mpv-nyc-EDSA-final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "931c9192",
   "metadata": {},
   "source": [
    "Useful link:\n",
    "- https://geographicdata.science/book/notebooks/08_point_pattern_analysis.html#centrography"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9901de8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "\n",
    "import statsmodels.api as sm # for linear regression\n",
    "\n",
    "import datetime\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('fivethirtyeight')\n",
    "import descartes\n",
    "import geopandas as gpd\n",
    "from shapely.geometry import Point, Polygon\n",
    "from shapely import wkt\n",
    "from pointpats import centrography # for centrographic statistics\n",
    "from matplotlib.patches import Ellipse # for standard deviational ellipse\n",
    "import contextily\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a03c069",
   "metadata": {},
   "source": [
    "### Clean MPV data to be just NYC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bdc7a32f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>race</th>\n",
       "      <th>victim_image</th>\n",
       "      <th>date</th>\n",
       "      <th>street_address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zip</th>\n",
       "      <th>...</th>\n",
       "      <th>congressperson_party</th>\n",
       "      <th>prosecutor_head</th>\n",
       "      <th>prosecutor_race</th>\n",
       "      <th>prosecutor_gender</th>\n",
       "      <th>prosecutor_party</th>\n",
       "      <th>prosecutor_term</th>\n",
       "      <th>prosecutor_in_court</th>\n",
       "      <th>prosecutor_special</th>\n",
       "      <th>independent_investigation</th>\n",
       "      <th>prosecutor_url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jesus Antonio Rodriguez</td>\n",
       "      <td>33.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>https://i0.wp.com/nbcpalmsprings.com/wp-conten...</td>\n",
       "      <td>2/6/2023</td>\n",
       "      <td>25400 block of Filaree Ave</td>\n",
       "      <td>Moreno Valley</td>\n",
       "      <td>CA</td>\n",
       "      <td>92551.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Evan Wollert</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>https://d1ldvf68ux039x.cloudfront.net/thumbs/f...</td>\n",
       "      <td>2/6/2023</td>\n",
       "      <td>3985 East Bijou Street</td>\n",
       "      <td>Colorado Springs</td>\n",
       "      <td>CO</td>\n",
       "      <td>80909.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alex Greene</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://cache.legacy.net/legacy/images/cobrand...</td>\n",
       "      <td>2/6/2023</td>\n",
       "      <td>Havendale Boulevard near 16th Street</td>\n",
       "      <td>Winter Haven</td>\n",
       "      <td>FL</td>\n",
       "      <td>33881.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Azaan Lee</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>https://encrypted-tbn0.gstatic.com/images?q=tb...</td>\n",
       "      <td>2/6/2023</td>\n",
       "      <td>200 block of Campbell Ave.</td>\n",
       "      <td>Rantoul</td>\n",
       "      <td>IL</td>\n",
       "      <td>61866.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antonio Garza</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2/5/2023</td>\n",
       "      <td>Whitewater Cutoff, north of the 10 Freeway</td>\n",
       "      <td>Palm Springs</td>\n",
       "      <td>CA</td>\n",
       "      <td>92282.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      name   age gender      race  \\\n",
       "0  Jesus Antonio Rodriguez  33.0   Male  Hispanic   \n",
       "1             Evan Wollert  44.0   Male     Black   \n",
       "2              Alex Greene  21.0   Male       NaN   \n",
       "3                Azaan Lee  21.0   Male     Black   \n",
       "4            Antonio Garza  44.0   Male       NaN   \n",
       "\n",
       "                                        victim_image      date  \\\n",
       "0  https://i0.wp.com/nbcpalmsprings.com/wp-conten...  2/6/2023   \n",
       "1  https://d1ldvf68ux039x.cloudfront.net/thumbs/f...  2/6/2023   \n",
       "2  https://cache.legacy.net/legacy/images/cobrand...  2/6/2023   \n",
       "3  https://encrypted-tbn0.gstatic.com/images?q=tb...  2/6/2023   \n",
       "4                                                NaN  2/5/2023   \n",
       "\n",
       "                               street_address              city state  \\\n",
       "0                  25400 block of Filaree Ave     Moreno Valley    CA   \n",
       "1                      3985 East Bijou Street  Colorado Springs    CO   \n",
       "2        Havendale Boulevard near 16th Street      Winter Haven    FL   \n",
       "3                  200 block of Campbell Ave.           Rantoul    IL   \n",
       "4  Whitewater Cutoff, north of the 10 Freeway      Palm Springs    CA   \n",
       "\n",
       "       zip  ... congressperson_party prosecutor_head prosecutor_race  \\\n",
       "0  92551.0  ...                  NaN             NaN             NaN   \n",
       "1  80909.0  ...                  NaN             NaN             NaN   \n",
       "2  33881.0  ...                  NaN             NaN             NaN   \n",
       "3  61866.0  ...                  NaN             NaN             NaN   \n",
       "4  92282.0  ...                  NaN             NaN             NaN   \n",
       "\n",
       "  prosecutor_gender prosecutor_party prosecutor_term prosecutor_in_court  \\\n",
       "0               NaN              NaN             NaN                 NaN   \n",
       "1               NaN              NaN             NaN                 NaN   \n",
       "2               NaN              NaN             NaN                 NaN   \n",
       "3               NaN              NaN             NaN                 NaN   \n",
       "4               NaN              NaN             NaN                 NaN   \n",
       "\n",
       "  prosecutor_special independent_investigation prosecutor_url  \n",
       "0                NaN                       NaN            NaN  \n",
       "1                NaN                       NaN            NaN  \n",
       "2                NaN                       NaN            NaN  \n",
       "3                NaN                       NaN            NaN  \n",
       "4                NaN                       NaN            NaN  \n",
       "\n",
       "[5 rows x 62 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read in the raw data from MappingPoliceViolence.org\n",
    "mpv_raw = pd.read_csv('/Users/taylor/Desktop/phd/research-local/data/police-violence-data/MPV03282023.csv')\n",
    "mpv_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "523f18b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'age', 'gender', 'race', 'victim_image', 'date',\n",
       "       'street_address', 'city', 'state', 'zip', 'county',\n",
       "       'agency_responsible', 'ori', 'cause_of_death', 'circumstances',\n",
       "       'disposition_official', 'officer_charged', 'news_urls',\n",
       "       'signs_of_mental_illness', 'allegedly_armed', 'wapo_armed',\n",
       "       'wapo_threat_level', 'wapo_flee', 'wapo_body_camera', 'wapo_id',\n",
       "       'off_duty_killing', 'geography', 'mpv_id', 'fe_id', 'encounter_type',\n",
       "       'initial_reason', 'officer_names', 'officer_races',\n",
       "       'officer_known_past_shootings', 'call_for_service', 'tract',\n",
       "       'urban_rural_uspsai', 'urban_rural_nchs',\n",
       "       'hhincome_median_census_tract', 'latitude', 'longitude',\n",
       "       'pop_total_census_tract', 'pop_white_census_tract',\n",
       "       'pop_black_census_tract', 'pop_native_american_census_tract',\n",
       "       'pop_asian_census_tract', 'pop_pacific_islander_census_tract',\n",
       "       'pop_other_multiple_census_tract', 'pop_hispanic_census_tract',\n",
       "       'congressional_district_113', 'congressperson_lastname',\n",
       "       'congressperson_firstname', 'congressperson_party', 'prosecutor_head',\n",
       "       'prosecutor_race', 'prosecutor_gender', 'prosecutor_party',\n",
       "       'prosecutor_term', 'prosecutor_in_court', 'prosecutor_special',\n",
       "       'independent_investigation', 'prosecutor_url'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# exploring the data\n",
    "mpv_raw.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3a69dedf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CA', 'CO', 'FL', 'IL', 'IN', 'MA', 'AL', 'NJ', 'TX', 'KS', 'KY',\n",
       "       'LA', 'MI', 'SD', 'GA', 'SC', 'TN', 'VA', 'MO', 'NE', 'OH', 'NM',\n",
       "       'MD', 'AZ', 'ID', 'AR', 'OR', 'WA', 'MS', 'WI', 'PA', 'NC', 'OK',\n",
       "       'UT', 'WV', 'NY', 'NV', 'NH', 'HI', 'IA', 'AK', 'MN', 'DC', 'DE',\n",
       "       'WY', 'CT', 'MT', 'ME', 'VT', 'ND', 'RI'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpv_raw['state'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bcffc2ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>race</th>\n",
       "      <th>victim_image</th>\n",
       "      <th>date</th>\n",
       "      <th>street_address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zip</th>\n",
       "      <th>...</th>\n",
       "      <th>congressperson_party</th>\n",
       "      <th>prosecutor_head</th>\n",
       "      <th>prosecutor_race</th>\n",
       "      <th>prosecutor_gender</th>\n",
       "      <th>prosecutor_party</th>\n",
       "      <th>prosecutor_term</th>\n",
       "      <th>prosecutor_in_court</th>\n",
       "      <th>prosecutor_special</th>\n",
       "      <th>independent_investigation</th>\n",
       "      <th>prosecutor_url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Christopher T. Torres</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1/10/2023</td>\n",
       "      <td>Fields Corner Road near Pugsley Road</td>\n",
       "      <td>Southeast</td>\n",
       "      <td>NY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Quayshawn Samuel</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/29/2022</td>\n",
       "      <td>439 Beach 56th</td>\n",
       "      <td>Queens</td>\n",
       "      <td>NY</td>\n",
       "      <td>11692.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>Enrique Lopez</td>\n",
       "      <td>56.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/28/2022</td>\n",
       "      <td>Birchwood Road</td>\n",
       "      <td>Medford</td>\n",
       "      <td>NY</td>\n",
       "      <td>11763.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Chatuma M. Crawford</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/17/2022</td>\n",
       "      <td>Northern Blvd.</td>\n",
       "      <td>Cicero</td>\n",
       "      <td>NY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>Daniel Rivera</td>\n",
       "      <td>39.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/4/2022</td>\n",
       "      <td>161st Street and Summit Avenue</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>10451.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11130</th>\n",
       "      <td>Kurt Myers</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>http://ww2.hdnux.com/photos/20/36/45/4319577/2...</td>\n",
       "      <td>3/14/2013</td>\n",
       "      <td>248 N Main St</td>\n",
       "      <td>Herkimer</td>\n",
       "      <td>NY</td>\n",
       "      <td>13350.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11142</th>\n",
       "      <td>Kimani Gray</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3/10/2013</td>\n",
       "      <td>East 52nd St and Snyder Avenue</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>11218.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11165</th>\n",
       "      <td>Richard J. Aubin</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>http://usgunviolence.files.wordpress.com/2013/...</td>\n",
       "      <td>3/5/2013</td>\n",
       "      <td>Liberty Road and Route 9</td>\n",
       "      <td>North Hudson</td>\n",
       "      <td>NY</td>\n",
       "      <td>12855.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11207</th>\n",
       "      <td>Charles A. Baker Jr.</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>http://www.buffalonews.com/storyimage/BN/20130...</td>\n",
       "      <td>2/16/2013</td>\n",
       "      <td>201 E 2nd St</td>\n",
       "      <td>Jamestown</td>\n",
       "      <td>NY</td>\n",
       "      <td>14701.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11326</th>\n",
       "      <td>Peter Jourdan</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>http://www.trbimg.com/img-50e7a90e/turbine/pet...</td>\n",
       "      <td>1/3/2013</td>\n",
       "      <td>Fort Hamilton Parkway &amp; 62nd Street</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>NY</td>\n",
       "      <td>11219.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>236 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        name   age gender      race  \\\n",
       "100    Christopher T. Torres  34.0   Male       NaN   \n",
       "154         Quayshawn Samuel  31.0   Male       NaN   \n",
       "159            Enrique Lopez  56.0   Male       NaN   \n",
       "191      Chatuma M. Crawford  20.0   Male       NaN   \n",
       "237            Daniel Rivera  39.0   Male  Hispanic   \n",
       "...                      ...   ...    ...       ...   \n",
       "11130             Kurt Myers  64.0   Male     White   \n",
       "11142            Kimani Gray  16.0   Male     Black   \n",
       "11165       Richard J. Aubin  28.0   Male     White   \n",
       "11207   Charles A. Baker Jr.  30.0   Male     Black   \n",
       "11326          Peter Jourdan  37.0   Male     Black   \n",
       "\n",
       "                                            victim_image        date  \\\n",
       "100                                                  NaN   1/10/2023   \n",
       "154                                                  NaN  12/29/2022   \n",
       "159                                                  NaN  12/28/2022   \n",
       "191                                                  NaN  12/17/2022   \n",
       "237                                                  NaN   12/4/2022   \n",
       "...                                                  ...         ...   \n",
       "11130  http://ww2.hdnux.com/photos/20/36/45/4319577/2...   3/14/2013   \n",
       "11142                                                NaN   3/10/2013   \n",
       "11165  http://usgunviolence.files.wordpress.com/2013/...    3/5/2013   \n",
       "11207  http://www.buffalonews.com/storyimage/BN/20130...   2/16/2013   \n",
