1_group_geo_users_in_clusters.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c613f715",
   "metadata": {},
   "source": [
    "# 1. Grouping users according to salient geographic characteristics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4971af5",
   "metadata": {},
   "source": [
    "In this first of 4 jupyter notebooks focused to data analaysis, I examine behavior among geotagged adopters as stratified by properties of their county subdivisions we've theorized as salient (i.e. racial makeup, class, rates of police violence).\n",
    "\n",
    "To do this, I first identify which of the neighborhood characteristics I examine is the most salient, then I use K means clustering to group users according to the six most salient characteristics.\n",
    "\n",
    "The second notebook `2_examine_population_characteristics`, includes the birds-eye view of adoption rates and also compares geotagged users to their peers, This notebook is provided \"first\" to researchers looking to reproduce my methods because it includes the connection of geotagged users with their salient neighborhood characteristics, which is necessary for some of the succeeding analayses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "075c1da7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import utils.common.constants as c\n",
    "from pyarrow import feather\n",
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.linear_model import (\n",
    "    Lasso,\n",
    "    Ridge,\n",
    "    LinearRegression\n",
    ")\n",
    "from sklearn.cluster import (\n",
    "    KMeans, \n",
    "    AgglomerativeClustering, \n",
    "    DBSCAN\n",
    ")\n",
    "\n",
    "from utils.data_analysis.load_locations import load_locations_gdf\n",
    "from utils.data_analysis.load_users import load_users_dfs\n",
    "\n",
    "pd.set_option('display.min_rows', 20)\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.width', 1000)\n",
    "pd.set_option('display.max_rows', 20)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b65dd5dc",
   "metadata": {},
   "source": [
    "## Load in County Subdivision Geospatial Boundaries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08e2b759",
   "metadata": {},
   "source": [
    "Load in the geolocation data about each county's name, and bounding polygon. Note that the Coordinate Reference System (CRS) for the county subdivisions is EPSG:4326 (geodetic lat/long coords.). We just need to make sure that whatever we compare this to is also in this projection.\n",
    "\n",
    "EPSG:4326 seems to be most common, but I didn't know there were alternative versions, so noting! You need a different (\"flat\") projection to add/subtract coordinates. I used EPSG:2163 to compute means and distances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f891d4ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "county_subd_gdf = gpd.read_file(\"utils/data_analysis/datasets/county_subdivs/COUSUB_2019_US_SL060_Coast_Clipped.dbf\")\n",
    "county_subd_gdf = county_subd_gdf[[\"Geo_FIPS\", \"Geo_QNAME\", \"geometry\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "2436809e",
   "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>Geo_FIPS</th>\n",
       "      <th>Geo_QNAME</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6005080100</td>\n",
       "      <td>Tualatai county</td>\n",
       "      <td>MULTIPOLYGON (((-170.77156 -14.36328, -170.771...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6005080900</td>\n",
       "      <td>Tualauta county</td>\n",
       "      <td>POLYGON ((-170.75416 -14.34793, -170.75400 -14...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6005042500</td>\n",
       "      <td>Lealataua county</td>\n",
       "      <td>MULTIPOLYGON (((-170.82896 -14.33140, -170.828...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6005043300</td>\n",
       "      <td>Leasina county</td>\n",
       "      <td>MULTIPOLYGON (((-170.77333 -14.34093, -170.773...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6001037700</td>\n",
       "      <td>Ituau county</td>\n",
       "      <td>MULTIPOLYGON (((-170.69943 -14.32473, -170.699...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6001051300</td>\n",
       "      <td>Ma'oputasi county</td>\n",
       "      <td>MULTIPOLYGON (((-170.68398 -14.26395, -170.683...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6001072100</td>\n",
       "      <td>Sua county</td>\n",
       "      <td>MULTIPOLYGON (((-170.65963 -14.26357, -170.659...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6001067300</td>\n",
       "      <td>Sa'ole county</td>\n",
       "      <td>MULTIPOLYGON (((-170.55324 -14.27831, -170.552...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6001086500</td>\n",
       "      <td>Vaifanua county</td>\n",
       "      <td>MULTIPOLYGON (((-170.59147 -14.25745, -170.591...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6002060900</td>\n",
       "      <td>Olosega county</td>\n",
       "      <td>POLYGON ((-169.62919 -14.17159, -169.62934 -14...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36564</th>\n",
       "      <td>6601028050</td>\n",
       "      <td>Dededo municipality</td>\n",
       "      <td>POLYGON ((144.84350 13.49876, 144.84330 13.498...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36565</th>\n",
       "      <td>6601083300</td>\n",
       "      <td>Yigo municipality</td>\n",
       "      <td>POLYGON ((144.87207 13.58439, 144.87181 13.585...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36566</th>\n",
       "      <td>6910014290</td>\n",
       "      <td>District 7</td>\n",
       "      <td>MULTIPOLYGON (((145.19508 14.11514, 145.19502 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36567</th>\n",
       "      <td>6912014270</td>\n",
       "      <td>District 6</td>\n",
       "      <td>MULTIPOLYGON (((145.53345 14.83529, 145.53351 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36568</th>\n",
       "      <td>6911013900</td>\n",
       "      <td>District 2</td>\n",
       "      <td>POLYGON ((145.72248 15.14267, 145.72244 15.142...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36569</th>\n",
       "      <td>6911013750</td>\n",
       "      <td>District 1</td>\n",
       "      <td>POLYGON ((145.72253 15.10729, 145.72237 15.107...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36570</th>\n",
       "      <td>6911014200</td>\n",
       "      <td>District 5</td>\n",
       "      <td>MULTIPOLYGON (((145.72226 15.15398, 145.72209 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36571</th>\n",
       "      <td>6911014050</td>\n",
       "      <td>District 3</td>\n",
       "      <td>MULTIPOLYGON (((145.72235 15.15976, 145.72063 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36572</th>\n",
       "      <td>6911014150</td>\n",
       "      <td>District 4</td>\n",
       "      <td>MULTIPOLYGON (((145.80504 15.28428, 145.80508 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36573</th>\n",
       "      <td>6908514075</td>\n",
       "      <td>District 4</td>\n",
       "      <td>MULTIPOLYGON (((146.06229 16.02764, 146.06236 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36574 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Geo_FIPS            Geo_QNAME                                           geometry\n",
       "0      6005080100      Tualatai county  MULTIPOLYGON (((-170.77156 -14.36328, -170.771...\n",
       "1      6005080900      Tualauta county  POLYGON ((-170.75416 -14.34793, -170.75400 -14...\n",
       "2      6005042500     Lealataua county  MULTIPOLYGON (((-170.82896 -14.33140, -170.828...\n",
       "3      6005043300       Leasina county  MULTIPOLYGON (((-170.77333 -14.34093, -170.773...\n",
       "4      6001037700         Ituau county  MULTIPOLYGON (((-170.69943 -14.32473, -170.699...\n",
       "5      6001051300    Ma'oputasi county  MULTIPOLYGON (((-170.68398 -14.26395, -170.683...\n",
       "6      6001072100           Sua county  MULTIPOLYGON (((-170.65963 -14.26357, -170.659...\n",
       "7      6001067300        Sa'ole county  MULTIPOLYGON (((-170.55324 -14.27831, -170.552...\n",
       "8      6001086500      Vaifanua county  MULTIPOLYGON (((-170.59147 -14.25745, -170.591...\n",
       "9      6002060900       Olosega county  POLYGON ((-169.62919 -14.17159, -169.62934 -14...\n",
       "...           ...                  ...                                                ...\n",
       "36564  6601028050  Dededo municipality  POLYGON ((144.84350 13.49876, 144.84330 13.498...\n",
       "36565  6601083300    Yigo municipality  POLYGON ((144.87207 13.58439, 144.87181 13.585...\n",
       "36566  6910014290           District 7  MULTIPOLYGON (((145.19508 14.11514, 145.19502 ...\n",
       "36567  6912014270           District 6  MULTIPOLYGON (((145.53345 14.83529, 145.53351 ...\n",
       "36568  6911013900           District 2  POLYGON ((145.72248 15.14267, 145.72244 15.142...\n",
       "36569  6911013750           District 1  POLYGON ((145.72253 15.10729, 145.72237 15.107...\n",
       "36570  6911014200           District 5  MULTIPOLYGON (((145.72226 15.15398, 145.72209 ...\n",
       "36571  6911014050           District 3  MULTIPOLYGON (((145.72235 15.15976, 145.72063 ...\n",
       "36572  6911014150           District 4  MULTIPOLYGON (((145.80504 15.28428, 145.80508 ...\n",
       "36573  6908514075           District 4  MULTIPOLYGON (((146.06229 16.02764, 146.06236 ...\n",
       "\n",
       "[36574 rows x 3 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "county_subd_gdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b5f870f",
   "metadata": {},
   "source": [
    "## Merging `geo_users_df` with American Community Survey (ACS), Mapping Police Violence (MPV) Datasets\n",
    "\n",
    "We use several different datasets in order to determine the characteristics of users' neighborhood.\n",
    "\n",
    "In order to load these various properties into a single DF, have to do a bit of data cleaning up and getting rid of 0-population subdivs (i.e. national parks...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "1f00bec3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "96.18941023269069"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acs_df = pd.read_csv(\"utils/data_analysis/datasets/ACS_data.csv\", low_memory=False, header=1)\n",
    "acs_df[\"Land_Area\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "a28322d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Geo_FIPS            36099\n",
       "Geo_NAME            36099\n",
       "Geo_STATE           36099\n",
       "Geo_COUNTY          36099\n",
       "Geo_COUSUB          36099\n",
       "Total_Pop           36099\n",
       "Pop_Dens            35935\n",
       "Land_Area           35935\n",
       "WhitePop            36099\n",
       "BlackPop            36099\n",
       "OtherRacePop        36099\n",
       "WhitePopPerc        36099\n",
       "BlackPopPerc        36099\n",
       "OtherRacePopPerc    36099\n",
       "PctLatinoPop        36099\n",
       "WhitePerCap         35499\n",
       "BlackPerCap         12917\n",
       "LatinoPerCap        17121\n",
       "PerCap              35572\n",
       "dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acs_df['OtherRacePop'] = acs_df['OtherRacePop'] + acs_df['NativePop'] + acs_df['AsianPop'] + acs_df['IslanderPop'] + acs_df['MultiRacePop']\n",
    "acs_df['OtherRacePopPerc'] = acs_df['OtherRacePopPerc'] + acs_df['NativePopPerc'] + acs_df['AsianPopPerc'] + acs_df['IslanderPopPerc'] + acs_df['MultiRacePopPerc']\n",
    "\n",
    "acs_df = acs_df[[\n",
    "    'Geo_FIPS',\n",
    "    'Geo_NAME',\n",
    "    'Geo_STATE',\n",
    "    'Geo_COUNTY',\n",
    "    'Geo_COUSUB',\n",
    "    'Total_Pop',\n",
    "    'Pop_Dens',\n",
    "    'Land_Area',\n",
    "    'WhitePop',\n",
    "    'BlackPop',\n",
    "    'OtherRacePop',\n",
    "    'WhitePopPerc',\n",
    "    'BlackPopPerc',\n",
    "    'OtherRacePopPerc',\n",
    "    'PctLatinoPop',\n",
    "    'WhitePerCap',\n",
    "    'BlackPerCap',\n",
    "    'LatinoPerCap',\n",
    "    'PerCap'\n",
    "]]\n",
    "acs_df = acs_df[acs_df[\"Total_Pop\"] > 0]\n",
    "\n",
    "acs_df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a3ad94c",
   "metadata": {},
   "source": [
    "Add in MPV Dataset to Salients:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "d50ae95a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/raycrist/opt/anaconda3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3364: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Geo_FIPS            36099\n",
       "Geo_NAME            36099\n",
       "Geo_STATE           36099\n",
       "Geo_COUNTY          36099\n",
       "Geo_COUSUB          36099\n",
       "Total_Pop           36099\n",
       "Pop_Dens            35935\n",
       "Land_Area           35935\n",
       "WhitePop            36099\n",
       "BlackPop            36099\n",
       "                    ...  \n",
       "BlackPopPerc        36099\n",
       "OtherRacePopPerc    36099\n",
