5016441, - 3133.409277, - 1265.047031 - ], - "xaxis": "x", - "y": [ - 36.088, - 63.3, - 45.252, - 40.317, - 63.11888, - 72, - 50.651, - 49.203, - 55.234, - 56.95, - 71.63, - 73.42, - 56.528, - 63.983, - 62.612, - 67.712, - 65.421, - 63.01, - 53.754, - 53.07, - 43.971, - 52.143, - 51.929, - 58.065, - 53.886, - 69.521, - 65.042, - 57.29600000000001, - 69.39, - 60.405, - 50.254, - 56.532, - 39.848 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Europe
year=1972
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Albania", - "Austria", - "Belgium", - "Bosnia and Herzegovina", - "Bulgaria", - "Croatia", - "Czech Republic", - "Denmark", - "Finland", - "France", - "Germany", - "Greece", - "Hungary", - "Iceland", - "Ireland", - "Italy", - "Montenegro", - "Netherlands", - "Norway", - "Poland", - "Portugal", - "Romania", - "Serbia", - "Slovak Republic", - "Slovenia", - "Spain", - "Sweden", - "Switzerland", - "Turkey", - "United Kingdom" - ], - "ids": [ - "Albania", - "Austria", - "Belgium", - "Bosnia and Herzegovina", - "Bulgaria", - "Croatia", - "Czech Republic", - "Denmark", - "Finland", - "France", - "Germany", - "Greece", - "Hungary", - "Iceland", - "Ireland", - "Italy", - "Montenegro", - "Netherlands", - "Norway", - "Poland", - "Portugal", - "Romania", - "Serbia", - "Slovak Republic", - "Slovenia", - "Spain", - "Sweden", - "Switzerland", - "Turkey", - "United Kingdom" - ], - "legendgroup": "Europe", - "marker": { - "color": "#EF553B", - "size": [ - 2263554, - 7544201, - 9709100, - 3819000, - 8576200, - 4225310, - 9862158, - 4991596, - 4639657, - 51732000, - 78717088, - 8888628, - 10394091, - 209275, - 3024400, - 54365564, - 527678, - 13329874, - 3933004, - 33039545, - 8970450, - 20662648, - 8313288, - 4593433, - 1694510, - 34513161, - 8122293, - 6401400, - 37492953, - 56079000 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Europe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 3313.422188, - 16661.6256, - 16672.14356, - 2860.16975, - 6597.494398, - 9164.090127, - 13108.4536, - 18866.20721, - 14358.8759, - 16107.19171, - 18016.18027, - 12724.82957, - 10168.65611, - 15798.06362, - 9530.772896, - 12269.27378, - 7778.414017, - 18794.74567, - 18965.05551, - 8006.506993000001, - 9022.247417, - 8011.4144019999985, - 10522.06749, - 9674.167626, - 12383.4862, - 10638.75131, - 17832.02464, - 27195.11304, - 3450.69638, - 15895.11641 - ], - "xaxis": "x", - "y": [ - 67.69, - 70.63, - 71.44, - 67.45, - 70.9, - 69.61, - 70.29, - 73.47, - 70.87, - 72.38, - 71, - 72.34, - 69.76, - 74.46, - 71.28, - 72.19, - 70.63600000000002, - 73.75, - 74.34, - 70.85, - 69.26, - 69.21, - 68.7, - 70.35, - 69.82, - 73.06, - 74.72, - 73.78, - 57.005, - 72.01 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Africa
year=1972
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Algeria", - "Angola", - "Benin", - "Botswana", - "Burkina Faso", - "Burundi", - "Cameroon", - "Central African Republic", - "Chad", - "Comoros", - "Congo, Dem. Rep.", - "Congo, Rep.", - "Cote d'Ivoire", - "Djibouti", - "Egypt", - "Equatorial Guinea", - "Eritrea", - "Ethiopia", - "Gabon", - "Gambia", - "Ghana", - "Guinea", - "Guinea-Bissau", - "Kenya", - "Lesotho", - "Liberia", - "Libya", - "Madagascar", - "Malawi", - "Mali", - "Mauritania", - "Mauritius", - "Morocco", - "Mozambique", - "Namibia", - "Niger", - "Nigeria", - "Reunion", - "Rwanda", - "Sao Tome and Principe", - "Senegal", - "Sierra Leone", - "Somalia", - "South Africa", - "Sudan", - "Swaziland", - "Tanzania", - "Togo", - "Tunisia", - "Uganda", - "Zambia", - "Zimbabwe" - ], - "ids": [ - "Algeria", - "Angola", - "Benin", - "Botswana", - "Burkina Faso", - "Burundi", - "Cameroon", - "Central African Republic", - "Chad", - "Comoros", - "Congo, Dem. Rep.", - "Congo, Rep.", - "Cote d'Ivoire", - "Djibouti", - "Egypt", - "Equatorial Guinea", - "Eritrea", - "Ethiopia", - "Gabon", - "Gambia", - "Ghana", - "Guinea", - "Guinea-Bissau", - "Kenya", - "Lesotho", - "Liberia", - "Libya", - "Madagascar", - "Malawi", - "Mali", - "Mauritania", - "Mauritius", - "Morocco", - "Mozambique", - "Namibia", - "Niger", - "Nigeria", - "Reunion", - "Rwanda", - "Sao Tome and Principe", - "Senegal", - "Sierra Leone", - "Somalia", - "South Africa", - "Sudan", - "Swaziland", - "Tanzania", - "Togo", - "Tunisia", - "Uganda", - "Zambia", - "Zimbabwe" - ], - "legendgroup": "Africa", - "marker": { - "color": "#00cc96", - "size": [ - 14760787, - 5894858, - 2761407, - 619351, - 5433886, - 3529983, - 7021028, - 1927260, - 3899068, - 250027, - 23007669, - 1340458, - 6071696, - 178848, - 34807417, - 277603, - 2260187, - 30770372, - 537977, - 517101, - 9354120, - 3811387, - 625361, - 12044785, - 1116779, - 1482628, - 2183877, - 7082430, - 4730997, - 5828158, - 1332786, - 851334, - 16660670, - 9809596, - 821782, - 5060262, - 53740085, - 461633, - 3992121, - 76595, - 4588696, - 2879013, - 3840161, - 23935810, - 14597019, - 480105, - 14706593, - 2056351, - 5303507, - 10190285, - 4506497, - 5861135 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Africa", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 4182.663766, - 5473.288004999999, - 1085.796879, - 2263.6111140000007, - 854.7359763000002, - 464.0995039, - 1684.1465280000002, - 1070.013275, - 1104.103987, - 1937.577675, - 904.8960685, - 3213.152683, - 2378.201111, - 3694.2123520000014, - 2024.008147, - 672.4122571, - 514.3242081999998, - 566.2439442000001, - 11401.94841, - 756.0868363, - 1178.223708, - 741.6662307, - 820.2245876000002, - 1222.359968, - 496.5815922000001, - 803.0054535, - 21011.49721, - 1748.562982, - 584.6219709, - 581.3688761, - 1586.851781, - 2575.484158, - 1930.194975, - 724.9178037, - 3746.080948, - 954.2092363, - 1698.388838, - 5047.658563, - 590.5806637999998, - 1532.985254, - 1597.712056, - 1353.759762, - 1254.576127, - 7765.962636, - 1659.652775, - 3364.836625, - 915.9850592, - 1649.660188, - 2753.2859940000008, - 950.735869, - 1773.498265, - 799.3621757999998 - ], - "xaxis": "x", - "y": [ - 54.518, - 37.928, - 47.014, - 56.024, - 43.591, - 44.057, - 47.049, - 43.457, - 45.569, - 48.944, - 45.989, - 54.907, - 49.801, - 44.36600000000001, - 51.137, - 40.516, - 44.142, - 43.515, - 48.69, - 38.308, - 49.875, - 38.842, - 36.486, - 53.559, - 49.767, - 42.614, - 52.773, - 44.851000000000006, - 41.76600000000001, - 39.977, - 48.437, - 62.944, - 52.862, - 40.328, - 53.867, - 40.546, - 42.82100000000001, - 64.274, - 44.6, - 56.48, - 45.815, - 35.4, - 40.973, - 53.69600000000001, - 45.083, - 49.552, - 47.62, - 49.75899999999999, - 55.602, - 51.01600000000001, - 50.107, - 55.635 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Americas
year=1972
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Argentina", - "Bolivia", - "Brazil", - "Canada", - "Chile", - "Colombia", - "Costa Rica", - "Cuba", - "Dominican Republic", - "Ecuador", - "El Salvador", - "Guatemala", - "Haiti", - "Honduras", - "Jamaica", - "Mexico", - "Nicaragua", - "Panama", - "Paraguay", - "Peru", - "Puerto Rico", - "Trinidad and Tobago", - "United States", - "Uruguay", - "Venezuela" - ], - "ids": [ - "Argentina", - "Bolivia", - "Brazil", - "Canada", - "Chile", - "Colombia", - "Costa Rica", - "Cuba", - "Dominican Republic", - "Ecuador", - "El Salvador", - "Guatemala", - "Haiti", - "Honduras", - "Jamaica", - "Mexico", - "Nicaragua", - "Panama", - "Paraguay", - "Peru", - "Puerto Rico", - "Trinidad and Tobago", - "United States", - "Uruguay", - "Venezuela" - ], - "legendgroup": "Americas", - "marker": { - "color": "#ab63fa", - "size": [ - 24779799, - 4565872, - 100840058, - 22284500, - 9717524, - 22542890, - 1834796, - 8831348, - 4671329, - 6298651, - 3790903, - 5149581, - 4698301, - 2965146, - 1997616, - 55984294, - 2182908, - 1616384, - 2614104, - 13954700, - 2847132, - 975199, - 209896000, - 2829526, - 11515649 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Americas", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 9443.038526, - 2980.331339, - 4985.711467, - 18970.57086, - 5494.024437, - 3264.660041, - 5118.146939, - 5305.445256, - 2189.874499, - 5280.99471, - 4520.246008, - 4031.408271, - 1654.456946, - 2529.842345, - 7433.889293000001, - 6809.406690000002, - 4688.593267, - 5364.249663000001, - 2523.337977, - 5937.827283, - 9123.041742, - 6619.551418999999, - 21806.03594, - 5703.408898, - 10505.25966 - ], - "xaxis": "x", - "y": [ - 67.065, - 46.714, - 59.504, - 72.88, - 63.441, - 61.62300000000001, - 67.84899999999999, - 70.723, - 59.631, - 58.79600000000001, - 58.207, - 53.738, - 48.042, - 53.88399999999999, - 69, - 62.361, - 55.151, - 66.21600000000001, - 65.815, - 55.448, - 72.16, - 65.9, - 71.34, - 68.673, - 65.712 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Oceania
year=1972
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Australia", - "New Zealand" - ], - "ids": [ - "Australia", - "New Zealand" - ], - "legendgroup": "Oceania", - "marker": { - "color": "#FFA15A", - "size": [ - 13177000, - 2929100 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Oceania", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 16788.62948, - 16046.03728 - ], - "xaxis": "x", - "y": [ - 71.93, - 71.89 - ], - "yaxis": "y" - } - ], - "name": "1972" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

continent=Asia
year=1977
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Afghanistan", - "Bahrain", - "Bangladesh", - "Cambodia", - "China", - "Hong Kong, China", - "India", - "Indonesia", - "Iran", - "Iraq", - "Israel", - "Japan", - "Jordan", - "Korea, Dem. Rep.", - "Korea, Rep.", - "Kuwait", - "Lebanon", - "Malaysia", - "Mongolia", - "Myanmar", - "Nepal", - "Oman", - "Pakistan", - "Philippines", - "Saudi Arabia", - "Singapore", - "Sri Lanka", - "Syria", - "Taiwan", - "Thailand", - "Vietnam", - "West Bank and Gaza", - "Yemen, Rep." - ], - "ids": [ - "Afghanistan", - "Bahrain", - "Bangladesh", - "Cambodia", - "China", - "Hong Kong, China", - "India", - "Indonesia", - "Iran", - "Iraq", - "Israel", - "Japan", - "Jordan", - "Korea, Dem. Rep.", - "Korea, Rep.", - "Kuwait", - "Lebanon", - "Malaysia", - "Mongolia", - "Myanmar", - "Nepal", - "Oman", - "Pakistan", - "Philippines", - "Saudi Arabia", - "Singapore", - "Sri Lanka", - "Syria", - "Taiwan", - "Thailand", - "Vietnam", - "West Bank and Gaza", - "Yemen, Rep." - ], - "legendgroup": "Asia", - "marker": { - "color": "#636efa", - "size": [ - 14880372, - 297410, - 80428306, - 6978607, - 943455000, - 4583700, - 634000000, - 136725000, - 35480679, - 11882916, - 3495918, - 113872473, - 1937652, - 16325320, - 36436000, - 1140357, - 3115787, - 12845381, - 1528000, - 31528087, - 13933198, - 1004533, - 78152686, - 46850962, - 8128505, - 2325300, - 14116836, - 7932503, - 16785196, - 44148285, - 50533506, - 1261091, - 8403990 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Asia", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 786.11336, - 19340.10196, - 659.8772322000002, - 524.9721831999999, - 741.2374699, - 11186.14125, - 813.3373230000002, - 1382.702056, - 11888.59508, - 14688.23507, - 13306.61921, - 16610.37701, - 2852.351568, - 4106.301249, - 4657.22102, - 59265.47714, - 8659.696836, - 3827.921571, - 1647.511665, - 371, - 694.1124398, - 11848.34392, - 1175.921193, - 2373.204287, - 34167.7626, - 11210.08948, - 1348.775651, - 3195.484582, - 5596.519826, - 1961.2246350000007, - 713.5371196000001, - 3682.831494, - 1829.765177 - ], - "xaxis": "x", - "y": [ - 38.438, - 65.593, - 46.923, - 31.22, - 63.96736, - 73.6, - 54.208, - 52.702, - 57.702, - 60.413, - 73.06, - 75.38, - 61.13399999999999, - 67.15899999999999, - 64.766, - 69.343, - 66.09899999999999, - 65.256, - 55.49100000000001, - 56.059, - 46.74800000000001, - 57.367, - 54.043, - 60.06, - 58.69, - 70.795, - 65.949, - 61.195, - 70.59, - 62.494, - 55.764, - 60.765, - 44.175 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Europe
year=1977
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Albania", - "Austria", - "Belgium", - "Bosnia and Herzegovina", - "Bulgaria", - "Croatia", - "Czech Republic", - "Denmark", - "Finland", - "France", - "Germany", - "Greece", - "Hungary", - "Iceland", - "Ireland", - "Italy", - "Montenegro", - "Netherlands", - "Norway", - "Poland", - "Portugal", - "Romania", - "Serbia", - "Slovak Republic", - "Slovenia", - "Spain", - "Sweden", - "Switzerland", - "Turkey", - "United Kingdom" - ], - "ids": [ - "Albania", - "Austria", - "Belgium", - "Bosnia and Herzegovina", - "Bulgaria", - "Croatia", - "Czech Republic", - "Denmark", - "Finland", - "France", - "Germany", - "Greece", - "Hungary", - "Iceland", - "Ireland", - "Italy", - "Montenegro", - "Netherlands", - "Norway", - "Poland", - "Portugal", - "Romania", - "Serbia", - "Slovak Republic", - "Slovenia", - "Spain", - "Sweden", - "Switzerland", - "Turkey", - "United Kingdom" - ], - "legendgroup": "Europe", - "marker": { - "color": "#EF553B", - "size": [ - 2509048, - 7568430, - 9821800, - 4086000, - 8797022, - 4318673, - 10161915, - 5088419, - 4738902, - 53165019, - 78160773, - 9308479, - 10637171, - 221823, - 3271900, - 56059245, - 560073, - 13852989, - 4043205, - 34621254, - 9662600, - 21658597, - 8686367, - 4827803, - 1746919, - 36439000, - 8251648, - 6316424, - 42404033, - 56179000 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Europe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 3533.003910000001, - 19749.4223, - 19117.97448, - 3528.481305, - 7612.240438, - 11305.38517, - 14800.16062, - 20422.9015, - 15605.42283, - 18292.63514, - 20512.92123, - 14195.52428, - 11674.83737, - 19654.96247, - 11150.98113, - 14255.98475, - 9595.929905, - 21209.0592, - 23311.34939, - 9508.141454, - 10172.48572, - 9356.39724, - 12980.66956, - 10922.66404, - 15277.030169999998, - 13236.92117, - 18855.72521, - 26982.29052, - 4269.122326, - 17428.74846 - ], - "xaxis": "x", - "y": [ - 68.93, - 72.17, - 72.8, - 69.86, - 70.81, - 70.64, - 70.71, - 74.69, - 72.52, - 73.83, - 72.5, - 73.68, - 69.95, - 76.11, - 72.03, - 73.48, - 73.066, - 75.24, - 75.37, - 70.67, - 70.41, - 69.46, - 70.3, - 70.45, - 70.97, - 74.39, - 75.44, - 75.39, - 59.507, - 72.76 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Africa
year=1977
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Algeria", - "Angola", - "Benin", - "Botswana", - "Burkina Faso", - "Burundi", - "Cameroon", - "Central African Republic", - "Chad", - "Comoros", - "Congo, Dem. Rep.", - "Congo, Rep.", - "Cote d'Ivoire", - "Djibouti", - "Egypt", - "Equatorial Guinea", - "Eritrea", - "Ethiopia", - "Gabon", - "Gambia", - "Ghana", - "Guinea", - "Guinea-Bissau", - "Kenya", - "Lesotho", - "Liberia", - "Libya", - "Madagascar", - "Malawi", - "Mali", - "Mauritania", - "Mauritius", - "Morocco", - "Mozambique", - "Namibia", - "Niger", - "Nigeria", - "Reunion", - "Rwanda", - "Sao Tome and Principe", - "Senegal", - "Sierra Leone", - "Somalia", - "South Africa", - "Sudan", - "Swaziland", - "Tanzania", - "Togo", - "Tunisia", - "Uganda", - "Zambia", - "Zimbabwe" - ], - "ids": [ - "Algeria", - "Angola", - "Benin", - "Botswana", - "Burkina Faso", - "Burundi", - "Cameroon", - "Central African Republic", - "Chad", - "Comoros", - "Congo, Dem. Rep.", - "Congo, Rep.", - "Cote d'Ivoire", - "Djibouti", - "Egypt", - "Equatorial Guinea", - "Eritrea", - "Ethiopia", - "Gabon", - "Gambia", - "Ghana", - "Guinea", - "Guinea-Bissau", - "Kenya", - "Lesotho", - "Liberia", - "Libya", - "Madagascar", - "Malawi", - "Mali", - "Mauritania", - "Mauritius", - "Morocco", - "Mozambique", - "Namibia", - "Niger", - "Nigeria", - "Reunion", - "Rwanda", - "Sao Tome and Principe", - "Senegal", - "Sierra Leone", - "Somalia", - "South Africa", - "Sudan", - "Swaziland", - "Tanzania", - "Togo", - "Tunisia", - "Uganda", - "Zambia", - "Zimbabwe" - ], - "legendgroup": "Africa", - "marker": { - "color": "#00cc96", - "size": [ - 17152804, - 6162675, - 3168267, - 781472, - 5889574, - 3834415, - 7959865, - 2167533, - 4388260, - 304739, - 26480870, - 1536769, - 7459574, - 228694, - 38783863, - 192675, - 2512642, - 34617799, - 706367, - 608274, - 10538093, - 4227026, - 745228, - 14500404, - 1251524, - 1703617, - 2721783, - 8007166, - 5637246, - 6491649, - 1456688, - 913025, - 18396941, - 11127868, - 977026, - 5682086, - 62209173, - 492095, - 4657072, - 86796, - 5260855, - 3140897, - 4353666, - 27129932, - 17104986, - 551425, - 17129565, - 2308582, - 6005061, - 11457758, - 5216550, - 6642107 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Africa", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 4910.416756000001, - 3008.647355, - 1029.161251, - 3214.857818, - 743.3870368, - 556.1032651, - 1783.432873, - 1109.374338, - 1133.98495, - 1172.603047, - 795.757282, - 3259.178978, - 2517.736547, - 3081.761022, - 2785.493582, - 958.5668124, - 505.7538077, - 556.8083834, - 21745.57328, - 884.7552507000001, - 993.2239571, - 874.6858642999998, - 764.7259627999998, - 1267.613204, - 745.3695408, - 640.3224382999998, - 21951.21176, - 1544.228586, - 663.2236766, - 686.3952693, - 1497.492223, - 3710.982963, - 2370.619976, - 502.3197334, - 3876.485958, - 808.8970727999998, - 1981.951806, - 4319.804067, - 670.0806011, - 1737.561657, - 1561.769116, - 1348.285159, - 1450.992513, - 8028.651439, - 2202.988423, - 3781.410618, - 962.4922932, - 1532.776998, - 3120.876811, - 843.7331372000001, - 1588.688299, - 685.5876821 - ], - "xaxis": "x", - "y": [ - 58.014, - 39.483, - 49.19, - 59.319, - 46.137, - 45.91, - 49.355, - 46.775, - 47.383, - 50.93899999999999, - 47.804, - 55.625, - 52.374, - 46.519, - 53.319, - 42.024, - 44.535, - 44.51, - 52.79, - 41.842, - 51.756, - 40.762, - 37.465, - 56.155, - 52.208, - 43.764, - 57.442, - 46.881, - 43.767, - 41.714, - 50.852, - 64.93, - 55.73, - 42.495, - 56.437, - 41.291, - 44.514, - 67.064, - 45, - 58.55, - 48.879, - 36.788, - 41.974, - 55.527, - 47.8, - 52.537, - 49.919, - 52.887, - 59.837, - 50.35, - 51.386, - 57.674 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Americas
year=1977
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Argentina", - "Bolivia", - "Brazil", - "Canada", - "Chile", - "Colombia", - "Costa Rica", - "Cuba", - "Dominican Republic", - "Ecuador", - "El Salvador", - "Guatemala", - "Haiti", - "Honduras", - "Jamaica", - "Mexico", - "Nicaragua", - "Panama", - "Paraguay", - "Peru", - "Puerto Rico", - "Trinidad and Tobago", - "United States", - "Uruguay", - "Venezuela" - ], - "ids": [ - "Argentina", - "Bolivia", - "Brazil", - "Canada", - "Chile", - "Colombia", - "Costa Rica", - "Cuba", - "Dominican Republic", - "Ecuador", - "El Salvador", - "Guatemala", - "Haiti", - "Honduras", - "Jamaica", - "Mexico", - "Nicaragua", - "Panama", - "Paraguay", - "Peru", - "Puerto Rico", - "Trinidad and Tobago", - "United States", - "Uruguay", - "Venezuela" - ], - "legendgroup": "Americas", - "marker": { - "color": "#ab63fa", - "size": [ - 26983828, - 5079716, - 114313951, - 23796400, - 10599793, - 25094412, - 2108457, - 9537988, - 5302800, - 7278866, - 4282586, - 5703430, - 4908554, - 3055235, - 2156814, - 63759976, - 2554598, - 1839782, - 2984494, - 15990099, - 3080828, - 1039009, - 220239000, - 2873520, - 13503563 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Americas", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 10079.02674, - 3548.097832, - 6660.118654, - 22090.88306, - 4756.763836, - 3815.80787, - 5926.876967, - 6380.494965999998, - 2681.9889, - 6679.62326, - 5138.922374, - 4879.992748, - 1874.298931, - 3203.208066, - 6650.195573, - 7674.929108, - 5486.371089, - 5351.912144, - 3248.373311, - 6281.290854999998, - 9770.524921, - 7899.554209000001, - 24072.63213, - 6504.339663000002, - 13143.95095 - ], - "xaxis": "x", - "y": [ - 68.48100000000001, - 50.023, - 61.489, - 74.21, - 67.05199999999999, - 63.837, - 70.75, - 72.649, - 61.788, - 61.31, - 56.69600000000001, - 56.029, - 49.923, - 57.402, - 70.11, - 65.032, - 57.47, - 68.681, - 66.35300000000001, - 58.447, - 73.44, - 68.3, - 73.38, - 69.48100000000001, - 67.456 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Oceania
year=1977
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Australia", - "New Zealand" - ], - "ids": [ - "Australia", - "New Zealand" - ], - "legendgroup": "Oceania", - "marker": { - "color": "#FFA15A", - "size": [ - 14074100, - 3164900 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Oceania", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 18334.19751, - 16233.7177 - ], - "xaxis": "x", - "y": [ - 73.49, - 72.22 - ], - "yaxis": "y" - } - ], - "name": "1977" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

continent=Asia
year=1982
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Afghanistan", - "Bahrain", - "Bangladesh", - "Cambodia", - "China", - "Hong Kong, China", - "India", - "Indonesia", - "Iran", - "Iraq", - "Israel", - "Japan", - "Jordan", - "Korea, Dem. Rep.", - "Korea, Rep.", - "Kuwait", - "Lebanon", - "Malaysia", - "Mongolia", - "Myanmar", - "Nepal", - "Oman", - "Pakistan", - "Philippines", - "Saudi Arabia", - "Singapore", - "Sri Lanka", - "Syria", - "Taiwan", - "Thailand", - "Vietnam", - "West Bank and Gaza", - "Yemen, Rep." - ], - "ids": [ - "Afghanistan", - "Bahrain", - "Bangladesh", - "Cambodia", - "China", - "Hong Kong, China", - "India", - "Indonesia", - "Iran", - "Iraq", - "Israel", - "Japan", - "Jordan", - "Korea, Dem. Rep.", - "Korea, Rep.", - "Kuwait", - "Lebanon", - "Malaysia", - "Mongolia", - "Myanmar", - "Nepal", - "Oman", - "Pakistan", - "Philippines", - "Saudi Arabia", - "Singapore", - "Sri Lanka", - "Syria", - "Taiwan", - "Thailand", - "Vietnam", - "West Bank and Gaza", - "Yemen, Rep." - ], - "legendgroup": "Asia", - "marker": { - "color": "#636efa", - "size": [ - 12881816, - 377967, - 93074406, - 7272485, - 1000281000, - 5264500, - 708000000, - 153343000, - 43072751, - 14173318, - 3858421, - 118454974, - 2347031, - 17647518, - 39326000, - 1497494, - 3086876, - 14441916, - 1756032, - 34680442, - 15796314, - 1301048, - 91462088, - 53456774, - 11254672, - 2651869, - 15410151, - 9410494, - 18501390, - 48827160, - 56142181, - 1425876, - 9657618 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Asia", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 978.0114388, - 19211.14731, - 676.9818656, - 624.4754784, - 962.4213805, - 14560.53051, - 855.7235377000002, - 1516.872988, - 7608.334602, - 14517.90711, - 15367.0292, - 19384.10571, - 4161.415959, - 4106.525293, - 5622.942464, - 31354.03573, - 7640.519520999998, - 4920.355951, - 2000.603139, - 424, - 718.3730947, - 12954.79101, - 1443.429832, - 2603.273765, - 33693.17525, - 15169.16112, - 1648.079789, - 3761.837715, - 7426.3547739999985, - 2393.219781, - 707.2357863, - 4336.032082, - 1977.55701 - ], - "xaxis": "x", - "y": [ - 39.854, - 69.05199999999999, - 50.00899999999999, - 50.957, - 65.525, - 75.45, - 56.596, - 56.159, - 59.62, - 62.038, - 74.45, - 77.11, - 63.739, - 69.1, - 67.123, - 71.309, - 66.983, - 68, - 57.489, - 58.056, - 49.594, - 62.728, - 56.158, - 62.082, - 63.012, - 71.76, - 68.757, - 64.59, - 72.16, - 64.597, - 58.816, - 64.406, - 49.113 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Europe
year=1982
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Albania", - "Austria", - "Belgium", - "Bosnia and Herzegovina", - "Bulgaria", - "Croatia", - "Czech Republic", - "Denmark", - "Finland", - "France", - "Germany", - "Greece", - "Hungary", - "Iceland", - "Ireland", - "Italy", - "Montenegro", - "Netherlands", - "Norway", - "Poland", - "Portugal", - "Romania", - "Serbia", - "Slovak Republic", - "Slovenia", - "Spain", - "Sweden", - "Switzerland", - "Turkey", - "United Kingdom" - ], - "ids": [ - "Albania", - "Austria", - "Belgium", - "Bosnia and Herzegovina", - "Bulgaria", - "Croatia", - "Czech Republic", - "Denmark", - "Finland", - "France", - "Germany", - "Greece", - "Hungary", - "Iceland", - "Ireland", - "Italy", - "Montenegro", - "Netherlands", - "Norway", - "Poland", - "Portugal", - "Romania", - "Serbia", - "Slovak Republic", - "Slovenia", - "Spain", - "Sweden", - "Switzerland", - "Turkey", - "United Kingdom" - ], - "legendgroup": "Europe", - "marker": { - "color": "#EF553B", - "size": [ - 2780097, - 7574613, - 9856303, - 4172693, - 8892098, - 4413368, - 10303704, - 5117810, - 4826933, - 54433565, - 78335266, - 9786480, - 10705535, - 233997, - 3480000, - 56535636, - 562548, - 14310401, - 4114787, - 36227381, - 9859650, - 22356726, - 9032824, - 5048043, - 1861252, - 37983310, - 8325260, - 6468126, - 47328791, - 56339704 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Europe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 3630.880722, - 21597.08362, - 20979.84589, - 4126.613157, - 8224.191647, - 13221.82184, - 15377.22855, - 21688.04048, - 18533.15761, - 20293.89746, - 22031.53274, - 15268.42089, - 12545.99066, - 23269.6075, - 12618.32141, - 16537.4835, - 11222.58762, - 21399.46046, - 26298.63531, - 8451.531004, - 11753.84291, - 9605.314053, - 15181.0927, - 11348.54585, - 17866.72175, - 13926.16997, - 20667.38125, - 28397.71512, - 4241.356344, - 18232.42452 - ], - "xaxis": "x", - "y": [ - 70.42, - 73.18, - 73.93, - 70.69, - 71.08, - 70.46, - 70.96, - 74.63, - 74.55, - 74.89, - 73.8, - 75.24, - 69.39, - 76.99, - 73.1, - 74.98, - 74.101, - 76.05, - 75.97, - 71.32, - 72.77, - 69.66, - 70.16199999999999, - 70.8, - 71.063, - 76.3, - 76.42, - 76.21, - 61.036, - 74.04 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Africa
year=1982
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Algeria", - "Angola", - "Benin", - "Botswana", - "Burkina Faso", - "Burundi", - "Cameroon", - "Central African Republic", - "Chad", - "Comoros", - "Congo, Dem. Rep.", - "Congo, Rep.", - "Cote d'Ivoire", - "Djibouti", - "Egypt", - "Equatorial Guinea", - "Eritrea", - "Ethiopia", - "Gabon", - "Gambia", - "Ghana", - "Guinea", - "Guinea-Bissau", - "Kenya", - "Lesotho", - "Liberia", - "Libya", - "Madagascar", - "Malawi", - "Mali", - "Mauritania", - "Mauritius", - "Morocco", - "Mozambique", - "Namibia", - "Niger", - "Nigeria", - "Reunion", - "Rwanda", - "Sao Tome and Principe", - "Senegal", - "Sierra Leone", - "Somalia", - "South Africa", - "Sudan", - "Swaziland", - "Tanzania", - "Togo", - "Tunisia", - "Uganda", - "Zambia", - "Zimbabwe" - ], - "ids": [ - "Algeria", - "Angola", - "Benin", - "Botswana", - "Burkina Faso", - "Burundi", - "Cameroon", - "Central African Republic", - "Chad", - "Comoros", - "Congo, Dem. Rep.", - "Congo, Rep.", - "Cote d'Ivoire", - "Djibouti", - "Egypt", - "Equatorial Guinea", - "Eritrea", - "Ethiopia", - "Gabon", - "Gambia", - "Ghana", - "Guinea", - "Guinea-Bissau", - "Kenya", - "Lesotho", - "Liberia", - "Libya", - "Madagascar", - "Malawi", - "Mali", - "Mauritania", - "Mauritius", - "Morocco", - "Mozambique", - "Namibia", - "Niger", - "Nigeria", - "Reunion", - "Rwanda", - "Sao Tome and Principe", - "Senegal", - "Sierra Leone", - "Somalia", - "South Africa", - "Sudan", - "Swaziland", - "Tanzania", - "Togo", - "Tunisia", - "Uganda", - "Zambia", - "Zimbabwe" - ], - "legendgroup": "Africa", - "marker": { - "color": "#00cc96", - "size": [ - 20033753, - 7016384, - 3641603, - 970347, - 6634596, - 4580410, - 9250831, - 2476971, - 4875118, - 348643, - 30646495, - 1774735, - 9025951, - 305991, - 45681811, - 285483, - 2637297, - 38111756, - 753874, - 715523, - 11400338, - 4710497, - 825987, - 17661452, - 1411807, - 1956875, - 3344074, - 9171477, - 6502825, - 6998256, - 1622136, - 992040, - 20198730, - 12587223, - 1099010, - 6437188, - 73039376, - 517810, - 5507565, - 98593, - 6147783, - 3464522, - 5828892, - 31140029, - 20367053, - 649901, - 19844382, - 2644765, - 6734098, - 12939400, - 6100407, - 7636524 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Africa", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5745.160213, - 2756.953672, - 1277.897616, - 4551.14215, - 807.1985855, - 559.6032309999998, - 2367.983282, - 956.7529907, - 797.9081006, - 1267.100083, - 673.7478181, - 4879.507522, - 2602.710169, - 2879.468067, - 3503.729636, - 927.8253427, - 524.8758493, - 577.8607471, - 15113.36194, - 835.8096107999999, - 876.032569, - 857.2503577, - 838.1239671, - 1348.225791, - 797.2631074, - 572.1995694, - 17364.275380000006, - 1302.878658, - 632.8039209, - 618.0140640999998, - 1481.150189, - 3688.037739, - 2702.620356, - 462.2114149, - 4191.100511, - 909.7221354, - 1576.97375, - 5267.219353, - 881.5706467, - 1890.218117, - 1518.479984, - 1465.010784, - 1176.807031, - 8568.266228, - 1895.544073, - 3895.384018, - 874.2426069, - 1344.577953, - 3560.2331740000004, - 682.2662267999998, - 1408.678565, - 788.8550411 - ], - "xaxis": "x", - "y": [ - 61.368, - 39.942, - 50.904, - 61.484, - 48.122, - 47.471, - 52.96100000000001, - 48.295, - 49.517, - 52.933, - 47.784, - 56.695, - 53.983, - 48.812, - 56.006, - 43.662, - 43.89, - 44.916, - 56.56399999999999, - 45.58, - 53.744, - 42.89100000000001, - 39.327, - 58.76600000000001, - 55.078, - 44.852, - 62.155, - 48.969, - 45.642, - 43.916, - 53.599, - 66.711, - 59.65, - 42.795, - 58.968, - 42.598, - 45.826, - 69.885, - 46.218, - 60.351000000000006, - 52.379, - 38.445, - 42.955, - 58.161, - 50.338, - 55.56100000000001, - 50.608, - 55.471, - 64.048, - 49.849, - 51.82100000000001, - 60.363 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Americas
year=1982
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Argentina", - "Bolivia", - "Brazil", - "Canada", - "Chile", - "Colombia", - "Costa Rica", - "Cuba", - "Dominican Republic", - "Ecuador", - "El Salvador", - "Guatemala", - "Haiti", - "Honduras", - "Jamaica", - "Mexico", - "Nicaragua", - "Panama", - "Paraguay", - "Peru", - "Puerto Rico", - "Trinidad and Tobago", - "United States", - "Uruguay", - "Venezuela" - ], - "ids": [ - "Argentina", - "Bolivia", - "Brazil", - "Canada", - "Chile", - "Colombia", - "Costa Rica", - "Cuba", - "Dominican Republic", - "Ecuador", - "El Salvador", - "Guatemala", - "Haiti", - "Honduras", - "Jamaica", - "Mexico", - "Nicaragua", - "Panama", - "Paraguay", - "Peru", - "Puerto Rico", - "Trinidad and Tobago", - "United States", - "Uruguay", - "Venezuela" - ], - "legendgroup": "Americas", - "marker": { - "color": "#ab63fa", - "size": [ - 29341374, - 5642224, - 128962939, - 25201900, - 11487112, - 27764644, - 2424367, - 9789224, - 5968349, - 8365850, - 4474873, - 6395630, - 5198399, - 3669448, - 2298309, - 71640904, - 2979423, - 2036305, - 3366439, - 18125129, - 3279001, - 1116479, - 232187835, - 2953997, - 15620766 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Americas", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 8997.897412, - 3156.510452, - 7030.835878, - 22898.79214, - 5095.6657380000015, - 4397.575659, - 5262.734751, - 7316.918106999998, - 2861.092386, - 7213.791267, - 4098.344175, - 4820.49479, - 2011.159549, - 3121.7607940000007, - 6068.05135, - 9611.147541, - 3470.3381560000007, - 7009.601598, - 4258.503604, - 6434.501797, - 10330.98915, - 9119.528607, - 25009.55914, - 6920.223051000001, - 11152.41011 - ], - "xaxis": "x", - "y": [ - 69.942, - 53.859, - 63.33600000000001, - 75.76, - 70.565, - 66.653, - 73.45, - 73.717, - 63.727, - 64.342, - 56.604, - 58.137, - 51.46100000000001, - 60.909, - 71.21, - 67.405, - 59.298, - 70.472, - 66.874, - 61.40600000000001, - 73.75, - 68.832, - 74.65, - 70.805, - 68.557 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Oceania
year=1982
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Australia", - "New Zealand" - ], - "ids": [ - "Australia", - "New Zealand" - ], - "legendgroup": "Oceania", - "marker": { - "color": "#FFA15A", - "size": [ - 15184200, - 3210650 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Oceania", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 19477.00928, - 17632.4104 - ], - "xaxis": "x", - "y": [ - 74.74, - 73.84 - ], - "yaxis": "y" - } - ], - "name": "1982" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

continent=Asia
year=1987
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Afghanistan", - "Bahrain", - "Bangladesh", - "Cambodia", - "China", - "Hong Kong, China", - "India", - "Indonesia", - "Iran", - "Iraq", - "Israel", - "Japan", - "Jordan", - "Korea, Dem. Rep.", - "Korea, Rep.", - "Kuwait", - "Lebanon", - "Malaysia", - "Mongolia", - "Myanmar", - "Nepal", - "Oman", - "Pakistan", - "Philippines", - "Saudi Arabia", - "Singapore", - "Sri Lanka", - "Syria", - "Taiwan", - "Thailand", - "Vietnam", - "West Bank and Gaza", - "Yemen, Rep." - ], - "ids": [ - "Afghanistan", - "Bahrain", - "Bangladesh", - "Cambodia", - "China", - "Hong Kong, China", - "India", - "Indonesia", - "Iran", - "Iraq", - "Israel", - "Japan", - "Jordan", - "Korea, Dem. Rep.", - "Korea, Rep.", - "Kuwait", - "Lebanon", - "Malaysia", - "Mongolia", - "Myanmar", - "Nepal", - "Oman", - "Pakistan", - "Philippines", - "Saudi Arabia", - "Singapore", - "Sri Lanka", - "Syria", - "Taiwan", - "Thailand", - "Vietnam", - "West Bank and Gaza", - "Yemen, Rep." - ], - "legendgroup": "Asia", - "marker": { - "color": "#636efa", - "size": [ - 13867957, - 454612, - 103764241, - 8371791, - 1084035000, - 5584510, - 788000000, - 169276000, - 51889696, - 16543189, - 4203148, - 122091325, - 2820042, - 19067554, - 41622000, - 1891487, - 3089353, - 16331785, - 2015133, - 38028578, - 17917180, - 1593882, - 105186881, - 60017788, - 14619745, - 2794552, - 16495304, - 11242847, - 19757799, - 52910342, - 62826491, - 1691210, - 11219340 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Asia", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 852.3959447999998, - 18524.02406, - 751.9794035, - 683.8955732000002, - 1378.904018, - 20038.47269, - 976.5126756, - 1748.356961, - 6642.881371, - 11643.57268, - 17122.47986, - 22375.94189, - 4448.679912, - 4106.492315, - 8533.088805, - 28118.42998, - 5377.091329, - 5249.802653, - 2338.008304, - 385, - 775.6324501, - 18115.22313, - 1704.686583, - 2189.634995, - 21198.26136, - 18861.53081, - 1876.766827, - 3116.774285, - 11054.56175, - 2982.653773, - 820.7994449, - 5107.197384, - 1971.741538 - ], - "xaxis": "x", - "y": [ - 40.822, - 70.75, - 52.819, - 53.914, - 67.274, - 76.2, - 58.553, - 60.137, - 63.04, - 65.044, - 75.6, - 78.67, - 65.869, - 70.64699999999998, - 69.81, - 74.17399999999998, - 67.926, - 69.5, - 60.222, - 58.339, - 52.537, - 67.734, - 58.245, - 64.15100000000001, - 66.295, - 73.56, - 69.01100000000001, - 66.97399999999999, - 73.4, - 66.084, - 62.82, - 67.046, - 52.922 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Europe
year=1987
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Albania", - "Austria", - "Belgium", - "Bosnia and Herzegovina", - "Bulgaria", - "Croatia", - "Czech Republic", - "Denmark", - "Finland", - "France", - "Germany", - "Greece", - "Hungary", - "Iceland", - "Ireland", - "Italy", - "Montenegro", - "Netherlands", - "Norway", - "Poland", - "Portugal", - "Romania", - "Serbia", - "Slovak Republic", - "Slovenia", - "Spain", - "Sweden", - "Switzerland", - "Turkey", - "United Kingdom" - ], - "ids": [ - "Albania", - "Austria", - "Belgium", - "Bosnia and Herzegovina", - "Bulgaria", - "Croatia", - "Czech Republic", - "Denmark", - "Finland", - "France", - "Germany", - "Greece", - "Hungary", - "Iceland", - "Ireland", - "Italy", - "Montenegro", - "Netherlands", - "Norway", - "Poland", - "Portugal", - "Romania", - "Serbia", - "Slovak Republic", - "Slovenia", - "Spain", - "Sweden", - "Switzerland", - "Turkey", - "United Kingdom" - ], - "legendgroup": "Europe", - "marker": { - "color": "#EF553B", - "size": [ - 3075321, - 7578903, - 9870200, - 4338977, - 8971958, - 4484310, - 10311597, - 5127024, - 4931729, - 55630100, - 77718298, - 9974490, - 10612740, - 244676, - 3539900, - 56729703, - 569473, - 14665278, - 4186147, - 37740710, - 9915289, - 22686371, - 9230783, - 5199318, - 1945870, - 38880702, - 8421403, - 6649942, - 52881328, - 56981620 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Europe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 3738.932735, - 23687.82607, - 22525.56308, - 4314.114757, - 8239.854824, - 13822.58394, - 16310.4434, - 25116.17581, - 21141.01223, - 22066.44214, - 24639.18566, - 16120.52839, - 12986.47998, - 26923.20628, - 13872.86652, - 19207.23482, - 11732.51017, - 23651.32361, - 31540.9748, - 9082.351172, - 13039.30876, - 9696.273295, - 15870.87851, - 12037.26758, - 18678.53492, - 15764.98313, - 23586.92927, - 30281.70459, - 5089.043686, - 21664.78767 - ], - "xaxis": "x", - "y": [ - 72, - 74.94, - 75.35, - 71.14, - 71.34, - 71.52, - 71.58, - 74.8, - 74.83, - 76.34, - 74.847, - 76.67, - 69.58, - 77.23, - 74.36, - 76.42, - 74.865, - 76.83, - 75.89, - 70.98, - 74.06, - 69.53, - 71.218, - 71.08, - 72.25, - 76.9, - 77.19, - 77.41, - 63.108, - 75.007 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Africa
year=1987
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Algeria", - "Angola", - "Benin", - "Botswana", - "Burkina Faso", - "Burundi", - "Cameroon", - "Central African Republic", - "Chad", - "Comoros", - "Congo, Dem. Rep.", - "Congo, Rep.", - "Cote d'Ivoire", - "Djibouti", - "Egypt", - "Equatorial Guinea", - "Eritrea", - "Ethiopia", - "Gabon", - "Gambia", - "Ghana", - "Guinea", - "Guinea-Bissau", - "Kenya", - "Lesotho", - "Liberia", - "Libya", - "Madagascar", - "Malawi", - "Mali", - "Mauritania", - "Mauritius", - "Morocco", - "Mozambique", - "Namibia", - "Niger", - "Nigeria", - "Reunion", - "Rwanda", - "Sao Tome and Principe", - "Senegal", - "Sierra Leone", - "Somalia", - "South Africa", - "Sudan", - "Swaziland", - "Tanzania", - "Togo", - "Tunisia", - "Uganda", - "Zambia", - "Zimbabwe" - ], - "ids": [ - "Algeria", - "Angola", - "Benin", - "Botswana", - "Burkina Faso", - "Burundi", - "Cameroon", - "Central African Republic", - "Chad", - "Comoros", - "Congo, Dem. Rep.", - "Congo, Rep.", - "Cote d'Ivoire", - "Djibouti", - "Egypt", - "Equatorial Guinea", - "Eritrea", - "Ethiopia", - "Gabon", - "Gambia", - "Ghana", - "Guinea", - "Guinea-Bissau", - "Kenya", - "Lesotho", - "Liberia", - "Libya", - "Madagascar", - "Malawi", - "Mali", - "Mauritania", - "Mauritius", - "Morocco", - "Mozambique", - "Namibia", - "Niger", - "Nigeria", - "Reunion", - "Rwanda", - "Sao Tome and Principe", - "Senegal", - "Sierra Leone", - "Somalia", - "South Africa", - "Sudan", - "Swaziland", - "Tanzania", - "Togo", - "Tunisia", - "Uganda", - "Zambia", - "Zimbabwe" - ], - "legendgroup": "Africa", - "marker": { - "color": "#00cc96", - "size": [ - 23254956, - 7874230, - 4243788, - 1151184, - 7586551, - 5126023, - 10780667, - 2840009, - 5498955, - 395114, - 35481645, - 2064095, - 10761098, - 311025, - 52799062, - 341244, - 2915959, - 42999530, - 880397, - 848406, - 14168101, - 5650262, - 927524, - 21198082, - 1599200, - 2269414, - 3799845, - 10568642, - 7824747, - 7634008, - 1841240, - 1042663, - 22987397, - 12891952, - 1278184, - 7332638, - 81551520, - 562035, - 6349365, - 110812, - 7171347, - 3868905, - 6921858, - 35933379, - 24725960, - 779348, - 23040630, - 3154264, - 7724976, - 15283050, - 7272406, - 9216418 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Africa", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5681.358539, - 2430.208311, - 1225.85601, - 6205.88385, - 912.0631417, - 621.8188188999999, - 2602.664206, - 844.8763504000002, - 952.386129, - 1315.980812, - 672.774812, - 4201.194936999998, - 2156.9560690000008, - 2880.102568, - 3885.46071, - 966.8968149, - 521.1341333, - 573.7413142000001, - 11864.40844, - 611.6588611000002, - 847.0061135, - 805.5724717999999, - 736.4153921, - 1361.936856, - 773.9932140999998, - 506.1138573, - 11770.5898, - 1155.441948, - 635.5173633999998, - 684.1715576, - 1421.603576, - 4783.586903, - 2755.046991, - 389.8761846, - 3693.731337, - 668.3000228, - 1385.029563, - 5303.377488, - 847.991217, - 1516.525457, - 1441.72072, - 1294.4477880000004, - 1093.244963, - 7825.823398, - 1507.819159, - 3984.839812, - 831.8220794, - 1202.201361, - 3810.419296, - 617.7244065, - 1213.315116, - 706.1573059 - ], - "xaxis": "x", - "y": [ - 65.79899999999999, - 39.906, - 52.337, - 63.622, - 49.557, - 48.21100000000001, - 54.985, - 50.485, - 51.051, - 54.926, - 47.412, - 57.47, - 54.655, - 50.04, - 59.797, - 45.664, - 46.453, - 46.684, - 60.19, - 49.265, - 55.729, - 45.552, - 41.245, - 59.339, - 57.18, - 46.027, - 66.234, - 49.35, - 47.457, - 46.364, - 56.145, - 68.74, - 62.677, - 42.861, - 60.835, - 44.555, - 46.886, - 71.913, - 44.02, - 61.728, - 55.769, - 40.006, - 44.50100000000001, - 60.834, - 51.744, - 57.678, - 51.535, - 56.941, - 66.89399999999999, - 51.50899999999999, - 50.82100000000001, - 62.351000000000006 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Americas
year=1987
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Argentina", - "Bolivia", - "Brazil", - "Canada", - "Chile", - "Colombia", - "Costa Rica", - "Cuba", - "Dominican Republic", - "Ecuador", - "El Salvador", - "Guatemala", - "Haiti", - "Honduras", - "Jamaica", - "Mexico", - "Nicaragua", - "Panama", - "Paraguay", - "Peru", - "Puerto Rico", - "Trinidad and Tobago", - "United States", - "Uruguay", - "Venezuela" - ], - "ids": [ - "Argentina", - "Bolivia", - "Brazil", - "Canada", - "Chile", - "Colombia", - "Costa Rica", - "Cuba", - "Dominican Republic", - "Ecuador", - "El Salvador", - "Guatemala", - "Haiti", - "Honduras", - "Jamaica", - "Mexico", - "Nicaragua", - "Panama", - "Paraguay", - "Peru", - "Puerto Rico", - "Trinidad and Tobago", - "United States", - "Uruguay", - "Venezuela" - ], - "legendgroup": "Americas", - "marker": { - "color": "#ab63fa", - "size": [ - 31620918, - 6156369, - 142938076, - 26549700, - 12463354, - 30964245, - 2799811, - 10239839, - 6655297, - 9545158, - 4842194, - 7326406, - 5756203, - 4372203, - 2326606, - 80122492, - 3344353, - 2253639, - 3886512, - 20195924, - 3444468, - 1191336, - 242803533, - 3045153, - 17910182 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Americas", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 9139.671389, - 2753.69149, - 7807.095818000002, - 26626.51503, - 5547.063754, - 4903.2191, - 5629.915318, - 7532.924762999999, - 2899.842175, - 6481.776993, - 4140.442097, - 4246.485974, - 1823.015995, - 3023.096699, - 6351.237495, - 8688.156003, - 2955.984375, - 7034.779161, - 3998.875695, - 6360.943444, - 12281.34191, - 7388.597823, - 29884.350410000006, - 7452.398969, - 9883.584648 - ], - "xaxis": "x", - "y": [ - 70.774, - 57.25100000000001, - 65.205, - 76.86, - 72.492, - 67.768, - 74.752, - 74.17399999999998, - 66.046, - 67.23100000000001, - 63.154, - 60.782, - 53.636, - 64.492, - 71.77, - 69.498, - 62.008, - 71.523, - 67.378, - 64.134, - 74.63, - 69.582, - 75.02, - 71.918, - 70.19 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Oceania
year=1987
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Australia", - "New Zealand" - ], - "ids": [ - "Australia", - "New Zealand" - ], - "legendgroup": "Oceania", - "marker": { - "color": "#FFA15A", - "size": [ - 16257249, - 3317166 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Oceania", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 21888.88903, - 19007.19129 - ], - "xaxis": "x", - "y": [ - 76.32, - 74.32 - ], - "yaxis": "y" - } - ], - "name": "1987" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

continent=Asia
year=1992
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Afghanistan", - "Bahrain", - "Bangladesh", - "Cambodia", - "China", - "Hong Kong, China", - "India", - "Indonesia", - "Iran", - "Iraq", - "Israel", - "Japan", - "Jordan", - "Korea, Dem. Rep.", - "Korea, Rep.", - "Kuwait", - "Lebanon", - "Malaysia", - "Mongolia", - "Myanmar", - "Nepal", - "Oman", - "Pakistan", - "Philippines", - "Saudi Arabia", - "Singapore", - "Sri Lanka", - "Syria", - "Taiwan", - "Thailand", - "Vietnam", - "West Bank and Gaza", - "Yemen, Rep." - ], - "ids": [ - "Afghanistan", - "Bahrain", - "Bangladesh", - "Cambodia", - "China", - "Hong Kong, China", - "India", - "Indonesia", - "Iran", - "Iraq", - "Israel", - "Japan", - "Jordan", - "Korea, Dem. Rep.", - "Korea, Rep.", - "Kuwait", - "Lebanon", - "Malaysia", - "Mongolia", - "Myanmar", - "Nepal", - "Oman", - "Pakistan", - "Philippines", - "Saudi Arabia", - "Singapore", - "Sri Lanka", - "Syria", - "Taiwan", - "Thailand", - "Vietnam", - "West Bank and Gaza", - "Yemen, Rep." - ], - "legendgroup": "Asia", - "marker": { - "color": "#636efa", - "size": [ - 16317921, - 529491, - 113704579, - 10150094, - 1164970000, - 5829696, - 872000000, - 184816000, - 60397973, - 17861905, - 4936550, - 124329269, - 3867409, - 20711375, - 43805450, - 1418095, - 3219994, - 18319502, - 2312802, - 40546538, - 20326209, - 1915208, - 120065004, - 67185766, - 16945857, - 3235865, - 17587060, - 13219062, - 20686918, - 56667095, - 69940728, - 2104779, - 13367997 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Asia", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 649.3413952000002, - 19035.57917, - 837.8101642999999, - 682.3031755, - 1655.784158, - 24757.60301, - 1164.406809, - 2383.140898, - 7235.653187999998, - 3745.640687, - 18051.52254, - 26824.89511, - 3431.593647, - 3726.063507, - 12104.27872, - 34932.91959, - 6890.806854, - 7277.912802, - 1785.402016, - 347, - 897.7403604, - 18616.70691, - 1971.829464, - 2279.324017000001, - 24841.61777, - 24769.8912, - 2153.739222, - 3340.542768, - 15215.6579, - 4616.896545000001, - 989.0231487, - 6017.654756, - 1879.496673 - ], - "xaxis": "x", - "y": [ - 41.674, - 72.601, - 56.018, - 55.803, - 68.69, - 77.601, - 60.223, - 62.681, - 65.742, - 59.46100000000001, - 76.93, - 79.36, - 68.015, - 69.97800000000001, - 72.244, - 75.19, - 69.292, - 70.693, - 61.271, - 59.32, - 55.727, - 71.197, - 60.838, - 66.458, - 68.768, - 75.788, - 70.37899999999998, - 69.249, - 74.26, - 67.298, - 67.66199999999999, - 69.718, - 55.599 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Europe
year=1992
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Albania", - "Austria", - "Belgium", - "Bosnia and Herzegovina", - "Bulgaria", - "Croatia", - "Czech Republic", - "Denmark", - "Finland", - "France", - "Germany", - "Greece", - "Hungary", - "Iceland", - "Ireland", - "Italy", - "Montenegro", - "Netherlands", - "Norway", - "Poland", - "Portugal", - "Romania", - "Serbia", - "Slovak Republic", - "Slovenia", - "Spain", - "Sweden", - "Switzerland", - "Turkey", - "United Kingdom" - ], - "ids": [ - "Albania", - "Austria", - "Belgium", - "Bosnia and Herzegovina", - "Bulgaria", - "Croatia", - "Czech Republic", - "Denmark", - "Finland", - "France", - "Germany", - "Greece", - "Hungary", - "Iceland", - "Ireland", - "Italy", - "Montenegro", - "Netherlands", - "Norway", - "Poland", - "Portugal", - "Romania", - "Serbia", - "Slovak Republic", - "Slovenia", - "Spain", - "Sweden", - "Switzerland", - "Turkey", - "United Kingdom" - ], - "legendgroup": "Europe", - "marker": { - "color": "#EF553B", - "size": [ - 3326498, - 7914969, - 10045622, - 4256013, - 8658506, - 4494013, - 10315702, - 5171393, - 5041039, - 57374179, - 80597764, - 10325429, - 10348684, - 259012, - 3557761, - 56840847, - 621621, - 15174244, - 4286357, - 38370697, - 9927680, - 22797027, - 9826397, - 5302888, - 1999210, - 39549438, - 8718867, - 6995447, - 58179144, - 57866349 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Europe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 2497.437901, - 27042.01868, - 25575.57069, - 2546.781445, - 6302.623438000001, - 8447.794873, - 14297.02122, - 26406.73985, - 20647.16499, - 24703.79615, - 26505.30317, - 17541.49634, - 10535.62855, - 25144.39201, - 17558.81555, - 22013.64486, - 7003.339037000002, - 26790.94961, - 33965.66115, - 7738.881247, - 16207.266630000002, - 6598.409903, - 9325.068238, - 9498.467723, - 14214.71681, - 18603.06452, - 23880.01683, - 31871.5303, - 5678.348271, - 22705.09254 - ], - "xaxis": "x", - "y": [ - 71.581, - 76.04, - 76.46, - 72.178, - 71.19, - 72.527, - 72.4, - 75.33, - 75.7, - 77.46, - 76.07, - 77.03, - 69.17, - 78.77, - 75.467, - 77.44, - 75.435, - 77.42, - 77.32, - 70.99, - 74.86, - 69.36, - 71.65899999999998, - 71.38, - 73.64, - 77.57, - 78.16, - 78.03, - 66.146, - 76.42 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Africa
year=1992
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Algeria", - "Angola", - "Benin", - "Botswana", - "Burkina Faso", - "Burundi", - "Cameroon", - "Central African Republic", - "Chad", - "Comoros", - "Congo, Dem. Rep.", - "Congo, Rep.", - "Cote d'Ivoire", - "Djibouti", - "Egypt", - "Equatorial Guinea", - "Eritrea", - "Ethiopia", - "Gabon", - "Gambia", - "Ghana", - "Guinea", - "Guinea-Bissau", - "Kenya", - "Lesotho", - "Liberia", - "Libya", - "Madagascar", - "Malawi", - "Mali", - "Mauritania", - "Mauritius", - "Morocco", - "Mozambique", - "Namibia", - "Niger", - "Nigeria", - "Reunion", - "Rwanda", - "Sao Tome and Principe", - "Senegal", - "Sierra Leone", - "Somalia", - "South Africa", - "Sudan", - "Swaziland", - "Tanzania", - "Togo", - "Tunisia", - "Uganda", - "Zambia", - "Zimbabwe" - ], - "ids": [ - "Algeria", - "Angola", - "Benin", - "Botswana", - "Burkina Faso", - "Burundi", - "Cameroon", - "Central African Republic", - "Chad", - "Comoros", - "Congo, Dem. Rep.", - "Congo, Rep.", - "Cote d'Ivoire", - "Djibouti", - "Egypt", - "Equatorial Guinea", - "Eritrea", - "Ethiopia", - "Gabon", - "Gambia", - "Ghana", - "Guinea", - "Guinea-Bissau", - "Kenya", - "Lesotho", - "Liberia", - "Libya", - "Madagascar", - "Malawi", - "Mali", - "Mauritania", - "Mauritius", - "Morocco", - "Mozambique", - "Namibia", - "Niger", - "Nigeria", - "Reunion", - "Rwanda", - "Sao Tome and Principe", - "Senegal", - "Sierra Leone", - "Somalia", - "South Africa", - "Sudan", - "Swaziland", - "Tanzania", - "Togo", - "Tunisia", - "Uganda", - "Zambia", - "Zimbabwe" - ], - "legendgroup": "Africa", - "marker": { - "color": "#00cc96", - "size": [ - 26298373, - 8735988, - 4981671, - 1342614, - 8878303, - 5809236, - 12467171, - 3265124, - 6429417, - 454429, - 41672143, - 2409073, - 12772596, - 384156, - 59402198, - 387838, - 3668440, - 52088559, - 985739, - 1025384, - 16278738, - 6990574, - 1050938, - 25020539, - 1803195, - 1912974, - 4364501, - 12210395, - 10014249, - 8416215, - 2119465, - 1096202, - 25798239, - 13160731, - 1554253, - 8392818, - 93364244, - 622191, - 7290203, - 125911, - 8307920, - 4260884, - 6099799, - 39964159, - 28227588, - 962344, - 26605473, - 3747553, - 8523077, - 18252190, - 8381163, - 10704340 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Africa", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5023.216647, - 2627.845685, - 1191.207681, - 7954.111645, - 931.7527731, - 631.6998778, - 1793.1632780000002, - 747.9055252, - 1058.0643, - 1246.90737, - 457.7191807, - 4016.239529, - 1648.073791, - 2377.156192000001, - 3794.755195, - 1132.055034, - 582.8585102000002, - 421.3534653, - 13522.15752, - 665.6244126, - 925.060154, - 794.3484384, - 745.5398706, - 1341.9217210000004, - 977.4862725, - 636.6229191000001, - 9640.138501, - 1040.67619, - 563.2000145, - 739.014375, - 1361.369784, - 6058.253846000001, - 2948.047252, - 410.8968239, - 3804.537999, - 581.182725, - 1619.848217, - 6101.255823, - 737.0685949, - 1428.777814, - 1367.899369, - 1068.696278, - 926.9602964, - 7225.069257999998, - 1492.197043, - 3553.0224, - 825.682454, - 1034.298904, - 4332.720164, - 644.1707968999998, - 1210.884633, - 693.4207856 - ], - "xaxis": "x", - "y": [ - 67.744, - 40.647, - 53.919, - 62.745, - 50.26, - 44.736, - 54.31399999999999, - 49.396, - 51.724, - 57.93899999999999, - 45.548, - 56.433, - 52.044, - 51.604, - 63.674, - 47.545, - 49.99100000000001, - 48.091, - 61.36600000000001, - 52.644, - 57.50100000000001, - 48.576, - 43.26600000000001, - 59.285, - 59.685, - 40.802, - 68.755, - 52.214, - 49.42, - 48.38800000000001, - 58.333, - 69.745, - 65.393, - 44.284, - 61.999, - 47.39100000000001, - 47.472, - 73.615, - 23.599, - 62.742, - 58.19600000000001, - 38.333, - 39.658, - 61.88800000000001, - 53.556, - 58.474, - 50.44, - 58.06100000000001, - 70.001, - 48.825, - 46.1, - 60.377 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Americas
year=1992
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Argentina", - "Bolivia", - "Brazil", - "Canada", - "Chile", - "Colombia", - "Costa Rica", - "Cuba", - "Dominican Republic", - "Ecuador", - "El Salvador", - "Guatemala", - "Haiti", - "Honduras", - "Jamaica", - "Mexico", - "Nicaragua", - "Panama", - "Paraguay", - "Peru", - "Puerto Rico", - "Trinidad and Tobago", - "United States", - "Uruguay", - "Venezuela" - ], - "ids": [ - "Argentina", - "Bolivia", - "Brazil", - "Canada", - "Chile", - "Colombia", - "Costa Rica", - "Cuba", - "Dominican Republic", - "Ecuador", - "El Salvador", - "Guatemala", - "Haiti", - "Honduras", - "Jamaica", - "Mexico", - "Nicaragua", - "Panama", - "Paraguay", - "Peru", - "Puerto Rico", - "Trinidad and Tobago", - "United States", - "Uruguay", - "Venezuela" - ], - "legendgroup": "Americas", - "marker": { - "color": "#ab63fa", - "size": [ - 33958947, - 6893451, - 155975974, - 28523502, - 13572994, - 34202721, - 3173216, - 10723260, - 7351181, - 10748394, - 5274649, - 8486949, - 6326682, - 5077347, - 2378618, - 88111030, - 4017939, - 2484997, - 4483945, - 22430449, - 3585176, - 1183669, - 256894189, - 3149262, - 20265563 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Americas", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 9308.41871, - 2961.699694, - 6950.283020999998, - 26342.88426, - 7596.125964, - 5444.648617, - 6160.416317, - 5592.843963, - 3044.214214, - 7103.702595000002, - 4444.2317, - 4439.45084, - 1456.309517, - 3081.694603, - 7404.923685, - 9472.384295, - 2170.151724, - 6618.74305, - 4196.411078, - 4446.380924, - 14641.58711, - 7370.990932, - 32003.93224, - 8137.004775, - 10733.92631 - ], - "xaxis": "x", - "y": [ - 71.868, - 59.957, - 67.057, - 77.95, - 74.126, - 68.421, - 75.71300000000002, - 74.414, - 68.457, - 69.613, - 66.798, - 63.37300000000001, - 55.089, - 66.399, - 71.766, - 71.455, - 65.843, - 72.462, - 68.225, - 66.458, - 73.911, - 69.862, - 76.09, - 72.752, - 71.15 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Oceania
year=1992
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Australia", - "New Zealand" - ], - "ids": [ - "Australia", - "New Zealand" - ], - "legendgroup": "Oceania", - "marker": { - "color": "#FFA15A", - "size": [ - 17481977, - 3437674 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Oceania", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 23424.76683, - 18363.32494 - ], - "xaxis": "x", - "y": [ - 77.56, - 76.33 - ], - "yaxis": "y" - } - ], - "name": "1992" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