       "11326  http://www.trbimg.com/img-50e7a90e/turbine/pet...    1/3/2013   \n",
       "\n",
       "                             street_address          city state      zip  ...  \\\n",
       "100    Fields Corner Road near Pugsley Road     Southeast    NY      NaN  ...   \n",
       "154                          439 Beach 56th        Queens    NY  11692.0  ...   \n",
       "159                          Birchwood Road       Medford    NY  11763.0  ...   \n",
       "191                          Northern Blvd.        Cicero    NY      NaN  ...   \n",
       "237          161st Street and Summit Avenue      New York    NY  10451.0  ...   \n",
       "...                                     ...           ...   ...      ...  ...   \n",
       "11130                         248 N Main St      Herkimer    NY  13350.0  ...   \n",
       "11142        East 52nd St and Snyder Avenue      New York    NY  11218.0  ...   \n",
       "11165              Liberty Road and Route 9  North Hudson    NY  12855.0  ...   \n",
       "11207                          201 E 2nd St     Jamestown    NY  14701.0  ...   \n",
       "11326   Fort Hamilton Parkway & 62nd Street      Brooklyn    NY  11219.0  ...   \n",
       "\n",
       "      congressperson_party prosecutor_head prosecutor_race prosecutor_gender  \\\n",
       "100                    NaN             NaN             NaN               NaN   \n",
       "154                    NaN             NaN             NaN               NaN   \n",
       "159                    NaN             NaN             NaN               NaN   \n",
       "191                    NaN             NaN             NaN               NaN   \n",
       "237                    NaN             NaN             NaN               NaN   \n",
       "...                    ...             ...             ...               ...   \n",
       "11130           Republican             NaN             NaN               NaN   \n",
       "11142             Democrat             NaN             NaN               NaN   \n",
       "11165           Republican             NaN             NaN               NaN   \n",
       "11207           Republican             NaN             NaN               NaN   \n",
       "11326             Democrat             NaN             NaN               NaN   \n",
       "\n",
       "      prosecutor_party prosecutor_term prosecutor_in_court prosecutor_special  \\\n",
       "100                NaN             NaN                 NaN                NaN   \n",
       "154                NaN             NaN                 NaN                NaN   \n",
       "159                NaN             NaN                 NaN                NaN   \n",
       "191                NaN             NaN                 NaN                NaN   \n",
       "237                NaN             NaN                 NaN                NaN   \n",
       "...                ...             ...                 ...                ...   \n",
       "11130              NaN             NaN                 NaN                NaN   \n",
       "11142              NaN             NaN                 NaN                NaN   \n",
       "11165              NaN             NaN                 NaN                NaN   \n",
       "11207              NaN             NaN                 NaN                NaN   \n",
       "11326              NaN             NaN                 NaN                NaN   \n",
       "\n",
       "      independent_investigation prosecutor_url  \n",
       "100                         NaN            NaN  \n",
       "154                         NaN            NaN  \n",
       "159                         NaN            NaN  \n",
       "191                         NaN            NaN  \n",
       "237                         NaN            NaN  \n",
       "...                         ...            ...  \n",
       "11130                       NaN            NaN  \n",
       "11142                       NaN            NaN  \n",
       "11165                       NaN            NaN  \n",
       "11207                       NaN            NaN  \n",
       "11326                       NaN            NaN  \n",
       "\n",
       "[236 rows x 62 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filtering data to New York State\n",
    "mpv_nys = mpv_raw[mpv_raw['state'] == 'NY']\n",
    "mpv_nys"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6b503d7",
   "metadata": {},
   "source": [
    "236 observations of police violence in New York State (even less in New York City, presumably). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d4103acf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Putnam', 'Queens', 'Suffolk', 'Onondaga', 'Bronx', 'Erie',\n",
       "       'Nassau', 'New York', 'Monroe', 'Kings', 'Oneida', 'Ulster',\n",
       "       'Franklin', 'Orange', 'Dutchess', 'Westchester', 'Herkimer',\n",
       "       'Otsego', 'Greene', 'Delaware', 'Cayuga', 'King', 'Chemung',\n",
       "       'Rensselaer', nan, 'Wayne', 'Livingston', 'Schenectady',\n",
       "       'Rockland', 'Tompkins', 'Hamilton', 'Richmond', 'Broome',\n",
       "       'Chautauqua', 'Albany', 'Genesee', 'Madison', 'Ontario',\n",
       "       'St. Lawrence', 'Fulton', 'Oswego', 'Orleans', 'Saratoga', 'Essex'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpv_nys['county'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8b74af1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Queens', 'Bronx', 'New York', 'Kings', 'Richmond'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filtering to only NYC counties\n",
    "mpv_nyc = mpv_nys[(mpv_nys['county'] == 'Queens') |\n",
    "                 (mpv_nys['county'] == 'Richmond')|\n",
    "                 (mpv_nys['county'] == 'New York')|\n",
    "                 (mpv_nys['county'] == 'Kings')|\n",
    "                 (mpv_nys['county'] == 'Bronx')\n",
    "                 ]\n",
    "# sanity check to make sure it's only NYC counties\n",
    "mpv_nyc['county'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fd8c0a69",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>race</th>\n",
       "      <th>victim_image</th>\n",
       "      <th>date</th>\n",
       "      <th>street_address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zip</th>\n",
       "      <th>...</th>\n",
       "      <th>congressperson_party</th>\n",
       "      <th>prosecutor_head</th>\n",
       "      <th>prosecutor_race</th>\n",
       "      <th>prosecutor_gender</th>\n",
       "      <th>prosecutor_party</th>\n",
       "      <th>prosecutor_term</th>\n",
       "      <th>prosecutor_in_court</th>\n",
       "      <th>prosecutor_special</th>\n",
       "      <th>independent_investigation</th>\n",
       "      <th>prosecutor_url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Quayshawn Samuel</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/29/2022</td>\n",
       "      <td>439 Beach 56th</td>\n",
       "      <td>Queens</td>\n",
       "      <td>NY</td>\n",
       "      <td>11692.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>Daniel Rivera</td>\n",
       "      <td>39.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/4/2022</td>\n",
       "      <td>161st Street and Summit Avenue</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>10451.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>Name Withheld</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11/18/2022</td>\n",
       "      <td>72 Vermilyea Ave</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>10034.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>Jermaine Hickson</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>https://www.google.com/url?sa=i&amp;url=https%3A%2...</td>\n",
       "      <td>11/10/2022</td>\n",
       "      <td>West 36th Street and Neptune Avenue</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>11224.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>Kyle Styles Lovera Lockett</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://i2.wp.com/www.norwoodnews.org/wp-conte...</td>\n",
       "      <td>11/3/2022</td>\n",
       "      <td>330 East Gun Hill Road</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>NY</td>\n",
       "      <td>10467.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10971</th>\n",
       "      <td>Deion Fludd</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>http://i.dailymail.co.uk/i/pix/2014/04/11/arti...</td>\n",
       "      <td>5/5/2013</td>\n",
       "      <td>Rockaway Avenue and Fulton Street</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>11233.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11046</th>\n",
       "      <td>Dylan Samuel-Peters</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4/15/2013</td>\n",
       "      <td>East 56th Street in the East Flatbush</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>NY</td>\n",
       "      <td>11203.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11047</th>\n",
       "      <td>Dason Peters</td>\n",
       "      <td>33.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4/15/2013</td>\n",
       "      <td>East 56th Street in the East Flatbush</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>NY</td>\n",
       "      <td>11203.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11142</th>\n",
       "      <td>Kimani Gray</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3/10/2013</td>\n",
       "      <td>East 52nd St and Snyder Avenue</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>11218.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11326</th>\n",
       "      <td>Peter Jourdan</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>http://www.trbimg.com/img-50e7a90e/turbine/pet...</td>\n",
       "      <td>1/3/2013</td>\n",
       "      <td>Fort Hamilton Parkway &amp; 62nd Street</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>NY</td>\n",
       "      <td>11219.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             name   age gender      race  \\\n",
       "154              Quayshawn Samuel  31.0   Male       NaN   \n",
       "237                 Daniel Rivera  39.0   Male  Hispanic   \n",
       "293                 Name Withheld   NaN   Male       NaN   \n",
       "323              Jermaine Hickson  42.0   Male     Black   \n",
       "345    Kyle Styles Lovera Lockett  21.0   Male       NaN   \n",
       "...                           ...   ...    ...       ...   \n",
       "10971                 Deion Fludd  17.0   Male     Black   \n",
       "11046         Dylan Samuel-Peters   1.0   Male     Black   \n",
       "11047                Dason Peters  33.0   Male     Black   \n",
       "11142                 Kimani Gray  16.0   Male     Black   \n",
       "11326               Peter Jourdan  37.0   Male     Black   \n",
       "\n",
       "                                            victim_image        date  \\\n",
       "154                                                  NaN  12/29/2022   \n",
       "237                                                  NaN   12/4/2022   \n",
       "293                                                  NaN  11/18/2022   \n",
       "323    https://www.google.com/url?sa=i&url=https%3A%2...  11/10/2022   \n",
       "345    https://i2.wp.com/www.norwoodnews.org/wp-conte...   11/3/2022   \n",
       "...                                                  ...         ...   \n",
       "10971  http://i.dailymail.co.uk/i/pix/2014/04/11/arti...    5/5/2013   \n",
       "11046                                                NaN   4/15/2013   \n",
       "11047                                                NaN   4/15/2013   \n",
       "11142                                                NaN   3/10/2013   \n",
       "11326  http://www.trbimg.com/img-50e7a90e/turbine/pet...    1/3/2013   \n",
       "\n",
       "                              street_address      city state      zip  ...  \\\n",
       "154                           439 Beach 56th    Queens    NY  11692.0  ...   \n",
       "237           161st Street and Summit Avenue  New York    NY  10451.0  ...   \n",
       "293                         72 Vermilyea Ave  New York    NY  10034.0  ...   \n",
       "323      West 36th Street and Neptune Avenue  New York    NY  11224.0  ...   \n",
       "345                   330 East Gun Hill Road     Bronx    NY  10467.0  ...   \n",
       "...                                      ...       ...   ...      ...  ...   \n",
       "10971      Rockaway Avenue and Fulton Street  New York    NY  11233.0  ...   \n",
       "11046  East 56th Street in the East Flatbush  Brooklyn    NY  11203.0  ...   \n",
       "11047  East 56th Street in the East Flatbush  Brooklyn    NY  11203.0  ...   \n",
       "11142         East 52nd St and Snyder Avenue  New York    NY  11218.0  ...   \n",
       "11326    Fort Hamilton Parkway & 62nd Street  Brooklyn    NY  11219.0  ...   \n",
       "\n",
       "      congressperson_party prosecutor_head prosecutor_race prosecutor_gender  \\\n",
       "154                    NaN             NaN             NaN               NaN   \n",
       "237                    NaN             NaN             NaN               NaN   \n",
       "293                    NaN             NaN             NaN               NaN   \n",
       "323                    NaN             NaN             NaN               NaN   \n",
       "345                    NaN             NaN             NaN               NaN   \n",
       "...                    ...             ...             ...               ...   \n",
       "10971             Democrat             NaN             NaN               NaN   \n",
       "11046             Democrat             NaN             NaN               NaN   \n",
       "11047             Democrat             NaN             NaN               NaN   \n",
       "11142             Democrat             NaN             NaN               NaN   \n",
       "11326             Democrat             NaN             NaN               NaN   \n",
       "\n",
       "      prosecutor_party prosecutor_term prosecutor_in_court prosecutor_special  \\\n",
       "154                NaN             NaN                 NaN                NaN   \n",
       "237                NaN             NaN                 NaN                NaN   \n",
       "293                NaN             NaN                 NaN                NaN   \n",
       "323                NaN             NaN                 NaN                NaN   \n",
       "345                NaN             NaN                 NaN                NaN   \n",
       "...                ...             ...                 ...                ...   \n",
       "10971              NaN             NaN                 NaN                NaN   \n",