       "PctLatinoPop        36099\n",
       "WhitePerCap         35499\n",
       "BlackPerCap         12917\n",
       "LatinoPerCap        17121\n",
       "PerCap              35572\n",
       "PoliceKillings      36099\n",
       "PKPerCap            36099\n",
       "PKPerSqMile         35935\n",
       "Length: 22, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from shapely.geometry import Point\n",
    "\n",
    "# Load in Mapping Police Violence dataset\n",
    "mpv_df = pd.read_csv(f\"utils/data_analysis/datasets/MPV_data.csv\", low_memory=False)\n",
    "mpv_df = mpv_df[['Latitude','Longitude', 'Street Address of Incident', 'Date of Incident (month/day/year)']]\n",
    "\n",
    "# Convert to GeoDataFrame\n",
    "mpv_geom = [Point(xy) for xy in zip(mpv_df['Longitude'], mpv_df['Latitude'])]\n",
    "mpv_gdf = gpd.GeoDataFrame(mpv_df, crs='EPSG:4326', geometry=mpv_geom) # Creating a Geographic data frame \n",
    "\n",
    "# Convert to count per subdivision\n",
    "mpv_with_subdiv = gpd.sjoin(mpv_gdf, county_subd_gdf, how=\"inner\", op='intersects')\n",
    "mpv_by_subdiv = mpv_with_subdiv.groupby('Geo_FIPS').agg({\n",
    "    'Street Address of Incident': 'count',\n",
    "}).reset_index()\n",
    "mpv_by_subdiv = mpv_by_subdiv.rename(columns={'Street Address of Incident': 'PoliceKillings'})\n",
    "mpv_by_subdiv['Geo_FIPS'] = mpv_by_subdiv['Geo_FIPS'].astype(int)\n",
    "\n",
    "\n",
    "salients_df = pd.merge(\n",
    "    acs_df,\n",
    "    mpv_by_subdiv,\n",
    "    how=\"left\",\n",
    "    on='Geo_FIPS',\n",
    ")\n",
    "salients_df = salients_df.fillna({\"PoliceKillings\": 0})\n",
    "salients_df[\"PKPerCap\"] = salients_df[\"PoliceKillings\"] / salients_df[\"Total_Pop\"]\n",
    "salients_df[\"PKPerSqMile\"] = salients_df[\"PoliceKillings\"] / salients_df[\"Land_Area\"]\n",
    "salients_df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "573bb0ad",
   "metadata": {},
   "source": [
    "Calculate Black/White Income Ratio:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "48c148e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "salients_df[\"Blck_Wht_PerCap_Ratio\"] = (salients_df[\"BlackPerCap\"] / salients_df[\"WhitePerCap\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89be5835",
   "metadata": {},
   "source": [
    "And merge with county subd. geospatial data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "1ab76bef",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Geo_FIPS</th>\n",
       "      <th>Geo_QNAME</th>\n",
       "      <th>geometry</th>\n",
       "      <th>Geo_NAME</th>\n",
       "      <th>Geo_STATE</th>\n",
       "      <th>Geo_COUNTY</th>\n",
       "      <th>Geo_COUSUB</th>\n",
       "      <th>Total_Pop</th>\n",
       "      <th>Pop_Dens</th>\n",
       "      <th>Land_Area</th>\n",
       "      <th>WhitePop</th>\n",
       "      <th>BlackPop</th>\n",
       "      <th>OtherRacePop</th>\n",
       "      <th>WhitePopPerc</th>\n",
       "      <th>BlackPopPerc</th>\n",
       "      <th>OtherRacePopPerc</th>\n",
       "      <th>PctLatinoPop</th>\n",
       "      <th>WhitePerCap</th>\n",
       "      <th>BlackPerCap</th>\n",
       "      <th>LatinoPerCap</th>\n",
       "      <th>PerCap</th>\n",
       "      <th>PoliceKillings</th>\n",
       "      <th>PKPerCap</th>\n",
       "      <th>PKPerSqMile</th>\n",
       "      <th>Blck_Wht_PerCap_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6005080100</td>\n",
       "      <td>Tualatai county</td>\n",
       "      <td>MULTIPOLYGON (((-170.77156 -14.36328, -170.771...</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",
       "      <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6005080900</td>\n",
       "      <td>Tualauta county</td>\n",
       "      <td>POLYGON ((-170.75416 -14.34793, -170.75400 -14...</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",
       "      <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6005042500</td>\n",
       "      <td>Lealataua county</td>\n",
       "      <td>MULTIPOLYGON (((-170.82896 -14.33140, -170.828...</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",
       "      <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6005043300</td>\n",
       "      <td>Leasina county</td>\n",
       "      <td>MULTIPOLYGON (((-170.77333 -14.34093, -170.773...</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",
       "      <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6001037700</td>\n",
       "      <td>Ituau county</td>\n",
       "      <td>MULTIPOLYGON (((-170.69943 -14.32473, -170.699...</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",
       "      <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6001051300</td>\n",
       "      <td>Ma'oputasi county</td>\n",
       "      <td>MULTIPOLYGON (((-170.68398 -14.26395, -170.683...</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",
       "      <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6001072100</td>\n",
       "      <td>Sua county</td>\n",
       "      <td>MULTIPOLYGON (((-170.65963 -14.26357, -170.659...</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",
       "      <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6001067300</td>\n",
       "      <td>Sa'ole county</td>\n",
       "      <td>MULTIPOLYGON (((-170.55324 -14.27831, -170.552...</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",
       "      <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6001086500</td>\n",
       "      <td>Vaifanua county</td>\n",
       "      <td>MULTIPOLYGON (((-170.59147 -14.25745, -170.591...</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",
       "      <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6002060900</td>\n",
       "      <td>Olosega county</td>\n",
       "      <td>POLYGON ((-169.62919 -14.17159, -169.62934 -14...</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",
       "      <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",
       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36564</th>\n",
       "      <td>6601028050</td>\n",
       "      <td>Dededo municipality</td>\n",
       "      <td>POLYGON ((144.84350 13.49876, 144.84330 13.498...</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",
       "      <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36565</th>\n",
       "      <td>6601083300</td>\n",
       "      <td>Yigo municipality</td>\n",
       "      <td>POLYGON ((144.87207 13.58439, 144.87181 13.585...</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",
       "      <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36566</th>\n",
       "      <td>6910014290</td>\n",
       "      <td>District 7</td>\n",
       "      <td>MULTIPOLYGON (((145.19508 14.11514, 145.19502 ...</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",
       "      <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36567</th>\n",
       "      <td>6912014270</td>\n",
       "      <td>District 6</td>\n",
       "      <td>MULTIPOLYGON (((145.53345 14.83529, 145.53351 ...</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",
       "      <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36568</th>\n",
       "      <td>6911013900</td>\n",
       "      <td>District 2</td>\n",
       "      <td>POLYGON ((145.72248 15.14267, 145.72244 15.142...</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",
       "      <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36569</th>\n",
       "      <td>6911013750</td>\n",
       "      <td>District 1</td>\n",
       "      <td>POLYGON ((145.72253 15.10729, 145.72237 15.107...</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",
       "      <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36570</th>\n",
       "      <td>6911014200</td>\n",
       "      <td>District 5</td>\n",
       "      <td>MULTIPOLYGON (((145.72226 15.15398, 145.72209 ...</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",
       "      <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36571</th>\n",
       "      <td>6911014050</td>\n",
       "      <td>District 3</td>\n",
       "      <td>MULTIPOLYGON (((145.72235 15.15976, 145.72063 ...</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",
       "      <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36572</th>\n",
       "      <td>6911014150</td>\n",
       "      <td>District 4</td>\n",
       "      <td>MULTIPOLYGON (((145.80504 15.28428, 145.80508 ...</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",
       "      <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36573</th>\n",
       "      <td>6908514075</td>\n",
       "      <td>District 4</td>\n",
       "      <td>MULTIPOLYGON (((146.06229 16.02764, 146.06236 ...</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",
       "      <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36574 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Geo_FIPS            Geo_QNAME                                           geometry Geo_NAME  Geo_STATE  Geo_COUNTY  Geo_COUSUB  Total_Pop  Pop_Dens  Land_Area  WhitePop  BlackPop  OtherRacePop  WhitePopPerc  BlackPopPerc  OtherRacePopPerc  PctLatinoPop  WhitePerCap  BlackPerCap  LatinoPerCap  PerCap  PoliceKillings  PKPerCap  PKPerSqMile  Blck_Wht_PerCap_Ratio\n",
       "0      6005080100      Tualatai county  MULTIPOLYGON (((-170.77156 -14.36328, -170.771...      NaN        NaN         NaN         NaN        NaN       NaN        NaN       NaN       NaN           NaN           NaN           NaN               NaN           NaN          NaN          NaN           NaN     NaN             NaN       NaN          NaN                    NaN\n",
       "1      6005080900      Tualauta county  POLYGON ((-170.75416 -14.34793, -170.75400 -14...      NaN        NaN         NaN         NaN        NaN       NaN        NaN       NaN       NaN           NaN           NaN           NaN               NaN           NaN          NaN          NaN           NaN     NaN             NaN       NaN          NaN                    NaN\n",
       "2      6005042500     Lealataua county  MULTIPOLYGON (((-170.82896 -14.33140, -170.828...      NaN        NaN         NaN         NaN        NaN       NaN        NaN       NaN       NaN           NaN           NaN           NaN               NaN           NaN          NaN          NaN           NaN     NaN             NaN       NaN          NaN                    NaN\n",
       "3      6005043300       Leasina county  MULTIPOLYGON (((-170.77333 -14.34093, -170.773...      NaN        NaN         NaN         NaN        NaN       NaN        NaN       NaN       NaN           NaN           NaN           NaN               NaN           NaN          NaN          NaN           NaN     NaN             NaN       NaN          NaN                    NaN\n",
       "4      6001037700         Ituau county  MULTIPOLYGON (((-170.69943 -14.32473, -170.699...      NaN        NaN         NaN         NaN        NaN       NaN        NaN       NaN       NaN           NaN           NaN           NaN               NaN           NaN          NaN          NaN           NaN     NaN             NaN       NaN          NaN                    NaN\n",
       "5      6001051300    Ma'oputasi county  MULTIPOLYGON (((-170.68398 -14.26395, -170.683...      NaN        NaN         NaN         NaN        NaN       NaN        NaN       NaN       NaN           NaN           NaN           NaN               NaN           NaN          NaN          NaN           NaN     NaN             NaN       NaN          NaN                    NaN\n",
       "6      6001072100           Sua county  MULTIPOLYGON (((-170.65963 -14.26357, -170.659...      NaN        NaN         NaN         NaN        NaN       NaN        NaN       NaN       NaN           NaN           NaN           NaN               NaN           NaN          NaN          NaN           NaN     NaN             NaN       NaN          NaN                    NaN\n",
       "7      6001067300        Sa'ole county  MULTIPOLYGON (((-170.55324 -14.27831, -170.552...      NaN        NaN         NaN         NaN        NaN       NaN        NaN       NaN       NaN           NaN           NaN           NaN               NaN           NaN          NaN          NaN           NaN     NaN             NaN       NaN          NaN                    NaN\n",
       "8      6001086500      Vaifanua county  MULTIPOLYGON (((-170.59147 -14.25745, -170.591...      NaN        NaN         NaN         NaN        NaN       NaN        NaN       NaN       NaN           NaN           NaN           NaN               NaN           NaN          NaN          NaN           NaN     NaN             NaN       NaN          NaN                    NaN\n",
       "9      6002060900       Olosega county  POLYGON ((-169.62919 -14.17159, -169.62934 -14...      NaN        NaN         NaN         NaN        NaN       NaN        NaN       NaN       NaN           NaN           NaN           NaN               NaN           NaN          NaN          NaN           NaN     NaN             NaN       NaN          NaN                    NaN\n",