continent=Asia
year=1997
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Afghanistan", - "Bahrain", - "Bangladesh", - "Cambodia", - "China", - "Hong Kong, China", - "India", - "Indonesia", - "Iran", - "Iraq", - "Israel", - "Japan", - "Jordan", - "Korea, Dem. Rep.", - "Korea, Rep.", - "Kuwait", - "Lebanon", - "Malaysia", - "Mongolia", - "Myanmar", - "Nepal", - "Oman", - "Pakistan", - "Philippines", - "Saudi Arabia", - "Singapore", - "Sri Lanka", - "Syria", - "Taiwan", - "Thailand", - "Vietnam", - "West Bank and Gaza", - "Yemen, Rep." - ], - "ids": [ - "Afghanistan", - "Bahrain", - "Bangladesh", - "Cambodia", - "China", - "Hong Kong, China", - "India", - "Indonesia", - "Iran", - "Iraq", - "Israel", - "Japan", - "Jordan", - "Korea, Dem. Rep.", - "Korea, Rep.", - "Kuwait", - "Lebanon", - "Malaysia", - "Mongolia", - "Myanmar", - "Nepal", - "Oman", - "Pakistan", - "Philippines", - "Saudi Arabia", - "Singapore", - "Sri Lanka", - "Syria", - "Taiwan", - "Thailand", - "Vietnam", - "West Bank and Gaza", - "Yemen, Rep." - ], - "legendgroup": "Asia", - "marker": { - "color": "#636efa", - "size": [ - 22227415, - 598561, - 123315288, - 11782962, - 1230075000, - 6495918, - 959000000, - 199278000, - 63327987, - 20775703, - 5531387, - 125956499, - 4526235, - 21585105, - 46173816, - 1765345, - 3430388, - 20476091, - 2494803, - 43247867, - 23001113, - 2283635, - 135564834, - 75012988, - 21229759, - 3802309, - 18698655, - 15081016, - 21628605, - 60216677, - 76048996, - 2826046, - 15826497 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Asia", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 635.341351, - 20292.01679, - 972.7700352, - 734.28517, - 2289.234136, - 28377.63219, - 1458.817442, - 3119.335603, - 8263.590301, - 3076.239795, - 20896.60924, - 28816.58499, - 3645.379572, - 1690.756814, - 15993.52796, - 40300.61996, - 8754.96385, - 10132.90964, - 1902.2521, - 415, - 1010.892138, - 19702.05581, - 2049.3505210000008, - 2536.534925, - 20586.69019, - 33519.4766, - 2664.477257, - 4014.238972, - 20206.82098, - 5852.625497, - 1385.896769, - 7110.667619, - 2117.484526 - ], - "xaxis": "x", - "y": [ - 41.76300000000001, - 73.925, - 59.412, - 56.534, - 70.426, - 80, - 61.765, - 66.041, - 68.042, - 58.81100000000001, - 78.26899999999998, - 80.69, - 69.77199999999999, - 67.727, - 74.64699999999998, - 76.156, - 70.265, - 71.938, - 63.625, - 60.328, - 59.426, - 72.499, - 61.81800000000001, - 68.564, - 70.533, - 77.158, - 70.457, - 71.527, - 75.25, - 67.521, - 70.672, - 71.096, - 58.02 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Europe
year=1997
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Albania", - "Austria", - "Belgium", - "Bosnia and Herzegovina", - "Bulgaria", - "Croatia", - "Czech Republic", - "Denmark", - "Finland", - "France", - "Germany", - "Greece", - "Hungary", - "Iceland", - "Ireland", - "Italy", - "Montenegro", - "Netherlands", - "Norway", - "Poland", - "Portugal", - "Romania", - "Serbia", - "Slovak Republic", - "Slovenia", - "Spain", - "Sweden", - "Switzerland", - "Turkey", - "United Kingdom" - ], - "ids": [ - "Albania", - "Austria", - "Belgium", - "Bosnia and Herzegovina", - "Bulgaria", - "Croatia", - "Czech Republic", - "Denmark", - "Finland", - "France", - "Germany", - "Greece", - "Hungary", - "Iceland", - "Ireland", - "Italy", - "Montenegro", - "Netherlands", - "Norway", - "Poland", - "Portugal", - "Romania", - "Serbia", - "Slovak Republic", - "Slovenia", - "Spain", - "Sweden", - "Switzerland", - "Turkey", - "United Kingdom" - ], - "legendgroup": "Europe", - "marker": { - "color": "#EF553B", - "size": [ - 3428038, - 8069876, - 10199787, - 3607000, - 8066057, - 4444595, - 10300707, - 5283663, - 5134406, - 58623428, - 82011073, - 10502372, - 10244684, - 271192, - 3667233, - 57479469, - 692651, - 15604464, - 4405672, - 38654957, - 10156415, - 22562458, - 10336594, - 5383010, - 2011612, - 39855442, - 8897619, - 7193761, - 63047647, - 58808266 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Europe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 3193.054604, - 29095.920660000003, - 27561.19663, - 4766.355904, - 5970.38876, - 9875.604515, - 16048.51424, - 29804.34567, - 23723.9502, - 25889.78487, - 27788.88416, - 18747.69814, - 11712.7768, - 28061.099660000003, - 24521.94713, - 24675.02446, - 6465.613349, - 30246.13063, - 41283.16433, - 10159.58368, - 17641.03156, - 7346.547556999999, - 7914.320304000002, - 12126.23065, - 17161.10735, - 20445.29896, - 25266.59499, - 32135.323010000004, - 6601.429915, - 26074.53136 - ], - "xaxis": "x", - "y": [ - 72.95, - 77.51, - 77.53, - 73.244, - 70.32, - 73.68, - 74.01, - 76.11, - 77.13, - 78.64, - 77.34, - 77.869, - 71.04, - 78.95, - 76.122, - 78.82, - 75.445, - 78.03, - 78.32, - 72.75, - 75.97, - 69.72, - 72.232, - 72.71, - 75.13, - 78.77, - 79.39, - 79.37, - 68.835, - 77.218 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Africa
year=1997
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Algeria", - "Angola", - "Benin", - "Botswana", - "Burkina Faso", - "Burundi", - "Cameroon", - "Central African Republic", - "Chad", - "Comoros", - "Congo, Dem. Rep.", - "Congo, Rep.", - "Cote d'Ivoire", - "Djibouti", - "Egypt", - "Equatorial Guinea", - "Eritrea", - "Ethiopia", - "Gabon", - "Gambia", - "Ghana", - "Guinea", - "Guinea-Bissau", - "Kenya", - "Lesotho", - "Liberia", - "Libya", - "Madagascar", - "Malawi", - "Mali", - "Mauritania", - "Mauritius", - "Morocco", - "Mozambique", - "Namibia", - "Niger", - "Nigeria", - "Reunion", - "Rwanda", - "Sao Tome and Principe", - "Senegal", - "Sierra Leone", - "Somalia", - "South Africa", - "Sudan", - "Swaziland", - "Tanzania", - "Togo", - "Tunisia", - "Uganda", - "Zambia", - "Zimbabwe" - ], - "ids": [ - "Algeria", - "Angola", - "Benin", - "Botswana", - "Burkina Faso", - "Burundi", - "Cameroon", - "Central African Republic", - "Chad", - "Comoros", - "Congo, Dem. Rep.", - "Congo, Rep.", - "Cote d'Ivoire", - "Djibouti", - "Egypt", - "Equatorial Guinea", - "Eritrea", - "Ethiopia", - "Gabon", - "Gambia", - "Ghana", - "Guinea", - "Guinea-Bissau", - "Kenya", - "Lesotho", - "Liberia", - "Libya", - "Madagascar", - "Malawi", - "Mali", - "Mauritania", - "Mauritius", - "Morocco", - "Mozambique", - "Namibia", - "Niger", - "Nigeria", - "Reunion", - "Rwanda", - "Sao Tome and Principe", - "Senegal", - "Sierra Leone", - "Somalia", - "South Africa", - "Sudan", - "Swaziland", - "Tanzania", - "Togo", - "Tunisia", - "Uganda", - "Zambia", - "Zimbabwe" - ], - "legendgroup": "Africa", - "marker": { - "color": "#00cc96", - "size": [ - 29072015, - 9875024, - 6066080, - 1536536, - 10352843, - 6121610, - 14195809, - 3696513, - 7562011, - 527982, - 47798986, - 2800947, - 14625967, - 417908, - 66134291, - 439971, - 4058319, - 59861301, - 1126189, - 1235767, - 18418288, - 8048834, - 1193708, - 28263827, - 1982823, - 2200725, - 4759670, - 14165114, - 10419991, - 9384984, - 2444741, - 1149818, - 28529501, - 16603334, - 1774766, - 9666252, - 106207839, - 684810, - 7212583, - 145608, - 9535314, - 4578212, - 6633514, - 42835005, - 32160729, - 1054486, - 30686889, - 4320890, - 9231669, - 21210254, - 9417789, - 11404948 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Africa", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 4797.295051, - 2277.140884, - 1232.975292, - 8647.142313, - 946.2949618, - 463.1151478, - 1694.337469, - 740.5063317, - 1004.961353, - 1173.618235, - 312.188423, - 3484.164376, - 1786.265407, - 1895.016984, - 4173.181797, - 2814.480755, - 913.47079, - 515.8894013, - 14722.841880000002, - 653.7301704, - 1005.245812, - 869.4497667999998, - 796.6644681, - 1360.4850210000004, - 1186.147994, - 609.1739508, - 9467.446056, - 986.2958956, - 692.2758102999999, - 790.2579846, - 1483.136136, - 7425.705295000002, - 2982.101858, - 472.34607710000006, - 3899.52426, - 580.3052092, - 1624.941275, - 6071.941411, - 589.9445051, - 1339.076036, - 1392.368347, - 574.6481576, - 930.5964284, - 7479.188244, - 1632.2107640000004, - 3876.76846, - 789.1862231, - 982.2869243, - 4876.798614, - 816.559081, - 1071.353818, - 792.4499602999998 - ], - "xaxis": "x", - "y": [ - 69.152, - 40.963, - 54.777, - 52.556, - 50.324, - 45.326, - 52.199, - 46.066, - 51.573, - 60.66, - 42.587, - 52.962, - 47.99100000000001, - 53.157, - 67.217, - 48.245, - 53.378, - 49.402, - 60.46100000000001, - 55.861, - 58.556, - 51.455, - 44.87300000000001, - 54.407, - 55.558, - 42.221, - 71.555, - 54.978, - 47.495, - 49.903, - 60.43, - 70.736, - 67.66, - 46.344, - 58.909, - 51.313, - 47.464, - 74.77199999999998, - 36.087, - 63.306, - 60.187, - 39.897, - 43.795, - 60.236, - 55.37300000000001, - 54.289, - 48.466, - 58.39, - 71.973, - 44.578, - 40.238, - 46.809 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Americas
year=1997
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Argentina", - "Bolivia", - "Brazil", - "Canada", - "Chile", - "Colombia", - "Costa Rica", - "Cuba", - "Dominican Republic", - "Ecuador", - "El Salvador", - "Guatemala", - "Haiti", - "Honduras", - "Jamaica", - "Mexico", - "Nicaragua", - "Panama", - "Paraguay", - "Peru", - "Puerto Rico", - "Trinidad and Tobago", - "United States", - "Uruguay", - "Venezuela" - ], - "ids": [ - "Argentina", - "Bolivia", - "Brazil", - "Canada", - "Chile", - "Colombia", - "Costa Rica", - "Cuba", - "Dominican Republic", - "Ecuador", - "El Salvador", - "Guatemala", - "Haiti", - "Honduras", - "Jamaica", - "Mexico", - "Nicaragua", - "Panama", - "Paraguay", - "Peru", - "Puerto Rico", - "Trinidad and Tobago", - "United States", - "Uruguay", - "Venezuela" - ], - "legendgroup": "Americas", - "marker": { - "color": "#ab63fa", - "size": [ - 36203463, - 7693188, - 168546719, - 30305843, - 14599929, - 37657830, - 3518107, - 10983007, - 7992357, - 11911819, - 5783439, - 9803875, - 6913545, - 5867957, - 2531311, - 95895146, - 4609572, - 2734531, - 5154123, - 24748122, - 3759430, - 1138101, - 272911760, - 3262838, - 22374398 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Americas", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 10967.28195, - 3326.143191, - 7957.980823999998, - 28954.92589, - 10118.05318, - 6117.361746000001, - 6677.045314, - 5431.990415, - 3614.101285, - 7429.4558769999985, - 5154.825496, - 4684.313807, - 1341.726931, - 3160.454906, - 7121.924704000001, - 9767.29753, - 2253.023004, - 7113.692252, - 4247.400261, - 5838.347657, - 16999.4333, - 8792.573126000001, - 35767.43303, - 9230.240708, - 10165.49518 - ], - "xaxis": "x", - "y": [ - 73.275, - 62.05, - 69.388, - 78.61, - 75.816, - 70.313, - 77.26, - 76.15100000000002, - 69.957, - 72.312, - 69.535, - 66.322, - 56.67100000000001, - 67.65899999999999, - 72.262, - 73.67, - 68.426, - 73.738, - 69.4, - 68.38600000000001, - 74.917, - 69.465, - 76.81, - 74.223, - 72.146 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Oceania
year=1997
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Australia", - "New Zealand" - ], - "ids": [ - "Australia", - "New Zealand" - ], - "legendgroup": "Oceania", - "marker": { - "color": "#FFA15A", - "size": [ - 18565243, - 3676187 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Oceania", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 26997.93657, - 21050.41377 - ], - "xaxis": "x", - "y": [ - 78.83, - 77.55 - ], - "yaxis": "y" - } - ], - "name": "1997" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

continent=Asia
year=2002
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Afghanistan", - "Bahrain", - "Bangladesh", - "Cambodia", - "China", - "Hong Kong, China", - "India", - "Indonesia", - "Iran", - "Iraq", - "Israel", - "Japan", - "Jordan", - "Korea, Dem. Rep.", - "Korea, Rep.", - "Kuwait", - "Lebanon", - "Malaysia", - "Mongolia", - "Myanmar", - "Nepal", - "Oman", - "Pakistan", - "Philippines", - "Saudi Arabia", - "Singapore", - "Sri Lanka", - "Syria", - "Taiwan", - "Thailand", - "Vietnam", - "West Bank and Gaza", - "Yemen, Rep." - ], - "ids": [ - "Afghanistan", - "Bahrain", - "Bangladesh", - "Cambodia", - "China", - "Hong Kong, China", - "India", - "Indonesia", - "Iran", - "Iraq", - "Israel", - "Japan", - "Jordan", - "Korea, Dem. Rep.", - "Korea, Rep.", - "Kuwait", - "Lebanon", - "Malaysia", - "Mongolia", - "Myanmar", - "Nepal", - "Oman", - "Pakistan", - "Philippines", - "Saudi Arabia", - "Singapore", - "Sri Lanka", - "Syria", - "Taiwan", - "Thailand", - "Vietnam", - "West Bank and Gaza", - "Yemen, Rep." - ], - "legendgroup": "Asia", - "marker": { - "color": "#636efa", - "size": [ - 25268405, - 656397, - 135656790, - 12926707, - 1280400000, - 6762476, - 1034172547, - 211060000, - 66907826, - 24001816, - 6029529, - 127065841, - 5307470, - 22215365, - 47969150, - 2111561, - 3677780, - 22662365, - 2674234, - 45598081, - 25873917, - 2713462, - 153403524, - 82995088, - 24501530, - 4197776, - 19576783, - 17155814, - 22454239, - 62806748, - 80908147, - 3389578, - 18701257 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Asia", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 726.7340548, - 23403.55927, - 1136.3904300000004, - 896.2260152999999, - 3119.280896, - 30209.015160000006, - 1746.769454, - 2873.91287, - 9240.761975, - 4390.717312, - 21905.59514, - 28604.5919, - 3844.917194, - 1646.758151, - 19233.98818, - 35110.10566, - 9313.93883, - 10206.97794, - 2140.739323, - 611, - 1057.206311, - 19774.83687, - 2092.712441, - 2650.921068, - 19014.54118, - 36023.1054, - 3015.378833, - 4090.925331, - 23235.42329, - 5913.187529, - 1764.456677, - 4515.487575, - 2234.820827 - ], - "xaxis": "x", - "y": [ - 42.129, - 74.795, - 62.01300000000001, - 56.752, - 72.028, - 81.495, - 62.879, - 68.58800000000001, - 69.45100000000001, - 57.04600000000001, - 79.696, - 82, - 71.263, - 66.66199999999999, - 77.045, - 76.904, - 71.028, - 73.044, - 65.033, - 59.908, - 61.34, - 74.193, - 63.61, - 70.303, - 71.626, - 78.77, - 70.815, - 73.053, - 76.99, - 68.564, - 73.017, - 72.37, - 60.308 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Europe
year=2002
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Albania", - "Austria", - "Belgium", - "Bosnia and Herzegovina", - "Bulgaria", - "Croatia", - "Czech Republic", - "Denmark", - "Finland", - "France", - "Germany", - "Greece", - "Hungary", - "Iceland", - "Ireland", - "Italy", - "Montenegro", - "Netherlands", - "Norway", - "Poland", - "Portugal", - "Romania", - "Serbia", - "Slovak Republic", - "Slovenia", - "Spain", - "Sweden", - "Switzerland", - "Turkey", - "United Kingdom" - ], - "ids": [ - "Albania", - "Austria", - "Belgium", - "Bosnia and Herzegovina", - "Bulgaria", - "Croatia", - "Czech Republic", - "Denmark", - "Finland", - "France", - "Germany", - "Greece", - "Hungary", - "Iceland", - "Ireland", - "Italy", - "Montenegro", - "Netherlands", - "Norway", - "Poland", - "Portugal", - "Romania", - "Serbia", - "Slovak Republic", - "Slovenia", - "Spain", - "Sweden", - "Switzerland", - "Turkey", - "United Kingdom" - ], - "legendgroup": "Europe", - "marker": { - "color": "#EF553B", - "size": [ - 3508512, - 8148312, - 10311970, - 4165416, - 7661799, - 4481020, - 10256295, - 5374693, - 5193039, - 59925035, - 82350671, - 10603863, - 10083313, - 288030, - 3879155, - 57926999, - 720230, - 16122830, - 4535591, - 38625976, - 10433867, - 22404337, - 10111559, - 5410052, - 2011497, - 40152517, - 8954175, - 7361757, - 67308928, - 59912431 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Europe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 4604.211737, - 32417.60769, - 30485.88375, - 6018.975239, - 7696.777725, - 11628.38895, - 17596.210219999994, - 32166.50006, - 28204.59057, - 28926.03234, - 30035.80198, - 22514.2548, - 14843.93556, - 31163.20196, - 34077.04939, - 27968.09817, - 6557.194282, - 33724.75778, - 44683.97525, - 12002.23908, - 19970.90787, - 7885.360081, - 7236.075251, - 13638.778369999998, - 20660.01936, - 24835.47166, - 29341.630930000007, - 34480.95771, - 6508.085718, - 29478.99919 - ], - "xaxis": "x", - "y": [ - 75.65100000000002, - 78.98, - 78.32, - 74.09, - 72.14, - 74.876, - 75.51, - 77.18, - 78.37, - 79.59, - 78.67, - 78.256, - 72.59, - 80.5, - 77.783, - 80.24, - 73.98100000000002, - 78.53, - 79.05, - 74.67, - 77.29, - 71.322, - 73.21300000000002, - 73.8, - 76.66, - 79.78, - 80.04, - 80.62, - 70.845, - 78.471 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Africa
year=2002
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Algeria", - "Angola", - "Benin", - "Botswana", - "Burkina Faso", - "Burundi", - "Cameroon", - "Central African Republic", - "Chad", - "Comoros", - "Congo, Dem. Rep.", - "Congo, Rep.", - "Cote d'Ivoire", - "Djibouti", - "Egypt", - "Equatorial Guinea", - "Eritrea", - "Ethiopia", - "Gabon", - "Gambia", - "Ghana", - "Guinea", - "Guinea-Bissau", - "Kenya", - "Lesotho", - "Liberia", - "Libya", - "Madagascar", - "Malawi", - "Mali", - "Mauritania", - "Mauritius", - "Morocco", - "Mozambique", - "Namibia", - "Niger", - "Nigeria", - "Reunion", - "Rwanda", - "Sao Tome and Principe", - "Senegal", - "Sierra Leone", - "Somalia", - "South Africa", - "Sudan", - "Swaziland", - "Tanzania", - "Togo", - "Tunisia", - "Uganda", - "Zambia", - "Zimbabwe" - ], - "ids": [ - "Algeria", - "Angola", - "Benin", - "Botswana", - "Burkina Faso", - "Burundi", - "Cameroon", - "Central African Republic", - "Chad", - "Comoros", - "Congo, Dem. Rep.", - "Congo, Rep.", - "Cote d'Ivoire", - "Djibouti", - "Egypt", - "Equatorial Guinea", - "Eritrea", - "Ethiopia", - "Gabon", - "Gambia", - "Ghana", - "Guinea", - "Guinea-Bissau", - "Kenya", - "Lesotho", - "Liberia", - "Libya", - "Madagascar", - "Malawi", - "Mali", - "Mauritania", - "Mauritius", - "Morocco", - "Mozambique", - "Namibia", - "Niger", - "Nigeria", - "Reunion", - "Rwanda", - "Sao Tome and Principe", - "Senegal", - "Sierra Leone", - "Somalia", - "South Africa", - "Sudan", - "Swaziland", - "Tanzania", - "Togo", - "Tunisia", - "Uganda", - "Zambia", - "Zimbabwe" - ], - "legendgroup": "Africa", - "marker": { - "color": "#00cc96", - "size": [ - 31287142, - 10866106, - 7026113, - 1630347, - 12251209, - 7021078, - 15929988, - 4048013, - 8835739, - 614382, - 55379852, - 3328795, - 16252726, - 447416, - 73312559, - 495627, - 4414865, - 67946797, - 1299304, - 1457766, - 20550751, - 8807818, - 1332459, - 31386842, - 2046772, - 2814651, - 5368585, - 16473477, - 11824495, - 10580176, - 2828858, - 1200206, - 31167783, - 18473780, - 1972153, - 11140655, - 119901274, - 743981, - 7852401, - 170372, - 10870037, - 5359092, - 7753310, - 44433622, - 37090298, - 1130269, - 34593779, - 4977378, - 9770575, - 24739869, - 10595811, - 11926563 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Africa", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5288.040382, - 2773.287312, - 1372.877931, - 11003.60508, - 1037.645221, - 446.4035126, - 1934.011449, - 738.6906068, - 1156.18186, - 1075.811558, - 241.1658765, - 3484.06197, - 1648.800823, - 1908.260867, - 4754.604414, - 7703.4959, - 765.3500015, - 530.0535319, - 12521.71392, - 660.5855997, - 1111.9845779999996, - 945.5835837, - 575.7047176, - 1287.514732, - 1275.184575, - 531.4823679, - 9534.677467, - 894.6370822, - 665.4231186000002, - 951.4097518, - 1579.019543, - 9021.815894, - 3258.495584, - 633.6179466, - 4072.324751, - 601.0745012, - 1615.286395, - 6316.1652, - 785.6537647999999, - 1353.09239, - 1519.635262, - 699.4897129999998, - 882.0818218000002, - 7710.946444, - 1993.398314, - 4128.116943, - 899.0742111, - 886.2205765000001, - 5722.895654999998, - 927.7210018, - 1071.6139380000004, - 672.0386227000001 - ], - "xaxis": "x", - "y": [ - 70.994, - 41.003, - 54.40600000000001, - 46.63399999999999, - 50.65, - 47.36, - 49.856, - 43.308, - 50.525, - 62.974, - 44.966, - 52.97, - 46.832, - 53.37300000000001, - 69.806, - 49.348, - 55.24, - 50.725, - 56.761, - 58.041, - 58.453, - 53.676, - 45.504, - 50.992, - 44.593, - 43.753, - 72.737, - 57.286, - 45.00899999999999, - 51.81800000000001, - 62.247, - 71.954, - 69.615, - 44.026, - 51.479, - 54.496, - 46.608, - 75.744, - 43.413, - 64.337, - 61.6, - 41.012, - 45.93600000000001, - 53.365, - 56.369, - 43.869, - 49.651, - 57.56100000000001, - 73.042, - 47.813, - 39.19300000000001, - 39.989 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Americas
year=2002
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Argentina", - "Bolivia", - "Brazil", - "Canada", - "Chile", - "Colombia", - "Costa Rica", - "Cuba", - "Dominican Republic", - "Ecuador", - "El Salvador", - "Guatemala", - "Haiti", - "Honduras", - "Jamaica", - "Mexico", - "Nicaragua", - "Panama", - "Paraguay", - "Peru", - "Puerto Rico", - "Trinidad and Tobago", - "United States", - "Uruguay", - "Venezuela" - ], - "ids": [ - "Argentina", - "Bolivia", - "Brazil", - "Canada", - "Chile", - "Colombia", - "Costa Rica", - "Cuba", - "Dominican Republic", - "Ecuador", - "El Salvador", - "Guatemala", - "Haiti", - "Honduras", - "Jamaica", - "Mexico", - "Nicaragua", - "Panama", - "Paraguay", - "Peru", - "Puerto Rico", - "Trinidad and Tobago", - "United States", - "Uruguay", - "Venezuela" - ], - "legendgroup": "Americas", - "marker": { - "color": "#ab63fa", - "size": [ - 38331121, - 8445134, - 179914212, - 31902268, - 15497046, - 41008227, - 3834934, - 11226999, - 8650322, - 12921234, - 6353681, - 11178650, - 7607651, - 6677328, - 2664659, - 102479927, - 5146848, - 2990875, - 5884491, - 26769436, - 3859606, - 1101832, - 287675526, - 3363085, - 24287670 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Americas", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 8797.640716, - 3413.26269, - 8131.212843000001, - 33328.96507, - 10778.78385, - 5755.259962, - 7723.447195000002, - 6340.646683, - 4563.808154, - 5773.044512, - 5351.568665999999, - 4858.347495, - 1270.364932, - 3099.72866, - 6994.774861, - 10742.44053, - 2474.548819, - 7356.0319340000015, - 3783.674243, - 5909.020073, - 18855.60618, - 11460.60023, - 39097.09955, - 7727.002004000001, - 8605.047831 - ], - "xaxis": "x", - "y": [ - 74.34, - 63.883, - 71.006, - 79.77, - 77.86, - 71.682, - 78.123, - 77.158, - 70.847, - 74.173, - 70.734, - 68.97800000000001, - 58.137, - 68.565, - 72.047, - 74.902, - 70.836, - 74.712, - 70.755, - 69.906, - 77.778, - 68.976, - 77.31, - 75.307, - 72.766 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Oceania
year=2002
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Australia", - "New Zealand" - ], - "ids": [ - "Australia", - "New Zealand" - ], - "legendgroup": "Oceania", - "marker": { - "color": "#FFA15A", - "size": [ - 19546792, - 3908037 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Oceania", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 30687.75473, - 23189.80135 - ], - "xaxis": "x", - "y": [ - 80.37, - 79.11 - ], - "yaxis": "y" - } - ], - "name": "2002" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

continent=Asia
year=2007
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Afghanistan", - "Bahrain", - "Bangladesh", - "Cambodia", - "China", - "Hong Kong, China", - "India", - "Indonesia", - "Iran", - "Iraq", - "Israel", - "Japan", - "Jordan", - "Korea, Dem. Rep.", - "Korea, Rep.", - "Kuwait", - "Lebanon", - "Malaysia", - "Mongolia", - "Myanmar", - "Nepal", - "Oman", - "Pakistan", - "Philippines", - "Saudi Arabia", - "Singapore", - "Sri Lanka", - "Syria", - "Taiwan", - "Thailand", - "Vietnam", - "West Bank and Gaza", - "Yemen, Rep." - ], - "ids": [ - "Afghanistan", - "Bahrain", - "Bangladesh", - "Cambodia", - "China", - "Hong Kong, China", - "India", - "Indonesia", - "Iran", - "Iraq", - "Israel", - "Japan", - "Jordan", - "Korea, Dem. Rep.", - "Korea, Rep.", - "Kuwait", - "Lebanon", - "Malaysia", - "Mongolia", - "Myanmar", - "Nepal", - "Oman", - "Pakistan", - "Philippines", - "Saudi Arabia", - "Singapore", - "Sri Lanka", - "Syria", - "Taiwan", - "Thailand", - "Vietnam", - "West Bank and Gaza", - "Yemen, Rep." - ], - "legendgroup": "Asia", - "marker": { - "color": "#636efa", - "size": [ - 31889923, - 708573, - 150448339, - 14131858, - 1318683096, - 6980412, - 1110396331, - 223547000, - 69453570, - 27499638, - 6426679, - 127467972, - 6053193, - 23301725, - 49044790, - 2505559, - 3921278, - 24821286, - 2874127, - 47761980, - 28901790, - 3204897, - 169270617, - 91077287, - 27601038, - 4553009, - 20378239, - 19314747, - 23174294, - 65068149, - 85262356, - 4018332, - 22211743 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Asia", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 974.5803384, - 29796.04834, - 1391.253792, - 1713.778686, - 4959.114854, - 39724.97867, - 2452.210407, - 3540.651564, - 11605.71449, - 4471.061906, - 25523.2771, - 31656.06806, - 4519.461171, - 1593.06548, - 23348.139730000006, - 47306.98978, - 10461.05868, - 12451.6558, - 3095.7722710000007, - 944, - 1091.359778, - 22316.19287, - 2605.94758, - 3190.481016, - 21654.83194, - 47143.17964, - 3970.095407, - 4184.548089, - 28718.27684, - 7458.396326999998, - 2441.576404, - 3025.349798, - 2280.769906 - ], - "xaxis": "x", - "y": [ - 43.828, - 75.635, - 64.062, - 59.723, - 72.961, - 82.208, - 64.69800000000001, - 70.65, - 70.964, - 59.545, - 80.745, - 82.603, - 72.535, - 67.297, - 78.623, - 77.58800000000002, - 71.993, - 74.241, - 66.803, - 62.069, - 63.785, - 75.64, - 65.483, - 71.688, - 72.777, - 79.972, - 72.396, - 74.143, - 78.4, - 70.616, - 74.249, - 73.422, - 62.698 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Europe
year=2007
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Albania", - "Austria", - "Belgium", - "Bosnia and Herzegovina", - "Bulgaria", - "Croatia", - "Czech Republic", - "Denmark", - "Finland", - "France", - "Germany", - "Greece", - "Hungary", - "Iceland", - "Ireland", - "Italy", - "Montenegro", - "Netherlands", - "Norway", - "Poland", - "Portugal", - "Romania", - "Serbia", - "Slovak Republic", - "Slovenia", - "Spain", - "Sweden", - "Switzerland", - "Turkey", - "United Kingdom" - ], - "ids": [ - "Albania", - "Austria", - "Belgium", - "Bosnia and Herzegovina", - "Bulgaria", - "Croatia", - "Czech Republic", - "Denmark", - "Finland", - "France", - "Germany", - "Greece", - "Hungary", - "Iceland", - "Ireland", - "Italy", - "Montenegro", - "Netherlands", - "Norway", - "Poland", - "Portugal", - "Romania", - "Serbia", - "Slovak Republic", - "Slovenia", - "Spain", - "Sweden", - "Switzerland", - "Turkey", - "United Kingdom" - ], - "legendgroup": "Europe", - "marker": { - "color": "#EF553B", - "size": [ - 3600523, - 8199783, - 10392226, - 4552198, - 7322858, - 4493312, - 10228744, - 5468120, - 5238460, - 61083916, - 82400996, - 10706290, - 9956108, - 301931, - 4109086, - 58147733, - 684736, - 16570613, - 4627926, - 38518241, - 10642836, - 22276056, - 10150265, - 5447502, - 2009245, - 40448191, - 9031088, - 7554661, - 71158647, - 60776238 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Europe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5937.029525999998, - 36126.4927, - 33692.60508, - 7446.298803, - 10680.79282, - 14619.222719999998, - 22833.30851, - 35278.41874, - 33207.0844, - 30470.0167, - 32170.37442, - 27538.41188, - 18008.94444, - 36180.78919, - 40675.99635, - 28569.7197, - 9253.896111, - 36797.93332, - 49357.19017, - 15389.924680000002, - 20509.64777, - 10808.47561, - 9786.534714, - 18678.31435, - 25768.25759, - 28821.0637, - 33859.74835, - 37506.41907, - 8458.276384, - 33203.26128 - ], - "xaxis": "x", - "y": [ - 76.423, - 79.829, - 79.441, - 74.852, - 73.005, - 75.748, - 76.486, - 78.332, - 79.313, - 80.657, - 79.406, - 79.483, - 73.33800000000002, - 81.757, - 78.885, - 80.546, - 74.543, - 79.762, - 80.196, - 75.563, - 78.098, - 72.476, - 74.002, - 74.663, - 77.926, - 80.941, - 80.884, - 81.70100000000002, - 71.777, - 79.425 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Africa
year=2007
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Algeria", - "Angola", - "Benin", - "Botswana", - "Burkina Faso", - "Burundi", - "Cameroon", - "Central African Republic", - "Chad", - "Comoros", - "Congo, Dem. Rep.", - "Congo, Rep.", - "Cote d'Ivoire", - "Djibouti", - "Egypt", - "Equatorial Guinea", - "Eritrea", - "Ethiopia", - "Gabon", - "Gambia", - "Ghana", - "Guinea", - "Guinea-Bissau", - "Kenya", - "Lesotho", - "Liberia", - "Libya", - "Madagascar", - "Malawi", - "Mali", - "Mauritania", - "Mauritius", - "Morocco", - "Mozambique", - "Namibia", - "Niger", - "Nigeria", - "Reunion", - "Rwanda", - "Sao Tome and Principe", - "Senegal", - "Sierra Leone", - "Somalia", - "South Africa", - "Sudan", - "Swaziland", - "Tanzania", - "Togo", - "Tunisia", - "Uganda", - "Zambia", - "Zimbabwe" - ], - "ids": [ - "Algeria", - "Angola", - "Benin", - "Botswana", - "Burkina Faso", - "Burundi", - "Cameroon", - "Central African Republic", - "Chad", - "Comoros", - "Congo, Dem. Rep.", - "Congo, Rep.", - "Cote d'Ivoire", - "Djibouti", - "Egypt", - "Equatorial Guinea", - "Eritrea", - "Ethiopia", - "Gabon", - "Gambia", - "Ghana", - "Guinea", - "Guinea-Bissau", - "Kenya", - "Lesotho", - "Liberia", - "Libya", - "Madagascar", - "Malawi", - "Mali", - "Mauritania", - "Mauritius", - "Morocco", - "Mozambique", - "Namibia", - "Niger", - "Nigeria", - "Reunion", - "Rwanda", - "Sao Tome and Principe", - "Senegal", - "Sierra Leone", - "Somalia", - "South Africa", - "Sudan", - "Swaziland", - "Tanzania", - "Togo", - "Tunisia", - "Uganda", - "Zambia", - "Zimbabwe" - ], - "legendgroup": "Africa", - "marker": { - "color": "#00cc96", - "size": [ - 33333216, - 12420476, - 8078314, - 1639131, - 14326203, - 8390505, - 17696293, - 4369038, - 10238807, - 710960, - 64606759, - 3800610, - 18013409, - 496374, - 80264543, - 551201, - 4906585, - 76511887, - 1454867, - 1688359, - 22873338, - 9947814, - 1472041, - 35610177, - 2012649, - 3193942, - 6036914, - 19167654, - 13327079, - 12031795, - 3270065, - 1250882, - 33757175, - 19951656, - 2055080, - 12894865, - 135031164, - 798094, - 8860588, - 199579, - 12267493, - 6144562, - 9118773, - 43997828, - 42292929, - 1133066, - 38139640, - 5701579, - 10276158, - 29170398, - 11746035, - 12311143 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Africa", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 6223.367465, - 4797.231267, - 1441.284873, - 12569.85177, - 1217.032994, - 430.0706916, - 2042.09524, - 706.016537, - 1704.063724, - 986.1478792, - 277.5518587, - 3632.557798, - 1544.750112, - 2082.4815670000007, - 5581.180998, - 12154.08975, - 641.3695236000002, - 690.8055759, - 13206.48452, - 752.7497265, - 1327.60891, - 942.6542111, - 579.2317429999998, - 1463.249282, - 1569.331442, - 414.5073415, - 12057.49928, - 1044.770126, - 759.3499101, - 1042.581557, - 1803.151496, - 10956.99112, - 3820.17523, - 823.6856205, - 4811.060429, - 619.6768923999998, - 2013.977305, - 7670.122558, - 863.0884639000002, - 1598.435089, - 1712.472136, - 862.5407561000002, - 926.1410683, - 9269.657808, - 2602.394995, - 4513.480643, - 1107.482182, - 882.9699437999999, - 7092.923025, - 1056.380121, - 1271.211593, - 469.70929810000007 - ], - "xaxis": "x", - "y": [ - 72.301, - 42.731, - 56.728, - 50.728, - 52.295, - 49.58, - 50.43, - 44.74100000000001, - 50.651, - 65.152, - 46.462, - 55.322, - 48.328, - 54.791, - 71.33800000000002, - 51.57899999999999, - 58.04, - 52.947, - 56.735, - 59.448, - 60.022, - 56.007, - 46.38800000000001, - 54.11, - 42.592, - 45.678, - 73.952, - 59.44300000000001, - 48.303, - 54.467, - 64.164, - 72.801, - 71.164, - 42.082, - 52.90600000000001, - 56.867, - 46.859, - 76.442, - 46.242, - 65.528, - 63.062, - 42.56800000000001, - 48.159, - 49.339, - 58.556, - 39.613, - 52.517, - 58.42, - 73.923, - 51.542, - 42.38399999999999, - 43.487 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Americas
year=2007
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Argentina", - "Bolivia", - "Brazil", - "Canada", - "Chile", - "Colombia", - "Costa Rica", - "Cuba", - "Dominican Republic", - "Ecuador", - "El Salvador", - "Guatemala", - "Haiti", - "Honduras", - "Jamaica", - "Mexico", - "Nicaragua", - "Panama", - "Paraguay", - "Peru", - "Puerto Rico", - "Trinidad and Tobago", - "United States", - "Uruguay", - "Venezuela" - ], - "ids": [ - "Argentina", - "Bolivia", - "Brazil", - "Canada", - "Chile", - "Colombia", - "Costa Rica", - "Cuba", - "Dominican Republic", - "Ecuador", - "El Salvador", - "Guatemala", - "Haiti", - "Honduras", - "Jamaica", - "Mexico", - "Nicaragua", - "Panama", - "Paraguay", - "Peru", - "Puerto Rico", - "Trinidad and Tobago", - "United States", - "Uruguay", - "Venezuela" - ], - "legendgroup": "Americas", - "marker": { - "color": "#ab63fa", - "size": [ - 40301927, - 9119152, - 190010647, - 33390141, - 16284741, - 44227550, - 4133884, - 11416987, - 9319622, - 13755680, - 6939688, - 12572928, - 8502814, - 7483763, - 2780132, - 108700891, - 5675356, - 3242173, - 6667147, - 28674757, - 3942491, - 1056608, - 301139947, - 3447496, - 26084662 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Americas", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 12779.37964, - 3822.137084, - 9065.800825, - 36319.23501, - 13171.63885, - 7006.580419, - 9645.06142, - 8948.102923, - 6025.3747520000015, - 6873.262326000001, - 5728.353514, - 5186.050003, - 1201.637154, - 3548.3308460000007, - 7320.8802620000015, - 11977.57496, - 2749.320965, - 9809.185636, - 4172.838464, - 7408.905561, - 19328.70901, - 18008.50924, - 42951.65309, - 10611.46299, - 11415.80569 - ], - "xaxis": "x", - "y": [ - 75.32, - 65.554, - 72.39, - 80.653, - 78.553, - 72.889, - 78.782, - 78.273, - 72.235, - 74.994, - 71.878, - 70.259, - 60.916, - 70.19800000000001, - 72.567, - 76.195, - 72.899, - 75.53699999999998, - 71.752, - 71.421, - 78.74600000000002, - 69.819, - 78.242, - 76.384, - 73.747 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

continent=Oceania
year=2007
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}", - "hovertext": [ - "Australia", - "New Zealand" - ], - "ids": [ - "Australia", - "New Zealand" - ], - "legendgroup": "Oceania", - "marker": { - "color": "#FFA15A", - "size": [ - 20434176, - 4115771 - ], - "sizemode": "area", - "sizeref": 435928.2961983471, - "symbol": "circle" - }, - "mode": "markers", - "name": "Oceania", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 34435.367439999995, - 25185.00911 - ], - "xaxis": "x", - "y": [ - 81.235, - 80.204 - ], - "yaxis": "y" - } - ], - "name": "2007" - } - ], - "layout": { - "height": 600, - "legend": { - "itemsizing": "constant", - "title": { - "text": "continent" - }, - "tracegroupgap": 0 - }, - "margin": { - "t": 60 - }, - "sliders": [ - { - "active": 0, - "currentvalue": { - "prefix": "year=" - }, - "len": 0.9, - "pad": { - "b": 10, - "t": 60 - }, - "steps": [ - { - "args": [ - [ - "1952" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "1952", - "method": "animate" - }, - { - "args": [ - [ - "1957" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "1957", - "method": "animate" - }, - { - "args": [ - [ - "1962" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "1962", - "method": "animate" - }, - { - "args": [ - [ - "1967" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "1967", - "method": "animate" - }, - { - "args": [ - [ - "1972" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "1972", - "method": "animate" - }, - { - "args": [ - [ - "1977" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "1977", - "method": "animate" - }, - { - "args": [ - [ - "1982" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "1982", - "method": "animate" - }, - { - "args": [ - [ - "1987" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "1987", - "method": "animate" - }, - { - "args": [ - [ - "1992" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "1992", - "method": "animate" - }, - { - "args": [ - [ - "1997" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "1997", - "method": "animate" - }, - { - "args": [ - [ - "2002" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "2002", - "method": "animate" - }, - { - "args": [ - [ - "2007" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "2007", - "method": "animate" - } - ], - "x": 0.1, - "xanchor": "left", - "y": 0, - "yanchor": "top" - } - ], - "template": { - "data": { - "bar": [ - { - "error_x": { - "color": "#2a3f5f" - }, - "error_y": { - "color": "#2a3f5f" - }, - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "bar" - } - ], - "barpolar": [ - { - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "barpolar" - } - ], - "carpet": [ - { - "aaxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "baxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "type": "carpet" - } - ], - "choropleth": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "choropleth" - } - ], - "contour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "contour" - } - ], - "contourcarpet": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "contourcarpet" - } - ], - "heatmap": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmap" - } - ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmapgl" - } - ], - "histogram": [ - { - "marker": { - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "histogram" - } - ], - "histogram2d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2d" - } - ], - "histogram2dcontour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2dcontour" - } - ], - "mesh3d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "mesh3d" - } - ], - "parcoords": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "parcoords" - } - ], - "pie": [ - { - "automargin": true, - "type": "pie" - } - ], - "scatter": [ - { - "fillpattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - }, - "type": "scatter" - } - ], - "scatter3d": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatter3d" - } - ], - "scattercarpet": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattercarpet" - } - ], - "scattergeo": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergeo" - } - ], - "scattergl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergl" - } - ], - "scattermapbox": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattermapbox" - } - ], - "scatterpolar": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolar" - } - ], - "scatterpolargl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolargl" - } - ], - "scatterternary": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterternary" - } - ], - "surface": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "surface" - } - ], - "table": [ - { - "cells": { - "fill": { - "color": "#EBF0F8" - }, - "line": { - "color": "white" - } - }, - "header": { - "fill": { - "color": "#C8D4E3" - }, - "line": { - "color": "white" - } - }, - "type": "table" - } - ] - }, - "layout": { - "annotationdefaults": { - "arrowcolor": "#2a3f5f", - "arrowhead": 0, - "arrowwidth": 1 - }, - "autotypenumbers": "strict", - "coloraxis": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "colorscale": { - "diverging": [ - [ - 0, - "#8e0152" - ], - [ - 0.1, - "#c51b7d" - ], - [ - 0.2, - "#de77ae" - ], - [ - 0.3, - "#f1b6da" - ], - [ - 0.4, - "#fde0ef" - ], - [ - 0.5, - "#f7f7f7" - ], - [ - 0.6, - "#e6f5d0" - ], - [ - 0.7, - "#b8e186" - ], - [ - 0.8, - "#7fbc41" - ], - [ - 0.9, - "#4d9221" - ], - [ - 1, - "#276419" - ] - ], - "sequential": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "sequentialminus": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ] - }, - "colorway": [ - "#636efa", - "#EF553B", - "#00cc96", - "#ab63fa", - "#FFA15A", - "#19d3f3", - "#FF6692", - "#B6E880", - "#FF97FF", - "#FECB52" - ], - "font": { - "color": "#2a3f5f" - }, - "geo": { - "bgcolor": "white", - "lakecolor": "white", - "landcolor": "#E5ECF6", - "showlakes": true, - "showland": true, - "subunitcolor": "white" - }, - "hoverlabel": { - "align": "left" - }, - "hovermode": "closest", - "mapbox": { - "style": "light" - }, - "paper_bgcolor": "white", - "plot_bgcolor": "#E5ECF6", - "polar": { - "angularaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "radialaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "width": 800, - "xaxis": { - "anchor": "y", - "domain": [ - 0, - 1 - ], - "range": [ - 2, - 5 - ], - "title": { - "text": "gdpPercap" - }, - "type": "log" - }, - "yaxis": { - "anchor": "x", - "domain": [ - 0, - 1 - ], - "range": [ - 25, - 90 - ], - "title": { - "text": "lifeExp" - }, - "type": "linear" - } - } - }, - "image/png": "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", - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df = px.data.gapminder()\n", - "fig = px.scatter(df, x=\"gdpPercap\", y=\"lifeExp\", animation_frame=\"year\", animation_group=\"country\",\n", - " size=\"pop\", color=\"continent\", hover_name=\"country\",\n", - " log_x=True, size_max=55, range_x=[100,100000], range_y=[25,90], width=800, height=600)\n", - "\n", - "fig[\"layout\"].pop(\"updatemenus\") # optional, drop animation buttons\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's try to create such a plot with some CBS data! To do so, we will use Statline data that I have already collected for you. The information we have collected contains a timeserie of data on *population*, *building stock* and the number of *primary school students* within each municipality between 2000 and 2020. The **.csv** file is a direct excerpt from Statline, I did not change anything to the data. \n", - "\n", - "We start with loading a file that allows us to connect the names of municipalities with each province." - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "gem_prov = pd.read_excel('Gemeenten alfabetisch 2022.xlsx')[['Gemeentenaam','Provincienaam']]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And now we load the data that I downloaded direct from [Statline](https://opendata.cbs.nl/statline): " - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "bevolking_gemeente = pd.read_csv('Regionale_kerncijfers_Nederland_17032023_094145.csv',sep=';')\n", - "bevolking_gemeente.dropna(inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "However, the column names are somewhat long and inconvenient to plot, so let's rename those column names:" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "bevolking_gemeente.columns= ['Year','Gemeente','Population','Building stock','Primary School Students']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And now we merge the Statline information with the dataframe containing municapility and province names." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "bevolking_gemeente = bevolking_gemeente.merge(gem_prov,left_on='Gemeente',right_on='Gemeentenaam')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And use the same code as above, but now with our own data:" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "hovertemplate": "%{hovertext}