       "11046              NaN             NaN                 NaN                NaN   \n",
       "11047              NaN             NaN                 NaN                NaN   \n",
       "11142              NaN             NaN                 NaN                NaN   \n",
       "11326              NaN             NaN                 NaN                NaN   \n",
       "\n",
       "      independent_investigation prosecutor_url  \n",
       "154                         NaN            NaN  \n",
       "237                         NaN            NaN  \n",
       "293                         NaN            NaN  \n",
       "323                         NaN            NaN  \n",
       "345                         NaN            NaN  \n",
       "...                         ...            ...  \n",
       "10971                       NaN            NaN  \n",
       "11046                       NaN            NaN  \n",
       "11047                       NaN            NaN  \n",
       "11142                       NaN            NaN  \n",
       "11326                       NaN            NaN  \n",
       "\n",
       "[100 rows x 62 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpv_nyc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a450e071",
   "metadata": {},
   "source": [
    "100 observations of police violence in New York City across all 5 counties since 2013"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c27bea5e",
   "metadata": {},
   "source": [
    "### Clean MPV date column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "acbbdfd7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: 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",
      "  \n"
     ]
    }
   ],
   "source": [
    "# convert date column to datetime object and THEN sort it\n",
    "mpv_nyc['date_new'] = pd.to_datetime(mpv_nyc['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bd727c6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "154     2022-12-29\n",
       "237     2022-12-04\n",
       "293     2022-11-18\n",
       "323     2022-11-10\n",
       "345     2022-11-03\n",
       "           ...    \n",
       "10971   2013-05-05\n",
       "11046   2013-04-15\n",
       "11047   2013-04-15\n",
       "11142   2013-03-10\n",
       "11326   2013-01-03\n",
       "Name: date_new, Length: 100, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpv_nyc['date_new']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e55e8b10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>race</th>\n",
       "      <th>date_new</th>\n",
       "      <th>county</th>\n",
       "      <th>tract</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>cause_of_death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Quayshawn Samuel</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-12-29</td>\n",
       "      <td>Queens</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-73.786248</td>\n",
       "      <td>40.594928</td>\n",
       "      <td>Gunshot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>Daniel Rivera</td>\n",
       "      <td>39.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2022-12-04</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>6302.0</td>\n",
       "      <td>-73.927844</td>\n",
       "      <td>40.829298</td>\n",
       "      <td>Gunshot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>Name Withheld</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-11-18</td>\n",
       "      <td>New York</td>\n",
       "      <td>29100.0</td>\n",
       "      <td>-73.923477</td>\n",
       "      <td>40.865709</td>\n",
       "      <td>Gunshot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>Jermaine Hickson</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>2022-11-10</td>\n",
       "      <td>Kings</td>\n",
       "      <td>33000.0</td>\n",
       "      <td>-74.002157</td>\n",
       "      <td>40.577008</td>\n",
       "      <td>Gunshot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>Kyle Styles Lovera Lockett</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-11-03</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>43102.0</td>\n",
       "      <td>-73.873480</td>\n",
       "      <td>40.878858</td>\n",
       "      <td>Gunshot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10971</th>\n",
       "      <td>Deion Fludd</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>2013-05-05</td>\n",
       "      <td>Kings</td>\n",
       "      <td>37100.0</td>\n",
       "      <td>-73.910799</td>\n",
       "      <td>40.678278</td>\n",
       "      <td>Beaten</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11046</th>\n",
       "      <td>Dylan Samuel-Peters</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>2013-04-15</td>\n",
       "      <td>Kings</td>\n",
       "      <td>93800.0</td>\n",
       "      <td>-73.924197</td>\n",
       "      <td>40.649604</td>\n",
       "      <td>Gunshot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11047</th>\n",
       "      <td>Dason Peters</td>\n",
       "      <td>33.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>2013-04-15</td>\n",
       "      <td>Kings</td>\n",
       "      <td>93800.0</td>\n",
       "      <td>-73.924197</td>\n",
       "      <td>40.649604</td>\n",
       "      <td>Gunshot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11142</th>\n",
       "      <td>Kimani Gray</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>2013-03-10</td>\n",
       "      <td>Kings</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>-73.970771</td>\n",
       "      <td>40.757544</td>\n",
       "      <td>Gunshot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11326</th>\n",
       "      <td>Peter Jourdan</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>2013-01-03</td>\n",
       "      <td>Kings</td>\n",
       "      <td>21800.0</td>\n",
       "      <td>-73.996953</td>\n",
       "      <td>40.633157</td>\n",
       "      <td>Gunshot</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             name   age gender      race   date_new    county  \\\n",
       "154              Quayshawn Samuel  31.0   Male       NaN 2022-12-29    Queens   \n",
       "237                 Daniel Rivera  39.0   Male  Hispanic 2022-12-04     Bronx   \n",
       "293                 Name Withheld   NaN   Male       NaN 2022-11-18  New York   \n",
       "323              Jermaine Hickson  42.0   Male     Black 2022-11-10     Kings   \n",
       "345    Kyle Styles Lovera Lockett  21.0   Male       NaN 2022-11-03     Bronx   \n",
       "...                           ...   ...    ...       ...        ...       ...   \n",
       "10971                 Deion Fludd  17.0   Male     Black 2013-05-05     Kings   \n",
       "11046         Dylan Samuel-Peters   1.0   Male     Black 2013-04-15     Kings   \n",
       "11047                Dason Peters  33.0   Male     Black 2013-04-15     Kings   \n",
       "11142                 Kimani Gray  16.0   Male     Black 2013-03-10     Kings   \n",
       "11326               Peter Jourdan  37.0   Male     Black 2013-01-03     Kings   \n",
       "\n",
       "         tract  longitude   latitude cause_of_death  \n",
       "154        NaN -73.786248  40.594928        Gunshot  \n",
       "237     6302.0 -73.927844  40.829298        Gunshot  \n",
       "293    29100.0 -73.923477  40.865709        Gunshot  \n",
       "323    33000.0 -74.002157  40.577008        Gunshot  \n",
       "345    43102.0 -73.873480  40.878858        Gunshot  \n",
       "...        ...        ...        ...            ...  \n",
       "10971  37100.0 -73.910799  40.678278         Beaten  \n",
       "11046  93800.0 -73.924197  40.649604        Gunshot  \n",
       "11047  93800.0 -73.924197  40.649604        Gunshot  \n",
       "11142  10000.0 -73.970771  40.757544        Gunshot  \n",
       "11326  21800.0 -73.996953  40.633157        Gunshot  \n",
       "\n",
       "[100 rows x 10 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# columns we care about for now: name, age, gender, race, date, city, zip, county, longitude, latitude, cause_of_death\n",
    "mpv_nyc_clean = mpv_nyc[['name','age','gender','race','date_new','county','tract','longitude','latitude','cause_of_death']]\n",
    "mpv_nyc_clean\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "35323325",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='longitude', ylabel='latitude'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# exploratory scatter plot of the killings based on long and lat\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.scatterplot(x='longitude', y='latitude', hue='county',s=20, data=mpv_nyc_clean)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1c23909d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A5TX7TsjMHQCZuSMi1tYnyswnI+KjwGPAGPCVzPzKbNNPGBkZKeYqukxZr2uuWnk/hoChAPbCw3tb9jKF8b0xqWz3Yv369U2nGRsbO2Lf/v0LGdnzaBFZaqvprr8tQS0iLgZGM/PeiNjYZNpVVEpkrwaeBf46Iq7IzFuaOc/w8HAzh/eEkZGRUl7XXHk/JnkvJnkvKpYuXXrEvsHBQYZPKVdXhHbVoZwHXBIRjwD/DXh9RNwC7IyIIYDq39EGaX8B+EFm7srMl4C/BX6m+txs0kuS+kRbglpmXpuZ6zLzVVQ6edyZmVcAtwJXVg+7EtjaIPljwE9HxLKICOACJjubzCa9JKlPdLq1ewuwKSJGgE3VbSLipIj4EkBm3g18Hvgm8G0qeb5xuvSSpP7U9sHXmbkN2FZ9vJtKyav+mKeAC2u2Pwh8sMFxDdNLkvpTp0tqkiQVxqAmSSoNg5okqTQMapKk0jCoSZJKw6AmSSoNg5okqTRcJFRqgU6slC3JoCa1RCdWypZk9aPUEp1YKVuSQU1qCVfKljrD6kepBSZWyq5tU5PUegY1qQUC2LB2gA1rF3c6K1Jf8eejJKk0DGqSpNIwqEmSSsOgJkkqDYOaJKk07P0oSX1g8zkrjtgXwAO7Dx7eXrNkgKHB3g4LvZ17SdKsbLnvuRmPuf7clQwNtiEzLWT1oySpNAxqkqTSMKhJkkrDoCZJKg2DmiSpNAxqkqTSMKhJkkrDcWrqC+MJX9s1PmV9s5g5WUf1Yp6lTjOoqS98bdc4l/7dDpLKLApbLxpiQ5cv3NmLeZY6zU+I+sKDz75EVh9ndbvb9WKepU4zqKkvnHHcosNVd1Hd7na9mGep06x+VF84f+0AWy8amtI+1Ug3tWPNNs+SJhnU1BcC2LB2gA1rF097XDe1Y802z5Im+dNPqmE7VvcYT7hrdJwbHzrAXaPjh/9fpOlYUpNqTLRjTZTUeqEdq1GVaRl0U6lZvaOpoBYRm4DLgbWZ+csRsR5YkZl3tiR3Upv1YjtWoy//oU5nqgCNSs1WxWoms/7ERsS7gE8CI8CG6u4x4A+bOMeCiPhWRNxW3T4+Im6PiJHq31UN0pweEffV/HsuIn6r+tx1EfFkzXMXzjYvUiMT7VhXnbaYDT0y2LmsVab2/tRcNFNS+y3ggsx8JCLeV933IHB6E+e4GtgOTKwrvhm4IzO3RMTm6vb7ahNk5veAc6ASFIEngS/UHPLxzPxoE3lQScWChdw12h09F9upYZXp3g5nqgC9WGpW5zUT1JYDj1cfT/wwXAQcnE3iiFgHXAR8GHhPdfelwMbq45uBbdQFtToXAP+SmY/ONtPqHyOxmjf3YRtMoy//h0sQ1Oz9qbloJqjdRaUk9eGafe8G/mGW6W8ArqESHCeckJk7ADJzR0SsneEclwOfrdv3zoh4C3AP8DuZ+UyjhCMjI7PMZm8p63XNxcNjJ0yphrv/6ecZ2ruzk1lqmyFgKIC9HA5ovjcmle1erF+/vuk0Y2NjMx6zf/9CRvZ0f5lhuutvJqi9C/hiRLwdWB4R3wOeA355poQRcTEwmpn3RsTGJl6z9hzHAJcA19bs/iTwISrfYR8CPga8tVH64eHhubxsVxsZGSnldc3Vwz94bko13NknHsvw2hUzpCon3xuTvBcVS5cunfGYwcFBhk85omtDT5l1UKuWpH4KOBc4hUpV5Dcyc3wWyc8DLql25FgCrIiIW4CdETFUPfcQMDrNOX4J+GZmHv7pXfs4Iv4UuG2216PyOS13s/WiV9oGI/Wxpj71WXF3Zv51Zn59lgGNzLw2M9dl5quoVCHemZlXALcCV1YPuxLYOs1pfoW6qsdqIJxwGfCdWV6KSmj85UM913NRUrGmLalFxOMw80D+zDxljq+/BfhcRLwNeAx4Y/V1TwJuyswLq9vLgE3Ab9Sl/0hEnFPN4yMNnpck9ZGZqh+vqHn8U1RKU38MPAq8Engn8F+aecHM3EallyOZuZtKj8b6Y54CLqzZfgFY3eC4X2vmtSVJ5TZtUMvMf5x4HBF/AvzrzHyyZt+Xgb+n0kFDkqSOaqZN7STg+bp9zwMnF5cdSZLmrpmgditwa0RsiogzI+J/ozKzx62tyZokSc1pJqi9A/gn4FPAN6t/767ulySp45oZp/YilRlFNrcuO5Ikzd2sg1pEvP5oz7n0jCSpGzQzTdan67bXAMcATwCvKSxHkiTNUTPVj6+u3a4uA/N+YF/RmZJmo37F55MXuJC71O/m/C2QmS9HxIeplNT+7+KyJM1O/YrPf7lxNa/tdKYkddR8f9puAmY1/6NUtPoVnx+2zkA6qs3nzLxiRQAP7J7VEplHWLNkgKHBzteWNNNRpH4eyGVUZtz/zaIzJc1G/YrPpy6fIYHUx7bc91xLz3/9uSsZGmzpS8xKM2H1irrt/cBDmdnaOyUdRf2Kzyc//yjQn+unSapoJqj9VGZ+tH5nRLwnM21TU9sFsGHtABvWLgZgZO+hzmZIUsc1M6PIB46y//1FZERqp/GEu0bHufGhA9w1Oj7z+kqSesKMJbWaQdcLIuLnYcrai6/BLv3qIRPDAB7dP867t+083B639aIhNrhSttTzZlP9ODHoegnwmZr9CTwNvKvoTEmtMjEM4FfPWDml5+SDz750uBpTUu+aMahNDLqOiP+SmW9pfZak1pkYBrD8mIEpPSfPOG5RZzMmqRDNzChiQFPPmxgG8LmHnuMdZ61izZIB1q85hvNLWvVYP+vK+WsHprQfSGUzbVCLiO2ZeWb1cf04tcMy85QW5E0qXP0wgLJ/ydfPumLbocpuppLa22se149Tk3pO/TCAsqufdcW2Q5XdtEEtM79Ws7k2M/+6/piI+N8Lz5WkQtTPumLbocqu2aVnjghqwI3A54vJjqQiNapulcpsNuPUJtZKG4iIV3PkOLUXW5ExqUw61WGj36pbpdmU1B5msvbiX+qeexq4ruA8SaVjhw2pPWb8VGXmQGYuAL5afVz776TMvLEN+ZR6WqMOG5KKN+ufipn5c63MiFRmEx02wA4bUis1s57aQuDfAz8HvIKatrXM3FB81qTysMOG1B7NfLI+DvwGcBfwk8DfAGuBO1uQL6lUJjpsXHXaYjaUfMC31EnNBLV/A/xSZn4COFT9+wbg51uRMWm+XF5G6j/NjFNbBjxefTwWEcsy88GI+F9bkC9p3uxxqFrOg9kfmglq24GfAr4B3ANcFxHPAU+2ImPSfDlFlGr5I6c/NPM/ejUw0Q/5PcBPABczdX5IqWvY41C1HFbRH2aapf/10+y7vvr3mKIzJRXBHoeq5TyY/WGm6sdPz/A8VN4jr5nxKKnNnCKqv020od0/dgI7Rsf5WX/k9IWZZul/dbsyIsnODEWa2oa273Abmj9yyq2ZjiKSWmzii/j4JQt402kruGfX5MrcBrfmFNlRyB8bvaOtQS0iFlDpOflkZl4cEccDfwW8CngEeFNmPlOX5vTqMRNeA3wgM2+YTXqpl0x8Eb/ptBV86oFn7Kk3D0W2odlzsne0+3/laipDAyZsBu7IzGHgjur2FJn5vcw8JzPPoTKTyQvAF2abXuolE1/E+w6O21NvniY6Cn3oJ5ez9aKhebWh2XOyd7QtqEXEOuAi4Kaa3ZcCN1cf30xlhpLpXAD8S2Y+Osf0Uleb+CL+VycucTjCPE10FPrFpTvnPTWZw0N6RzurH28ArgGW1+w7ITN3AGTmjohYO8M5Lgc+O4/0Uleb+CJOBnjloD31uoXDQ3pHW4JaRFwMjGbmvRGxcY7nOAa4BLh2LulHRkbmkqzrlfW65qpM92MIGApgLzy8t/n0ZboX81XEvZjP/0csWMhIrObhfXDqcjg9nuV7edzh7dNyN+MvH5r1+davX99cBoCxsbGm0zRj//6FjOx5dOYDCzDd9berpHYecElEXAgsAVZExC3AzogYqpayhoDRac7xS8A3M3Nnzb5Zpx8eHi7gMrrLyMhIKa9rrrwfk7wXk7rhXtw1Os6bazqafGbTOt56e23Hk1e2vOPJ0qVLW3r+wcFBhk9Z1dLXmI22lKEz89rMXJeZr6JShXhnZl4B3ApcWT3sSmDrNKf5FaZWPdJkekk9oIyrK9R3NNm+54AdT1qk0+PUtgCfi4i3AY8BbwSIiJOAmzLzwur2MmATlfXcZkwvqXcV1X2+fkaRTo4tqx9ecObxi52yq0XaHtQycxuwrfp4N5UejfXHPAVcWLP9ArC6wXEN00vqXUUNmj7ajCKdUN/RxCm7WqfTJTVJTSr77BZFDZrupqWHGs1D2u4puzafs6Kl5w/ggd0HW/oaa5YMMDQ4fdgyqEk9puyzWxTVfd5Z+afact9znc7CvF1/7kqGBqc/xqAm9ZhuKoG0QlGrK0wEx/uffp6zTzzWKr4+YVBT3+n16jtLILMzERyH9u5keG1rq97UPQxq6judqL4rMpA6u4V0dAY19Z1OVN8VGUhd/FQ6On/iqe90YnJaZ3mX2sOSmkqtUbVfJ6rvbAeT2sOgplI7WrVfu6vvbAeT2sOgplLrlu7vtoNJ7WFQU6kVWe3X60MBpH5gUFOpFVntV/aZPKQyMKip1Iqs9uuWqkxLjI15XwQGNWnWuqUHoyXGxrwvAoOaNGvd0oOx2RJjv5RguqUkrc4yqEmz1KoejM0GnWZLjP1SgumWkrQ6y6CmntdNKxzPRbNBp9kSY7+UYLqlJK3OMqip53XTCsdz0WzQabbE2EwJpperKjs5FrCX71vZGNTU83q9JNLqarNmSjD9UlVZNO9b9zCoqef1eltKq6vNminB9PoPhE7xvnUPg5p6Xq+vcNxNU2j1+g+ETvG+dQ+Dmnpet65wPF07S1FtMEW35cxUarTtaFLtvfjxVYvspNIlDGpSi0zXzlJUG8w//XCcLz/+IvsOjvODfS+zMJbwM2vm/oU6U6nRtqNJje7FVad1vrTd7/rz3Si1wXQLgxa1aOj3nx/nUw88wy0P7uVTDzzDvzw/Pp8sz6ifFzsdT7hrdJwbHzrAXaPj/GBf/96LbmZJTWqR6dpZimqD2fXCoSlfrPsOHOKu0YGWjdnr57aj+pLZZzYN9e296GYGNalFpmufOn/tALddPMT3nx9n1wuHgMkvx2asX3PMlC/WoWOPaemYvX4e4FxfSt31wkt9ey+6mUFNapHp2qcCeDnh3dt2zqp96mgdNOqDTKu7lndTT812qy+lLl20AIC3n7a4bzvLdCODmtQhMwWg2kC2Ztki3vvVUXa/+PKUAHhkkOnf6sFWm/gBcc+ug+x6cZzrvv5D9rz4cl93lulGBjWpQ2Zqn6pvw3nHWav45APPTFsC6/Uxe91s4gfEg88Gf/CNZw7vd6B1dzGoSR0yU/tUfUlu38FKz8bpSmDdOmavTPq5s0wvMKhJHTJT+1T9l+cFP7KMs1YvslNCh/VzZ5leYFCTulSjL89g7tVczgZSjH7uLNMLDGpSm802uBT95elsIOoHBjWpzToVXJxJXv3An2lSm3VqqqmJNjqwg4PKy5Ka1Gad6j1nB4f+tvmc4nvDLl4Aixe0r2V2zZKZ37MGNanNOhVc7ODQ37bc91zh57z+3JWctfqYws87H239qRYRCyLiWxFxW3X7+Ii4PSJGqn9XHSXdcRHx+Yh4MCK2R8Trqvuvi4gnI+K+6r8L23k90lxMBJerTlt8eFYQScVod/3D1cD2mu3NwB2ZOQzcUd1u5BPA32fmGcDZdef4eGaeU/33pVZkWmqV+uVMXhqHLzwxzvUPjPGFJ8Zp7UIyUvm0rfoxItYBFwEfBt5T3X0psLH6+GZgG/C+unQrgA3AvwPIzIPAwVbnV2qH+p6Q//mCIX7jjqnLm1y2zrYvabba2aZ2A3ANsLxm3wmZuQMgM3dExNoG6V4D7AL+LCLOBu4Frs7M/dXn3xkRbwHuAX4nM59pcA5GRkaKuYouU9brmqteux/3j50wpSfk9/YcmLL93d0v8uNjT87p3L12L1qpbPdi/fr1TacZGxsrPB/79y9kZM+jhZ93JtNdf