       "...           ...                  ...                                                ...      ...        ...         ...         ...        ...       ...        ...       ...       ...           ...           ...           ...               ...           ...          ...          ...           ...     ...             ...       ...          ...                    ...\n",
       "36564  6601028050  Dededo municipality  POLYGON ((144.84350 13.49876, 144.84330 13.498...      NaN        NaN         NaN         NaN        NaN       NaN        NaN       NaN       NaN           NaN           NaN           NaN               NaN           NaN          NaN          NaN           NaN     NaN             NaN       NaN          NaN                    NaN\n",
       "36565  6601083300    Yigo municipality  POLYGON ((144.87207 13.58439, 144.87181 13.585...      NaN        NaN         NaN         NaN        NaN       NaN        NaN       NaN       NaN           NaN           NaN           NaN               NaN           NaN          NaN          NaN           NaN     NaN             NaN       NaN          NaN                    NaN\n",
       "36566  6910014290           District 7  MULTIPOLYGON (((145.19508 14.11514, 145.19502 ...      NaN        NaN         NaN         NaN        NaN       NaN        NaN       NaN       NaN           NaN           NaN           NaN               NaN           NaN          NaN          NaN           NaN     NaN             NaN       NaN          NaN                    NaN\n",
       "36567  6912014270           District 6  MULTIPOLYGON (((145.53345 14.83529, 145.53351 ...      NaN        NaN         NaN         NaN        NaN       NaN        NaN       NaN       NaN           NaN           NaN           NaN               NaN           NaN          NaN          NaN           NaN     NaN             NaN       NaN          NaN                    NaN\n",
       "36568  6911013900           District 2  POLYGON ((145.72248 15.14267, 145.72244 15.142...      NaN        NaN         NaN         NaN        NaN       NaN        NaN       NaN       NaN           NaN           NaN           NaN               NaN           NaN          NaN          NaN           NaN     NaN             NaN       NaN          NaN                    NaN\n",
       "36569  6911013750           District 1  POLYGON ((145.72253 15.10729, 145.72237 15.107...      NaN        NaN         NaN         NaN        NaN       NaN        NaN       NaN       NaN           NaN           NaN           NaN               NaN           NaN          NaN          NaN           NaN     NaN             NaN       NaN          NaN                    NaN\n",
       "36570  6911014200           District 5  MULTIPOLYGON (((145.72226 15.15398, 145.72209 ...      NaN        NaN         NaN         NaN        NaN       NaN        NaN       NaN       NaN           NaN           NaN           NaN               NaN           NaN          NaN          NaN           NaN     NaN             NaN       NaN          NaN                    NaN\n",
       "36571  6911014050           District 3  MULTIPOLYGON (((145.72235 15.15976, 145.72063 ...      NaN        NaN         NaN         NaN        NaN       NaN        NaN       NaN       NaN           NaN           NaN           NaN               NaN           NaN          NaN          NaN           NaN     NaN             NaN       NaN          NaN                    NaN\n",
       "36572  6911014150           District 4  MULTIPOLYGON (((145.80504 15.28428, 145.80508 ...      NaN        NaN         NaN         NaN        NaN       NaN        NaN       NaN       NaN           NaN           NaN           NaN               NaN           NaN          NaN          NaN           NaN     NaN             NaN       NaN          NaN                    NaN\n",
       "36573  6908514075           District 4  MULTIPOLYGON (((146.06229 16.02764, 146.06236 ...      NaN        NaN         NaN         NaN        NaN       NaN        NaN       NaN       NaN           NaN           NaN           NaN               NaN           NaN          NaN          NaN           NaN     NaN             NaN       NaN          NaN                    NaN\n",
       "\n",
       "[36574 rows x 25 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salients_df[\"Geo_FIPS\"] = salients_df[\"Geo_FIPS\"].astype(int)\n",
    "county_subd_gdf[\"Geo_FIPS\"] = county_subd_gdf[\"Geo_FIPS\"].astype(int)\n",
    "\n",
    "county_subd_gdf_2 = county_subd_gdf.merge(\n",
    "    salients_df,\n",
    "    how=\"left\",\n",
    "    on='Geo_FIPS',\n",
    ")\n",
    "county_subd_gdf = county_subd_gdf_2\n",
    "county_subd_gdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7660092",
   "metadata": {},
   "source": [
    "Filter out areas without a defined population, as these are often national parks / do not contain users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "d84fdd4e",
   "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>Geo_FIPS</th>\n",
       "      <th>Geo_QNAME</th>\n",
       "      <th>geometry</th>\n",
       "      <th>Geo_NAME</th>\n",
       "      <th>Geo_STATE</th>\n",
       "      <th>Geo_COUNTY</th>\n",
       "      <th>Geo_COUSUB</th>\n",
       "      <th>Total_Pop</th>\n",
       "      <th>Pop_Dens</th>\n",
       "      <th>Land_Area</th>\n",
       "      <th>WhitePop</th>\n",
       "      <th>BlackPop</th>\n",
       "      <th>OtherRacePop</th>\n",
       "      <th>WhitePopPerc</th>\n",
       "      <th>BlackPopPerc</th>\n",
       "      <th>OtherRacePopPerc</th>\n",
       "      <th>PctLatinoPop</th>\n",
       "      <th>WhitePerCap</th>\n",
       "      <th>BlackPerCap</th>\n",
       "      <th>LatinoPerCap</th>\n",
       "      <th>PerCap</th>\n",
       "      <th>PoliceKillings</th>\n",
       "      <th>PKPerCap</th>\n",
       "      <th>PKPerSqMile</th>\n",
       "      <th>Blck_Wht_PerCap_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1500791620</td>\n",
       "      <td>Koloa-Poipu CCD</td>\n",
       "      <td>POLYGON ((-159.47758 21.94446, -159.47681 21.9...</td>\n",
       "      <td>Koloa-Poipu CCD</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>91620.0</td>\n",
       "      <td>6513.0</td>\n",
       "      <td>267.959400</td>\n",
       "      <td>24.305920</td>\n",
       "      <td>2611.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3869.0</td>\n",
       "      <td>40.09</td>\n",
       "      <td>0.51</td>\n",
       "      <td>59.40</td>\n",
       "      <td>13.86</td>\n",
       "      <td>49205.0</td>\n",
       "      <td>24639.0</td>\n",
       "      <td>12977.0</td>\n",
       "      <td>35558.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1500791440</td>\n",
       "      <td>Kekaha-Waimea CCD</td>\n",
       "      <td>MULTIPOLYGON (((-159.78534 22.01540, -159.7854...</td>\n",
       "      <td>Kekaha-Waimea CCD</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>91440.0</td>\n",
       "      <td>5424.0</td>\n",
       "      <td>60.076390</td>\n",
       "      <td>90.285045</td>\n",
       "      <td>987.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>4402.0</td>\n",
       "      <td>18.20</td>\n",
       "      <td>0.65</td>\n",
       "      <td>81.16</td>\n",
       "      <td>13.74</td>\n",
       "      <td>40439.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22627.0</td>\n",
       "      <td>33280.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1500790180</td>\n",
       "      <td>Eleele-Kalaheo CCD</td>\n",
       "      <td>POLYGON ((-159.55515 21.94415, -159.55503 21.9...</td>\n",
       "      <td>Eleele-Kalaheo CCD</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>90180.0</td>\n",
       "      <td>9552.0</td>\n",
       "      <td>265.937900</td>\n",
       "      <td>35.918159</td>\n",
       "      <td>2719.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6829.0</td>\n",
       "      <td>28.47</td>\n",
       "      <td>0.04</td>\n",
       "      <td>71.50</td>\n",
       "      <td>13.30</td>\n",
       "      <td>43940.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22011.0</td>\n",
       "      <td>31674.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1500791260</td>\n",
       "      <td>Kaumakani-Hanapepe CCD</td>\n",
       "      <td>MULTIPOLYGON (((-159.66121 21.94349, -159.6611...</td>\n",
       "      <td>Kaumakani-Hanapepe CCD</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>91260.0</td>\n",
       "      <td>4201.0</td>\n",
       "      <td>34.652970</td>\n",
       "      <td>121.230572</td>\n",
       "      <td>506.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3690.0</td>\n",
       "      <td>12.04</td>\n",
       "      <td>0.12</td>\n",
       "      <td>87.83</td>\n",
       "      <td>9.31</td>\n",
       "      <td>30089.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24595.0</td>\n",
       "      <td>26411.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1500792160</td>\n",
       "      <td>Lihue CCD</td>\n",
       "      <td>POLYGON ((-159.34601 21.95641, -159.34656 21.9...</td>\n",
       "      <td>Lihue CCD</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>92160.0</td>\n",
       "      <td>7058.0</td>\n",
       "      <td>1147.636000</td>\n",
       "      <td>6.150035</td>\n",
       "      <td>1528.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>5403.0</td>\n",
       "      <td>21.65</td>\n",
       "      <td>1.80</td>\n",
       "      <td>76.55</td>\n",
       "      <td>12.99</td>\n",
       "      <td>50783.0</td>\n",
       "      <td>45691.0</td>\n",
       "      <td>20009.0</td>\n",
       "      <td>33228.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.899730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1500792970</td>\n",
       "      <td>Puhi-Hanamaulu CCD</td>\n",
       "      <td>POLYGON ((-159.45922 21.94382, -159.45917 21.9...</td>\n",
       "      <td>Puhi-Hanamaulu CCD</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>92970.0</td>\n",
       "      <td>10871.0</td>\n",
       "      <td>137.945100</td>\n",
       "      <td>78.806697</td>\n",
       "      <td>1781.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>9041.0</td>\n",
       "      <td>16.38</td>\n",
       "      <td>0.45</td>\n",
       "      <td>83.16</td>\n",
       "      <td>8.41</td>\n",
       "      <td>45971.0</td>\n",
       "      <td>66349.0</td>\n",
       "      <td>16632.0</td>\n",
       "      <td>30505.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000092</td>\n",
       "      <td>0.012689</td>\n",
       "      <td>1.443279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1500790540</td>\n",
       "      <td>Hanalei CCD</td>\n",
       "      <td>MULTIPOLYGON (((-159.60977 22.15846, -159.6100...</td>\n",
       "      <td>Hanalei CCD</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>90540.0</td>\n",
       "      <td>6073.0</td>\n",
       "      <td>49.390540</td>\n",
       "      <td>122.958766</td>\n",
       "      <td>4126.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1945.0</td>\n",
       "      <td>67.94</td>\n",
       "      <td>0.03</td>\n",
       "      <td>32.03</td>\n",
       "      <td>7.85</td>\n",
       "      <td>46478.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18531.0</td>\n",
       "      <td>37766.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1500793780</td>\n",
       "      <td>Wailua-Anahola CCD</td>\n",
       "      <td>POLYGON ((-159.43426 22.06419, -159.43446 22.0...</td>\n",
       "      <td>Wailua-Anahola CCD</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>93780.0</td>\n",
       "      <td>13913.0</td>\n",
       "      <td>201.309600</td>\n",
       "      <td>69.112462</td>\n",
       "      <td>6115.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>7771.0</td>\n",
       "      <td>43.95</td>\n",
       "      <td>0.19</td>\n",
       "      <td>55.87</td>\n",
       "      <td>12.25</td>\n",
       "      <td>42550.0</td>\n",
       "      <td>75693.0</td>\n",
       "      <td>23750.0</td>\n",
       "      <td>35067.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.778919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1500791080</td>\n",
       "      <td>Kapaa CCD</td>\n",
       "      <td>MULTIPOLYGON (((-159.32028 22.07209, -159.3201...</td>\n",
       "      <td>Kapaa CCD</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>91080.0</td>\n",
       "      <td>8164.0</td>\n",
       "      <td>2276.402000</td>\n",
       "      <td>3.586361</td>\n",
       "      <td>2856.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5281.0</td>\n",
       "      <td>34.98</td>\n",
       "      <td>0.33</td>\n",
       "      <td>64.69</td>\n",
       "      <td>7.50</td>\n",
       "      <td>37725.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26687.0</td>\n",
       "      <td>33026.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1500393600</td>\n",
       "      <td>Waianae CCD</td>\n",
       "      <td>POLYGON ((-158.20327 21.45708, -158.20328 21.4...</td>\n",