Provincienaam=Drenthe
Year=2021
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "ids": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "legendgroup": "Drenthe", - "marker": { - "color": "#636efa", - "size": [ - 25399, - 68836, - 25598, - 35317, - 107024, - 55603, - 34386, - 33381, - 31214, - 33978, - 19661, - 24374 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Drenthe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11207, - 32538, - 11224, - 15580, - 49528, - 25496, - 16106, - 14413, - 14855, - 14596, - 8553, - 10460 - ], - "xaxis": "x", - "y": [ - 1591, - 5561, - 1696, - 2586, - 7965, - 4658, - 2944, - 2328, - 2304, - 3281, - 1241, - 1772 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Holland
Year=2021
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Gooise Meren", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hollands Kroon", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "ids": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Gooise Meren", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hollands Kroon", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "legendgroup": "Noord-Holland", - "marker": { - "color": "#EF553B", - "size": [ - 31991, - 109896, - 90829, - 873338, - 29715, - 41863, - 11954, - 23478, - 36086, - 31334, - 19838, - 36268, - 18637, - 58524, - 162543, - 157789, - 39191, - 27545, - 24144, - 56582, - 91235, - 48583, - 73619, - 41090, - 22940, - 11565, - 45165, - 9689, - 12009, - 14125, - 81683, - 46532, - 21743, - 13656, - 13632, - 30206, - 68617, - 17312, - 20445, - 24463, - 16333, - 156901, - 17168 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 13284, - 51656, - 42251, - 449989, - 15353, - 19511, - 5175, - 10166, - 16231, - 15467, - 8410, - 15432, - 8880, - 26896, - 77201, - 65214, - 17607, - 12817, - 10939, - 28390, - 42979, - 20988, - 33656, - 19018, - 9608, - 4897, - 19211, - 4100, - 4974, - 6301, - 37124, - 20581, - 9253, - 6770, - 5728, - 13264, - 31439, - 7411, - 9386, - 10800, - 6919, - 69114, - 9706 - ], - "xaxis": "x", - "y": [ - 2757, - 8313, - 7165, - 59846, - 1821, - 3078, - 1085, - 3300, - 2825, - 2349, - 1569, - 2749, - 1401, - 5818, - 13643, - 13115, - 3148, - 2543, - 1829, - 4105, - 7921, - 3637, - 6397, - 3028, - 1809, - 1084, - 3542, - 863, - 875, - 1219, - 5650, - 3341, - 1802, - 879, - 1104, - 2499, - 5009, - 1465, - 1822, - 1932, - 1225, - 12689, - 1012 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Gelderland
Year=2021
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berg en Dal", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Betuwe", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "ids": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berg en Dal", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Betuwe", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "legendgroup": "Gelderland", - "marker": { - "color": "#00cc96", - "size": [ - 27120, - 164781, - 162424, - 59992, - 35010, - 43846, - 26157, - 36087, - 20884, - 27009, - 29121, - 11064, - 58270, - 18991, - 25066, - 118530, - 23429, - 33198, - 27016, - 48726, - 12228, - 18776, - 16569, - 46822, - 33948, - 25452, - 36031, - 24648, - 43600, - 177359, - 28021, - 23760, - 29574, - 39346, - 48214, - 24365, - 31417, - 43525, - 1726, - 10128, - 41920, - 24790, - 39635, - 51496, - 19581, - 15014, - 41261, - 29022, - 29447, - 44096, - 48111 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Gelderland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 12028, - 74184, - 77839, - 23146, - 15854, - 19594, - 11262, - 16213, - 9416, - 10933, - 12729, - 5313, - 26509, - 8202, - 10760, - 50056, - 9595, - 14697, - 11360, - 20677, - 5262, - 8017, - 7274, - 20056, - 15165, - 10207, - 16016, - 8912, - 17349, - 82317, - 11199, - 9475, - 12949, - 17454, - 20128, - 9904, - 15003, - 21281, - 694, - 4114, - 18271, - 10744, - 18086, - 20996, - 8375, - 6744, - 18190, - 13245, - 11852, - 20227, - 22651 - ], - "xaxis": "x", - "y": [ - 2134, - 12514, - 12351, - 6886, - 2165, - 2838, - 1907, - 2184, - 1336, - 1983, - 2394, - 573, - 4617, - 1417, - 1826, - 10707, - 2225, - 2482, - 2072, - 4606, - 1110, - 1517, - 1203, - 3801, - 2353, - 1982, - 2344, - 2631, - 4051, - 12064, - 2606, - 2188, - 2079, - 2753, - 4402, - 2084, - 2108, - 3057, - 229, - 1175, - 3089, - 2339, - 2466, - 4351, - 1343, - 1144, - 3011, - 1984, - 2597, - 3353, - 3530 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Fryslân
Year=2021
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Achtkarspelen", - "Ameland", - "Dantumadiel", - "De Fryske Marren", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Noardeast-Fryslân", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Súdwest-Fryslân", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Waadhoeke", - "Weststellingwerf" - ], - "ids": [ - "Achtkarspelen", - "Ameland", - "Dantumadiel", - "De Fryske Marren", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Noardeast-Fryslân", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Súdwest-Fryslân", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Waadhoeke", - "Weststellingwerf" - ], - "legendgroup": "Fryslân", - "marker": { - "color": "#ab63fa", - "size": [ - 27900, - 3746, - 18943, - 51778, - 15807, - 50650, - 124481, - 45481, - 25464, - 29812, - 931, - 56040, - 89999, - 4870, - 32060, - 1194, - 46149, - 26130 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Fryslân", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 12181, - 1753, - 8202, - 23460, - 7909, - 23767, - 63067, - 20690, - 11419, - 13202, - 588, - 25833, - 42501, - 2293, - 14093, - 603, - 21375, - 11757 - ], - "xaxis": "x", - "y": [ - 2431, - 295, - 1638, - 3987, - 1153, - 4163, - 9337, - 3790, - 1901, - 2308, - 48, - 4434, - 7062, - 320, - 2579, - 52, - 3574, - 1896 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zuid-Holland
Year=2021
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Bodegraven-Reeuwijk", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Goeree-Overflakkee", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Hoeksche Waard", - "Kaag en Braassem", - "Katwijk", - "Krimpen aan den IJssel", - "Krimpenerwaard", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Molenlanden", - "Nieuwkoop", - "Nissewaard", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zuidplas", - "Zwijndrecht" - ], - "ids": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Bodegraven-Reeuwijk", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Goeree-Overflakkee", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Hoeksche Waard", - "Kaag en Braassem", - "Katwijk", - "Krimpen aan den IJssel", - "Krimpenerwaard", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Molenlanden", - "Nieuwkoop", - "Nissewaard", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zuidplas", - "Zwijndrecht" - ], - "legendgroup": "Zuid-Holland", - "marker": { - "color": "#FFA15A", - "size": [ - 20136, - 25814, - 112587, - 48643, - 35278, - 17439, - 67319, - 103581, - 119115, - 50589, - 37410, - 73681, - 18413, - 40312, - 31258, - 22197, - 88047, - 27541, - 65995, - 29410, - 56622, - 63363, - 124093, - 27377, - 76433, - 22982, - 33567, - 19414, - 44130, - 29151, - 85440, - 44062, - 25064, - 32171, - 55674, - 46671, - 651631, - 79279, - 25597, - 37791, - 73924, - 25650, - 30479, - 26949, - 111382, - 14900, - 125267, - 8843, - 45064, - 44775 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zuid-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 8408, - 10573, - 48893, - 19433, - 14676, - 8110, - 31556, - 51495, - 55813, - 22098, - 17288, - 33725, - 7514, - 18067, - 12332, - 9863, - 38203, - 11895, - 27165, - 12648, - 23944, - 24686, - 60538, - 12379, - 36955, - 10411, - 15389, - 7996, - 17340, - 11660, - 39469, - 20120, - 11014, - 14479, - 21798, - 21308, - 317945, - 37472, - 11086, - 16286, - 35551, - 11504, - 12796, - 12287, - 45956, - 6943, - 56421, - 3740, - 18566, - 20720 - ], - "xaxis": "x", - "y": [ - 1949, - 2477, - 9547, - 4716, - 3202, - 1338, - 5195, - 6688, - 9825, - 4090, - 3249, - 6336, - 1725, - 3061, - 3255, - 1537, - 7112, - 2107, - 5748, - 2630, - 4462, - 6813, - 7937, - 2486, - 5726, - 1831, - 2550, - 2361, - 4083, - 2206, - 6940, - 3421, - 2782, - 2628, - 5304, - 4010, - 50878, - 6770, - 2286, - 2899, - 5938, - 2346, - 2516, - 2046, - 9910, - 968, - 10991, - 589, - 4284, - 3683 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Overijssel
Year=2021
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "ids": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "legendgroup": "Overijssel", - "marker": { - "color": "#19d3f3", - "size": [ - 73132, - 23668, - 28901, - 101236, - 26606, - 159732, - 24229, - 61357, - 35932, - 35040, - 54474, - 22888, - 31701, - 18361, - 18295, - 37911, - 38204, - 17261, - 44341, - 21315, - 33699, - 24538, - 22823, - 129840 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Overijssel", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 33827, - 10105, - 12056, - 46224, - 11047, - 75925, - 10571, - 25177, - 14959, - 15279, - 22785, - 9863, - 14662, - 7828, - 7601, - 16049, - 14867, - 5988, - 20276, - 8720, - 13847, - 9930, - 9013, - 59491 - ], - "xaxis": "x", - "y": [ - 5464, - 2132, - 2510, - 8154, - 1941, - 11945, - 1740, - 5365, - 2794, - 2532, - 5216, - 1690, - 2485, - 1176, - 1519, - 3034, - 3853, - 2022, - 3492, - 1795, - 2946, - 2222, - 2346, - 11313 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Flevoland
Year=2021
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "ids": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "legendgroup": "Flevoland", - "marker": { - "color": "#FF6692", - "size": [ - 214715, - 42011, - 79811, - 47583, - 21227, - 22879 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Flevoland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 87259, - 17730, - 33734, - 20336, - 6763, - 8837 - ], - "xaxis": "x", - "y": [ - 19944, - 3607, - 7174, - 4354, - 3034, - 1999 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Brabant
Year=2021
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alphen-Chaam", - "Altena", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Meierijstad", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "ids": [ - "Alphen-Chaam", - "Altena", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Meierijstad", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "legendgroup": "Noord-Brabant", - "marker": { - "color": "#B6E880", - "size": [ - 10373, - 56352, - 16817, - 6899, - 18754, - 67514, - 31455, - 30216, - 20529, - 10959, - 32973, - 184126, - 21001, - 32437, - 26368, - 27325, - 19528, - 235691, - 43869, - 21770, - 40066, - 30760, - 26723, - 23952, - 30430, - 16243, - 92627, - 155490, - 45005, - 15698, - 22805, - 23504, - 81647, - 37185, - 23702, - 18842, - 32373, - 56206, - 92526, - 13127, - 77200, - 23080, - 29498, - 19428, - 17552, - 24310, - 221947, - 31221, - 45500, - 31669, - 17544, - 48815, - 22028, - 21988 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Brabant", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 4432, - 22587, - 7069, - 2916, - 8027, - 30921, - 12798, - 12977, - 8841, - 4397, - 14390, - 85230, - 9044, - 14052, - 11456, - 11845, - 8511, - 114398, - 19154, - 9648, - 18078, - 12649, - 11392, - 10423, - 13488, - 6925, - 41222, - 73424, - 19357, - 6865, - 9793, - 10478, - 34895, - 16629, - 10450, - 8036, - 14130, - 25194, - 40746, - 5523, - 35618, - 9916, - 12274, - 8181, - 7318, - 10484, - 102471, - 14885, - 20085, - 13724, - 7832, - 21996, - 9928, - 9649 - ], - "xaxis": "x", - "y": [ - 727, - 5026, - 1180, - 416, - 1278, - 5241, - 2372, - 2217, - 1601, - 850, - 2374, - 14618, - 1467, - 2511, - 1899, - 1914, - 1495, - 17068, - 3578, - 1665, - 2949, - 2596, - 2179, - 2341, - 2233, - 1194, - 7661, - 12380, - 3566, - 1175, - 1641, - 1701, - 6256, - 2655, - 1816, - 1410, - 2365, - 4191, - 6857, - 1134, - 5850, - 1478, - 2448, - 1429, - 1564, - 1621, - 15812, - 2074, - 3382, - 2904, - 1452, - 3694, - 1359, - 1359 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Utrecht
Year=2021
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Stichtse Vecht", - "Utrechtse Heuvelrug", - "Veenendaal", - "Vijfheerenlanden", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "ids": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Stichtse Vecht", - "Utrechtse Heuvelrug", - "Veenendaal", - "Vijfheerenlanden", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "legendgroup": "Utrecht", - "marker": { - "color": "#FF97FF", - "size": [ - 157462, - 24792, - 43384, - 15341, - 22019, - 9362, - 50223, - 33819, - 30544, - 14456, - 13896, - 63866, - 10138, - 5556, - 20203, - 44720, - 46906, - 65108, - 49946, - 66912, - 57829, - 23925, - 52694, - 13639, - 65043 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Utrecht", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 68809, - 11700, - 19738, - 6484, - 8550, - 4091, - 20681, - 14444, - 13229, - 5838, - 5756, - 29445, - 4323, - 2154, - 8287, - 18580, - 20953, - 28739, - 21890, - 28607, - 24196, - 10273, - 22437, - 5286, - 29638 - ], - "xaxis": "x", - "y": [ - 14233, - 1969, - 4138, - 1456, - 2098, - 710, - 4729, - 2707, - 2519, - 1088, - 1209, - 4972, - 881, - 405, - 1650, - 3400, - 3663, - 5660, - 4148, - 6340, - 5151, - 1960, - 4766, - 1291, - 5640 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Limburg
Year=2021
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Beekdaelen", - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Eijsden-Margraten", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Peel en Maas", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "ids": [ - "Beekdaelen", - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Eijsden-Margraten", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Peel en Maas", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "legendgroup": "Limburg", - "marker": { - "color": "#FECB52", - "size": [ - 36065, - 13450, - 13108, - 27670, - 31751, - 25900, - 17035, - 14206, - 86936, - 42487, - 45442, - 37262, - 36045, - 23947, - 120227, - 18661, - 7909, - 17171, - 43660, - 20580, - 58763, - 10477, - 91743, - 10084, - 16365, - 101988, - 43713, - 12466, - 50011 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Limburg", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 16711, - 6040, - 5793, - 14464, - 14713, - 11367, - 7490, - 6880, - 45982, - 17937, - 23937, - 18152, - 16157, - 10965, - 62839, - 8640, - 3667, - 7534, - 18527, - 9554, - 27749, - 5185, - 46709, - 5822, - 8337, - 47809, - 19300, - 5813, - 23236 - ], - "xaxis": "x", - "y": [ - 2409, - 880, - 835, - 1920, - 1963, - 1746, - 1186, - 776, - 5567, - 3005, - 2610, - 2381, - 2050, - 1344, - 6449, - 1187, - 439, - 1148, - 3067, - 1288, - 4466, - 618, - 5717, - 463, - 795, - 7124, - 3017, - 928, - 3521 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zeeland
Year=2021
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "ids": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "legendgroup": "Zeeland", - "marker": { - "color": "#636efa", - "size": [ - 22818, - 38594, - 27575, - 12882, - 7581, - 22896, - 34065, - 23166, - 54463, - 26085, - 21953, - 44358 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zeeland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 9850, - 18633, - 13618, - 5459, - 4553, - 9332, - 16983, - 14152, - 27080, - 11112, - 10997, - 22885 - ], - "xaxis": "x", - "y": [ - 1849, - 3012, - 1544, - 1100, - 429, - 2320, - 2447, - 1277, - 3625, - 2422, - 1607, - 3123 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Groningen
Year=2021
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Eemsdelta", - "Het Hogeland", - "Midden-Groningen", - "Oldambt", - "Pekela", - "Stadskanaal", - "Veendam", - "Westerkwartier", - "Westerwolde" - ], - "ids": [ - "Eemsdelta", - "Het Hogeland", - "Midden-Groningen", - "Oldambt", - "Pekela", - "Stadskanaal", - "Veendam", - "Westerkwartier", - "Westerwolde" - ], - "legendgroup": "Groningen", - "marker": { - "color": "#EF553B", - "size": [ - 45587, - 47834, - 60726, - 38277, - 12176, - 31754, - 27417, - 63678, - 26215 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Groningen", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 23083, - 22878, - 28154, - 18648, - 5782, - 15208, - 12906, - 27494, - 11937 - ], - "xaxis": "x", - "y": [ - 3251, - 3516, - 4276, - 2548, - 958, - 2392, - 2030, - 5396, - 1700 - ], - "yaxis": "y" - } - ], - "frames": [ - { - "data": [ - { - "hovertemplate": "%{hovertext}

Provincienaam=Drenthe
Year=2000
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "ids": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "legendgroup": "Drenthe", - "marker": { - "color": "#636efa", - "size": [ - 24824, - 58445, - 26201, - 34821, - 105972, - 52790, - 29652, - 32394, - 30740, - 31508, - 18888, - 23571 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Drenthe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 9964, - 24943, - 10586, - 14048, - 44065, - 21182, - 12548, - 12532, - 12392, - 12697, - 7651, - 9017 - ], - "xaxis": "x", - "y": [ - 2329, - 5896, - 2598, - 3364, - 9981, - 5235, - 3044, - 3271, - 3102, - 3256, - 1610, - 2360 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Holland
Year=2000
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hoorn", - "Huizen", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "ids": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hoorn", - "Huizen", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "legendgroup": "Noord-Holland", - "marker": { - "color": "#EF553B", - "size": [ - 22461, - 92836, - 77623, - 731288, - 13736, - 35802, - 9525, - 16716, - 22907, - 23807, - 9878, - 27460, - 16946, - 148484, - 111155, - 35921, - 25879, - 21751, - 59441, - 82177, - 64604, - 42141, - 10265, - 7601, - 8921, - 10848, - 12784, - 70284, - 17173, - 21179, - 13425, - 11312, - 26090, - 66553, - 17389, - 18111, - 15202, - 135762, - 15877 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 8759, - 39891, - 35946, - 371800, - 6068, - 15996, - 3864, - 6752, - 9274, - 9940, - 3606, - 10664, - 7271, - 67093, - 43492, - 14528, - 10743, - 8911, - 26003, - 37154, - 27082, - 16891, - 4121, - 3158, - 3558, - 3995, - 5512, - 29219, - 7046, - 7922, - 5595, - 4441, - 10842, - 28386, - 6802, - 8078, - 6059, - 56722, - 7722 - ], - "xaxis": "x", - "y": [ - 2260, - 8678, - 6181, - 57023, - 1171, - 2894, - 993, - 2603, - 2310, - 2575, - 1110, - 2758, - 1742, - 11866, - 11731, - 4119, - 2422, - 2157, - 5813, - 7240, - 6663, - 4311, - 1130, - 774, - 936, - 1295, - 1538, - 7499, - 1597, - 2199, - 1474, - 1213, - 2605, - 6586, - 1894, - 1256, - 1499, - 12700, - 1144 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Gelderland
Year=2000
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Beuningen", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lochem", - "Maasdriel", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "ids": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Beuningen", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lochem", - "Maasdriel", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "legendgroup": "Gelderland", - "marker": { - "color": "#00cc96", - "size": [ - 18699, - 153261, - 138154, - 47744, - 25422, - 21548, - 24990, - 24768, - 11434, - 46967, - 16751, - 25182, - 101700, - 21624, - 33081, - 26760, - 39745, - 11637, - 18130, - 16237, - 19189, - 23045, - 36315, - 152200, - 26149, - 22374, - 22697, - 32155, - 44313, - 1422, - 9090, - 38326, - 23685, - 33440, - 18092, - 16225, - 38112, - 28561, - 25545, - 26299, - 35165 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Gelderland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 7028, - 61895, - 63102, - 15923, - 9495, - 8291, - 8841, - 9703, - 4604, - 19385, - 6237, - 9519, - 37654, - 7646, - 12767, - 8830, - 14966, - 4643, - 6900, - 6101, - 7366, - 8531, - 13131, - 64645, - 8990, - 7158, - 7635, - 13683, - 19091, - 601, - 3241, - 15206, - 8421, - 12321, - 6802, - 6047, - 14646, - 11393, - 9184, - 10918, - 15533 - ], - "xaxis": "x", - "y": [ - 2028, - 13872, - 11434, - 6362, - 2934, - 2095, - 2441, - 2896, - 1139, - 4792, - 1736, - 3135, - 11397, - 2320, - 3069, - 2663, - 4229, - 1229, - 1706, - 2060, - 1769, - 2397, - 3826, - 12237, - 3028, - 2468, - 2484, - 2745, - 3689, - 212, - 1147, - 4005, - 2929, - 2575, - 1843, - 1993, - 4082, - 2713, - 2686, - 2443, - 3210 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Fryslân
Year=2000
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Achtkarspelen", - "Ameland", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "ids": [ - "Achtkarspelen", - "Ameland", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "legendgroup": "Fryslân", - "marker": { - "color": "#ab63fa", - "size": [ - 28011, - 3488, - 15426, - 40437, - 88887, - 25790, - 28769, - 1000, - 52437, - 4723, - 31037, - 1201, - 24990 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Fryslân", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 10894, - 1735, - 6562, - 17768, - 42549, - 10459, - 11237, - 524, - 21817, - 1891, - 12124, - 512, - 10435 - ], - "xaxis": "x", - "y": [ - 2848, - 408, - 1555, - 3798, - 7390, - 2649, - 3079, - 94, - 5196, - 411, - 3098, - 157, - 2266 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zuid-Holland
Year=2000
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Katwijk", - "Krimpen aan den IJssel", - "Leiden", - "Leiderdorp", - "Lisse", - "Maassluis", - "Nieuwkoop", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zwijndrecht" - ], - "ids": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Katwijk", - "Krimpen aan den IJssel", - "Leiden", - "Leiderdorp", - "Lisse", - "Maassluis", - "Nieuwkoop", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zwijndrecht" - ], - "legendgroup": "Zuid-Holland", - "marker": { - "color": "#FFA15A", - "size": [ - 18183, - 16420, - 69928, - 28848, - 15869, - 64251, - 96095, - 119821, - 33602, - 71918, - 17760, - 38358, - 20986, - 20664, - 40753, - 28705, - 117191, - 25279, - 21942, - 33110, - 11070, - 24879, - 20785, - 29694, - 46839, - 592673, - 75589, - 23703, - 73535, - 22662, - 26644, - 25999, - 13857, - 109941, - 8679, - 41515 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zuid-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 7290, - 6416, - 27834, - 11286, - 6575, - 28278, - 42456, - 51694, - 14863, - 29848, - 6468, - 15254, - 8036, - 8311, - 14985, - 11114, - 49760, - 10595, - 8785, - 13940, - 4068, - 10063, - 8285, - 12276, - 19727, - 283667, - 34689, - 9588, - 34064, - 9445, - 10169, - 11519, - 5663, - 44604, - 3067, - 17354 - ], - "xaxis": "x", - "y": [ - 2020, - 1712, - 7309, - 3425, - 1505, - 6375, - 8085, - 11740, - 3381, - 7777, - 2099, - 4016, - 2432, - 1955, - 4608, - 3071, - 9024, - 2929, - 2509, - 3017, - 1255, - 2200, - 2593, - 2871, - 4134, - 53975, - 7423, - 2317, - 6556, - 2411, - 2847, - 2132, - 1291, - 12011, - 935, - 4187 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Overijssel
Year=2000
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Kampen", - "Losser", - "Oldenzaal", - "Ommen", - "Raalte", - "Staphorst", - "Tubbergen", - "Wierden", - "Zwolle" - ], - "ids": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Kampen", - "Losser", - "Oldenzaal", - "Ommen", - "Raalte", - "Staphorst", - "Tubbergen", - "Wierden", - "Zwolle" - ], - "legendgroup": "Overijssel", - "marker": { - "color": "#19d3f3", - "size": [ - 66263, - 22316, - 17499, - 83956, - 149505, - 23885, - 35028, - 35674, - 32705, - 22595, - 30746, - 16686, - 28294, - 15245, - 19938, - 23392, - 105801 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Overijssel", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 28329, - 8618, - 6132, - 35778, - 63434, - 8992, - 12163, - 13219, - 13509, - 8235, - 12485, - 6007, - 10165, - 4820, - 6401, - 8210, - 44856 - ], - "xaxis": "x", - "y": [ - 6225, - 2461, - 1952, - 8168, - 13155, - 2318, - 4225, - 3384, - 3353, - 2142, - 3096, - 1928, - 3187, - 2080, - 2598, - 2485, - 9727 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Flevoland
Year=2000
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "ids": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "legendgroup": "Flevoland", - "marker": { - "color": "#FF6692", - "size": [ - 142765, - 35244, - 63098, - 43122, - 15719, - 17258 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Flevoland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 55600, - 12792, - 26528, - 16835, - 4435, - 5843 - ], - "xaxis": "x", - "y": [ - 19247, - 4387, - 6445, - 5172, - 2836, - 2883 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Brabant
Year=2000
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "ids": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "legendgroup": "Noord-Brabant", - "marker": { - "color": "#B6E880", - "size": [ - 9460, - 15779, - 6192, - 17906, - 65104, - 28579, - 25944, - 18997, - 9184, - 29250, - 160615, - 20278, - 32028, - 24812, - 26643, - 18301, - 201728, - 37023, - 21139, - 27499, - 24241, - 22303, - 29645, - 15324, - 80098, - 129034, - 42779, - 14629, - 21536, - 22817, - 36312, - 23783, - 17683, - 25365, - 52291, - 65763, - 12434, - 75157, - 22477, - 27684, - 18277, - 14717, - 23338, - 193116, - 31089, - 42003, - 25334, - 16260, - 45333, - 20995, - 20155 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Brabant", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 3491, - 5694, - 2355, - 6566, - 27370, - 10068, - 10027, - 6880, - 3129, - 11368, - 69742, - 7959, - 11629, - 9522, - 10066, - 6522, - 89509, - 14506, - 8218, - 9821, - 9011, - 8420, - 11382, - 5746, - 33790, - 54513, - 15976, - 5327, - 7864, - 8720, - 14455, - 9042, - 6177, - 9648, - 21363, - 26563, - 4310, - 30763, - 8769, - 10177, - 6541, - 5889, - 9316, - 80650, - 12767, - 16624, - 9842, - 6641, - 18027, - 8350, - 7687 - ], - "xaxis": "x", - "y": [ - 934, - 1758, - 527, - 1909, - 6214, - 3099, - 2985, - 1954, - 1115, - 3156, - 14637, - 1971, - 3311, - 2489, - 2716, - 2037, - 16767, - 3652, - 1866, - 3062, - 2699, - 2106, - 2765, - 1573, - 8072, - 12014, - 4662, - 1654, - 2197, - 2258, - 3414, - 2454, - 1945, - 2748, - 5184, - 6374, - 1392, - 7474, - 2020, - 2666, - 2120, - 1545, - 2204, - 17352, - 2877, - 4250, - 2650, - 1697, - 4322, - 1850, - 2088 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Utrecht
Year=2000
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "ids": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "legendgroup": "Utrecht", - "marker": { - "color": "#FF97FF", - "size": [ - 126143, - 24508, - 32662, - 13916, - 19115, - 8619, - 33313, - 28714, - 28747, - 13374, - 13372, - 63118, - 9789, - 4005, - 17272, - 34202, - 44251, - 59875, - 23079, - 38095, - 10914, - 60020 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Utrecht", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 52322, - 10309, - 14528, - 5404, - 6475, - 3297, - 11828, - 11464, - 11010, - 4694, - 4825, - 25543, - 3698, - 1331, - 6655, - 12534, - 18316, - 22715, - 8596, - 14759, - 3936, - 24746 - ], - "xaxis": "x", - "y": [ - 12646, - 2173, - 3581, - 1498, - 2294, - 908, - 4870, - 3389, - 3131, - 1548, - 1583, - 5567, - 1064, - 343, - 1940, - 3656, - 4129, - 6969, - 2667, - 4184, - 1226, - 5046 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Limburg
Year=2000
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Kerkrade", - "Landgraaf", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Roerdalen", - "Roermond", - "Simpelveld", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "ids": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Kerkrade", - "Landgraaf", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Roerdalen", - "Roermond", - "Simpelveld", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "legendgroup": "Limburg", - "marker": { - "color": "#FECB52", - "size": [ - 13158, - 13362, - 30464, - 16811, - 15460, - 95147, - 51458, - 41009, - 122070, - 20267, - 7833, - 16129, - 10495, - 44952, - 11589, - 10821, - 17879, - 64864, - 37868, - 13066, - 47959 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Limburg", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5117, - 5052, - 13536, - 6105, - 6096, - 44153, - 22768, - 17324, - 53461, - 8025, - 3162, - 5888, - 4297, - 20075, - 4725, - 4569, - 7339, - 28695, - 14426, - 5186, - 19606 - ], - "xaxis": "x", - "y": [ - 1290, - 1437, - 2778, - 1594, - 1498, - 7734, - 3733, - 3587, - 9383, - 1887, - 753, - 1716, - 899, - 4179, - 994, - 663, - 1424, - 5974, - 4032, - 1291, - 4583 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zeeland
Year=2000
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "ids": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "legendgroup": "Zeeland", - "marker": { - "color": "#636efa", - "size": [ - 21867, - 35768, - 19547, - 11174, - 6901, - 20524, - 33601, - 34539, - 23618, - 22069, - 44342 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zeeland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 8657, - 15362, - 8378, - 4313, - 3761, - 7890, - 14669, - 15650, - 9216, - 9621, - 20268 - ], - "xaxis": "x", - "y": [ - 2387, - 3460, - 1612, - 1243, - 549, - 2369, - 3232, - 3409, - 2802, - 2275, - 4060 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Groningen
Year=2000
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Pekela", - "Stadskanaal", - "Veendam" - ], - "ids": [ - "Pekela", - "Stadskanaal", - "Veendam" - ], - "legendgroup": "Groningen", - "marker": { - "color": "#EF553B", - "size": [ - 13408, - 33097, - 28218 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Groningen", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5635, - 13802, - 11923 - ], - "xaxis": "x", - "y": [ - 1238, - 3074, - 2622 - ], - "yaxis": "y" - } - ], - "name": "2000" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

Provincienaam=Drenthe
Year=2001
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "ids": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "legendgroup": "Drenthe", - "marker": { - "color": "#636efa", - "size": [ - 25208, - 59006, - 26298, - 35502, - 107422, - 52782, - 30037, - 32813, - 30959, - 31752, - 18943, - 23784 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Drenthe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 10116, - 25285, - 10689, - 14120, - 44592, - 21277, - 12720, - 12553, - 12420, - 12793, - 7666, - 9061 - ], - "xaxis": "x", - "y": [ - 2305, - 6151, - 2631, - 3366, - 10046, - 5199, - 2988, - 3246, - 3067, - 3284, - 1626, - 2400 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Holland
Year=2001
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hoorn", - "Huizen", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "ids": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hoorn", - "Huizen", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "legendgroup": "Noord-Holland", - "marker": { - "color": "#EF553B", - "size": [ - 22662, - 93022, - 77370, - 734594, - 31687, - 35938, - 9376, - 16831, - 23132, - 23835, - 10049, - 27687, - 17033, - 148377, - 113553, - 36074, - 26003, - 21897, - 59822, - 82773, - 65764, - 42099, - 10243, - 7671, - 8909, - 11041, - 12988, - 72142, - 17126, - 21081, - 13535, - 11669, - 26237, - 66977, - 17224, - 18092, - 15383, - 136115, - 16297 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 8864, - 40042, - 36114, - 373198, - 13404, - 16014, - 3864, - 6767, - 9417, - 10001, - 3677, - 10823, - 7298, - 67192, - 44751, - 14785, - 10914, - 8986, - 26116, - 37410, - 27618, - 17076, - 4166, - 3161, - 3575, - 4076, - 5629, - 29940, - 7072, - 7946, - 5660, - 4534, - 11013, - 28276, - 6802, - 8081, - 6164, - 57124, - 7993 - ], - "xaxis": "x", - "y": [ - 2282, - 8408, - 6267, - 57119, - 2859, - 2840, - 991, - 2678, - 2291, - 2630, - 1107, - 2777, - 1705, - 11668, - 12118, - 4088, - 2448, - 2171, - 5689, - 7176, - 6658, - 4085, - 1129, - 781, - 1008, - 1329, - 1524, - 7530, - 1606, - 2224, - 1467, - 1286, - 2687, - 6658, - 1853, - 1251, - 1540, - 12935, - 1189 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Gelderland
Year=2001
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Beuningen", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lochem", - "Maasdriel", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "ids": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Beuningen", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lochem", - "Maasdriel", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "legendgroup": "Gelderland", - "marker": { - "color": "#00cc96", - "size": [ - 18773, - 153683, - 139329, - 48298, - 25439, - 21593, - 25239, - 25252, - 11518, - 47742, - 17054, - 25751, - 102405, - 21689, - 33156, - 26784, - 40186, - 11672, - 18083, - 16503, - 19179, - 23370, - 36721, - 153705, - 26139, - 22645, - 39731, - 22889, - 32236, - 44386, - 1506, - 9138, - 39608, - 23713, - 33826, - 18181, - 16153, - 38428, - 28672, - 25660, - 26191, - 35694 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Gelderland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 7045, - 62314, - 63541, - 16087, - 9537, - 8313, - 8912, - 9952, - 4634, - 19722, - 6358, - 9651, - 37983, - 7708, - 12790, - 8868, - 15074, - 4645, - 6908, - 6167, - 7359, - 8650, - 13478, - 65206, - 9015, - 7222, - 14884, - 7696, - 13681, - 19170, - 627, - 3260, - 15691, - 8436, - 12466, - 6829, - 6093, - 14760, - 11432, - 9295, - 10956, - 15647 - ], - "xaxis": "x", - "y": [ - 2018, - 14005, - 11609, - 6482, - 2896, - 2103, - 2434, - 2954, - 1142, - 4826, - 1790, - 3231, - 11309, - 2289, - 3070, - 2682, - 4219, - 1233, - 1685, - 2089, - 1763, - 2440, - 3890, - 12341, - 3014, - 2437, - 4275, - 2505, - 2760, - 3747, - 238, - 1191, - 4281, - 2908, - 2537, - 1831, - 1919, - 4057, - 2760, - 2649, - 2413, - 3361 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Fryslân
Year=2001
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Achtkarspelen", - "Ameland", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "ids": [ - "Achtkarspelen", - "Ameland", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "legendgroup": "Fryslân", - "marker": { - "color": "#ab63fa", - "size": [ - 28013, - 3545, - 15444, - 41250, - 89453, - 26238, - 28833, - 1017, - 53010, - 4768, - 31049, - 1215, - 25247 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Fryslân", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 10929, - 1756, - 6543, - 17895, - 42986, - 10610, - 11324, - 524, - 21990, - 1923, - 12239, - 521, - 10487 - ], - "xaxis": "x", - "y": [ - 2818, - 409, - 1512, - 3810, - 7485, - 2715, - 3064, - 96, - 5276, - 413, - 3116, - 154, - 2358 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zuid-Holland
Year=2001
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Katwijk", - "Krimpen aan den IJssel", - "Leiden", - "Leiderdorp", - "Lisse", - "Maassluis", - "Nieuwkoop", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zwijndrecht" - ], - "ids": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Katwijk", - "Krimpen aan den IJssel", - "Leiden", - "Leiderdorp", - "Lisse", - "Maassluis", - "Nieuwkoop", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zwijndrecht" - ], - "legendgroup": "Zuid-Holland", - "marker": { - "color": "#FFA15A", - "size": [ - 18410, - 17721, - 70162, - 30976, - 15965, - 65018, - 96180, - 120021, - 33994, - 71782, - 17811, - 38861, - 21179, - 20855, - 41005, - 28698, - 117022, - 26179, - 22002, - 32981, - 11019, - 24656, - 21117, - 30088, - 46542, - 595255, - 76102, - 23664, - 73675, - 22621, - 26843, - 26019, - 13899, - 110129, - 8683, - 41409 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zuid-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 7433, - 7036, - 27903, - 12006, - 6695, - 28521, - 42641, - 52034, - 14872, - 29880, - 6482, - 15497, - 8093, - 8383, - 15223, - 11122, - 49794, - 10874, - 8875, - 13775, - 4091, - 10075, - 8397, - 12434, - 19777, - 285041, - 34689, - 9620, - 34106, - 9471, - 10208, - 11519, - 5676, - 44924, - 3074, - 17422 - ], - "xaxis": "x", - "y": [ - 2042, - 1975, - 7261, - 3928, - 1518, - 6426, - 8013, - 11720, - 3495, - 7720, - 2087, - 3927, - 2670, - 1922, - 4485, - 2918, - 8987, - 3005, - 2524, - 3003, - 1260, - 2136, - 2581, - 2906, - 3900, - 53801, - 7462, - 2280, - 6621, - 2437, - 2811, - 2172, - 1320, - 11721, - 930, - 4054 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Overijssel
Year=2001
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Ommen", - "Raalte", - "Staphorst", - "Tubbergen", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "ids": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Ommen", - "Raalte", - "Staphorst", - "Tubbergen", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "legendgroup": "Overijssel", - "marker": { - "color": "#19d3f3", - "size": [ - 70416, - 20717, - 25842, - 85008, - 150449, - 24129, - 57254, - 35789, - 34855, - 47941, - 22757, - 31111, - 16772, - 36403, - 15329, - 20022, - 23415, - 22097, - 107373 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Overijssel", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 29623, - 8129, - 9222, - 36104, - 63898, - 9118, - 20343, - 13336, - 13645, - 18522, - 8322, - 12674, - 6068, - 13192, - 4881, - 6450, - 8254, - 7589, - 45430 - ], - "xaxis": "x", - "y": [ - 6502, - 2268, - 2951, - 8190, - 13181, - 2303, - 6470, - 3358, - 3483, - 5104, - 2074, - 3143, - 1883, - 4083, - 2091, - 2580, - 2461, - 2954, - 9904 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Flevoland
Year=2001
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "ids": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "legendgroup": "Flevoland", - "marker": { - "color": "#FF6692", - "size": [ - 150398, - 35591, - 64668, - 43669, - 16231, - 18379 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Flevoland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 58478, - 12930, - 27240, - 17039, - 4618, - 6237 - ], - "xaxis": "x", - "y": [ - 19882, - 4424, - 6631, - 5128, - 2846, - 2947 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Brabant
Year=2001
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "ids": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "legendgroup": "Noord-Brabant", - "marker": { - "color": "#B6E880", - "size": [ - 9486, - 15789, - 6312, - 17966, - 65363, - 28664, - 26825, - 18992, - 9230, - 29265, - 162308, - 20295, - 32045, - 24856, - 26624, - 18315, - 203397, - 37784, - 20989, - 27683, - 24544, - 22389, - 29502, - 15285, - 80932, - 130477, - 42935, - 14804, - 21540, - 22911, - 36456, - 23679, - 17703, - 25281, - 52749, - 66887, - 12457, - 76769, - 22586, - 27830, - 18452, - 14819, - 23354, - 195819, - 31008, - 42489, - 25469, - 16266, - 45278, - 21456, - 20272 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Brabant", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 3511, - 5734, - 2352, - 6635, - 27547, - 10102, - 10448, - 6932, - 3143, - 11419, - 70509, - 7999, - 11745, - 9569, - 10161, - 6569, - 90399, - 14848, - 8225, - 9886, - 9152, - 8477, - 11406, - 5760, - 34128, - 55536, - 16039, - 5391, - 7955, - 8794, - 14513, - 9051, - 6201, - 9735, - 21608, - 27159, - 4343, - 31714, - 8842, - 10299, - 6581, - 5913, - 9374, - 81948, - 12780, - 16898, - 9970, - 6677, - 18025, - 8431, - 7690 - ], - "xaxis": "x", - "y": [ - 955, - 1708, - 523, - 1935, - 6148, - 3126, - 3054, - 1973, - 1113, - 2908, - 14758, - 1937, - 3354, - 2602, - 2718, - 2077, - 16835, - 3766, - 1789, - 2991, - 2673, - 2119, - 2762, - 1624, - 8268, - 12289, - 4500, - 1670, - 2126, - 2256, - 3414, - 2426, - 1933, - 2661, - 5208, - 6420, - 1319, - 7509, - 2015, - 2913, - 2097, - 1552, - 2203, - 17409, - 2886, - 4259, - 2590, - 1683, - 4393, - 1856, - 2146 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Utrecht
Year=2001
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "ids": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "legendgroup": "Utrecht", - "marker": { - "color": "#FF97FF", - "size": [ - 128035, - 24526, - 42056, - 13774, - 19260, - 8660, - 35978, - 30923, - 28833, - 13514, - 13285, - 62345, - 9742, - 4055, - 17344, - 34226, - 44622, - 60392, - 23155, - 46921, - 11025, - 59844 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Utrecht", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 53069, - 10318, - 18205, - 5426, - 6591, - 3318, - 12857, - 12309, - 11132, - 4783, - 4973, - 25551, - 3702, - 1406, - 6685, - 12732, - 18618, - 22786, - 8787, - 18433, - 3992, - 24819 - ], - "xaxis": "x", - "y": [ - 13429, - 2159, - 4568, - 1446, - 2256, - 910, - 4934, - 3515, - 3093, - 1534, - 1559, - 5496, - 1090, - 310, - 1932, - 3664, - 4125, - 6965, - 2678, - 4989, - 1233, - 5001 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Limburg
Year=2001
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "ids": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "legendgroup": "Limburg", - "marker": { - "color": "#FECB52", - "size": [ - 13189, - 13444, - 30395, - 16822, - 15525, - 95149, - 28334, - 51066, - 40769, - 122163, - 20246, - 7978, - 16171, - 10528, - 45159, - 11500, - 97950, - 10838, - 17885, - 90500, - 38139, - 13142, - 48151 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Limburg", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5224, - 5074, - 13580, - 6100, - 6130, - 44636, - 10722, - 22943, - 17431, - 53753, - 8066, - 3213, - 5925, - 4216, - 20223, - 4754, - 42028, - 4574, - 7338, - 39168, - 14540, - 5245, - 19729 - ], - "xaxis": "x", - "y": [ - 1304, - 1427, - 2685, - 1584, - 1412, - 7558, - 3064, - 3608, - 3534, - 9288, - 1911, - 753, - 1699, - 888, - 4168, - 989, - 8724, - 669, - 1414, - 8277, - 4018, - 1297, - 4655 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zeeland
Year=2001
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "ids": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "legendgroup": "Zeeland", - "marker": { - "color": "#636efa", - "size": [ - 22009, - 35953, - 19691, - 11442, - 6971, - 20726, - 34194, - 34498, - 23763, - 22031, - 44776 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zeeland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 8754, - 15481, - 8462, - 4447, - 3762, - 7977, - 14763, - 15502, - 9261, - 9649, - 20626 - ], - "xaxis": "x", - "y": [ - 2438, - 3551, - 1617, - 1241, - 549, - 2410, - 3245, - 3391, - 2862, - 2275, - 4149 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Groningen
Year=2001
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Pekela", - "Stadskanaal", - "Veendam" - ], - "ids": [ - "Pekela", - "Stadskanaal", - "Veendam" - ], - "legendgroup": "Groningen", - "marker": { - "color": "#EF553B", - "size": [ - 13359, - 33517, - 28328 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Groningen", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5635, - 14041, - 11880 - ], - "xaxis": "x", - "y": [ - 1298, - 3152, - 2583 - ], - "yaxis": "y" - } - ], - "name": "2001" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

Provincienaam=Drenthe
Year=2002
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "ids": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "legendgroup": "Drenthe", - "marker": { - "color": "#636efa", - "size": [ - 25552, - 60230, - 26333, - 35756, - 108367, - 53186, - 30221, - 32691, - 31565, - 31865, - 19158, - 23875 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Drenthe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 10172, - 25512, - 10744, - 14151, - 44987, - 21260, - 12860, - 12553, - 12479, - 12874, - 7668, - 9148 - ], - "xaxis": "x", - "y": [ - 2307, - 6291, - 2654, - 3387, - 10072, - 5110, - 3011, - 3189, - 3137, - 3270, - 1636, - 2391 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Holland
Year=2002
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hoorn", - "Huizen", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "ids": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hoorn", - "Huizen", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "legendgroup": "Noord-Holland", - "marker": { - "color": "#EF553B", - "size": [ - 22786, - 92992, - 77256, - 735526, - 31926, - 35780, - 9343, - 17097, - 35177, - 23971, - 10078, - 27833, - 17076, - 147831, - 118553, - 36281, - 25898, - 21976, - 60083, - 83096, - 66458, - 42230, - 10296, - 7671, - 9062, - 11165, - 13029, - 73476, - 17214, - 21128, - 13728, - 11768, - 26423, - 67407, - 17172, - 17958, - 23331, - 15467, - 137669, - 16809 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 8981, - 40185, - 36121, - 374952, - 13394, - 16052, - 3887, - 6815, - 13900, - 10045, - 3712, - 10993, - 7384, - 66964, - 47138, - 14804, - 10914, - 9030, - 26364, - 37685, - 28007, - 17204, - 4167, - 3211, - 3662, - 4106, - 5627, - 30651, - 7141, - 8012, - 5698, - 4567, - 11059, - 28402, - 6832, - 8083, - 9434, - 6164, - 57814, - 8135 - ], - "xaxis": "x", - "y": [ - 2274, - 8280, - 6410, - 56938, - 2854, - 3020, - 1021, - 2735, - 3674, - 2663, - 1126, - 2826, - 1669, - 11373, - 12498, - 3957, - 2415, - 2211, - 5611, - 7101, - 6707, - 4051, - 1134, - 787, - 1023, - 1311, - 1503, - 7572, - 1581, - 2223, - 1468, - 1293, - 2780, - 6767, - 1801, - 1204, - 2396, - 1643, - 13153, - 1170 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Gelderland
Year=2002
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Beuningen", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lochem", - "Maasdriel", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "ids": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Beuningen", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lochem", - "Maasdriel", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "legendgroup": "Gelderland", - "marker": { - "color": "#00cc96", - "size": [ - 18811, - 154859, - 140736, - 48958, - 25429, - 21613, - 25609, - 26203, - 11476, - 48701, - 17461, - 26051, - 103708, - 21704, - 33224, - 26760, - 40399, - 11693, - 17997, - 16595, - 19413, - 23576, - 37327, - 154616, - 26317, - 22680, - 40284, - 23161, - 32250, - 44831, - 1529, - 9073, - 40205, - 23736, - 33987, - 18241, - 16212, - 38830, - 28750, - 25759, - 26155, - 36179 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Gelderland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 7055, - 63033, - 63898, - 16278, - 9576, - 8305, - 9049, - 10180, - 4634, - 19956, - 6537, - 9652, - 38701, - 7743, - 12840, - 8923, - 15192, - 4651, - 6914, - 6223, - 7453, - 8700, - 13697, - 65908, - 9042, - 7260, - 15083, - 7742, - 13759, - 19280, - 628, - 3270, - 15786, - 8518, - 12482, - 6869, - 6106, - 14890, - 11448, - 9323, - 10993, - 15697 - ], - "xaxis": "x", - "y": [ - 2001, - 13936, - 11595, - 6445, - 2876, - 2093, - 2419, - 3019, - 1148, - 4852, - 1849, - 3252, - 11249, - 2293, - 3054, - 2676, - 4208, - 1227, - 1769, - 2110, - 1772, - 2398, - 3922, - 12396, - 2996, - 2396, - 4313, - 2471, - 2766, - 3730, - 239, - 1143, - 4332, - 2867, - 2565, - 1823, - 1849, - 4160, - 2749, - 2688, - 2375, - 3408 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Fryslân
Year=2002
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Achtkarspelen", - "Ameland", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "ids": [ - "Achtkarspelen", - "Ameland", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "legendgroup": "Fryslân", - "marker": { - "color": "#ab63fa", - "size": [ - 28045, - 3564, - 15533, - 41784, - 90516, - 26726, - 28828, - 1025, - 53493, - 4769, - 31413, - 1183, - 25403 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Fryslân", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 10976, - 1770, - 6590, - 18038, - 43323, - 10707, - 11312, - 524, - 22210, - 1922, - 12485, - 523, - 10588 - ], - "xaxis": "x", - "y": [ - 2769, - 417, - 1516, - 3853, - 7468, - 2744, - 3027, - 87, - 5287, - 421, - 3114, - 153, - 2385 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zuid-Holland
Year=2002
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Katwijk", - "Krimpen aan den IJssel", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Nieuwkoop", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zwijndrecht" - ], - "ids": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Katwijk", - "Krimpen aan den IJssel", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Nieuwkoop", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zwijndrecht" - ], - "legendgroup": "Zuid-Holland", - "marker": { - "color": "#FFA15A", - "size": [ - 18388, - 18092, - 70649, - 33894, - 16001, - 65226, - 96936, - 120222, - 34324, - 71688, - 17741, - 39017, - 21495, - 20845, - 41307, - 28944, - 117170, - 26709, - 74085, - 22051, - 32876, - 11005, - 24548, - 21252, - 30276, - 35123, - 46196, - 598660, - 76576, - 23807, - 73935, - 22558, - 26884, - 25801, - 14195, - 110500, - 8659, - 41473 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zuid-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 7406, - 7125, - 28100, - 13150, - 6733, - 28756, - 42694, - 52331, - 14904, - 29920, - 6497, - 15510, - 8186, - 8404, - 15548, - 11288, - 50271, - 10987, - 34234, - 8892, - 13665, - 4128, - 10130, - 8490, - 12463, - 12855, - 19828, - 286286, - 35453, - 9712, - 34130, - 9484, - 10288, - 11566, - 5927, - 45248, - 3075, - 17423 - ], - "xaxis": "x", - "y": [ - 2048, - 2009, - 7122, - 3938, - 1534, - 6333, - 7922, - 11542, - 3513, - 7635, - 2053, - 3810, - 2616, - 1864, - 4394, - 3051, - 8833, - 3022, - 6191, - 2455, - 3010, - 1233, - 2107, - 2556, - 2902, - 4460, - 4121, - 53115, - 7361, - 2293, - 6558, - 2466, - 2779, - 2207, - 1309, - 11620, - 882, - 3957 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Overijssel
Year=2002
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Ommen", - "Raalte", - "Staphorst", - "Tubbergen", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "ids": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Ommen", - "Raalte", - "Staphorst", - "Tubbergen", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "legendgroup": "Overijssel", - "marker": { - "color": "#19d3f3", - "size": [ - 71026, - 20729, - 26061, - 86072, - 151346, - 24193, - 57483, - 36011, - 35041, - 48394, - 22703, - 31180, - 16890, - 36632, - 15438, - 20161, - 23429, - 22178, - 109000 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Overijssel", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 30071, - 8135, - 9256, - 36507, - 64198, - 9124, - 20617, - 13441, - 13721, - 18638, - 8359, - 12768, - 6127, - 13363, - 4918, - 6506, - 8309, - 7605, - 46311 - ], - "xaxis": "x", - "y": [ - 6682, - 2258, - 2940, - 8184, - 13302, - 2284, - 6494, - 3341, - 3558, - 5107, - 2088, - 3092, - 1784, - 4044, - 2097, - 2614, - 2440, - 2926, - 9964 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Flevoland
Year=2002
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "ids": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "legendgroup": "Flevoland", - "marker": { - "color": "#FF6692", - "size": [ - 158902, - 36369, - 66460, - 44252, - 16489, - 19249 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Flevoland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 61873, - 13194, - 27447, - 17155, - 4712, - 6388 - ], - "xaxis": "x", - "y": [ - 20425, - 4387, - 6784, - 4998, - 2843, - 2935 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Brabant
Year=2002
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "ids": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "legendgroup": "Noord-Brabant", - "marker": { - "color": "#B6E880", - "size": [ - 9447, - 15936, - 6468, - 17974, - 65793, - 28780, - 27450, - 19046, - 9295, - 29371, - 163427, - 20320, - 32124, - 25143, - 26772, - 18386, - 204776, - 38613, - 20988, - 27729, - 24737, - 22517, - 29625, - 15333, - 82853, - 131697, - 43138, - 14851, - 21543, - 23058, - 36433, - 23645, - 17808, - 25327, - 52968, - 67383, - 12429, - 77640, - 22601, - 27975, - 18480, - 14805, - 23390, - 197358, - 31163, - 42718, - 25610, - 16193, - 45453, - 21548, - 20341 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Brabant", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 3522, - 5804, - 2366, - 6683, - 27734, - 10142, - 10687, - 6967, - 3164, - 11491, - 71053, - 8085, - 11873, - 9595, - 10216, - 6642, - 91261, - 15255, - 8358, - 9967, - 9175, - 8616, - 11455, - 5801, - 34982, - 55940, - 16229, - 5453, - 7981, - 8899, - 14533, - 9060, - 6212, - 9772, - 21768, - 27347, - 4384, - 32050, - 8881, - 10351, - 6605, - 5943, - 9367, - 82507, - 13109, - 17001, - 10111, - 6704, - 18197, - 8468, - 7686 - ], - "xaxis": "x", - "y": [ - 959, - 1675, - 516, - 1959, - 6144, - 3106, - 3068, - 2033, - 1126, - 2821, - 14761, - 1923, - 3380, - 2535, - 2652, - 2026, - 16747, - 3803, - 1915, - 2901, - 2601, - 2127, - 2732, - 1609, - 8330, - 11944, - 4572, - 1678, - 2148, - 2169, - 3422, - 2411, - 1920, - 2663, - 5217, - 6490, - 1275, - 7466, - 2009, - 2898, - 2100, - 1598, - 2203, - 17363, - 2799, - 4191, - 2641, - 1644, - 4322, - 1863, - 2182 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Utrecht
Year=2002
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "ids": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "legendgroup": "Utrecht", - "marker": { - "color": "#FF97FF", - "size": [ - 129720, - 24469, - 42309, - 13845, - 19327, - 8609, - 38106, - 32401, - 29038, - 13571, - 13298, - 62140, - 9699, - 4138, - 17529, - 34416, - 44705, - 60669, - 23209, - 47549, - 11019, - 59682 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Utrecht", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 53612, - 10330, - 18224, - 5516, - 6618, - 3326, - 13646, - 12831, - 11190, - 4855, - 4977, - 25551, - 3771, - 1436, - 6787, - 12745, - 18772, - 22786, - 8838, - 18653, - 4004, - 25300 - ], - "xaxis": "x", - "y": [ - 13749, - 2184, - 4612, - 1519, - 2195, - 896, - 5080, - 3595, - 3048, - 1582, - 1607, - 5346, - 1107, - 355, - 1946, - 3645, - 4198, - 7003, - 2590, - 5020, - 1269, - 5030 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Limburg
Year=2002
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "ids": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "legendgroup": "Limburg", - "marker": { - "color": "#FECB52", - "size": [ - 13403, - 13539, - 30158, - 16847, - 15435, - 95004, - 28476, - 50680, - 40345, - 122005, - 20223, - 7947, - 16136, - 10512, - 45332, - 11418, - 97953, - 10732, - 17834, - 91400, - 38630, - 13163, - 48479 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Limburg", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5316, - 5113, - 13654, - 6111, - 6136, - 44763, - 10826, - 23066, - 17597, - 54333, - 8107, - 3225, - 5928, - 4267, - 20130, - 4764, - 42361, - 4597, - 7433, - 39536, - 14750, - 5316, - 19904 - ], - "xaxis": "x", - "y": [ - 1300, - 1423, - 2621, - 1619, - 1360, - 7305, - 3116, - 3441, - 3415, - 9118, - 1842, - 748, - 1723, - 901, - 4099, - 969, - 8684, - 662, - 1382, - 8137, - 4078, - 1256, - 4638 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zeeland
Year=2002
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "ids": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "legendgroup": "Zeeland", - "marker": { - "color": "#636efa", - "size": [ - 22251, - 36047, - 19836, - 11575, - 7052, - 20831, - 34503, - 34572, - 23884, - 21985, - 45416 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zeeland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 8786, - 15523, - 8547, - 4490, - 3762, - 8046, - 14912, - 15582, - 9293, - 9654, - 20740 - ], - "xaxis": "x", - "y": [ - 2413, - 3550, - 1636, - 1235, - 557, - 2373, - 3229, - 3341, - 2851, - 2296, - 4099 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Groningen
Year=2002
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Pekela", - "Stadskanaal", - "Veendam" - ], - "ids": [ - "Pekela", - "Stadskanaal", - "Veendam" - ], - "legendgroup": "Groningen", - "marker": { - "color": "#EF553B", - "size": [ - 13599, - 33632, - 28304 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Groningen", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5675, - 14096, - 11876 - ], - "xaxis": "x", - "y": [ - 1325, - 3220, - 2478 - ], - "yaxis": "y" - } - ], - "name": "2002" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

Provincienaam=Drenthe
Year=2003
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "ids": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "legendgroup": "Drenthe", - "marker": { - "color": "#636efa", - "size": [ - 25305, - 61577, - 26440, - 36008, - 108198, - 53312, - 30588, - 32826, - 31936, - 31998, - 19097, - 23969 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Drenthe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 10239, - 25951, - 10763, - 14247, - 45063, - 21528, - 12881, - 12730, - 12891, - 12934, - 7687, - 9177 - ], - "xaxis": "x", - "y": [ - 2282, - 6322, - 2643, - 3400, - 10177, - 5219, - 3066, - 3237, - 3150, - 3250, - 1670, - 2379 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Holland
Year=2003
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hoorn", - "Huizen", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "ids": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hoorn", - "Huizen", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "legendgroup": "Noord-Holland", - "marker": { - "color": "#EF553B", - "size": [ - 22839, - 93390, - 78095, - 736562, - 31742, - 36409, - 9309, - 17045, - 35327, - 24046, - 10194, - 28063, - 17117, - 147097, - 122902, - 36421, - 25760, - 22048, - 60026, - 83306, - 67515, - 42125, - 10352, - 7902, - 9118, - 11220, - 13054, - 74921, - 17488, - 21171, - 13825, - 11772, - 26680, - 67527, - 17150, - 17885, - 23237, - 15510, - 139464, - 16864 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 9034, - 41008, - 36415, - 377069, - 13396, - 16303, - 3899, - 6830, - 13900, - 10039, - 3799, - 11160, - 7519, - 67026, - 48928, - 14875, - 10937, - 9044, - 26345, - 37895, - 28472, - 17269, - 4227, - 3259, - 3662, - 4167, - 5644, - 30965, - 7415, - 8147, - 5753, - 4571, - 11170, - 28497, - 6870, - 8102, - 9451, - 6183, - 58512, - 8135 - ], - "xaxis": "x", - "y": [ - 2299, - 8447, - 6429, - 56916, - 2834, - 3128, - 1008, - 2780, - 3711, - 2234, - 1135, - 2840, - 1610, - 11433, - 13052, - 3850, - 2452, - 2176, - 5486, - 7059, - 6740, - 3923, - 1143, - 817, - 1043, - 1305, - 1530, - 7593, - 1727, - 2284, - 1438, - 1307, - 2811, - 6716, - 1771, - 1202, - 2422, - 1659, - 13152, - 1171 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Gelderland
Year=2003
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Beuningen", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "ids": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Beuningen", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "legendgroup": "Gelderland", - "marker": { - "color": "#00cc96", - "size": [ - 18855, - 155741, - 141528, - 49423, - 25361, - 21604, - 25689, - 26362, - 11412, - 49099, - 17570, - 26163, - 104771, - 21662, - 33233, - 26755, - 40603, - 11688, - 18270, - 16557, - 42467, - 19370, - 23519, - 37631, - 156198, - 26456, - 22892, - 40604, - 23192, - 32098, - 45012, - 1527, - 9039, - 40515, - 23602, - 34841, - 18202, - 16162, - 39564, - 28975, - 25999, - 25965, - 36924 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Gelderland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 7105, - 63353, - 63747, - 16481, - 9595, - 8314, - 9073, - 10119, - 4633, - 20308, - 6619, - 9660, - 39094, - 7814, - 12827, - 8982, - 15454, - 4641, - 6934, - 6250, - 16354, - 7584, - 8750, - 13869, - 66194, - 9108, - 7361, - 15274, - 7758, - 13856, - 19279, - 631, - 3283, - 15848, - 8614, - 12631, - 6869, - 6113, - 15294, - 11465, - 9493, - 11052, - 15997 - ], - "xaxis": "x", - "y": [ - 1986, - 13984, - 11597, - 6529, - 2839, - 2083, - 2446, - 2992, - 1134, - 4821, - 1836, - 3242, - 11219, - 2278, - 3042, - 2619, - 4197, - 1210, - 1762, - 2108, - 4352, - 1783, - 2354, - 3933, - 12387, - 2989, - 2356, - 4423, - 2463, - 2768, - 3657, - 249, - 1160, - 4302, - 2809, - 2533, - 1845, - 1739, - 4155, - 2847, - 2782, - 2344, - 3401 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Fryslân
Year=2003
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Achtkarspelen", - "Ameland", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "ids": [ - "Achtkarspelen", - "Ameland", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "legendgroup": "Fryslân", - "marker": { - "color": "#ab63fa", - "size": [ - 28102, - 3576, - 15561, - 42190, - 91284, - 26761, - 29000, - 1000, - 53740, - 4723, - 31696, - 1240, - 25802 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Fryslân", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11048, - 1778, - 6629, - 18517, - 43478, - 10726, - 11312, - 525, - 22439, - 1934, - 12582, - 523, - 10690 - ], - "xaxis": "x", - "y": [ - 2798, - 398, - 1444, - 3845, - 7484, - 2706, - 3132, - 94, - 5297, - 445, - 3172, - 123, - 2394 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zuid-Holland
Year=2003
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Katwijk", - "Krimpen aan den IJssel", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Nieuwkoop", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zwijndrecht" - ], - "ids": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Katwijk", - "Krimpen aan den IJssel", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Nieuwkoop", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zwijndrecht" - ], - "legendgroup": "Zuid-Holland", - "marker": { - "color": "#FFA15A", - "size": [ - 18425, - 18737, - 70706, - 35880, - 15999, - 65318, - 96588, - 120043, - 34509, - 71641, - 17841, - 39692, - 22096, - 20670, - 41535, - 29075, - 117689, - 26402, - 73747, - 22026, - 32915, - 11056, - 24542, - 21228, - 30510, - 35855, - 45873, - 599651, - 75802, - 23820, - 74322, - 22542, - 26797, - 25662, - 14243, - 112594, - 8593, - 45071 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zuid-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 7424, - 7406, - 28170, - 13888, - 6760, - 28986, - 42799, - 52338, - 15095, - 30095, - 6553, - 15966, - 8454, - 8415, - 15808, - 11305, - 50425, - 11000, - 34221, - 9019, - 14034, - 4136, - 10253, - 8550, - 12463, - 13165, - 19718, - 286762, - 35547, - 9740, - 34385, - 9555, - 10288, - 11609, - 5968, - 46544, - 3054, - 19128 - ], - "xaxis": "x", - "y": [ - 2039, - 1945, - 7140, - 4167, - 1544, - 6283, - 7630, - 11401, - 3532, - 7582, - 2011, - 3835, - 2555, - 1792, - 4382, - 3020, - 8827, - 2993, - 6110, - 2372, - 2930, - 1263, - 2045, - 2495, - 3021, - 4678, - 4045, - 52705, - 7305, - 2294, - 6437, - 2419, - 2647, - 2160, - 1285, - 11462, - 851, - 4386 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Overijssel
Year=2003
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "ids": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "legendgroup": "Overijssel", - "marker": { - "color": "#19d3f3", - "size": [ - 71729, - 20651, - 26281, - 87526, - 25997, - 152321, - 24109, - 57731, - 36146, - 35038, - 48919, - 22587, - 31374, - 17002, - 16966, - 36956, - 15565, - 42358, - 20262, - 33427, - 23444, - 22206, - 109955 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Overijssel", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 30146, - 8157, - 9394, - 37174, - 9172, - 64383, - 9143, - 20767, - 13535, - 13761, - 18733, - 8415, - 12923, - 6514, - 6126, - 13595, - 5009, - 17488, - 6536, - 11871, - 8363, - 7617, - 46700 - ], - "xaxis": "x", - "y": [ - 6607, - 2550, - 2960, - 7959, - 3052, - 13266, - 2317, - 6470, - 3306, - 3576, - 5111, - 2094, - 3030, - 1688, - 1678, - 3979, - 2103, - 4302, - 2624, - 3354, - 2472, - 2851, - 9953 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Flevoland
Year=2003
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "ids": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "legendgroup": "Flevoland", - "marker": { - "color": "#FF6692", - "size": [ - 165106, - 37132, - 68555, - 44741, - 16748, - 19398 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Flevoland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 63771, - 13552, - 28103, - 17324, - 4740, - 6437 - ], - "xaxis": "x", - "y": [ - 20510, - 4389, - 6839, - 5096, - 2831, - 2753 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Brabant
Year=2003
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "ids": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "legendgroup": "Noord-Brabant", - "marker": { - "color": "#B6E880", - "size": [ - 9413, - 16091, - 6507, - 18093, - 66024, - 28848, - 28244, - 19092, - 9372, - 29513, - 164397, - 20336, - 32130, - 25148, - 26747, - 18389, - 206118, - 39352, - 20939, - 27816, - 24894, - 22510, - 29746, - 15353, - 84233, - 132501, - 43161, - 14852, - 21590, - 22952, - 36553, - 23514, - 17838, - 25588, - 53136, - 76184, - 12353, - 78110, - 22607, - 27961, - 18413, - 15017, - 23386, - 197917, - 31111, - 42795, - 25273, - 16339, - 45584, - 21557, - 20386 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Brabant", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 3528, - 5933, - 2374, - 6713, - 27770, - 10198, - 11068, - 6992, - 3171, - 11661, - 71716, - 8114, - 11925, - 9586, - 10262, - 6679, - 91718, - 15401, - 8388, - 10010, - 9363, - 8674, - 11618, - 5821, - 35441, - 56697, - 16257, - 5493, - 8043, - 8900, - 14682, - 9081, - 6251, - 9897, - 21907, - 30520, - 4405, - 32295, - 8964, - 10373, - 6622, - 6062, - 9439, - 82757, - 13125, - 17001, - 10134, - 6782, - 18179, - 8518, - 7764 - ], - "xaxis": "x", - "y": [ - 952, - 1690, - 502, - 1988, - 6114, - 3134, - 3176, - 2032, - 1146, - 2740, - 14794, - 1872, - 3367, - 2539, - 2640, - 1999, - 16740, - 3838, - 1893, - 2904, - 2640, - 2118, - 2700, - 1575, - 8428, - 11960, - 4528, - 1661, - 2112, - 2172, - 3457, - 2437, - 1925, - 2580, - 5158, - 7427, - 1204, - 7476, - 1957, - 2909, - 2031, - 1600, - 2180, - 17317, - 2779, - 4110, - 2642, - 1645, - 4268, - 1819, - 2148 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Utrecht
Year=2003
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "ids": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "legendgroup": "Utrecht", - "marker": { - "color": "#FF97FF", - "size": [ - 131221, - 24423, - 42308, - 14111, - 19396, - 8630, - 41254, - 33269, - 28998, - 13728, - 13339, - 62124, - 9754, - 4205, - 17690, - 34499, - 44792, - 60953, - 23330, - 47762, - 11151, - 59799 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Utrecht", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 54031, - 10352, - 18224, - 5614, - 6679, - 3341, - 14878, - 13074, - 11257, - 4876, - 4977, - 25552, - 3784, - 1471, - 6856, - 12772, - 18806, - 22980, - 8932, - 18744, - 4103, - 25366 - ], - "xaxis": "x", - "y": [ - 14191, - 2158, - 4565, - 1512, - 2193, - 938, - 5101, - 3693, - 2975, - 1628, - 1629, - 5315, - 1124, - 346, - 1970, - 3603, - 4247, - 6986, - 2518, - 5091, - 1273, - 5166 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Limburg
Year=2003
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "ids": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "legendgroup": "Limburg", - "marker": { - "color": "#FECB52", - "size": [ - 13466, - 13463, - 29942, - 32107, - 16867, - 15340, - 93969, - 28655, - 50295, - 40055, - 121982, - 20039, - 8011, - 16104, - 10557, - 45344, - 11448, - 97806, - 10628, - 17896, - 91780, - 39122, - 13087, - 48785 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Limburg", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5323, - 5181, - 13891, - 12927, - 6171, - 6201, - 44800, - 10974, - 23130, - 17469, - 54744, - 8137, - 3237, - 6010, - 4291, - 20274, - 4789, - 42391, - 4624, - 7491, - 39894, - 14986, - 5343, - 20115 - ], - "xaxis": "x", - "y": [ - 1282, - 1393, - 2544, - 2739, - 1592, - 1313, - 7097, - 3122, - 3304, - 3287, - 8993, - 1817, - 745, - 1673, - 904, - 4112, - 932, - 8643, - 627, - 1360, - 8152, - 4108, - 1252, - 4581 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zeeland
Year=2003
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "ids": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "legendgroup": "Zeeland", - "marker": { - "color": "#636efa", - "size": [ - 22351, - 36251, - 27890, - 11568, - 7127, - 20839, - 34484, - 24828, - 55520, - 23990, - 22087, - 45199 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zeeland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 8831, - 15673, - 12083, - 4502, - 3851, - 8084, - 14950, - 13457, - 24872, - 9298, - 9728, - 20879 - ], - "xaxis": "x", - "y": [ - 2430, - 3551, - 2370, - 1248, - 551, - 2380, - 3225, - 2063, - 4996, - 2891, - 2291, - 4043 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Groningen
Year=2003
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Pekela", - "Stadskanaal", - "Veendam" - ], - "ids": [ - "Pekela", - "Stadskanaal", - "Veendam" - ], - "legendgroup": "Groningen", - "marker": { - "color": "#EF553B", - "size": [ - 13580, - 33695, - 28397 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Groningen", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5678, - 14118, - 12078 - ], - "xaxis": "x", - "y": [ - 1321, - 3204, - 2440 - ], - "yaxis": "y" - } - ], - "name": "2003" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