1uCWkRcDIxm5r0RsbHJ5AuBnwDelZl3R8QnqFRT/j7wSeBDVD7/HwI+Bry10UmGh4fnlvkuNjIyUsrrmqtevB87RscJ9h0umZ1+/OIpPSN/dPUShtc1f029eC9axXtRsXTp0sLPOTg4yPApDbtCdEy7SmrnAZdUO3IsAVZExC3AzogYqpbShoDRBmmfAJ7IzLur25+n2vaWmTsnDoqIPwVua+VFSEWr7wl53toBFm0aYvueA5x5/GIurat6dKoraXptCWqZeS1wLUC1pPbezLwiIv4TcCWwpfp3a4O0T0fE4xFxemZ+D7gA+G71XEMT1ZfAZcB3Wn0tUpEadbO/bN0Al61r/Kvaqa6k6XX607AF2BQRI8Cm6jYRcVJE1PZkfBfwFxHxAHAOcH11/0ci4tvV/T8P/Hbbci51wGxnI4kFC6f0qsyGR0nl0/bB15m5jUovRzJzN5WSV/0xTwEX1mzfBxzRMpiZv9aibEpdabazkYzEat5siU59yBlFpB4y29lIHt6HkxerLxnUpIK1sjPHbKe6OnU5rs6svmRQkwrWDZ05TsvdbL3olU5erL5jUOtDdgtvrW5Yt2z85UNOXqy+ZFDrQ91QkiizTi0tI8mg1pe6oSRRZq5bJnWOQa0PWZJoLdctkzrHoNaHylqSmBhwbFuh1L8Man2orCUJBxxL8hPfQP3CjU4x1BsaDTiW1F8sqTVg78De5IBjSQa1Buwd2JuKGHB8aBy++NT4lKVf/Dkj9Q6DWgP2DuxNRQw4/uJT47z19slS+mc2DXHZOsOa1CsMag2UtXegZrZ9z4EppfTtew4cdW0zSd3HoNZAWXsHamZnHr94Sin9zON9D0i9xKAm1bh03QCf2TQ0pU1NUu8wqEk1BoDL1g1Y5Sj1KH+GSpJKw6AmSSoNg5okqTQMapKk0rCjSB9wpWtJ/cKg1gecy1JSv/CbrQ80mstSksrIoNYHJuayBOeylFRuVj/2AeeylLT5nBWzPnbxAli8YOaW9zVLuu+7xKDWB5zLUtKW+56b9bHXn7uSs1Yf08LctE73hVlJkubIoCZJKg2DmiSpNGxTU9dy0LikZhnUNEU3BRIHjUtqlkFNU3RTIGk0aNwenJKm489eTdFNs484aFxSsyypaYqJQDJRUutkIHHQuKRmGdQ0RTcFEgeNS2pWW7+xImJBRHwrIm6rbh8fEbdHxEj176qjpDsuIj4fEQ9GxPaIeF0z6TV7E4HkqtMWs8HehpJ6TLt/hl8NbK/Z3gzckZnDwB3V7UY+Afx9Zp4BnF1zjtmmV8mNJzy88ARufOgAd42OH24XlNRf2hbUImIdcBFwU83uS4Gbq49vBt7QIN0KYAPwaYDMPJiZz842vfrD13aN8+Zt+7jm//shl/7dDr46Ot7pLEnqgHa2qd0AXAMsr9l3QmbuAMjMHRGxtkG61wC7gD+LiLOBe4GrM3P/LNMDMDIyUsxVdJmyXlez7h87YUqvzfuffp6hvTs7maWO870xqWz3Yv369U2nGRsbm/Wx+/cvZGTPo02/RrtMd/1tCWoRcTEwmpn3RsTGJpMvBH4CeFdm3h0Rn6BSzfj7zZxkeHi4yZftfiMjI6W8rrnYMTpOsO9wr82zTzyW4bWzX2qjbHxvTPJeVCxdunTWxw4ODjJ8Sm92UWhXSe084JKIuBBYAqyIiFuAnRExVC1lDQGjDdI+ATyRmXdXtz/PZNvZbNKrD5y/doC/3Licx19a3PFem5I6py2f/My8NjPXZeargMuBOzPzCuBW4MrqYVcCWxukfRp4PCJOr+66APhu9fGM6dUfAjj10E57baop4wl3jY7bwahEOj1ObQvwuYh4G/AY8EaAiDgJuCkzL6we9y7gLyLiGOD7wK9Pl16SZqObpoVTMdoe1DJzG7Ct+ng3lZJX/TFPARfWbN8HHNEyeLT0kjQbzi9aPv4kkdS3nF+0fDpd/ShJHdNN08KpGAY1qUW6aW06Neb8ouVjUJNaxE4IUvv5CZNapJvWppP6hUFNahE7IUjtZ/Wj1CJ2QpDaz6AmtYidEKT286ejJKk0DGqSpNIwqEmSSsOgJkkqDYOaJKk07P2onlU/DdXJC3w7S/3ObwH1rPppqP5y42peW9C5nbdR6k0GNfWs+mmoHt5X3Lmdt1HqTQY19ayJaagmAs+py4s7t4tHqmyuP3flrI9ds6R3f8AZ1NSz6qehOvn5R4EVhZy7PmA6b6N63Vmrj+l0FtrCoKaeVT8N1cjeQ4Wd23kbpd5kUJMacN5GqTf581OSVBoGNUlSaRjUJEmlYVCTJJWGQU2SVBoGNUlSaRjUJEmlYVCTJJWGQU2SVBqRmTMf1aP27t1b3ouT1PdWrlw57YpIZf0OnO66nSZLkvrATAGwLKx+lCSVRqmrHyVJ/cWSmiSpNAxqkqTSMKh1uYj4q4i4r/rvkYi4r+75UyLi+Yh471HSvzEi/jkixiNifVsy3SIF3IvjI+L2iBip/l3Vloy3yNHuR0ScW7P//oi47Cjpz46If4qIb0fEFyOimGXDO6CAe3FORHy9etw9EXFuWy9AhbFNrYdExMeAvZn5BzX7/gYYB+7OzI82SHNm9fn/DLw3M+9pV35baY734iPAnszcEhGbgVWZ+b62ZbqFau9HRCwDDmbmoYgYAu4HTsrMQ3Vp/ieV98Q/RsRbgVdn5u+3P/fFmuO9+Arw8cz8ckRcCFyTmRvbnnnNm136e0REBPAm4PU1+94AfB/Yf7R0mbm9emyLc9g+c70XwKXAxurjm4FtQM8Htfr7kZkv1Dy9BDjaL9fTgbuqj28H/jvQ00FtHvcigYmS6krgqVblUa1l9WPvOB/YmZkjABExSOUL+T92NFedMdd7cUJm7gCo/l3b0ly2z5T7ARAR/yoi/hn4NvCO+pJJ1XeAS6qP3wj8SMtz2npzvRe/BfyniHgc+ChwbTsyq+JZUusCEfE/gBMbPPUfMnNr9fGvAJ+tee4/Uqkueb5kpTDvRY053g8y827gx6rVzzdHxJcz88W6c7wV+OOI+ABwK3Cw2NwXq8X34v8Efjsz/yYi3gR8GviFYq9A7WCbWg+IiIXAk8BPZuYT1X1fZfKX9XFU2pI+kJn/z1HOsY0StKnN515ExPeAjZm5o9q+si0zT29b5lug0f1ocMw/AL873f99RJwG3JKZPdtBYj73IiL2AsdlZlarMPdmZs92nOlnltR6wy8AD9Z+UDPz/InHEXEd8PzRAlrJzOde3ApcCWyp/t3a4Jhec8T9iIhXA49XO0e8kkrb2SP1CSNibWaORsQA8H7gU23Kc6vM+V5QaUP7OSrtrK8HRhocox5gm1pvuJy6KpXpRMRNE933I+KyiHgCeB3wdxHx31uUx3aZ872gEsw2RcQIsKm63esa3Y+fBe6vdmv/AvDvM/OHcMT9+JWIeAh4kMqX+p+1J8stM5978XbgYxFxP3A9cFV7sqyiWf0oSSoNS2qSpNIwqEmSSsOgJkkqDYOaJKk0DGqSpNIwqKnUqjO2t3RmiKisDPCaAs+XEXFqUeeT+omDr6V5ysxjJx5HxJ8DT2Tm+zuXI6l/WVKTJJWGQU19ISIWR8QNEfFU9d8NEbG4+tzGiHgiIn4nIkYjYkdE/HpN2tVRWUTzuYj4nxHxhxHxtZrnMyJOjYirgF8FrqlWSX6x9vma4/88Iv6wZvt3q6/5VHVds/p8fzQiHouInRHxqYhY2ro7JfU2g5r6xX8Afho4BzgbOJfKfIcTTqSyjtbJwNuAP4nJlbH/hMo6bSdSmTPyykYvkJk3An8BfCQzj83MX54pUxHxi8B7qUzbNcyRM8P/EXBaNd+nVvP3gZnOK/Urg5r6xa8Cf5CZo5m5i8pyNb9W8/xL1edfyswvAc8Dp0fEAuDfAh/MzBcy87tUFhgtypuAP8vM72TmfuC6iSeqs8W/ncqSKHsycx+VeQkvL/D1pVKxo4j6xUnAozXbj1b3Tdhdt3jkC8CxwBoqn5PHa56rfVxEvu6ty9eENcAy4N6adeICWFDg60ulYklN/eIp4JU126dU981kF3AIWFezb7oVohvNEP4CleA0oXahyx115zul5vEPgTHgxzLzuOq/lbW9LSVNZVBTv/gs8P6IWBMRr6DSLnXLTIky82Xgb4HrImJZRJwBvGWaJDuB+jFr9wFvjogF1Ta0n6t57nPAv4uIH42IZcAHa157HPhT4OMRsRYgIk6OiH89U76lfmVQU7/4Q+Ae4AHg28A3q/tm451UOpE8DfxXKgHywFGO/TTwoxHxbET8v9V9VwO/DDxLpW1vYj+Z+WXgBuBO4OHq31rvq+7/ekQ8B/wPKgtdSmrA9dSkJkXEHwEnZmbDXpCSOseSmjSDiDgjIs6KinOpdPn/QqfzJelI9n6UZracSpXjScAo8DFga0dzJKkhqx8lSaVh9aMkqTQMapKk0jCoSZJKw6AmSSoNg5okqTT+f0XkOIuQrHKTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate scatter plot\n",
    "sns.jointplot(x=\"longitude\", y=\"latitude\", data=mpv_nyc_clean, s=20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9b4d5a8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Get a count by county\n",
    "county_count = mpv_nyc_clean.groupby('county').agg('count').reset_index()\n",
    "\n",
    "#Plot the count by county\n",
    "fig, ax1 = plt.subplots(1,1, figsize=(6,6)\n",
    "                       )\n",
    "sns.barplot(x='county', y='name', data=county_count, ax=ax1)\n",
    "\n",
    "ax1.set_title('Number of police killings by county since 2013', fontsize=15)\n",
    "ax1.set_xlabel('County', fontsize=12)\n",
    "ax1.set_ylabel('Count', fontsize=12)\n",
    "ax1.tick_params(axis='both', labelsize=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "debff9cc",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/geopandas/plotting.py:51: ShapelyDeprecationWarning: The 'type' attribute is deprecated, and will be removed in the future. You can use the 'geom_type' attribute instead.\n",
      "  if geom is not None and geom.type.startswith(prefix) and not geom.is_empty:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'NYC shape file')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# read in the shapefile for NYC and plot bare map\n",
    "nyc_map = gpd.read_file('/Users/taylor/Desktop/phd/research-local/data/shapefiles/nyc/nyc-shapefile-with-borough-lines/geo_export_712ccf97-7583-4635-9e4e-49905e30e6f4.shp')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15,15))\n",
    "nyc_map.plot(ax=ax)\n",
    "\n",
    "ax.set_title('NYC shape file', fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "02792b07",
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize coordinate reference system; there are different crs for different places\n",
    "crs = {'init':'epsg:2263'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b04a51c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'age', 'gender', 'race', 'victim_image', 'date',\n",
       "       'street_address', 'city', 'state', 'zip', 'county',\n",
       "       'agency_responsible', 'ori', 'cause_of_death', 'circumstances',\n",
       "       'disposition_official', 'officer_charged', 'news_urls',\n",
       "       'signs_of_mental_illness', 'allegedly_armed', 'wapo_armed',\n",
       "       'wapo_threat_level', 'wapo_flee', 'wapo_body_camera', 'wapo_id',\n",
       "       'off_duty_killing', 'geography', 'mpv_id', 'fe_id', 'encounter_type',\n",
       "       'initial_reason', 'officer_names', 'officer_races',\n",
       "       'officer_known_past_shootings', 'call_for_service', 'tract',\n",
       "       'urban_rural_uspsai', 'urban_rural_nchs',\n",
       "       'hhincome_median_census_tract', 'latitude', 'longitude',\n",
       "       'pop_total_census_tract', 'pop_white_census_tract',\n",
       "       'pop_black_census_tract', 'pop_native_american_census_tract',\n",
       "       'pop_asian_census_tract', 'pop_pacific_islander_census_tract',\n",
       "       'pop_other_multiple_census_tract', 'pop_hispanic_census_tract',\n",
       "       'congressional_district_113', 'congressperson_lastname',\n",
       "       'congressperson_firstname', 'congressperson_party', 'prosecutor_head',\n",
       "       'prosecutor_race', 'prosecutor_gender', 'prosecutor_party',\n",
       "       'prosecutor_term', 'prosecutor_in_court', 'prosecutor_special',\n",
       "       'independent_investigation', 'prosecutor_url'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpv_nyc_clean_from_R = pd.read_csv(\"/Users/taylor/Desktop/phd/coursework/fall2023/gis-spatial/hw6/mpv_nyc_clean.csv\")\n",
    "mpv_nyc_clean_from_R.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2a170da3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use longitude and latitude to create the 'geometry' variable\n",
    "geometry = [Point(xy) for xy in zip(mpv_nyc_clean['longitude'], mpv_nyc_clean['latitude'])]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3182ff73",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/pyproj/crs/crs.py:131: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>race</th>\n",
       "      <th>victim_image</th>\n",
       "      <th>date</th>\n",
       "      <th>street_address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zip</th>\n",
       "      <th>...</th>\n",
       "      <th>prosecutor_head</th>\n",
       "      <th>prosecutor_race</th>\n",
       "      <th>prosecutor_gender</th>\n",
       "      <th>prosecutor_party</th>\n",
       "      <th>prosecutor_term</th>\n",
       "      <th>prosecutor_in_court</th>\n",
       "      <th>prosecutor_special</th>\n",
       "      <th>independent_investigation</th>\n",
       "      <th>prosecutor_url</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Daniel Rivera</td>\n",
       "      <td>39.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/4/2022</td>\n",
       "      <td>161st Street and Summit Avenue</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>10451.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-73.928 40.829)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jermaine Hickson</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>https://www.google.com/url?sa=i&amp;url=https%3A%2...</td>\n",
       "      <td>11/10/2022</td>\n",
       "      <td>West 36th Street and Neptune Avenue</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>11224.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-74.002 40.577)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Joel Capellan</td>\n",
       "      <td>29.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>https://newyorkfolk.com/wp-content/uploads/202...</td>\n",
       "      <td>10/16/2022</td>\n",
       "      <td>Nagle Avenue and Dyckman Street</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>10040.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-73.925 40.862)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Raul Hardy</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>https://nypost.com/2022/07/10/nyc-man-shot-by-...</td>\n",
       "      <td>7/9/2022</td>\n",
       "      <td>205-17 116th Ave</td>\n",
       "      <td>Queens</td>\n",
       "      <td>NY</td>\n",
       "      <td>11412.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-73.748 40.697)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Malik Williams</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7/9/2022</td>\n",
       "      <td>Flatbush Avenue and Nevins Street</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>NY</td>\n",
       "      <td>11217.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-73.981 40.689)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Deion Fludd</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>http://i.dailymail.co.uk/i/pix/2014/04/11/arti...</td>\n",
       "      <td>5/5/2013</td>\n",
       "      <td>Rockaway Avenue and Fulton Street</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>11233.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-73.911 40.678)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Dylan Samuel-Peters</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4/15/2013</td>\n",
       "      <td>East 56th Street in the East Flatbush</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>NY</td>\n",
       "      <td>11203.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-73.924 40.650)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Dason Peters</td>\n",
       "      <td>33.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4/15/2013</td>\n",
       "      <td>East 56th Street in the East Flatbush</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>NY</td>\n",
       "      <td>11203.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-73.924 40.650)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Kimani Gray</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3/10/2013</td>\n",
       "      <td>East 52nd St and Snyder Avenue</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>11218.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-73.971 40.758)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Peter Jourdan</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>http://www.trbimg.com/img-50e7a90e/turbine/pet...</td>\n",
       "      <td>1/3/2013</td>\n",
       "      <td>Fort Hamilton Parkway &amp; 62nd Street</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>NY</td>\n",
       "      <td>11219.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-73.997 40.633)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   name   age gender      race  \\\n",
       "0         Daniel Rivera  39.0   Male  Hispanic   \n",
       "1      Jermaine Hickson  42.0   Male     Black   \n",
       "2        Joel Capellan   29.0   Male     Black   \n",
       "3            Raul Hardy  60.0   Male  Hispanic   \n",
       "4        Malik Williams  19.0   Male     Black   \n",
       "..                  ...   ...    ...       ...   \n",
       "92          Deion Fludd  17.0   Male     Black   \n",
       "93  Dylan Samuel-Peters   1.0   Male     Black   \n",
       "94         Dason Peters  33.0   Male     Black   \n",
       "95          Kimani Gray  16.0   Male     Black   \n",
       "96        Peter Jourdan  37.0   Male     Black   \n",
       "\n",
       "                                         victim_image        date  \\\n",
       "0                                                 NaN   12/4/2022   \n",
       "1   https://www.google.com/url?sa=i&url=https%3A%2...  11/10/2022   \n",
       "2   https://newyorkfolk.com/wp-content/uploads/202...  10/16/2022   \n",
       "3   https://nypost.com/2022/07/10/nyc-man-shot-by-...    7/9/2022   \n",
       "4                                                 NaN    7/9/2022   \n",
       "..                                                ...         ...   \n",
       "92  http://i.dailymail.co.uk/i/pix/2014/04/11/arti...    5/5/2013   \n",
       "93                                                NaN   4/15/2013   \n",
       "94                                                NaN   4/15/2013   \n",
       "95                                                NaN   3/10/2013   \n",
       "96  http://www.trbimg.com/img-50e7a90e/turbine/pet...    1/3/2013   \n",