       "      <td>Waianae CCD</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>93600.0</td>\n",
       "      <td>49971.0</td>\n",
       "      <td>819.848900</td>\n",
       "      <td>60.951473</td>\n",
       "      <td>5115.0</td>\n",
       "      <td>672.0</td>\n",
       "      <td>44184.0</td>\n",
       "      <td>10.24</td>\n",
       "      <td>1.34</td>\n",
       "      <td>88.42</td>\n",
       "      <td>17.33</td>\n",
       "      <td>37636.0</td>\n",
       "      <td>25388.0</td>\n",
       "      <td>15417.0</td>\n",
       "      <td>21903.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.016406</td>\n",
       "      <td>0.674567</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36536</th>\n",
       "      <td>2707752672</td>\n",
       "      <td>Prosper township</td>\n",
       "      <td>POLYGON ((-94.96000 48.93073, -94.96000 48.933...</td>\n",
       "      <td>Prosper township</td>\n",
       "      <td>27.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>52672.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>10.602660</td>\n",
       "      <td>12.544018</td>\n",
       "      <td>133.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>33277.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33277.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36537</th>\n",
       "      <td>2707704027</td>\n",
       "      <td>Baudette township</td>\n",
       "      <td>POLYGON ((-94.69569 48.79691, -94.69497 48.792...</td>\n",
       "      <td>Baudette township</td>\n",
       "      <td>27.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>4027.0</td>\n",
       "      <td>316.0</td>\n",
       "      <td>32.499560</td>\n",
       "      <td>9.723208</td>\n",
       "      <td>225.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>71.20</td>\n",
       "      <td>0.32</td>\n",
       "      <td>28.48</td>\n",
       "      <td>0.32</td>\n",
       "      <td>41095.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30317.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36538</th>\n",
       "      <td>2707704024</td>\n",
       "      <td>Baudette city</td>\n",
       "      <td>POLYGON ((-94.58328 48.70025, -94.58326 48.703...</td>\n",
       "      <td>Baudette city</td>\n",
       "      <td>27.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>4024.0</td>\n",
       "      <td>1030.0</td>\n",
       "      <td>242.557600</td>\n",
       "      <td>4.246415</td>\n",
       "      <td>897.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>87.09</td>\n",
       "      <td>0.10</td>\n",
       "      <td>12.82</td>\n",
       "      <td>0.19</td>\n",
       "      <td>21833.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19529.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36539</th>\n",
       "      <td>2707147352</td>\n",
       "      <td>Northwest Koochiching UT</td>\n",
       "      <td>POLYGON ((-94.22250 48.08743, -94.22462 48.087...</td>\n",
       "      <td>Northwest Koochiching UT</td>\n",
       "      <td>27.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>47352.0</td>\n",
       "      <td>494.0</td>\n",
       "      <td>0.458804</td>\n",
       "      <td>1076.712621</td>\n",
       "      <td>482.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>97.57</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.01</td>\n",
       "      <td>33531.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32926.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36541</th>\n",
       "      <td>2707137592</td>\n",
       "      <td>Littlefork city</td>\n",
       "      <td>POLYGON ((-93.54943 48.40461, -93.54945 48.404...</td>\n",
       "      <td>Littlefork city</td>\n",
       "      <td>27.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>37592.0</td>\n",
       "      <td>701.0</td>\n",
       "      <td>590.555800</td>\n",
       "      <td>1.187017</td>\n",
       "      <td>700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>99.86</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.14</td>\n",
       "      <td>3.28</td>\n",
       "      <td>30227.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30184.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36542</th>\n",
       "      <td>2707152990</td>\n",
       "      <td>Rainy Lake UT</td>\n",
       "      <td>MULTIPOLYGON (((-93.51728 48.35536, -93.51727 ...</td>\n",
       "      <td>Rainy Lake UT</td>\n",
       "      <td>27.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>52990.0</td>\n",
       "      <td>3084.0</td>\n",
       "      <td>8.768746</td>\n",
       "      <td>351.703622</td>\n",
       "      <td>2977.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>96.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.47</td>\n",
       "      <td>3.18</td>\n",
       "      <td>40708.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24543.0</td>\n",
       "      <td>40094.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36543</th>\n",
       "      <td>2707131040</td>\n",
       "      <td>International Falls city</td>\n",
       "      <td>POLYGON ((-93.41918 48.57652, -93.42108 48.576...</td>\n",
       "      <td>International Falls city</td>\n",
       "      <td>27.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>31040.0</td>\n",
       "      <td>5981.0</td>\n",
       "      <td>932.249700</td>\n",
       "      <td>6.415663</td>\n",
       "      <td>5584.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>327.0</td>\n",
       "      <td>93.36</td>\n",
       "      <td>1.17</td>\n",
       "      <td>5.47</td>\n",
       "      <td>0.48</td>\n",
       "      <td>26352.0</td>\n",
       "      <td>3049.0</td>\n",
       "      <td>4041.0</td>\n",
       "      <td>25347.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.115703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36544</th>\n",
       "      <td>2707153134</td>\n",
       "      <td>Ranier city</td>\n",
       "      <td>POLYGON ((-93.34008 48.61384, -93.34018 48.613...</td>\n",
       "      <td>Ranier city</td>\n",
       "      <td>27.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>53134.0</td>\n",
       "      <td>626.0</td>\n",
       "      <td>4356.886000</td>\n",
       "      <td>0.143681</td>\n",
       "      <td>552.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>88.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11.83</td>\n",
       "      <td>1.44</td>\n",
       "      <td>40754.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38208.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36545</th>\n",
       "      <td>2713732252</td>\n",
       "      <td>Kabetogama township</td>\n",
       "      <td>MULTIPOLYGON (((-93.09089 48.48749, -93.09086 ...</td>\n",
       "      <td>Kabetogama township</td>\n",
       "      <td>27.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>32252.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>6.397906</td>\n",
       "      <td>16.255319</td>\n",
       "      <td>104.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>37804.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37804.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36546</th>\n",
       "      <td>201601615</td>\n",
       "      <td>Aleutians West census subarea</td>\n",
       "      <td>MULTIPOLYGON (((-179.10743 51.30124, -179.1053...</td>\n",
       "      <td>Aleutians West census subarea</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1615.0</td>\n",
       "      <td>5698.0</td>\n",
       "      <td>1.297868</td>\n",
       "      <td>4390.276032</td>\n",
       "      <td>1660.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>3847.0</td>\n",
       "      <td>29.13</td>\n",
       "      <td>3.35</td>\n",
       "      <td>67.52</td>\n",
       "      <td>14.06</td>\n",
       "      <td>57717.0</td>\n",
       "      <td>50725.0</td>\n",
       "      <td>30963.0</td>\n",
       "      <td>39647.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.878857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36099 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Geo_FIPS                      Geo_QNAME                                           geometry                       Geo_NAME  Geo_STATE  Geo_COUNTY  Geo_COUSUB  Total_Pop     Pop_Dens    Land_Area  WhitePop  BlackPop  OtherRacePop  WhitePopPerc  BlackPopPerc  OtherRacePopPerc  PctLatinoPop  WhitePerCap  BlackPerCap  LatinoPerCap   PerCap  PoliceKillings  PKPerCap  PKPerSqMile  Blck_Wht_PerCap_Ratio\n",
       "17     1500791620                Koloa-Poipu CCD  POLYGON ((-159.47758 21.94446, -159.47681 21.9...                Koloa-Poipu CCD       15.0         7.0     91620.0     6513.0   267.959400    24.305920    2611.0      33.0        3869.0         40.09          0.51             59.40         13.86      49205.0      24639.0       12977.0  35558.0             0.0  0.000000     0.000000               0.500742\n",
       "18     1500791440              Kekaha-Waimea CCD  MULTIPOLYGON (((-159.78534 22.01540, -159.7854...              Kekaha-Waimea CCD       15.0         7.0     91440.0     5424.0    60.076390    90.285045     987.0      35.0        4402.0         18.20          0.65             81.16         13.74      40439.0          NaN       22627.0  33280.0             0.0  0.000000     0.000000                    NaN\n",
       "19     1500790180             Eleele-Kalaheo CCD  POLYGON ((-159.55515 21.94415, -159.55503 21.9...             Eleele-Kalaheo CCD       15.0         7.0     90180.0     9552.0   265.937900    35.918159    2719.0       4.0        6829.0         28.47          0.04             71.50         13.30      43940.0          NaN       22011.0  31674.0             0.0  0.000000     0.000000                    NaN\n",
       "20     1500791260         Kaumakani-Hanapepe CCD  MULTIPOLYGON (((-159.66121 21.94349, -159.6611...         Kaumakani-Hanapepe CCD       15.0         7.0     91260.0     4201.0    34.652970   121.230572     506.0       5.0        3690.0         12.04          0.12             87.83          9.31      30089.0          NaN       24595.0  26411.0             0.0  0.000000     0.000000                    NaN\n",
       "21     1500792160                      Lihue CCD  POLYGON ((-159.34601 21.95641, -159.34656 21.9...                      Lihue CCD       15.0         7.0     92160.0     7058.0  1147.636000     6.150035    1528.0     127.0        5403.0         21.65          1.80             76.55         12.99      50783.0      45691.0       20009.0  33228.0             0.0  0.000000     0.000000               0.899730\n",
       "22     1500792970             Puhi-Hanamaulu CCD  POLYGON ((-159.45922 21.94382, -159.45917 21.9...             Puhi-Hanamaulu CCD       15.0         7.0     92970.0    10871.0   137.945100    78.806697    1781.0      49.0        9041.0         16.38          0.45             83.16          8.41      45971.0      66349.0       16632.0  30505.0             1.0  0.000092     0.012689               1.443279\n",
       "23     1500790540                    Hanalei CCD  MULTIPOLYGON (((-159.60977 22.15846, -159.6100...                    Hanalei CCD       15.0         7.0     90540.0     6073.0    49.390540   122.958766    4126.0       2.0        1945.0         67.94          0.03             32.03          7.85      46478.0          NaN       18531.0  37766.0             0.0  0.000000     0.000000                    NaN\n",
       "24     1500793780             Wailua-Anahola CCD  POLYGON ((-159.43426 22.06419, -159.43446 22.0...             Wailua-Anahola CCD       15.0         7.0     93780.0    13913.0   201.309600    69.112462    6115.0      27.0        7771.0         43.95          0.19             55.87         12.25      42550.0      75693.0       23750.0  35067.0             0.0  0.000000     0.000000               1.778919\n",
       "25     1500791080                      Kapaa CCD  MULTIPOLYGON (((-159.32028 22.07209, -159.3201...                      Kapaa CCD       15.0         7.0     91080.0     8164.0  2276.402000     3.586361    2856.0      27.0        5281.0         34.98          0.33             64.69          7.50      37725.0          NaN       26687.0  33026.0             0.0  0.000000     0.000000                    NaN\n",
       "26     1500393600                    Waianae CCD  POLYGON ((-158.20327 21.45708, -158.20328 21.4...                    Waianae CCD       15.0         3.0     93600.0    49971.0   819.848900    60.951473    5115.0     672.0       44184.0         10.24          1.34             88.42         17.33      37636.0      25388.0       15417.0  21903.0             1.0  0.000020     0.016406               0.674567\n",
       "...           ...                            ...                                                ...                            ...        ...         ...         ...        ...          ...          ...       ...       ...           ...           ...           ...               ...           ...          ...          ...           ...      ...             ...       ...          ...                    ...\n",