Provincienaam=Drenthe
Year=2004
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "ids": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "legendgroup": "Drenthe", - "marker": { - "color": "#636efa", - "size": [ - 25218, - 61925, - 26360, - 36220, - 108354, - 53663, - 30651, - 32806, - 32129, - 32203, - 19036, - 23850 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Drenthe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 10316, - 26358, - 10751, - 14401, - 45436, - 21464, - 12917, - 12974, - 12955, - 12985, - 7687, - 9183 - ], - "xaxis": "x", - "y": [ - 2297, - 6395, - 2601, - 3428, - 10287, - 5247, - 3024, - 3294, - 3042, - 3266, - 1682, - 2396 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Holland
Year=2004
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hoorn", - "Huizen", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "ids": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hoorn", - "Huizen", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "legendgroup": "Noord-Holland", - "marker": { - "color": "#EF553B", - "size": [ - 22915, - 94121, - 78866, - 739104, - 31738, - 36995, - 9257, - 16922, - 35291, - 24049, - 10378, - 28194, - 17241, - 147343, - 127750, - 36294, - 25660, - 22058, - 59795, - 83454, - 67952, - 41974, - 10315, - 8037, - 9176, - 11201, - 13055, - 75831, - 18198, - 21386, - 13735, - 11783, - 26763, - 67642, - 17266, - 17843, - 23333, - 15765, - 139774, - 16866 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 9034, - 41826, - 36417, - 378573, - 13635, - 16790, - 3951, - 6853, - 14220, - 10039, - 3926, - 11194, - 7637, - 67042, - 51027, - 14974, - 10951, - 9112, - 26435, - 37928, - 28904, - 17254, - 4226, - 3258, - 3695, - 4181, - 5644, - 31451, - 7648, - 8208, - 5760, - 4589, - 11205, - 28537, - 6943, - 8109, - 9462, - 6294, - 59441, - 8135 - ], - "xaxis": "x", - "y": [ - 2280, - 8310, - 6432, - 56344, - 2797, - 3183, - 1033, - 2839, - 3672, - 2572, - 1148, - 2880, - 1622, - 11428, - 13539, - 3676, - 2410, - 2219, - 5382, - 7082, - 6843, - 3864, - 1141, - 858, - 1075, - 1302, - 1549, - 7656, - 1754, - 2276, - 1420, - 1341, - 2861, - 6760, - 1723, - 1206, - 2433, - 1653, - 13284, - 1130 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Gelderland
Year=2004
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Beuningen", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "ids": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Beuningen", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "legendgroup": "Gelderland", - "marker": { - "color": "#00cc96", - "size": [ - 18998, - 156000, - 141601, - 50019, - 25459, - 21473, - 25756, - 26594, - 11446, - 49503, - 17786, - 25812, - 105495, - 21753, - 33309, - 26590, - 40559, - 11782, - 18349, - 16686, - 43019, - 19388, - 23521, - 22289, - 37983, - 157466, - 26634, - 22951, - 41176, - 23180, - 31908, - 44886, - 1509, - 9033, - 40609, - 23639, - 35137, - 18232, - 16068, - 39878, - 29299, - 26191, - 26014, - 36552 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Gelderland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 7178, - 63573, - 63832, - 16662, - 9598, - 8314, - 9141, - 10362, - 4639, - 20485, - 6668, - 9711, - 39396, - 7840, - 12957, - 9037, - 15681, - 4757, - 6926, - 6326, - 16605, - 7573, - 8785, - 7446, - 14112, - 66186, - 9246, - 7652, - 15666, - 7837, - 13876, - 19292, - 631, - 3294, - 15900, - 8669, - 12676, - 6877, - 6129, - 15409, - 11484, - 9501, - 11194, - 16087 - ], - "xaxis": "x", - "y": [ - 1978, - 14007, - 11669, - 6550, - 2838, - 2079, - 2490, - 3057, - 1158, - 4829, - 1875, - 3144, - 11279, - 2332, - 3057, - 2608, - 4272, - 1179, - 1749, - 2098, - 4447, - 1798, - 2360, - 2730, - 3998, - 12343, - 3100, - 2473, - 4613, - 2288, - 2750, - 3702, - 249, - 1178, - 4327, - 2816, - 2558, - 1863, - 1659, - 4155, - 2849, - 3080, - 2317, - 3634 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Fryslân
Year=2004
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Achtkarspelen", - "Ameland", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "ids": [ - "Achtkarspelen", - "Ameland", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "legendgroup": "Fryslân", - "marker": { - "color": "#ab63fa", - "size": [ - 28156, - 3525, - 15839, - 42642, - 91354, - 26683, - 29436, - 992, - 54053, - 4725, - 31963, - 1157, - 25990 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Fryslân", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11087, - 1786, - 6696, - 18552, - 43673, - 10743, - 11481, - 525, - 22676, - 1934, - 12704, - 523, - 10694 - ], - "xaxis": "x", - "y": [ - 2831, - 424, - 1470, - 3901, - 7475, - 2700, - 3182, - 91, - 5325, - 446, - 3157, - 127, - 2413 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zuid-Holland
Year=2004
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Katwijk", - "Krimpen aan den IJssel", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Nieuwkoop", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zwijndrecht" - ], - "ids": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Katwijk", - "Krimpen aan den IJssel", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Nieuwkoop", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zwijndrecht" - ], - "legendgroup": "Zuid-Holland", - "marker": { - "color": "#FFA15A", - "size": [ - 18386, - 19607, - 70477, - 37257, - 15948, - 65354, - 95817, - 119649, - 34623, - 71797, - 17828, - 40164, - 22966, - 20588, - 41822, - 29046, - 118702, - 26182, - 73832, - 22061, - 32847, - 17072, - 11092, - 24452, - 21188, - 30914, - 37696, - 45528, - 598923, - 75619, - 23837, - 74058, - 22505, - 26304, - 25506, - 97270, - 14265, - 114216, - 8526, - 45384 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zuid-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 7355, - 7826, - 28267, - 14469, - 6713, - 28871, - 42867, - 52229, - 15096, - 30139, - 6584, - 16156, - 8749, - 8426, - 15978, - 11326, - 50549, - 11087, - 34518, - 9015, - 14103, - 6300, - 4136, - 10287, - 8557, - 12463, - 14145, - 19688, - 286287, - 35513, - 9755, - 34455, - 9603, - 10287, - 11608, - 37622, - 6053, - 47740, - 3082, - 19259 - ], - "xaxis": "x", - "y": [ - 2025, - 1986, - 7161, - 4430, - 1543, - 6319, - 7386, - 11366, - 3518, - 7499, - 1999, - 3809, - 2619, - 1783, - 4308, - 3015, - 8727, - 2975, - 6025, - 2352, - 2901, - 2244, - 1262, - 2005, - 2507, - 3055, - 4858, - 3900, - 52308, - 7171, - 2292, - 6369, - 2424, - 2619, - 2154, - 10213, - 1278, - 11308, - 853, - 4358 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Overijssel
Year=2004
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "ids": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "legendgroup": "Overijssel", - "marker": { - "color": "#19d3f3", - "size": [ - 72227, - 20600, - 26428, - 89142, - 26094, - 152989, - 24123, - 57820, - 36105, - 35000, - 48949, - 22508, - 31392, - 17035, - 16938, - 37144, - 36170, - 15665, - 42846, - 20298, - 33493, - 23343, - 22034, - 110880 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Overijssel", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 30411, - 8163, - 9480, - 37561, - 9330, - 64943, - 9297, - 20835, - 13604, - 13791, - 18846, - 8424, - 12898, - 6527, - 6140, - 13596, - 12677, - 5045, - 17629, - 6602, - 11863, - 8472, - 7707, - 47137 - ], - "xaxis": "x", - "y": [ - 6684, - 2211, - 2955, - 8327, - 3013, - 13380, - 2354, - 6453, - 3259, - 3590, - 5077, - 2096, - 3008, - 1701, - 1648, - 3937, - 4266, - 2112, - 4319, - 2521, - 3476, - 2492, - 2894, - 10098 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Flevoland
Year=2004
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "ids": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "legendgroup": "Flevoland", - "marker": { - "color": "#FF6692", - "size": [ - 170704, - 37768, - 69640, - 45568, - 17044, - 19180 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Flevoland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 66301, - 13729, - 28627, - 17489, - 4902, - 6582 - ], - "xaxis": "x", - "y": [ - 21042, - 4350, - 6962, - 5077, - 2854, - 2735 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Brabant
Year=2004
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "ids": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "legendgroup": "Noord-Brabant", - "marker": { - "color": "#B6E880", - "size": [ - 9414, - 16138, - 6545, - 18137, - 66140, - 28868, - 28658, - 19153, - 9396, - 29512, - 166035, - 20149, - 32083, - 25306, - 26663, - 18286, - 207870, - 39657, - 20940, - 37680, - 27841, - 25093, - 22578, - 29679, - 15258, - 85127, - 133511, - 43108, - 15016, - 21583, - 22976, - 36775, - 23367, - 17839, - 25784, - 53121, - 76307, - 12371, - 77916, - 22624, - 27886, - 18223, - 15070, - 23484, - 198767, - 31091, - 42545, - 25157, - 16502, - 45597, - 21415, - 20431 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Brabant", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 3539, - 6088, - 2380, - 6816, - 27765, - 10244, - 11209, - 7045, - 3194, - 11678, - 72150, - 8189, - 12015, - 9652, - 10294, - 6717, - 92968, - 15499, - 8434, - 15792, - 10110, - 9476, - 8680, - 11696, - 5829, - 35788, - 57622, - 16287, - 5585, - 8095, - 8986, - 14779, - 9085, - 6265, - 9935, - 21924, - 30518, - 4445, - 32236, - 8980, - 10474, - 6659, - 6109, - 9472, - 82902, - 13142, - 17121, - 10191, - 6848, - 18336, - 8563, - 7797 - ], - "xaxis": "x", - "y": [ - 991, - 1678, - 489, - 1963, - 6042, - 3198, - 3211, - 2044, - 1118, - 2709, - 14854, - 1865, - 3321, - 2593, - 2605, - 1949, - 16682, - 3784, - 1884, - 3661, - 2884, - 2598, - 2101, - 2692, - 1558, - 8589, - 11949, - 4449, - 1661, - 2141, - 2170, - 3479, - 2347, - 1941, - 2560, - 5098, - 7416, - 1143, - 7441, - 1901, - 2886, - 1984, - 1590, - 2145, - 17339, - 2743, - 4133, - 2608, - 1651, - 4239, - 1812, - 2128 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Utrecht
Year=2004
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "ids": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "legendgroup": "Utrecht", - "marker": { - "color": "#FF97FF", - "size": [ - 132851, - 24503, - 42208, - 14087, - 19450, - 8670, - 42350, - 33577, - 29106, - 13870, - 13367, - 61803, - 9795, - 4266, - 17875, - 34437, - 44906, - 61111, - 23511, - 48096, - 11228, - 60373 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Utrecht", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 55059, - 10362, - 18243, - 5615, - 6793, - 3367, - 15662, - 13090, - 11321, - 4890, - 5081, - 25596, - 3836, - 1485, - 6998, - 12778, - 18835, - 23161, - 8989, - 18805, - 4144, - 25503 - ], - "xaxis": "x", - "y": [ - 14544, - 2172, - 4573, - 1468, - 2183, - 935, - 5101, - 3674, - 2924, - 1656, - 1645, - 5283, - 1124, - 355, - 2013, - 3600, - 4302, - 7109, - 2497, - 5107, - 1294, - 5179 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Limburg
Year=2004
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "ids": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "legendgroup": "Limburg", - "marker": { - "color": "#FECB52", - "size": [ - 13341, - 13477, - 29595, - 32194, - 16754, - 15135, - 93523, - 28813, - 50035, - 39778, - 122183, - 19986, - 7977, - 16100, - 10418, - 45159, - 11436, - 97487, - 10423, - 17768, - 92094, - 39085, - 12996, - 48724 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Limburg", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5326, - 5198, - 13953, - 12935, - 6214, - 6228, - 44659, - 10998, - 23080, - 17411, - 55072, - 8152, - 3237, - 6014, - 4295, - 20545, - 4812, - 42590, - 4645, - 7513, - 40244, - 15123, - 5369, - 20040 - ], - "xaxis": "x", - "y": [ - 1304, - 1370, - 2543, - 2658, - 1586, - 1286, - 6916, - 3112, - 3214, - 3212, - 8812, - 1835, - 751, - 1677, - 882, - 4135, - 926, - 8486, - 624, - 1347, - 7937, - 4132, - 1214, - 4578 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zeeland
Year=2004
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "ids": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "legendgroup": "Zeeland", - "marker": { - "color": "#636efa", - "size": [ - 22318, - 36591, - 27877, - 11627, - 7124, - 20966, - 34415, - 24596, - 55412, - 24386, - 22130, - 45236 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zeeland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 8919, - 15812, - 12141, - 4420, - 3850, - 8071, - 14970, - 13507, - 24898, - 9456, - 9818, - 20959 - ], - "xaxis": "x", - "y": [ - 2441, - 3544, - 2400, - 1212, - 565, - 2425, - 3255, - 2004, - 4965, - 2885, - 2296, - 3948 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Groningen
Year=2004
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Pekela", - "Stadskanaal", - "Veendam" - ], - "ids": [ - "Pekela", - "Stadskanaal", - "Veendam" - ], - "legendgroup": "Groningen", - "marker": { - "color": "#EF553B", - "size": [ - 13482, - 33708, - 28275 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Groningen", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5598, - 14194, - 12168 - ], - "xaxis": "x", - "y": [ - 1308, - 3205, - 2495 - ], - "yaxis": "y" - } - ], - "name": "2004" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

Provincienaam=Drenthe
Year=2005
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "ids": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "legendgroup": "Drenthe", - "marker": { - "color": "#636efa", - "size": [ - 25329, - 62755, - 26327, - 36086, - 108617, - 53886, - 30397, - 32984, - 31712, - 32301, - 19131, - 23844 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Drenthe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 10374, - 26715, - 10776, - 14516, - 45534, - 21867, - 13212, - 13054, - 13076, - 13028, - 7702, - 9222 - ], - "xaxis": "x", - "y": [ - 2364, - 6507, - 2533, - 3479, - 10344, - 5292, - 3075, - 3347, - 3047, - 3267, - 1707, - 2391 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Holland
Year=2005
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hoorn", - "Huizen", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "ids": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hoorn", - "Huizen", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "legendgroup": "Noord-Holland", - "marker": { - "color": "#EF553B", - "size": [ - 23296, - 94266, - 79036, - 742783, - 31694, - 36860, - 9179, - 17005, - 35091, - 23932, - 10505, - 28333, - 17308, - 146739, - 131816, - 36439, - 25644, - 22106, - 59446, - 83682, - 68136, - 42098, - 10272, - 8047, - 9182, - 11252, - 13177, - 77068, - 18602, - 21480, - 13739, - 11787, - 26969, - 67516, - 17343, - 17671, - 23298, - 15836, - 139817, - 16709 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 9118, - 41991, - 36424, - 380143, - 13738, - 16864, - 3946, - 6891, - 14237, - 10039, - 3978, - 11195, - 7729, - 67134, - 52764, - 15028, - 10976, - 9108, - 26508, - 38171, - 29089, - 17383, - 4228, - 3269, - 3701, - 4246, - 5641, - 32142, - 7863, - 8344, - 5800, - 4607, - 11267, - 28699, - 7057, - 8125, - 9475, - 6299, - 59613, - 8184 - ], - "xaxis": "x", - "y": [ - 2353, - 8088, - 6395, - 56610, - 2782, - 3160, - 1036, - 2865, - 3568, - 2471, - 1154, - 2901, - 1604, - 11504, - 14081, - 3772, - 2438, - 2295, - 5266, - 7073, - 6820, - 3822, - 1134, - 880, - 1040, - 1285, - 1574, - 7620, - 1802, - 2315, - 1421, - 1333, - 2885, - 6784, - 1659, - 1209, - 2451, - 1678, - 13402, - 1150 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Gelderland
Year=2005
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "ids": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "legendgroup": "Gelderland", - "marker": { - "color": "#00cc96", - "size": [ - 27446, - 156064, - 141321, - 50026, - 45227, - 25291, - 37616, - 21403, - 25648, - 27143, - 11381, - 56754, - 17889, - 25714, - 106416, - 22032, - 33108, - 26414, - 40879, - 11756, - 18334, - 16795, - 43176, - 32816, - 23554, - 35263, - 22359, - 38419, - 158215, - 26677, - 22966, - 40377, - 42272, - 23183, - 31903, - 44773, - 1513, - 8961, - 40542, - 23557, - 35219, - 18315, - 15945, - 39912, - 29275, - 26145, - 31499, - 46192 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Gelderland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 10676, - 64151, - 64370, - 16893, - 17448, - 9603, - 14414, - 8329, - 9174, - 10736, - 4683, - 23026, - 6764, - 9736, - 39900, - 8053, - 13040, - 9027, - 15949, - 4761, - 6934, - 6407, - 16748, - 13131, - 8823, - 13441, - 7456, - 14285, - 66564, - 9280, - 7823, - 15812, - 16061, - 7842, - 13899, - 19371, - 631, - 3295, - 15962, - 8663, - 12679, - 6957, - 6149, - 15477, - 11566, - 9518, - 13209, - 19901 - ], - "xaxis": "x", - "y": [ - 2815, - 13964, - 11584, - 6582, - 4286, - 2761, - 3685, - 2063, - 2542, - 3041, - 1182, - 5515, - 1915, - 3212, - 11343, - 2338, - 3075, - 2570, - 4229, - 1163, - 1775, - 2068, - 4551, - 2990, - 2369, - 3591, - 2733, - 4144, - 12308, - 2897, - 2489, - 4030, - 4689, - 2456, - 2738, - 3655, - 250, - 1200, - 4390, - 2800, - 2575, - 1895, - 1580, - 4141, - 2880, - 3120, - 2925, - 4763 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Fryslân
Year=2005
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Achtkarspelen", - "Ameland", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "ids": [ - "Achtkarspelen", - "Ameland", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "legendgroup": "Fryslân", - "marker": { - "color": "#ab63fa", - "size": [ - 28134, - 3520, - 15959, - 42802, - 91749, - 26491, - 29472, - 997, - 54432, - 4731, - 32016, - 1133, - 25958 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Fryslân", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11204, - 1804, - 6773, - 18892, - 43927, - 10700, - 11693, - 521, - 22969, - 1956, - 12781, - 524, - 10756 - ], - "xaxis": "x", - "y": [ - 2857, - 385, - 1403, - 3923, - 7384, - 2614, - 3194, - 88, - 5410, - 448, - 3180, - 123, - 2422 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zuid-Holland
Year=2005
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Katwijk", - "Krimpen aan den IJssel", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Nieuwkoop", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zwijndrecht" - ], - "ids": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Katwijk", - "Krimpen aan den IJssel", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Nieuwkoop", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zwijndrecht" - ], - "legendgroup": "Zuid-Holland", - "marker": { - "color": "#FFA15A", - "size": [ - 18356, - 20606, - 70591, - 39294, - 16031, - 65480, - 95031, - 119263, - 34352, - 71781, - 17799, - 40125, - 23690, - 20451, - 42024, - 28983, - 118563, - 26154, - 73793, - 21909, - 32451, - 17054, - 11122, - 24561, - 21309, - 31224, - 39882, - 45106, - 596407, - 75487, - 23902, - 73394, - 22712, - 26217, - 25557, - 97858, - 14243, - 115792, - 8509, - 45209 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zuid-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 7484, - 8241, - 28487, - 15312, - 6824, - 28937, - 42701, - 52460, - 15126, - 30131, - 6566, - 16212, - 9097, - 8390, - 16169, - 11419, - 50671, - 11058, - 34796, - 9129, - 13859, - 6426, - 4228, - 10457, - 8698, - 12853, - 15023, - 19462, - 285982, - 35171, - 9793, - 34259, - 9809, - 10296, - 11631, - 38118, - 6053, - 48717, - 3089, - 19261 - ], - "xaxis": "x", - "y": [ - 2024, - 2042, - 7213, - 4660, - 1569, - 6219, - 7287, - 11437, - 3596, - 7434, - 1986, - 3742, - 2631, - 1777, - 4280, - 2975, - 8656, - 2952, - 5910, - 2343, - 2865, - 2223, - 1263, - 1995, - 2499, - 3113, - 5049, - 3860, - 51438, - 7017, - 2283, - 6175, - 2389, - 2574, - 2135, - 10101, - 1246, - 11333, - 806, - 4211 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Overijssel
Year=2005
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "ids": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "legendgroup": "Overijssel", - "marker": { - "color": "#19d3f3", - "size": [ - 72293, - 20503, - 26459, - 95620, - 26103, - 153679, - 24267, - 57855, - 36172, - 34941, - 48980, - 22487, - 31373, - 17079, - 17028, - 37206, - 36362, - 15709, - 42833, - 20508, - 33549, - 23493, - 21877, - 111900 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Overijssel", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 30511, - 8094, - 9486, - 40351, - 9388, - 65270, - 9403, - 20943, - 13714, - 13791, - 18988, - 8471, - 12897, - 6586, - 6141, - 13736, - 12720, - 5072, - 17715, - 6667, - 12054, - 8522, - 7802, - 48205 - ], - "xaxis": "x", - "y": [ - 6720, - 2206, - 3003, - 9048, - 3051, - 13433, - 2404, - 6398, - 3309, - 3588, - 5122, - 2100, - 2977, - 1731, - 1652, - 3927, - 4279, - 2137, - 4376, - 2688, - 3479, - 2483, - 2910, - 10252 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Flevoland
Year=2005
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "ids": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "legendgroup": "Flevoland", - "marker": { - "color": "#FF6692", - "size": [ - 175007, - 38010, - 70860, - 45600, - 17262, - 19120 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Flevoland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 67873, - 13787, - 28864, - 17621, - 5053, - 6643 - ], - "xaxis": "x", - "y": [ - 21279, - 4308, - 7011, - 5116, - 2876, - 2750 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Brabant
Year=2005
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "ids": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "legendgroup": "Noord-Brabant", - "marker": { - "color": "#B6E880", - "size": [ - 9513, - 16261, - 6581, - 18100, - 66298, - 29216, - 28925, - 19135, - 9412, - 29520, - 168054, - 20039, - 32003, - 25533, - 26658, - 18295, - 208455, - 39860, - 20856, - 37846, - 27882, - 25196, - 22392, - 29537, - 15207, - 85829, - 133978, - 43051, - 15072, - 21642, - 22918, - 36785, - 23182, - 17904, - 25917, - 52921, - 76300, - 12301, - 77734, - 22527, - 27902, - 18113, - 15136, - 23435, - 199068, - 31057, - 42763, - 25239, - 16477, - 45708, - 21217, - 20554 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Brabant", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 3573, - 6102, - 2403, - 6847, - 27984, - 10555, - 11275, - 7082, - 3224, - 11732, - 73732, - 8203, - 12048, - 9876, - 10398, - 6749, - 93752, - 15611, - 8455, - 15970, - 10192, - 9524, - 8712, - 11694, - 5839, - 35872, - 57634, - 16413, - 5601, - 8200, - 8992, - 14863, - 9098, - 6299, - 10025, - 22032, - 30912, - 4504, - 32351, - 9038, - 10488, - 6712, - 6186, - 9513, - 83628, - 13265, - 17596, - 10204, - 6860, - 18647, - 8668, - 7896 - ], - "xaxis": "x", - "y": [ - 974, - 1669, - 462, - 1924, - 6028, - 3278, - 3209, - 2013, - 1143, - 2660, - 15107, - 1864, - 3224, - 2562, - 2571, - 1868, - 16728, - 3808, - 1867, - 3648, - 2827, - 2549, - 2124, - 2682, - 1506, - 8647, - 12002, - 4427, - 1650, - 2150, - 2125, - 3446, - 2327, - 1900, - 2574, - 5086, - 7369, - 1108, - 7318, - 1843, - 2913, - 1904, - 1626, - 2100, - 17291, - 2707, - 4047, - 2569, - 1641, - 4239, - 1783, - 2015 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Utrecht
Year=2005
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "ids": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "legendgroup": "Utrecht", - "marker": { - "color": "#FF97FF", - "size": [ - 134906, - 24416, - 42195, - 14137, - 19487, - 8785, - 43529, - 33608, - 28977, - 13968, - 13438, - 61449, - 9947, - 4305, - 17966, - 34438, - 45087, - 61381, - 23580, - 48118, - 11237, - 60408 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Utrecht", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 55860, - 10389, - 18246, - 5691, - 6809, - 3466, - 15957, - 13097, - 11324, - 4952, - 5083, - 25761, - 3891, - 1492, - 7015, - 12805, - 19053, - 23635, - 9012, - 18827, - 4107, - 25676 - ], - "xaxis": "x", - "y": [ - 14935, - 2212, - 4512, - 1448, - 2156, - 945, - 5129, - 3728, - 2888, - 1670, - 1660, - 5430, - 1148, - 375, - 2068, - 3575, - 4341, - 7185, - 2501, - 5180, - 1268, - 5266 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Limburg
Year=2005
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "ids": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "legendgroup": "Limburg", - "marker": { - "color": "#FECB52", - "size": [ - 13401, - 13576, - 29777, - 32322, - 16823, - 15044, - 92542, - 28722, - 49563, - 39477, - 121456, - 19857, - 8010, - 16265, - 10331, - 45348, - 11430, - 97055, - 10229, - 17561, - 92263, - 39114, - 12953, - 48707 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Limburg", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5413, - 5237, - 13672, - 13158, - 6282, - 6273, - 44813, - 11034, - 23005, - 17451, - 55311, - 8165, - 3263, - 6167, - 4316, - 20684, - 4811, - 42722, - 4657, - 7524, - 40565, - 15201, - 5370, - 20192 - ], - "xaxis": "x", - "y": [ - 1298, - 1377, - 2501, - 2655, - 1569, - 1260, - 6766, - 3042, - 3123, - 3164, - 8615, - 1802, - 752, - 1666, - 864, - 4183, - 926, - 8301, - 617, - 1339, - 7930, - 4115, - 1195, - 4546 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zeeland
Year=2005
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "ids": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "legendgroup": "Zeeland", - "marker": { - "color": "#636efa", - "size": [ - 22397, - 36783, - 27883, - 11609, - 7195, - 21019, - 34491, - 24605, - 55361, - 24676, - 22039, - 45372 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zeeland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 8949, - 15886, - 12254, - 4453, - 3850, - 8147, - 15007, - 13606, - 25027, - 9604, - 9863, - 21101 - ], - "xaxis": "x", - "y": [ - 2462, - 3575, - 2394, - 1211, - 571, - 2421, - 3227, - 1955, - 4895, - 2908, - 2263, - 3924 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Groningen
Year=2005
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Pekela", - "Stadskanaal", - "Veendam" - ], - "ids": [ - "Pekela", - "Stadskanaal", - "Veendam" - ], - "legendgroup": "Groningen", - "marker": { - "color": "#EF553B", - "size": [ - 13449, - 33804, - 28250 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Groningen", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5523, - 14211, - 12248 - ], - "xaxis": "x", - "y": [ - 1278, - 3248, - 2556 - ], - "yaxis": "y" - } - ], - "name": "2005" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

Provincienaam=Drenthe
Year=2006
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "ids": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "legendgroup": "Drenthe", - "marker": { - "color": "#636efa", - "size": [ - 25507, - 63383, - 26303, - 36135, - 108589, - 54150, - 30539, - 33282, - 31573, - 31977, - 19256, - 23787 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Drenthe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 10471, - 27270, - 10860, - 14557, - 45988, - 22057, - 13220, - 13137, - 13121, - 13032, - 7745, - 9271 - ], - "xaxis": "x", - "y": [ - 2379, - 6628, - 2527, - 3556, - 10530, - 5324, - 3174, - 3332, - 3046, - 3206, - 1730, - 2416 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Holland
Year=2006
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hoorn", - "Huizen", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "ids": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hoorn", - "Huizen", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "legendgroup": "Noord-Holland", - "marker": { - "color": "#EF553B", - "size": [ - 24145, - 94455, - 78774, - 743079, - 31505, - 36546, - 9114, - 16974, - 35020, - 23834, - 18539, - 28459, - 17715, - 147015, - 135136, - 37423, - 25650, - 22123, - 58957, - 83652, - 67846, - 42091, - 10301, - 8096, - 9200, - 11268, - 13142, - 77922, - 18749, - 21400, - 13708, - 11915, - 26844, - 67678, - 17267, - 17533, - 23435, - 15796, - 140270, - 16651 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 9211, - 42362, - 36536, - 381832, - 13744, - 16525, - 3946, - 6895, - 14291, - 10039, - 7036, - 11231, - 7842, - 67323, - 54256, - 15438, - 10998, - 9142, - 26550, - 37964, - 29208, - 17541, - 4233, - 3345, - 3727, - 4248, - 5655, - 32445, - 8023, - 8348, - 5842, - 4732, - 11287, - 29260, - 7061, - 8223, - 9504, - 6299, - 60081, - 8204 - ], - "xaxis": "x", - "y": [ - 2448, - 8197, - 6183, - 56437, - 2775, - 3292, - 1022, - 2910, - 3549, - 2359, - 1963, - 2962, - 1544, - 11665, - 14686, - 3674, - 2389, - 2303, - 5084, - 7351, - 6829, - 3696, - 1143, - 815, - 1073, - 1273, - 1597, - 7546, - 1825, - 2328, - 1420, - 1419, - 2921, - 6638, - 1641, - 1199, - 2423, - 1679, - 13637, - 1143 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Gelderland
Year=2006
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "ids": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "legendgroup": "Gelderland", - "marker": { - "color": "#00cc96", - "size": [ - 27476, - 156051, - 142195, - 50448, - 45294, - 25078, - 37700, - 21385, - 25584, - 27157, - 11539, - 56755, - 18022, - 25577, - 107048, - 22086, - 32978, - 26251, - 41388, - 11702, - 18330, - 16827, - 43719, - 32861, - 23565, - 35190, - 22378, - 38695, - 159522, - 26583, - 22880, - 40214, - 42737, - 23173, - 31603, - 44189, - 1502, - 8933, - 40966, - 23522, - 35315, - 18376, - 15730, - 39509, - 29137, - 26191, - 31680, - 46642 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Gelderland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 10726, - 64377, - 64995, - 17114, - 17485, - 9676, - 14414, - 8334, - 9206, - 10821, - 4836, - 23172, - 6782, - 9780, - 39984, - 8173, - 13050, - 9052, - 16247, - 4758, - 6973, - 6433, - 17004, - 13316, - 8854, - 13634, - 7473, - 14485, - 67990, - 9499, - 7868, - 15883, - 16248, - 7899, - 13971, - 19399, - 631, - 3339, - 16391, - 8674, - 12724, - 7041, - 6151, - 15505, - 11681, - 9580, - 13330, - 20073 - ], - "xaxis": "x", - "y": [ - 2831, - 13940, - 11719, - 6577, - 4227, - 2710, - 3671, - 2041, - 2546, - 3048, - 1166, - 5449, - 1942, - 3123, - 11295, - 2403, - 3058, - 2551, - 4354, - 1157, - 1780, - 1983, - 4717, - 3075, - 2431, - 3543, - 2750, - 4206, - 12314, - 2914, - 2393, - 4043, - 4850, - 2437, - 2750, - 3689, - 253, - 1196, - 4414, - 2735, - 2554, - 1926, - 1452, - 4101, - 2898, - 2851, - 2906, - 4762 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Fryslân
Year=2006
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Achtkarspelen", - "Ameland", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "ids": [ - "Achtkarspelen", - "Ameland", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "legendgroup": "Fryslân", - "marker": { - "color": "#ab63fa", - "size": [ - 28223, - 3475, - 15642, - 42757, - 91817, - 26125, - 29565, - 986, - 54859, - 4729, - 32142, - 1127, - 25507 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Fryslân", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11268, - 1816, - 6779, - 18988, - 44322, - 10745, - 11770, - 522, - 23303, - 1967, - 12898, - 524, - 10783 - ], - "xaxis": "x", - "y": [ - 2886, - 375, - 1391, - 3973, - 7340, - 2624, - 3201, - 82, - 5477, - 432, - 3215, - 113, - 2375 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zuid-Holland
Year=2006
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Katwijk", - "Krimpen aan den IJssel", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Nieuwkoop", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zwijndrecht" - ], - "ids": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Katwijk", - "Krimpen aan den IJssel", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Nieuwkoop", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zwijndrecht" - ], - "legendgroup": "Zuid-Holland", - "marker": { - "color": "#FFA15A", - "size": [ - 18426, - 21059, - 70955, - 41249, - 15990, - 65602, - 95090, - 118821, - 34250, - 71386, - 17775, - 40004, - 24458, - 20317, - 60932, - 28783, - 118069, - 25990, - 73111, - 21877, - 31956, - 17435, - 11122, - 24673, - 21891, - 31553, - 41695, - 44757, - 588697, - 75389, - 23801, - 34683, - 72553, - 22887, - 26062, - 25622, - 98328, - 14224, - 116979, - 8501, - 44588 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zuid-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 7578, - 8462, - 28918, - 16062, - 6858, - 29054, - 42971, - 52676, - 15291, - 30076, - 6618, - 16410, - 9536, - 8445, - 23072, - 11438, - 50772, - 10999, - 35196, - 9247, - 13859, - 6590, - 4237, - 10654, - 8815, - 13029, - 15629, - 19547, - 287262, - 35271, - 9755, - 13622, - 34340, - 9974, - 10292, - 11774, - 38647, - 6061, - 49788, - 3116, - 19445 - ], - "xaxis": "x", - "y": [ - 2037, - 1962, - 7197, - 4947, - 1529, - 6133, - 7112, - 11363, - 3581, - 7404, - 1970, - 3626, - 2686, - 1793, - 6198, - 2970, - 8636, - 2905, - 5821, - 2343, - 2805, - 2220, - 1227, - 2012, - 2486, - 3088, - 5195, - 3777, - 50604, - 6934, - 2286, - 3740, - 6075, - 2381, - 2528, - 2156, - 10091, - 1251, - 11180, - 793, - 4112 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Overijssel
Year=2006
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "ids": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "legendgroup": "Overijssel", - "marker": { - "color": "#19d3f3", - "size": [ - 72048, - 20547, - 26439, - 96540, - 26092, - 154377, - 24344, - 57909, - 36152, - 35041, - 49295, - 22489, - 31297, - 17247, - 17303, - 37259, - 36417, - 15783, - 43122, - 20601, - 33438, - 23540, - 21872, - 113078 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Overijssel", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 30719, - 8130, - 9546, - 40786, - 9417, - 66123, - 9451, - 21060, - 13787, - 13860, - 19121, - 8507, - 13034, - 6685, - 6164, - 13815, - 12817, - 5125, - 17837, - 6723, - 12092, - 8551, - 7846, - 49090 - ], - "xaxis": "x", - "y": [ - 6727, - 2170, - 3079, - 9141, - 3078, - 13387, - 2417, - 6447, - 3356, - 3640, - 5308, - 2093, - 3045, - 1771, - 1657, - 3889, - 4272, - 2154, - 4390, - 2716, - 3514, - 2451, - 2953, - 10449 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Flevoland
Year=2006
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "ids": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "legendgroup": "Flevoland", - "marker": { - "color": "#FF6692", - "size": [ - 178466, - 38125, - 71447, - 45739, - 17458, - 19421 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Flevoland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 69030, - 13922, - 29096, - 17700, - 5067, - 6800 - ], - "xaxis": "x", - "y": [ - 21357, - 4290, - 7244, - 5216, - 2852, - 2745 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Brabant
Year=2006
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "ids": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "legendgroup": "Noord-Brabant", - "marker": { - "color": "#B6E880", - "size": [ - 9491, - 16299, - 6648, - 18060, - 65767, - 29544, - 29026, - 19130, - 9407, - 29676, - 169709, - 20133, - 31853, - 25558, - 26649, - 18183, - 209172, - 40322, - 20855, - 37738, - 27913, - 25266, - 22146, - 29482, - 15150, - 85682, - 134717, - 43103, - 15069, - 21771, - 22807, - 36676, - 22873, - 17812, - 25908, - 52853, - 76416, - 12314, - 77703, - 22412, - 28013, - 18118, - 15297, - 23356, - 200380, - 30924, - 43716, - 25235, - 16487, - 45762, - 21141, - 20797 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Brabant", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 3599, - 6208, - 2413, - 6873, - 28107, - 10683, - 11418, - 7134, - 3263, - 11940, - 73956, - 8217, - 12095, - 9949, - 10453, - 6763, - 94308, - 16068, - 8480, - 16056, - 10192, - 9597, - 8774, - 11667, - 5866, - 36118, - 58334, - 16538, - 5651, - 8241, - 8983, - 14970, - 9094, - 6341, - 10040, - 22224, - 31173, - 4537, - 32381, - 9060, - 10518, - 6767, - 6197, - 9536, - 84986, - 13325, - 17808, - 10178, - 6878, - 18620, - 8666, - 7912 - ], - "xaxis": "x", - "y": [ - 947, - 1655, - 448, - 1889, - 5937, - 3305, - 3187, - 2039, - 1159, - 2700, - 15078, - 1788, - 3128, - 2603, - 2567, - 1803, - 16692, - 3768, - 1852, - 3644, - 2747, - 2514, - 2132, - 2654, - 1477, - 8798, - 12115, - 4419, - 1620, - 2129, - 2105, - 3487, - 2270, - 1877, - 2545, - 5049, - 7329, - 1080, - 7205, - 1808, - 2933, - 1874, - 1627, - 2085, - 17243, - 2636, - 3948, - 2581, - 1664, - 4167, - 1829, - 1988 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Utrecht
Year=2006
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Utrechtse Heuvelrug", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "ids": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Utrechtse Heuvelrug", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "legendgroup": "Utrecht", - "marker": { - "color": "#FF97FF", - "size": [ - 136999, - 24513, - 42037, - 14234, - 19468, - 8859, - 44499, - 33976, - 28889, - 13908, - 13506, - 61605, - 9941, - 4346, - 18364, - 34353, - 45217, - 48957, - 61669, - 23464, - 47912, - 11211, - 60369 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Utrecht", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 57177, - 10500, - 18246, - 5741, - 6826, - 3498, - 16603, - 13392, - 11326, - 4967, - 5168, - 26155, - 3894, - 1510, - 7145, - 12825, - 19247, - 19758, - 23909, - 9016, - 18883, - 4115, - 25743 - ], - "xaxis": "x", - "y": [ - 15184, - 2210, - 4561, - 1463, - 2124, - 958, - 5190, - 3784, - 2878, - 1663, - 1665, - 5437, - 1158, - 384, - 1997, - 3502, - 4357, - 4749, - 7244, - 2490, - 5240, - 1300, - 5335 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Limburg
Year=2006
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "ids": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "legendgroup": "Limburg", - "marker": { - "color": "#FECB52", - "size": [ - 13521, - 13612, - 29623, - 32295, - 16742, - 14954, - 91499, - 28750, - 49323, - 39189, - 120175, - 19762, - 8016, - 16446, - 10288, - 45457, - 11317, - 96648, - 10068, - 17280, - 92052, - 39043, - 12959, - 48575 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Limburg", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5495, - 5248, - 13817, - 13271, - 6348, - 6281, - 44837, - 11078, - 23055, - 17553, - 55569, - 8203, - 3270, - 6167, - 4392, - 20968, - 4838, - 43252, - 4661, - 7543, - 40633, - 15294, - 5413, - 20322 - ], - "xaxis": "x", - "y": [ - 1309, - 1338, - 2491, - 2647, - 1581, - 1170, - 6644, - 3018, - 3080, - 3095, - 8451, - 1734, - 750, - 1669, - 879, - 4149, - 897, - 8083, - 586, - 1308, - 7830, - 4137, - 1140, - 4460 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zeeland
Year=2006
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "ids": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "legendgroup": "Zeeland", - "marker": { - "color": "#636efa", - "size": [ - 22415, - 36690, - 27893, - 11755, - 7200, - 21142, - 34435, - 24357, - 55414, - 24892, - 21946, - 45073 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zeeland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 8999, - 15982, - 12435, - 4547, - 3886, - 8224, - 15052, - 13661, - 25204, - 9710, - 9915, - 21332 - ], - "xaxis": "x", - "y": [ - 2423, - 3573, - 2401, - 1284, - 570, - 2392, - 3164, - 1928, - 4810, - 2948, - 2249, - 3845 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Groningen
Year=2006
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Pekela", - "Stadskanaal", - "Veendam" - ], - "ids": [ - "Pekela", - "Stadskanaal", - "Veendam" - ], - "legendgroup": "Groningen", - "marker": { - "color": "#EF553B", - "size": [ - 13395, - 34146, - 28185 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Groningen", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5517, - 14544, - 12229 - ], - "xaxis": "x", - "y": [ - 1312, - 3246, - 2540 - ], - "yaxis": "y" - } - ], - "name": "2006" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

Provincienaam=Drenthe
Year=2007
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "ids": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "legendgroup": "Drenthe", - "marker": { - "color": "#636efa", - "size": [ - 25563, - 64391, - 26297, - 36040, - 108832, - 54383, - 31087, - 33545, - 31439, - 31731, - 19287, - 23602 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Drenthe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 10508, - 27850, - 10906, - 14696, - 46204, - 22336, - 13511, - 13413, - 13091, - 13029, - 7768, - 9287 - ], - "xaxis": "x", - "y": [ - 2405, - 6769, - 2447, - 3561, - 10560, - 5395, - 3302, - 3371, - 3038, - 3190, - 1744, - 2441 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Holland
Year=2007
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "ids": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "legendgroup": "Noord-Holland", - "marker": { - "color": "#EF553B", - "size": [ - 25019, - 94174, - 78945, - 742884, - 31332, - 36835, - 9112, - 17028, - 34863, - 23888, - 18596, - 28494, - 17826, - 146960, - 138255, - 38006, - 25575, - 21979, - 58227, - 83669, - 68174, - 41927, - 21214, - 10264, - 26569, - 9248, - 11212, - 13038, - 77955, - 19083, - 21345, - 13618, - 12061, - 26977, - 67635, - 17183, - 17556, - 23571, - 15856, - 141402, - 16599 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 9833, - 42509, - 36653, - 383078, - 13941, - 16594, - 3969, - 6906, - 14323, - 10129, - 7161, - 11262, - 7904, - 67485, - 55344, - 15693, - 11091, - 9160, - 26578, - 38429, - 29525, - 17650, - 8071, - 4252, - 10505, - 3749, - 4272, - 5631, - 32700, - 8143, - 8341, - 5905, - 4830, - 11455, - 29268, - 7069, - 8229, - 9655, - 6360, - 61071, - 8207 - ], - "xaxis": "x", - "y": [ - 2542, - 8127, - 6177, - 56928, - 2688, - 3373, - 1071, - 2943, - 3535, - 2333, - 1939, - 3043, - 1549, - 11835, - 15090, - 3611, - 2359, - 2292, - 5007, - 7461, - 6877, - 3662, - 2454, - 1125, - 2789, - 1065, - 1277, - 1580, - 7525, - 1783, - 2282, - 1379, - 1447, - 2934, - 6567, - 1585, - 1179, - 2441, - 1695, - 13818, - 1128 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Gelderland
Year=2007
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "ids": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "legendgroup": "Gelderland", - "marker": { - "color": "#00cc96", - "size": [ - 27570, - 155564, - 142569, - 50953, - 45213, - 25231, - 37788, - 21229, - 25647, - 27178, - 11593, - 56238, - 18114, - 25605, - 107500, - 22216, - 32989, - 26305, - 42079, - 11630, - 18013, - 16673, - 44344, - 32832, - 23514, - 35054, - 22309, - 38879, - 160907, - 26583, - 22800, - 29853, - 40068, - 43161, - 23041, - 31640, - 43950, - 1527, - 8926, - 41191, - 23510, - 35680, - 18261, - 15599, - 39479, - 29249, - 26194, - 31884, - 46635 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Gelderland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 10889, - 64673, - 65441, - 17607, - 17735, - 9851, - 14655, - 8360, - 9241, - 10914, - 4827, - 23215, - 6892, - 9855, - 40291, - 8243, - 13084, - 9128, - 16578, - 4754, - 7002, - 6448, - 17409, - 13365, - 8928, - 13700, - 7553, - 14619, - 68943, - 9511, - 7926, - 11366, - 15881, - 16518, - 8007, - 14027, - 19506, - 631, - 3371, - 16548, - 8794, - 12707, - 7057, - 6152, - 15626, - 11848, - 9674, - 13428, - 20208 - ], - "xaxis": "x", - "y": [ - 2795, - 13897, - 11872, - 6580, - 4255, - 2694, - 3692, - 2068, - 2527, - 3072, - 1129, - 5469, - 1991, - 3067, - 11262, - 2391, - 3074, - 2498, - 4316, - 1166, - 1780, - 1939, - 4818, - 3117, - 2417, - 3582, - 2733, - 4287, - 12219, - 2872, - 2397, - 3266, - 4043, - 5021, - 2544, - 2726, - 3698, - 250, - 1169, - 4450, - 2746, - 2580, - 1900, - 1428, - 4111, - 2941, - 2884, - 2892, - 4847 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Fryslân
Year=2007
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Achtkarspelen", - "Ameland", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "ids": [ - "Achtkarspelen", - "Ameland", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "legendgroup": "Fryslân", - "marker": { - "color": "#ab63fa", - "size": [ - 28143, - 3460, - 15465, - 42776, - 92342, - 26224, - 29583, - 946, - 54956, - 4707, - 32322, - 1137, - 25500 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Fryslân", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11303, - 1562, - 6836, - 19084, - 44865, - 10828, - 11945, - 522, - 23412, - 2013, - 12950, - 524, - 10842 - ], - "xaxis": "x", - "y": [ - 2929, - 385, - 1411, - 4083, - 7381, - 2571, - 3194, - 85, - 5437, - 430, - 3190, - 116, - 2426 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zuid-Holland
Year=2007
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Katwijk", - "Krimpen aan den IJssel", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Nieuwkoop", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zwijndrecht" - ], - "ids": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Katwijk", - "Krimpen aan den IJssel", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Nieuwkoop", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zwijndrecht" - ], - "legendgroup": "Zuid-Holland", - "marker": { - "color": "#FFA15A", - "size": [ - 18666, - 21523, - 71100, - 43044, - 15918, - 65374, - 95379, - 118541, - 34288, - 70953, - 17693, - 39633, - 24825, - 20295, - 61111, - 28719, - 47927, - 117485, - 26077, - 72824, - 21975, - 31567, - 17536, - 26987, - 24700, - 22022, - 31403, - 42769, - 44679, - 584058, - 75162, - 23774, - 35008, - 71461, - 22785, - 25896, - 25608, - 98869, - 14128, - 118024, - 8400, - 44400 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zuid-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 7516, - 8563, - 29396, - 16807, - 6858, - 29075, - 43498, - 52664, - 15392, - 30009, - 6650, - 16413, - 9664, - 8510, - 23370, - 11477, - 18140, - 50988, - 11107, - 35347, - 9262, - 13954, - 6605, - 9956, - 10682, - 8956, - 13092, - 16155, - 19717, - 288349, - 35655, - 9790, - 13773, - 34256, - 9992, - 10292, - 11862, - 38984, - 6058, - 50802, - 3101, - 19599 - ], - "xaxis": "x", - "y": [ - 2043, - 2204, - 7221, - 5275, - 1532, - 6023, - 6924, - 11212, - 3610, - 7391, - 1984, - 3598, - 2660, - 1775, - 6176, - 2959, - 5937, - 8589, - 2906, - 5787, - 2374, - 2738, - 2250, - 2645, - 2066, - 2484, - 3095, - 5301, - 3716, - 50193, - 6871, - 2290, - 3801, - 5990, - 2358, - 2469, - 2139, - 10039, - 1232, - 11167, - 768, - 4081 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Overijssel
Year=2007
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "ids": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "legendgroup": "Overijssel", - "marker": { - "color": "#19d3f3", - "size": [ - 72096, - 20521, - 26541, - 96617, - 26061, - 154476, - 24348, - 58105, - 36046, - 35137, - 49359, - 22485, - 31416, - 17327, - 17311, - 37311, - 36584, - 15899, - 43127, - 20724, - 33461, - 23439, - 21919, - 114635 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Overijssel", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 30838, - 8144, - 9663, - 41043, - 9448, - 66281, - 9525, - 21192, - 13863, - 13916, - 19428, - 8505, - 13214, - 6724, - 6301, - 13978, - 12959, - 5252, - 17965, - 6855, - 12186, - 8581, - 7830, - 49879 - ], - "xaxis": "x", - "y": [ - 6832, - 2156, - 3071, - 9371, - 3084, - 13491, - 2403, - 6482, - 3453, - 3647, - 5397, - 2141, - 3084, - 1769, - 1675, - 3843, - 4283, - 2187, - 4457, - 2754, - 3577, - 2482, - 2912, - 10719 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Flevoland
Year=2007
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "ids": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "legendgroup": "Flevoland", - "marker": { - "color": "#FF6692", - "size": [ - 180924, - 38182, - 72252, - 45777, - 17585, - 19704 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Flevoland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 70181, - 14114, - 29472, - 17817, - 5124, - 6911 - ], - "xaxis": "x", - "y": [ - 21553, - 4283, - 7379, - 5210, - 2885, - 2779 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Brabant
Year=2007
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "ids": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "legendgroup": "Noord-Brabant", - "marker": { - "color": "#B6E880", - "size": [ - 9461, - 16374, - 6674, - 18094, - 65440, - 29644, - 29006, - 19058, - 9503, - 30001, - 170349, - 20281, - 31841, - 25475, - 26575, - 18041, - 209699, - 40591, - 20724, - 37823, - 27890, - 25376, - 22084, - 29331, - 15153, - 86061, - 135648, - 42883, - 15107, - 21748, - 22870, - 36645, - 22692, - 17852, - 25743, - 53295, - 76652, - 12367, - 77450, - 22408, - 27921, - 18134, - 15306, - 23301, - 201259, - 30908, - 43284, - 25239, - 16521, - 45667, - 21497, - 20849 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Brabant", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 3644, - 6327, - 2463, - 6880, - 28151, - 10824, - 11420, - 7369, - 3326, - 12163, - 74517, - 8245, - 12131, - 9985, - 10514, - 6803, - 95094, - 16254, - 8557, - 16296, - 10421, - 9679, - 8892, - 11846, - 5971, - 36760, - 59066, - 16657, - 5748, - 8278, - 9110, - 15122, - 9095, - 6452, - 10136, - 22515, - 31392, - 4574, - 32446, - 9187, - 10572, - 6828, - 6218, - 9568, - 85827, - 13455, - 17850, - 10232, - 6933, - 18785, - 8802, - 7943 - ], - "xaxis": "x", - "y": [ - 922, - 1660, - 447, - 1883, - 5907, - 3320, - 3239, - 2038, - 1163, - 2730, - 15082, - 1761, - 3044, - 2602, - 2539, - 1720, - 16833, - 3774, - 1835, - 3645, - 2746, - 2542, - 2120, - 2678, - 1482, - 8914, - 12187, - 4329, - 1604, - 2159, - 2126, - 3496, - 2235, - 1890, - 2522, - 5060, - 7440, - 1064, - 7094, - 1799, - 2952, - 1828, - 1634, - 2044, - 17326, - 2630, - 3928, - 2694, - 1674, - 4169, - 1873, - 1891 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Utrecht
Year=2007
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Utrechtse Heuvelrug", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "ids": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Utrechtse Heuvelrug", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "legendgroup": "Utrecht", - "marker": { - "color": "#FF97FF", - "size": [ - 139054, - 24375, - 42016, - 14160, - 19475, - 8891, - 45568, - 33994, - 28610, - 13999, - 13474, - 61365, - 9953, - 4463, - 18644, - 34565, - 45360, - 48846, - 61706, - 23366, - 48016, - 11403, - 60326 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Utrecht", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 57910, - 10516, - 18246, - 5775, - 6953, - 3517, - 16949, - 13430, - 11309, - 5026, - 5168, - 26075, - 3897, - 1562, - 7253, - 13052, - 19314, - 19758, - 24063, - 9079, - 18949, - 4205, - 25767 - ], - "xaxis": "x", - "y": [ - 15444, - 2220, - 4504, - 1452, - 2134, - 954, - 5278, - 3865, - 2852, - 1678, - 1661, - 5490, - 1117, - 425, - 2043, - 3479, - 4404, - 4810, - 7162, - 2485, - 5367, - 1316, - 5363 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Limburg
Year=2007
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "ids": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "legendgroup": "Limburg", - "marker": { - "color": "#FECB52", - "size": [ - 13646, - 13561, - 29590, - 32345, - 16863, - 14841, - 90537, - 28852, - 48769, - 38866, - 36818, - 24497, - 119038, - 19739, - 8029, - 16539, - 21320, - 54248, - 11319, - 96245, - 9959, - 17186, - 92091, - 39069, - 12848, - 48484 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Limburg", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5522, - 5316, - 13895, - 13333, - 6487, - 6294, - 45007, - 11233, - 23285, - 17796, - 14376, - 9890, - 56209, - 8252, - 3270, - 6211, - 8848, - 24754, - 4883, - 43407, - 4579, - 7640, - 41026, - 15569, - 5434, - 20471 - ], - "xaxis": "x", - "y": [ - 1321, - 1287, - 2501, - 2580, - 1585, - 1109, - 6481, - 2983, - 3042, - 3058, - 3390, - 1994, - 8312, - 1696, - 756, - 1620, - 1783, - 4655, - 873, - 7913, - 573, - 1267, - 7780, - 4091, - 1083, - 4321 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zeeland
Year=2007
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "ids": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "legendgroup": "Zeeland", - "marker": { - "color": "#636efa", - "size": [ - 22387, - 36600, - 28013, - 12001, - 7267, - 21109, - 34132, - 24325, - 55268, - 25155, - 21950, - 45023 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zeeland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 9097, - 16173, - 12520, - 4654, - 3920, - 8313, - 15140, - 13747, - 25342, - 9817, - 9987, - 21252 - ], - "xaxis": "x", - "y": [ - 2389, - 3508, - 2412, - 1277, - 579, - 2410, - 3172, - 1851, - 4769, - 2919, - 2239, - 3841 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Groningen
Year=2007
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Pekela", - "Stadskanaal", - "Veendam" - ], - "ids": [ - "Pekela", - "Stadskanaal", - "Veendam" - ], - "legendgroup": "Groningen", - "marker": { - "color": "#EF553B", - "size": [ - 13304, - 34123, - 28117 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Groningen", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5506, - 14583, - 12239 - ], - "xaxis": "x", - "y": [ - 1310, - 3281, - 2530 - ], - "yaxis": "y" - } - ], - "name": "2007" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