       "\n",
       "                           street_address      city state      zip  ...  \\\n",
       "0          161st Street and Summit Avenue  New York    NY  10451.0  ...   \n",
       "1     West 36th Street and Neptune Avenue  New York    NY  11224.0  ...   \n",
       "2        Nagle Avenue and Dyckman Street   New York    NY  10040.0  ...   \n",
       "3                        205-17 116th Ave    Queens    NY  11412.0  ...   \n",
       "4       Flatbush Avenue and Nevins Street  Brooklyn    NY  11217.0  ...   \n",
       "..                                    ...       ...   ...      ...  ...   \n",
       "92      Rockaway Avenue and Fulton Street  New York    NY  11233.0  ...   \n",
       "93  East 56th Street in the East Flatbush  Brooklyn    NY  11203.0  ...   \n",
       "94  East 56th Street in the East Flatbush  Brooklyn    NY  11203.0  ...   \n",
       "95         East 52nd St and Snyder Avenue  New York    NY  11218.0  ...   \n",
       "96    Fort Hamilton Parkway & 62nd Street  Brooklyn    NY  11219.0  ...   \n",
       "\n",
       "   prosecutor_head prosecutor_race prosecutor_gender prosecutor_party  \\\n",
       "0              NaN             NaN               NaN              NaN   \n",
       "1              NaN             NaN               NaN              NaN   \n",
       "2              NaN             NaN               NaN              NaN   \n",
       "3              NaN             NaN               NaN              NaN   \n",
       "4              NaN             NaN               NaN              NaN   \n",
       "..             ...             ...               ...              ...   \n",
       "92             NaN             NaN               NaN              NaN   \n",
       "93             NaN             NaN               NaN              NaN   \n",
       "94             NaN             NaN               NaN              NaN   \n",
       "95             NaN             NaN               NaN              NaN   \n",
       "96             NaN             NaN               NaN              NaN   \n",
       "\n",
       "   prosecutor_term prosecutor_in_court prosecutor_special  \\\n",
       "0              NaN                 NaN                NaN   \n",
       "1              NaN                 NaN                NaN   \n",
       "2              NaN                 NaN                NaN   \n",
       "3              NaN                 NaN                NaN   \n",
       "4              NaN                 NaN                NaN   \n",
       "..             ...                 ...                ...   \n",
       "92             NaN                 NaN                NaN   \n",
       "93             NaN                 NaN                NaN   \n",
       "94             NaN                 NaN                NaN   \n",
       "95             NaN                 NaN                NaN   \n",
       "96             NaN                 NaN                NaN   \n",
       "\n",
       "   independent_investigation prosecutor_url                geometry  \n",
       "0                        NaN            NaN  POINT (-73.928 40.829)  \n",
       "1                        NaN            NaN  POINT (-74.002 40.577)  \n",
       "2                        NaN            NaN  POINT (-73.925 40.862)  \n",
       "3                        NaN            NaN  POINT (-73.748 40.697)  \n",
       "4                        NaN            NaN  POINT (-73.981 40.689)  \n",
       "..                       ...            ...                     ...  \n",
       "92                       NaN            NaN  POINT (-73.911 40.678)  \n",
       "93                       NaN            NaN  POINT (-73.924 40.650)  \n",
       "94                       NaN            NaN  POINT (-73.924 40.650)  \n",
       "95                       NaN            NaN  POINT (-73.971 40.758)  \n",
       "96                       NaN            NaN  POINT (-73.997 40.633)  \n",
       "\n",
       "[97 rows x 63 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use longitude and latitude to create the 'geometry' variable\n",
    "geometry_R_dataset = [Point(xy) for xy in zip(mpv_nyc_clean_from_R['longitude'], mpv_nyc_clean_from_R['latitude'])]\n",
    "\n",
    "# converting the pandas dataframe to a geodataframe, which uses longitude and latitude to create 'geometry variable'\n",
    "mpv_nyc_clean_from_R_geo_df = gpd.GeoDataFrame(mpv_nyc_clean_from_R, #specify  data\n",
    "                          crs=crs, #specify our coordinate reference system\n",
    "                          geometry=geometry_R_dataset) #specify the geometry list we created\n",
    "\n",
    "# take a look at the gdf with 'geometry' variable and all the data from the R dataset\n",
    "mpv_nyc_clean_from_R_geo_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "520e41fe",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/pyproj/crs/crs.py:131: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>race</th>\n",
       "      <th>date_new</th>\n",
       "      <th>county</th>\n",
       "      <th>tract</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>cause_of_death</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Quayshawn Samuel</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-12-29</td>\n",
       "      <td>Queens</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>40.594928</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>POINT (-73.786 40.595)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>Daniel Rivera</td>\n",
       "      <td>39.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2022-12-04</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>6302.0</td>\n",
       "      <td>-73.927844</td>\n",
       "      <td>40.829298</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>POINT (-73.928 40.829)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>Name Withheld</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-11-18</td>\n",
       "      <td>New York</td>\n",
       "      <td>29100.0</td>\n",
       "      <td>-73.923477</td>\n",
       "      <td>40.865709</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>POINT (-73.923 40.866)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>Jermaine Hickson</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>2022-11-10</td>\n",
       "      <td>Kings</td>\n",
       "      <td>33000.0</td>\n",
       "      <td>-74.002157</td>\n",
       "      <td>40.577008</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>POINT (-74.002 40.577)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>Kyle Styles Lovera Lockett</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-11-03</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>43102.0</td>\n",
       "      <td>-73.873480</td>\n",
       "      <td>40.878858</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>POINT (-73.873 40.879)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10971</th>\n",
       "      <td>Deion Fludd</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>2013-05-05</td>\n",
       "      <td>Kings</td>\n",
       "      <td>37100.0</td>\n",
       "      <td>-73.910799</td>\n",
       "      <td>40.678278</td>\n",
       "      <td>Beaten</td>\n",
       "      <td>POINT (-73.911 40.678)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11046</th>\n",
       "      <td>Dylan Samuel-Peters</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>2013-04-15</td>\n",
       "      <td>Kings</td>\n",
       "      <td>93800.0</td>\n",
       "      <td>-73.924197</td>\n",
       "      <td>40.649604</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>POINT (-73.924 40.650)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11047</th>\n",
       "      <td>Dason Peters</td>\n",
       "      <td>33.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>2013-04-15</td>\n",
       "      <td>Kings</td>\n",
       "      <td>93800.0</td>\n",
       "      <td>-73.924197</td>\n",
       "      <td>40.649604</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>POINT (-73.924 40.650)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11142</th>\n",
       "      <td>Kimani Gray</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>2013-03-10</td>\n",
       "      <td>Kings</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>-73.970771</td>\n",
       "      <td>40.757544</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>POINT (-73.971 40.758)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11326</th>\n",
       "      <td>Peter Jourdan</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>2013-01-03</td>\n",
       "      <td>Kings</td>\n",
       "      <td>21800.0</td>\n",
       "      <td>-73.996953</td>\n",
       "      <td>40.633157</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>POINT (-73.997 40.633)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             name   age gender      race   date_new    county  \\\n",
       "154              Quayshawn Samuel  31.0   Male       NaN 2022-12-29    Queens   \n",
       "237                 Daniel Rivera  39.0   Male  Hispanic 2022-12-04     Bronx   \n",
       "293                 Name Withheld   NaN   Male       NaN 2022-11-18  New York   \n",
       "323              Jermaine Hickson  42.0   Male     Black 2022-11-10     Kings   \n",
       "345    Kyle Styles Lovera Lockett  21.0   Male       NaN 2022-11-03     Bronx   \n",
       "...                           ...   ...    ...       ...        ...       ...   \n",
       "10971                 Deion Fludd  17.0   Male     Black 2013-05-05     Kings   \n",
       "11046         Dylan Samuel-Peters   1.0   Male     Black 2013-04-15     Kings   \n",
       "11047                Dason Peters  33.0   Male     Black 2013-04-15     Kings   \n",
       "11142                 Kimani Gray  16.0   Male     Black 2013-03-10     Kings   \n",
       "11326               Peter Jourdan  37.0   Male     Black 2013-01-03     Kings   \n",
       "\n",
       "         tract  longitude   latitude cause_of_death                geometry  \n",
       "154        NaN -73.786248  40.594928        Gunshot  POINT (-73.786 40.595)  \n",
       "237     6302.0 -73.927844  40.829298        Gunshot  POINT (-73.928 40.829)  \n",
       "293    29100.0 -73.923477  40.865709        Gunshot  POINT (-73.923 40.866)  \n",
       "323    33000.0 -74.002157  40.577008        Gunshot  POINT (-74.002 40.577)  \n",
       "345    43102.0 -73.873480  40.878858        Gunshot  POINT (-73.873 40.879)  \n",
       "...        ...        ...        ...            ...                     ...  \n",
       "10971  37100.0 -73.910799  40.678278         Beaten  POINT (-73.911 40.678)  \n",
       "11046  93800.0 -73.924197  40.649604        Gunshot  POINT (-73.924 40.650)  \n",
       "11047  93800.0 -73.924197  40.649604        Gunshot  POINT (-73.924 40.650)  \n",
       "11142  10000.0 -73.970771  40.757544        Gunshot  POINT (-73.971 40.758)  \n",
       "11326  21800.0 -73.996953  40.633157        Gunshot  POINT (-73.997 40.633)  \n",
       "\n",
       "[100 rows x 11 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# converting the pandas dataframe to a geodataframe, which uses longitude and latitude to create 'geometry variable'\n",
    "mpv_nyc_geo_df = gpd.GeoDataFrame(mpv_nyc_clean, #specify  data\n",
    "                          crs=crs, #specify our coordinate reference system\n",
    "                          geometry=geometry) #specify the geometry list we created\n",
    "\n",
    "# take a look at the df with 'geometry' variable\n",
    "mpv_nyc_geo_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4fed705f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/geopandas/plotting.py:51: ShapelyDeprecationWarning: The 'type' attribute is deprecated, and will be removed in the future. You can use the 'geom_type' attribute instead.\n",
      "  if geom is not None and geom.type.startswith(prefix) and not geom.is_empty:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Police killings in NYC since 2013')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# add these points to the map from above\n",
    "fig, ax1 = plt.subplots(figsize=(15,15))\n",
    "nyc_map.plot(ax=ax1, alpha=0.3, color='black')\n",
    "mpv_nyc_geo_df.plot(ax=ax1, \n",
    "            markersize=20, \n",
    "            color='red', \n",
    "            marker='o')\n",
    "\n",
    "# label the points with the date that they occurred\n",
    "#for x, y, label in zip(mpv_nyc_geo_df.geometry.x, mpv_nyc_geo_df.geometry.y):\n",
    " #   ax1.annotate(label, xy=(x, y), xytext=(2, 2), textcoords=\"offset points\")\n",
    "\n",
    "#plt.legend(prop={'size':15})\n",
    "\n",
    "# set title\n",
    "ax1.set_title('Police killings in NYC since 2013', fontsize=15)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7cd305a1",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'GeoDataFrame' object has no attribute 'dtype'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-52-8521eb090bd4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmpv_nyc_clean_from_R_geo_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5485\u001b[0m         ):\n\u001b[1;32m   5486\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5487\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5488\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5489\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'GeoDataFrame' object has no attribute 'dtype'"
     ]
    }
   ],
   "source": [
    "mpv_nyc_clean_from_R_geo_df.dtype # so it's a geodataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4e9d905d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'to_file'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-57-515b5d601ddb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfiona\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mmpv_nyc_clean_from_R_geo_df_shp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{filepath}/mpv_nyc_clean_from_R_geo_df_shp.shp.shp\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdriver\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ESRI Shapefile'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5485\u001b[0m         ):\n\u001b[1;32m   5486\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5487\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5488\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5489\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'to_file'"
     ]
    }
   ],
   "source": [
    "# export geodataframe to shapefile\n",
    "mpv_nyc_clean_from_R_geo_df_shp = mpv_nyc_clean_from_R_geo_df[['age', 'race', 'gender', 'encounter_type', 'county', 'allegedly_armed']]\n",
    "\n",
    "filepath = \"/Users/taylor/Desktop/phd/coursework/fall2023/gis-spatial/hw6\"\n",
    "\n",
