data prep page

ci/power.ipynb

@@ -2354,7 +2354,7 @@
    "id": "e3a66d7e-6cb8-4cf1-aa79-90df85ae6991",
    "metadata": {},
    "source": [
-    "Plot location of most damaged healthcare facilities"
+    "Plot location of most damaged power infrastructure assets."
    ]
   },
   {

ci/telecom.ipynb

@@ -1920,7 +1920,7 @@
    "id": "e3a66d7e-6cb8-4cf1-aa79-90df85ae6991",
    "metadata": {},
    "source": [
-    "Plot location of most damaged healthcare facilities"
+    "Plot location of most damaged power infrastructure assets."
    ]
   },
   {

howto/using_osm.md

@@ -122,7 +122,7 @@ print(max_damage_df)
 
 Each infrastructure type requires a vulnerability curve to determine expected damage based on hazard intensity. Vulnerability curves are hazard-specific and should be reviewed for accuracy. A useful reference is [Nirandjan et al. (2024)](https://nhess.copernicus.org/articles/24/4341/2024/nhess-24-4341-2024-discussion.html), but local validation is necessary.
 
-To facilitate vulnerability curve selection, a predefined dictionary linking OSM categories to damage curves is available in [`base_values.py`](https://github.com/VU-IVM/DamageScanner/blob/DS1.0/src/damagescanner/base_values.py). Users can use this as a reference and adjust as needed based on local validation.
+To facilitate vulnerability curve selection, a predefined dictionary linking OSM categories to damage curves is available in [`base_values.py`](https://github.com/VU-IVM/DamageScanner/blob/DS1.0/src/damagescanner/base_values.py). Users can use this as a reference and adjust as needed based on local validation. The code below provides an example how one could add their own defined curves.
 
 ```python
 import numpy as np

intro/ci_explained.md

@@ -2,7 +2,7 @@
 
 Critical Infrastructure (CI) refers to the essential services and assets that form the backbone of a functioning society and economy. These infrastructure systems are vital for public health, safety, and economic well-being, supporting various activities ranging from transportation to energy distribution. CI systems are susceptible to a wide range of threats, both natural and human-made, including natural disasters, terrorism, and poor maintenance. We categorize critical infrastructure (CI) network into seven overarching systems: transportation, energy, water, waste, telecommunication, education, and health. This classification aligns with widely accepted frameworks in the literature (Hallegatte et al., 2019; Hall et al., 2019; Thacker et al., 2019; United Nations Office for Disaster Risk Reduction, 2015), and highlights the growing recognition of the importance of educational and health-related infrastructure (United Nations Office for Disaster Risk Reduction, 2015). 
 
-Each of these seven CI systems is further subdivided into ten subsystems, with each subsystem containing two or more specific types of infrastructure. For example, the telecommunication subsystem encompasses communication towers and masts. To represent the global landscape of CI, a total of 81 OpenStreetMap (OSM) tags were selected to identify 39 different infrastructure types (Popescu & Simion, 2011). This breakdown of critical infrastructure types enables a more granular approach to risk and damage assessments by providing detailed categorizations and geospatial data on the global infrastructure network.
+ To represent the global landscape of CI, a total of 81 OpenStreetMap (OSM) tags were selected to identify 39 different infrastructure types (Popescu & Simion, 2011). This breakdown of critical infrastructure types enables a more granular approach to risk and damage assessments by providing detailed categorizations and geospatial data on the global infrastructure network.
 
 ![Distribution and statistics of unique CI assets extracted from OSM](https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41597-022-01218-4/MediaObjects/41597_2022_1218_Fig3_HTML.png?as=webp)
 *Figure 1: Distribution and statistics of unique CI assets extracted from OSM (Nirandjan et al., 2022).*
@@ -66,4 +66,4 @@ More specifically, we categorize CI into the following types and subtypes:
    - Kindergartens
    - Libraries
 
-As part of our damage and risk assessments, understanding these infrastructure types and their vulnerabilities is crucial. In the sections that follow, we will explore how each CI system can be assessed for risks and damages caused by natural or human-made threats.