Provincienaam=Drenthe
Year=2008
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "ids": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "legendgroup": "Drenthe", - "marker": { - "color": "#636efa", - "size": [ - 25598, - 65487, - 26201, - 35993, - 109151, - 54468, - 31536, - 33587, - 31253, - 31974, - 19333, - 23554 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Drenthe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 10636, - 28565, - 10891, - 14740, - 46570, - 22583, - 13683, - 13521, - 13171, - 13285, - 7832, - 9389 - ], - "xaxis": "x", - "y": [ - 2390, - 6539, - 2397, - 3573, - 10524, - 5463, - 3393, - 3328, - 2987, - 3190, - 1780, - 2412 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Holland
Year=2008
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "ids": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "legendgroup": "Noord-Holland", - "marker": { - "color": "#EF553B", - "size": [ - 26386, - 93876, - 78980, - 747093, - 31220, - 37347, - 9036, - 16873, - 34705, - 24046, - 18818, - 28448, - 17804, - 147640, - 140648, - 38381, - 25626, - 22024, - 57795, - 83815, - 68696, - 41880, - 21495, - 10173, - 26682, - 9205, - 11244, - 13046, - 78434, - 19064, - 21372, - 13547, - 12205, - 27501, - 67556, - 17026, - 17575, - 23432, - 15901, - 142863, - 16665 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 10436, - 42648, - 36328, - 387531, - 13983, - 16691, - 3973, - 6908, - 14400, - 10127, - 7345, - 11285, - 7959, - 69402, - 56394, - 16170, - 11176, - 9303, - 26603, - 38768, - 30005, - 17992, - 8211, - 4259, - 10699, - 3749, - 4306, - 5679, - 33459, - 8284, - 8481, - 5958, - 4945, - 11724, - 29325, - 7072, - 8182, - 9666, - 6455, - 61788, - 8384 - ], - "xaxis": "x", - "y": [ - 2771, - 8133, - 6187, - 57696, - 2693, - 3489, - 1094, - 2946, - 3469, - 2253, - 1994, - 3053, - 1547, - 11880, - 15366, - 3628, - 2352, - 2313, - 4914, - 7605, - 6939, - 3700, - 2504, - 1098, - 2807, - 1053, - 1263, - 1596, - 7432, - 1753, - 2243, - 1372, - 1423, - 2960, - 6364, - 1586, - 1216, - 2426, - 1663, - 14042, - 1120 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Gelderland
Year=2008
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "ids": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "legendgroup": "Gelderland", - "marker": { - "color": "#00cc96", - "size": [ - 27568, - 155108, - 143582, - 51486, - 45150, - 25321, - 37833, - 21200, - 25727, - 27298, - 11567, - 56253, - 18082, - 25560, - 107686, - 22231, - 32970, - 26270, - 42333, - 11657, - 18212, - 16628, - 44617, - 33045, - 23631, - 34995, - 22424, - 38982, - 161251, - 26567, - 22771, - 29873, - 39905, - 44226, - 23183, - 31719, - 43763, - 1511, - 9037, - 41132, - 23660, - 36215, - 18320, - 15478, - 39660, - 29182, - 26185, - 31816, - 46762 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Gelderland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11059, - 64891, - 66264, - 18068, - 17832, - 10007, - 14738, - 8529, - 9388, - 10997, - 4887, - 23892, - 6881, - 10000, - 41159, - 8317, - 13175, - 9297, - 16713, - 4746, - 7290, - 6520, - 17738, - 13552, - 8941, - 13980, - 7589, - 14845, - 69422, - 9713, - 8043, - 11504, - 15935, - 17345, - 8125, - 14115, - 19540, - 631, - 3463, - 16650, - 8874, - 12977, - 7098, - 6166, - 15790, - 12016, - 9859, - 13508, - 20459 - ], - "xaxis": "x", - "y": [ - 2782, - 14374, - 12122, - 6588, - 4282, - 2647, - 3688, - 2048, - 2564, - 3096, - 1087, - 5477, - 1982, - 2996, - 11260, - 2405, - 3093, - 2449, - 4480, - 1184, - 1795, - 1894, - 4841, - 3142, - 2450, - 3561, - 2772, - 4316, - 12144, - 2851, - 2417, - 3252, - 3982, - 5100, - 2557, - 2673, - 3658, - 243, - 1166, - 4405, - 2417, - 2617, - 1867, - 1402, - 4137, - 2937, - 2861, - 2892, - 4900 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Fryslân
Year=2008
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Achtkarspelen", - "Ameland", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "ids": [ - "Achtkarspelen", - "Ameland", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "legendgroup": "Fryslân", - "marker": { - "color": "#ab63fa", - "size": [ - 28088, - 3456, - 15567, - 43027, - 92864, - 26398, - 29703, - 951, - 54962, - 4705, - 32243, - 1124, - 25674 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Fryslân", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11425, - 1554, - 6926, - 19214, - 45661, - 10855, - 12004, - 529, - 23535, - 2054, - 13044, - 553, - 10976 - ], - "xaxis": "x", - "y": [ - 2938, - 363, - 1428, - 4095, - 7465, - 2526, - 3200, - 93, - 5408, - 418, - 3228, - 112, - 2432 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zuid-Holland
Year=2008
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Katwijk", - "Krimpen aan den IJssel", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Nieuwkoop", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zwijndrecht" - ], - "ids": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Katwijk", - "Krimpen aan den IJssel", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Nieuwkoop", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zwijndrecht" - ], - "legendgroup": "Zuid-Holland", - "marker": { - "color": "#FFA15A", - "size": [ - 18718, - 22453, - 71658, - 44962, - 15762, - 65022, - 96168, - 118182, - 34472, - 70857, - 17604, - 39620, - 24854, - 20348, - 61180, - 28807, - 49411, - 116878, - 26376, - 72862, - 22131, - 31394, - 17451, - 26777, - 24906, - 22367, - 31573, - 43762, - 44689, - 582951, - 74947, - 23765, - 35308, - 70860, - 22861, - 25638, - 25763, - 99299, - 14084, - 119504, - 8303, - 44472 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zuid-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 7644, - 9033, - 29726, - 17637, - 6858, - 29186, - 43836, - 52804, - 15453, - 30309, - 6652, - 16564, - 9575, - 8632, - 23684, - 11843, - 18886, - 50994, - 11261, - 35326, - 9353, - 14235, - 6641, - 9994, - 10821, - 9104, - 13405, - 16443, - 19860, - 288677, - 35449, - 9799, - 14009, - 34462, - 10068, - 10292, - 11967, - 39384, - 6058, - 51796, - 3127, - 19740 - ], - "xaxis": "x", - "y": [ - 2070, - 2283, - 7180, - 5500, - 1479, - 5961, - 6881, - 11160, - 3680, - 7384, - 1928, - 3551, - 2716, - 1809, - 6178, - 2934, - 6149, - 8521, - 2899, - 5782, - 2389, - 2751, - 2278, - 2581, - 2129, - 2476, - 3114, - 5462, - 3664, - 50092, - 6852, - 2266, - 3813, - 5847, - 2353, - 2398, - 2138, - 9978, - 1236, - 11337, - 761, - 4056 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Overijssel
Year=2008
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "ids": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "legendgroup": "Overijssel", - "marker": { - "color": "#19d3f3", - "size": [ - 72287, - 20764, - 26735, - 97342, - 26116, - 154753, - 24378, - 58207, - 36059, - 35181, - 49385, - 22492, - 31584, - 17334, - 17419, - 37030, - 36667, - 15973, - 43282, - 20797, - 33461, - 23365, - 21930, - 116365 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Overijssel", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 31219, - 8371, - 9804, - 41620, - 9734, - 66882, - 9671, - 21587, - 13874, - 14082, - 20043, - 8522, - 13349, - 6795, - 6414, - 14089, - 13159, - 5283, - 18200, - 7164, - 12489, - 8729, - 7953, - 50541 - ], - "xaxis": "x", - "y": [ - 6880, - 2171, - 3103, - 9504, - 3081, - 13484, - 2420, - 6554, - 3540, - 3623, - 5507, - 2122, - 3109, - 1770, - 1654, - 3860, - 4280, - 2177, - 4468, - 2767, - 3628, - 2494, - 2902, - 10887 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Flevoland
Year=2008
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "ids": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "legendgroup": "Flevoland", - "marker": { - "color": "#FF6692", - "size": [ - 183270, - 38528, - 73063, - 45716, - 17825, - 20286 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Flevoland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 71029, - 14371, - 30010, - 17971, - 5166, - 7324 - ], - "xaxis": "x", - "y": [ - 21554, - 4261, - 7475, - 5176, - 2836, - 2787 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Brabant
Year=2008
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "ids": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "legendgroup": "Noord-Brabant", - "marker": { - "color": "#B6E880", - "size": [ - 9455, - 16392, - 6664, - 18099, - 65242, - 29663, - 29089, - 19114, - 9646, - 30241, - 170960, - 20263, - 31643, - 25442, - 26623, - 18072, - 210333, - 40435, - 20742, - 37982, - 28103, - 25644, - 22319, - 29488, - 15133, - 86767, - 136481, - 42942, - 15107, - 21783, - 22885, - 36724, - 22598, - 17855, - 25743, - 53785, - 76732, - 12384, - 77277, - 22473, - 28100, - 18211, - 15448, - 23211, - 202091, - 30867, - 43056, - 25337, - 16695, - 45641, - 21637, - 20947 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Brabant", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 3705, - 6408, - 2476, - 6929, - 28413, - 10926, - 11567, - 7379, - 3376, - 12278, - 75323, - 8274, - 12375, - 10028, - 10633, - 6828, - 95530, - 16538, - 8706, - 16373, - 10669, - 10062, - 9106, - 12093, - 6002, - 37346, - 60332, - 16925, - 5862, - 8435, - 9102, - 15316, - 9155, - 6518, - 10295, - 23026, - 31780, - 4612, - 32755, - 9227, - 10938, - 6904, - 6295, - 9622, - 86976, - 13602, - 17888, - 10198, - 7038, - 19009, - 8947, - 8099 - ], - "xaxis": "x", - "y": [ - 891, - 1620, - 431, - 1818, - 5871, - 3306, - 3251, - 1976, - 1161, - 2779, - 15301, - 1738, - 2977, - 2579, - 2448, - 1674, - 16892, - 3808, - 1876, - 3668, - 2691, - 2549, - 2132, - 2638, - 1470, - 9080, - 12191, - 4236, - 1610, - 2130, - 2090, - 3517, - 2186, - 1874, - 2516, - 5044, - 7384, - 1043, - 7006, - 1789, - 2969, - 1786, - 1647, - 2014, - 17410, - 2570, - 3930, - 2472, - 1635, - 4126, - 1875, - 1837 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Utrecht
Year=2008
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Utrechtse Heuvelrug", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "ids": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Utrechtse Heuvelrug", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "legendgroup": "Utrecht", - "marker": { - "color": "#FF97FF", - "size": [ - 141211, - 24406, - 41998, - 14258, - 19611, - 8964, - 46475, - 34059, - 28540, - 14047, - 13493, - 61087, - 9917, - 4482, - 18779, - 34569, - 45560, - 48979, - 61769, - 23306, - 48383, - 11592, - 60488 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Utrecht", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 58618, - 10514, - 18246, - 5817, - 7026, - 3552, - 17438, - 13668, - 11472, - 5117, - 5181, - 26181, - 3962, - 1585, - 7369, - 13157, - 19410, - 19896, - 24254, - 9083, - 19275, - 4379, - 26159 - ], - "xaxis": "x", - "y": [ - 15789, - 2223, - 4519, - 1478, - 2134, - 959, - 5412, - 3873, - 2892, - 1703, - 1654, - 5504, - 1105, - 424, - 2070, - 3429, - 4397, - 4886, - 7142, - 2467, - 5422, - 1328, - 5425 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Limburg
Year=2008
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "ids": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "legendgroup": "Limburg", - "marker": { - "color": "#FECB52", - "size": [ - 13643, - 13499, - 29742, - 32172, - 16890, - 14712, - 89671, - 28975, - 48334, - 38748, - 36744, - 24545, - 118004, - 19744, - 8084, - 16665, - 21142, - 54446, - 11206, - 95691, - 9838, - 17099, - 91872, - 39100, - 12757, - 48305 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Limburg", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5738, - 5335, - 14102, - 13540, - 6542, - 6331, - 45020, - 11365, - 23380, - 17644, - 14615, - 9983, - 56472, - 8454, - 3337, - 6299, - 8882, - 25152, - 4936, - 44336, - 4631, - 7691, - 41297, - 15583, - 5466, - 20715 - ], - "xaxis": "x", - "y": [ - 1303, - 1286, - 2441, - 2525, - 1572, - 1052, - 6285, - 2987, - 3041, - 3033, - 3292, - 1977, - 8105, - 1682, - 759, - 1575, - 1752, - 4351, - 845, - 7777, - 559, - 1255, - 7812, - 3982, - 1073, - 4228 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zeeland
Year=2008
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "ids": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "legendgroup": "Zeeland", - "marker": { - "color": "#636efa", - "size": [ - 22531, - 36706, - 27944, - 12047, - 7289, - 21296, - 33994, - 24238, - 55155, - 25264, - 21998, - 44798 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zeeland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 9241, - 16420, - 12648, - 4717, - 3927, - 8370, - 15455, - 13899, - 25552, - 10017, - 10156, - 21495 - ], - "xaxis": "x", - "y": [ - 2371, - 3496, - 2385, - 1298, - 565, - 2395, - 3126, - 1816, - 4702, - 2829, - 2183, - 3504 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Groningen
Year=2008
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Pekela", - "Stadskanaal", - "Veendam" - ], - "ids": [ - "Pekela", - "Stadskanaal", - "Veendam" - ], - "legendgroup": "Groningen", - "marker": { - "color": "#EF553B", - "size": [ - 13262, - 33817, - 28058 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Groningen", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5490, - 14747, - 12366 - ], - "xaxis": "x", - "y": [ - 1257, - 3149, - 2480 - ], - "yaxis": "y" - } - ], - "name": "2008" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

Provincienaam=Drenthe
Year=2009
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "ids": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "legendgroup": "Drenthe", - "marker": { - "color": "#636efa", - "size": [ - 25675, - 66369, - 26151, - 35887, - 109441, - 54652, - 32026, - 33560, - 31075, - 32236, - 19302, - 23544 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Drenthe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 10649, - 29223, - 10925, - 14803, - 46629, - 22738, - 13879, - 13637, - 13189, - 13338, - 7847, - 9474 - ], - "xaxis": "x", - "y": [ - 2394, - 6842, - 2375, - 3548, - 10345, - 5437, - 3462, - 3258, - 2963, - 3318, - 1765, - 2384 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Holland
Year=2009
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "ids": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "legendgroup": "Noord-Holland", - "marker": { - "color": "#EF553B", - "size": [ - 28006, - 93416, - 79768, - 755605, - 31154, - 38284, - 8987, - 22069, - 34739, - 24361, - 19091, - 28483, - 18104, - 148191, - 142042, - 38485, - 25680, - 22216, - 57526, - 84422, - 69358, - 42040, - 21792, - 10139, - 27017, - 9201, - 11289, - 13107, - 78862, - 18759, - 21289, - 13691, - 12434, - 27660, - 67528, - 16954, - 17577, - 23327, - 15900, - 144055, - 16607 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11278, - 42844, - 36981, - 391181, - 14049, - 16908, - 3974, - 9126, - 14428, - 10126, - 7497, - 11367, - 8081, - 69505, - 56811, - 16415, - 11262, - 9389, - 26746, - 38765, - 30550, - 18194, - 8390, - 4292, - 11039, - 3774, - 4421, - 5716, - 33766, - 8277, - 8515, - 6030, - 5061, - 11916, - 29446, - 7039, - 8260, - 9814, - 6513, - 62445, - 8384 - ], - "xaxis": "x", - "y": [ - 2971, - 8139, - 6197, - 58458, - 2614, - 3601, - 1112, - 3603, - 3412, - 2176, - 1999, - 3080, - 1544, - 12045, - 15672, - 3546, - 2356, - 2314, - 4817, - 7717, - 7001, - 3716, - 2457, - 1102, - 2846, - 1017, - 1297, - 1582, - 7386, - 1744, - 2145, - 1352, - 1411, - 2922, - 6197, - 1598, - 1274, - 2414, - 1638, - 14133, - 1138 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Gelderland
Year=2009
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "ids": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "legendgroup": "Gelderland", - "marker": { - "color": "#00cc96", - "size": [ - 27447, - 155332, - 145574, - 52066, - 45142, - 25304, - 37897, - 21159, - 25667, - 27414, - 11503, - 56136, - 18102, - 25510, - 107623, - 22112, - 32954, - 26306, - 43092, - 11708, - 18313, - 16623, - 45084, - 33313, - 23798, - 35103, - 22561, - 39080, - 161817, - 26714, - 22622, - 30012, - 40010, - 45097, - 23458, - 31752, - 43679, - 1515, - 9141, - 41070, - 23706, - 36695, - 18374, - 15277, - 39948, - 29028, - 26218, - 31809, - 46953 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Gelderland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11130, - 65685, - 67018, - 18224, - 17978, - 10057, - 14863, - 8574, - 9446, - 11200, - 4912, - 23934, - 7010, - 10128, - 41379, - 8326, - 13273, - 9626, - 17300, - 4803, - 7410, - 6581, - 17895, - 13778, - 9018, - 14073, - 7665, - 15063, - 69924, - 9737, - 8083, - 11714, - 16064, - 17520, - 8347, - 14401, - 19678, - 632, - 3520, - 16657, - 8944, - 13055, - 7159, - 6221, - 16143, - 12129, - 9890, - 13516, - 20606 - ], - "xaxis": "x", - "y": [ - 2791, - 14321, - 12267, - 6512, - 4264, - 2583, - 3709, - 1988, - 2518, - 3069, - 1061, - 5431, - 1974, - 2849, - 11154, - 2351, - 3002, - 2318, - 4557, - 1209, - 1776, - 1840, - 4850, - 3139, - 2459, - 3517, - 2763, - 4348, - 12052, - 2836, - 2413, - 3182, - 3914, - 5113, - 2524, - 2700, - 3643, - 228, - 1152, - 4362, - 2369, - 2646, - 1801, - 1375, - 4124, - 2909, - 2946, - 2913, - 4860 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Fryslân
Year=2009
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Achtkarspelen", - "Ameland", - "Dantumadiel", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "ids": [ - "Achtkarspelen", - "Ameland", - "Dantumadiel", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "legendgroup": "Fryslân", - "marker": { - "color": "#ab63fa", - "size": [ - 28139, - 3466, - 19367, - 15689, - 43334, - 93498, - 26288, - 29777, - 946, - 55201, - 4740, - 32211, - 1146, - 25802 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Fryslân", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11493, - 1563, - 7694, - 6987, - 19443, - 46201, - 10918, - 12004, - 529, - 23859, - 2056, - 13141, - 553, - 11075 - ], - "xaxis": "x", - "y": [ - 2945, - 367, - 1830, - 1433, - 4080, - 7420, - 2467, - 3212, - 83, - 5388, - 415, - 3177, - 113, - 2406 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zuid-Holland
Year=2009
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Kaag en Braassem", - "Katwijk", - "Krimpen aan den IJssel", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Nieuwkoop", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zwijndrecht" - ], - "ids": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Kaag en Braassem", - "Katwijk", - "Krimpen aan den IJssel", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Nieuwkoop", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zwijndrecht" - ], - "legendgroup": "Zuid-Holland", - "marker": { - "color": "#FFA15A", - "size": [ - 18839, - 22974, - 72178, - 45861, - 15597, - 65273, - 96517, - 118408, - 34554, - 70828, - 17538, - 39629, - 25881, - 20487, - 25412, - 61337, - 28907, - 51019, - 116787, - 26470, - 72697, - 22301, - 31389, - 17598, - 26757, - 25335, - 22564, - 31730, - 45900, - 44646, - 587134, - 75326, - 23895, - 35533, - 70433, - 22996, - 25436, - 25909, - 99436, - 14031, - 120881, - 8193, - 44312 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zuid-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 7692, - 9187, - 30033, - 18029, - 6715, - 29321, - 44160, - 53390, - 15473, - 30507, - 6747, - 16765, - 10287, - 8758, - 9632, - 23829, - 11926, - 19441, - 51352, - 11330, - 35403, - 9486, - 13989, - 6801, - 9992, - 10946, - 9305, - 13565, - 17697, - 20019, - 289405, - 35590, - 10046, - 14153, - 34403, - 10133, - 10320, - 11999, - 39625, - 6091, - 52274, - 3124, - 19792 - ], - "xaxis": "x", - "y": [ - 2104, - 2394, - 7170, - 5570, - 1475, - 5872, - 6845, - 11019, - 3619, - 7335, - 1908, - 3495, - 2816, - 1809, - 2601, - 6287, - 2909, - 6324, - 8473, - 2828, - 5904, - 2366, - 2705, - 2221, - 2538, - 2141, - 2438, - 3167, - 5602, - 3589, - 50083, - 6981, - 2273, - 3802, - 5663, - 2364, - 2354, - 2100, - 9835, - 1216, - 11345, - 735, - 4011 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Overijssel
Year=2009
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "ids": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "legendgroup": "Overijssel", - "marker": { - "color": "#19d3f3", - "size": [ - 72428, - 21106, - 27044, - 97892, - 26066, - 156071, - 24495, - 58650, - 35846, - 35151, - 49860, - 22589, - 31764, - 17482, - 17446, - 36891, - 36787, - 16016, - 43215, - 20992, - 33605, - 23467, - 21944, - 117703 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Overijssel", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 31479, - 8591, - 9922, - 41506, - 9739, - 67760, - 9675, - 22077, - 13999, - 14305, - 20133, - 8730, - 13444, - 6920, - 6545, - 14153, - 13265, - 5367, - 18110, - 7219, - 12625, - 8799, - 7965, - 51226 - ], - "xaxis": "x", - "y": [ - 6921, - 2156, - 3121, - 9577, - 3014, - 13493, - 2426, - 6529, - 3582, - 3605, - 5554, - 2147, - 3098, - 1750, - 1700, - 3742, - 4289, - 2168, - 4424, - 2757, - 3658, - 2470, - 2857, - 11127 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Flevoland
Year=2009
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "ids": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "legendgroup": "Flevoland", - "marker": { - "color": "#FF6692", - "size": [ - 185746, - 39206, - 73848, - 45814, - 18062, - 20773 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Flevoland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 72373, - 14789, - 30450, - 18203, - 5291, - 7557 - ], - "xaxis": "x", - "y": [ - 21437, - 4238, - 7609, - 5203, - 2838, - 2758 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Brabant
Year=2009
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "ids": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "legendgroup": "Noord-Brabant", - "marker": { - "color": "#B6E880", - "size": [ - 9421, - 16363, - 6682, - 18087, - 65582, - 29615, - 29017, - 19129, - 9692, - 30281, - 171916, - 20272, - 31466, - 25411, - 26624, - 18104, - 212269, - 40997, - 20794, - 37996, - 28508, - 25789, - 22466, - 29271, - 15194, - 87757, - 137775, - 43060, - 15041, - 21717, - 22934, - 36648, - 22437, - 17806, - 25738, - 54198, - 77097, - 12432, - 77482, - 22549, - 28267, - 18229, - 15527, - 23229, - 203464, - 30871, - 43007, - 25288, - 16543, - 45744, - 21700, - 21083 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Brabant", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 3703, - 6457, - 2513, - 7012, - 28766, - 10971, - 11617, - 7566, - 3374, - 12283, - 75885, - 8290, - 12428, - 10226, - 10673, - 7060, - 95251, - 17001, - 8772, - 16486, - 10919, - 10092, - 9170, - 12087, - 6072, - 37693, - 61236, - 16981, - 5878, - 8459, - 9176, - 15350, - 9155, - 6566, - 10440, - 23042, - 32020, - 4691, - 33086, - 9235, - 11048, - 6926, - 6314, - 9665, - 88270, - 13680, - 17956, - 10495, - 7097, - 19079, - 8977, - 8186 - ], - "xaxis": "x", - "y": [ - 876, - 1606, - 438, - 1787, - 5836, - 3241, - 3196, - 1965, - 1130, - 2787, - 15382, - 1751, - 2865, - 2495, - 2426, - 1606, - 16943, - 3838, - 1882, - 3657, - 2691, - 2490, - 2141, - 2593, - 1424, - 9104, - 12222, - 4127, - 1552, - 2144, - 2048, - 3459, - 2125, - 1866, - 2474, - 4966, - 7303, - 1042, - 6828, - 1716, - 2952, - 1738, - 1684, - 1987, - 17390, - 2558, - 3876, - 2413, - 1626, - 4147, - 1833, - 1731 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Utrecht
Year=2009
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Utrechtse Heuvelrug", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "ids": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Utrechtse Heuvelrug", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "legendgroup": "Utrecht", - "marker": { - "color": "#FF97FF", - "size": [ - 143212, - 24335, - 41984, - 14407, - 19753, - 8880, - 47243, - 34163, - 28583, - 14156, - 13484, - 61007, - 9898, - 4532, - 18833, - 34528, - 45711, - 48893, - 62008, - 23252, - 48885, - 11744, - 60383 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Utrecht", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 59653, - 10561, - 18246, - 5842, - 7283, - 3552, - 17887, - 13698, - 11660, - 5186, - 5234, - 26156, - 3964, - 1594, - 7387, - 13155, - 19548, - 19903, - 24458, - 9148, - 19537, - 4528, - 26240 - ], - "xaxis": "x", - "y": [ - 15835, - 2218, - 4507, - 1459, - 2147, - 917, - 5587, - 3880, - 2881, - 1688, - 1608, - 5451, - 1067, - 428, - 2085, - 3328, - 4364, - 4836, - 7054, - 2433, - 5479, - 1316, - 5523 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Limburg
Year=2009
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "ids": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "legendgroup": "Limburg", - "marker": { - "color": "#FECB52", - "size": [ - 13795, - 13402, - 29532, - 32049, - 17101, - 14585, - 89356, - 29286, - 48076, - 38713, - 36751, - 24473, - 118286, - 19695, - 8040, - 16695, - 21333, - 54731, - 11090, - 95327, - 9825, - 17071, - 91467, - 39224, - 12749, - 48332 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Limburg", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5825, - 5378, - 14169, - 13539, - 6702, - 6327, - 45077, - 11518, - 23375, - 17692, - 14706, - 10001, - 56425, - 8463, - 3343, - 6536, - 8985, - 25362, - 4938, - 44413, - 4710, - 7777, - 41306, - 15678, - 5465, - 20862 - ], - "xaxis": "x", - "y": [ - 1287, - 1234, - 2376, - 2447, - 1578, - 1013, - 6224, - 2914, - 2966, - 2948, - 3216, - 1946, - 7891, - 1620, - 743, - 1538, - 1752, - 4573, - 816, - 7520, - 536, - 1225, - 7804, - 3873, - 1044, - 4167 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zeeland
Year=2009
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "ids": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "legendgroup": "Zeeland", - "marker": { - "color": "#636efa", - "size": [ - 22551, - 36754, - 27886, - 12288, - 7327, - 21345, - 33929, - 24156, - 55149, - 25368, - 21960, - 44712 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zeeland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 9287, - 16551, - 12714, - 4833, - 4019, - 8421, - 15626, - 13903, - 25774, - 10066, - 10204, - 21624 - ], - "xaxis": "x", - "y": [ - 2342, - 3439, - 2277, - 1332, - 558, - 2368, - 3090, - 1766, - 4594, - 2813, - 2101, - 3449 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Groningen
Year=2009
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Pekela", - "Stadskanaal", - "Veendam" - ], - "ids": [ - "Pekela", - "Stadskanaal", - "Veendam" - ], - "legendgroup": "Groningen", - "marker": { - "color": "#EF553B", - "size": [ - 13130, - 33430, - 27995 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Groningen", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5492, - 14891, - 12496 - ], - "xaxis": "x", - "y": [ - 1192, - 3097, - 2420 - ], - "yaxis": "y" - } - ], - "name": "2009" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

Provincienaam=Drenthe
Year=2010
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "ids": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "legendgroup": "Drenthe", - "marker": { - "color": "#636efa", - "size": [ - 25563, - 66857, - 26072, - 36121, - 109491, - 54805, - 32378, - 33560, - 30808, - 32408, - 19342, - 23576 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Drenthe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 10763, - 29622, - 11017, - 14861, - 46797, - 22856, - 14066, - 13799, - 13197, - 13471, - 7877, - 9561 - ], - "xaxis": "x", - "y": [ - 2351, - 6870, - 2279, - 3535, - 10171, - 5395, - 3496, - 3169, - 2899, - 3300, - 1745, - 2324 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Holland
Year=2010
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "ids": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "legendgroup": "Noord-Holland", - "marker": { - "color": "#EF553B", - "size": [ - 29187, - 93861, - 80695, - 767457, - 30985, - 38883, - 8955, - 22023, - 34652, - 24685, - 19291, - 28506, - 18153, - 149579, - 142788, - 38848, - 26058, - 22451, - 57403, - 84573, - 70252, - 41934, - 21969, - 10233, - 27384, - 9149, - 11386, - 13099, - 79038, - 18736, - 21274, - 13779, - 12664, - 28053, - 67281, - 17057, - 17636, - 23391, - 15862, - 145332, - 16632 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11741, - 43067, - 37171, - 394196, - 14055, - 16917, - 4008, - 9117, - 14503, - 10425, - 7624, - 11441, - 8137, - 70674, - 57059, - 16844, - 11445, - 9524, - 26930, - 38830, - 30985, - 18198, - 8442, - 4329, - 11189, - 3777, - 4472, - 5727, - 33772, - 8275, - 8597, - 6063, - 5090, - 12046, - 29470, - 7082, - 8300, - 9867, - 6515, - 63700, - 8455 - ], - "xaxis": "x", - "y": [ - 3115, - 8096, - 6152, - 58773, - 2512, - 3661, - 1086, - 3527, - 3302, - 2170, - 1924, - 3081, - 1530, - 12009, - 15692, - 3509, - 2567, - 2245, - 4752, - 7683, - 6958, - 3697, - 2424, - 1097, - 2833, - 997, - 1286, - 1553, - 7232, - 1727, - 2125, - 1310, - 1383, - 2805, - 5897, - 1584, - 1283, - 2350, - 1568, - 14122, - 1104 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Gelderland
Year=2010
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "ids": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "legendgroup": "Gelderland", - "marker": { - "color": "#00cc96", - "size": [ - 27500, - 155726, - 147018, - 52490, - 44978, - 25481, - 37751, - 21139, - 25887, - 27501, - 11600, - 56108, - 18084, - 25593, - 107756, - 22176, - 32881, - 26264, - 44010, - 11792, - 18313, - 16632, - 45445, - 33395, - 23756, - 35179, - 22553, - 39538, - 162963, - 26728, - 22750, - 30026, - 39897, - 45577, - 23592, - 31656, - 43724, - 1495, - 9316, - 41192, - 23772, - 37359, - 18413, - 15237, - 40148, - 29051, - 26428, - 31907, - 46870 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Gelderland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11218, - 65956, - 68096, - 18509, - 18011, - 10227, - 14946, - 8584, - 9659, - 11343, - 5072, - 24125, - 6986, - 10377, - 41648, - 8332, - 13335, - 9748, - 17868, - 4811, - 7410, - 6652, - 18336, - 13905, - 9099, - 14200, - 7757, - 15340, - 70402, - 9819, - 8264, - 11795, - 16264, - 17855, - 8600, - 14556, - 19928, - 632, - 3609, - 16857, - 9115, - 13272, - 7209, - 6362, - 16346, - 12248, - 10115, - 13760, - 20705 - ], - "xaxis": "x", - "y": [ - 2753, - 14209, - 12300, - 6521, - 4265, - 2555, - 3633, - 1987, - 2510, - 3010, - 1015, - 5384, - 1916, - 2750, - 11121, - 2305, - 2953, - 2293, - 4595, - 1185, - 1762, - 1750, - 4773, - 3129, - 2415, - 3422, - 2741, - 4304, - 11892, - 2754, - 2435, - 3088, - 3787, - 5086, - 2452, - 2683, - 3630, - 242, - 1155, - 4246, - 2327, - 2615, - 1702, - 1386, - 4099, - 2887, - 2903, - 2916, - 4828 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Fryslân
Year=2010
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Achtkarspelen", - "Ameland", - "Dantumadiel", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "ids": [ - "Achtkarspelen", - "Ameland", - "Dantumadiel", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "legendgroup": "Fryslân", - "marker": { - "color": "#ab63fa", - "size": [ - 28088, - 3501, - 19283, - 15809, - 43418, - 94073, - 26235, - 30039, - 942, - 55271, - 4733, - 32237, - 1160, - 25829 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Fryslân", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11537, - 1568, - 7701, - 7033, - 19623, - 46391, - 10938, - 12084, - 529, - 23988, - 2056, - 13189, - 553, - 11140 - ], - "xaxis": "x", - "y": [ - 2939, - 356, - 1796, - 1417, - 4071, - 7453, - 2401, - 3137, - 93, - 5330, - 401, - 3162, - 122, - 2388 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zuid-Holland
Year=2010
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Kaag en Braassem", - "Katwijk", - "Krimpen aan den IJssel", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Nieuwkoop", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zuidplas", - "Zwijndrecht" - ], - "ids": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Kaag en Braassem", - "Katwijk", - "Krimpen aan den IJssel", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Nieuwkoop", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zuidplas", - "Zwijndrecht" - ], - "legendgroup": "Zuid-Holland", - "marker": { - "color": "#FFA15A", - "size": [ - 19014, - 24191, - 72534, - 46449, - 15759, - 65345, - 96760, - 118480, - 34663, - 71122, - 17497, - 39756, - 26897, - 20484, - 25642, - 61828, - 28812, - 52565, - 117123, - 26426, - 72160, - 22335, - 31591, - 17890, - 26929, - 25407, - 22597, - 31853, - 48013, - 44746, - 593049, - 75565, - 24051, - 35759, - 70533, - 23462, - 25400, - 25816, - 99717, - 14057, - 121532, - 8118, - 40410, - 44404 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zuid-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 7745, - 9777, - 30280, - 18286, - 7010, - 29849, - 44457, - 53349, - 15474, - 30781, - 6738, - 16968, - 10709, - 8885, - 9901, - 24028, - 11939, - 20369, - 51931, - 11457, - 35537, - 9649, - 14235, - 7014, - 10105, - 10988, - 9352, - 13697, - 18463, - 20333, - 290027, - 35558, - 10188, - 14388, - 34271, - 10473, - 10315, - 12057, - 39943, - 6278, - 52681, - 3156, - 15596, - 20171 - ], - "xaxis": "x", - "y": [ - 2067, - 2459, - 7128, - 5596, - 1447, - 5817, - 6757, - 10821, - 3634, - 7169, - 1908, - 3429, - 2870, - 1810, - 2588, - 6283, - 2881, - 6954, - 8513, - 2788, - 5903, - 2335, - 2748, - 2202, - 2520, - 2151, - 2437, - 3125, - 5696, - 3546, - 49455, - 6976, - 2249, - 3748, - 5660, - 2358, - 2261, - 2075, - 9784, - 1188, - 11115, - 727, - 4090, - 4021 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Overijssel
Year=2010
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "ids": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "legendgroup": "Overijssel", - "marker": { - "color": "#19d3f3", - "size": [ - 72602, - 21330, - 27259, - 98523, - 26058, - 157052, - 24486, - 59014, - 35791, - 35468, - 50051, - 22647, - 31974, - 17540, - 17404, - 36796, - 37080, - 16153, - 43208, - 21145, - 33580, - 23447, - 21935, - 119030 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Overijssel", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 31514, - 8671, - 10053, - 41796, - 9767, - 68124, - 9719, - 22407, - 14021, - 14758, - 20160, - 8743, - 13579, - 6970, - 6631, - 14172, - 13502, - 5398, - 18243, - 7382, - 12642, - 8867, - 7970, - 51912 - ], - "xaxis": "x", - "y": [ - 6892, - 2143, - 3070, - 9451, - 2972, - 13353, - 2398, - 6428, - 3603, - 3545, - 5562, - 2095, - 3133, - 1692, - 1702, - 3700, - 4305, - 2144, - 4340, - 2699, - 3664, - 2458, - 2761, - 11277 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Flevoland
Year=2010
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "ids": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "legendgroup": "Flevoland", - "marker": { - "color": "#FF6692", - "size": [ - 188160, - 39787, - 74628, - 46090, - 18310, - 20906 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Flevoland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 73672, - 15114, - 30953, - 18381, - 5450, - 7712 - ], - "xaxis": "x", - "y": [ - 21165, - 4189, - 7598, - 5153, - 2818, - 2723 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Brabant
Year=2010
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "ids": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "legendgroup": "Noord-Brabant", - "marker": { - "color": "#B6E880", - "size": [ - 9436, - 16335, - 6703, - 18061, - 65845, - 29655, - 28953, - 19177, - 9772, - 30276, - 173299, - 20343, - 31526, - 25061, - 26591, - 18157, - 213809, - 41524, - 21087, - 38117, - 28763, - 25975, - 22761, - 29291, - 15260, - 88291, - 139607, - 42995, - 14978, - 21581, - 23016, - 36536, - 22213, - 17750, - 25827, - 54136, - 77392, - 12555, - 77566, - 22484, - 28138, - 18229, - 15543, - 23224, - 204853, - 30725, - 43243, - 25400, - 16533, - 45751, - 21635, - 21025 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Brabant", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 3735, - 6522, - 2531, - 7059, - 28987, - 11174, - 11632, - 7666, - 3374, - 12372, - 76276, - 8415, - 12770, - 10325, - 10690, - 7196, - 96969, - 17244, - 9108, - 16655, - 11138, - 10161, - 9299, - 12254, - 6197, - 37871, - 62526, - 16985, - 5932, - 8519, - 9177, - 15375, - 9229, - 6648, - 10505, - 23206, - 32264, - 4841, - 33173, - 9279, - 11097, - 7029, - 6372, - 9805, - 89501, - 13768, - 18710, - 10569, - 7138, - 19354, - 9060, - 8300 - ], - "xaxis": "x", - "y": [ - 838, - 1587, - 441, - 1728, - 5742, - 3163, - 3144, - 1976, - 1099, - 2828, - 15471, - 1705, - 2802, - 2394, - 2343, - 1564, - 17062, - 3868, - 1880, - 3540, - 2618, - 2484, - 2139, - 2546, - 1405, - 9108, - 12303, - 4014, - 1548, - 2086, - 2053, - 3390, - 2070, - 1794, - 2423, - 4886, - 7103, - 1055, - 6702, - 1636, - 2898, - 1698, - 1630, - 1943, - 17254, - 2505, - 3813, - 2388, - 1599, - 4117, - 1772, - 1641 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Utrecht
Year=2010
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Utrechtse Heuvelrug", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "ids": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Utrechtse Heuvelrug", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "legendgroup": "Utrecht", - "marker": { - "color": "#FF97FF", - "size": [ - 144862, - 24317, - 42017, - 14459, - 20000, - 8837, - 47622, - 34226, - 28812, - 14174, - 13494, - 60896, - 9843, - 4602, - 18860, - 34400, - 45680, - 48801, - 62053, - 23157, - 49334, - 11905, - 60286 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Utrecht", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 60605, - 10669, - 18278, - 5879, - 7327, - 3620, - 18176, - 13850, - 11827, - 5201, - 5256, - 26157, - 3964, - 1646, - 7411, - 13188, - 19651, - 19916, - 24603, - 9187, - 19750, - 4583, - 26266 - ], - "xaxis": "x", - "y": [ - 15888, - 2208, - 4507, - 1470, - 2132, - 903, - 5667, - 3936, - 2832, - 1633, - 1582, - 5424, - 1049, - 452, - 2091, - 3206, - 4289, - 4819, - 7005, - 2396, - 5453, - 1347, - 5522 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Limburg
Year=2010
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Peel en Maas", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "ids": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Peel en Maas", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "legendgroup": "Limburg", - "marker": { - "color": "#FECB52", - "size": [ - 13902, - 13391, - 29448, - 32282, - 17153, - 14594, - 89236, - 41465, - 47684, - 38457, - 36741, - 24275, - 118533, - 19565, - 8049, - 16607, - 43058, - 21307, - 55212, - 10996, - 95243, - 9870, - 17090, - 100301, - 42718, - 12703, - 48456 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Limburg", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5862, - 5398, - 14129, - 13785, - 6771, - 6315, - 45321, - 16382, - 23540, - 17809, - 14819, - 10021, - 56662, - 8445, - 3373, - 6560, - 16802, - 9078, - 25882, - 4920, - 44428, - 4753, - 7811, - 45091, - 17180, - 5469, - 21107 - ], - "xaxis": "x", - "y": [ - 1266, - 1187, - 2289, - 2414, - 1547, - 980, - 6176, - 4131, - 2933, - 2826, - 3107, - 1826, - 7612, - 1563, - 721, - 1432, - 3959, - 1697, - 4541, - 798, - 7252, - 518, - 1220, - 8512, - 4063, - 1000, - 4026 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zeeland
Year=2010
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "ids": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "legendgroup": "Zeeland", - "marker": { - "color": "#636efa", - "size": [ - 22618, - 36605, - 27860, - 12402, - 7397, - 21476, - 34118, - 24089, - 54878, - 25410, - 21914, - 44645 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zeeland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 9395, - 16616, - 12795, - 4891, - 4038, - 8431, - 15752, - 13931, - 25991, - 10197, - 10213, - 21744 - ], - "xaxis": "x", - "y": [ - 2284, - 3311, - 2180, - 1298, - 545, - 2421, - 2998, - 1735, - 4455, - 2746, - 1967, - 3494 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Groningen
Year=2010
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Oldambt", - "Pekela", - "Stadskanaal", - "Veendam" - ], - "ids": [ - "Oldambt", - "Pekela", - "Stadskanaal", - "Veendam" - ], - "legendgroup": "Groningen", - "marker": { - "color": "#EF553B", - "size": [ - 39486, - 13054, - 33416, - 28024 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Groningen", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 17972, - 5487, - 14968, - 12562 - ], - "xaxis": "x", - "y": [ - 3349, - 1136, - 2987, - 2385 - ], - "yaxis": "y" - } - ], - "name": "2010" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

Provincienaam=Drenthe
Year=2011
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "ids": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "legendgroup": "Drenthe", - "marker": { - "color": "#636efa", - "size": [ - 25785, - 67177, - 25941, - 36067, - 109259, - 54844, - 32511, - 33581, - 30794, - 32450, - 19365, - 23637 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Drenthe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 10797, - 29702, - 11062, - 14898, - 46837, - 22990, - 14157, - 13864, - 13137, - 13495, - 7935, - 9588 - ], - "xaxis": "x", - "y": [ - 2264, - 6813, - 2153, - 3412, - 9798, - 5370, - 3485, - 3079, - 2833, - 3241, - 1687, - 2209 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Holland
Year=2011
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "ids": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "legendgroup": "Noord-Holland", - "marker": { - "color": "#EF553B", - "size": [ - 30189, - 93936, - 81796, - 779808, - 30868, - 39329, - 8959, - 22039, - 34593, - 25012, - 19335, - 28583, - 18173, - 150670, - 143374, - 39206, - 26297, - 22580, - 57207, - 84984, - 70697, - 41726, - 22082, - 10253, - 42891, - 9114, - 11420, - 13146, - 79193, - 18671, - 21402, - 13728, - 12819, - 28114, - 67347, - 17140, - 17987, - 23312, - 15801, - 146940, - 16632 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11764, - 43231, - 37442, - 397460, - 14108, - 17253, - 4081, - 9140, - 14562, - 10426, - 7667, - 11471, - 8214, - 70976, - 57265, - 16900, - 11555, - 9570, - 26965, - 38827, - 31088, - 18211, - 8540, - 4336, - 17208, - 3776, - 4562, - 5718, - 34024, - 8278, - 8758, - 6105, - 5179, - 12074, - 29444, - 7083, - 8408, - 9912, - 6515, - 64185, - 8455 - ], - "xaxis": "x", - "y": [ - 3238, - 7990, - 6193, - 59674, - 2444, - 3652, - 1075, - 3513, - 3199, - 2130, - 1859, - 3068, - 1514, - 12209, - 15690, - 3452, - 2575, - 2178, - 4671, - 7661, - 6870, - 3636, - 2345, - 1064, - 4309, - 966, - 1244, - 1588, - 7024, - 1707, - 2061, - 1238, - 1359, - 2746, - 5645, - 1548, - 1276, - 2263, - 1514, - 14040, - 1087 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Gelderland
Year=2011
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "ids": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "legendgroup": "Gelderland", - "marker": { - "color": "#00cc96", - "size": [ - 27439, - 156199, - 148070, - 53026, - 44863, - 25507, - 37677, - 21219, - 26013, - 27584, - 11636, - 56037, - 18097, - 25550, - 108285, - 22310, - 32875, - 26133, - 44932, - 11762, - 18300, - 16494, - 45589, - 33278, - 23970, - 34976, - 22620, - 39788, - 164223, - 26685, - 22659, - 30113, - 39922, - 45953, - 23746, - 31559, - 43982, - 1512, - 9327, - 41181, - 23703, - 36642, - 18301, - 15336, - 40507, - 29026, - 26643, - 32234, - 47084 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Gelderland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11267, - 66160, - 68127, - 18779, - 18242, - 10293, - 15000, - 8584, - 9741, - 11409, - 5080, - 24142, - 7101, - 10385, - 41883, - 8475, - 13358, - 9765, - 18298, - 4810, - 7410, - 6646, - 18499, - 13984, - 9175, - 14400, - 7781, - 15463, - 71083, - 9867, - 8365, - 11841, - 16342, - 18033, - 8703, - 14553, - 19928, - 632, - 3609, - 17158, - 9125, - 13493, - 7261, - 6385, - 16792, - 12266, - 10129, - 13760, - 20874 - ], - "xaxis": "x", - "y": [ - 2736, - 14103, - 12421, - 6406, - 4151, - 2417, - 3527, - 1880, - 2415, - 2943, - 939, - 5276, - 1867, - 2551, - 11005, - 2282, - 2846, - 2262, - 4702, - 1171, - 1715, - 1659, - 4732, - 3043, - 2387, - 3227, - 2702, - 4313, - 11756, - 2723, - 2386, - 3039, - 3733, - 4908, - 2343, - 2641, - 3539, - 249, - 1147, - 4170, - 2309, - 2579, - 1643, - 1348, - 4005, - 2771, - 2882, - 2889, - 4735 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Fryslân
Year=2011
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Achtkarspelen", - "Ameland", - "Dantumadiel", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Súdwest-Fryslân", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "ids": [ - "Achtkarspelen", - "Ameland", - "Dantumadiel", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Súdwest-Fryslân", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "legendgroup": "Fryslân", - "marker": { - "color": "#ab63fa", - "size": [ - 28123, - 3503, - 19310, - 15878, - 43454, - 94838, - 26004, - 29991, - 957, - 55436, - 82445, - 4721, - 32178, - 1151, - 25846 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Fryslân", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11566, - 1577, - 7707, - 7091, - 19661, - 46635, - 11011, - 12215, - 559, - 24055, - 35825, - 2063, - 13223, - 566, - 11183 - ], - "xaxis": "x", - "y": [ - 2921, - 360, - 1792, - 1418, - 3995, - 7429, - 2350, - 3047, - 84, - 5209, - 8181, - 394, - 3121, - 113, - 2318 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zuid-Holland
Year=2011
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Bodegraven-Reeuwijk", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Kaag en Braassem", - "Katwijk", - "Krimpen aan den IJssel", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Nieuwkoop", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zuidplas", - "Zwijndrecht" - ], - "ids": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Bodegraven-Reeuwijk", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Kaag en Braassem", - "Katwijk", - "Krimpen aan den IJssel", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Nieuwkoop", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zuidplas", - "Zwijndrecht" - ], - "legendgroup": "Zuid-Holland", - "marker": { - "color": "#FFA15A", - "size": [ - 19208, - 24674, - 72680, - 46831, - 32728, - 15978, - 66104, - 97690, - 118810, - 34895, - 71047, - 17535, - 39739, - 27616, - 20627, - 25744, - 62044, - 28626, - 54090, - 117915, - 26609, - 72068, - 22685, - 31910, - 18154, - 26988, - 25438, - 22767, - 31883, - 49286, - 44889, - 610386, - 75718, - 24061, - 35812, - 71269, - 23865, - 25337, - 25830, - 99776, - 13992, - 121911, - 8130, - 40521, - 44445 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zuid-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 7933, - 9996, - 30315, - 18356, - 12832, - 7189, - 30320, - 44631, - 53437, - 15668, - 30730, - 6887, - 16910, - 11148, - 8893, - 9935, - 24083, - 11870, - 20624, - 52269, - 11563, - 35776, - 9659, - 14459, - 7111, - 10191, - 11150, - 9453, - 13801, - 19083, - 20379, - 297737, - 35588, - 10203, - 14376, - 34651, - 10747, - 10342, - 12139, - 40246, - 6432, - 52929, - 3156, - 15768, - 20175 - ], - "xaxis": "x", - "y": [ - 2060, - 2510, - 7050, - 5586, - 3359, - 1408, - 5731, - 6748, - 10614, - 3587, - 7281, - 1886, - 3352, - 2979, - 1801, - 2576, - 6213, - 2858, - 7049, - 8444, - 2724, - 5905, - 2313, - 2677, - 2175, - 2407, - 2141, - 2457, - 3080, - 5772, - 3507, - 50280, - 6914, - 2286, - 3624, - 5650, - 2395, - 2176, - 2042, - 9747, - 1153, - 11128, - 701, - 4053, - 3961 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Overijssel
Year=2011
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "ids": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "legendgroup": "Overijssel", - "marker": { - "color": "#19d3f3", - "size": [ - 72599, - 21557, - 27313, - 98737, - 26067, - 157838, - 24448, - 59283, - 35747, - 35573, - 50403, - 22664, - 32176, - 17502, - 17329, - 36688, - 37433, - 16179, - 43281, - 21171, - 33727, - 23630, - 22018, - 120355 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Overijssel", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 31477, - 8801, - 10149, - 41914, - 9809, - 68516, - 9722, - 22509, - 14123, - 14775, - 20453, - 8716, - 13703, - 6972, - 6640, - 14304, - 13648, - 5420, - 18592, - 7475, - 12758, - 8963, - 8042, - 52155 - ], - "xaxis": "x", - "y": [ - 6786, - 2092, - 3040, - 9483, - 2911, - 13226, - 2349, - 6285, - 3606, - 3478, - 5489, - 2068, - 3156, - 1675, - 1708, - 3608, - 4264, - 2100, - 4197, - 2637, - 3649, - 2440, - 2732, - 11527 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Flevoland
Year=2011
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "ids": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "legendgroup": "Flevoland", - "marker": { - "color": "#FF6692", - "size": [ - 190655, - 40164, - 75111, - 46253, - 18678, - 21106 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Flevoland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 75304, - 15375, - 31283, - 18432, - 5570, - 7889 - ], - "xaxis": "x", - "y": [ - 21146, - 4093, - 7667, - 5049, - 2832, - 2609 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Brabant
Year=2011
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "ids": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "legendgroup": "Noord-Brabant", - "marker": { - "color": "#B6E880", - "size": [ - 9466, - 16296, - 6704, - 18073, - 66074, - 29728, - 28810, - 19386, - 9865, - 30280, - 174599, - 20371, - 31676, - 25101, - 26477, - 18166, - 216036, - 41800, - 21307, - 38389, - 28906, - 25764, - 22807, - 29292, - 15295, - 88560, - 140786, - 43119, - 15035, - 21532, - 22973, - 36547, - 22242, - 17845, - 25721, - 54072, - 84201, - 12606, - 77541, - 22430, - 28114, - 18317, - 15653, - 23273, - 206240, - 30622, - 43592, - 25654, - 16482, - 46211, - 21682, - 21163 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Brabant", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 3727, - 6599, - 2526, - 7143, - 29080, - 11217, - 11640, - 7825, - 3568, - 12442, - 76982, - 8421, - 12904, - 10402, - 10754, - 7297, - 97617, - 17258, - 9268, - 16906, - 11252, - 10208, - 9499, - 12306, - 6235, - 37987, - 63252, - 16971, - 5912, - 8504, - 9365, - 15496, - 9564, - 6767, - 10659, - 23234, - 35000, - 4911, - 33311, - 9307, - 11105, - 7168, - 6474, - 9885, - 89808, - 13818, - 18764, - 10562, - 7138, - 19475, - 9131, - 8337 - ], - "xaxis": "x", - "y": [ - 842, - 1559, - 434, - 1682, - 5629, - 3094, - 3042, - 1929, - 1077, - 2866, - 15608, - 1653, - 2684, - 2329, - 2274, - 1518, - 17135, - 3860, - 1929, - 3506, - 2607, - 2459, - 2160, - 2537, - 1393, - 9097, - 12358, - 3913, - 1513, - 2036, - 2014, - 3328, - 1984, - 1715, - 2363, - 4772, - 7566, - 1061, - 6556, - 1582, - 2865, - 1627, - 1599, - 1883, - 17096, - 2416, - 3777, - 2372, - 1561, - 4042, - 1729, - 1550 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Utrecht
Year=2011
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Stichtse Vecht", - "Utrechtse Heuvelrug", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "ids": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Stichtse Vecht", - "Utrechtse Heuvelrug", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "legendgroup": "Utrecht", - "marker": { - "color": "#FF97FF", - "size": [ - 146592, - 24379, - 42049, - 14437, - 20111, - 8845, - 47935, - 34348, - 28609, - 14121, - 13500, - 60947, - 9815, - 4752, - 18951, - 43004, - 45611, - 63050, - 48726, - 62267, - 23115, - 49748, - 12008, - 60824 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Utrecht", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 61314, - 10709, - 18291, - 5903, - 7414, - 3620, - 18117, - 13883, - 11942, - 5203, - 5285, - 26216, - 3968, - 1661, - 7543, - 16880, - 19712, - 25633, - 19936, - 25025, - 9182, - 19945, - 4630, - 26391 - ], - "xaxis": "x", - "y": [ - 15872, - 2162, - 4503, - 1467, - 2111, - 908, - 5681, - 3858, - 2855, - 1594, - 1549, - 5360, - 1003, - 449, - 2055, - 4237, - 4156, - 6050, - 4734, - 6980, - 2328, - 5401, - 1335, - 5541 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Limburg
Year=2011
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Eijsden-Margraten", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Peel en Maas", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "ids": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Eijsden-Margraten", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Peel en Maas", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "legendgroup": "Limburg", - "marker": { - "color": "#FECB52", - "size": [ - 13844, - 13350, - 29375, - 32264, - 24940, - 17383, - 14496, - 89212, - 41814, - 47409, - 38186, - 36600, - 24272, - 119664, - 19495, - 7947, - 16678, - 43188, - 21239, - 55595, - 10953, - 94814, - 9805, - 17024, - 99793, - 42784, - 12664, - 48563 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Limburg", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5866, - 5440, - 14163, - 13855, - 10284, - 6849, - 6426, - 45062, - 16520, - 23437, - 17810, - 14871, - 10031, - 56825, - 8494, - 3374, - 6672, - 16906, - 9092, - 26153, - 4961, - 45041, - 4782, - 7816, - 45737, - 17292, - 5471, - 21247 - ], - "xaxis": "x", - "y": [ - 1238, - 1128, - 2208, - 2370, - 1871, - 1508, - 920, - 6109, - 3959, - 2927, - 2730, - 2969, - 1732, - 7310, - 1542, - 685, - 1389, - 3853, - 1621, - 4443, - 769, - 6944, - 499, - 1153, - 8412, - 3915, - 984, - 3957 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zeeland
Year=2011
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "ids": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "legendgroup": "Zeeland", - "marker": { - "color": "#636efa", - "size": [ - 22707, - 36665, - 27719, - 12365, - 7474, - 21614, - 34203, - 23979, - 54823, - 25489, - 21926, - 44536 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zeeland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 9445, - 16725, - 12840, - 4949, - 4053, - 8542, - 15817, - 13931, - 25964, - 10221, - 10294, - 21772 - ], - "xaxis": "x", - "y": [ - 2234, - 3228, - 2100, - 1278, - 562, - 2419, - 2892, - 1686, - 4305, - 2677, - 1923, - 3478 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Groningen
Year=2011
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Oldambt", - "Pekela", - "Stadskanaal", - "Veendam" - ], - "ids": [ - "Oldambt", - "Pekela", - "Stadskanaal", - "Veendam" - ], - "legendgroup": "Groningen", - "marker": { - "color": "#EF553B", - "size": [ - 39396, - 12954, - 33122, - 27981 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Groningen", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 17968, - 5485, - 14979, - 12590 - ], - "xaxis": "x", - "y": [ - 3326, - 1045, - 2948, - 2366 - ], - "yaxis": "y" - } - ], - "name": "2011" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