       "36536  2707752672               Prosper township  POLYGON ((-94.96000 48.93073, -94.96000 48.933...               Prosper township       27.0        77.0     52672.0      133.0    10.602660    12.544018     133.0       0.0           0.0        100.00          0.00              0.00          0.00      33277.0          NaN           NaN  33277.0             0.0  0.000000     0.000000                    NaN\n",
       "36537  2707704027              Baudette township  POLYGON ((-94.69569 48.79691, -94.69497 48.792...              Baudette township       27.0        77.0      4027.0      316.0    32.499560     9.723208     225.0       1.0          90.0         71.20          0.32             28.48          0.32      41095.0          NaN           NaN  30317.0             0.0  0.000000     0.000000                    NaN\n",
       "36538  2707704024                  Baudette city  POLYGON ((-94.58328 48.70025, -94.58326 48.703...                  Baudette city       27.0        77.0      4024.0     1030.0   242.557600     4.246415     897.0       1.0         132.0         87.09          0.10             12.82          0.19      21833.0          NaN           NaN  19529.0             0.0  0.000000     0.000000                    NaN\n",
       "36539  2707147352       Northwest Koochiching UT  POLYGON ((-94.22250 48.08743, -94.22462 48.087...       Northwest Koochiching UT       27.0        71.0     47352.0      494.0     0.458804  1076.712621     482.0       0.0          12.0         97.57          0.00              2.43          1.01      33531.0          NaN           NaN  32926.0             0.0  0.000000     0.000000                    NaN\n",
       "36541  2707137592                Littlefork city  POLYGON ((-93.54943 48.40461, -93.54945 48.404...                Littlefork city       27.0        71.0     37592.0      701.0   590.555800     1.187017     700.0       0.0           1.0         99.86          0.00              0.14          3.28      30227.0          NaN           NaN  30184.0             0.0  0.000000     0.000000                    NaN\n",
       "36542  2707152990                  Rainy Lake UT  MULTIPOLYGON (((-93.51728 48.35536, -93.51727 ...                  Rainy Lake UT       27.0        71.0     52990.0     3084.0     8.768746   351.703622    2977.0       0.0         107.0         96.53          0.00              3.47          3.18      40708.0          NaN       24543.0  40094.0             0.0  0.000000     0.000000                    NaN\n",
       "36543  2707131040       International Falls city  POLYGON ((-93.41918 48.57652, -93.42108 48.576...       International Falls city       27.0        71.0     31040.0     5981.0   932.249700     6.415663    5584.0      70.0         327.0         93.36          1.17              5.47          0.48      26352.0       3049.0        4041.0  25347.0             0.0  0.000000     0.000000               0.115703\n",
       "36544  2707153134                    Ranier city  POLYGON ((-93.34008 48.61384, -93.34018 48.613...                    Ranier city       27.0        71.0     53134.0      626.0  4356.886000     0.143681     552.0       0.0          74.0         88.18          0.00             11.83          1.44      40754.0          NaN           NaN  38208.0             0.0  0.000000     0.000000                    NaN\n",
       "36545  2713732252            Kabetogama township  MULTIPOLYGON (((-93.09089 48.48749, -93.09086 ...            Kabetogama township       27.0       137.0     32252.0      104.0     6.397906    16.255319     104.0       0.0           0.0        100.00          0.00              0.00          0.00      37804.0          NaN           NaN  37804.0             0.0  0.000000     0.000000                    NaN\n",
       "36546   201601615  Aleutians West census subarea  MULTIPOLYGON (((-179.10743 51.30124, -179.1053...  Aleutians West census subarea        2.0        16.0      1615.0     5698.0     1.297868  4390.276032    1660.0     191.0        3847.0         29.13          3.35             67.52         14.06      57717.0      50725.0       30963.0  39647.0             0.0  0.000000     0.000000               0.878857\n",
       "\n",
       "[36099 rows x 25 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "county_subd_gdf = county_subd_gdf.loc[county_subd_gdf['Total_Pop'].notna()]\n",
    "county_subd_gdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef297b99",
   "metadata": {},
   "source": [
    "## Connecting Users to County Subdivisions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4fce992",
   "metadata": {},
   "source": [
    "Load in the locations of users, and convert exact location into user's county subdivision. I briefly had an issue using `sjoin()` where some users' mean locations were computed in bodies of water or ever-so-slightly off-coast, which is why I used `sjoin_nearest()` instead.\n",
    "\n",
    "(I believe EPSG:2613 is in meters!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2d1635c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "locs_gdf = load_locations_gdf()\n",
    "locs_gdf.drop(columns=[\"centroid\",\"PL_ID\",\"Twts\"], inplace=True)\n",
    "locs_gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17fb7b9c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "locs_gdf = locs_gdf.to_crs(\"epsg:2163\")\n",
    "locs_gdf = locs_gdf.sjoin_nearest(\n",
    "    county_subd_gdf.to_crs(\"epsg:2163\"), \n",
    "    how=\"left\", \n",
    "    max_distance=800\n",
    ")\n",
    "locs_gdf.drop(columns=[\"index_right\", \"Geo_NAME\"], inplace=True)\n",
    "locs_gdf = locs_gdf.to_crs(\"epsg:4326\")\n",
    "locs_gdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "673029f1",
   "metadata": {},
   "source": [
    "A handful of users' locations were too off-coast to reliably assign a county subdivision. See these users below -- filtered out for the rest of the analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f08fefcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "locs_gdf[locs_gdf['BlackPerCap'].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "b2df0aa1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "ax = county_subd_gdf.plot()\n",
    "locs_gdf[locs_gdf['PerCap'].isna() | locs_gdf['WhitePopPerc'].isna()].plot(ax=ax, color=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "5e0a6fe4",
   "metadata": {},
   "outputs": [],
   "source": [
    "locs_gdf = locs_gdf[locs_gdf['PerCap'].notna() & locs_gdf['WhitePopPerc'].notna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34ba5b99",
   "metadata": {},
   "source": [
    "Combine with user information from Twitter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "34fb47db",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/raycrist/opt/anaconda3/lib/python3.9/site-packages/pandas/core/frame.py:4906: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\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",
      "  return super().drop(\n"
     ]
    }
   ],
   "source": [
    "_, geo_users_df = load_users_dfs()\n",
    "geo_users_df.drop(columns=[\"Locations\",\"Has Geotags\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "432f35ad",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "locs_gdf = locs_gdf.merge(geo_users_df, how=\"left\", on=\"U_ID\")\n",
    "locs_gdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09e62f8d",
   "metadata": {},
   "source": [
    "Users who are private are not defined in geo_users_df, but may be in locs_gdf (since locations came from tweets downloaded at a different time). Thus, we sift out those users:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4e5e4d5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "locs_gdf = locs_gdf.loc[locs_gdf[\"Private\"].notna()]\n",
    "locs_gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24c1287e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "locs_gdf = locs_gdf.reset_index()\n",
    "locs_gdf.drop(columns=[\"index\"], inplace=True)\n",
    "locs_gdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b41652f",
   "metadata": {},
   "source": [
    "Finally, loop back to `county_subd_gdf` to add a per-cap adopters value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b980bcf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "users_by_subdiv_df = locs_gdf.groupby('Geo_FIPS').agg({\n",
    "    'U_ID': 'nunique',\n",
    "}).reset_index()\n",
    "users_by_subdiv_df.rename(columns={'U_ID': 'Adopters'}, inplace=True)\n",
    "county_subd_gdf = pd.merge(\n",
    "    county_subd_gdf,\n",
    "    users_by_subdiv_df,\n",
    "    how=\"left\",\n",
    "    on='Geo_FIPS',\n",
    ")\n",
    "\n",
    "county_subd_gdf['Adopters'] = county_subd_gdf['Adopters'].fillna(0)\n",
    "county_subd_gdf['AdoptersPerCap'] = (county_subd_gdf['Adopters'] / county_subd_gdf['Total_Pop'])\n",
    "county_subd_gdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e52c3d64",
   "metadata": {},
   "source": [
    "## General Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "81ae14e9",
   "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>mean-mode dev.</th>\n",
       "      <th>Geo_FIPS</th>\n",
       "      <th>Geo_STATE</th>\n",
       "      <th>Geo_COUNTY</th>\n",
       "      <th>Geo_COUSUB</th>\n",
       "      <th>Total_Pop</th>\n",
       "      <th>Pop_Dens</th>\n",
       "      <th>Land_Area</th>\n",
       "      <th>WhitePop</th>\n",
       "      <th>BlackPop</th>\n",
       "      <th>OtherRacePop</th>\n",
       "      <th>WhitePopPerc</th>\n",
       "      <th>BlackPopPerc</th>\n",
       "      <th>OtherRacePopPerc</th>\n",
       "      <th>PctLatinoPop</th>\n",
       "      <th>WhitePerCap</th>\n",
       "      <th>BlackPerCap</th>\n",
       "      <th>LatinoPerCap</th>\n",
       "      <th>PerCap</th>\n",
       "      <th>PoliceKillings</th>\n",
       "      <th>PKPerCap</th>\n",
       "      <th>PKPerSqMile</th>\n",
       "      <th>Blck_Wht_PerCap_Ratio</th>\n",
       "      <th>Following</th>\n",
       "      <th>Followers</th>\n",
       "      <th>Tweets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.437900e+04</td>\n",
       "      <td>3.437900e+04</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>3.437900e+04</td>\n",
       "      <td>34300.000000</td>\n",
       "      <td>34300.000000</td>\n",
       "      <td>3.437900e+04</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>3.437900e+04</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34050.000000</td>\n",
       "      <td>34194.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34300.000000</td>\n",
       "      <td>34050.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>3.437900e+04</td>\n",
       "      <td>3.437900e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.579819e+03</td>\n",
       "      <td>2.598706e+09</td>\n",
       "      <td>25.902382</td>\n",
       "      <td>83.953693</td>\n",
       "      <td>71933.772506</td>\n",
       "      <td>8.556566e+05</td>\n",
       "      <td>8036.880040</td>\n",
       "      <td>192.025560</td>\n",
       "      <td>4.641589e+05</td>\n",
       "      <td>156743.689229</td>\n",
       "      <td>2.347540e+05</td>\n",
       "      <td>61.736517</td>\n",
       "      <td>18.027244</td>\n",
       "      <td>20.236247</td>\n",
       "      <td>23.307587</td>\n",
       "      <td>45324.986096</td>\n",
       "      <td>26451.629134</td>\n",
       "      <td>22605.882348</td>\n",
       "      <td>37659.224439</td>\n",
       "      <td>25.720033</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.160133</td>\n",
       "      <td>0.630862</td>\n",
       "      <td>1196.417231</td>\n",
       "      <td>3.282625e+03</td>\n",
       "      <td>1.658021e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.955855e+03</td>\n",
       "      <td>1.652377e+09</td>\n",
       "      <td>16.489786</td>\n",
       "      <td>108.017975</td>\n",
       "      <td>29841.414150</td>\n",
       "      <td>9.993318e+05</td>\n",
       "      <td>13604.529302</td>\n",
       "      <td>466.710048</td>\n",
       "      <td>5.485587e+05</td>\n",
       "      <td>221619.030296</td>\n",
       "      <td>3.245024e+05</td>\n",
       "      <td>18.226162</td>\n",
       "      <td>16.546325</td>\n",
       "      <td>13.338321</td>\n",
       "      <td>18.519561</td>\n",
       "      <td>18702.669909</td>\n",
       "      <td>9201.380373</td>\n",
       "      <td>8103.623555</td>\n",
       "      <td>13530.812948</td>\n",
       "      <td>36.069957</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.161441</td>\n",
       "      <td>0.235241</td>\n",
       "      <td>4387.541000</td>\n",
       "      <td>5.706542e+04</td>\n",
       "      <td>3.645231e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.001926e+08</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.660000e+02</td>\n",
       "      <td>0.434984</td>\n",
       "      <td>0.032625</td>\n",
       "      <td>1.630000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.380000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4226.000000</td>\n",