    "import fiona\n",
    "mpv_nyc_clean_from_R_geo_df_shp.to_file(f\"{filepath}/mpv_nyc_clean_from_R_geo_df_shp.shp.shp\", driver='ESRI Shapefile')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1dd53610",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/geopandas/plotting.py:51: ShapelyDeprecationWarning: The 'type' attribute is deprecated, and will be removed in the future. You can use the 'geom_type' attribute instead.\n",
      "  if geom is not None and geom.type.startswith(prefix) and not geom.is_empty:\n",
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:29: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# add these points to the map from above\n",
    "fig, ax2 = plt.subplots(figsize=(15,15))\n",
    "nyc_map.plot(ax=ax2, alpha=0.3, color='black')\n",
    "mpv_nyc_geo_df.plot(ax=ax2, \n",
    "            markersize=20, \n",
    "            color='red', \n",
    "            marker='o')\n",
    "\n",
    "# label the points with the date that they occurred\n",
    "#for x, y, label in zip(mpv_nyc_geo_df.geometry.x, mpv_nyc_geo_df.geometry.y):\n",
    " #   ax1.annotate(label, xy=(x, y), xytext=(2, 2), textcoords=\"offset points\")\n",
    "\n",
    "#plt.legend(prop={'size':15})\n",
    "\n",
    "# set title\n",
    "ax2.set_title('Police killings in NYC since 2013', fontsize=15)\n",
    "\n",
    "\n",
    "# Generate and add KDE with a shading of 50 gradients\n",
    "# coloured contours, 75% of transparency,\n",
    "# and the reverse viridis colormap\n",
    "sns.kdeplot(\n",
    "    x=\"longitude\",\n",
    "    y=\"latitude\",\n",
    "    data=mpv_nyc_clean,\n",
    "    n_levels=50,\n",
    "    shade=True,\n",
    "    alpha=0.55,\n",
    "    cmap=\"viridis_r\",\n",
    ")\n",
    "\n",
    "# Remove axes\n",
    "ax.set_axis_off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d2bfbe9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#The rows of our dataset are borough,\n",
    "#so we iterate through each borough\n",
    "\n",
    "\n",
    "#Add counts of stations per neighborhood\n",
    "for county in nyc_map.index:\n",
    "    mask = mpv_nyc_geo_df.within(nyc_map.loc[county, \"geometry\"])\n",
    "    killings_count = mask.sum()\n",
    "    nyc_map.loc[county, \"killings_count\"] = killings_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1a864141",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>boro_code</th>\n",
       "      <th>shape_area</th>\n",
       "      <th>shape_leng</th>\n",
       "      <th>killings_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.684576e+09</td>\n",
       "      <td>552611.743115</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.581139</td>\n",
       "      <td>9.017828e+08</td>\n",
       "      <td>245398.447747</td>\n",
       "      <td>12.980755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.365208e+08</td>\n",
       "      <td>325917.353702</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.187175e+09</td>\n",
       "      <td>357564.316391</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.623621e+09</td>\n",
       "      <td>463179.772813</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.934143e+09</td>\n",
       "      <td>728197.541089</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.041419e+09</td>\n",
       "      <td>888199.731579</td>\n",
       "      <td>38.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       boro_code    shape_area     shape_leng  killings_count\n",
       "count   5.000000  5.000000e+00       5.000000        5.000000\n",
       "mean    3.000000  1.684576e+09  552611.743115       20.000000\n",
       "std     1.581139  9.017828e+08  245398.447747       12.980755\n",
       "min     1.000000  6.365208e+08  325917.353702        4.000000\n",
       "25%     2.000000  1.187175e+09  357564.316391       14.000000\n",
       "50%     3.000000  1.623621e+09  463179.772813       17.000000\n",
       "75%     4.000000  1.934143e+09  728197.541089       27.000000\n",
       "max     5.000000  3.041419e+09  888199.731579       38.000000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking killings count per county/borough\n",
    "nyc_map.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca1b983a",
   "metadata": {},
   "source": [
    "# Centrographic Stats on just MPV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a49d0210",
   "metadata": {},
   "outputs": [],
   "source": [
    "# centrographic statistics\n",
    "mean_center = centrography.mean_center(mpv_nyc_geo_df[[\"longitude\", \"latitude\"]])\n",
    "med_center = centrography.euclidean_median(mpv_nyc_geo_df[[\"longitude\", \"latitude\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ca3e4d52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1176027051661323"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centrography.std_distance(mpv_nyc_clean[[\"longitude\", \"latitude\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6e2cbf37",
   "metadata": {},
   "outputs": [],
   "source": [
    "major, minor, rotation = centrography.ellipse(mpv_nyc_clean[[\"longitude\", \"latitude\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7efc882a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/geopandas/plotting.py:51: ShapelyDeprecationWarning: The 'type' attribute is deprecated, and will be removed in the future. You can use the 'geom_type' attribute instead.\n",
      "  if geom is not None and geom.type.startswith(prefix) and not geom.is_empty:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# add these points to the map from above\n",
    "fig, ax3 = plt.subplots(figsize=(15,15))\n",
    "nyc_map.plot(ax=ax3, alpha=0.3, color='black')\n",
    "mpv_nyc_geo_df.plot(ax=ax3, \n",
    "            markersize=20, \n",
    "            color='red', \n",
    "            marker='o')\n",
    "\n",
    "# set title\n",
    "ax3.set_title('Police killings in NYC since 2013', fontsize=15)\n",
    "\n",
    "# plot mean center\n",
    "ax3.scatter(*mean_center, color=\"limegreen\", marker=\"x\", label=\"Mean Center\")\n",
    "\n",
    "\n",
    "# Construct the standard ellipse using matplotlib\n",
    "ellipse = Ellipse(\n",
    "    xy=mean_center,  # center the ellipse on our mean center\n",
    "    width=major * 2,  # centrography.ellipse only gives half the axis\n",
    "    height=minor * 2,\n",
    "    angle=np.rad2deg(\n",
    "        rotation\n",
    "    ),  # Angles for this are in degrees, not radians\n",
    "    facecolor=\"none\",\n",
    "    edgecolor=\"blue\",\n",
    "    linestyle=\"--\",\n",
    "    label=\"Std. Ellipse\",\n",
    ")\n",
    "ax3.add_patch(ellipse)\n",
    "\n",
    "ax3.legend()\n",
    "# Display\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6d23f7f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-73.90944376,  40.73256801])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_center"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0df432d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>tract_id</th>\n",
       "      <th>state_abr</th>\n",
       "      <th>crime_type</th>\n",
       "      <th>crimeCount</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>36005001600</td>\n",
       "      <td>NY</td>\n",
       "      <td>400</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>2022</td>\n",
       "      <td>6144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-04-01</td>\n",
       "      <td>36005001600</td>\n",
       "      <td>NY</td>\n",
       "      <td>400</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>6056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>36005001902</td>\n",
       "      <td>NY</td>\n",
       "      <td>400</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>1464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-02-01</td>\n",
       "      <td>36005001902</td>\n",
       "      <td>NY</td>\n",
       "      <td>400</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>1334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>36005002001</td>\n",
       "      <td>NY</td>\n",
       "      <td>400</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>2019</td>\n",
       "      <td>4837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1738</th>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>36085031902</td>\n",
       "      <td>NY</td>\n",
       "      <td>400</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2020</td>\n",
       "      <td>4953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1739</th>\n",
       "      <td>2019-10-01</td>\n",
       "      <td>36085031902</td>\n",
       "      <td>NY</td>\n",
       "      <td>400</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2019</td>\n",
       "      <td>5343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1740</th>\n",
       "      <td>2014-06-01</td>\n",
       "      <td>36085031902</td>\n",
       "      <td>NY</td>\n",
       "      <td>400</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2014</td>\n",
       "      <td>4535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1741</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>36085032300</td>\n",
       "      <td>NY</td>\n",
       "      <td>400</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "      <td>1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1742</th>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>36085032300</td>\n",
       "      <td>NY</td>\n",
       "      <td>400</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>2017</td>\n",
       "      <td>1136</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1743 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date     tract_id state_abr  crime_type  crimeCount  month  year  \\\n",
       "0     2022-12-01  36005001600        NY         400           1     12  2022   \n",
       "1     2016-04-01  36005001600        NY         400           1      4  2016   \n",
       "2     2022-01-01  36005001902        NY         400           1      1  2022   \n",
       "3     2017-02-01  36005001902        NY         400           1      2  2017   \n",
       "4     2019-11-01  36005002001        NY         400           1     11  2019   \n",
       "...          ...          ...       ...         ...         ...    ...   ...   \n",
       "1738  2020-09-01  36085031902        NY         400           2      9  2020   \n",
       "1739  2019-10-01  36085031902        NY         400           1     10  2019   \n",
       "1740  2014-06-01  36085031902        NY         400           1      6  2014   \n",
       "1741  2020-07-01  36085032300        NY         400           1      7  2020   \n",
       "1742  2017-12-01  36085032300        NY         400           1     12  2017   \n",
       "\n",
       "      population  \n",
       "0           6144  \n",
       "1           6056  \n",
       "2           1464  \n",
       "3           1334  \n",
       "4           4837  \n",
       "...          ...  \n",
       "1738        4953  \n",
       "1739        5343  \n",
       "1740        4535  \n",
       "1741        1200  \n",
       "1742        1136  \n",
       "\n",
       "[1743 rows x 8 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_fatal_shootings_raw = pd.read_csv('/Users/taylor/Desktop/phd/research-local/data/crime-data/low-nyc/AV.org - Fatal Shootings NYC for 2010 − 2022.csv.csv')\n",
    "nyc_fatal_shootings_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "67620f4f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/geopandas/plotting.py:51: ShapelyDeprecationWarning: The 'type' attribute is deprecated, and will be removed in the future. You can use the 'geom_type' attribute instead.\n",
      "  if geom is not None and geom.type.startswith(prefix) and not geom.is_empty:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'NYC public housing')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's look at a map of NYC housing projects\n",
    "# read in the shapefile for NYC projects\n",
    "nyc_public_housing = gpd.read_file('/Users/taylor/Desktop/phd/research-local/data/shapefiles/nyc/Map of NYCHA Developments/geo_export_a8617639-f8c6-4ee8-8710-a9b8391d9ba2.shp')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15,15))\n",
    "nyc_public_housing.plot(ax=ax)\n",
    "\n",
    "ax.set_title('NYC public housing', fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "572e63c1",
   "metadata": {},
   "outputs": [
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       "          borough              developmen tds_num  \\\n",
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       "top     MANHATTAN  1010 EAST 178TH STREET     180   \n",
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       "\n",
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  {
   "cell_type": "code",
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   "id": "d37e92c2",
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   "outputs": [
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       "       borough                          developmen tds_num  \\\n",
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       "..         ...                                 ...     ...   \n",
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       "276   BROOKLYN                     WYCKOFF GARDENS     163   \n",
       "\n",
       "                                              geometry  \n",
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       "1    POLYGON ((-73.94445 40.78071, -73.94456 40.780...  \n",
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       "4    POLYGON ((-73.95286 40.80376, -73.95317 40.803...  \n",
       "..                                                 ...  \n",
       "272  MULTIPOLYGON (((-73.96927 40.78820, -73.96945 ...  \n",
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       "275  POLYGON ((-73.97318 40.78935, -73.97336 40.789...  \n",
       "276  POLYGON ((-73.98258 40.68276, -73.98332 40.681...  \n",
       "\n",
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       "       borough                          developmen tds_num  \\\n",
       "0        BRONX              1010 EAST 178TH STREET     180   \n",
       "1    MANHATTAN                       HOLMES TOWERS     159   \n",
       "2     BROOKLYN              104-14 TAPSCOTT STREET     242   \n",
       "3        BRONX         1162-1176 WASHINGTON AVENUE     233   \n",
       "4    MANHATTAN           131 SAINT NICHOLAS AVENUE     154   \n",
       "..         ...                                 ...     ...   \n",
       "272  MANHATTAN                  WSUR (BROWNSTONES)     178   \n",
       "273  MANHATTAN  WSUR (SITE A) 120 WEST 94TH STREET     151   \n",
       "274  MANHATTAN   WSUR (SITE B) 74 WEST 92ND STREET     173   \n",
       "275  MANHATTAN  WSUR (SITE C) 589 AMSTERDAM AVENUE     174   \n",