+As part of our damage and risk assessments, understanding these infrastructure types and their vulnerabilities is crucial. In the sections that follow, we will explore how each CI system can be assessed for risks and damages caused by natural threats.

intro/damage_risk.md

@@ -12,7 +12,7 @@ This framework is applied to estimate the risks posed by natural hazards such as
 
 The **conditional probability of failure** of infrastructure assets when subject to an extreme hazard depends on their design and site-specific conditions. For global risk assessments, generic fragility curves are often used to estimate damage due to a lack of localized data. These curves relate the intensity of hazards to a **damage probability** based on infrastructure characteristics. For example, flood damage curves are often linear, relating flood depth to expected infrastructure damage (See Vulnerability Section).
 
-The general approach is outlined in **Figure 1**. After processing the OSM data, we perform an exact overlay with the provided natural hazard data (a flood in **Figure 1**) Within our analysis, we identify each unique hazard intensity for each element. Following, knowing the affected area (in the case of a polygon element) or the length (in the case of a line element) of the affected asset, we use the pre-defined vulnerability curves to assess the damage for to that asset for each unique hazard intensity. Finally, this can be summed to get a total damage per asset. If required, these damage values can be scaled according to Gross Domestic Products (GDP) or corrected based on available information design standards.
+The general approach is outlined in **Figure 1**. After processing the OSM data, we perform an exact overlay with the provided natural hazard data (a flood in **Figure 1**) Within our analysis, we identify each unique hazard intensity for each element. Following, knowing the affected area (in the case of a polygon element) or the length (in the case of a line element) of the affected asset, we use the pre-defined vulnerability curves to assess the damage for that asset for each unique hazard intensity. Finally, this can be summed to get a total damage per asset. If required, these damage values can be scaled according to Gross Domestic Products (GDP) or corrected based on available information design standards.
 
 ![Risk assessment framework for critical infrastructure](https://nhess.copernicus.org/articles/21/1011/2021/nhess-21-1011-2021-f02-web.png)
 *Figure 1: Damage assessment framework for critical infrastructure. Source: Van Ginkel et al., (2021).*

intro/exposure.md

@@ -10,6 +10,6 @@ Geographical features in OSM are projected in the form of nodes, ways, and relat
 An example of unprocessed OSM data, including a breakdown of the basic datatypes, is shown in **Figure 2**. Each georeferenced element in OSM has an ID number that uniquely identifies it, along with details such as the user who modified the element and the time of last modification. Elements can be further specified by a list of attribute tags in the form of key-value pairs, whereby the value provides more detail to the key identifier. For example, primary roads that often link larger towns are specified under the key ‘highway’ in combination with the value ‘primary’.
 
 ![OSM Data Types](https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41597-022-01218-4/MediaObjects/41597_2022_1218_Fig2_HTML.png?as=webp)
-*Figure 2: Visualization of raw OpenStreetMap data of a given area, with a breakdown by the datatypes (Nirandjan et al., 2022).*
+*Figure 1: Visualization of raw OpenStreetMap data of a given area, with a breakdown by the datatypes (Nirandjan et al., 2022).*
 
 

use_cases/all_ci_vulnerability.ipynb

@@ -445,7 +445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "70b64900-3a6c-450b-9a1e-3e49c86d71b2",
    "metadata": {},
    "outputs": [
@@ -476,7 +476,7 @@
     "pie = plt.pie(sizes,autopct='%1.1f%%', labeldistance=1.15, wedgeprops = { 'linewidth' : 1, 'edgecolor' : 'white' }, colors=colors);\n",
     "plt.axis('equal')\n",
     "plt.legend(loc = 'right', labels=labels,bbox_to_anchor=(1.15, 0.5),)\n",
-    "plt.title(f'River Flood Damage for {country_full_name} for multiple \\n Critical Infrastructure systems',fontweight='bold')"
+    "plt.title(f'River Flood Vulnerability for {country_full_name} for multiple \\n Critical Infrastructure systems',fontweight='bold')"
    ]
   }
  ],