       "      <td>602.000000</td>\n",
       "      <td>1611.000000</td>\n",
       "      <td>4725.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020028</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.001336e+08</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>8.099300e+04</td>\n",
       "      <td>1933.357000</td>\n",
       "      <td>36.339705</td>\n",
       "      <td>5.553000e+04</td>\n",
       "      <td>6863.500000</td>\n",
       "      <td>9.244500e+03</td>\n",
       "      <td>47.780000</td>\n",
       "      <td>5.780000</td>\n",
       "      <td>9.550000</td>\n",
       "      <td>8.210000</td>\n",
       "      <td>33715.000000</td>\n",
       "      <td>20324.000000</td>\n",
       "      <td>18142.000000</td>\n",
       "      <td>29147.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.023944</td>\n",
       "      <td>0.498853</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>1.900000e+02</td>\n",
       "      <td>2.039000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.328306e-10</td>\n",
       "      <td>2.605938e+09</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>90884.000000</td>\n",
       "      <td>3.957770e+05</td>\n",
       "      <td>3570.421000</td>\n",
       "      <td>87.658840</td>\n",
       "      <td>2.094240e+05</td>\n",
       "      <td>45888.000000</td>\n",
       "      <td>6.624500e+04</td>\n",
       "      <td>60.300000</td>\n",
       "      <td>11.880000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>18.750000</td>\n",
       "      <td>41883.000000</td>\n",
       "      <td>25557.000000</td>\n",
       "      <td>20597.000000</td>\n",
       "      <td>35563.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.107292</td>\n",
       "      <td>0.611203</td>\n",
       "      <td>597.000000</td>\n",
       "      <td>4.600000e+02</td>\n",
       "      <td>6.379000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.275834e+02</td>\n",
       "      <td>3.906102e+09</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>92522.000000</td>\n",
       "      <td>1.579075e+06</td>\n",
       "      <td>9067.710000</td>\n",
       "      <td>235.938329</td>\n",
       "      <td>7.563910e+05</td>\n",
       "      <td>240993.000000</td>\n",
       "      <td>4.233490e+05</td>\n",
       "      <td>76.380000</td>\n",
       "      <td>28.280000</td>\n",
       "      <td>28.450000</td>\n",
       "      <td>33.035000</td>\n",
       "      <td>49305.000000</td>\n",
       "      <td>30095.000000</td>\n",
       "      <td>26033.000000</td>\n",
       "      <td>41569.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.272115</td>\n",
       "      <td>0.726091</td>\n",
       "      <td>1227.000000</td>\n",
       "      <td>1.169000e+03</td>\n",
       "      <td>1.745500e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.993679e+04</td>\n",
       "      <td>7.214985e+09</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>840.000000</td>\n",
       "      <td>98000.000000</td>\n",
       "      <td>3.433961e+06</td>\n",
       "      <td>71488.690000</td>\n",
       "      <td>24607.898341</td>\n",
       "      <td>2.556502e+06</td>\n",
       "      <td>833683.000000</td>\n",
       "      <td>1.050342e+06</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>94.340000</td>\n",
       "      <td>98.430000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>156737.000000</td>\n",
       "      <td>192355.000000</td>\n",
       "      <td>238039.000000</td>\n",
       "      <td>164307.000000</td>\n",
       "      <td>204.000000</td>\n",
       "      <td>0.004762</td>\n",
       "      <td>2.579073</td>\n",
       "      <td>5.684942</td>\n",
       "      <td>362676.000000</td>\n",
       "      <td>6.848484e+06</td>\n",
       "      <td>2.355436e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean-mode dev.      Geo_FIPS     Geo_STATE    Geo_COUNTY    Geo_COUSUB     Total_Pop      Pop_Dens     Land_Area      WhitePop       BlackPop  OtherRacePop  WhitePopPerc  BlackPopPerc  OtherRacePopPerc  PctLatinoPop    WhitePerCap    BlackPerCap   LatinoPerCap         PerCap  PoliceKillings      PKPerCap   PKPerSqMile  Blck_Wht_PerCap_Ratio      Following     Followers        Tweets\n",
       "count    3.437900e+04  3.437900e+04  34379.000000  34379.000000  34379.000000  3.437900e+04  34300.000000  34300.000000  3.437900e+04   34379.000000  3.437900e+04  34379.000000  34379.000000      34379.000000  34379.000000   34379.000000   34050.000000   34194.000000   34379.000000    34379.000000  34379.000000  34300.000000           34050.000000   34379.000000  3.437900e+04  3.437900e+04\n",
       "mean     1.579819e+03  2.598706e+09     25.902382     83.953693  71933.772506  8.556566e+05   8036.880040    192.025560  4.641589e+05  156743.689229  2.347540e+05     61.736517     18.027244         20.236247     23.307587   45324.986096   26451.629134   22605.882348   37659.224439       25.720033      0.000028      0.160133               0.630862    1196.417231  3.282625e+03  1.658021e+04\n",
       "std      3.955855e+03  1.652377e+09     16.489786    108.017975  29841.414150  9.993318e+05  13604.529302    466.710048  5.485587e+05  221619.030296  3.245024e+05     18.226162     16.546325         13.338321     18.519561   18702.669909    9201.380373    8103.623555   13530.812948       36.069957      0.000037      0.161441               0.235241    4387.541000  5.706542e+04  3.645231e+04\n",
       "min      0.000000e+00  1.001926e+08      1.000000      1.000000     70.000000  1.660000e+02      0.434984      0.032625  1.630000e+02       0.000000  0.000000e+00      1.380000      0.000000          0.000000      0.000000    4226.000000     602.000000    1611.000000    4725.000000        0.000000      0.000000      0.000000               0.020028       0.000000  0.000000e+00  1.000000e+00\n",
       "25%      0.000000e+00  9.001336e+08      9.000000     31.000000  50000.000000  8.099300e+04   1933.357000     36.339705  5.553000e+04    6863.500000  9.244500e+03     47.780000      5.780000          9.550000      8.210000   33715.000000   20324.000000   18142.000000   29147.000000        1.000000      0.000011      0.023944               0.498853     290.000000  1.900000e+02  2.039000e+03\n",
       "50%      2.328306e-10  2.605938e+09     26.000000     55.000000  90884.000000  3.957770e+05   3570.421000     87.658840  2.094240e+05   45888.000000  6.624500e+04     60.300000     11.880000         17.200000     18.750000   41883.000000   25557.000000   20597.000000   35563.000000       10.000000      0.000027      0.107292               0.611203     597.000000  4.600000e+02  6.379000e+03\n",
       "75%      9.275834e+02  3.906102e+09     39.000000     97.000000  92522.000000  1.579075e+06   9067.710000    235.938329  7.563910e+05  240993.000000  4.233490e+05     76.380000     28.280000         28.450000     33.035000   49305.000000   30095.000000   26033.000000   41569.000000       34.000000      0.000037      0.272115               0.726091    1227.000000  1.169000e+03  1.745500e+04\n",
       "max      2.993679e+04  7.214985e+09     72.000000    840.000000  98000.000000  3.433961e+06  71488.690000  24607.898341  2.556502e+06  833683.000000  1.050342e+06    100.000000     94.340000         98.430000    100.000000  156737.000000  192355.000000  238039.000000  164307.000000      204.000000      0.004762      2.579073               5.684942  362676.000000  6.848484e+06  2.355436e+06"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "locs_gdf.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db80d092",
   "metadata": {},
   "source": [
    "## Histograms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a57a3eeb",
   "metadata": {},
   "source": [
    "To get a better grip on my data, construct histograms to examine feature distributions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "ab685ccc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Total_Pop                            float64\n",
       "Pop_Dens                             float64\n",
       "Land_Area                            float64\n",
       "WhitePop                             float64\n",
       "BlackPop                             float64\n",
       "OtherRacePop                         float64\n",
       "WhitePopPerc                         float64\n",
       "BlackPopPerc                         float64\n",
       "OtherRacePopPerc                     float64\n",
       "PctLatinoPop                         float64\n",
       "                                ...         \n",
       "PoliceKillings                       float64\n",
       "PKPerCap                             float64\n",
       "PKPerSqMile                          float64\n",
       "Blck_Wht_PerCap_Ratio                float64\n",
       "Following                            float64\n",
       "Followers                            float64\n",
       "Tweets                               float64\n",
       "Private                               object\n",
       "Born                     datetime64[ns, UTC]\n",
       "prior_adopter                         object\n",
       "Length: 24, dtype: object"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_plot_df = locs_gdf.drop(labels=[\n",
    "    'Geo_QNAME',\n",
    "    'Geo_FIPS', \n",
    "    'Geo_STATE', \n",
    "    'Geo_COUNTY', \n",
    "    'Geo_COUSUB', \n",
    "    'U_ID',\n",
    "    'mean-mode dev.',\n",
    "    'geometry',\n",
    "], axis=1)\n",
    "to_plot_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "459f3c60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'Total_Pop'}>,\n",
       "        <AxesSubplot:title={'center':'Pop_Dens'}>,\n",
       "        <AxesSubplot:title={'center':'Land_Area'}>,\n",
       "        <AxesSubplot:title={'center':'WhitePop'}>,\n",
       "        <AxesSubplot:title={'center':'BlackPop'}>,\n",
       "        <AxesSubplot:title={'center':'OtherRacePop'}>,\n",
       "        <AxesSubplot:title={'center':'WhitePopPerc'}>,\n",
       "        <AxesSubplot:title={'center':'BlackPopPerc'}>,\n",
       "        <AxesSubplot:title={'center':'OtherRacePopPerc'}>,\n",
       "        <AxesSubplot:title={'center':'PctLatinoPop'}>,\n",
       "        <AxesSubplot:title={'center':'WhitePerCap'}>],\n",
       "       [<AxesSubplot:title={'center':'BlackPerCap'}>,\n",
       "        <AxesSubplot:title={'center':'LatinoPerCap'}>,\n",
       "        <AxesSubplot:title={'center':'PerCap'}>,\n",
       "        <AxesSubplot:title={'center':'PoliceKillings'}>,\n",
       "        <AxesSubplot:title={'center':'PKPerCap'}>,\n",
       "        <AxesSubplot:title={'center':'PKPerSqMile'}>,\n",
       "        <AxesSubplot:title={'center':'Blck_Wht_PerCap_Ratio'}>,\n",
       "        <AxesSubplot:title={'center':'Following'}>,\n",
       "        <AxesSubplot:title={'center':'Followers'}>,\n",
       "        <AxesSubplot:title={'center':'Tweets'}>,\n",
       "        <AxesSubplot:title={'center':'Born'}>]], dtype=object)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x8640 with 22 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "fig, axis = plt.subplots(nrows=2,ncols=11,figsize=(20, 120))\n",
    "to_plot_df.hist(ax=axis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "c909548f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f96ba85fc70>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "pk_per_cap_df = locs_gdf.groupby('PKPerCap').agg({'U_ID': 'count'}).reset_index()\n",
    "\n",
    "plt.xlim([0.00, 0.0003])\n",
    "plt.ylim([0, 3000])\n",
    "\n",
    "plt.title(\"Police Killings: Per-Capita\")\n",