Provincienaam=Drenthe
Year=2012
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "ids": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "legendgroup": "Drenthe", - "marker": { - "color": "#636efa", - "size": [ - 25738, - 67208, - 25859, - 35881, - 108838, - 54889, - 32573, - 33558, - 30955, - 32357, - 19198, - 23753 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Drenthe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 12414, - 29854, - 11644, - 16846, - 51220, - 23279, - 14352, - 13950, - 14094, - 14119, - 8087, - 9573 - ], - "xaxis": "x", - "y": [ - 2178, - 6831, - 2100, - 3290, - 9640, - 5320, - 3443, - 2955, - 2765, - 3169, - 1584, - 2108 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Holland
Year=2012
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hollands Kroon", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "ids": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hollands Kroon", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "legendgroup": "Noord-Holland", - "marker": { - "color": "#EF553B", - "size": [ - 30364, - 94269, - 83363, - 790110, - 30682, - 39844, - 9038, - 22056, - 34474, - 24935, - 19300, - 28700, - 18268, - 151818, - 143943, - 39268, - 26242, - 22650, - 57065, - 85537, - 47703, - 71254, - 41574, - 22345, - 10334, - 43117, - 9097, - 11393, - 13232, - 79266, - 18653, - 21468, - 13668, - 12897, - 28307, - 67286, - 17010, - 18022, - 23297, - 15781, - 148281, - 16651 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 12267, - 43703, - 41304, - 408888, - 14658, - 18325, - 3888, - 9439, - 14939, - 11843, - 7804, - 11761, - 8343, - 71173, - 58757, - 17035, - 11751, - 9864, - 26990, - 38937, - 20011, - 30947, - 18242, - 8847, - 4391, - 17346, - 3890, - 4562, - 5733, - 34901, - 8302, - 8808, - 6249, - 5221, - 12279, - 29640, - 7026, - 8387, - 10182, - 6520, - 65099, - 9204 - ], - "xaxis": "x", - "y": [ - 3308, - 7904, - 6214, - 60149, - 2368, - 3595, - 1082, - 3489, - 3065, - 2100, - 1796, - 3028, - 1523, - 12339, - 15607, - 3413, - 2587, - 2157, - 4666, - 7668, - 4686, - 6721, - 3569, - 2272, - 1080, - 4181, - 906, - 1225, - 1568, - 6805, - 1723, - 2020, - 1154, - 1344, - 2746, - 5480, - 1545, - 1265, - 2229, - 1445, - 13979, - 1093 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Gelderland
Year=2012
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "ids": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "legendgroup": "Gelderland", - "marker": { - "color": "#00cc96", - "size": [ - 27305, - 156961, - 149271, - 53521, - 44911, - 25433, - 37441, - 21327, - 25949, - 27632, - 11598, - 56252, - 18193, - 25525, - 108763, - 22416, - 32519, - 26127, - 45429, - 11805, - 18244, - 16413, - 45770, - 33263, - 24022, - 35043, - 22614, - 40126, - 165182, - 26609, - 22716, - 30005, - 39839, - 46269, - 23860, - 31630, - 43612, - 1514, - 9354, - 41527, - 23719, - 37049, - 18325, - 15294, - 40734, - 28946, - 26774, - 32429, - 47144 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Gelderland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11504, - 67968, - 69605, - 19699, - 18879, - 10374, - 15566, - 9039, - 10018, - 11348, - 5207, - 24288, - 7290, - 10390, - 46871, - 8659, - 13662, - 10296, - 18433, - 5035, - 7402, - 6700, - 18539, - 14119, - 9484, - 14625, - 8085, - 15519, - 74162, - 10167, - 8610, - 12288, - 16689, - 18190, - 9825, - 14486, - 20189, - 628, - 3632, - 17320, - 9408, - 14486, - 7520, - 6414, - 16920, - 12321, - 10096, - 14062, - 20988 - ], - "xaxis": "x", - "y": [ - 2639, - 14027, - 12575, - 6276, - 4049, - 2291, - 3389, - 1767, - 2331, - 2813, - 877, - 5168, - 1799, - 2438, - 10930, - 2226, - 2744, - 2198, - 4689, - 1178, - 1702, - 1574, - 4592, - 2936, - 2343, - 3077, - 2621, - 4349, - 11812, - 2673, - 2361, - 2933, - 3639, - 4763, - 2339, - 2550, - 3395, - 246, - 1087, - 4050, - 2287, - 2551, - 1586, - 1316, - 3898, - 2693, - 2914, - 2858, - 4535 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Fryslân
Year=2012
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Achtkarspelen", - "Ameland", - "Dantumadiel", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Súdwest-Fryslân", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "ids": [ - "Achtkarspelen", - "Ameland", - "Dantumadiel", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Súdwest-Fryslân", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "legendgroup": "Fryslân", - "marker": { - "color": "#ab63fa", - "size": [ - 28091, - 3526, - 19264, - 15844, - 43514, - 95321, - 25858, - 29952, - 932, - 55456, - 82634, - 4748, - 32164, - 1105, - 25780 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Fryslân", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11663, - 1547, - 7858, - 7371, - 19712, - 46778, - 11137, - 12520, - 590, - 24316, - 36706, - 2156, - 13621, - 571, - 11210 - ], - "xaxis": "x", - "y": [ - 2935, - 359, - 1750, - 1401, - 3919, - 7536, - 2259, - 2975, - 85, - 5106, - 7955, - 385, - 3033, - 98, - 2241 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zuid-Holland
Year=2012
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Bodegraven-Reeuwijk", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Kaag en Braassem", - "Katwijk", - "Krimpen aan den IJssel", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Nieuwkoop", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zuidplas", - "Zwijndrecht" - ], - "ids": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Bodegraven-Reeuwijk", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Kaag en Braassem", - "Katwijk", - "Krimpen aan den IJssel", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Nieuwkoop", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zuidplas", - "Zwijndrecht" - ], - "legendgroup": "Zuid-Holland", - "marker": { - "color": "#FFA15A", - "size": [ - 19467, - 25003, - 72853, - 47053, - 32834, - 16072, - 66122, - 98675, - 118862, - 35083, - 71235, - 17654, - 39442, - 28257, - 20831, - 25737, - 62476, - 28692, - 55265, - 118748, - 26706, - 72405, - 22511, - 31849, - 18225, - 26974, - 25517, - 22788, - 32032, - 50103, - 45208, - 616260, - 76244, - 24232, - 35686, - 71042, - 24310, - 25280, - 25762, - 101980, - 13901, - 122331, - 8171, - 40673, - 44499 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zuid-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 8083, - 9822, - 31005, - 18421, - 13441, - 7157, - 30308, - 48035, - 53973, - 15569, - 30704, - 7012, - 17131, - 11585, - 8978, - 10520, - 24307, - 12109, - 21230, - 53613, - 11838, - 35969, - 9637, - 14511, - 7195, - 10346, - 12847, - 9822, - 13938, - 19154, - 19862, - 306227, - 35834, - 10413, - 14339, - 34339, - 10951, - 10398, - 12078, - 40761, - 6484, - 53029, - 3204, - 16175, - 20154 - ], - "xaxis": "x", - "y": [ - 2111, - 2581, - 6962, - 5544, - 3303, - 1401, - 5635, - 6537, - 10412, - 3473, - 7186, - 1878, - 3320, - 2999, - 1779, - 2505, - 6235, - 2814, - 7200, - 8383, - 2687, - 5966, - 2264, - 2628, - 2157, - 2336, - 2166, - 2439, - 3047, - 5812, - 3441, - 50280, - 6889, - 2266, - 3505, - 5524, - 2413, - 2121, - 2054, - 9792, - 1107, - 11079, - 663, - 4045, - 3856 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Overijssel
Year=2012
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "ids": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "legendgroup": "Overijssel", - "marker": { - "color": "#19d3f3", - "size": [ - 72757, - 21586, - 27432, - 98672, - 26073, - 158048, - 24419, - 59430, - 35796, - 35599, - 50705, - 22673, - 32176, - 17659, - 17380, - 36603, - 37561, - 16224, - 43402, - 21206, - 33929, - 23824, - 22048, - 121527 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Overijssel", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 31995, - 8951, - 10869, - 42269, - 10105, - 74142, - 9790, - 23260, - 14319, - 14643, - 20845, - 9291, - 13948, - 7066, - 8137, - 15228, - 13714, - 5456, - 19461, - 7866, - 13097, - 9326, - 8352, - 53314 - ], - "xaxis": "x", - "y": [ - 6720, - 2109, - 2953, - 9380, - 2821, - 13110, - 2294, - 6211, - 3546, - 3370, - 5414, - 2045, - 3153, - 1597, - 1688, - 3512, - 4243, - 2089, - 4022, - 2570, - 3599, - 2431, - 2668, - 11663 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Flevoland
Year=2012
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "ids": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "legendgroup": "Flevoland", - "marker": { - "color": "#FF6692", - "size": [ - 193163, - 40470, - 75312, - 46342, - 18950, - 21288 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Flevoland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 76755, - 17043, - 32216, - 18759, - 5375, - 7913 - ], - "xaxis": "x", - "y": [ - 21046, - 3943, - 7700, - 4944, - 2884, - 2493 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Brabant
Year=2012
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "ids": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "legendgroup": "Noord-Brabant", - "marker": { - "color": "#B6E880", - "size": [ - 9525, - 16404, - 6722, - 18123, - 66130, - 29802, - 28696, - 19582, - 9967, - 30284, - 176401, - 20432, - 31762, - 25205, - 26664, - 18166, - 217225, - 42048, - 21471, - 38671, - 29041, - 25589, - 22805, - 29325, - 15352, - 88801, - 141893, - 43112, - 15047, - 21608, - 23071, - 36522, - 22548, - 17861, - 25775, - 54006, - 84639, - 12654, - 77426, - 22320, - 28128, - 18580, - 15822, - 23354, - 207580, - 30666, - 43880, - 25789, - 16503, - 46422, - 21675, - 21221 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Brabant", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 3936, - 6337, - 2681, - 7456, - 29604, - 11307, - 11703, - 8061, - 3670, - 12729, - 77017, - 8567, - 12829, - 10520, - 11105, - 7647, - 101413, - 17468, - 9376, - 17105, - 11772, - 10237, - 9703, - 12664, - 6341, - 38363, - 63223, - 17748, - 6115, - 8755, - 9864, - 15708, - 9449, - 6913, - 10768, - 23318, - 35396, - 5021, - 33879, - 9194, - 11310, - 7236, - 6437, - 10015, - 90698, - 13931, - 18796, - 10619, - 7030, - 20138, - 9226, - 8972 - ], - "xaxis": "x", - "y": [ - 806, - 1546, - 445, - 1630, - 5502, - 3008, - 2872, - 1870, - 1066, - 2866, - 15630, - 1583, - 2639, - 2277, - 2243, - 1499, - 17197, - 3908, - 1865, - 3417, - 2587, - 2397, - 2155, - 2422, - 1330, - 9023, - 12308, - 3835, - 1463, - 2010, - 1923, - 3299, - 1928, - 1641, - 2321, - 4681, - 7370, - 1087, - 6454, - 1525, - 2792, - 1535, - 1599, - 1873, - 16964, - 2336, - 3658, - 2390, - 1555, - 4023, - 1668, - 1502 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Utrecht
Year=2012
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Stichtse Vecht", - "Utrechtse Heuvelrug", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "ids": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Stichtse Vecht", - "Utrechtse Heuvelrug", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "legendgroup": "Utrecht", - "marker": { - "color": "#FF97FF", - "size": [ - 148250, - 24352, - 42079, - 14451, - 20200, - 8824, - 48309, - 34271, - 28905, - 14050, - 13601, - 60720, - 9850, - 4822, - 19064, - 42977, - 45612, - 63315, - 48267, - 62870, - 23064, - 50052, - 12034, - 61233 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Utrecht", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 63442, - 11071, - 19274, - 5940, - 7603, - 3438, - 18971, - 13726, - 12133, - 5315, - 5350, - 26411, - 3989, - 1752, - 7492, - 17836, - 19835, - 26617, - 21205, - 24636, - 9337, - 20524, - 4662, - 26534 - ], - "xaxis": "x", - "y": [ - 15884, - 2125, - 4425, - 1477, - 2103, - 876, - 5727, - 3773, - 2845, - 1484, - 1545, - 5203, - 983, - 444, - 2062, - 4147, - 4121, - 5987, - 4545, - 6819, - 2242, - 5361, - 1310, - 5497 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Limburg
Year=2012
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Eijsden-Margraten", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Peel en Maas", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "ids": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Eijsden-Margraten", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Peel en Maas", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "legendgroup": "Limburg", - "marker": { - "color": "#FECB52", - "size": [ - 13742, - 13286, - 29199, - 32165, - 24994, - 17367, - 14479, - 89016, - 41917, - 47280, - 38067, - 36462, - 24095, - 121050, - 19430, - 7874, - 16771, - 43271, - 21092, - 56165, - 10914, - 94535, - 9749, - 16945, - 100027, - 42959, - 12664, - 48668 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Limburg", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5939, - 5480, - 14255, - 14086, - 10415, - 7088, - 6362, - 45243, - 16594, - 21338, - 17833, - 15231, - 10280, - 59234, - 8345, - 3476, - 6916, - 17198, - 9016, - 26531, - 5101, - 44973, - 5157, - 7870, - 46150, - 18121, - 5439, - 21276 - ], - "xaxis": "x", - "y": [ - 1205, - 1098, - 2147, - 2327, - 1740, - 1479, - 886, - 5970, - 3818, - 2883, - 2661, - 2810, - 1649, - 7079, - 1493, - 667, - 1339, - 3734, - 1565, - 4454, - 756, - 6770, - 492, - 1103, - 8290, - 3743, - 963, - 3806 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zeeland
Year=2012
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "ids": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "legendgroup": "Zeeland", - "marker": { - "color": "#636efa", - "size": [ - 22676, - 36921, - 27632, - 12398, - 7522, - 21704, - 34151, - 23892, - 54742, - 25540, - 21959, - 44502 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zeeland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 9543, - 17321, - 13091, - 5005, - 4103, - 8662, - 16457, - 15755, - 25924, - 10352, - 10548, - 21907 - ], - "xaxis": "x", - "y": [ - 2159, - 3140, - 2007, - 1314, - 533, - 2431, - 2787, - 1639, - 4154, - 2649, - 1857, - 3444 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Groningen
Year=2012
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Oldambt", - "Pekela", - "Stadskanaal", - "Veendam" - ], - "ids": [ - "Oldambt", - "Pekela", - "Stadskanaal", - "Veendam" - ], - "legendgroup": "Groningen", - "marker": { - "color": "#EF553B", - "size": [ - 39095, - 12868, - 32998, - 27920 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Groningen", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 18090, - 5648, - 14895, - 12508 - ], - "xaxis": "x", - "y": [ - 3211, - 1025, - 2813, - 2319 - ], - "yaxis": "y" - } - ], - "name": "2012" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

Provincienaam=Drenthe
Year=2013
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "ids": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "legendgroup": "Drenthe", - "marker": { - "color": "#636efa", - "size": [ - 25541, - 67204, - 25662, - 35765, - 108392, - 54874, - 32726, - 33422, - 31024, - 32456, - 19091, - 23761 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Drenthe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11214, - 30183, - 11619, - 15237, - 48540, - 23398, - 14686, - 13986, - 14204, - 13989, - 8235, - 9599 - ], - "xaxis": "x", - "y": [ - 2062, - 6782, - 1994, - 3238, - 9402, - 5298, - 3401, - 2811, - 2691, - 3158, - 1494, - 2006 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Holland
Year=2013
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hollands Kroon", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "ids": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hollands Kroon", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "legendgroup": "Noord-Holland", - "marker": { - "color": "#EF553B", - "size": [ - 30618, - 94505, - 84379, - 799278, - 30333, - 40070, - 9107, - 22195, - 34402, - 25218, - 19298, - 28754, - 18315, - 153093, - 144153, - 39117, - 26317, - 22594, - 56947, - 86017, - 47643, - 71360, - 41445, - 22376, - 10454, - 43248, - 9141, - 11379, - 13249, - 79482, - 46207, - 21430, - 13662, - 13061, - 28387, - 67122, - 17091, - 18151, - 23221, - 15740, - 149622, - 16593 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 12354, - 44049, - 41616, - 411127, - 14635, - 18483, - 4078, - 9513, - 15128, - 11891, - 7876, - 11876, - 8399, - 71725, - 58925, - 17012, - 11813, - 9907, - 27138, - 39673, - 20184, - 31354, - 18316, - 8963, - 4432, - 17528, - 3981, - 4564, - 5752, - 34893, - 19439, - 8850, - 6281, - 5327, - 12393, - 29798, - 7033, - 8421, - 10315, - 6512, - 66110, - 9304 - ], - "xaxis": "x", - "y": [ - 3299, - 7871, - 6309, - 61011, - 2255, - 3632, - 991, - 3479, - 2938, - 2136, - 1726, - 2988, - 1543, - 12615, - 15446, - 3309, - 2565, - 2095, - 4514, - 7693, - 4458, - 6585, - 3505, - 2156, - 1055, - 4038, - 872, - 1176, - 1520, - 6571, - 4281, - 1933, - 1069, - 1322, - 2675, - 5266, - 1576, - 1283, - 2142, - 1339, - 13743, - 1047 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Gelderland
Year=2013
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "ids": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "legendgroup": "Gelderland", - "marker": { - "color": "#00cc96", - "size": [ - 27082, - 157315, - 149827, - 53751, - 44769, - 25324, - 37216, - 21245, - 25939, - 27681, - 11539, - 56414, - 18203, - 25554, - 109823, - 22510, - 32385, - 26120, - 45650, - 11769, - 18399, - 16455, - 45818, - 33308, - 24092, - 34834, - 22593, - 40355, - 166382, - 26628, - 22774, - 29873, - 39779, - 46531, - 23971, - 31565, - 43679, - 1501, - 9403, - 41751, - 23724, - 37408, - 18410, - 15199, - 41004, - 28967, - 26953, - 32405, - 47240 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Gelderland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11451, - 68319, - 70571, - 19835, - 19205, - 10365, - 15600, - 9080, - 10041, - 11598, - 5168, - 24523, - 7348, - 10513, - 47600, - 8762, - 13698, - 10301, - 18517, - 5128, - 7580, - 6713, - 18781, - 14271, - 9531, - 14738, - 8052, - 15675, - 76518, - 10354, - 8766, - 12434, - 17252, - 18388, - 9382, - 14565, - 20334, - 628, - 3663, - 17446, - 9443, - 18578, - 7647, - 6427, - 17187, - 12685, - 10336, - 14507, - 21324 - ], - "xaxis": "x", - "y": [ - 2604, - 13697, - 12575, - 6311, - 3884, - 2172, - 3254, - 1698, - 2252, - 2754, - 828, - 4989, - 1742, - 2282, - 10775, - 2180, - 2697, - 2132, - 4656, - 1151, - 1634, - 1494, - 4524, - 2835, - 2277, - 2906, - 2555, - 4231, - 11804, - 2667, - 2348, - 2797, - 3496, - 4675, - 2289, - 2541, - 3365, - 250, - 1083, - 3913, - 2265, - 2512, - 1508, - 1293, - 3786, - 2663, - 2892, - 2793, - 4485 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Fryslân
Year=2013
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Achtkarspelen", - "Ameland", - "Dantumadiel", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Súdwest-Fryslân", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "ids": [ - "Achtkarspelen", - "Ameland", - "Dantumadiel", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Súdwest-Fryslân", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "legendgroup": "Fryslân", - "marker": { - "color": "#ab63fa", - "size": [ - 28110, - 3525, - 19130, - 15854, - 43323, - 95949, - 25836, - 29896, - 960, - 55454, - 82639, - 4795, - 31979, - 1114, - 25595 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Fryslân", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11711, - 1568, - 7867, - 7390, - 19709, - 47727, - 11160, - 12460, - 591, - 24469, - 37852, - 2210, - 13252, - 570, - 11275 - ], - "xaxis": "x", - "y": [ - 2871, - 361, - 1704, - 1378, - 3831, - 7577, - 2155, - 2933, - 73, - 5051, - 7749, - 364, - 3058, - 94, - 2149 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zuid-Holland
Year=2013
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Bodegraven-Reeuwijk", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Goeree-Overflakkee", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Kaag en Braassem", - "Katwijk", - "Krimpen aan den IJssel", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Nieuwkoop", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zuidplas", - "Zwijndrecht" - ], - "ids": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Bodegraven-Reeuwijk", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Goeree-Overflakkee", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Kaag en Braassem", - "Katwijk", - "Krimpen aan den IJssel", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Nieuwkoop", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zuidplas", - "Zwijndrecht" - ], - "legendgroup": "Zuid-Holland", - "marker": { - "color": "#FFA15A", - "size": [ - 19643, - 25101, - 72913, - 47371, - 32817, - 16320, - 66024, - 99097, - 118466, - 48259, - 35128, - 70904, - 17722, - 39086, - 28641, - 20868, - 25715, - 62688, - 28855, - 56506, - 119800, - 26743, - 72588, - 22393, - 31985, - 18251, - 27082, - 25671, - 23153, - 32082, - 50454, - 45330, - 616294, - 76216, - 24389, - 35800, - 70905, - 24658, - 25217, - 25656, - 102698, - 13890, - 123092, - 8122, - 40779, - 44610 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zuid-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 8111, - 10059, - 31280, - 18760, - 13699, - 7478, - 30440, - 48329, - 54565, - 20182, - 15622, - 31067, - 7047, - 17241, - 11392, - 9163, - 10647, - 24614, - 12461, - 21913, - 54400, - 11843, - 35868, - 9653, - 14459, - 7281, - 10420, - 12921, - 10010, - 14036, - 19367, - 20504, - 307352, - 36011, - 10503, - 14346, - 34046, - 10975, - 10529, - 12100, - 41185, - 6509, - 53868, - 3250, - 16350, - 20195 - ], - "xaxis": "x", - "y": [ - 2098, - 2581, - 6839, - 5401, - 3223, - 1331, - 5631, - 6434, - 10299, - 4486, - 3405, - 7017, - 1829, - 3320, - 2972, - 1737, - 2453, - 6212, - 2817, - 6843, - 8287, - 2599, - 5952, - 2199, - 2591, - 2108, - 2289, - 2140, - 2436, - 2974, - 5807, - 3490, - 50719, - 6810, - 2278, - 3367, - 5497, - 2458, - 2048, - 2009, - 9768, - 1084, - 11072, - 642, - 3986, - 3839 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Overijssel
Year=2013
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "ids": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "legendgroup": "Overijssel", - "marker": { - "color": "#19d3f3", - "size": [ - 72729, - 21770, - 27570, - 98581, - 26056, - 158627, - 24322, - 59585, - 35743, - 35215, - 50924, - 22554, - 32200, - 17751, - 17314, - 36486, - 37608, - 16275, - 43437, - 21172, - 33971, - 23807, - 22139, - 122562 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Overijssel", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 32117, - 9078, - 11022, - 42092, - 10215, - 74502, - 9904, - 23396, - 14359, - 14655, - 20887, - 9429, - 13986, - 7212, - 8135, - 15385, - 13855, - 5450, - 19497, - 8045, - 13124, - 9409, - 8376, - 53978 - ], - "xaxis": "x", - "y": [ - 6553, - 2086, - 2863, - 9223, - 2677, - 13041, - 2216, - 6040, - 3468, - 3242, - 5377, - 2010, - 3156, - 1559, - 1675, - 3422, - 4218, - 2069, - 3860, - 2437, - 3555, - 2384, - 2643, - 11716 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Flevoland
Year=2013
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "ids": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "legendgroup": "Flevoland", - "marker": { - "color": "#FF6692", - "size": [ - 195213, - 40679, - 75778, - 46284, - 19225, - 21262 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Flevoland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 77609, - 17192, - 32379, - 18928, - 5429, - 7959 - ], - "xaxis": "x", - "y": [ - 20706, - 3894, - 7708, - 4801, - 2920, - 2368 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Brabant
Year=2013
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "ids": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "legendgroup": "Noord-Brabant", - "marker": { - "color": "#B6E880", - "size": [ - 9640, - 16392, - 6699, - 18191, - 66287, - 29775, - 28637, - 19635, - 10062, - 30436, - 178140, - 20330, - 31733, - 25382, - 26737, - 18196, - 218433, - 42274, - 21513, - 38768, - 29101, - 25858, - 22933, - 29231, - 15405, - 89023, - 142817, - 43244, - 15086, - 21767, - 23083, - 36625, - 22645, - 17926, - 25770, - 53686, - 84861, - 12670, - 77155, - 22268, - 28040, - 18628, - 16138, - 23400, - 208527, - 30584, - 44092, - 25564, - 16725, - 46438, - 21648, - 21241 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Brabant", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 3984, - 6464, - 2706, - 7573, - 29739, - 11368, - 11769, - 8076, - 3807, - 12934, - 78852, - 8630, - 12994, - 10645, - 11156, - 7725, - 101970, - 18043, - 9395, - 17157, - 12042, - 10757, - 9792, - 13034, - 6424, - 38662, - 64413, - 17874, - 6176, - 9125, - 9961, - 15812, - 9867, - 6906, - 11037, - 23337, - 35747, - 5086, - 33869, - 9271, - 11383, - 7320, - 6711, - 10046, - 91012, - 13950, - 19061, - 10800, - 7035, - 20338, - 9295, - 9049 - ], - "xaxis": "x", - "y": [ - 784, - 1503, - 442, - 1552, - 5368, - 2913, - 2773, - 1834, - 1032, - 2834, - 15637, - 1536, - 2553, - 2209, - 2156, - 1475, - 17311, - 3898, - 1858, - 3287, - 2625, - 2393, - 2195, - 2379, - 1281, - 8852, - 12278, - 3819, - 1426, - 1977, - 1860, - 3191, - 1861, - 1590, - 2216, - 4501, - 7124, - 1086, - 6398, - 1474, - 2690, - 1454, - 1560, - 1815, - 16816, - 2295, - 3613, - 2354, - 1513, - 3922, - 1612, - 1426 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Utrecht
Year=2013
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Stichtse Vecht", - "Utrechtse Heuvelrug", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "ids": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Stichtse Vecht", - "Utrechtse Heuvelrug", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "legendgroup": "Utrecht", - "marker": { - "color": "#FF97FF", - "size": [ - 149662, - 24277, - 42032, - 14563, - 20316, - 8795, - 48429, - 34254, - 28969, - 13992, - 13621, - 60895, - 9872, - 4893, - 19047, - 42846, - 45508, - 63491, - 48092, - 63032, - 23035, - 50346, - 12321, - 61420 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Utrecht", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 63856, - 11228, - 19363, - 6082, - 7719, - 3591, - 19155, - 13857, - 12415, - 5347, - 5443, - 26647, - 4073, - 1827, - 7482, - 18048, - 19910, - 26732, - 21144, - 25457, - 9461, - 20720, - 4821, - 26976 - ], - "xaxis": "x", - "y": [ - 15956, - 2088, - 4335, - 1471, - 2068, - 856, - 5686, - 3658, - 2762, - 1443, - 1487, - 5117, - 944, - 412, - 1996, - 3956, - 4069, - 5973, - 4367, - 6635, - 2131, - 5242, - 1294, - 5367 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Limburg
Year=2013
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Eijsden-Margraten", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Peel en Maas", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "ids": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Eijsden-Margraten", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Peel en Maas", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "legendgroup": "Limburg", - "marker": { - "color": "#FECB52", - "size": [ - 13688, - 13275, - 29081, - 32072, - 25049, - 17319, - 14444, - 88747, - 41810, - 47194, - 37911, - 36426, - 24017, - 121819, - 19362, - 7827, - 16785, - 43302, - 20996, - 56690, - 10920, - 94024, - 9771, - 16814, - 100159, - 43038, - 12617, - 48587 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Limburg", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5925, - 5497, - 14355, - 14148, - 10486, - 7199, - 6636, - 45326, - 16797, - 21387, - 17899, - 15327, - 10298, - 60360, - 8360, - 3489, - 6991, - 17410, - 9203, - 26670, - 5123, - 45144, - 5192, - 7878, - 46199, - 18394, - 5549, - 21619 - ], - "xaxis": "x", - "y": [ - 1155, - 1028, - 2125, - 2299, - 1642, - 1486, - 824, - 5811, - 3653, - 2819, - 2569, - 2687, - 1524, - 6881, - 1480, - 625, - 1263, - 3687, - 1503, - 4410, - 749, - 6544, - 474, - 1053, - 8133, - 3641, - 897, - 3731 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zeeland
Year=2013
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "ids": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "legendgroup": "Zeeland", - "marker": { - "color": "#636efa", - "size": [ - 22683, - 36971, - 27514, - 12495, - 7509, - 21859, - 34040, - 23886, - 54729, - 25514, - 21903, - 44451 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zeeland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 9641, - 17410, - 13046, - 5055, - 4096, - 8709, - 16732, - 13611, - 26102, - 10413, - 10661, - 22154 - ], - "xaxis": "x", - "y": [ - 2091, - 3072, - 1919, - 1287, - 515, - 2435, - 2728, - 1545, - 4017, - 2613, - 1728, - 3475 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Groningen
Year=2013
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Oldambt", - "Pekela", - "Stadskanaal", - "Veendam" - ], - "ids": [ - "Oldambt", - "Pekela", - "Stadskanaal", - "Veendam" - ], - "legendgroup": "Groningen", - "marker": { - "color": "#EF553B", - "size": [ - 38748, - 12804, - 32885, - 27914 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Groningen", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 18073, - 5649, - 14922, - 12553 - ], - "xaxis": "x", - "y": [ - 3130, - 951, - 2790, - 2248 - ], - "yaxis": "y" - } - ], - "name": "2013" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

Provincienaam=Drenthe
Year=2014
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "ids": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "legendgroup": "Drenthe", - "marker": { - "color": "#636efa", - "size": [ - 25357, - 67190, - 25627, - 35769, - 108052, - 54664, - 32867, - 33366, - 31087, - 32493, - 18933, - 23583 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Drenthe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11173, - 30539, - 11710, - 15330, - 49264, - 24275, - 15101, - 13976, - 14516, - 13983, - 8250, - 9908 - ], - "xaxis": "x", - "y": [ - 1946, - 6594, - 1913, - 3109, - 9168, - 5228, - 3371, - 2695, - 2611, - 3155, - 1413, - 1858 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Holland
Year=2014
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hollands Kroon", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "ids": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hollands Kroon", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "legendgroup": "Noord-Holland", - "marker": { - "color": "#EF553B", - "size": [ - 30759, - 94866, - 85015, - 810937, - 30076, - 40093, - 9094, - 22059, - 34288, - 25930, - 19250, - 28920, - 18376, - 155147, - 144061, - 39088, - 26364, - 22636, - 56597, - 86426, - 47502, - 71703, - 41245, - 22485, - 10444, - 43320, - 9139, - 11368, - 13271, - 79576, - 45978, - 21485, - 13552, - 13234, - 28418, - 67220, - 17134, - 18172, - 23187, - 15777, - 150598, - 16575 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 12780, - 44884, - 42395, - 413697, - 14988, - 18736, - 4151, - 9565, - 15151, - 12657, - 8018, - 12161, - 8540, - 72356, - 59214, - 17273, - 12247, - 10075, - 27689, - 40667, - 20445, - 31943, - 18434, - 9128, - 4463, - 18124, - 3878, - 4606, - 5775, - 35119, - 19565, - 8924, - 6401, - 5473, - 12571, - 30584, - 7076, - 8673, - 10347, - 6664, - 66585, - 9280 - ], - "xaxis": "x", - "y": [ - 3319, - 7886, - 6470, - 61809, - 2161, - 3535, - 932, - 3459, - 2799, - 2148, - 1691, - 2901, - 1524, - 12901, - 15157, - 3178, - 2570, - 2018, - 4439, - 7655, - 4253, - 6575, - 3404, - 2068, - 1045, - 3967, - 854, - 1139, - 1484, - 6475, - 4171, - 1851, - 1038, - 1264, - 2587, - 5151, - 1561, - 1308, - 2123, - 1268, - 13539, - 1043 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Gelderland
Year=2014
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "ids": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "legendgroup": "Gelderland", - "marker": { - "color": "#00cc96", - "size": [ - 27013, - 157545, - 150823, - 54152, - 44666, - 25288, - 36932, - 21177, - 26019, - 27590, - 11437, - 56344, - 18210, - 25609, - 110656, - 22645, - 32351, - 26045, - 45732, - 11732, - 18490, - 16334, - 45776, - 33248, - 24156, - 34987, - 22555, - 40638, - 168292, - 26680, - 22835, - 29700, - 39595, - 46665, - 23872, - 31580, - 43640, - 1503, - 9498, - 41775, - 23767, - 37429, - 18419, - 15138, - 41043, - 28881, - 27182, - 32283, - 47164 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Gelderland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11687, - 69507, - 71114, - 20113, - 19390, - 10538, - 15997, - 9097, - 10196, - 11630, - 5171, - 24848, - 7495, - 10605, - 47757, - 8983, - 13802, - 10620, - 18562, - 5052, - 7756, - 6729, - 18884, - 14396, - 9566, - 14933, - 8048, - 15777, - 77248, - 10208, - 8894, - 12587, - 17277, - 18517, - 9495, - 14648, - 20909, - 627, - 3709, - 17521, - 9621, - 19236, - 7765, - 6453, - 17440, - 12795, - 10654, - 14541, - 21638 - ], - "xaxis": "x", - "y": [ - 2585, - 13368, - 12637, - 6262, - 3777, - 2100, - 3047, - 1643, - 2129, - 2695, - 797, - 4849, - 1665, - 2189, - 10687, - 2182, - 2600, - 2114, - 4666, - 1130, - 1613, - 1422, - 4324, - 2647, - 2190, - 2783, - 2561, - 4125, - 11914, - 2584, - 2331, - 2681, - 3335, - 4648, - 2231, - 2490, - 3301, - 237, - 1058, - 3757, - 2216, - 2480, - 1475, - 1254, - 3659, - 2606, - 2856, - 2771, - 4374 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Fryslân
Year=2014
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Achtkarspelen", - "Ameland", - "Dantumadiel", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Súdwest-Fryslân", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "ids": [ - "Achtkarspelen", - "Ameland", - "Dantumadiel", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Súdwest-Fryslân", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "legendgroup": "Fryslân", - "marker": { - "color": "#ab63fa", - "size": [ - 28016, - 3578, - 19030, - 15821, - 49899, - 107342, - 25672, - 29863, - 942, - 55467, - 84180, - 4780, - 31973, - 1110, - 25454 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Fryslân", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11894, - 1657, - 8039, - 7425, - 22974, - 53112, - 11158, - 12709, - 581, - 25063, - 38448, - 2213, - 13457, - 568, - 11358 - ], - "xaxis": "x", - "y": [ - 2803, - 358, - 1675, - 1334, - 4512, - 8650, - 2045, - 2827, - 73, - 4957, - 7761, - 343, - 2990, - 85, - 2131 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zuid-Holland
Year=2014
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Bodegraven-Reeuwijk", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Goeree-Overflakkee", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Kaag en Braassem", - "Katwijk", - "Krimpen aan den IJssel", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Nieuwkoop", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zuidplas", - "Zwijndrecht" - ], - "ids": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Bodegraven-Reeuwijk", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Goeree-Overflakkee", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Kaag en Braassem", - "Katwijk", - "Krimpen aan den IJssel", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Nieuwkoop", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zuidplas", - "Zwijndrecht" - ], - "legendgroup": "Zuid-Holland", - "marker": { - "color": "#FFA15A", - "size": [ - 19801, - 25069, - 106785, - 47377, - 32910, - 16312, - 66178, - 100046, - 118691, - 48245, - 35242, - 70941, - 17758, - 38953, - 28911, - 20944, - 25745, - 62782, - 28825, - 57122, - 121163, - 26813, - 73356, - 22336, - 32080, - 18456, - 27104, - 25691, - 22910, - 32117, - 51071, - 45253, - 618357, - 76450, - 24528, - 35735, - 70981, - 24951, - 25508, - 25675, - 103241, - 13964, - 123561, - 8075, - 40892, - 44547 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zuid-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 8122, - 10165, - 45433, - 18806, - 13715, - 7617, - 30727, - 48438, - 54528, - 20574, - 16019, - 31542, - 7058, - 17249, - 11533, - 9297, - 10764, - 25022, - 12329, - 22158, - 55202, - 11891, - 36175, - 9782, - 14805, - 7449, - 10597, - 12154, - 10269, - 14088, - 19663, - 20686, - 308291, - 36044, - 10464, - 14687, - 34857, - 11086, - 10784, - 12152, - 41692, - 6695, - 54809, - 3334, - 16712, - 20355 - ], - "xaxis": "x", - "y": [ - 2084, - 2583, - 10131, - 5249, - 3178, - 1302, - 5582, - 6417, - 10157, - 4344, - 3381, - 6945, - 1834, - 3297, - 2967, - 1733, - 2343, - 6100, - 2743, - 6910, - 8236, - 2588, - 5932, - 2165, - 2540, - 2124, - 2205, - 2129, - 2458, - 2914, - 5748, - 3442, - 50985, - 6825, - 2259, - 3253, - 5497, - 2450, - 2025, - 2011, - 9650, - 984, - 11079, - 614, - 3908, - 3882 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Overijssel
Year=2014
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "ids": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "legendgroup": "Overijssel", - "marker": { - "color": "#19d3f3", - "size": [ - 72459, - 21884, - 27674, - 98322, - 25947, - 158586, - 24344, - 59577, - 35711, - 34997, - 51092, - 22612, - 32137, - 17770, - 17361, - 36519, - 37661, - 16367, - 43350, - 21206, - 33929, - 23909, - 22167, - 123159 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Overijssel", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 32159, - 9155, - 11134, - 42203, - 10445, - 74787, - 10072, - 23554, - 14371, - 14790, - 21069, - 9462, - 14204, - 7200, - 6933, - 15464, - 14006, - 5537, - 19529, - 8113, - 13488, - 9542, - 8461, - 54274 - ], - "xaxis": "x", - "y": [ - 6440, - 2002, - 2702, - 9208, - 2525, - 13109, - 2143, - 5912, - 3356, - 3162, - 5364, - 1959, - 3151, - 1452, - 1616, - 3330, - 4182, - 2015, - 3733, - 2330, - 3530, - 2351, - 2619, - 11750 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Flevoland
Year=2014
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "ids": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "legendgroup": "Flevoland", - "marker": { - "color": "#FF6692", - "size": [ - 196013, - 40413, - 76142, - 46356, - 19470, - 21499 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Flevoland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 78330, - 17231, - 32578, - 19357, - 5996, - 8020 - ], - "xaxis": "x", - "y": [ - 20394, - 3817, - 7741, - 4729, - 2973, - 2244 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Brabant
Year=2014
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "ids": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "legendgroup": "Noord-Brabant", - "marker": { - "color": "#B6E880", - "size": [ - 9717, - 16440, - 6612, - 18256, - 66419, - 29690, - 28617, - 19834, - 10089, - 30320, - 179623, - 20344, - 31659, - 25358, - 26695, - 18183, - 220920, - 42357, - 21571, - 38854, - 29315, - 26069, - 23098, - 29340, - 15353, - 89256, - 143733, - 43165, - 15092, - 21802, - 23080, - 36729, - 22620, - 17980, - 25802, - 53717, - 84954, - 12713, - 77027, - 22180, - 28121, - 18690, - 16235, - 23374, - 210270, - 30335, - 44155, - 25638, - 16765, - 46498, - 21621, - 21399 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Brabant", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 4050, - 6662, - 2727, - 7653, - 29887, - 11941, - 11965, - 8302, - 3882, - 13192, - 80717, - 8715, - 13198, - 10725, - 11364, - 7856, - 103507, - 18152, - 9542, - 17217, - 12224, - 11067, - 9843, - 12933, - 6518, - 39072, - 65174, - 18050, - 6289, - 9138, - 10033, - 15970, - 9881, - 6936, - 11108, - 23788, - 36290, - 5178, - 34091, - 9513, - 11463, - 7516, - 6725, - 10067, - 94072, - 14130, - 19119, - 11058, - 7436, - 20578, - 9538, - 9076 - ], - "xaxis": "x", - "y": [ - 741, - 1490, - 440, - 1515, - 5285, - 2817, - 2688, - 1752, - 977, - 2790, - 15576, - 1689, - 2510, - 2151, - 2103, - 1448, - 17418, - 3877, - 1829, - 3143, - 2622, - 2380, - 2185, - 2281, - 1222, - 8776, - 12324, - 3774, - 1388, - 1937, - 1802, - 3065, - 1761, - 1515, - 2223, - 4387, - 6911, - 1109, - 6286, - 1449, - 2586, - 1376, - 1552, - 1785, - 16746, - 2202, - 3544, - 2375, - 1486, - 3899, - 1555, - 1349 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Utrecht
Year=2014
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Stichtse Vecht", - "Utrechtse Heuvelrug", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "ids": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Stichtse Vecht", - "Utrechtse Heuvelrug", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "legendgroup": "Utrecht", - "marker": { - "color": "#FF97FF", - "size": [ - 150897, - 24314, - 42036, - 14626, - 20492, - 8779, - 48421, - 34275, - 28997, - 13999, - 13639, - 61038, - 9873, - 4924, - 19116, - 42642, - 45493, - 63856, - 47951, - 63252, - 23043, - 50577, - 12422, - 61250 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Utrecht", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 64455, - 11439, - 19195, - 6101, - 7867, - 3590, - 19365, - 13968, - 12389, - 5364, - 5483, - 27068, - 4147, - 1839, - 7602, - 17586, - 20057, - 27284, - 21358, - 25922, - 9644, - 20995, - 4826, - 27033 - ], - "xaxis": "x", - "y": [ - 15804, - 2052, - 4243, - 1401, - 2083, - 820, - 5598, - 3524, - 2700, - 1367, - 1451, - 5112, - 917, - 405, - 1968, - 3801, - 3981, - 5948, - 4254, - 6516, - 2047, - 5149, - 1274, - 5423 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Limburg
Year=2014
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Eijsden-Margraten", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Peel en Maas", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "ids": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Eijsden-Margraten", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Peel en Maas", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "legendgroup": "Limburg", - "marker": { - "color": "#FECB52", - "size": [ - 13617, - 13237, - 28958, - 31976, - 24979, - 17286, - 14484, - 88259, - 41727, - 46784, - 37573, - 36219, - 23907, - 122488, - 19254, - 7796, - 16751, - 43314, - 20832, - 56929, - 10844, - 93691, - 9685, - 16675, - 100428, - 43112, - 12454, - 48721 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Limburg", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5904, - 5552, - 14555, - 14360, - 10712, - 7236, - 6818, - 45373, - 17116, - 22468, - 17969, - 15520, - 10559, - 61021, - 8385, - 3518, - 7076, - 17796, - 9330, - 26832, - 5210, - 45775, - 5229, - 8066, - 46291, - 18514, - 5618, - 22380 - ], - "xaxis": "x", - "y": [ - 1097, - 981, - 2089, - 2214, - 1604, - 1457, - 810, - 5658, - 3481, - 2797, - 2541, - 2568, - 1467, - 6786, - 1442, - 577, - 1243, - 3586, - 1415, - 4420, - 729, - 6343, - 473, - 998, - 7924, - 3466, - 869, - 3689 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zeeland
Year=2014
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "ids": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "legendgroup": "Zeeland", - "marker": { - "color": "#636efa", - "size": [ - 22579, - 36954, - 27388, - 12500, - 7530, - 21927, - 33852, - 23820, - 54709, - 25408, - 21868, - 44444 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zeeland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 9719, - 17548, - 13103, - 5171, - 4168, - 8889, - 16765, - 13986, - 26287, - 10515, - 10724, - 22324 - ], - "xaxis": "x", - "y": [ - 2039, - 3006, - 1793, - 1287, - 505, - 2446, - 2648, - 1472, - 3871, - 2502, - 1664, - 3468 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Groningen
Year=2014
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Oldambt", - "Pekela", - "Stadskanaal", - "Veendam" - ], - "ids": [ - "Oldambt", - "Pekela", - "Stadskanaal", - "Veendam" - ], - "legendgroup": "Groningen", - "marker": { - "color": "#EF553B", - "size": [ - 38560, - 12706, - 32803, - 27792 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Groningen", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 18100, - 5776, - 15304, - 12720 - ], - "xaxis": "x", - "y": [ - 3011, - 969, - 2653, - 2223 - ], - "yaxis": "y" - } - ], - "name": "2014" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

Provincienaam=Drenthe
Year=2015
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "ids": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "legendgroup": "Drenthe", - "marker": { - "color": "#636efa", - "size": [ - 25203, - 67165, - 25502, - 35535, - 107775, - 54860, - 32799, - 33284, - 31137, - 32570, - 19085, - 23661 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Drenthe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11150, - 30737, - 11734, - 15388, - 49120, - 24443, - 15076, - 14045, - 14558, - 13857, - 8257, - 10018 - ], - "xaxis": "x", - "y": [ - 1864, - 6427, - 1902, - 2957, - 9008, - 5334, - 3314, - 2692, - 2534, - 3140, - 1362, - 1764 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Holland
Year=2015
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hollands Kroon", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "ids": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hollands Kroon", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "legendgroup": "Noord-Holland", - "marker": { - "color": "#EF553B", - "size": [ - 31077, - 107106, - 87162, - 821752, - 30005, - 40182, - 9312, - 22256, - 34361, - 26666, - 19294, - 29087, - 18345, - 156645, - 144152, - 39138, - 26480, - 22553, - 56483, - 87161, - 47546, - 71880, - 41315, - 22426, - 10823, - 43604, - 9187, - 11301, - 13289, - 79611, - 46137, - 21498, - 13581, - 13291, - 28731, - 67166, - 17143, - 18348, - 23176, - 15740, - 151418, - 16692 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 12803, - 50076, - 42736, - 416966, - 14986, - 18760, - 4272, - 9619, - 15317, - 13132, - 8045, - 12285, - 8613, - 73230, - 59292, - 17361, - 12321, - 10207, - 27869, - 40861, - 20543, - 32226, - 18544, - 9148, - 4646, - 18237, - 3932, - 4629, - 5781, - 35353, - 19770, - 9000, - 6405, - 5520, - 12603, - 30621, - 7228, - 8715, - 10344, - 6696, - 66549, - 9403 - ], - "xaxis": "x", - "y": [ - 3321, - 8861, - 6686, - 62371, - 2106, - 3465, - 959, - 3425, - 2716, - 2135, - 1657, - 2770, - 1525, - 13098, - 14833, - 3205, - 2471, - 1973, - 4415, - 7749, - 4080, - 6547, - 3221, - 2028, - 1048, - 3848, - 846, - 1055, - 1449, - 6287, - 4169, - 1812, - 977, - 1266, - 2514, - 5032, - 1540, - 1302, - 2032, - 1205, - 13434, - 1042 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Gelderland
Year=2015
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "ids": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "legendgroup": "Gelderland", - "marker": { - "color": "#00cc96", - "size": [ - 26904, - 158099, - 152293, - 54703, - 44364, - 25282, - 36726, - 20983, - 26117, - 27560, - 11355, - 56484, - 18294, - 25548, - 111575, - 22843, - 32214, - 26190, - 45776, - 11821, - 18512, - 16383, - 45788, - 33244, - 24185, - 35150, - 22728, - 40870, - 170681, - 26744, - 23001, - 29533, - 39558, - 46833, - 24377, - 31408, - 43625, - 1509, - 9522, - 41590, - 23913, - 37786, - 18570, - 14992, - 40886, - 28977, - 27358, - 32265, - 46849 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Gelderland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11775, - 69804, - 71962, - 20598, - 19299, - 10581, - 16074, - 9250, - 10323, - 11529, - 5167, - 25039, - 7607, - 10616, - 48250, - 9101, - 13839, - 10781, - 18712, - 5160, - 7782, - 6945, - 19214, - 14519, - 9639, - 15170, - 8250, - 15921, - 78678, - 10386, - 8982, - 12654, - 17240, - 18838, - 9570, - 14658, - 20931, - 635, - 3711, - 17654, - 9791, - 19530, - 7871, - 6544, - 17506, - 12861, - 10801, - 14614, - 21695 - ], - "xaxis": "x", - "y": [ - 2504, - 13238, - 12583, - 6361, - 3690, - 1972, - 2913, - 1524, - 2050, - 2537, - 782, - 4777, - 1652, - 2081, - 10665, - 2208, - 2526, - 2143, - 4678, - 1124, - 1603, - 1357, - 4243, - 2555, - 2119, - 2638, - 2572, - 4102, - 11935, - 2536, - 2304, - 2573, - 3250, - 4562, - 2184, - 2418, - 3291, - 231, - 1069, - 3629, - 2191, - 2492, - 1446, - 1205, - 3577, - 2493, - 2784, - 2698, - 4312 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Fryslân
Year=2015
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Achtkarspelen", - "Ameland", - "Dantumadiel", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Súdwest-Fryslân", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "ids": [ - "Achtkarspelen", - "Ameland", - "Dantumadiel", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Súdwest-Fryslân", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "legendgroup": "Fryslân", - "marker": { - "color": "#ab63fa", - "size": [ - 27983, - 3590, - 19059, - 15779, - 50141, - 107691, - 25617, - 29859, - 926, - 55635, - 84164, - 4827, - 31957, - 1103, - 25525 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Fryslân", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11917, - 1670, - 8046, - 7477, - 23134, - 53792, - 11154, - 12716, - 578, - 25256, - 38939, - 2222, - 13553, - 527, - 11410 - ], - "xaxis": "x", - "y": [ - 2778, - 359, - 1667, - 1321, - 4411, - 8610, - 2040, - 2761, - 67, - 4842, - 7540, - 317, - 2957, - 78, - 2092 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zuid-Holland
Year=2015
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Bodegraven-Reeuwijk", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Goeree-Overflakkee", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Kaag en Braassem", - "Katwijk", - "Krimpen aan den IJssel", - "Krimpenerwaard", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Nieuwkoop", - "Nissewaard", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zuidplas", - "Zwijndrecht" - ], - "ids": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Bodegraven-Reeuwijk", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Goeree-Overflakkee", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Kaag en Braassem", - "Katwijk", - "Krimpen aan den IJssel", - "Krimpenerwaard", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Nieuwkoop", - "Nissewaard", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zuidplas", - "Zwijndrecht" - ], - "legendgroup": "Zuid-Holland", - "marker": { - "color": "#FFA15A", - "size": [ - 19845, - 25148, - 107396, - 47521, - 33208, - 16467, - 66478, - 101030, - 118899, - 48206, - 35338, - 71105, - 17802, - 38882, - 29156, - 21101, - 25844, - 63633, - 28970, - 54208, - 58133, - 121562, - 26853, - 73979, - 22539, - 32201, - 18709, - 27114, - 85121, - 25604, - 22997, - 32188, - 51203, - 45149, - 623652, - 76869, - 24758, - 35646, - 71645, - 25150, - 25657, - 25731, - 104302, - 14083, - 124025, - 8114, - 40771, - 44501 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zuid-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 8138, - 10167, - 45753, - 18867, - 13824, - 7678, - 30728, - 49199, - 54548, - 20845, - 15980, - 31694, - 7073, - 17298, - 11640, - 9399, - 10835, - 25491, - 12322, - 23265, - 22523, - 55231, - 11958, - 36383, - 9935, - 14846, - 7578, - 10741, - 38989, - 12058, - 10366, - 14181, - 19721, - 20747, - 311324, - 36581, - 10598, - 14716, - 34927, - 11090, - 11021, - 12192, - 42339, - 6708, - 55050, - 3359, - 16707, - 20356 - ], - "xaxis": "x", - "y": [ - 2057, - 2556, - 10034, - 5103, - 3165, - 1299, - 5500, - 6417, - 10180, - 4293, - 3350, - 6908, - 1784, - 3273, - 3022, - 1713, - 2301, - 6273, - 2678, - 4558, - 6850, - 8218, - 2591, - 5881, - 2098, - 2544, - 2143, - 2196, - 6723, - 2144, - 2497, - 2868, - 5755, - 3465, - 51402, - 6900, - 2250, - 3185, - 5596, - 2440, - 2039, - 2003, - 9623, - 949, - 11185, - 611, - 3897, - 3832 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Overijssel
Year=2015
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "ids": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "legendgroup": "Overijssel", - "marker": { - "color": "#19d3f3", - "size": [ - 72291, - 21992, - 27677, - 98540, - 25928, - 158553, - 24307, - 59577, - 35622, - 34917, - 51432, - 22467, - 32120, - 17839, - 17341, - 36603, - 37830, - 16421, - 43219, - 21142, - 33874, - 23874, - 22166, - 123861 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Overijssel", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 32281, - 9231, - 11238, - 42858, - 10461, - 75030, - 10134, - 23662, - 14414, - 14903, - 21149, - 9497, - 14227, - 7302, - 6831, - 15231, - 14106, - 5655, - 19627, - 8167, - 13574, - 9596, - 8574, - 54761 - ], - "xaxis": "x", - "y": [ - 6386, - 1985, - 2579, - 8982, - 2357, - 12976, - 2050, - 5711, - 3297, - 3045, - 5315, - 1857, - 3055, - 1380, - 1591, - 3241, - 4153, - 2012, - 3556, - 2198, - 3450, - 2290, - 2610, - 11791 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Flevoland
Year=2015
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "ids": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "legendgroup": "Flevoland", - "marker": { - "color": "#FF6692", - "size": [ - 196932, - 40363, - 76418, - 46479, - 19705, - 21894 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Flevoland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 78974, - 17369, - 32658, - 19493, - 6110, - 8127 - ], - "xaxis": "x", - "y": [ - 20087, - 3784, - 7685, - 4711, - 3011, - 2185 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Brabant
Year=2015
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "ids": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "legendgroup": "Noord-Brabant", - "marker": { - "color": "#B6E880", - "size": [ - 9753, - 16559, - 6599, - 18209, - 66320, - 29729, - 28737, - 19869, - 10119, - 30337, - 180937, - 20542, - 31765, - 25395, - 26703, - 18347, - 223209, - 42503, - 21574, - 38879, - 29513, - 26065, - 23014, - 29484, - 15477, - 89718, - 150889, - 43132, - 15042, - 21913, - 22960, - 36816, - 22620, - 18079, - 25732, - 53793, - 89799, - 12774, - 76874, - 22233, - 28395, - 18695, - 16344, - 23638, - 211648, - 30234, - 44166, - 25853, - 16874, - 46713, - 21644, - 21363 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Brabant", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 4149, - 6698, - 2744, - 7679, - 30010, - 12000, - 11952, - 8346, - 3890, - 13214, - 81291, - 8756, - 13344, - 10779, - 11435, - 7946, - 104287, - 18183, - 9560, - 17269, - 12334, - 11076, - 9859, - 12991, - 6558, - 39162, - 68962, - 18199, - 6297, - 9227, - 10025, - 16077, - 9918, - 7031, - 11091, - 24141, - 38519, - 5255, - 34437, - 9505, - 11690, - 7754, - 6874, - 9996, - 95264, - 14121, - 19181, - 11179, - 7506, - 20761, - 9647, - 9163 - ], - "xaxis": "x", - "y": [ - 722, - 1411, - 414, - 1454, - 5230, - 2719, - 2549, - 1695, - 969, - 2714, - 15741, - 1636, - 2518, - 2100, - 2043, - 1467, - 17573, - 3822, - 1818, - 3131, - 2614, - 2381, - 2232, - 2256, - 1177, - 8643, - 12896, - 3723, - 1359, - 1913, - 1699, - 3041, - 1720, - 1472, - 2164, - 4279, - 7133, - 1102, - 6213, - 1409, - 2571, - 1337, - 1530, - 1719, - 16751, - 2165, - 3436, - 2437, - 1478, - 3882, - 1504, - 1314 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Utrecht
Year=2015
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Stichtse Vecht", - "Utrechtse Heuvelrug", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "ids": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Stichtse Vecht", - "Utrechtse Heuvelrug", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "legendgroup": "Utrecht", - "marker": { - "color": "#FF97FF", - "size": [ - 152481, - 24406, - 42169, - 14662, - 20647, - 8807, - 48637, - 34061, - 29062, - 14099, - 13672, - 61264, - 9924, - 4976, - 19308, - 42588, - 45454, - 63943, - 48183, - 63440, - 23222, - 50631, - 12487, - 61641 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Utrecht", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 64833, - 11586, - 19220, - 6207, - 7977, - 3619, - 19441, - 14006, - 12400, - 5383, - 5506, - 27507, - 4177, - 1837, - 7692, - 17570, - 20281, - 27368, - 21055, - 26411, - 9753, - 21272, - 4849, - 27830 - ], - "xaxis": "x", - "y": [ - 15781, - 2039, - 4218, - 1388, - 2082, - 839, - 5483, - 3392, - 2642, - 1328, - 1410, - 5037, - 921, - 393, - 1922, - 3720, - 3873, - 5979, - 4139, - 6470, - 2012, - 5060, - 1261, - 5404 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Limburg
Year=2015
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Eijsden-Margraten", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Peel en Maas", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "ids": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Eijsden-Margraten", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Peel en Maas", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "legendgroup": "Limburg", - "marker": { - "color": "#FECB52", - "size": [ - 13511, - 13152, - 28656, - 31947, - 24967, - 17280, - 14497, - 87500, - 41661, - 46524, - 37456, - 36244, - 23766, - 122397, - 19063, - 7762, - 16776, - 43448, - 20699, - 57005, - 10844, - 93724, - 9694, - 16618, - 100536, - 43202, - 12397, - 48914 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Limburg", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5911, - 5594, - 14534, - 14402, - 10781, - 7324, - 6840, - 45169, - 17256, - 24014, - 17897, - 15669, - 10599, - 60764, - 8445, - 3544, - 7118, - 18001, - 9382, - 26992, - 5211, - 46055, - 5238, - 8148, - 46276, - 18706, - 5680, - 22349 - ], - "xaxis": "x", - "y": [ - 1085, - 937, - 2107, - 2175, - 1534, - 1424, - 849, - 5542, - 3433, - 2785, - 2542, - 2464, - 1391, - 6835, - 1388, - 557, - 939, - 3461, - 1367, - 4489, - 716, - 6339, - 470, - 971, - 7815, - 3355, - 861, - 3887 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zeeland
Year=2015
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "ids": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "legendgroup": "Zeeland", - "marker": { - "color": "#636efa", - "size": [ - 22568, - 37153, - 27360, - 12545, - 7433, - 22058, - 33821, - 23747, - 54577, - 25440, - 21926, - 44485 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zeeland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 9657, - 17684, - 13190, - 5195, - 4324, - 8916, - 16669, - 13974, - 26359, - 10597, - 10717, - 22378 - ], - "xaxis": "x", - "y": [ - 2011, - 3042, - 1728, - 1233, - 446, - 2446, - 2559, - 1408, - 3836, - 2494, - 1583, - 3451 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Groningen
Year=2015
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Oldambt", - "Pekela", - "Stadskanaal", - "Veendam" - ], - "ids": [ - "Oldambt", - "Pekela", - "Stadskanaal", - "Veendam" - ], - "legendgroup": "Groningen", - "marker": { - "color": "#EF553B", - "size": [ - 38420, - 12678, - 32610, - 27695 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Groningen", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 18287, - 5775, - 15274, - 12719 - ], - "xaxis": "x", - "y": [ - 2920, - 973, - 2625, - 2198 - ], - "yaxis": "y" - } - ], - "name": "2015" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