       "276   BROOKLYN                     WYCKOFF GARDENS     163   \n",
       "\n",
       "                                              geometry          long  \\\n",
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       "2    POLYGON ((1006336.700 181555.555, 1006237.842 ...  1.006278e+06   \n",
       "3    POLYGON ((1010003.163 241714.476, 1009973.882 ...  1.009915e+06   \n",
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       "..                                                 ...           ...   \n",
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       "273  POLYGON ((992722.671 227774.167, 992668.149 22...  9.926050e+05   \n",
       "274  POLYGON ((992864.899 227095.408, 992767.142 22...  9.927606e+05   \n",
       "275  POLYGON ((991675.582 226859.333, 991625.873 22...  9.915910e+05   \n",
       "276  POLYGON ((989081.312 188023.288, 988875.469 18...  9.887377e+05   \n",
       "\n",
       "               lat  \n",
       "0    245624.691893  \n",
       "1    223715.907493  \n",
       "2    181595.462956  \n",
       "3    241671.034570  \n",
       "4    232240.601749  \n",
       "..             ...  \n",
       "272  226891.834804  \n",
       "273  227777.605294  \n",
       "274  227036.902850  \n",
       "275  226777.282192  \n",
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       "\n",
       "[277 rows x 6 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_public_housing = nyc_public_housing.to_crs(2263)\n",
    "nyc_public_housing['long'] = nyc_public_housing.centroid.x\n",
    "nyc_public_housing['lat'] = nyc_public_housing.centroid.y\n",
    "nyc_public_housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e18a5061",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/geopandas/plotting.py:51: ShapelyDeprecationWarning: The 'type' attribute is deprecated, and will be removed in the future. You can use the 'geom_type' attribute instead.\n",
      "  if geom is not None and geom.type.startswith(prefix) and not geom.is_empty:\n",
      "/opt/anaconda3/lib/python3.7/site-packages/geopandas/plotting.py:51: ShapelyDeprecationWarning: The 'type' attribute is deprecated, and will be removed in the future. You can use the 'geom_type' attribute instead.\n",
      "  if geom is not None and geom.type.startswith(prefix) and not geom.is_empty:\n",
      "/opt/anaconda3/lib/python3.7/site-packages/geopandas/plotting.py:51: ShapelyDeprecationWarning: The 'type' attribute is deprecated, and will be removed in the future. You can use the 'geom_type' attribute instead.\n",
      "  if geom is not None and geom.type.startswith(prefix) and not geom.is_empty:\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# add nyc public housing points to the map above\n",
    "\n",
    "# centrographic statistics\n",
    "nyhousing_mean_center = centrography.mean_center(nyc_public_housing[[\"long\", \"lat\"]])\n",
    "centrography.std_distance(nyc_public_housing[[\"long\", \"lat\"]])\n",
    "major1, minor1, rotation1 = centrography.ellipse(nyc_public_housing[[\"long\", \"lat\"]])\n",
    "\n",
    "# add these points to the map from above\n",
    "fig, ax7 = plt.subplots(figsize=(15,15))\n",
    "nyc_map.plot(ax=ax7, alpha=0.3, color='black')\n",
    "nyc_public_housing.plot(ax=ax7, alpha=0.3, color='black')\n",
    "nyc_public_housing.plot(ax=ax7, \n",
    "            markersize=20, \n",
    "            color='red', \n",
    "            marker='o')\n",
    "\n",
    "# set title\n",
    "ax7.set_title('NYC public housing units', fontsize=15)\n",
    "\n",
    "# plot mean center\n",
    "ax7.scatter(*nyhousing_mean_center, color=\"limegreen\", marker=\"x\", label=\"Mean Center\")\n",
    "\n",
    "\n",
    "# Construct the standard ellipse using matplotlib\n",
    "ellipse = Ellipse(\n",
    "    xy=nyhousing_mean_center,  # center the ellipse on our mean center\n",
    "    width=major1 * 2,  # centrography.ellipse only gives half the axis\n",
    "    height=minor1 * 2,\n",
    "    angle=np.rad2deg(\n",
    "        rotation1\n",
    "    ),  # Angles for this are in degrees, not radians\n",
    "    facecolor=\"none\",\n",
    "    edgecolor=\"blue\",\n",
    "    linestyle=\"--\",\n",
    "    label=\"Std. Ellipse\",\n",
    ")\n",
    "ax7.add_patch(ellipse)\n",
    "\n",
    "ax7.legend()\n",
    "# Display\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ab5d628f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1004924.19642296,  210313.78373731])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyhousing_mean_center"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f3b5d66c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wFZSZmRXJCcrMzIrkBGVmZkVygjIzsyI5QZmZWZGcoMzMrEhOUGZmViQnKDMzK5ITlJmZFckJyszMiuQEZWZmRXKCMjOzIjlBmZlZkZygzMysSLUkKEkbSppX+XtA0sGSJkiaJak//1+njnjMzKx8tSSoiLgpIqZGxFTgVcDDwHnA4cDsiOgDZuduMzOznlTxTQNuiYi/AtOBmbn/TGC3HsRjZmYF6kWC2gc4I39eLyIWAuT/E3sQj5mZFajWV75LWgXYFfhEp9P29/d3P6AxaCTlMKmLcYwFIymrWg+MAvj4srr19fUNOU7dx+FOwFURsSh3L5I0OSIWSpoM3NVqwpYrM2tu14MsWTsbtZUlXYxjLBhJWc0fZ+frkZSV2Wipu4pvX56p3gO4EJiRP88ALqg5HjMzK1RtCUrSasB2wLmV3kcD20nqz8OOriseMzMrW21VfBHxMPDchn73kFr1mZmZLcVPkjAzsyI5QZmZWZGcoMzMrEhOUGZmViQnKDMzK5ITlJmZFckJyszMiuQEZWZmRXKCMjOzIjlBmZlZkZygzMysSE5QZmZWJCcoMzMrkhOUmZkVyQnKzMyK5ARlZmZFcoIyM7MiOUGZmVmRnKDMzKxITlBmZlYkJygzMyuSE5SZmRXJCcrMzIrkBGVmZkVygjIzsyI5QZmZWZGcoMzMrEhOUGZmViQnKDMzK1JtCUrS2pLOkXSjpD9L2krSBEmzJPXn/+vUFY+ZmZWtziuorwMXR8TLgE2APwOHA7Mjog+YnbvNzMzqSVCS1gReD5wMEBGPR8R9wHRgZh5tJrBbHfGYmVn56rqCegmwGPiepKslfVfSc4D1ImIhQP4/saZ4zMyscCvVuJzNgIMi4gpJX6fD6rz+/v5RCWysGUk5TOpiHGPBSMqqrgOjFD6+rG59fX1DjlPXcXg7cHtEXJG7zyElqEWSJkfEQkmTgbtazaDlysya2+VQy9bORm1lSRfjGAtGUlbzx9n5eiRlZTZaaqnii4g7gb9J2jD3mgbcAFwIzMj9ZgAX1BGPmZmVr86ajIOAH0paBbgVeDcpQZ4l6QBgAbBXjfGYmVnBaktQETEP2LzJoGl1xWBmZmOHnyRhZmZFcoIyM7MiOUGZmVmRnKDMzKxITlBmZlYkJygzMyuSE5SZmRXJCcrMzIrkBGVmZkVygjIzsyI5QZmZWZGcoMzMrEhOUGZmViQnKDMzK5ITlJmZFckJyszMiuQEZWZmRXKCMjOzIjlBmZlZkZygzMysSE5QZmZWJCcoMzMrkhOUmZkVyQnKzMyK5ARlZmZFcoIyM7MiOUGZmVmRnKDMzKxITlBmZlaklepakKTbgCXAk8ATEbG5pAnAmcAU4DbgbRFxb10xmZlZueq+gnpjREyNiM1z9+HA7IjoA2bnbjMzs55X8U0HZubPM4HdeheKmZmVpM4EFcClkq6U9L7cb72IWAiQ/0+sMR4zMytYbfeggK0j4g5JE4FZkm7sZOL+/v5RCmtsGUk5TOpiHGPBSMqqzgOjBD6+rG59fX1DjlPbcRgRd+T/d0k6D9gCWCRpckQslDQZuKvV9C1XZtbcUYi2XO1s1FaWdDGOsWAkZTV/nJ2vR1JWZqOllio+Sc+RtMbAZ2B74DrgQmBGHm0GcEEd8ZiZWfnquoJaDzhP0sAyT4+IiyX9AThL0gHAAmCvmuIxM7PC1ZKgIuJWYJMm/e8BptURg5mZjS29bmZuZmbWlBOUmZkVyQnKzMyK5ARlZmZFcoIyM7MitZ2gJDVtAi5pz+6FY2ZmlnRyBXVyi/7f6UYgZmZmVUP+DkrSS/LHFSRtAKgy+CXAo6MRmJmZjW/t/FD3ZtKTyAXc0jDsTuDILsdkZmY2dIKKiBUAJP0yIt4w+iGZmZl1cA/KycnMzOrU9rP48v2no4CpwOrVYRHxou6GZWZm410nD4s9nXQP6mPAw6MTjpmZWdJJgtqI9Fbcp0YrGDMzswGd/A7qV8CmoxWImZlZVSdXULcBl0g6l9S8/GkR8dluBmVmZtZJgnoO8BNgZWD90QnHzMwsaTtBRcS7RzMQMzOzqk6amb+k1bD8SnczM7Ou6aSKr/rIowGR/6/YtYjMzMzorIpvqRZ/kiYBRwC/7nZQZmZmw35hYUTcCRwMfKlr0ZiZmWUjfaPuhsBq3QjEzMysqpNGEr/mmXtOkBLTRsDnux2UmZlZJ40kvtvQ/RBwTUT0dzEeMzMzoLNGEjNHMxAzM7Oqtu9BSVpZ0uck3Srp0fz/c5JWGc0AzcxsfOqkiu8rwBbAB4C/Ai8GPgOsCRzS/dDMzGw86yRB7QVsEhH35O6bJF0FXIMTlJmZdVknzczVYf9lR5RWlHS1pIty9wRJsyT15//rdBCPmZktxzpJUGcDP5G0g6SXS9oROD/3b9dHgD9Xug8HZkdEHzA7d5uZmXWUoA4Dfg4cD1wJfBO4DDi0nYklvRDYmaWbq08HBloHzgR26yAeMzNbjg2ZoCRtLenLEfF4RHw2Il4aEavlq55nAZu1uaz/ISW56ivj14uIhQD5/8TOwjczs+VVO40kPgmc0GLYL4BPAf8+2Awk7QLcFRFXStq2kwAH9Pf798AwsnKY1MU4xoKRlFUnrYeWBz6+rG59fX1DjtPOcTgVuLjFsJ8Dp7Qxj62BXSW9BXg2sKakHwCLJE2OiIWSJgN3tZpBy5WZNbeNxS8/2tmorSzpYhxjwUjKav44O1+PpKzMRks796DWBFr9GHdlYI2hZhARn4iIF0bEFGAf4LKIeAdwITAjjzYDuKCNeMzMbBxoJ0HdCGzfYtj2efhwHQ1sJ6kf2C53m5mZtVXFdxxwoqQVgfMj4ilJK5Ba3B0PfLSTBUbEHGBO/nwPMK2T6c3MbHwYMkFFxOn57bkzgWdJuht4HvAocEREnDHKMZqZ2TjUVmOliPiapO8CWwHPBe4BLo+IB0YzODMzG786ed3GA8AloxiLmZnZ00b6ynczM7NR4QRlZmZFcoIyM7MiOUGZmVmRnKDMzKxITlBmZlYkJygzMyuSE5SZmRXJCcrMzIrkBGVmZkVygjIzsyI5QZmZWZGcoMzMrEhOUGZmViQnKDMzK5ITlJmZFckJyszMiuQEZWZmRXKCMjOzIjlBmZlZkZygzMysSE5QZmZWJCcoMzMrkhOUmZkVyQnKzMyK5ARlZmZFqiVBSXq2pLmSrpF0vaTP5f4TJM2S1J//r1NHPGZmVr66rqAeA94UEZsAU4EdJb0GOByYHRF9wOzcbWZmVk+CiuTB3Lly/gtgOjAz958J7FZHPGZmVr7a7kFJWlHSPOAuYFZEXAGsFxELAfL/iXXFY2ZmZVuprgVFxJPAVElrA+dJ2riT6fv7+0clrrFmJOUwqYtxjAUjKavaDoxC+PiyuvX19Q05Tu3HYUTcJ2kOsCOwSNLkiFgoaTLp6qqplisza+5ohFmsdjZqK0u6GMdYMJKymj/OztcjKSuz0VJXK75185UTklYF3gzcCFwIzMijzQAuqCMeMzMrX11XUJOBmZJWJCXFsyLiIkmXA2dJOgBYAOxVUzxmZla4WhJURFwLbNqk/z3AtDpiMDOzscVPkjAzsyI5QZmZWZGcoMzMrEhOUGZmViQnKDMzK5ITlJmZFckJyszMiuQEZWZmRXKCMjOzIjlBmZlZkZygzMysSE5QZmZWJCcoMzMrkhOUmZkVyQnKzMyK5ARlZmZFcoIyM7MiOUGZmVmRnKDMzKxITlBmZlYkJygzMyuSE5SZmRXJCcrMzIrkBGVmZkVygjIzsyI5QZmZWZGcoMzMrEhOUGZmViQnKDMzK9JKdSxE0vrAacAk4CngOxHxdUkTgDOBKcBtwNsi4t46YjKz7vjs/76/1yHU7vMfOLHXIYwLdV1BPQF8LCJeDrwG+JCkVwCHA7Mjog+YnbvNzMzqSVARsTAirsqflwB/Bl4ATAdm5tFmArvVEY+ZmZWv9ntQkqYAmwJXAOtFxEJISQyYWHc8ZmZWplruQQ2QtDrwY+DgiHhAUtvT9vf3j1pcY8lIymFSF+MYC0ZSVrUeGAXw8dUZl9fI9fX1DTlObcehpJVJyemHEXFu7r1I0uSIWChpMnBXq+lbrsysud0OtWjtbNRWlnQxjrFgJGU1f5ydf0ZSVszuXhxjxYjKy9pWSxWf0qXSycCfI+JrlUEXAjPy5xnABXXEY2Zm5avrCmpr4J3AnyTNy/0+CRwNnCXpAGABsFdN8ZiZWeFqSVAR8Rug1Q2naXXEYGZmY4ufJGFmZkVygjIzsyI5QZmZWZGcoMzMrEhOUGZmViQnKDMzK5ITlJmZFckJyszMiuQEZWZmRXKCMjOzIjlBmZlZkZygzMysSE5QZmZWJCcoMzMrkhOUmZkVyQnKzMyK5ARlZmZFcoIyM7MiOUGZmVmRnKDMzKxITlBmZlYkJygzMyuSE5SZmRXJCcrMzIrkBGVmZkVygjIzsyI5QZmZWZGcoMzMrEi1JChJp0i6S9J1lX4TJM2S1J//r1NHLGZmNjbUdQV1KrBjQ7/DgdkR0QfMzt1mZmZATQkqIn4F/KOh93RgZv48E9itjljMzGxs6OU9qPUiYiFA/j+xh7GYmVlhVup1AO3q7+/vdQhFGEk5TOpiHGPBSMpqzBwYXeLjqzMjKa85c+Z0L5AxYNttt23av6+vb8hpe3kcLpI0OSIWSpoM3DXYyC1XZtbcUQitXO1s1FaWdDGOsWAkZTV/nJ2vR1JWzO5eHGPFSMprvCWokZRVL6v4LgRm5M8zgAt6GIuZmRWmrmbmZwCXAxtKul3SAcDRwHaS+oHtcreZmRlQUxVfROzbYtC0OpZvZmZjj58kYWZmRXKCMjOzIjlBmZlZkZygzMysSE5QZmZWJCcoMzMrkhOUmZkVyQnKzMyK5ARlZmZFcoIyM7MiOUGZmVmRnKDMzKxITlBmZlYkJygzMyuSE5SZmRXJCcrMzIrkBGVmZkVygjIzsyI5QZmZWZGcoMzMrEhOUGZmViQnKDMzK5ITlJmZFckJyszMiuQEZWZmRXKCMjOzIjlBmZlZkZygzMysSE5QZmZWpJ4nKEk7SrpJ0s2SDu91PGZmVoaeJihJKwLHAzsBrwD2lfSKXsZkZmZl6PUV1BbAzRFxa0Q8DvwImN7jmMzMrACKiN4tXNoT2DEi3pO73wlsGREHAtx///29C87MzGqx1lprqVn/Xl9BNQvKScnMzHqeoG4H1q90vxC4o0exmJlZQXpdxbcS8BdgGvB34A/A2yPi+p4FZWZmRViplwuPiCckHQhcAqwInOLkZGZmAETEuPsDngTmAdcAVwGvzf2nAI/kYTcApwErV6bbBpgL3Jj/3lcZdiTwMDCx0u/B/H99YD4wIXevk7tfvDyWRR7+vsqwucA2lWFzgM0ry+kHdgC2BS7K/Z+dp/23ynSHAf+bpwngoMqwbwH7d7FcAvh+pXslYPFAfJX+FwCXN/RruS80fs7d+wPfauh3DXBG/vzuvB3mAY8Df8qf/1np/2Tl84fzdF8n1Uys0LCsp4BXVvpdB0ypaX97YS6zfuDWvN2e1aIMqvvJbZX1ngd8I/c/lXQsDfT/XTvrCfxHnt+1uf/0Lq7jp4Dr87znAVsC5+XPNwP3V+IdON7a2d5H5/VaXBk+j/QTnSm0eUyQ9s+P58+vAa7I8/kzcGSrfbLhWLgb+FKT7fXHSvfmwJz8edu83lcDNwG/AnYZsizr2ClL+2Ppk8UOwC/z5ynAdfnzisBlwH65exKwANgsdz8PuBLYubLRFwBfbrGcw4Dv5M8nAp/odTmMYlnskrufl7s3y+NPquzIm5NOVjcBu1Z24osq8ewI/JrUmOYFwC2k5D4FWEQ62FfJ43Y7QT2YD6ZVc/dO+SCuxrc28Ld8YG9Q6T/UvjBoggJeTjop/R14TsO4tw2Ua6vtmLtXyDH8Hti2YVkLgDMr/WpJUHk7zgXeXdmvTiYl0qXKoLqfDLHepwJ7Nunfcj3zfncLsFbuv3p1+41wHbcCLgeeVTk2nl8ZvtQ+3un2blZO8czx2tYxwdIJ6iZgk8r2eMVgy8nD3gL8NpehGrbXAmCn3N2YoKrHztS8btMGK89eN5IowZrAvY09I+JJ0sH0gtzrQ8CpEXFVHn43KelUn35xCrC3pAlNlnMc8BpJB5OuPr7arRXoom6VxX8Bh+b+5PFm5ukGTAIuBT4dERc2CyYiLgYWAu8ild+RETEQ32JgNjBjWGvanv8Dds6f9wXOaBj+VuAnpN/v7dMwbLB9YShvB75PKp9dhzE9wBtJJ+Rvk2KvugjYSNKGw5z3cL0JeDQivgdP71eHkLbv6qOwvFbrORFYQvoSQkQ8GBHzu7TMycDdEfFYnvfdETFUw69ubG8Y3jExkXSMERFPRsQNbUyzL+lLxQLSFVjVMcCnh5pBRMwDPg8cONh44zVBrSppnqQbge8C/904gqRnky7NL869NiJdFVT9Mfcf8CDpxPSRxvlFxD+BQ0kn2oMj/TC5BKNRFu2U1Wmkb2hnDxHfwcBRwLoR8f2GYUcDH8tPJBkNPwL2yev/SlJVSNVA0jqDZZNAy32BZ8p8nqR5pAO1am/gzBbzbddAbOcBu0hauTLsKeArwCeHOe/hWma/iIgHSN+k27kf/otKuR1S6X9Mpf8PK/1brec1pKuN+ZK+J+nfO12RQVwKrC/pL5JOkPSGNqbpdHvvXd1/JK1aGdbpMXEccJOk8yS9P+/rLeVlTSMl/2bxXg48JumNbSz7KuBlg40wXhPUIxExNSJeRqpGOk3SwG+y/iWfNO4BFkTEtbm/aP4brcZ+3wBmSFqzybg7kb6tbDzSFeii0SyLqsZpfg68U9JqgwWXv31eRroSaBw2n3Rl9/bB5jFceX2nkA7Cn1WHSVoPeCnwm4j4C/CEpMbt2mpfGCjzqRExFfhsZb6vBhZHxF9J34Y3k7ROJ3FLWoVUDXN+TgBXANs3jHY66Yp+g07mPUKt9hsBa7WYpjr+Gyvldlyl/6GV/vs1TL/MeuYrtx2BPUmtiI+TdGSH69I82IgHgVeR7sEuBs6UtH+r8Ye5vc+s7j8R8Uhl+R0dExHxeVJV3KV5mosHn4JdgF9ExMPAj4HdmyTDL9DGVRTNfwe7lPGaoJ4WEZeT6onXzb1uySeNl5J27IFL7utJG7LqVaQGBNX53Uc6KD5Y7S9pKrAd6ZL4EEmTu7YSXdLFsrghd1dtxtJl9RXSifPs/HODwTyV/5r5IqlKcbT25QuBY1m2em9vcmMXSbeREtlS1Xyt9oUh7Au8LM/zFlK161s7jHlH0gn/T3k+29DwTTciniBVM/9Xh/MeiWX2m5y81yN9m248MU8g3YwftlbrGcnciPgSabt1WsaDLfPJiJgTEUeQqrAGm3c3tnejjo6JiLglIr5NujLaRNJzBxl9X+DNOd4rgeeSqpOr87uM1Mipsfqv0aak+7ctjfsEJellpJuD91T7R8RC0j2VT+RexwP750RD3ohfJp1oG30NeD+52iJfkXybVLW3gFRPe2y312WkulgWXwG+PLCj5/H2B05oWOQhwAPAyZWrto5ExI2kxLfLcKZvwynA5yPiTw399yU9pmtKREwhJeTG+1DQsC8MRtIKwF6klmcD851O59V8+wLvqcxjA2D7JlerpwJv5pkvJKNtNrCapHfB0w+L/irpZv5cYGtJk/KwzUmt+/7WheWeSmU9JT1f0maV4VOBv3ZhOUjaUFJfO/Pu4vZeSifHhKSdK8deH6k16H0txl2T9GXnRZV4P9Qi3qNI96VbLfeVwGdI55KWxmuCevoeAKnud0a+7G90PumAel0+Sb8DOCnfr/kd6XdbP2mcKDcOOI90gAG8l1RFNit3n0D61tRO/fRo63pZ5EYPpwC/y8NPAt6Rp3tapOY8M0g3lgeS2zRJt1f+tmpjHY4itczquoi4PSK+Xu0naQrwIlILuYHx5gMPSNqyYfrGfWEwrwf+HhF/r/T7FfCKdq+4cxLaAfhpJYaHgN8AS91ryfdBv0G6UT7q8vbeHdhTUj/pi9BTEXFURCwi3a/7Wd4X/wfYNyKqV87Ve1CnVfpX70HNy1Wc1eU2rufKwLGSbszL2pvm9wqHY3VgpqQbJF1LagJ+ZItxh7u9G+9BvbbJOO0eE+8k3YOaR2qosV/l+N+/eiwCHwYuG2gAkl0A7Cppqf07In5GquKsep2kqyXdREpMH46I2YMF19MnSZjZ+JVPrGcAe0REY6MaMycoMzMr03it4jMzs8I5QZmZWZGcoMzMrEhOUGZmViQnKLPljKQpkqKNH0CbFc0JyqwDkm6T9IikByXdK+mnktYfekoz65QTlFnn/j0iVif9wHgR8M1OZ+CrG7OhOUGZDVNEPAqcQ3paAJLWknSapMWS/irp0/lxNkjaX9JvJR0n6R/AkUOMf6SkHwwsq7HaTtIGkn4laYmkn0s6vjp+tp+kBZLulvSpOsrErJv8Lc5smPJjhfbmmUcefZP0kNaXkB6ieSnp6fUn5+Fbkl7hMZH0uJ0Thxh/MKeTXhr3ZmAL0tPWG9+rtQ2wIfCvwFxJ50bEoA/nNCuJE5RZ586X9ATpuWt3ATvkB5/uDWwaEUuAJZK+SnrW2UDCuSMivgkgKdoYvylJLwJeTXob6ePAbyQ1e+nj5/KrGK6RdA2wCUM8PdqsJK7iM+vcbhGxNukBsAcCvyQ9mHMVln5y9V955i3EsPSTuZ/XxvitPB/4R34nT7N5D7iz8vlhRuettWajxgnKbJjye3/OJb2i4DXAP4EXV0Z5EVB9UnX1wZd3DzH+Q0D19RiTKp8XAhMaXp/hloS23HGCMhsmJdNJL9q7DjgLOErSGpJeDHwUaGy4ADz9VtfBxp8HvF7SiyStxTPv4iK/ffWPpIYWq+RXknTzteVmRXCCMuvcTyQ9SHrZ4lGkd2hdDxxEuvK5lfT+pdNJ78VqpeX4+d1hZwLXkt5celHDtPsBW5HeqfSFPO5jmC1H/LoNs+WApDOBG/Nrxs2WC76CMhuDJL1a0r9IWkHSjqRXhZ/f47DMusrNzM3GpknAuaTfT90O/GdEXN3bkMy6y1V8ZmZWJFfxmZlZkZygzMysSE5QZmZWJCcoMzMrkhOUmZkVyQnKzMyK9P8GTW3gN/VCUwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Get a count by county\n",
    "housing_by_borough = nyc_public_housing.groupby('borough').agg('count').reset_index()\n",
    "\n",
    "#Plot the count by county\n",
    "fig, ax1 = plt.subplots(1,1, figsize=(6,6)\n",
    "                       )\n",
    "sns.barplot(x='borough', y='developmen', data=housing_by_borough, ax=ax1)\n",
    "\n",
    "ax1.set_title('Number of public housing units by borough', fontsize=15)\n",
    "ax1.set_xlabel('Borough', fontsize=12)\n",
    "ax1.set_ylabel('Count', fontsize=12)\n",
    "ax1.tick_params(axis='both', labelsize=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b90d09b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>borough</th>\n",
       "      <th>developmen</th>\n",
       "      <th>tds_num</th>\n",
       "      <th>geometry</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BRONX</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>79</td>\n",
       "      <td>79</td>\n",
       "      <td>79</td>\n",