    "plt.scatter(pk_per_cap_df['PKPerCap'], pk_per_cap_df['U_ID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "7898e22d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f95fe1810d0>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEICAYAAACzliQjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAc2ElEQVR4nO3df5RcZZ3n8fcnTYONiA3SsEknkLiTiQO4gpRMduPOOKImB0eTVdG4KHGG2ZzhMDsy62YmcRx/zaqZwZ1V5iy4iEpYEMwIhvgjZiA466r8sGOCMWAkyI90J5JGaEXpDUn47h/36VjdXd11q7u6qlP38zqnTt166t5bz617+nNvP/ep5yoiMDOzYpjR7AqYmVnjOPTNzArEoW9mViAOfTOzAnHom5kViEPfzKxAHPo2KZIelfS6NP1+Sdc1+PM/I+lv0vRrJPVOl7q1OkkXS/rnstch6beaWSerzqFvwJGAHJT0K0lPSPqCpBNqWUdEfDwi/qTO9XqPpO+UvT5R0ncl3SqpPSL+NCL+thl1q5Wk6yU9l77jpyTdIelldVr37PSdPCnpF5J2SHrPJOoZkt48ovxTqfw9ABFxU0S8YfK1t0Zy6Fu5N0XECcArgVcBH2hyfYaRdBJwJ/AY8I6IONjkKk3E36fveDawH7i+1hVIOqZC8f8G9gBnAC8BLgGemHg1+QmwYsRnXgQ8PIl12jTg0LdRIqIP2AScDSDpzZJ2ShqQ9C+SfqfScpI+LOnGstevlvS9tNyeoTNEScdJ+qSkx9N/FZ+R1DFenSSdAtwF7ATeFRGHUvn1kv5btW0qr5ukuemMdUWqw5OS/rps3g5J6yQ9LelBSX85otnoryT1SXpG0i5JF1T7/JEi4lngi/zmO56VztT7JT0i6c9H1P3Lkm6U9EvgPRVW+Srg+oj4dUQciohtEbGpbB3vlvSYpJ9L+uvypq8xfBVYlA60AEuAHwI/K1vnsP/Cyk1kH1tjOPRtFElzgAuBbZJ+G7gZuALoAr4BfFXSsVXWcTrZgeMf03LnANvT238H/HYq+y2gG/jgOKs7Gfg/wL3AH0fE87VvVUWvBhYAFwAfLDuYfQiYC7wUeD3wrqEFJC0A/gx4VUS8CFgMPJree7WkgTwfnJrOLib7jmeQhez9ZN/FBcAVkhaXLbIU+DLQCdxUYZX3AP9T0vL03Zd/1pnANcC7gVlk/wnMrlLF/wdsBJan15cAN+TZtqTWfWwN4tC3chtSaH2HLGQ/DrwD+HpE3JGaUz4JdAD/rsq6LgbujIibI+JgRPw8IrZLEvCfgL+IiKci4pn0OcvHWdccsgD5QtR3sKiPRMRgRNxPFrivSOVvBz4eEU9HRC9wVdkyh4HjgDPTNYVHI+JhgIj4TkR0VvnM/5q+493ACWRn7a8CuiLioxHxXET8FPgsw7+TuyNiQ0Q8HxGDFdZ7EfB/gb8BHpG0XdKr0ntvA74WEd+OiANpnjwHzhuASyS9GPh9YEOOZZjgPrYGqdQ2aMW1LCLuLC+QNIusDR2AiHhe0h6yM7fxzKFy+28XcDywNcuG7GOAtnHWdT/wT8AmSRdExLYqn53Xz8qmnyULYcjOhveUvXdkOiJ2S7oC+DBwlqTNwH+JiL05P/OTETHsWomk84BZI/5LaCML8VF1qCQingZWA6tTU9gnyQ7is0duT0T8WtLPq1U0Ir4jqYvs2s7XImKwbJ+NZyL72BrEZ/pWzV6yi4PAkbO4OUBfleX2AP+6QvmTwCBwVkR0pseL08XNMUXEp4G1wB2Szq5lAyZgH8ObP+aMqMsXI+LVZN9LkDVlTMYe4JGy76MzIl4UEReWf2zelUXEk2ShP4usaWxf+TZIOp6siSePG4H3UVvTzoT2sTWGQ9+qWQ+8UdIFktrJAuAA8L0qy90EvE7S2yUdI+klks5J7fGfBf6HpFMBJHWPaL+uKCL+Hvg0cGdqW58q64E1kk6S1E3Whg9kbfqSXivpOLJ270GyJp/JuA/4ZbpA3CGpTdLZZc0zVUn6u7TMMZJeBFwG7I6In5NdC/jDdM3hWOCj5P/bv4rsusa389ZlMvvYpp5D38YVEbvILmT+I9kZ3JvIunY+V2W5x8kuBr8PeIrsIu5Qm/lfkbVp35N6o9xJdkE1T33+FrgO2CKp0n8S9fBRoBd4JNXty2QHOsja89eSfRc/A04F3g8g6d9L+lWtHxYRh8m+13PSZz5Jto0vrmE1xwNfAQaAn5L9F/LmtP6dwOVkvYX2AU+n7ctTt6ciYssErqVMeB/b1JJvomI2PkmXAcsj4vebXZd6kfQo8Ccjr+FY6/OZvtkIkmZKWiRpRmpGeh/ZWbTZUc+9d8xGOxb4X8A8suaSW4Crm1khs3px846ZWYG4ecfMrECmffPOKaecEnPnzm12NczMjipbt259MiK6RpbnCn1JnWRdyM4m+5HIHwO7gC+RjVHyKPD29KtAJK0BLiXrv/znEbE5lZ9HNqpgB9kYLu+t1hVs7ty59PT05KmmmZklkh6rVJ63eefTwDcj4mVkfa0fJPvJ95aImA9sSa+HBndaDpxFNjLf1ZKGfn59DbASmJ8eSya0NWZmNiFVQ1/SicDvAZ8DSANCDZCN+rcuzbYOWJamlwK3RMSBiHiE7Aca50uaCZwYEXens/sbypYxM7MGyHOm/1KgH/iCpG2SrpP0QuC0iNgHkJ5PTfN3M3xwqN5U1s3wXwEOlY8iaaWkHkk9/f39NW2QmZmNLU/oH0N2J6VrIuJc4NekppwxVBqGL8YpH10YcW1ElCKi1NU16jqEmZlNUJ7Q7wV6I+Le9PrLZAeBJ1KTDel5f9n85aMSziYbqbGX4SMXDpWbmVmDVA39iPgZsKdsVMMLgAfI7qozdA/NFcDtaXojsDzdLm0e2QXb+1IT0DOSFqbheS8pW6auNmzrY9Hau5i3+ussWnsXG7ZVGwXYzKwY8vbT/8/ATWlY1p8Cf0R2wFgv6VLgcbI79xAROyWtJzswHAIuT6MIQjbc6/VkXTY3pUddbdjWx5rbdjB4MPvIvoFB1ty2A4Bl51a774eZWWub9sMwlEqlqKWf/qK1d9E3MPpuct2dHXx39WvrWTUzs2lL0taIKI0sb7lhGPZWCPzxys3MiqTlQn9WZ0dN5WZmRdJyob9q8QI62offf7mjvY1Vi33THjOzaT/gWq2GLtZeuXkXewcGmdXZwarFC3wR18yMFgx9yILfIW9mNlrLNe+YmdnYHPpmZgXi0DczKxCHvplZgTj0zcwKxKFvZlYgDn0zswJx6JuZFYhD38ysQBz6ZmYF4tA3MysQh76ZWYE49M3MCsShb2ZWIA59M7MCceibmRWIQ9/MrEAc+mZmBeLQNzMrEIe+mVmB5Ap9SY9K2iFpu6SeVHaypDskPZSeTyqbf42k3ZJ2SVpcVn5eWs9uSVdJUv03yczMxlLLmf4fRMQ5EVFKr1cDWyJiPrAlvUbSmcBy4CxgCXC1pLa0zDXASmB+eiyZ/CaYmVlek2neWQqsS9PrgGVl5bdExIGIeATYDZwvaSZwYkTcHREB3FC2jJmZNUDe0A/gnyVtlbQylZ0WEfsA0vOpqbwb2FO2bG8q607TI8tHkbRSUo+knv7+/pxVNDOzao7JOd+iiNgr6VTgDkk/HmfeSu30MU756MKIa4FrAUqlUsV5zMysdrnO9CNib3reD3wFOB94IjXZkJ73p9l7gTlli88G9qby2RXKzcysQaqGvqQXSnrR0DTwBuBHwEZgRZptBXB7mt4ILJd0nKR5ZBds70tNQM9IWph67VxStoyZmTVAnuad04CvpN6VxwBfjIhvSvo+sF7SpcDjwEUAEbFT0nrgAeAQcHlEHE7rugy4HugANqWHmZk1iLKONNNXqVSKnp6eZlfDzOyoImlrWRf7I/yLXDOzAnHom5kViEPfzKxAHPpmZgXi0DczKxCHvplZgTj0zcwKxKFvZlYgDn0zswJx6JuZFYhD38ysQBz6ZmYF4tA3MysQh76ZWYE49M3MCsShb2ZWIA59M7MCceibmRWIQ9/MrEAc+mZmBeLQNzMrEIe+mVmBOPTNzArEoW9mViAOfTOzAskd+pLaJG2T9LX0+mRJd0h6KD2fVDbvGkm7Je2StLis/DxJO9J7V0lSfTfHzMzGU8uZ/nuBB8terwa2RMR8YEt6jaQzgeXAWcAS4GpJbWmZa4CVwPz0WDKp2puZWU1yhb6k2cAbgevKipcC69L0OmBZWfktEXEgIh4BdgPnS5oJnBgRd0dEADeULWNmZg2Q90z/U8BfAs+XlZ0WEfsA0vOpqbwb2FM2X28q607TI8tHkbRSUo+knv7+/pxVNDOzaqqGvqQ/BPZHxNac66zUTh/jlI8ujLg2IkoRUerq6sr5sWZmVs0xOeZZBLxZ0oXAC4ATJd0IPCFpZkTsS003+9P8vcCcsuVnA3tT+ewK5WZm1iBVz/QjYk1EzI6IuWQXaO+KiHcBG4EVabYVwO1peiOwXNJxkuaRXbC9LzUBPSNpYeq1c0nZMmZm1gB5zvTHshZYL+lS4HHgIoCI2ClpPfAAcAi4PCIOp2UuA64HOoBN6WFmZg2irCPN9FUqlaKnp6fZ1TAzO6pI2hoRpZHl/kWumVmBOPTNzArEoW9mViAOfTOzAnHom5kViEPfzKxAHPpmZgXi0DczKxCHvplZgTj0zcwKxKFvZlYgDn0zswJx6JuZFYhD38ysQBz6ZmYF4tA3MysQh76ZWYE49M3MCsShb2ZWIA59M7MCceibmRWIQ9/MrEAc+mZmBeLQNzMrEIe+mVmBVA19SS+QdJ+k+yXtlPSRVH6ypDskPZSeTypbZo2k3ZJ2SVpcVn6epB3pvaskaWo2y8zMKslzpn8AeG1EvAI4B1giaSGwGtgSEfOBLek1ks4ElgNnAUuAqyW1pXVdA6wE5qfHkvptipmZVVM19CPzq/SyPT0CWAqsS+XrgGVpeilwS0QciIhHgN3A+ZJmAidGxN0REcANZcuYmVkD5GrTl9QmaTuwH7gjIu4FTouIfQDp+dQ0ezewp2zx3lTWnaZHllf6vJWSeiT19Pf317A5ZmY2nlyhHxGHI+IcYDbZWfvZ48xeqZ0+ximv9HnXRkQpIkpdXV15qmhmZjnU1HsnIgaAfyFri38iNdmQnven2XqBOWWLzQb2pvLZFcrNzKxB8vTe6ZLUmaY7gNcBPwY2AivSbCuA29P0RmC5pOMkzSO7YHtfagJ6RtLC1GvnkrJlzMysAY7JMc9MYF3qgTMDWB8RX5N0N7Be0qXA48BFABGxU9J64AHgEHB5RBxO67oMuB7oADalh5mZNYiyjjTTV6lUip6enmZXw8zsqCJpa0SURpb7F7lmZgXi0DczKxCHvplZgTj0zcwKxKFvZlYgDn0zswJx6JuZFYhD38ysQPL8Item0IZtfVy5eRd7BwaZ1dnBqsULWHZuxcFHzcwmzaHfRBu29bHmth0MHsxGqegbGGTNbTsAHPxmNiXcvNNEV27edSTwhwwePMyVm3c1qUZm1uoc+k20d2CwpnIzs8ly6DfRrM6OmsrNzCbLod9EqxYvoKO9bVhZR3sbqxYvaFKNzKzV+UJuEw1drHXvHTNrFId+ky07t9shb2YN4+YdM7MCceibmRWIQ9/MrEAc+mZmBeLQNzMrEIe+mVmBOPTNzArEoW9mViAOfTOzAqka+pLmSPqWpAcl7ZT03lR+sqQ7JD2Unk8qW2aNpN2SdklaXFZ+nqQd6b2rJGlqNsvMzCrJc6Z/CHhfRPwOsBC4XNKZwGpgS0TMB7ak16T3lgNnAUuAqyUNjSp2DbASmJ8eS+q4LWZmVkXV0I+IfRHxgzT9DPAg0A0sBdal2dYBy9L0UuCWiDgQEY8Au4HzJc0EToyIuyMigBvKljEzswaoqU1f0lzgXOBe4LSI2AfZgQE4Nc3WDewpW6w3lXWn6ZHllT5npaQeST39/f21VNHMzMaRO/QlnQDcClwREb8cb9YKZTFO+ejCiGsjohQRpa6urrxVNDOzKnKFvqR2ssC/KSJuS8VPpCYb0vP+VN4LzClbfDawN5XPrlBuZmYNkqf3joDPAQ9GxD+UvbURWJGmVwC3l5Uvl3ScpHlkF2zvS01Az0hamNZ5SdkyZmbWAHluorIIeDewQ9L2VPZ+YC2wXtKlwOPARQARsVPSeuABsp4/l0fE4bTcZcD1QAewKT3MzKxBlHWkmb5KpVL09PQ0uxpmZkcVSVsjojSy3L/INTMrEIe+mVmBOPTNzArEoW9mViAOfTOzAnHom5kViEPfzKxAHPpmZgXi0DczKxCHvplZgTj0zcwKxKFvZlYgDn0zswJx6JuZFYhD38ysQBz6ZmYF4tA3MysQh76ZWYE49M3MCsShb2ZWIA59M7MCOabZFbDf2LCtjys372LvwCCzOjtYtXgBy87tbna1zKyFOPSniQ3b+lhz2w4GDx4GoG9gkDW37QBw8JtZ3bh5Z5q4cvOuI4E/ZPDgYa7cvKtJNTKzVuTQnyb2DgzWVG5mNhFVQ1/S5yXtl/SjsrKTJd0h6aH0fFLZe2sk7Za0S9LisvLzJO1I710lSfXfnKPXrM6OmsrNzCYiz5n+9cCSEWWrgS0RMR/Ykl4j6UxgOXBWWuZqSW1pmWuAlcD89Bi5zkJbtXgBHe1tw8o62ttYtXhBk2pkZq2oauhHxLeBp0YULwXWpel1wLKy8lsi4kBEPALsBs6XNBM4MSLujogAbihbxsgu1n7iLS+nu7MDAd2dHXziLS/3RVwzq6uJ9t45LSL2AUTEPkmnpvJu4J6y+XpT2cE0PbK8Ikkryf4r4PTTT59gFY8+y87trhjy7sppZvVS7wu5ldrpY5zyiiLi2ogoRUSpq6urbpU7Gg115ewbGCT4TVfODdv6ml01MzsKTTT0n0hNNqTn/am8F5hTNt9sYG8qn12h3MiCfdHau5i3+ussWnvXsEB3V04zq6eJhv5GYEWaXgHcXla+XNJxkuaRXbC9LzUFPSNpYeq1c0nZMoVW7UzeXTnNrJ7ydNm8GbgbWCCpV9KlwFrg9ZIeAl6fXhMRO4H1wAPAN4HLI2LoNPUy4Dqyi7sPA5vqvC1HpWpn8u7KaWb1VPVCbkS8c4y3Lhhj/o8BH6tQ3gOcXVPtCqDamfyqxQuGDc8A7sppZhPnX+Q2WbUzeXflNLN68oBrSbO6ReY5kx+rK6eZWa0c+jR3hMuh9bsfvpk1gkOf8S+mNiJ8fSZvZo3iNn3cLdLMisOhj7tFmllxOPSZviNcjvdLXTOziXCbPtPzYqpvn2hmU6HQoT+dR69s9sVlM2tNhQ39D2zYwU33PH5kqM96nknX42Dii8tmNhUK2aa/YVvfsMAfUo/RK+s1FLIvLpvZVCjkmf6Vm3eNOZj/eGfSI8/g/+BlXXzrx/3Dzujr1SzjMXfMbCoUMvTHC/YZEvNWf31Ys8yGbX18eONOBgYPHpmvb2CQG+95fNjrkSGd9zMrWXZuNz2PPcXN9+7hcARtEm89zz/iMrPJKWToz+rsoG+MED4c2f8AQyHe89hT3Lq1b8wwLzd48DBt0pF1jPzMWmzY1setW/uOrOtwBLdu7aN0xskOfjObsMK16W/Y1sevDxzKNe/gwcPcfO+eXIE/5HBEXfr8+45ZZjYVChX6QxdZy5tpAI5vH/trqHTWPp6hoY8nOxSye++Y2VQoVOhXOnsGOHBo7GBvU6V7uo/t2ecO0fPYUzXXbST33jGzqVCo0B/rLHm8s/l3/u6cUc0143n62YPceM/jw7psXvGl7cytcSiF6To0hJkd3QoV+rWeJQsonXEybz2vm9rO9yurpc/+yDtmdXa084L2GfzFl7aPOnh4jB4zy0tRY5t1o5VKpejp6anLukaOZ5NH+4ysN87zdfyaujs7+O7q1475fqXfA4zsQdTR3sYn3vJygIr9+X1LRbNik7Q1IkojywvVZXOo73t5//pqDtYz7ZNqPwAbOdBatV8Pe4weM8urUKE/1Pe92cZrZqp0sXkivx52Lx8zq6TlQ7/Sr2mbSTDuxdhawnro4FHph2b16OUznUchNbOJadnQz5pJfsjgweebXZVhgvFH8Rzr18Ji+Bl/eU+eVf90/6hmqL6BQRatvatiUOcJ82rj+fuAYHZ0askLuRd/9m6++/Dk+8pPBQEXLzx91EBtkDXt9A0MVgz4t57XPWyZocHexhpOotxJx7fzoTeddSSs81z4XbT2rorr7k719cXj2vlAaY001oXchoe+pCXAp4E24LqIWDve/LWG/gc27KjpQm1RtLeJK9/2Cj7y1Z08/Wzlpq6Tjm/njf9mZtWDyVjjC3V2tLP9Q28ARjerHd8+g+Pa2xh49uC4B60XHtvGx/5D6x088h5sG1mfPP/t+SDVePX63qdF6EtqA34CvB7oBb4PvDMiHhhrmVpDf+7qr0+2mi3r+PYZPDvFzV2fesc5QOUmp7zaZoj/ftErWipgxvvPabzuu1MhzwFouh2kiqKe3/tYod/oH2edD+yOiJ9GxHPALcDSBtehsKY68CFrorpy865JdXU9/Hy03MBy02kspTyD+XnAv+ZoxPfe6NDvBvaUve5NZcNIWimpR1JPf39/wypnk7d3YLAuQdZqXU6n01hKeQ5A0+kgVSSN+N4bHfqVRjMYdUoYEddGRCkiSl1dXQ2oVjHUOHbchMzq7KhLkLXawHLTaSylPAeg6XSQKpJGfO+NDv1eYE7Z69nA3gbXoZAEXPy7p9c0eNxI7TNEe9vYR46hEFu1eAHtMyZ+hGmboZYbWG7kWEoTHXK7HvIcgKbTQapIGvG9N7qf/veB+ZLmAX3AcuA/1vMDHl37xpa6mNs+A8Zrih/q3jnUo6a7s4O5L+ngew8/deRfqPIeMaUzTj7SM+DFHe08d+jwkbb+8t47Q+9LHOlxU961dKz3y0PMvXeGW3bu9Ljd5VAdxushkmceq79GfO/N6LJ5IfApsi6bn4+Ij403fz0HXDMzK4ppM+BaRHwD+EajP9fMzAo2nr6ZWdE59M3MCsShb2ZWIA59M7MCmfajbErqBx6b4OKnAE/WsTrTmbe1NXlbW1MjtvWMiBj169ZpH/qTIamnUpelVuRtbU3e1tbUzG11846ZWYE49M3MCqTVQ//aZleggbytrcnb2pqatq0t3aZvZmbDtfqZvpmZlXHom5kVSEuEvqQlknZJ2i1pdYX3Jemq9P4PJb2yGfWshxzb+hpJv5C0PT0+2Ix6Tpakz0vaL+lHY7zfSvu02ra2xD4FkDRH0rckPShpp6T3VpinJfZtzm1t/L6NiKP6QTZE88PAS4FjgfuBM0fMcyGwiWz4+YXAvc2u9xRu62uArzW7rnXY1t8DXgn8aIz3W2Kf5tzWltinaVtmAq9M0y8CftLCf695trXh+7YVzvTz3Gx9KXBDZO4BOiXNbHRF66AwN5aPiG8DT40zS6vs0zzb2jIiYl9E/CBNPwM8yOj7ZLfEvs25rQ3XCqGf52bruW7IfhTIux3/VtL9kjZJOqsxVWu4VtmnebXcPpU0FzgXuHfEWy23b8fZVmjwvm34TVSmQJ6bree6IftRIM92/IBszI1fpbuUbQDmT3XFmqBV9mkeLbdPJZ0A3ApcERG/HPl2hUWO2n1bZVsbvm9b4Uw/z83WW+WG7FW3IyJ+GRG/StPfANolndK4KjZMq+zTqlptn0pqJwvBmyLitgqztMy+rbatzdi3rRD6R262LulYsputbxwxz0bgktQrYCHwi4jY1+iK1kHVbZX0ryQpTZ9Pto9/3vCaTr1W2adVtdI+TdvxOeDBiPiHMWZriX2bZ1ubsW+P+uadiDgk6c+AzfzmZus7Jf1pev8zZPfkvRDYDTwL/FGz6jsZObf1bcBlkg4Bg8DySN0EjiaSbibr2XCKpF7gQ0A7tNY+hVzb2hL7NFkEvBvYIWl7Kns/cDq03L7Ns60N37cehsHMrEBaoXnHzMxycuibmRWIQ9/MrEAc+mZmBeLQNzMrEIe+mVmBOPTNzArk/wOqlQaHn9ksOAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "pk_per_cap_df = locs_gdf.groupby('PKPerSqMile').agg({'U_ID': 'count'}).reset_index()\n",
    "\n",
    "# plt.xlim([0.00, 0.0003])\n",
    "# plt.ylim([0, 3000])\n",
    "\n",
    "plt.title(\"Police Killings: Per Sq Mile\")\n",
    "plt.scatter(pk_per_cap_df['PKPerSqMile'], pk_per_cap_df['U_ID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "762e9a42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f95feba46d0>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "plt.xlim([0.000, 0.0005])\n",
    "plt.title(\"Police Killings: Per-Capita x Count\")\n",
    "plt.scatter(locs_gdf['PKPerCap'], locs_gdf['PoliceKillings'], marker='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "3dc98c79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f95fb89a790>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "plt.title(\"Per-Capita Income: Black x White\")\n",
    "plt.scatter(locs_gdf['BlackPerCap'], locs_gdf['WhitePerCap'], marker='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "7654a16d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f963e23c3d0>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "plt.title(\"% of Population: Black x White\")\n",
    "plt.scatter(locs_gdf['BlackPopPerc'], locs_gdf['WhitePopPerc'], marker='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "a67fe1fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f968c74ee50>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "plt.title(\"Police Killings x Black Pop. %\")\n",
    "plt.xlim([0, 50])\n",
    "plt.scatter(locs_gdf['PoliceKillings'], locs_gdf['BlackPopPerc'], marker='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "7046d0db",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "plt.title(\"Pop Density x Black/White Income Ratio\")\n",
    "plt.ylim([0,2])\n",
    "plt.xscale(\"log\")\n",
    "fig = plt.scatter(\n",
    "    locs_gdf['Pop_Dens'], \n",
    "    locs_gdf['Blck_Wht_PerCap_Ratio'], \n",
    "    c=locs_gdf['WhitePerCap'], \n",
    "    cmap = 'nipy_spectral',\n",
    "    marker='.'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f87b1a0",
   "metadata": {},
   "source": [
    "## Linear Regression\n",
    "\n",
    "First, a helper function to view the regression coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "1b23ee1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_coeffs(coeffs, alpha):\n",
    "    print()\n",
    "    print(f\"_______ COEFFS at ALPHA={alpha} ______\")\n",
    "    for tup in coeffs:\n",
    "        print(f\"{tup}\")  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d390b3d",
   "metadata": {},
   "source": [
    "### 1. Per-Capita #BLM Adoption\n",
    "\n",
    "Conduct regression with Lasso regularization, where the y-value is per-capita Twitter adopters, to identify which coefficients have the highest impact on behavior. We will use these coefficients to reduce the dimensions of our feature-space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "75663572",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = county_subd_gdf.drop(\n",
    "    labels=[\n",
    "        'Geo_QNAME',\n",
    "        'Total_Pop',\n",
    "        'WhitePop',\n",
    "        'BlackPop',\n",
    "        'OtherRacePop',\n",
    "        'Geo_FIPS', \n",
    "        'Geo_STATE', \n",
    "        'Geo_NAME', \n",
    "        'Geo_COUNTY', \n",
    "        'Geo_COUSUB', \n",
    "        'Adopters', \n",
    "        'AdoptersPerCap',\n",
    "        'PoliceKillings',\n",
    "        'PKPerCap',\n",
    "        'geometry'\n",
    "    ], \n",
    "    axis=1)\n",
    "y = county_subd_gdf['AdoptersPerCap']\n",
    "\n",
    "X.dtypes\n",
    "X = county_subd_gdf.drop(\n",
    "    labels=[\n",
    "        'Geo_QNAME',\n",
    "        'Total_Pop',\n",
    "        'WhitePop',\n",
    "        'BlackPop',\n",
    "        'OtherRacePop',\n",
    "        'Geo_FIPS', \n",
    "        'Geo_STATE', \n",
    "        'Geo_NAME', \n",
    "        'Geo_COUNTY', \n",
    "        'Geo_COUSUB', \n",
    "        'Adopters', \n",
    "        'AdoptersPerCap',\n",
    "        'PoliceKillings',\n",
    "        'PKPerCap',\n",
    "        'geometry'\n",
    "    ], \n",
    "    axis=1)\n",
    "y = county_subd_gdf['AdoptersPerCap']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "78e60eac",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "_______ COEFFS at ALPHA=0 ______\n",