Provincienaam=Drenthe
Year=2016
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "ids": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "legendgroup": "Drenthe", - "marker": { - "color": "#636efa", - "size": [ - 25243, - 67061, - 25371, - 35381, - 107584, - 55240, - 32794, - 33450, - 31039, - 32804, - 18940, - 23722 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Drenthe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11131, - 30691, - 11714, - 15342, - 49118, - 24504, - 15177, - 14030, - 14555, - 13958, - 8312, - 10093 - ], - "xaxis": "x", - "y": [ - 1754, - 6350, - 1817, - 2838, - 8807, - 5247, - 3221, - 2576, - 2459, - 3154, - 1274, - 1701 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Holland
Year=2016
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Gooise Meren", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hollands Kroon", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "ids": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Gooise Meren", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hollands Kroon", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "legendgroup": "Noord-Holland", - "marker": { - "color": "#EF553B", - "size": [ - 31299, - 107615, - 88602, - 833624, - 29943, - 40318, - 9622, - 22296, - 34604, - 26840, - 19400, - 35465, - 18455, - 56696, - 158140, - 144518, - 39299, - 26766, - 22689, - 56275, - 87830, - 47546, - 72172, - 41373, - 22471, - 10977, - 43725, - 9504, - 11336, - 13411, - 79889, - 46159, - 21493, - 13574, - 13360, - 29181, - 67448, - 17304, - 18572, - 23275, - 15664, - 152466, - 16792 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 12893, - 50346, - 43045, - 424390, - 14976, - 18846, - 4363, - 9653, - 15459, - 13216, - 8107, - 14939, - 8642, - 26137, - 73674, - 59699, - 17457, - 12520, - 10233, - 27939, - 41123, - 20554, - 32423, - 18592, - 9220, - 4668, - 18402, - 4048, - 4680, - 5851, - 35581, - 19814, - 9025, - 6470, - 5548, - 12837, - 30603, - 7280, - 8640, - 10381, - 6684, - 66961, - 9450 - ], - "xaxis": "x", - "y": [ - 3275, - 8816, - 6837, - 62468, - 2017, - 3363, - 968, - 3453, - 2663, - 2140, - 1594, - 3203, - 1536, - 6198, - 13339, - 14253, - 3201, - 2474, - 1877, - 4251, - 7753, - 3954, - 6477, - 3084, - 1935, - 1083, - 3761, - 861, - 1006, - 1379, - 6219, - 3989, - 1818, - 963, - 1239, - 2471, - 5000, - 1499, - 1315, - 1953, - 1169, - 13249, - 1053 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Gelderland
Year=2016
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berg en Dal", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "ids": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berg en Dal", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "legendgroup": "Gelderland", - "marker": { - "color": "#00cc96", - "size": [ - 26912, - 159025, - 153818, - 55441, - 34574, - 44437, - 25289, - 36510, - 20938, - 26202, - 27644, - 11336, - 56827, - 18407, - 25433, - 112427, - 22929, - 32282, - 26507, - 45966, - 11890, - 18556, - 16360, - 45950, - 33333, - 24084, - 35173, - 23049, - 41199, - 172064, - 26835, - 23104, - 29537, - 39657, - 47002, - 24516, - 31254, - 43824, - 1498, - 9529, - 41510, - 23984, - 37837, - 18693, - 15001, - 40814, - 28939, - 27543, - 32269, - 46997 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Gelderland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11811, - 70615, - 72711, - 20938, - 15282, - 19466, - 10667, - 16117, - 9260, - 10386, - 11862, - 5238, - 25180, - 7662, - 10635, - 48915, - 9170, - 14097, - 10785, - 18873, - 5240, - 7803, - 6991, - 19423, - 14624, - 9695, - 15281, - 8391, - 16131, - 79593, - 10449, - 9090, - 12692, - 17218, - 19251, - 9628, - 14626, - 20970, - 635, - 3725, - 17682, - 10174, - 19648, - 7960, - 6578, - 17518, - 12891, - 10985, - 14677, - 21698 - ], - "xaxis": "x", - "y": [ - 2448, - 13074, - 12708, - 6373, - 2382, - 3533, - 1890, - 2761, - 1494, - 1982, - 2448, - 771, - 4737, - 1588, - 2018, - 10649, - 2247, - 2504, - 2082, - 4672, - 1108, - 1574, - 1321, - 4138, - 2542, - 2060, - 2483, - 2558, - 4057, - 12029, - 2505, - 2290, - 2438, - 3162, - 4528, - 2182, - 2400, - 3267, - 229, - 1119, - 3515, - 2228, - 2472, - 1436, - 1199, - 3488, - 2333, - 2757, - 2625, - 4246 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Fryslân
Year=2016
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Achtkarspelen", - "Ameland", - "Dantumadiel", - "De Fryske Marren", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Súdwest-Fryslân", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "ids": [ - "Achtkarspelen", - "Ameland", - "Dantumadiel", - "De Fryske Marren", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Súdwest-Fryslân", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "legendgroup": "Fryslân", - "marker": { - "color": "#ab63fa", - "size": [ - 28007, - 3611, - 19015, - 51265, - 15813, - 50290, - 107897, - 25571, - 29830, - 919, - 55439, - 84048, - 4870, - 32077, - 1083, - 25520 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Fryslân", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11946, - 1674, - 8065, - 22818, - 7533, - 23230, - 54245, - 11172, - 12873, - 583, - 25391, - 39013, - 2229, - 13646, - 540, - 11495 - ], - "xaxis": "x", - "y": [ - 2707, - 348, - 1618, - 4566, - 1331, - 4367, - 8630, - 2006, - 2677, - 58, - 4697, - 7295, - 312, - 2842, - 69, - 2042 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zuid-Holland
Year=2016
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Bodegraven-Reeuwijk", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Goeree-Overflakkee", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Kaag en Braassem", - "Katwijk", - "Krimpen aan den IJssel", - "Krimpenerwaard", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Nieuwkoop", - "Nissewaard", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zuidplas", - "Zwijndrecht" - ], - "ids": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Bodegraven-Reeuwijk", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Goeree-Overflakkee", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Kaag en Braassem", - "Katwijk", - "Krimpen aan den IJssel", - "Krimpenerwaard", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Nieuwkoop", - "Nissewaard", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zuidplas", - "Zwijndrecht" - ], - "legendgroup": "Zuid-Holland", - "marker": { - "color": "#FFA15A", - "size": [ - 19955, - 24985, - 107960, - 47861, - 33451, - 16640, - 66486, - 101034, - 118801, - 48321, - 35260, - 71189, - 17774, - 38634, - 29408, - 21089, - 26108, - 64239, - 29054, - 54653, - 59035, - 122561, - 26968, - 74223, - 22606, - 32292, - 18873, - 27433, - 85293, - 25760, - 23209, - 32248, - 51894, - 45097, - 629606, - 77108, - 24968, - 36013, - 71808, - 25211, - 26072, - 25885, - 104960, - 14197, - 124107, - 8119, - 40937, - 44454 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zuid-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 8220, - 10208, - 45968, - 18918, - 14008, - 7683, - 30727, - 49627, - 54853, - 20915, - 16092, - 31868, - 7130, - 17305, - 11725, - 9398, - 11056, - 25814, - 12318, - 23331, - 22942, - 56636, - 12016, - 36398, - 10080, - 14938, - 7649, - 10835, - 39083, - 12267, - 10379, - 14251, - 20135, - 20680, - 311168, - 36751, - 10722, - 14952, - 34768, - 11210, - 11248, - 12213, - 42622, - 6732, - 55377, - 3365, - 16787, - 20411 - ], - "xaxis": "x", - "y": [ - 2054, - 2527, - 9891, - 5049, - 3126, - 1269, - 5397, - 6490, - 10026, - 4222, - 3310, - 6793, - 1786, - 3214, - 3098, - 1672, - 2306, - 6191, - 2703, - 4506, - 6871, - 8169, - 2586, - 5891, - 2012, - 2537, - 2151, - 2162, - 6728, - 2136, - 2590, - 2864, - 5641, - 3602, - 51836, - 6856, - 2247, - 3034, - 5660, - 2482, - 2054, - 2027, - 9588, - 952, - 11256, - 627, - 3939, - 3873 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Overijssel
Year=2016
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "ids": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "legendgroup": "Overijssel", - "marker": { - "color": "#19d3f3", - "size": [ - 72425, - 22343, - 27916, - 98869, - 26120, - 158351, - 24332, - 59687, - 35651, - 34881, - 51950, - 22444, - 32110, - 17886, - 17696, - 36700, - 37875, - 16544, - 43333, - 21120, - 33846, - 23952, - 22278, - 124896 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Overijssel", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 32417, - 9316, - 11391, - 43643, - 10528, - 75056, - 10162, - 23864, - 14526, - 14868, - 21343, - 9559, - 14284, - 7448, - 7108, - 15301, - 14298, - 5688, - 19636, - 8247, - 13609, - 9456, - 8648, - 55473 - ], - "xaxis": "x", - "y": [ - 6199, - 1990, - 2529, - 8857, - 2226, - 12732, - 1975, - 5593, - 3170, - 2907, - 5308, - 1798, - 2999, - 1295, - 1558, - 3189, - 4079, - 1987, - 3487, - 2096, - 3373, - 2277, - 2553, - 11935 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Flevoland
Year=2016
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "ids": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "legendgroup": "Flevoland", - "marker": { - "color": "#FF6692", - "size": [ - 198145, - 40592, - 76792, - 46439, - 19987, - 22113 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Flevoland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 79721, - 16495, - 32755, - 19583, - 6198, - 8166 - ], - "xaxis": "x", - "y": [ - 19895, - 3768, - 7612, - 4573, - 3041, - 2195 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Brabant
Year=2016
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "ids": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "legendgroup": "Noord-Brabant", - "marker": { - "color": "#B6E880", - "size": [ - 9924, - 16580, - 6611, - 18253, - 66237, - 29880, - 28976, - 19966, - 10254, - 30406, - 181611, - 20660, - 31878, - 25413, - 26815, - 18551, - 224755, - 42832, - 21630, - 38893, - 29647, - 26152, - 23111, - 29531, - 15650, - 90127, - 151608, - 43274, - 15164, - 21965, - 22929, - 36762, - 22763, - 18199, - 25835, - 54018, - 90003, - 12811, - 76960, - 22276, - 28403, - 18914, - 16425, - 23477, - 212941, - 30262, - 44317, - 25973, - 17023, - 47021, - 21682, - 21488 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Brabant", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 4214, - 6717, - 2746, - 7742, - 29738, - 12044, - 12031, - 8417, - 3975, - 13296, - 81649, - 8781, - 13433, - 10792, - 11501, - 8008, - 106732, - 18408, - 9553, - 17305, - 12451, - 11138, - 9906, - 13027, - 6628, - 39407, - 69726, - 18281, - 6476, - 9237, - 10110, - 16116, - 10017, - 7437, - 11178, - 24248, - 38831, - 5275, - 34718, - 9534, - 11738, - 7783, - 6906, - 10006, - 95779, - 14272, - 19280, - 11257, - 7556, - 20952, - 9642, - 9197 - ], - "xaxis": "x", - "y": [ - 705, - 1347, - 402, - 1434, - 5159, - 2649, - 2412, - 1672, - 935, - 2660, - 15643, - 1515, - 2498, - 2044, - 2002, - 1414, - 17429, - 3800, - 1786, - 3082, - 2595, - 2289, - 2291, - 2215, - 1165, - 8432, - 12833, - 3703, - 1302, - 1872, - 1672, - 2976, - 1695, - 1480, - 2113, - 4186, - 6975, - 1138, - 6133, - 1390, - 2499, - 1294, - 1522, - 1680, - 16629, - 2142, - 3394, - 2470, - 1471, - 3888, - 1495, - 1274 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Utrecht
Year=2016
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Stichtse Vecht", - "Utrechtse Heuvelrug", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "ids": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Stichtse Vecht", - "Utrechtse Heuvelrug", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "legendgroup": "Utrecht", - "marker": { - "color": "#FF97FF", - "size": [ - 153602, - 24521, - 42375, - 14773, - 20823, - 8877, - 48765, - 34101, - 29309, - 14156, - 13783, - 61749, - 10049, - 5051, - 19400, - 42576, - 45487, - 64061, - 48506, - 63816, - 23384, - 51161, - 12550, - 62258 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Utrecht", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 65289, - 11586, - 19260, - 6269, - 8087, - 3665, - 19542, - 14169, - 12425, - 5488, - 5572, - 27799, - 4270, - 1885, - 7755, - 17608, - 20282, - 27561, - 21216, - 26702, - 9857, - 21403, - 4893, - 27957 - ], - "xaxis": "x", - "y": [ - 15628, - 2041, - 4189, - 1363, - 2115, - 787, - 5349, - 3223, - 2625, - 1256, - 1377, - 4977, - 906, - 396, - 1887, - 3532, - 3803, - 6004, - 4083, - 6280, - 1995, - 4969, - 1265, - 5486 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Limburg
Year=2016
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Eijsden-Margraten", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Peel en Maas", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "ids": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Eijsden-Margraten", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Peel en Maas", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "legendgroup": "Limburg", - "marker": { - "color": "#FECB52", - "size": [ - 13388, - 13090, - 28448, - 31943, - 25123, - 17085, - 14508, - 87406, - 41675, - 46023, - 37465, - 36140, - 23757, - 122533, - 19040, - 7755, - 16793, - 43316, - 20686, - 57010, - 10741, - 93555, - 9632, - 16518, - 100371, - 43291, - 12482, - 49100 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Limburg", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5882, - 5619, - 14511, - 14450, - 10888, - 7277, - 6789, - 45227, - 17431, - 23967, - 17923, - 15718, - 10601, - 60948, - 8569, - 3561, - 7165, - 17898, - 9405, - 27111, - 5225, - 46058, - 5241, - 8169, - 46424, - 18790, - 5733, - 22504 - ], - "xaxis": "x", - "y": [ - 1050, - 916, - 2073, - 2173, - 1523, - 1408, - 806, - 5490, - 3358, - 2777, - 2474, - 2368, - 1318, - 6739, - 1318, - 518, - 1188, - 3381, - 1357, - 4476, - 673, - 6279, - 466, - 929, - 7731, - 3247, - 835, - 3662 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zeeland
Year=2016
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "ids": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "legendgroup": "Zeeland", - "marker": { - "color": "#636efa", - "size": [ - 22612, - 37207, - 27372, - 12639, - 7421, - 22265, - 33735, - 23639, - 54657, - 25421, - 21960, - 44451 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zeeland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 9687, - 17748, - 13272, - 5223, - 4347, - 8996, - 16717, - 14051, - 26675, - 10626, - 10831, - 22434 - ], - "xaxis": "x", - "y": [ - 1983, - 2939, - 1629, - 1220, - 458, - 2411, - 2458, - 1362, - 3814, - 2495, - 1559, - 3456 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Groningen
Year=2016
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Oldambt", - "Pekela", - "Stadskanaal", - "Veendam" - ], - "ids": [ - "Oldambt", - "Pekela", - "Stadskanaal", - "Veendam" - ], - "legendgroup": "Groningen", - "marker": { - "color": "#EF553B", - "size": [ - 38228, - 12641, - 32621, - 27467 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Groningen", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 18396, - 5777, - 15282, - 12723 - ], - "xaxis": "x", - "y": [ - 2809, - 973, - 2531, - 2239 - ], - "yaxis": "y" - } - ], - "name": "2016" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

Provincienaam=Drenthe
Year=2017
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "ids": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "legendgroup": "Drenthe", - "marker": { - "color": "#636efa", - "size": [ - 25286, - 67551, - 25355, - 35286, - 107490, - 55311, - 33025, - 33399, - 32981, - 33280, - 19084, - 23744 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Drenthe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11145, - 30995, - 11572, - 15283, - 48789, - 24684, - 15388, - 14156, - 14630, - 14119, - 8395, - 10084 - ], - "xaxis": "x", - "y": [ - 1730, - 6249, - 1794, - 2739, - 8542, - 5151, - 3211, - 2427, - 2384, - 3202, - 1215, - 1724 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Holland
Year=2017
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Gooise Meren", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hollands Kroon", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "ids": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Gooise Meren", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hollands Kroon", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "legendgroup": "Noord-Holland", - "marker": { - "color": "#EF553B", - "size": [ - 31373, - 108373, - 89294, - 844947, - 29844, - 40709, - 10201, - 22826, - 35216, - 27272, - 19384, - 35800, - 18479, - 56935, - 159229, - 146003, - 39171, - 26936, - 22857, - 56020, - 88888, - 47600, - 72493, - 41382, - 22522, - 11275, - 44058, - 9652, - 11420, - 13419, - 79928, - 46193, - 21552, - 13545, - 13465, - 29201, - 67619, - 17290, - 18751, - 23447, - 15820, - 153679, - 16899 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 12919, - 50513, - 41649, - 428035, - 14992, - 19106, - 4639, - 9727, - 15666, - 12770, - 8154, - 14819, - 8689, - 26249, - 74037, - 60085, - 17513, - 12511, - 10366, - 27986, - 41638, - 20616, - 32640, - 18718, - 9237, - 4739, - 18607, - 4068, - 4713, - 5851, - 35631, - 19993, - 9049, - 6501, - 5606, - 12771, - 30820, - 7241, - 8748, - 10583, - 6764, - 67476, - 9517 - ], - "xaxis": "x", - "y": [ - 3179, - 8747, - 6905, - 62134, - 1986, - 3312, - 990, - 3477, - 2690, - 2183, - 1546, - 3061, - 1508, - 6096, - 13376, - 13842, - 3220, - 2487, - 1764, - 4146, - 7766, - 3864, - 6409, - 3076, - 1881, - 1104, - 3690, - 884, - 947, - 1334, - 6107, - 3830, - 1804, - 953, - 1166, - 2465, - 5025, - 1477, - 1333, - 1933, - 1165, - 13084, - 1047 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Gelderland
Year=2017
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berg en Dal", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "ids": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berg en Dal", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "legendgroup": "Gelderland", - "marker": { - "color": "#00cc96", - "size": [ - 27047, - 160047, - 155699, - 56376, - 34764, - 44238, - 25424, - 36410, - 20843, - 26348, - 27864, - 11341, - 57068, - 18563, - 25398, - 113421, - 23026, - 32537, - 26590, - 46352, - 12040, - 18561, - 16475, - 46182, - 33545, - 24250, - 35316, - 23303, - 41775, - 173556, - 26892, - 23256, - 29634, - 39568, - 47394, - 24428, - 31391, - 43645, - 1498, - 9704, - 41488, - 24199, - 38458, - 18820, - 14991, - 40876, - 28912, - 27728, - 32379, - 47340 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Gelderland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11872, - 71050, - 73640, - 21330, - 15397, - 19458, - 10829, - 16063, - 9249, - 10459, - 12024, - 5248, - 25477, - 7807, - 10645, - 49703, - 9186, - 14177, - 10861, - 19185, - 5270, - 7810, - 7064, - 19638, - 14778, - 9749, - 15385, - 8510, - 16344, - 78200, - 10463, - 9163, - 12751, - 17225, - 19390, - 9649, - 14653, - 21117, - 635, - 3819, - 17839, - 10447, - 16836, - 7959, - 6613, - 17535, - 12933, - 11030, - 14795, - 21806 - ], - "xaxis": "x", - "y": [ - 2411, - 12989, - 12824, - 6408, - 2284, - 3371, - 1876, - 2636, - 1438, - 1985, - 2419, - 736, - 4624, - 1532, - 1971, - 10656, - 2287, - 2514, - 2114, - 4681, - 1125, - 1578, - 1292, - 4086, - 2513, - 1980, - 2434, - 2576, - 4099, - 12132, - 2477, - 2277, - 2346, - 3086, - 4474, - 2146, - 2348, - 3188, - 214, - 1145, - 3394, - 2247, - 2446, - 1388, - 1176, - 3320, - 2310, - 2703, - 2628, - 4135 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Fryslân
Year=2017
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Achtkarspelen", - "Ameland", - "Dantumadiel", - "De Fryske Marren", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Súdwest-Fryslân", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "ids": [ - "Achtkarspelen", - "Ameland", - "Dantumadiel", - "De Fryske Marren", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Súdwest-Fryslân", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Weststellingwerf" - ], - "legendgroup": "Fryslân", - "marker": { - "color": "#ab63fa", - "size": [ - 27893, - 3633, - 18942, - 51585, - 15860, - 50203, - 108667, - 25540, - 29718, - 941, - 55695, - 84158, - 4859, - 31963, - 1085, - 25608 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Fryslân", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 12028, - 1686, - 8084, - 22887, - 7561, - 23250, - 54461, - 11202, - 12926, - 584, - 25377, - 39117, - 2243, - 13730, - 542, - 11536 - ], - "xaxis": "x", - "y": [ - 2629, - 341, - 1564, - 4514, - 1325, - 4289, - 8630, - 1968, - 2592, - 44, - 4655, - 7219, - 315, - 2745, - 60, - 2004 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zuid-Holland
Year=2017
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Bodegraven-Reeuwijk", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Goeree-Overflakkee", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Kaag en Braassem", - "Katwijk", - "Krimpen aan den IJssel", - "Krimpenerwaard", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Nieuwkoop", - "Nissewaard", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zuidplas", - "Zwijndrecht" - ], - "ids": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Bodegraven-Reeuwijk", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Goeree-Overflakkee", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Kaag en Braassem", - "Katwijk", - "Krimpen aan den IJssel", - "Krimpenerwaard", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Nieuwkoop", - "Nissewaard", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zuidplas", - "Zwijndrecht" - ], - "legendgroup": "Zuid-Holland", - "marker": { - "color": "#FFA15A", - "size": [ - 20008, - 25105, - 108915, - 48344, - 33733, - 16833, - 66421, - 101381, - 118731, - 48653, - 35755, - 71752, - 17872, - 38722, - 29731, - 21316, - 26374, - 64532, - 29123, - 55204, - 60105, - 123661, - 27128, - 74604, - 22717, - 32450, - 19034, - 27914, - 85401, - 25867, - 23608, - 32292, - 52656, - 45408, - 634660, - 77838, - 25012, - 36093, - 71999, - 25315, - 26536, - 26055, - 105632, - 14257, - 124763, - 8367, - 41468, - 44417 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zuid-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 8252, - 10286, - 46723, - 19131, - 14090, - 7798, - 30745, - 49838, - 54868, - 21089, - 16300, - 32131, - 7211, - 17377, - 11842, - 9419, - 11204, - 26265, - 12333, - 23541, - 23255, - 57476, - 12068, - 36444, - 10183, - 14943, - 7727, - 11113, - 39192, - 12309, - 10459, - 14289, - 20414, - 20795, - 309693, - 36763, - 10902, - 15096, - 34901, - 11367, - 11339, - 12220, - 43187, - 6747, - 55826, - 3527, - 17034, - 20451 - ], - "xaxis": "x", - "y": [ - 2030, - 2499, - 9905, - 5023, - 3067, - 1254, - 5353, - 6600, - 10076, - 4130, - 3332, - 6658, - 1773, - 3217, - 3157, - 1606, - 2260, - 6095, - 2662, - 4475, - 6983, - 8174, - 2554, - 5832, - 1940, - 2485, - 2242, - 2177, - 6685, - 2185, - 2650, - 2832, - 5617, - 3691, - 51761, - 6803, - 2280, - 2938, - 5778, - 2445, - 2098, - 2014, - 9682, - 948, - 11180, - 616, - 3993, - 3886 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Overijssel
Year=2017
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "ids": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "legendgroup": "Overijssel", - "marker": { - "color": "#19d3f3", - "size": [ - 72479, - 22795, - 28070, - 99295, - 26244, - 158140, - 24272, - 60211, - 35772, - 35011, - 52511, - 22482, - 32006, - 17957, - 17531, - 36907, - 37983, - 16691, - 43448, - 21153, - 33845, - 24225, - 22309, - 125548 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Overijssel", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 32859, - 9537, - 11444, - 44653, - 10661, - 74564, - 10195, - 24113, - 14593, - 14969, - 21535, - 9626, - 14311, - 7529, - 7184, - 15359, - 14387, - 5756, - 19787, - 8348, - 13621, - 9645, - 8711, - 56066 - ], - "xaxis": "x", - "y": [ - 5978, - 2015, - 2481, - 8686, - 2126, - 12503, - 1923, - 5595, - 3050, - 2779, - 5362, - 1756, - 2957, - 1281, - 1551, - 3148, - 4022, - 1959, - 3434, - 1985, - 3324, - 2261, - 2501, - 11834 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Flevoland
Year=2017
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "ids": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "legendgroup": "Flevoland", - "marker": { - "color": "#FF6692", - "size": [ - 200914, - 40746, - 76937, - 46544, - 20220, - 22457 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Flevoland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 80582, - 16603, - 32895, - 19535, - 6309, - 8305 - ], - "xaxis": "x", - "y": [ - 19813, - 3685, - 7407, - 4521, - 3115, - 2110 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Brabant
Year=2017
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Meierijstad", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "ids": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Meierijstad", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "legendgroup": "Noord-Brabant", - "marker": { - "color": "#B6E880", - "size": [ - 10058, - 16714, - 6657, - 18342, - 66164, - 30252, - 29180, - 20063, - 10414, - 30655, - 182304, - 20676, - 31933, - 25509, - 26869, - 18554, - 226868, - 43027, - 21504, - 39078, - 30024, - 26220, - 23317, - 29579, - 15714, - 90602, - 152411, - 43516, - 15283, - 21942, - 23093, - 79869, - 36944, - 22866, - 18499, - 25936, - 54604, - 90376, - 12908, - 77163, - 22345, - 28584, - 19036, - 16626, - 23970, - 213804, - 30516, - 44724, - 26183, - 16898, - 47410, - 21837, - 21657 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Brabant", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 4254, - 6768, - 2770, - 7770, - 29916, - 12059, - 12326, - 8486, - 4034, - 13308, - 82231, - 8841, - 13523, - 10838, - 11601, - 8035, - 107812, - 18513, - 9594, - 17354, - 12597, - 11149, - 9961, - 13060, - 6655, - 39533, - 70255, - 18352, - 6493, - 9231, - 10169, - 33148, - 16315, - 10032, - 7650, - 11265, - 24441, - 39130, - 5344, - 34962, - 9604, - 11776, - 7863, - 6978, - 10178, - 96235, - 14444, - 19395, - 11349, - 7551, - 21242, - 9779, - 9280 - ], - "xaxis": "x", - "y": [ - 716, - 1296, - 410, - 1394, - 5130, - 2557, - 2358, - 1638, - 926, - 2568, - 15514, - 1492, - 2539, - 2011, - 1980, - 1406, - 17361, - 3740, - 1745, - 3040, - 2634, - 2286, - 2333, - 2214, - 1148, - 8272, - 12822, - 3633, - 1262, - 1780, - 1704, - 6280, - 2937, - 1658, - 1436, - 2076, - 4178, - 6902, - 1129, - 6012, - 1391, - 2481, - 1286, - 1503, - 1625, - 16445, - 2064, - 3313, - 2507, - 1447, - 3850, - 1460, - 1313 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Utrecht
Year=2017
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Stichtse Vecht", - "Utrechtse Heuvelrug", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "ids": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Stichtse Vecht", - "Utrechtse Heuvelrug", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "legendgroup": "Utrecht", - "marker": { - "color": "#FF97FF", - "size": [ - 154337, - 24529, - 42754, - 15156, - 21020, - 8999, - 49300, - 34208, - 29677, - 14208, - 13866, - 61868, - 10187, - 5101, - 19597, - 42763, - 45874, - 64450, - 49035, - 64277, - 23502, - 51513, - 12701, - 62830 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Utrecht", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 66034, - 11676, - 19375, - 6342, - 8135, - 3687, - 19808, - 14319, - 12495, - 5526, - 5667, - 27976, - 4322, - 1904, - 7935, - 17786, - 20361, - 27858, - 21516, - 27055, - 9934, - 21625, - 4963, - 28251 - ], - "xaxis": "x", - "y": [ - 15304, - 2065, - 4181, - 1391, - 2089, - 763, - 5206, - 3108, - 2603, - 1213, - 1329, - 4921, - 885, - 388, - 1884, - 3485, - 3765, - 5980, - 4100, - 6266, - 1986, - 4911, - 1287, - 5478 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Limburg
Year=2017
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Eijsden-Margraten", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Peel en Maas", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "ids": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Eijsden-Margraten", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Peel en Maas", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "legendgroup": "Limburg", - "marker": { - "color": "#FECB52", - "size": [ - 13405, - 13095, - 28358, - 31815, - 25297, - 17129, - 14337, - 87189, - 42139, - 45937, - 37458, - 35878, - 23774, - 122753, - 19072, - 7775, - 16864, - 43347, - 20700, - 57390, - 10571, - 93319, - 9710, - 16440, - 101059, - 43565, - 12462, - 49574 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Limburg", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5888, - 5669, - 14464, - 14497, - 10979, - 7361, - 6791, - 45619, - 17480, - 23883, - 18038, - 15735, - 10641, - 61485, - 8590, - 3563, - 7240, - 18059, - 9410, - 27140, - 5201, - 46253, - 5555, - 8182, - 46691, - 18883, - 5766, - 22402 - ], - "xaxis": "x", - "y": [ - 1013, - 910, - 1988, - 2135, - 1567, - 1373, - 794, - 5508, - 3240, - 2741, - 2450, - 2271, - 1312, - 6536, - 1237, - 505, - 1152, - 3307, - 1336, - 4532, - 660, - 6211, - 463, - 857, - 7651, - 3207, - 848, - 3659 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zeeland
Year=2017
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "ids": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "legendgroup": "Zeeland", - "marker": { - "color": "#636efa", - "size": [ - 22688, - 37274, - 27395, - 12622, - 7382, - 22356, - 33765, - 23658, - 54588, - 25527, - 21900, - 44394 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zeeland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 9735, - 17939, - 13323, - 5275, - 4361, - 8994, - 16753, - 13952, - 26754, - 10729, - 10884, - 22488 - ], - "xaxis": "x", - "y": [ - 1958, - 2928, - 1591, - 1194, - 439, - 2389, - 2444, - 1332, - 3763, - 2450, - 1564, - 3354 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Groningen
Year=2017
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Oldambt", - "Pekela", - "Stadskanaal", - "Veendam" - ], - "ids": [ - "Oldambt", - "Pekela", - "Stadskanaal", - "Veendam" - ], - "legendgroup": "Groningen", - "marker": { - "color": "#EF553B", - "size": [ - 38108, - 12517, - 32252, - 27527 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Groningen", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 18382, - 5776, - 15276, - 12756 - ], - "xaxis": "x", - "y": [ - 2742, - 941, - 2472, - 2244 - ], - "yaxis": "y" - } - ], - "name": "2017" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

Provincienaam=Drenthe
Year=2018
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "ids": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "legendgroup": "Drenthe", - "marker": { - "color": "#636efa", - "size": [ - 25390, - 67708, - 25351, - 35299, - 107192, - 55677, - 33410, - 33172, - 32370, - 33462, - 19152, - 23917 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Drenthe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11165, - 31424, - 11608, - 15478, - 48923, - 24867, - 15586, - 14304, - 14649, - 14211, - 8454, - 10149 - ], - "xaxis": "x", - "y": [ - 1735, - 6139, - 1757, - 2669, - 8335, - 5080, - 3155, - 2370, - 2355, - 3251, - 1222, - 1773 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Holland
Year=2018
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Gooise Meren", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hollands Kroon", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "ids": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Gooise Meren", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hollands Kroon", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "legendgroup": "Noord-Holland", - "marker": { - "color": "#EF553B", - "size": [ - 31499, - 108470, - 89870, - 854047, - 29941, - 41077, - 10795, - 23208, - 35608, - 28121, - 19440, - 35953, - 18476, - 57337, - 159709, - 147282, - 39146, - 27080, - 23099, - 55760, - 89521, - 47681, - 72806, - 41369, - 22659, - 11435, - 44480, - 9735, - 11526, - 13496, - 79983, - 46379, - 21670, - 13584, - 13520, - 29445, - 67831, - 17259, - 19147, - 23659, - 15995, - 154865, - 16970 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 12888, - 50830, - 41782, - 432715, - 15113, - 19189, - 4865, - 9894, - 15814, - 13136, - 8202, - 14915, - 8717, - 26342, - 74796, - 60622, - 17439, - 12561, - 10528, - 27988, - 41942, - 20689, - 32783, - 18833, - 9291, - 4835, - 18774, - 4093, - 4747, - 5897, - 35751, - 20103, - 9121, - 6552, - 5619, - 12948, - 30697, - 7260, - 8951, - 10618, - 6803, - 68025, - 9523 - ], - "xaxis": "x", - "y": [ - 3110, - 8576, - 6993, - 62109, - 1940, - 3252, - 1058, - 3446, - 2669, - 2244, - 1546, - 2916, - 1481, - 5858, - 13663, - 13541, - 3211, - 2495, - 1825, - 4159, - 7862, - 3813, - 6359, - 3069, - 1873, - 1097, - 3685, - 907, - 907, - 1323, - 5893, - 3736, - 1775, - 924, - 1176, - 2484, - 5026, - 1481, - 1548, - 1938, - 1162, - 12932, - 1032 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Gelderland
Year=2018
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berg en Dal", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "ids": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berg en Dal", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "legendgroup": "Gelderland", - "marker": { - "color": "#00cc96", - "size": [ - 26962, - 161156, - 157223, - 57339, - 34748, - 44032, - 25798, - 36352, - 20771, - 26365, - 28195, - 11328, - 57382, - 18701, - 25438, - 114682, - 23107, - 32863, - 26793, - 46832, - 12154, - 18603, - 16462, - 46372, - 33574, - 24350, - 35627, - 23615, - 42307, - 175948, - 27114, - 23504, - 29672, - 39520, - 47481, - 24313, - 31338, - 43527, - 1575, - 9751, - 41465, - 24310, - 38412, - 18891, - 15015, - 40847, - 28987, - 28014, - 43402, - 47537 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Gelderland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11922, - 71526, - 74416, - 21926, - 15518, - 19492, - 11043, - 16088, - 9279, - 10529, - 12177, - 5279, - 25628, - 7983, - 10678, - 48007, - 9274, - 14243, - 10948, - 19289, - 5284, - 7815, - 7123, - 19646, - 14916, - 9830, - 15525, - 8625, - 16669, - 79474, - 10611, - 9202, - 12787, - 17267, - 19486, - 9655, - 14763, - 21088, - 662, - 3921, - 17902, - 10495, - 17181, - 8016, - 6662, - 17843, - 12993, - 11216, - 19684, - 21971 - ], - "xaxis": "x", - "y": [ - 2302, - 12777, - 12765, - 6464, - 2251, - 3212, - 1836, - 2472, - 1411, - 1962, - 2409, - 664, - 4630, - 1489, - 1974, - 10632, - 2291, - 2482, - 2097, - 4621, - 1119, - 1539, - 1266, - 4061, - 2428, - 2030, - 2392, - 2581, - 4091, - 12207, - 2590, - 2246, - 2280, - 2993, - 4401, - 2141, - 2275, - 3194, - 205, - 1152, - 3334, - 2293, - 2451, - 1384, - 1163, - 3185, - 2198, - 2651, - 3323, - 4030 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Fryslân
Year=2018
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Achtkarspelen", - "Ameland", - "Dantumadiel", - "De Fryske Marren", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Súdwest-Fryslân", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Waadhoeke", - "Weststellingwerf" - ], - "ids": [ - "Achtkarspelen", - "Ameland", - "Dantumadiel", - "De Fryske Marren", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Súdwest-Fryslân", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Waadhoeke", - "Weststellingwerf" - ], - "legendgroup": "Fryslân", - "marker": { - "color": "#ab63fa", - "size": [ - 27935, - 3654, - 18904, - 51742, - 15783, - 50192, - 122415, - 25459, - 29753, - 932, - 55889, - 89594, - 4906, - 31870, - 1132, - 46101, - 25720 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Fryslân", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 12075, - 1712, - 8144, - 23086, - 7621, - 23367, - 60905, - 11247, - 13009, - 582, - 25454, - 41599, - 2276, - 13800, - 551, - 21003, - 11599 - ], - "xaxis": "x", - "y": [ - 2599, - 332, - 1627, - 4325, - 1262, - 4299, - 9746, - 1982, - 2535, - 45, - 4623, - 7523, - 333, - 2683, - 56, - 3790, - 1972 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zuid-Holland
Year=2018
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Bodegraven-Reeuwijk", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Goeree-Overflakkee", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Kaag en Braassem", - "Katwijk", - "Krimpen aan den IJssel", - "Krimpenerwaard", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Nieuwkoop", - "Nissewaard", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zuidplas", - "Zwijndrecht" - ], - "ids": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Bodegraven-Reeuwijk", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Goeree-Overflakkee", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Kaag en Braassem", - "Katwijk", - "Krimpen aan den IJssel", - "Krimpenerwaard", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Nieuwkoop", - "Nissewaard", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zuidplas", - "Zwijndrecht" - ], - "legendgroup": "Zuid-Holland", - "marker": { - "color": "#FFA15A", - "size": [ - 20014, - 25218, - 109682, - 48477, - 33948, - 17040, - 66854, - 102253, - 118426, - 49129, - 36284, - 72700, - 17958, - 39992, - 30677, - 21812, - 26625, - 64956, - 29306, - 55644, - 61155, - 124306, - 27197, - 74947, - 22746, - 32518, - 19338, - 28269, - 84593, - 26056, - 23887, - 32264, - 53634, - 45789, - 638712, - 77907, - 25020, - 36584, - 72050, - 25453, - 27578, - 26084, - 107492, - 14508, - 124695, - 8430, - 41882, - 44586 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zuid-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 8276, - 10333, - 47099, - 19184, - 14260, - 7918, - 30748, - 49855, - 55059, - 21374, - 16392, - 32758, - 7247, - 17923, - 12141, - 9681, - 11294, - 26329, - 12451, - 23569, - 23565, - 58079, - 12118, - 36481, - 10049, - 15058, - 7836, - 11298, - 38832, - 12389, - 10475, - 14331, - 20810, - 21009, - 311336, - 36914, - 10846, - 15515, - 35126, - 11442, - 11746, - 12176, - 43636, - 6793, - 55944, - 3493, - 17237, - 20394 - ], - "xaxis": "x", - "y": [ - 2052, - 2503, - 9895, - 4947, - 3078, - 1277, - 5344, - 6602, - 10011, - 4141, - 3293, - 6587, - 1754, - 3195, - 3337, - 1607, - 2237, - 6075, - 2660, - 4487, - 6949, - 8096, - 2536, - 5871, - 1884, - 2500, - 2268, - 2186, - 6627, - 2184, - 2693, - 2825, - 5501, - 3751, - 52154, - 6726, - 2324, - 2972, - 5773, - 2453, - 2189, - 1993, - 9746, - 947, - 11134, - 606, - 4074, - 3733 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Overijssel
Year=2018
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "ids": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "legendgroup": "Overijssel", - "marker": { - "color": "#19d3f3", - "size": [ - 72629, - 23124, - 28242, - 99653, - 26291, - 158261, - 24291, - 60539, - 35796, - 34930, - 53259, - 22547, - 31915, - 18023, - 17630, - 37158, - 38097, - 16797, - 43768, - 21213, - 33903, - 24258, - 22468, - 126116 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Overijssel", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 32988, - 9748, - 11511, - 44707, - 10774, - 73672, - 10315, - 24316, - 14645, - 15054, - 21843, - 9712, - 14402, - 7613, - 7289, - 15589, - 14574, - 5853, - 19951, - 8490, - 13664, - 9660, - 8817, - 56595 - ], - "xaxis": "x", - "y": [ - 5835, - 2044, - 2497, - 8569, - 2029, - 12420, - 1886, - 5489, - 2941, - 2723, - 5348, - 1722, - 2786, - 1261, - 1542, - 3131, - 3968, - 1974, - 3460, - 1960, - 3228, - 2243, - 2441, - 11768 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Flevoland
Year=2018
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "ids": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "legendgroup": "Flevoland", - "marker": { - "color": "#FF6692", - "size": [ - 203990, - 40735, - 77389, - 46625, - 20524, - 22407 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Flevoland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 82312, - 16901, - 32767, - 19786, - 6381, - 8340 - ], - "xaxis": "x", - "y": [ - 19733, - 3696, - 7315, - 4454, - 3112, - 1975 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Brabant
Year=2018
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Meierijstad", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "ids": [ - "Alphen-Chaam", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Meierijstad", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "legendgroup": "Noord-Brabant", - "marker": { - "color": "#B6E880", - "size": [ - 10083, - 16719, - 6799, - 18398, - 66354, - 30550, - 29497, - 20144, - 10502, - 30672, - 183448, - 20336, - 32137, - 25777, - 27063, - 18778, - 229126, - 43532, - 21517, - 39252, - 30340, - 26313, - 23621, - 29888, - 15886, - 90903, - 153434, - 43723, - 15366, - 22158, - 23120, - 80148, - 36967, - 23019, - 18558, - 26132, - 55147, - 90951, - 13040, - 77000, - 22401, - 28673, - 19120, - 16753, - 24781, - 215521, - 30654, - 44925, - 26418, - 17075, - 47725, - 21800, - 21525 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Brabant", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 4280, - 6803, - 2832, - 7810, - 30047, - 12301, - 12353, - 8552, - 4082, - 13333, - 83253, - 8887, - 13644, - 11075, - 11708, - 8071, - 109468, - 18732, - 9614, - 17429, - 12683, - 11199, - 10136, - 13093, - 6772, - 39798, - 71196, - 18523, - 6522, - 9318, - 10209, - 33475, - 16340, - 10131, - 7728, - 11439, - 24665, - 39505, - 5385, - 35103, - 9605, - 11985, - 7964, - 7067, - 10258, - 97612, - 14548, - 19502, - 11394, - 7564, - 21538, - 9837, - 9408 - ], - "xaxis": "x", - "y": [ - 740, - 1252, - 420, - 1379, - 5170, - 2505, - 2301, - 1595, - 883, - 2506, - 15326, - 1476, - 2531, - 2014, - 1994, - 1441, - 17436, - 3683, - 1747, - 3063, - 2674, - 2174, - 2340, - 2219, - 1148, - 8154, - 12659, - 3671, - 1191, - 1744, - 1673, - 6301, - 2943, - 1707, - 1446, - 2057, - 4184, - 6836, - 1123, - 6004, - 1435, - 2485, - 1269, - 1522, - 1605, - 16288, - 2066, - 3342, - 2532, - 1449, - 3780, - 1422, - 1304 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Utrecht
Year=2018
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Stichtse Vecht", - "Utrechtse Heuvelrug", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "ids": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Stichtse Vecht", - "Utrechtse Heuvelrug", - "Veenendaal", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "legendgroup": "Utrecht", - "marker": { - "color": "#FF97FF", - "size": [ - 155226, - 24630, - 42846, - 15214, - 21266, - 9112, - 49579, - 34302, - 29755, - 14395, - 13879, - 62426, - 10180, - 5175, - 19816, - 43620, - 46089, - 64513, - 49314, - 64918, - 23678, - 51758, - 13021, - 63322 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Utrecht", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 66530, - 11564, - 19412, - 6371, - 8251, - 3812, - 20032, - 14344, - 12637, - 5581, - 5730, - 28495, - 4315, - 1999, - 8034, - 18145, - 20479, - 28121, - 21636, - 27512, - 9979, - 21756, - 5006, - 28555 - ], - "xaxis": "x", - "y": [ - 15136, - 2081, - 4153, - 1396, - 2110, - 730, - 5052, - 2992, - 2592, - 1143, - 1296, - 4961, - 891, - 389, - 1832, - 3435, - 3730, - 5883, - 4059, - 6295, - 1990, - 4895, - 1296, - 5511 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Limburg
Year=2018
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Eijsden-Margraten", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Peel en Maas", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "ids": [ - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Eijsden-Margraten", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Peel en Maas", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "legendgroup": "Limburg", - "marker": { - "color": "#FECB52", - "size": [ - 13444, - 13106, - 28241, - 31751, - 25566, - 17052, - 14196, - 86762, - 42271, - 45823, - 37612, - 35857, - 23697, - 122723, - 19039, - 7768, - 17038, - 43312, - 20728, - 57761, - 10561, - 92956, - 9874, - 16431, - 101192, - 43341, - 12390, - 49855 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Limburg", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 5946, - 5683, - 14508, - 14532, - 11181, - 7387, - 6784, - 45758, - 17610, - 23904, - 18128, - 15745, - 10678, - 61955, - 8587, - 3617, - 7337, - 18151, - 9449, - 27276, - 5207, - 46369, - 5524, - 8185, - 46833, - 18905, - 5759, - 22626 - ], - "xaxis": "x", - "y": [ - 957, - 882, - 1971, - 2114, - 1595, - 1330, - 771, - 5555, - 3135, - 2654, - 2477, - 2179, - 1270, - 6517, - 1209, - 496, - 1148, - 3257, - 1331, - 4503, - 659, - 6127, - 451, - 828, - 7613, - 3141, - 910, - 3673 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zeeland
Year=2018
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "ids": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "legendgroup": "Zeeland", - "marker": { - "color": "#636efa", - "size": [ - 22716, - 37636, - 27472, - 12720, - 7314, - 22555, - 33687, - 23526, - 54440, - 25583, - 21867, - 44485 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zeeland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 9781, - 18062, - 13384, - 5317, - 4394, - 9049, - 16661, - 13960, - 26812, - 10777, - 10922, - 22796 - ], - "xaxis": "x", - "y": [ - 1952, - 2886, - 1589, - 1214, - 442, - 2370, - 2438, - 1293, - 3698, - 2460, - 1594, - 3299 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Groningen
Year=2018
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Midden-Groningen", - "Oldambt", - "Pekela", - "Stadskanaal", - "Veendam", - "Westerwolde" - ], - "ids": [ - "Midden-Groningen", - "Oldambt", - "Pekela", - "Stadskanaal", - "Veendam", - "Westerwolde" - ], - "legendgroup": "Groningen", - "marker": { - "color": "#EF553B", - "size": [ - 60953, - 38075, - 12245, - 32258, - 27508, - 24684 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Groningen", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 28025, - 18382, - 5755, - 15266, - 12788, - 11963 - ], - "xaxis": "x", - "y": [ - 4570, - 2678, - 968, - 2373, - 2198, - 1863 - ], - "yaxis": "y" - } - ], - "name": "2018" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

Provincienaam=Drenthe
Year=2019
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "ids": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "legendgroup": "Drenthe", - "marker": { - "color": "#636efa", - "size": [ - 25386, - 67963, - 25372, - 35483, - 107113, - 55662, - 33564, - 33178, - 31290, - 33698, - 19348, - 24110 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Drenthe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11257, - 31570, - 11645, - 15521, - 49187, - 25147, - 15658, - 14313, - 14716, - 14403, - 8515, - 10265 - ], - "xaxis": "x", - "y": [ - 1723, - 5898, - 1699, - 2625, - 8194, - 4949, - 3101, - 2309, - 2297, - 3265, - 1227, - 1736 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Holland
Year=2019
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Gooise Meren", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hollands Kroon", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "ids": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Gooise Meren", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hollands Kroon", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "legendgroup": "Noord-Holland", - "marker": { - "color": "#EF553B", - "size": [ - 31728, - 108558, - 90838, - 862965, - 29974, - 41176, - 11202, - 23410, - 35772, - 29196, - 19597, - 36099, - 18507, - 57715, - 161265, - 154235, - 39164, - 27286, - 23464, - 55604, - 90238, - 47815, - 73004, - 41273, - 22738, - 11488, - 44809, - 9757, - 11779, - 13916, - 80117, - 46553, - 21706, - 13547, - 13528, - 29424, - 68348, - 17315, - 19334, - 24013, - 16329, - 155885, - 17011 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 13091, - 51127, - 42035, - 441490, - 15209, - 19280, - 4941, - 9992, - 15973, - 13803, - 8280, - 15029, - 8754, - 26508, - 75632, - 63511, - 17501, - 12587, - 10574, - 28056, - 42324, - 20786, - 32744, - 18874, - 9395, - 4853, - 18957, - 4099, - 4783, - 6155, - 36141, - 20287, - 9207, - 6594, - 5633, - 12856, - 30913, - 7289, - 9008, - 10707, - 6962, - 68402, - 9557 - ], - "xaxis": "x", - "y": [ - 3003, - 8534, - 7091, - 62182, - 1886, - 3225, - 1082, - 3438, - 2776, - 2351, - 1570, - 2888, - 1463, - 5850, - 13787, - 13811, - 3169, - 2487, - 1844, - 4169, - 7850, - 3726, - 6375, - 3063, - 1841, - 1088, - 3634, - 884, - 898, - 1326, - 5815, - 3587, - 1758, - 913, - 1154, - 2436, - 5080, - 1480, - 1593, - 1947, - 1180, - 12759, - 1058 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Gelderland
Year=2019
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berg en Dal", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Betuwe", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "ids": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berg en Dal", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Betuwe", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "legendgroup": "Gelderland", - "marker": { - "color": "#00cc96", - "size": [ - 27011, - 162445, - 159265, - 57971, - 34798, - 43904, - 25882, - 36212, - 20698, - 26568, - 28555, - 11148, - 57555, - 18797, - 25332, - 115710, - 23086, - 33145, - 26858, - 47581, - 12173, - 18546, - 16486, - 46475, - 33590, - 24693, - 36026, - 24034, - 42943, - 176731, - 27481, - 23598, - 29704, - 39473, - 47543, - 24198, - 31302, - 43640, - 1654, - 9873, - 41978, - 24417, - 38774, - 50697, - 19076, - 14944, - 40951, - 28903, - 28451, - 43488, - 47609 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Gelderland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11971, - 72445, - 75632, - 22249, - 15619, - 19541, - 11090, - 16152, - 9310, - 10604, - 12390, - 5286, - 25926, - 8047, - 10708, - 48552, - 9268, - 14349, - 11002, - 19450, - 5277, - 7888, - 7160, - 19783, - 14997, - 9947, - 15727, - 8748, - 17125, - 80736, - 10851, - 9265, - 12854, - 17385, - 19702, - 9670, - 14908, - 21189, - 683, - 3967, - 18011, - 10545, - 17483, - 20710, - 8100, - 6690, - 17885, - 13087, - 11401, - 19796, - 22095 - ], - "xaxis": "x", - "y": [ - 2270, - 12781, - 12693, - 6533, - 2216, - 3070, - 1860, - 2361, - 1380, - 1997, - 2444, - 638, - 4554, - 1469, - 1979, - 10790, - 2294, - 2496, - 2055, - 4664, - 1122, - 1556, - 1248, - 3952, - 2405, - 2001, - 2363, - 2582, - 4081, - 12132, - 2621, - 2252, - 2213, - 2917, - 4412, - 2119, - 2223, - 3118, - 212, - 1134, - 3266, - 2298, - 2467, - 4412, - 1401, - 1147, - 3122, - 2094, - 2611, - 3322, - 3843 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Fryslân
Year=2019
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Achtkarspelen", - "Ameland", - "Dantumadiel", - "De Fryske Marren", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Noardeast-Fryslân", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Súdwest-Fryslân", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Waadhoeke", - "Weststellingwerf" - ], - "ids": [ - "Achtkarspelen", - "Ameland", - "Dantumadiel", - "De Fryske Marren", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Noardeast-Fryslân", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Súdwest-Fryslân", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Waadhoeke", - "Weststellingwerf" - ], - "legendgroup": "Fryslân", - "marker": { - "color": "#ab63fa", - "size": [ - 27852, - 3673, - 18923, - 51430, - 15758, - 50257, - 123107, - 45181, - 25497, - 29723, - 936, - 55938, - 89710, - 4890, - 31780, - 1138, - 46039, - 25840 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Fryslân", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 12118, - 1731, - 8147, - 23209, - 7627, - 23532, - 61492, - 20395, - 11303, - 12987, - 587, - 25675, - 41915, - 2281, - 13943, - 564, - 21112, - 11620 - ], - "xaxis": "x", - "y": [ - 2530, - 326, - 1635, - 4248, - 1214, - 4242, - 9635, - 3985, - 1957, - 2461, - 48, - 4578, - 7350, - 323, - 2682, - 55, - 3691, - 1947 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zuid-Holland
Year=2019
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Bodegraven-Reeuwijk", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Goeree-Overflakkee", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Hoeksche Waard", - "Kaag en Braassem", - "Katwijk", - "Krimpen aan den IJssel", - "Krimpenerwaard", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Molenlanden", - "Nieuwkoop", - "Nissewaard", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zuidplas", - "Zwijndrecht" - ], - "ids": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Bodegraven-Reeuwijk", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Goeree-Overflakkee", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Hoeksche Waard", - "Kaag en Braassem", - "Katwijk", - "Krimpen aan den IJssel", - "Krimpenerwaard", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Molenlanden", - "Nieuwkoop", - "Nissewaard", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zuidplas", - "Zwijndrecht" - ], - "legendgroup": "Zuid-Holland", - "marker": { - "color": "#FFA15A", - "size": [ - 20069, - 25271, - 110986, - 48673, - 34462, - 17182, - 66818, - 103163, - 118654, - 49611, - 36682, - 73181, - 18051, - 40049, - 30966, - 21966, - 86656, - 26866, - 65302, - 29376, - 56048, - 61601, - 124899, - 27109, - 75425, - 22800, - 32768, - 19391, - 43858, - 28628, - 84797, - 42859, - 24426, - 32290, - 54331, - 46241, - 644618, - 77999, - 25026, - 37061, - 72404, - 25479, - 28316, - 26211, - 108603, - 14626, - 124944, - 8450, - 42762, - 44639 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zuid-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 8324, - 10347, - 47753, - 19256, - 14360, - 7963, - 30853, - 50610, - 55008, - 21597, - 16599, - 33174, - 7312, - 17907, - 12214, - 9766, - 37542, - 11486, - 26724, - 12527, - 23787, - 23802, - 59088, - 12121, - 36781, - 10217, - 15127, - 7883, - 17160, - 11361, - 39036, - 19688, - 10691, - 14383, - 21134, - 21140, - 311597, - 36914, - 11018, - 15588, - 35159, - 11448, - 11970, - 12191, - 44441, - 6831, - 56199, - 3493, - 17563, - 20454 - ], - "xaxis": "x", - "y": [ - 2066, - 2482, - 9822, - 4874, - 3110, - 1296, - 5385, - 6636, - 10027, - 4118, - 3232, - 6494, - 1767, - 3118, - 3371, - 1593, - 7117, - 2179, - 5983, - 2645, - 4535, - 6892, - 8034, - 2553, - 5836, - 1866, - 2508, - 2309, - 4159, - 2182, - 6703, - 3528, - 2745, - 2747, - 5440, - 3822, - 51993, - 6735, - 2291, - 2941, - 5815, - 2399, - 2320, - 2013, - 9843, - 967, - 11101, - 609, - 4111, - 3680 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Overijssel
Year=2019
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "ids": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "legendgroup": "Overijssel", - "marker": { - "color": "#19d3f3", - "size": [ - 72849, - 23210, - 28499, - 99957, - 26350, - 158986, - 24277, - 60574, - 35808, - 34940, - 53779, - 22622, - 31840, - 18071, - 17813, - 37511, - 38300, - 17003, - 43940, - 21276, - 33792, - 24351, - 22503, - 127497 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Overijssel", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 33472, - 9817, - 11722, - 45313, - 10900, - 74588, - 10410, - 24513, - 14734, - 15122, - 22097, - 9733, - 14434, - 7669, - 7429, - 15759, - 14740, - 5914, - 20014, - 8525, - 13705, - 9727, - 8903, - 57730 - ], - "xaxis": "x", - "y": [ - 5741, - 2072, - 2471, - 8382, - 1965, - 12218, - 1837, - 5443, - 2846, - 2643, - 5300, - 1687, - 2673, - 1232, - 1534, - 3042, - 3949, - 1993, - 3463, - 1833, - 3153, - 2232, - 2427, - 11723 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Flevoland
Year=2019
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "ids": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "legendgroup": "Flevoland", - "marker": { - "color": "#FF6692", - "size": [ - 207904, - 40815, - 77893, - 46849, - 20776, - 22309 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Flevoland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 84161, - 17140, - 32893, - 20018, - 6472, - 8553 - ], - "xaxis": "x", - "y": [ - 19833, - 3648, - 7278, - 4386, - 3104, - 1964 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Brabant
Year=2019
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alphen-Chaam", - "Altena", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Meierijstad", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "ids": [ - "Alphen-Chaam", - "Altena", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Meierijstad", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "legendgroup": "Noord-Brabant", - "marker": { - "color": "#B6E880", - "size": [ - 10149, - 55386, - 16710, - 6847, - 18491, - 66811, - 30806, - 29821, - 20175, - 10588, - 30747, - 183873, - 20440, - 32362, - 26051, - 27150, - 19110, - 231642, - 43774, - 21515, - 39595, - 30447, - 26431, - 23793, - 30194, - 15964, - 91524, - 154205, - 44135, - 15334, - 22333, - 23327, - 80815, - 36961, - 23186, - 18623, - 26140, - 55616, - 91451, - 13060, - 77032, - 22572, - 28991, - 19322, - 16904, - 25054, - 217259, - 30910, - 45337, - 26396, - 17247, - 48240, - 21866, - 21612 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Brabant", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 4342, - 22221, - 6853, - 2852, - 7870, - 30380, - 12451, - 12535, - 8618, - 4168, - 13353, - 83793, - 8962, - 13887, - 11241, - 11779, - 8277, - 111295, - 18967, - 9638, - 17640, - 12754, - 11270, - 10291, - 13310, - 6817, - 40319, - 72003, - 18759, - 6591, - 9446, - 10317, - 34102, - 16407, - 10165, - 7861, - 11567, - 25115, - 39955, - 5422, - 35188, - 9690, - 12101, - 8057, - 7112, - 10335, - 99004, - 14647, - 19722, - 11491, - 7607, - 21782, - 9844, - 9455 - ], - "xaxis": "x", - "y": [ - 734, - 5199, - 1238, - 412, - 1340, - 5227, - 2420, - 2287, - 1612, - 870, - 2398, - 15195, - 1574, - 2533, - 1958, - 1951, - 1465, - 17446, - 3625, - 1734, - 3046, - 2670, - 2188, - 2360, - 2214, - 1155, - 7996, - 12689, - 3652, - 1204, - 1724, - 1646, - 6253, - 2873, - 1741, - 1430, - 2029, - 4224, - 6854, - 1142, - 5955, - 1421, - 2465, - 1328, - 1581, - 1623, - 16143, - 2061, - 3365, - 2502, - 1465, - 3744, - 1409, - 1351 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Utrecht
Year=2019
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Stichtse Vecht", - "Utrechtse Heuvelrug", - "Veenendaal", - "Vijfheerenlanden", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "ids": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Stichtse Vecht", - "Utrechtse Heuvelrug", - "Veenendaal", - "Vijfheerenlanden", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "legendgroup": "Utrecht", - "marker": { - "color": "#FF97FF", - "size": [ - 156286, - 24767, - 42824, - 15192, - 21576, - 9113, - 49911, - 34160, - 30030, - 14473, - 13996, - 63036, - 10201, - 5259, - 20004, - 44059, - 46194, - 64336, - 49515, - 65589, - 55712, - 23762, - 52197, - 13166, - 63934 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Utrecht", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 67181, - 11647, - 19544, - 6379, - 8414, - 3910, - 20271, - 14377, - 12905, - 5660, - 5733, - 28710, - 4338, - 2017, - 8131, - 18332, - 20567, - 28206, - 21679, - 27793, - 23418, - 9968, - 22002, - 5102, - 28777 - ], - "xaxis": "x", - "y": [ - 14867, - 2058, - 4147, - 1441, - 2112, - 717, - 4936, - 2900, - 2564, - 1080, - 1245, - 4977, - 891, - 390, - 1752, - 3382, - 3709, - 5821, - 4132, - 6229, - 4956, - 2008, - 4887, - 1293, - 5547 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Limburg
Year=2019
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Beekdaelen", - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Eijsden-Margraten", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Peel en Maas", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "ids": [ - "Beekdaelen", - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Eijsden-Margraten", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Peel en Maas", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "legendgroup": "Limburg", - "marker": { - "color": "#FECB52", - "size": [ - 35727, - 13519, - 13140, - 28103, - 31638, - 25658, - 17071, - 14246, - 86832, - 42291, - 45642, - 37591, - 35681, - 23716, - 121565, - 18923, - 7806, - 17001, - 43311, - 20615, - 58209, - 10516, - 92661, - 10092, - 16470, - 101603, - 43326, - 12452, - 49842 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Limburg", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 16635, - 5959, - 5723, - 14479, - 14540, - 11208, - 7408, - 6821, - 45834, - 17672, - 23679, - 18139, - 15832, - 10702, - 61884, - 8603, - 3579, - 7385, - 18302, - 9463, - 27465, - 5320, - 46435, - 5748, - 8291, - 47173, - 19004, - 5777, - 22769 - ], - "xaxis": "x", - "y": [ - 2431, - 945, - 869, - 1948, - 2082, - 1632, - 1269, - 771, - 5594, - 3101, - 2636, - 2459, - 2145, - 1277, - 6444, - 1208, - 478, - 1141, - 3146, - 1301, - 4561, - 642, - 5982, - 462, - 837, - 7353, - 3124, - 911, - 3621 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zeeland
Year=2019
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "ids": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "legendgroup": "Zeeland", - "marker": { - "color": "#636efa", - "size": [ - 22800, - 37653, - 27524, - 12785, - 7308, - 22678, - 33779, - 23386, - 54589, - 25780, - 21835, - 44371 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zeeland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 9785, - 18251, - 13520, - 5350, - 4400, - 9159, - 16746, - 14138, - 26952, - 10888, - 10964, - 22843 - ], - "xaxis": "x", - "y": [ - 1954, - 2944, - 1592, - 1178, - 442, - 2350, - 2458, - 1277, - 3655, - 2449, - 1595, - 3260 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Groningen
Year=2019
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Het Hogeland", - "Midden-Groningen", - "Oldambt", - "Pekela", - "Stadskanaal", - "Veendam", - "Westerkwartier", - "Westerwolde" - ], - "ids": [ - "Het Hogeland", - "Midden-Groningen", - "Oldambt", - "Pekela", - "Stadskanaal", - "Veendam", - "Westerkwartier", - "Westerwolde" - ], - "legendgroup": "Groningen", - "marker": { - "color": "#EF553B", - "size": [ - 47888, - 60899, - 38129, - 12214, - 31789, - 27491, - 63031, - 25199 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Groningen", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 22631, - 28074, - 18462, - 5767, - 15253, - 12816, - 27045, - 11969 - ], - "xaxis": "x", - "y": [ - 3669, - 4490, - 2583, - 968, - 2385, - 2140, - 5506, - 1824 - ], - "yaxis": "y" - } - ], - "name": "2019" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