       "      <td>79</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MANHATTAN</td>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>QUEENS</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>STATEN ISLAND</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         borough  developmen  tds_num  geometry  long  lat\n",
       "0          BRONX          75       75        75    75   75\n",
       "1       BROOKLYN          79       79        79    79   79\n",
       "2      MANHATTAN          82       82        82    82   82\n",
       "3         QUEENS          21       21        21    21   21\n",
       "4  STATEN ISLAND          10       10        10    10   10"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_by_borough#.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbceaccc",
   "metadata": {},
   "source": [
    "# Non-spatial regressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c98cc491",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'age', 'gender', 'race', 'victim_image', 'date',\n",
       "       'street_address', 'city', 'state', 'zip', 'county',\n",
       "       'agency_responsible', 'ori', 'cause_of_death', 'circumstances',\n",
       "       'disposition_official', 'officer_charged', 'news_urls',\n",
       "       'signs_of_mental_illness', 'allegedly_armed', 'wapo_armed',\n",
       "       'wapo_threat_level', 'wapo_flee', 'wapo_body_camera', 'wapo_id',\n",
       "       'off_duty_killing', 'geography', 'mpv_id', 'fe_id', 'encounter_type',\n",
       "       'initial_reason', 'officer_names', 'officer_races',\n",
       "       'officer_known_past_shootings', 'call_for_service', 'tract',\n",
       "       'urban_rural_uspsai', 'urban_rural_nchs',\n",
       "       'hhincome_median_census_tract', 'latitude', 'longitude',\n",
       "       'pop_total_census_tract', 'pop_white_census_tract',\n",
       "       'pop_black_census_tract', 'pop_native_american_census_tract',\n",
       "       'pop_asian_census_tract', 'pop_pacific_islander_census_tract',\n",
       "       'pop_other_multiple_census_tract', 'pop_hispanic_census_tract',\n",
       "       'congressional_district_113', 'congressperson_lastname',\n",
       "       'congressperson_firstname', 'congressperson_party', 'prosecutor_head',\n",
       "       'prosecutor_race', 'prosecutor_gender', 'prosecutor_party',\n",
       "       'prosecutor_term', 'prosecutor_in_court', 'prosecutor_special',\n",
       "       'independent_investigation', 'prosecutor_url', 'date_new'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpv_nyc.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c93cbff3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'white', 'Asian', 'White, White', 'Hispanic', 'Black'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpv_nyc.officer_races.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d283e2f",
   "metadata": {},
   "source": [
    "Variables of interest for linear regression: \n",
    "\n",
    "Group_by census tract?\n",
    "\n",
    "Categorical variables: group_by('tract'), population_white, population_black, controlling for the total population, cause of death, race, officer charged, officer_race\n",
    "\n",
    "Continuous variables: population_white, black, total, age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "02462fe5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Hispanic', 'Black', 'White', 'Unknown race', 'Asian'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpv_nyc_new = mpv_nyc.dropna(subset=['race'])\n",
    "mpv_nyc_new.race.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e1f1d929",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Pandas data cast to numpy dtype of object. Check input data with np.asarray(data). The types seen wereNone and None. The data was\n237      39.0\n323      42.0\n404      29.0\n729      60.0\n730      19.0\n         ... \n10971    17.0\n11046     1.0\n11047    33.0\n11142    16.0\n11326    37.0\nName: age, Length: 97, dtype: float64\nand\n 237      Hispanic\n323         Black\n404         Black\n729      Hispanic\n730         Black\n           ...   \n10971       Black\n11046       Black\n11047       Black\n11142       Black\n11326       Black\nName: race, Length: 97, dtype: object\nbefore. After,\n[39. 42. 29. 60. 19. 51. 25. 53. 47. 27. 26. nan 65. 24. 24. 53. nan 18.\n 52. 35. 41. 24. 20. 33. 19. 37. 36. 29. 31. 30. 33. 27. 39. 29. 27. 58.\n 42. 32. 42. 32. 50. 54. 34. 19. 52. 69. 67. 31. 32. 34. 18. 17. 28. 18.\n 63. 21. 35. 49. 66. 42. 39. 37. 25. 46. 32. 30. 38. 58. 19. 51. 51. 24.\n 30. 49. 28. 29. 32. 28. 47. 51. 22. 32. 43. 39. 45. 86. 44. 22. 26. 50.\n 46. 28. 17.  1. 33. 16. 37.]\n['Hispanic' 'Black' 'Black' 'Hispanic' 'Black' 'White' 'Black' 'Black'\n 'Black' 'Unknown race' 'Black' 'Unknown race' 'White' 'Asian' 'Hispanic'\n 'Unknown race' 'Unknown race' 'Hispanic' 'Hispanic' 'Black' 'Black'\n 'Black' 'Hispanic' 'Black' 'Black' 'Black' 'Black' 'Hispanic' 'Black'\n 'Black' 'White' 'Black' 'Black' 'Unknown race' 'Unknown race'\n 'Unknown race' 'Hispanic' 'Black' 'White' 'Black' 'Unknown race' 'White'\n 'Black' 'Black' 'Black' 'Hispanic' 'Black' 'Black' 'Black' 'Black'\n 'Hispanic' 'Black' 'Black' 'Hispanic' 'Black' 'Black' 'Hispanic'\n 'Hispanic' 'Black' 'Black' 'Black' 'Black' 'Black' 'White' 'Black'\n 'Black' 'Black' 'White' 'Black' 'Hispanic' 'Black' 'Black' 'White'\n 'Black' 'Black' 'Black' 'Black' 'White' 'Hispanic' 'Hispanic' 'Black'\n 'White' 'Black' 'Black' 'Black' 'White' 'Black' 'Black' 'Unknown race'\n 'Black' 'Black' 'Unknown race' 'Black' 'Black' 'Black' 'Black' 'Black'].",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-42-894719089e57>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# performing the regression\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# and fitting the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0msm1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOLS\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmpv_nyc_new\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmpv_nyc_new\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# printing the summary table\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/statsmodels/regression/linear_model.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, endog, exog, missing, hasconst, **kwargs)\u001b[0m\n\u001b[1;32m    905\u001b[0m             \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueWarning\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    906\u001b[0m         super(OLS, self).__init__(endog, exog, missing=missing,\n\u001b[0;32m--> 907\u001b[0;31m                                   hasconst=hasconst, **kwargs)\n\u001b[0m\u001b[1;32m    908\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;34m\"weights\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_keys\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    909\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_keys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"weights\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/statsmodels/regression/linear_model.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, endog, exog, weights, missing, hasconst, **kwargs)\u001b[0m\n\u001b[1;32m    732\u001b[0m             \u001b[0mweights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    733\u001b[0m         super(WLS, self).__init__(endog, exog, missing=missing,\n\u001b[0;32m--> 734\u001b[0;31m                                   weights=weights, hasconst=hasconst, **kwargs)\n\u001b[0m\u001b[1;32m    735\u001b[0m         \u001b[0mnobs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    736\u001b[0m         \u001b[0mweights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/statsmodels/regression/linear_model.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, endog, exog, **kwargs)\u001b[0m\n\u001b[1;32m    188\u001b[0m     \"\"\"\n\u001b[1;32m    189\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mendog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 190\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRegressionModel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    191\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data_attr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'pinv_wexog'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'wendog'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'wexog'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'weights'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/statsmodels/base/model.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, endog, exog, **kwargs)\u001b[0m\n\u001b[1;32m    265\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    266\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mendog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 267\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    268\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minitialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    269\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/statsmodels/base/model.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, endog, exog, **kwargs)\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0mhasconst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'hasconst'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m         self.data = self._handle_data(endog, exog, missing, hasconst,\n\u001b[0;32m---> 93\u001b[0;31m                                       **kwargs)\n\u001b[0m\u001b[1;32m     94\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mk_constant\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mk_constant\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexog\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexog\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/statsmodels/base/model.py\u001b[0m in \u001b[0;36m_handle_data\u001b[0;34m(self, endog, exog, missing, hasconst, **kwargs)\u001b[0m\n\u001b[1;32m    130\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_handle_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mendog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmissing\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhasconst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhandle_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmissing\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhasconst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    133\u001b[0m         \u001b[0;31m# kwargs arrays could have changed, easier to just attach here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/statsmodels/base/data.py\u001b[0m in \u001b[0;36mhandle_data\u001b[0;34m(endog, exog, missing, hasconst, **kwargs)\u001b[0m\n\u001b[1;32m    699\u001b[0m     \u001b[0mklass\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhandle_data_class_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    700\u001b[0m     return klass(endog, exog=exog, missing=missing, hasconst=hasconst,\n\u001b[0;32m--> 701\u001b[0;31m                  **kwargs)\n\u001b[0m",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/statsmodels/base/data.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, endog, exog, missing, hasconst, **kwargs)\u001b[0m\n\u001b[1;32m     82\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0morig_endog\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mendog\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0morig_exog\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexog\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexog\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_endog_exog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     85\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconst_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/statsmodels/base/data.py\u001b[0m in \u001b[0;36m_convert_endog_exog\u001b[0;34m(self, endog, exog)\u001b[0m\n\u001b[1;32m    529\u001b[0m                 \u001b[0mexog_dtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    530\u001b[0m             raise ValueError(\n\u001b[0;32m--> 531\u001b[0;31m                 \u001b[0;34m\"Pandas data cast to numpy dtype of object. Check input data \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    532\u001b[0m                 \u001b[0;34m\"with np.asarray(data). The types seen were\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    533\u001b[0m                 \u001b[0;34mf\"{endog_dtype} and {exog_dtype}. The data was\\n\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Pandas data cast to numpy dtype of object. Check input data with np.asarray(data). The types seen wereNone and None. The data was\n237      39.0\n323      42.0\n404      29.0\n729      60.0\n730      19.0\n         ... \n10971    17.0\n11046     1.0\n11047    33.0\n11142    16.0\n11326    37.0\nName: age, Length: 97, dtype: float64\nand\n 237      Hispanic\n323         Black\n404         Black\n729      Hispanic\n730         Black\n           ...   \n10971       Black\n11046       Black\n11047       Black\n11142       Black\n11326       Black\nName: race, Length: 97, dtype: object\nbefore. After,\n[39. 42. 29. 60. 19. 51. 25. 53. 47. 27. 26. nan 65. 24. 24. 53. nan 18.\n 52. 35. 41. 24. 20. 33. 19. 37. 36. 29. 31. 30. 33. 27. 39. 29. 27. 58.\n 42. 32. 42. 32. 50. 54. 34. 19. 52. 69. 67. 31. 32. 34. 18. 17. 28. 18.\n 63. 21. 35. 49. 66. 42. 39. 37. 25. 46. 32. 30. 38. 58. 19. 51. 51. 24.\n 30. 49. 28. 29. 32. 28. 47. 51. 22. 32. 43. 39. 45. 86. 44. 22. 26. 50.\n 46. 28. 17.  1. 33. 16. 37.]\n['Hispanic' 'Black' 'Black' 'Hispanic' 'Black' 'White' 'Black' 'Black'\n 'Black' 'Unknown race' 'Black' 'Unknown race' 'White' 'Asian' 'Hispanic'\n 'Unknown race' 'Unknown race' 'Hispanic' 'Hispanic' 'Black' 'Black'\n 'Black' 'Hispanic' 'Black' 'Black' 'Black' 'Black' 'Hispanic' 'Black'\n 'Black' 'White' 'Black' 'Black' 'Unknown race' 'Unknown race'\n 'Unknown race' 'Hispanic' 'Black' 'White' 'Black' 'Unknown race' 'White'\n 'Black' 'Black' 'Black' 'Hispanic' 'Black' 'Black' 'Black' 'Black'\n 'Hispanic' 'Black' 'Black' 'Hispanic' 'Black' 'Black' 'Hispanic'\n 'Hispanic' 'Black' 'Black' 'Black' 'Black' 'Black' 'White' 'Black'\n 'Black' 'Black' 'White' 'Black' 'Hispanic' 'Black' 'Black' 'White'\n 'Black' 'Black' 'Black' 'Black' 'White' 'Hispanic' 'Hispanic' 'Black'\n 'White' 'Black' 'Black' 'Black' 'White' 'Black' 'Black' 'Unknown race'\n 'Black' 'Black' 'Unknown race' 'Black' 'Black' 'Black' 'Black' 'Black']."
     ]
    }
   ],
   "source": [
    "# performing the regression\n",
    "# and fitting the model\n",
    "sm1 = sm.OLS(mpv_nyc_new.age, mpv_nyc_new.race).fit()\n",
    "\n",
    "# printing the summary table\n",
    "print(sm1.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc34cf95",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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