Provincienaam=Drenthe
Year=2020
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "ids": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "legendgroup": "Drenthe", - "marker": { - "color": "#636efa", - "size": [ - 25445, - 68599, - 25559, - 35297, - 107048, - 55699, - 33920, - 33185, - 31253, - 33887, - 19460, - 24330 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Drenthe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11179, - 32154, - 11374, - 15555, - 49308, - 25236, - 15851, - 14378, - 14817, - 14500, - 8529, - 10370 - ], - "xaxis": "x", - "y": [ - 1649, - 5715, - 1722, - 2592, - 7985, - 4785, - 3022, - 2290, - 2301, - 3281, - 1203, - 1761 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Holland
Year=2020
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Gooise Meren", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hollands Kroon", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "ids": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Gooise Meren", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hollands Kroon", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "legendgroup": "Noord-Holland", - "marker": { - "color": "#EF553B", - "size": [ - 31859, - 109436, - 91675, - 872757, - 29839, - 41626, - 11540, - 23571, - 35986, - 30780, - 19719, - 36197, - 18591, - 58055, - 162902, - 156002, - 39182, - 27234, - 23968, - 56296, - 90831, - 48432, - 73261, - 41273, - 22749, - 11491, - 45101, - 9735, - 11836, - 14026, - 81249, - 46483, - 21726, - 13575, - 13666, - 29478, - 68648, - 17424, - 19738, - 24358, - 16270, - 156794, - 17116 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 13217, - 51339, - 42310, - 447351, - 15344, - 19403, - 5075, - 10034, - 16056, - 15015, - 8373, - 15120, - 8824, - 26679, - 76436, - 64195, - 17578, - 12628, - 10775, - 28213, - 42676, - 20882, - 33086, - 18924, - 9473, - 4896, - 19100, - 4108, - 4852, - 6296, - 36701, - 20334, - 9199, - 6651, - 5713, - 12836, - 31226, - 7319, - 9197, - 10691, - 6971, - 68947, - 9708 - ], - "xaxis": "x", - "y": [ - 2865, - 8442, - 7207, - 61473, - 1864, - 3153, - 1063, - 3381, - 2797, - 2360, - 1576, - 2805, - 1432, - 5783, - 13810, - 13475, - 3147, - 2539, - 1833, - 4093, - 7878, - 3685, - 6356, - 3027, - 1807, - 1093, - 3602, - 885, - 875, - 1261, - 5776, - 3455, - 1778, - 901, - 1136, - 2480, - 5100, - 1480, - 1738, - 1936, - 1163, - 12782, - 1050 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Gelderland
Year=2020
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berg en Dal", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Betuwe", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "ids": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berg en Dal", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Betuwe", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "legendgroup": "Gelderland", - "marker": { - "color": "#00cc96", - "size": [ - 27121, - 163818, - 161348, - 59082, - 34992, - 43747, - 25890, - 36055, - 20726, - 26749, - 28955, - 11077, - 58001, - 18926, - 25126, - 117165, - 23161, - 33178, - 27008, - 48414, - 12209, - 18589, - 16454, - 46601, - 33729, - 25030, - 36011, - 24339, - 43171, - 177659, - 27851, - 23646, - 29627, - 39388, - 47906, - 24112, - 31419, - 43761, - 1704, - 9880, - 42159, - 24552, - 39664, - 51128, - 19324, - 14971, - 41110, - 28854, - 28881, - 43750, - 47934 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Gelderland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11976, - 73244, - 76737, - 22799, - 15802, - 19525, - 11137, - 16198, - 9348, - 10829, - 12530, - 5293, - 26129, - 8126, - 10743, - 49152, - 9356, - 14586, - 11156, - 19942, - 5298, - 7924, - 7206, - 19872, - 15096, - 10115, - 15917, - 8867, - 17321, - 81717, - 10957, - 9318, - 12910, - 17401, - 19947, - 9727, - 14985, - 21212, - 688, - 4002, - 18080, - 10641, - 17721, - 20875, - 8222, - 6723, - 18023, - 13139, - 11623, - 19970, - 22267 - ], - "xaxis": "x", - "y": [ - 2218, - 12745, - 12532, - 6678, - 2231, - 2991, - 1885, - 2276, - 1374, - 2017, - 2427, - 600, - 4584, - 1451, - 1892, - 10865, - 2257, - 2524, - 2058, - 4658, - 1127, - 1524, - 1210, - 3878, - 2361, - 1972, - 2314, - 2605, - 4054, - 12029, - 2637, - 2263, - 2118, - 2827, - 4406, - 2108, - 2158, - 3142, - 222, - 1184, - 3163, - 2325, - 2527, - 4348, - 1389, - 1144, - 3047, - 2024, - 2608, - 3307, - 3722 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Fryslân
Year=2020
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Achtkarspelen", - "Ameland", - "Dantumadiel", - "De Fryske Marren", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Noardeast-Fryslân", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Súdwest-Fryslân", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Waadhoeke", - "Weststellingwerf" - ], - "ids": [ - "Achtkarspelen", - "Ameland", - "Dantumadiel", - "De Fryske Marren", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Noardeast-Fryslân", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Súdwest-Fryslân", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Waadhoeke", - "Weststellingwerf" - ], - "legendgroup": "Fryslân", - "marker": { - "color": "#ab63fa", - "size": [ - 27843, - 3716, - 18922, - 51564, - 15722, - 50493, - 124084, - 45228, - 25469, - 29733, - 947, - 56150, - 89987, - 4888, - 32052, - 1155, - 46090, - 25914 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Fryslân", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 12129, - 1737, - 8164, - 23347, - 7798, - 23528, - 62599, - 20630, - 11253, - 13064, - 587, - 25768, - 42119, - 2293, - 14044, - 579, - 21228, - 11711 - ], - "xaxis": "x", - "y": [ - 2482, - 298, - 1623, - 4144, - 1209, - 4195, - 9460, - 3908, - 1907, - 2392, - 52, - 4477, - 7224, - 317, - 2600, - 53, - 3662, - 1912 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zuid-Holland
Year=2020
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Bodegraven-Reeuwijk", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Goeree-Overflakkee", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Hoeksche Waard", - "Kaag en Braassem", - "Katwijk", - "Krimpen aan den IJssel", - "Krimpenerwaard", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Molenlanden", - "Nieuwkoop", - "Nissewaard", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zuidplas", - "Zwijndrecht" - ], - "ids": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Bodegraven-Reeuwijk", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Goeree-Overflakkee", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Hoeksche Waard", - "Kaag en Braassem", - "Katwijk", - "Krimpen aan den IJssel", - "Krimpenerwaard", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Molenlanden", - "Nieuwkoop", - "Nissewaard", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zuidplas", - "Zwijndrecht" - ], - "legendgroup": "Zuid-Holland", - "marker": { - "color": "#FFA15A", - "size": [ - 20165, - 25590, - 111897, - 48714, - 34872, - 17271, - 67122, - 103595, - 119284, - 50049, - 37022, - 73427, - 18295, - 40142, - 31202, - 22209, - 87401, - 27297, - 65753, - 29526, - 56319, - 62384, - 125099, - 27056, - 76534, - 22955, - 33213, - 19341, - 43909, - 28811, - 85219, - 43508, - 24840, - 32136, - 55308, - 46189, - 651157, - 78730, - 25220, - 37440, - 73397, - 25596, - 29291, - 26305, - 110375, - 14731, - 125285, - 8605, - 43885, - 44737 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zuid-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 8353, - 10517, - 48178, - 19416, - 14499, - 8029, - 31270, - 50904, - 55279, - 21855, - 17210, - 33439, - 7451, - 17967, - 12314, - 9849, - 37821, - 11726, - 26869, - 12636, - 23933, - 24245, - 59832, - 12119, - 36944, - 10330, - 15186, - 7904, - 17205, - 11498, - 39225, - 19930, - 10862, - 14397, - 21536, - 20960, - 315565, - 37292, - 10898, - 16048, - 35470, - 11465, - 12332, - 12222, - 45063, - 6890, - 56458, - 3532, - 18031, - 20537 - ], - "xaxis": "x", - "y": [ - 2029, - 2479, - 9704, - 4832, - 3189, - 1299, - 5355, - 6734, - 9937, - 4093, - 3256, - 6386, - 1745, - 3104, - 3255, - 1585, - 7168, - 2098, - 5838, - 2689, - 4545, - 6828, - 8011, - 2540, - 5818, - 1834, - 2531, - 2360, - 4122, - 2233, - 6859, - 3455, - 2783, - 2678, - 5384, - 4000, - 51502, - 6753, - 2282, - 2921, - 5892, - 2389, - 2393, - 2045, - 9980, - 987, - 11107, - 597, - 4215, - 3720 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Overijssel
Year=2020
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "ids": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "legendgroup": "Overijssel", - "marker": { - "color": "#19d3f3", - "size": [ - 73107, - 23312, - 28587, - 100719, - 26461, - 159640, - 24311, - 60948, - 35916, - 35017, - 54319, - 22683, - 31836, - 18252, - 18009, - 37712, - 38177, - 17145, - 44126, - 21275, - 33743, - 24446, - 22685, - 128840 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Overijssel", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 33549, - 9954, - 11889, - 45988, - 10939, - 75222, - 10497, - 24810, - 14889, - 15211, - 22517, - 9789, - 14614, - 7724, - 7519, - 15902, - 14760, - 5933, - 20172, - 8604, - 13819, - 9860, - 8942, - 58492 - ], - "xaxis": "x", - "y": [ - 5604, - 2108, - 2511, - 8251, - 1926, - 12202, - 1771, - 5390, - 2822, - 2571, - 5316, - 1681, - 2613, - 1222, - 1514, - 3041, - 3954, - 2022, - 3485, - 1800, - 3074, - 2235, - 2374, - 11481 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Flevoland
Year=2020
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "ids": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "legendgroup": "Flevoland", - "marker": { - "color": "#FF6692", - "size": [ - 211893, - 41555, - 78598, - 47291, - 21031, - 22653 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Flevoland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 85977, - 17390, - 33385, - 20134, - 6663, - 8678 - ], - "xaxis": "x", - "y": [ - 20029, - 3641, - 7235, - 4391, - 3084, - 1997 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Brabant
Year=2020
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alphen-Chaam", - "Altena", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Meierijstad", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "ids": [ - "Alphen-Chaam", - "Altena", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Meierijstad", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "legendgroup": "Noord-Brabant", - "marker": { - "color": "#B6E880", - "size": [ - 10203, - 55967, - 16721, - 6859, - 18635, - 67496, - 31240, - 29988, - 20390, - 10785, - 30801, - 184069, - 21138, - 32471, - 26222, - 27272, - 19313, - 234394, - 43878, - 21544, - 39726, - 30723, - 26431, - 23904, - 30284, - 16152, - 92423, - 155111, - 44692, - 15518, - 22523, - 23408, - 81194, - 37129, - 23383, - 18714, - 26245, - 55982, - 91915, - 13112, - 77251, - 22878, - 29208, - 19368, - 17322, - 24416, - 219789, - 31193, - 45466, - 26558, - 17456, - 48637, - 21876, - 21829 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Brabant", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 4371, - 22399, - 6899, - 2875, - 7970, - 30884, - 12714, - 12698, - 8746, - 4248, - 13401, - 84567, - 9007, - 13955, - 11289, - 11761, - 8402, - 112965, - 19093, - 9639, - 17746, - 12635, - 11301, - 10363, - 13399, - 6861, - 40788, - 72991, - 19193, - 6724, - 9531, - 10409, - 34544, - 16501, - 10285, - 7965, - 11619, - 25158, - 40325, - 5484, - 35363, - 9822, - 12183, - 8113, - 7192, - 10433, - 100418, - 14805, - 19913, - 11519, - 7765, - 21948, - 9866, - 9472 - ], - "xaxis": "x", - "y": [ - 738, - 5096, - 1190, - 404, - 1284, - 5201, - 2384, - 2258, - 1597, - 838, - 2316, - 14910, - 1511, - 2527, - 1935, - 1928, - 1476, - 17332, - 3590, - 1676, - 2990, - 2639, - 2193, - 2346, - 2236, - 1169, - 7789, - 12542, - 3602, - 1203, - 1692, - 1707, - 6290, - 2723, - 1792, - 1445, - 1995, - 4215, - 6824, - 1110, - 5935, - 1450, - 2476, - 1357, - 1558, - 1635, - 15984, - 2098, - 3366, - 2591, - 1482, - 3709, - 1372, - 1349 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Utrecht
Year=2020
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Stichtse Vecht", - "Utrechtse Heuvelrug", - "Veenendaal", - "Vijfheerenlanden", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "ids": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Stichtse Vecht", - "Utrechtse Heuvelrug", - "Veenendaal", - "Vijfheerenlanden", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "legendgroup": "Utrecht", - "marker": { - "color": "#FF97FF", - "size": [ - 157276, - 24868, - 43137, - 15191, - 21866, - 9247, - 50146, - 34109, - 30401, - 14467, - 13917, - 63462, - 10230, - 5444, - 20119, - 44456, - 46606, - 64931, - 49580, - 66493, - 56811, - 23914, - 52299, - 13362, - 64905 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Utrecht", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 67722, - 11679, - 19577, - 6392, - 8536, - 4008, - 20417, - 14407, - 13043, - 5741, - 5740, - 28844, - 4372, - 2134, - 8225, - 18517, - 20699, - 28549, - 21821, - 28353, - 23824, - 10107, - 22106, - 5188, - 29245 - ], - "xaxis": "x", - "y": [ - 14613, - 2025, - 4186, - 1471, - 2096, - 742, - 4800, - 2787, - 2550, - 1066, - 1239, - 4929, - 872, - 387, - 1718, - 3413, - 3704, - 5808, - 4160, - 6311, - 5104, - 1973, - 4844, - 1293, - 5579 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Limburg
Year=2020
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Beekdaelen", - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Eijsden-Margraten", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Peel en Maas", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "ids": [ - "Beekdaelen", - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Eijsden-Margraten", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Peel en Maas", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "legendgroup": "Limburg", - "marker": { - "color": "#FECB52", - "size": [ - 35938, - 13482, - 13085, - 27821, - 31610, - 25768, - 16921, - 14171, - 87086, - 42429, - 45749, - 37445, - 35879, - 23965, - 121575, - 18828, - 7847, - 17019, - 43425, - 20574, - 58260, - 10555, - 92429, - 10105, - 16367, - 101802, - 43614, - 12475, - 50105 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Limburg", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 16635, - 5965, - 5760, - 14493, - 14595, - 11326, - 7450, - 6867, - 45976, - 17839, - 23771, - 18142, - 15992, - 10856, - 61833, - 8629, - 3591, - 7457, - 18399, - 9477, - 27649, - 5185, - 46625, - 5806, - 8309, - 47412, - 19137, - 5819, - 22948 - ], - "xaxis": "x", - "y": [ - 2414, - 930, - 852, - 1938, - 2015, - 1704, - 1213, - 760, - 5541, - 3045, - 2632, - 2439, - 2102, - 1340, - 6504, - 1221, - 460, - 1149, - 3154, - 1293, - 4441, - 632, - 5846, - 473, - 823, - 7216, - 3055, - 931, - 3572 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zeeland
Year=2020
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "ids": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "legendgroup": "Zeeland", - "marker": { - "color": "#636efa", - "size": [ - 22739, - 38082, - 27556, - 12695, - 7392, - 22730, - 33839, - 23210, - 54426, - 25757, - 21880, - 44360 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zeeland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 9821, - 18553, - 13587, - 5403, - 4460, - 9298, - 16783, - 14200, - 26966, - 10983, - 10967, - 22907 - ], - "xaxis": "x", - "y": [ - 1924, - 2956, - 1570, - 1130, - 427, - 2351, - 2427, - 1254, - 3632, - 2414, - 1594, - 3183 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Groningen
Year=2020
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Het Hogeland", - "Midden-Groningen", - "Oldambt", - "Pekela", - "Stadskanaal", - "Veendam", - "Westerkwartier", - "Westerwolde" - ], - "ids": [ - "Het Hogeland", - "Midden-Groningen", - "Oldambt", - "Pekela", - "Stadskanaal", - "Veendam", - "Westerkwartier", - "Westerwolde" - ], - "legendgroup": "Groningen", - "marker": { - "color": "#EF553B", - "size": [ - 47801, - 60797, - 38209, - 12196, - 31686, - 27384, - 63329, - 25733 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Groningen", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 22743, - 28167, - 18540, - 5774, - 15292, - 12843, - 27309, - 11982 - ], - "xaxis": "x", - "y": [ - 3571, - 4349, - 2578, - 977, - 2440, - 2154, - 5483, - 1749 - ], - "yaxis": "y" - } - ], - "name": "2020" - }, - { - "data": [ - { - "hovertemplate": "%{hovertext}

Provincienaam=Drenthe
Year=2021
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "ids": [ - "Aa en Hunze", - "Assen", - "Borger-Odoorn", - "Coevorden", - "Emmen", - "Hoogeveen", - "Meppel", - "Midden-Drenthe", - "Noordenveld", - "Tynaarlo", - "Westerveld", - "De Wolden" - ], - "legendgroup": "Drenthe", - "marker": { - "color": "#636efa", - "size": [ - 25399, - 68836, - 25598, - 35317, - 107024, - 55603, - 34386, - 33381, - 31214, - 33978, - 19661, - 24374 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Drenthe", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11207, - 32538, - 11224, - 15580, - 49528, - 25496, - 16106, - 14413, - 14855, - 14596, - 8553, - 10460 - ], - "xaxis": "x", - "y": [ - 1591, - 5561, - 1696, - 2586, - 7965, - 4658, - 2944, - 2328, - 2304, - 3281, - 1241, - 1772 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Holland
Year=2021
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Gooise Meren", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hollands Kroon", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "ids": [ - "Aalsmeer", - "Alkmaar", - "Amstelveen", - "Amsterdam", - "Bergen (NH.)", - "Beverwijk", - "Blaricum", - "Bloemendaal", - "Castricum", - "Diemen", - "Drechterland", - "Edam-Volendam", - "Enkhuizen", - "Gooise Meren", - "Haarlem", - "Haarlemmermeer", - "Heemskerk", - "Heemstede", - "Heiloo", - "Den Helder", - "Hilversum", - "Hollands Kroon", - "Hoorn", - "Huizen", - "Koggenland", - "Landsmeer", - "Medemblik", - "Oostzaan", - "Opmeer", - "Ouder-Amstel", - "Purmerend", - "Schagen", - "Stede Broec", - "Texel", - "Uitgeest", - "Uithoorn", - "Velsen", - "Waterland", - "Weesp", - "Wijdemeren", - "Wormerland", - "Zaanstad", - "Zandvoort" - ], - "legendgroup": "Noord-Holland", - "marker": { - "color": "#EF553B", - "size": [ - 31991, - 109896, - 90829, - 873338, - 29715, - 41863, - 11954, - 23478, - 36086, - 31334, - 19838, - 36268, - 18637, - 58524, - 162543, - 157789, - 39191, - 27545, - 24144, - 56582, - 91235, - 48583, - 73619, - 41090, - 22940, - 11565, - 45165, - 9689, - 12009, - 14125, - 81683, - 46532, - 21743, - 13656, - 13632, - 30206, - 68617, - 17312, - 20445, - 24463, - 16333, - 156901, - 17168 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 13284, - 51656, - 42251, - 449989, - 15353, - 19511, - 5175, - 10166, - 16231, - 15467, - 8410, - 15432, - 8880, - 26896, - 77201, - 65214, - 17607, - 12817, - 10939, - 28390, - 42979, - 20988, - 33656, - 19018, - 9608, - 4897, - 19211, - 4100, - 4974, - 6301, - 37124, - 20581, - 9253, - 6770, - 5728, - 13264, - 31439, - 7411, - 9386, - 10800, - 6919, - 69114, - 9706 - ], - "xaxis": "x", - "y": [ - 2757, - 8313, - 7165, - 59846, - 1821, - 3078, - 1085, - 3300, - 2825, - 2349, - 1569, - 2749, - 1401, - 5818, - 13643, - 13115, - 3148, - 2543, - 1829, - 4105, - 7921, - 3637, - 6397, - 3028, - 1809, - 1084, - 3542, - 863, - 875, - 1219, - 5650, - 3341, - 1802, - 879, - 1104, - 2499, - 5009, - 1465, - 1822, - 1932, - 1225, - 12689, - 1012 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Gelderland
Year=2021
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berg en Dal", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Betuwe", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "ids": [ - "Aalten", - "Apeldoorn", - "Arnhem", - "Barneveld", - "Berg en Dal", - "Berkelland", - "Beuningen", - "Bronckhorst", - "Brummen", - "Buren", - "Culemborg", - "Doesburg", - "Doetinchem", - "Druten", - "Duiven", - "Ede", - "Elburg", - "Epe", - "Ermelo", - "Harderwijk", - "Hattem", - "Heerde", - "Heumen", - "Lingewaard", - "Lochem", - "Maasdriel", - "Montferland", - "Neder-Betuwe", - "Nijkerk", - "Nijmegen", - "Nunspeet", - "Oldebroek", - "Oost Gelre", - "Oude IJsselstreek", - "Overbetuwe", - "Putten", - "Renkum", - "Rheden", - "Rozendaal", - "Scherpenzeel", - "Tiel", - "Voorst", - "Wageningen", - "West Betuwe", - "West Maas en Waal", - "Westervoort", - "Wijchen", - "Winterswijk", - "Zaltbommel", - "Zevenaar", - "Zutphen" - ], - "legendgroup": "Gelderland", - "marker": { - "color": "#00cc96", - "size": [ - 27120, - 164781, - 162424, - 59992, - 35010, - 43846, - 26157, - 36087, - 20884, - 27009, - 29121, - 11064, - 58270, - 18991, - 25066, - 118530, - 23429, - 33198, - 27016, - 48726, - 12228, - 18776, - 16569, - 46822, - 33948, - 25452, - 36031, - 24648, - 43600, - 177359, - 28021, - 23760, - 29574, - 39346, - 48214, - 24365, - 31417, - 43525, - 1726, - 10128, - 41920, - 24790, - 39635, - 51496, - 19581, - 15014, - 41261, - 29022, - 29447, - 44096, - 48111 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Gelderland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 12028, - 74184, - 77839, - 23146, - 15854, - 19594, - 11262, - 16213, - 9416, - 10933, - 12729, - 5313, - 26509, - 8202, - 10760, - 50056, - 9595, - 14697, - 11360, - 20677, - 5262, - 8017, - 7274, - 20056, - 15165, - 10207, - 16016, - 8912, - 17349, - 82317, - 11199, - 9475, - 12949, - 17454, - 20128, - 9904, - 15003, - 21281, - 694, - 4114, - 18271, - 10744, - 18086, - 20996, - 8375, - 6744, - 18190, - 13245, - 11852, - 20227, - 22651 - ], - "xaxis": "x", - "y": [ - 2134, - 12514, - 12351, - 6886, - 2165, - 2838, - 1907, - 2184, - 1336, - 1983, - 2394, - 573, - 4617, - 1417, - 1826, - 10707, - 2225, - 2482, - 2072, - 4606, - 1110, - 1517, - 1203, - 3801, - 2353, - 1982, - 2344, - 2631, - 4051, - 12064, - 2606, - 2188, - 2079, - 2753, - 4402, - 2084, - 2108, - 3057, - 229, - 1175, - 3089, - 2339, - 2466, - 4351, - 1343, - 1144, - 3011, - 1984, - 2597, - 3353, - 3530 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Fryslân
Year=2021
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Achtkarspelen", - "Ameland", - "Dantumadiel", - "De Fryske Marren", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Noardeast-Fryslân", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Súdwest-Fryslân", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Waadhoeke", - "Weststellingwerf" - ], - "ids": [ - "Achtkarspelen", - "Ameland", - "Dantumadiel", - "De Fryske Marren", - "Harlingen", - "Heerenveen", - "Leeuwarden", - "Noardeast-Fryslân", - "Ooststellingwerf", - "Opsterland", - "Schiermonnikoog", - "Smallingerland", - "Súdwest-Fryslân", - "Terschelling", - "Tytsjerksteradiel", - "Vlieland", - "Waadhoeke", - "Weststellingwerf" - ], - "legendgroup": "Fryslân", - "marker": { - "color": "#ab63fa", - "size": [ - 27900, - 3746, - 18943, - 51778, - 15807, - 50650, - 124481, - 45481, - 25464, - 29812, - 931, - 56040, - 89999, - 4870, - 32060, - 1194, - 46149, - 26130 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Fryslân", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 12181, - 1753, - 8202, - 23460, - 7909, - 23767, - 63067, - 20690, - 11419, - 13202, - 588, - 25833, - 42501, - 2293, - 14093, - 603, - 21375, - 11757 - ], - "xaxis": "x", - "y": [ - 2431, - 295, - 1638, - 3987, - 1153, - 4163, - 9337, - 3790, - 1901, - 2308, - 48, - 4434, - 7062, - 320, - 2579, - 52, - 3574, - 1896 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zuid-Holland
Year=2021
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Bodegraven-Reeuwijk", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Goeree-Overflakkee", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Hoeksche Waard", - "Kaag en Braassem", - "Katwijk", - "Krimpen aan den IJssel", - "Krimpenerwaard", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Molenlanden", - "Nieuwkoop", - "Nissewaard", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zuidplas", - "Zwijndrecht" - ], - "ids": [ - "Alblasserdam", - "Albrandswaard", - "Alphen aan den Rijn", - "Barendrecht", - "Bodegraven-Reeuwijk", - "Brielle", - "Capelle aan den IJssel", - "Delft", - "Dordrecht", - "Goeree-Overflakkee", - "Gorinchem", - "Gouda", - "Hardinxveld-Giessendam", - "Hellevoetsluis", - "Hendrik-Ido-Ambacht", - "Hillegom", - "Hoeksche Waard", - "Kaag en Braassem", - "Katwijk", - "Krimpen aan den IJssel", - "Krimpenerwaard", - "Lansingerland", - "Leiden", - "Leiderdorp", - "Leidschendam-Voorburg", - "Lisse", - "Maassluis", - "Midden-Delfland", - "Molenlanden", - "Nieuwkoop", - "Nissewaard", - "Noordwijk", - "Oegstgeest", - "Papendrecht", - "Pijnacker-Nootdorp", - "Ridderkerk", - "Rotterdam", - "Schiedam", - "Sliedrecht", - "Teylingen", - "Vlaardingen", - "Voorschoten", - "Waddinxveen", - "Wassenaar", - "Westland", - "Westvoorne", - "Zoetermeer", - "Zoeterwoude", - "Zuidplas", - "Zwijndrecht" - ], - "legendgroup": "Zuid-Holland", - "marker": { - "color": "#FFA15A", - "size": [ - 20136, - 25814, - 112587, - 48643, - 35278, - 17439, - 67319, - 103581, - 119115, - 50589, - 37410, - 73681, - 18413, - 40312, - 31258, - 22197, - 88047, - 27541, - 65995, - 29410, - 56622, - 63363, - 124093, - 27377, - 76433, - 22982, - 33567, - 19414, - 44130, - 29151, - 85440, - 44062, - 25064, - 32171, - 55674, - 46671, - 651631, - 79279, - 25597, - 37791, - 73924, - 25650, - 30479, - 26949, - 111382, - 14900, - 125267, - 8843, - 45064, - 44775 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zuid-Holland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 8408, - 10573, - 48893, - 19433, - 14676, - 8110, - 31556, - 51495, - 55813, - 22098, - 17288, - 33725, - 7514, - 18067, - 12332, - 9863, - 38203, - 11895, - 27165, - 12648, - 23944, - 24686, - 60538, - 12379, - 36955, - 10411, - 15389, - 7996, - 17340, - 11660, - 39469, - 20120, - 11014, - 14479, - 21798, - 21308, - 317945, - 37472, - 11086, - 16286, - 35551, - 11504, - 12796, - 12287, - 45956, - 6943, - 56421, - 3740, - 18566, - 20720 - ], - "xaxis": "x", - "y": [ - 1949, - 2477, - 9547, - 4716, - 3202, - 1338, - 5195, - 6688, - 9825, - 4090, - 3249, - 6336, - 1725, - 3061, - 3255, - 1537, - 7112, - 2107, - 5748, - 2630, - 4462, - 6813, - 7937, - 2486, - 5726, - 1831, - 2550, - 2361, - 4083, - 2206, - 6940, - 3421, - 2782, - 2628, - 5304, - 4010, - 50878, - 6770, - 2286, - 2899, - 5938, - 2346, - 2516, - 2046, - 9910, - 968, - 10991, - 589, - 4284, - 3683 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Overijssel
Year=2021
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "ids": [ - "Almelo", - "Borne", - "Dalfsen", - "Deventer", - "Dinkelland", - "Enschede", - "Haaksbergen", - "Hardenberg", - "Hellendoorn", - "Hof van Twente", - "Kampen", - "Losser", - "Oldenzaal", - "Olst-Wijhe", - "Ommen", - "Raalte", - "Rijssen-Holten", - "Staphorst", - "Steenwijkerland", - "Tubbergen", - "Twenterand", - "Wierden", - "Zwartewaterland", - "Zwolle" - ], - "legendgroup": "Overijssel", - "marker": { - "color": "#19d3f3", - "size": [ - 73132, - 23668, - 28901, - 101236, - 26606, - 159732, - 24229, - 61357, - 35932, - 35040, - 54474, - 22888, - 31701, - 18361, - 18295, - 37911, - 38204, - 17261, - 44341, - 21315, - 33699, - 24538, - 22823, - 129840 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Overijssel", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 33827, - 10105, - 12056, - 46224, - 11047, - 75925, - 10571, - 25177, - 14959, - 15279, - 22785, - 9863, - 14662, - 7828, - 7601, - 16049, - 14867, - 5988, - 20276, - 8720, - 13847, - 9930, - 9013, - 59491 - ], - "xaxis": "x", - "y": [ - 5464, - 2132, - 2510, - 8154, - 1941, - 11945, - 1740, - 5365, - 2794, - 2532, - 5216, - 1690, - 2485, - 1176, - 1519, - 3034, - 3853, - 2022, - 3492, - 1795, - 2946, - 2222, - 2346, - 11313 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Flevoland
Year=2021
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "ids": [ - "Almere", - "Dronten", - "Lelystad", - "Noordoostpolder", - "Urk", - "Zeewolde" - ], - "legendgroup": "Flevoland", - "marker": { - "color": "#FF6692", - "size": [ - 214715, - 42011, - 79811, - 47583, - 21227, - 22879 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Flevoland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 87259, - 17730, - 33734, - 20336, - 6763, - 8837 - ], - "xaxis": "x", - "y": [ - 19944, - 3607, - 7174, - 4354, - 3034, - 1999 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Noord-Brabant
Year=2021
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Alphen-Chaam", - "Altena", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Meierijstad", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "ids": [ - "Alphen-Chaam", - "Altena", - "Asten", - "Baarle-Nassau", - "Bergeijk", - "Bergen op Zoom", - "Bernheze", - "Best", - "Bladel", - "Boekel", - "Boxtel", - "Breda", - "Cranendonck", - "Deurne", - "Dongen", - "Drimmelen", - "Eersel", - "Eindhoven", - "Etten-Leur", - "Geertruidenberg", - "Geldrop-Mierlo", - "Gemert-Bakel", - "Gilze en Rijen", - "Goirle", - "Halderberge", - "Heeze-Leende", - "Helmond", - "'s-Hertogenbosch", - "Heusden", - "Hilvarenbeek", - "Laarbeek", - "Loon op Zand", - "Meierijstad", - "Moerdijk", - "Nuenen, Gerwen en Nederwetten", - "Oirschot", - "Oisterwijk", - "Oosterhout", - "Oss", - "Reusel-De Mierden", - "Roosendaal", - "Rucphen", - "Sint-Michielsgestel", - "Someren", - "Son en Breugel", - "Steenbergen", - "Tilburg", - "Valkenswaard", - "Veldhoven", - "Vught", - "Waalre", - "Waalwijk", - "Woensdrecht", - "Zundert" - ], - "legendgroup": "Noord-Brabant", - "marker": { - "color": "#B6E880", - "size": [ - 10373, - 56352, - 16817, - 6899, - 18754, - 67514, - 31455, - 30216, - 20529, - 10959, - 32973, - 184126, - 21001, - 32437, - 26368, - 27325, - 19528, - 235691, - 43869, - 21770, - 40066, - 30760, - 26723, - 23952, - 30430, - 16243, - 92627, - 155490, - 45005, - 15698, - 22805, - 23504, - 81647, - 37185, - 23702, - 18842, - 32373, - 56206, - 92526, - 13127, - 77200, - 23080, - 29498, - 19428, - 17552, - 24310, - 221947, - 31221, - 45500, - 31669, - 17544, - 48815, - 22028, - 21988 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Noord-Brabant", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 4432, - 22587, - 7069, - 2916, - 8027, - 30921, - 12798, - 12977, - 8841, - 4397, - 14390, - 85230, - 9044, - 14052, - 11456, - 11845, - 8511, - 114398, - 19154, - 9648, - 18078, - 12649, - 11392, - 10423, - 13488, - 6925, - 41222, - 73424, - 19357, - 6865, - 9793, - 10478, - 34895, - 16629, - 10450, - 8036, - 14130, - 25194, - 40746, - 5523, - 35618, - 9916, - 12274, - 8181, - 7318, - 10484, - 102471, - 14885, - 20085, - 13724, - 7832, - 21996, - 9928, - 9649 - ], - "xaxis": "x", - "y": [ - 727, - 5026, - 1180, - 416, - 1278, - 5241, - 2372, - 2217, - 1601, - 850, - 2374, - 14618, - 1467, - 2511, - 1899, - 1914, - 1495, - 17068, - 3578, - 1665, - 2949, - 2596, - 2179, - 2341, - 2233, - 1194, - 7661, - 12380, - 3566, - 1175, - 1641, - 1701, - 6256, - 2655, - 1816, - 1410, - 2365, - 4191, - 6857, - 1134, - 5850, - 1478, - 2448, - 1429, - 1564, - 1621, - 15812, - 2074, - 3382, - 2904, - 1452, - 3694, - 1359, - 1359 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Utrecht
Year=2021
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Stichtse Vecht", - "Utrechtse Heuvelrug", - "Veenendaal", - "Vijfheerenlanden", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "ids": [ - "Amersfoort", - "Baarn", - "De Bilt", - "Bunnik", - "Bunschoten", - "Eemnes", - "Houten", - "IJsselstein", - "Leusden", - "Lopik", - "Montfoort", - "Nieuwegein", - "Oudewater", - "Renswoude", - "Rhenen", - "De Ronde Venen", - "Soest", - "Stichtse Vecht", - "Utrechtse Heuvelrug", - "Veenendaal", - "Vijfheerenlanden", - "Wijk bij Duurstede", - "Woerden", - "Woudenberg", - "Zeist" - ], - "legendgroup": "Utrecht", - "marker": { - "color": "#FF97FF", - "size": [ - 157462, - 24792, - 43384, - 15341, - 22019, - 9362, - 50223, - 33819, - 30544, - 14456, - 13896, - 63866, - 10138, - 5556, - 20203, - 44720, - 46906, - 65108, - 49946, - 66912, - 57829, - 23925, - 52694, - 13639, - 65043 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Utrecht", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 68809, - 11700, - 19738, - 6484, - 8550, - 4091, - 20681, - 14444, - 13229, - 5838, - 5756, - 29445, - 4323, - 2154, - 8287, - 18580, - 20953, - 28739, - 21890, - 28607, - 24196, - 10273, - 22437, - 5286, - 29638 - ], - "xaxis": "x", - "y": [ - 14233, - 1969, - 4138, - 1456, - 2098, - 710, - 4729, - 2707, - 2519, - 1088, - 1209, - 4972, - 881, - 405, - 1650, - 3400, - 3663, - 5660, - 4148, - 6340, - 5151, - 1960, - 4766, - 1291, - 5640 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Limburg
Year=2021
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Beekdaelen", - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Eijsden-Margraten", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Peel en Maas", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "ids": [ - "Beekdaelen", - "Beesel", - "Bergen (L.)", - "Brunssum", - "Echt-Susteren", - "Eijsden-Margraten", - "Gennep", - "Gulpen-Wittem", - "Heerlen", - "Horst aan de Maas", - "Kerkrade", - "Landgraaf", - "Leudal", - "Maasgouw", - "Maastricht", - "Meerssen", - "Mook en Middelaar", - "Nederweert", - "Peel en Maas", - "Roerdalen", - "Roermond", - "Simpelveld", - "Sittard-Geleen", - "Vaals", - "Valkenburg aan de Geul", - "Venlo", - "Venray", - "Voerendaal", - "Weert" - ], - "legendgroup": "Limburg", - "marker": { - "color": "#FECB52", - "size": [ - 36065, - 13450, - 13108, - 27670, - 31751, - 25900, - 17035, - 14206, - 86936, - 42487, - 45442, - 37262, - 36045, - 23947, - 120227, - 18661, - 7909, - 17171, - 43660, - 20580, - 58763, - 10477, - 91743, - 10084, - 16365, - 101988, - 43713, - 12466, - 50011 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Limburg", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 16711, - 6040, - 5793, - 14464, - 14713, - 11367, - 7490, - 6880, - 45982, - 17937, - 23937, - 18152, - 16157, - 10965, - 62839, - 8640, - 3667, - 7534, - 18527, - 9554, - 27749, - 5185, - 46709, - 5822, - 8337, - 47809, - 19300, - 5813, - 23236 - ], - "xaxis": "x", - "y": [ - 2409, - 880, - 835, - 1920, - 1963, - 1746, - 1186, - 776, - 5567, - 3005, - 2610, - 2381, - 2050, - 1344, - 6449, - 1187, - 439, - 1148, - 3067, - 1288, - 4466, - 618, - 5717, - 463, - 795, - 7124, - 3017, - 928, - 3521 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Zeeland
Year=2021
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "ids": [ - "Borsele", - "Goes", - "Hulst", - "Kapelle", - "Noord-Beveland", - "Reimerswaal", - "Schouwen-Duiveland", - "Sluis", - "Terneuzen", - "Tholen", - "Veere", - "Vlissingen" - ], - "legendgroup": "Zeeland", - "marker": { - "color": "#636efa", - "size": [ - 22818, - 38594, - 27575, - 12882, - 7581, - 22896, - 34065, - 23166, - 54463, - 26085, - 21953, - 44358 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Zeeland", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 9850, - 18633, - 13618, - 5459, - 4553, - 9332, - 16983, - 14152, - 27080, - 11112, - 10997, - 22885 - ], - "xaxis": "x", - "y": [ - 1849, - 3012, - 1544, - 1100, - 429, - 2320, - 2447, - 1277, - 3625, - 2422, - 1607, - 3123 - ], - "yaxis": "y" - }, - { - "hovertemplate": "%{hovertext}

Provincienaam=Groningen
Year=2021
Building stock=%{x}
Primary School Students=%{y}
Population=%{marker.size}", - "hovertext": [ - "Eemsdelta", - "Het Hogeland", - "Midden-Groningen", - "Oldambt", - "Pekela", - "Stadskanaal", - "Veendam", - "Westerkwartier", - "Westerwolde" - ], - "ids": [ - "Eemsdelta", - "Het Hogeland", - "Midden-Groningen", - "Oldambt", - "Pekela", - "Stadskanaal", - "Veendam", - "Westerkwartier", - "Westerwolde" - ], - "legendgroup": "Groningen", - "marker": { - "color": "#EF553B", - "size": [ - 45587, - 47834, - 60726, - 38277, - 12176, - 31754, - 27417, - 63678, - 26215 - ], - "sizemode": "area", - "sizeref": 1397.3408, - "symbol": "circle" - }, - "mode": "markers", - "name": "Groningen", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 23083, - 22878, - 28154, - 18648, - 5782, - 15208, - 12906, - 27494, - 11937 - ], - "xaxis": "x", - "y": [ - 3251, - 3516, - 4276, - 2548, - 958, - 2392, - 2030, - 5396, - 1700 - ], - "yaxis": "y" - } - ], - "name": "2021" - } - ], - "layout": { - "height": 600, - "legend": { - "itemsizing": "constant", - "title": { - "text": "Provincienaam" - }, - "tracegroupgap": 0 - }, - "margin": { - "t": 60 - }, - "sliders": [ - { - "active": 21, - "currentvalue": { - "prefix": "Year=" - }, - "len": 0.9, - "pad": { - "b": 10, - "t": 60 - }, - "steps": [ - { - "args": [ - [ - "2000" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "2000", - "method": "animate" - }, - { - "args": [ - [ - "2001" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "2001", - "method": "animate" - }, - { - "args": [ - [ - "2002" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "2002", - "method": "animate" - }, - { - "args": [ - [ - "2003" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "2003", - "method": "animate" - }, - { - "args": [ - [ - "2004" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "2004", - "method": "animate" - }, - { - "args": [ - [ - "2005" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "2005", - "method": "animate" - }, - { - "args": [ - [ - "2006" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "2006", - "method": "animate" - }, - { - "args": [ - [ - "2007" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "2007", - "method": "animate" - }, - { - "args": [ - [ - "2008" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "2008", - "method": "animate" - }, - { - "args": [ - [ - "2009" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "2009", - "method": "animate" - }, - { - "args": [ - [ - "2010" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "2010", - "method": "animate" - }, - { - "args": [ - [ - "2011" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "2011", - "method": "animate" - }, - { - "args": [ - [ - "2012" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "2012", - "method": "animate" - }, - { - "args": [ - [ - "2013" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "2013", - "method": "animate" - }, - { - "args": [ - [ - "2014" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "2014", - "method": "animate" - }, - { - "args": [ - [ - "2015" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "2015", - "method": "animate" - }, - { - "args": [ - [ - "2016" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "2016", - "method": "animate" - }, - { - "args": [ - [ - "2017" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "2017", - "method": "animate" - }, - { - "args": [ - [ - "2018" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "2018", - "method": "animate" - }, - { - "args": [ - [ - "2019" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "2019", - "method": "animate" - }, - { - "args": [ - [ - "2020" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "2020", - "method": "animate" - }, - { - "args": [ - [ - "2021" - ], - { - "frame": { - "duration": 0, - "redraw": false - }, - "fromcurrent": true, - "mode": "immediate", - "transition": { - "duration": 0, - "easing": "linear" - } - } - ], - "label": "2021", - "method": "animate" - } - ], - "x": 0.1, - "xanchor": "left", - "y": 0, - "yanchor": "top" - } - ], - "template": { - "data": { - "bar": [ - { - "error_x": { - "color": "#2a3f5f" - }, - "error_y": { - "color": "#2a3f5f" - }, - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "bar" - } - ], - "barpolar": [ - { - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "barpolar" - } - ], - "carpet": [ - { - "aaxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "baxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "type": "carpet" - } - ], - "choropleth": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "choropleth" - } - ], - "contour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "contour" - } - ], - "contourcarpet": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "contourcarpet" - } - ], - "heatmap": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmap" - } - ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmapgl" - } - ], - "histogram": [ - { - "marker": { - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "histogram" - } - ], - "histogram2d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2d" - } - ], - "histogram2dcontour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2dcontour" - } - ], - "mesh3d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "mesh3d" - } - ], - "parcoords": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "parcoords" - } - ], - "pie": [ - { - "automargin": true, - "type": "pie" - } - ], - "scatter": [ - { - "fillpattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - }, - "type": "scatter" - } - ], - "scatter3d": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatter3d" - } - ], - "scattercarpet": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattercarpet" - } - ], - "scattergeo": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergeo" - } - ], - "scattergl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergl" - } - ], - "scattermapbox": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattermapbox" - } - ], - "scatterpolar": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolar" - } - ], - "scatterpolargl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolargl" - } - ], - "scatterternary": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterternary" - } - ], - "surface": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "surface" - } - ], - "table": [ - { - "cells": { - "fill": { - "color": "#EBF0F8" - }, - "line": { - "color": "white" - } - }, - "header": { - "fill": { - "color": "#C8D4E3" - }, - "line": { - "color": "white" - } - }, - "type": "table" - } - ] - }, - "layout": { - "annotationdefaults": { - "arrowcolor": "#2a3f5f", - "arrowhead": 0, - "arrowwidth": 1 - }, - "autotypenumbers": "strict", - "coloraxis": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "colorscale": { - "diverging": [ - [ - 0, - "#8e0152" - ], - [ - 0.1, - "#c51b7d" - ], - [ - 0.2, - "#de77ae" - ], - [ - 0.3, - "#f1b6da" - ], - [ - 0.4, - "#fde0ef" - ], - [ - 0.5, - "#f7f7f7" - ], - [ - 0.6, - "#e6f5d0" - ], - [ - 0.7, - "#b8e186" - ], - [ - 0.8, - "#7fbc41" - ], - [ - 0.9, - "#4d9221" - ], - [ - 1, - "#276419" - ] - ], - "sequential": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "sequentialminus": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ] - }, - "colorway": [ - "#636efa", - "#EF553B", - "#00cc96", - "#ab63fa", - "#FFA15A", - "#19d3f3", - "#FF6692", - "#B6E880", - "#FF97FF", - "#FECB52" - ], - "font": { - "color": "#2a3f5f" - }, - "geo": { - "bgcolor": "white", - "lakecolor": "white", - "landcolor": "#E5ECF6", - "showlakes": true, - "showland": true, - "subunitcolor": "white" - }, - "hoverlabel": { - "align": "left" - }, - "hovermode": "closest", - "mapbox": { - "style": "light" - }, - "paper_bgcolor": "white", - "plot_bgcolor": "#E5ECF6", - "polar": { - "angularaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "radialaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "width": 800, - "xaxis": { - "anchor": "y", - "autorange": true, - "domain": [ - 0, - 1 - ], - "range": [ - 2.583703819595895, - 5.938961396505756 - ], - "title": { - "text": "Building stock" - }, - "type": "log" - }, - "yaxis": { - "anchor": "x", - "autorange": true, - "domain": [ - 0, - 1 - ], - "range": [ - 1.468583818540921, - 5.149559767876395 - ], - "title": { - "text": "Primary School Students" - }, - "type": "log" - } - } - }, - "image/png": "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", - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = px.scatter(bevolking_gemeente, x=\"Building stock\", y=\"Primary School Students\", animation_frame=\"Year\", \n", - " animation_group='Gemeentenaam',\n", - " size='Population',color=\"Provincienaam\", hover_name='Gemeentenaam',\n", - " log_x=True, log_y=True, size_max=25, width=800, height=600)\n", - "\n", - "fig[\"layout\"].pop(\"updatemenus\") \n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Using Folium for interactive spatial visualization\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Folium is a Python package for creating interactive leaflet maps. It allows users to create different types of maps including scatter plots, choropleth maps, and marker maps. Folium is built on top of the JavaScript leaflet library, which is one of the most widely used open-source libraries for interactive maps.\n", - "\n", - "Folium can work with data from various sources such as GeoJSON files, Pandas data frames, and CSV files. It also provides various customization options such as choosing different tile sets, adding markers, popups, and tooltips to the map." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Visualizing point data\n", - "\n", - "Let's first play around with creating a Point Cluster map. We do so through the `MarkerCluster` function." - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": [ - "from folium.plugins import MarkerCluster" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's load some Flickr data we extracted for Ameland:" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.read_csv('Ameland_Flickr.csv')\n", - "df = df.dropna()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And quickly make sure that we have the coordinates to be able to plot here. Note (which is a bit confusing) that Folium want that the order of the coordinates is first the latitude (y) and then the longitude (x). Before, we always specified the x-coordinate before the y-coordinate." - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "df['longitude'] = df['longitude'].astype(float)\n", - "df['latitude'] = df['latitude'].astype(float)\n", - "df['coordinates'] = [list(x) for x in list(zip(df['latitude'],df['longitude']))]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's add a `nature` tag to our data again, so we can visualize which photos are tagged as nature, and which not. " - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "def find_nature_tags(row):\n", - " matches = [\"seehund\", \"zeehond\",\"vis\",\"wadden\",\n", - " \"natuur\",\"nature\",\"natur\",\n", - " \"landschaft\",\n", - " \"strand\",\"beach\",\"zee\",\"sea\",\"meer\",\n", - " \"bos\",\"forest\",\n", - " \"animal\",\"bird\",\"vogel\",\"dier\"]\n", - "\n", - " overlap = set(row.split()).intersection(set(matches))\n", - " \n", - " if len(overlap) :\n", - " return 'yes'\n", - " \n", - "df['nature'] = df.tags.apply(lambda x: find_nature_tags(x))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we convert the data to plot to a dictioniary, which is easy to read by the Folium algorithm." - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "data_sites = df[['nature','coordinates']].to_dict(orient='records')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And prepare the map." - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "map_1 = folium.Map(location=[53.429979, 5.762622])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On the map, we will plot two different feature groups: Photos that are tagged as *Nature* and photos that are **not** tagged as *Nature*." - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [], - "source": [ - "feature_group_nature = folium.FeatureGroup(name='Nature')\n", - "feature_group_nonature = folium.FeatureGroup(name='No Nature')" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], - "source": [ - "marker_cluster_nature = MarkerCluster()\n", - "marker_cluster_nonature =MarkerCluster()\n", - "for site in data_sites:\n", - " if(site[\"nature\"]==\"yes\"):\n", - " marker_nature = folium.Marker(site[\"coordinates\"],popup=\"Nature\",icon = folium.Icon(color='green',icon='ok-sign'))\n", - " marker_cluster_nature.add_child(marker_nature)\n", - " else:\n", - " marker_nonature = folium.Marker(site[\"coordinates\"],popup=\"No Nature\",icon = folium.Icon(color='red',icon='exclamation-sign'))\n", - " marker_cluster_nonature.add_child(marker_nonature)" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "feature_group_nature.add_child(marker_cluster_nature)\n", - "feature_group_nonature.add_child(marker_cluster_nonature)" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "map_1.add_child(feature_group_nature)\n", - "map_1.add_child(feature_group_nonature)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Visualizing polygon data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's use the municipality data again to show how we can interactively chloropleth maps through Folium.\n", - "\n", - "To make sure it works well with the background maps, we convert the data to a global coordinate reference system again (**EPSG:4326**)." - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "gemeentedata = gemeentedata.to_crs(4326)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To make sure we get tooltips (i.e. pop-ups for each polygon), we will use two important functions for this method:\n", - "\n", - "- **style_function** - A function that will be called for each feature in the GeoJSON data to determine its style. The function should take one argument (the feature) and return a dictionary of style options.\n", - "- **highlight_function** - A function that will be called when a feature is clicked. The function should take two arguments (the feature and the layer) and return a dictionary of style options to apply to the highlighted feature." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "colormap = cm.linear.YlOrRd_09.to_step(data=gemeentedata['bevolkings'], n=9, method=\"linear\")\n", - "colormap.caption = \"Population per km2\"\n", - "\n", - "style_function = lambda x: {\"weight\":0.5, \n", - " 'color':'black',\n", - " 'fillColor':colormap(x['properties']['bevolkings']), \n", - " 'fillOpacity':0.75}\n", - "highlight_function = lambda x: {'fillColor': '#000000', \n", - " 'color':'#000000', \n", - " 'fillOpacity': 0.50, \n", - " 'weight': 0.1}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can plot the map using the `folium.features.GeoJson` function. As you notice, you see the word **GeoJson**. A GeoJSON object is a data format for storing geographic data in JSON (JavaScript Object Notation) format. It's essentially a way of representing geospatial data using text-based data structures that can be easily read and parsed by computers. A GeoJSON object can be thought of as the underlying data structure that's used to store the geometry information in a GeoPandas DataFrame. When you create a GeoPandas DataFrame, you're essentially creating a pandas DataFrame that includes a geometry column, where each element in that column is a GeoJSON object that contains the spatial information for the corresponding feature.\n", - "\n", - "Let's break down the different arguments of the `folium.features.GeoJson` that we are using (besides the style and highlight function):\n", - "\n", - "- **data** - This is the GeoJSON data that you want to visualize on the map. It can be a Python dictionary or a JSON string.\n", - "- **tooltip** - A string that will be displayed as a tooltip when the mouse is hovered over a feature.\n", - "- **control** - A boolean that determines whether the layer should be added to the LayerControl object. If set to False, the layer won't be displayed in the layer control." - ] - }, - { - "cell_type": "code", - "execution_count": 160, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 160, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mymap = folium.Map(location=[52.10771254281107, 5.4293253455879675], zoom_start=7,tiles=None)\n", - "\n", - "folium.TileLayer('CartoDB positron',name=\"Light Map\",control=False).add_to(mymap)\n", - "\n", - "NIL=folium.features.GeoJson(\n", - " gemeentedata,\n", - " style_function=style_function,\n", - " control=False,\n", - " highlight_function=highlight_function,\n", - " tooltip=folium.features.GeoJsonTooltip(fields=['statnaam','bevolkings'],\n", - " aliases=['Gemeente','Population Density (people/km2)'],\n", - " style=(\"background-color: white; color: #333333; font-family: arial; font-size: 12px; padding: 10px;\"),\n", - " sticky=True\n", - " )\n", - " )\n", - "colormap.add_to(mymap)\n", - "mymap.add_child(NIL)\n", - "mymap" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/_toc.yml b/_toc.yml index 287cbdc..b92c426 100644 --- a/_toc.yml +++ b/_toc.yml @@ -27,8 +27,8 @@ parts: chapters: - file: TAA3/lecture - file: TAA3/tutorial - # - caption: TAA4 - # chapters: + - caption: TAA4 + chapters: # - file: TAA4/lecture - # - file: TAA4/assignment + - file: TAA4/assignment