      "(1.698438696384654e-05, 'Pop_Dens')\n",
      "(-5.118571808623745e-07, 'Land_Area')\n",
      "(-8.06738572177152e-06, 'WhitePopPerc')\n",
      "(1.5248336857492696e-06, 'BlackPopPerc')\n",
      "(-1.906677580042007e-06, 'OtherRacePopPerc')\n",
      "(3.3667166448969606e-06, 'PctLatinoPop')\n",
      "(7.3099655793000285e-06, 'WhitePerCap')\n",
      "(3.387218392698343e-06, 'BlackPerCap')\n",
      "(2.039346006364348e-06, 'LatinoPerCap')\n",
      "(-9.882291643963782e-07, 'PerCap')\n",
      "(4.903368938758977e-06, 'PKPerSqMile')\n",
      "(-2.7036100937901504e-06, 'Blck_Wht_PerCap_Ratio')\n",
      "\n",
      "_______ COEFFS at ALPHA=1e-06 ______\n",
      "(1.6325581040083313e-05, 'Pop_Dens')\n",
      "(-0.0, 'Land_Area')\n",
      "(-6.661022823196301e-06, 'WhitePopPerc')\n",
      "(1.804957696180346e-06, 'BlackPopPerc')\n",
      "(-0.0, 'OtherRacePopPerc')\n",
      "(1.8063651708052482e-06, 'PctLatinoPop')\n",
      "(5.593669179804927e-06, 'WhitePerCap')\n",
      "(1.383150335409054e-06, 'BlackPerCap')\n",
      "(1.5047735568175274e-06, 'LatinoPerCap')\n",
      "(0.0, 'PerCap')\n",
      "(4.228615353272555e-06, 'PKPerSqMile')\n",
      "(-7.57094527565095e-08, 'Blck_Wht_PerCap_Ratio')\n",
      "\n",
      "_______ COEFFS at ALPHA=5e-06 ______\n",
      "(1.4254965993124549e-05, 'Pop_Dens')\n",
      "(-0.0, 'Land_Area')\n",
      "(-3.857183321716553e-06, 'WhitePopPerc')\n",
      "(1.183326902776129e-07, 'BlackPopPerc')\n",
      "(0.0, 'OtherRacePopPerc')\n",
      "(0.0, 'PctLatinoPop')\n",
      "(1.5668208921621293e-06, 'WhitePerCap')\n",
      "(0.0, 'BlackPerCap')\n",
      "(0.0, 'LatinoPerCap')\n",
      "(0.0, 'PerCap')\n",
      "(1.4559186005471143e-06, 'PKPerSqMile')\n",
      "(0.0, 'Blck_Wht_PerCap_Ratio')\n",
      "\n",
      "_______ COEFFS at ALPHA=1e-05 ______\n",
      "(1.0496913567859663e-05, 'Pop_Dens')\n",
      "(-0.0, 'Land_Area')\n",
      "(-0.0, 'WhitePopPerc')\n",
      "(0.0, 'BlackPopPerc')\n",
      "(0.0, 'OtherRacePopPerc')\n",
      "(0.0, 'PctLatinoPop')\n",
      "(0.0, 'WhitePerCap')\n",
      "(0.0, 'BlackPerCap')\n",
      "(0.0, 'LatinoPerCap')\n",
      "(0.0, 'PerCap')\n",
      "(0.0, 'PKPerSqMile')\n",
      "(0.0, 'Blck_Wht_PerCap_Ratio')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/raycrist/opt/anaconda3/lib/python3.9/site-packages/sklearn/pipeline.py:394: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  self._final_estimator.fit(Xt, y, **fit_params_last_step)\n",
      "/Users/raycrist/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/raycrist/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e-04, tolerance: 5.004e-08 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/raycrist/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.162e-05, tolerance: 5.004e-08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/raycrist/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.036e-08, tolerance: 5.004e-08\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    }
   ],
   "source": [
    "alphas = [0, 0.000001, 0.000005, 0.00001]\n",
    "\n",
    "for alpha in alphas:\n",
    "\n",
    "    lasso = make_pipeline(\n",
    "        SimpleImputer(missing_values=np.nan, strategy='median'), \n",
    "        StandardScaler(), \n",
    "        Lasso(alpha=alpha, max_iter=2)\n",
    "    )\n",
    "    lasso.fit(X, y)\n",
    "\n",
    "    coeffs = list(zip(lasso[-1].coef_, X.columns))\n",
    "    print_coeffs(coeffs, alpha)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "849f021f",
   "metadata": {},
   "source": [
    "#### Graph Per-Capita Twitter Usage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "0f5923b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Per-Capita Geotagged Adopters'}>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "ax.set_title(\"Per-Capita Geotagged Adopters\")\n",
    "county_subd_gdf.plot(\n",
    "    ax=ax,\n",
    "    column='AdoptersPerCap', \n",
    "    legend=True, \n",
    "    vmin=0.0, \n",
    "    vmax=0.00008,\n",
    "    legend_kwds={'label': \"Per-Capita Geo-located Adopters\"}\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd8a4578",
   "metadata": {},
   "source": [
    "### 2. Mean Number of Friends\n",
    "\n",
    "Conduct a similiar regression, but to see what factors impact number of Twitter friends (people a user has followed). First, load the dataframe: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aadc0463",
   "metadata": {},
   "outputs": [],
   "source": [
    "subdivs_by_followers = locs_gdf.groupby(\"Geo_FIPS\").mean()\n",
    "subdivs_by_followers.reset_index(inplace=True)\n",
    "subdivs_by_followers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f6928b3",
   "metadata": {},
   "source": [
    "Now run the regression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "1391cd06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "_______ COEFFS at ALPHA=1.0 ______\n",
      "(0.0, 'Total_Pop')\n",
      "(-4.080630956706902, 'Pop_Dens')\n",
      "(1.0241831919654256, 'WhitePop')\n",
      "(10.64303913189807, 'BlackPop')\n",
      "(-3.2411602713322147, 'OtherRacePop')\n",
      "(-0.0, 'WhitePopPerc')\n",
      "(56.34024486374839, 'BlackPopPerc')\n",
      "(-16.440927325973824, 'OtherRacePopPerc')\n",
      "(-34.25091138118526, 'PctLatinoPop')\n",
      "(-206.25367810776612, 'WhitePerCap')\n",
      "(-55.91881729166822, 'BlackPerCap')\n",
      "(-66.20470122944758, 'LatinoPerCap')\n",
      "(394.919941575889, 'PerCap')\n",
      "(-2.1896132157232175, 'PKPerSqMile')\n",
      "(62.81201134464202, 'Blck_Wht_PerCap_Ratio')\n",
      "\n",
      "_______ COEFFS at ALPHA=2.0 ______\n",
      "(0.0, 'Total_Pop')\n",
      "(-2.005089306486521, 'Pop_Dens')\n",
      "(0.0, 'WhitePop')\n",
      "(1.943006937448537, 'BlackPop')\n",
      "(-0.0, 'OtherRacePop')\n",
      "(-0.0, 'WhitePopPerc')\n",
      "(34.56279223643958, 'BlackPopPerc')\n",
      "(-33.42083741822134, 'OtherRacePopPerc')\n",
      "(-27.44213650097295, 'PctLatinoPop')\n",
      "(-37.43584083594625, 'WhitePerCap')\n",
      "(-38.99025607699926, 'BlackPerCap')\n",
      "(-58.75029007085805, 'LatinoPerCap')\n",
      "(210.30840633352747, 'PerCap')\n",
      "(-1.474467854805777, 'PKPerSqMile')\n",
      "(55.32919109214217, 'Blck_Wht_PerCap_Ratio')\n",
      "\n",
      "_______ COEFFS at ALPHA=3.0 ______\n",
      "(-0.0, 'Total_Pop')\n",
      "(-0.0, 'Pop_Dens')\n",
      "(-0.0, 'WhitePop')\n",
      "(0.0, 'BlackPop')\n",
      "(-0.0, 'OtherRacePop')\n",
      "(-0.0, 'WhitePopPerc')\n",
      "(24.420276950816383, 'BlackPopPerc')\n",
      "(-38.1682640633124, 'OtherRacePopPerc')\n",
      "(-21.02893618709723, 'PctLatinoPop')\n",
      "(0.0, 'WhitePerCap')\n",
      "(-0.0, 'BlackPerCap')\n",
      "(-49.47435729838343, 'LatinoPerCap')\n",
      "(141.87366882282592, 'PerCap')\n",
      "(-0.0, 'PKPerSqMile')\n",
      "(22.123116397409625, 'Blck_Wht_PerCap_Ratio')\n"
     ]
    }
   ],
   "source": [
    "alphas = [1.0, 2.0, 3.0]\n",
    "\n",
    "X = subdivs_by_followers.drop([\n",
    "    \"Geo_FIPS\",\n",
    "    'mean-mode dev.',\n",
    "    'Land_Area',\n",
    "    \"Geo_STATE\",\n",
    "    \"Geo_COUNTY\",\n",
    "    \"Geo_COUSUB\",\n",
    "    \"Following\",\n",
    "    \"Followers\",\n",
    "    \"PoliceKillings\",\n",
    "    \"PKPerCap\",\n",
    "    \"Tweets\"\n",
    "], axis=1)\n",
    "y = subdivs_by_followers[\"Following\"]\n",
    "\n",
    "for alpha in alphas:\n",
    "\n",
    "    lasso = make_pipeline(\n",
    "        SimpleImputer(missing_values=np.nan, strategy='median'), \n",
    "        StandardScaler(), \n",
    "        Lasso(alpha=alpha, max_iter=2000)\n",
    "    )\n",
    "\n",
    "    lasso.fit(X, y)\n",
    "    y = lasso.predict(X)\n",
    "    coeffs = list(zip(lasso[-1].coef_, X.columns))\n",
    "    print_coeffs(coeffs, alpha)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a30a431e",
   "metadata": {},
   "source": [
    "#### Graph Mean Following Count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "092ab6e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Avg. Friends by Subdivision'}>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LuXHxWmblFR0xuToWbl25nmvnrqA3GSfs84+rctf4s2js9xB/DJyR/fKHK/eM23UNhpnCOIULvhLYr6qtACLyC+ACoFlEZnmj91lAi1e/AZib1n4OrklnQslJBX80c8dhUnI8+hhE48+hXX/rHRzrBnlcHKPgDYax4IYLHpeFTnXAehHJwzXRXAFsAvqBW4A7vc9fe/UfBO4Vka/gTrIuBV4YD0HGwoxQ8FMPZ2yKHcA3B8l7z6kRx2DIYcYjmJiqbhSRB4CXgBTwMnAXUADcLyIfwH0IvMOrv83ztHndq3/7RHvQgFHw0werGsn/wGRLYTBMK9xokuPjRaOqnwM+d1RxHHc0P1L9LwJfHJeLnyBGwU8wqoqqcuQcTPrcSxiYBez3jr16Mr6TugbDTMANVTB9V7KKSOWg3f9EMAp+ook/AV1/fWRZYB0ETgeCoK0Q/aVbnv+3WIUfn2ABDYZcYtqHKviziOzHXS37i8FVstmS1Z2LSImIPCAiO0Rku4icLyJlIvKoiOz2PktPRPoZh+8o3/nwdVD0T4hvNjj1EHkXUvh/wLcA8c+bHBkNhhzCQTJuUxVVXQr8H9wVsZtF5LcZ3DOPINtH238CD6nqCuAMYDvuktzHPAEe844NmZDI8H7k3VD4Gej/H7T3XyD2CGLlI/nvRcofQCJ/MXlyGgw5wKAXTaZtKqOqL6jq3+GuhO3AXTGbFRkVvIgUARcD3/UullDVLlyn/8EL3Q28ZUxSz1Tiz7qfvoVQ8LcghZDa651MQtwNUSyWifluMIwHjloZt6mKiBSJyC0i8gfgz0AjrqLPimxs8IuAVuD7InIGsBn4GHC8JbpHC3gbcBvAvHnG5KCJ59yQAwUfRXxVOD1fgNQu96R/DeT91aTKZzDkEoM5WacxrwC/Ar6gqmOOY5ONgvcDZwEf8XxB/5MxmGO8eA53Aaxbt27Cl+pOJTT2EEg+Uvq/SPAct9B/Afg7IDQHyXvfUFISg8Fw8iiQmsIj9CxYpKonrDezUfANQIOqbvSOH8BV8Mdboms4CtUk2vM58J8FBZ8EX/VQOQM/g0AV+C5AfOWTLKnBkHtMZRPM8RCRr6nqx4EHReQYBa+q12fTT0YFr6pNIlIvIstVdSeuU//r3jbSEl3DMfgh9ihYGyG1By38FCI+RAJQ/i1ApszI/cmWnxC3o6yvuIGigHngGKY5Om1NND/yPr98Mp1k6wf/EeDHIhIE9uFmMrEYYYmu4VhEBIo+D9oH4Tcili/t3NQaXTRF97GzdyMvdPyW62s/wumll022SAbDCTNeCT8mGlXd7H0+eTL9ZKXgVXULMFIozRGX6BqORSJvnGwRsuK62bdzYNdWkhrnwcNfZ1ZkCZXhuZkbGgxTlOk4gheR1xglvLCqnp5NP2Ylq+EI8v0l/PWSr9Gb6uCxprsp8JdMtkgGwwkzhoQfU43rvM/bvc9Bk817gIFsOzEK3nAMJcFqSoLV3LLw/06ZuQGD4URQhJQztcyg2aCqBwFEZIOqbkg79WkReRb4Qjb9TL87N0wYRrkbcoHpHKoAyBeRCwcPROQCID/bxmYEbzAYchedtiaaQT4AfE9EBpNKdwHvz7axUfAGgyFnmcY2eGDIm+YML2SMqGr3WNobE41hTHQkOrhr73dpj7dPtigGQ1Y4ni/8aNtURUTKReS/gCeAx0XkP0Uk6wUqZgRvyApV5f6GB2iPd7Cx4wUSTpwPL/3byRbLYBgVRbCn4SRrGvcBTwFv847fgxsb/spsGhsFb8iKxlgj0f4oL/ZsYnXhKjZ1vkTUjhLxRTI3NhgmkSk+iZqJMlX9l7TjfxWRt2TbeFo/2gwTR22kltrCWjaUnE9cYyhK/UDDZItlMIyK6vQ20QB/EpGbRMTytncCv8u2sVHwhqy5vPpSLi6/iLid4Izi01lasGSyRTIYMqIqGbephoj0ikgP8NfAvUDC2+4DPpFtP8ZEY8gav9/PsvKlfKzgI5QFS42fvGEaMOVH6COiqoXj0Y9R8IYxUxmqmGwRDIasmYoj9GwRkYtHKlfVp7JpbxS8wWDIWVTBdqavggc+lbYfxk3Xtxm4PJvGRsEbDIacZjp70ajqm9OPRWQu8KVs2xsFbzAYchZleptoRqABWJNtZaPgDQZDDjM9J1kHEZGvMxwX3gLW4ibizgqj4A0GQ05z4imrpwSb0vZTwE9U9dlsGxsFbzAYcprpbKJR1bsBRCSAa5o5NJb2ZqGTwWDIWVwvGivjNtUQkf8RkdXefjGuWeaHwMsi8q5s+5l6d2YwGAzjiGrmbQpykapu8/bfB+xS1dOAs4F/yLYTY6IxGAw5zTQ10STS9q8Cfgagqk1jWUGelYIXkQNAL2ADKVVdJyJluGErFwAHgHeqamfWVzYYDIZTjDI1Y81kQZeIXIdrc9+Am9kJEfEDWYdwHYuJ5jJVXauq67zjTwOPqepS4DHv2GAwGKYUmsU2Bflr4MPA94GPq2qTV34FExRN8gbgbm//buAtJ9GXwWAwjD8K6kjGLRtEpEREHhCRHSKyXUTOF5EyEXlURHZ7n6Vp9e8QkT0islNErhmT2Kq7VPVab1D9g7Tyh1X177PtJ1sFr8AjIrJZRG7zyqpVtdG7aCNQle1FDQaDYaIYx3DB/wk8pKorgDOA7RzHkiEiq4CbgNXAtcA3RcQ3zreWkWwnWTeo6mERqQIeFZEd2V7AeyDcBjBv3rwTENFgMBhOnPHwkvGSXl8M/JXbpyaAhIjcAFzqVbsbN3fqP+JaOO5T1TiwX0T24AYKe+7kpcmerEbwqnrY+2wBfokraLOIzALwPluO0/YuVV2nqusqKyvHR2qDwWDIgsFYNFmM4CtEZFPadttRXS0CWoHvi8jLIvK/IpLP8S0Zs4H6tPYNXllWiMjHvM8NJ3Lfg2RU8CKSLyKFg/vA1cBW4EHgFq/aLcCvT0YQw8xCNYbaI44JDIbxQwGVzBu0DQ5Eve2uo3ryA2cB31LVM4F+RncsGcnuM5Z3ifd5n18fQ5tjyMZEUw380vO99AP3qupDIvIicL+IfACoA95xMoIYZhYiYZ5q3cEFlWX4fad+OYbtOPQm4pSETZLwmcY4LWRqABpUdaN3/ACugm8WkVmq2niUJaMBmJvWfg5weAzX2+65p1eKyKtp5QKoqp6eTScZf1mqug93QuHo8nZclx2DYcw4jkN5fwFOSSf4Ttx0pxoHpxPx1Yxar663m2cPHeS9q9ae8LXc60VRZwDLV35S/Rgmiuy9ZEbDW2BULyLLVXUnru573dtuAe7kSEvGg8C9IvIVoBZYCrwwhuu9S0RqgIeB609UbrOS1TDhNLf3ICLMq6jkkZd/Tu2sazhrwejmSU1sgcAZI+SB9YHkZ7zmwuJSyiN5Jy60h0gErEDGet3RGMWR8ElfzzAOjJ+j+0eAH4tIENiHa0axGMGSoarbROR+3AdACrhdVe0xie36vp/hXW+ZV7xTVZPZ9mEUvGHCqS4v4mB3Oz3RR6mtPsjaki+hsTfjBC+nP5mkKBQ6or46HWj8Cb72ag/vXXkWxaEwIb/7ryviB8kuP3FRMJS5Uha4iwlHpyAURNVmEjzjDOno+IUqUNUtwLoRTo1oyVDVLwJfPJlrisgluEHGDuCaZ+aKyC3Z5mQ1wcYMk0Jh0GFOqJkd20rZ3xiB0IU4quzZcQiNPYJjtw9Xdroh71ZuXn4Wv3nsNfa0dUye4Fnis6wjlLvjJHGc+NCxOr0k+/5ANJH1YMxwokzTpaweXwGuVtVLVPVi4Brgq9k2NgreMKEc7KsnaafY1/xHWpMB5i9ZQ1PsNmwniN9KcObpC8G/mIN9XUNtxL8Qy5ePz7EI2hara45cU6fJXWj8+Qm+k9Fx7H40bXbPsgJY1vAbhFiFBAreQCQ4urlnjG/1hhGRLLYpS8Cz+QPuClcgs43Qwyh4w4Qyv2AuAZ+fudVXUBp5F9o5nw1nLsJnxSH2GJBA/ItZWLz4iHYvHjrExo5D/OVbzzu2U6cTUtuGDhsG6nml6+VTdg+OOvQlR4+r58bdS2Xsq7HrMD3RVgCSqWOVefpbQCqVuT/DCDhZbFOXTSLyXRG51Nu+A2zOtrGxwRsmheqIOwq/8AI3dIdIBEdtvrvrUc4uOANVi7PnDE+8rqvqAt+CEfuS0HkQGlb8PvFxWvExjl/jhiUWBYHSUev4/HOwbYeUkyIUOP7PbFZJ7XAba/Tx1vH0UFdvLyWF2c1DzDgG/eCnLx8Cbgc+ivuq8RTwzWwbGwVvmFA0VYf455FINRP0Vx9xzsq7gQ8sc1DgN9tfJdzip6IIqsPVSGB11teYFanNXGkCsCwh5PMPmWoyxfG2rNHPB/0j/1yNch+dKZrQIyu8UAdf8bYxY0w0hgnFSTbT0z1Af7/S2PofOE4SdXqGzlti4ROLXe2b+bfnnqI6XD1Kb6OjmsDp+w468JMjypvae0+4z7EwqNCTtj2kZBKJxCgthulLRIf2k7axw58U03uS9aQwI3jDhPLcc3kc9D3OmrLtVNYUQd+/Q/6tQNER9d625q289zgj1mwRCaJ2E9r/CyRvOI1lTfmpGfE6ThTLOnalbPrI25YYrT11lOaXIVJyXLNMyB8c2g/4ju9qadsOPp8Zp43K9DbRnBRGwRsmlA2XrWKDzqW5r4iqggsRcRhpCLW4tGxcridF/wj5WecoPilGUu5HEwkUIck2/rTrHjbMezN5+QtR1WPMNxZxYHhhluPoiCacdOVuawpfFj76pwrVVFZrBCYayZERuohYQIGq9mSs7GEe/YYJpTVWjyb3EqEeIYqIH5Gsvb7GjEgQ8S85Zf2no1kaezt7kszOz6MrEUdVSSRtoqlhs1EymcKSEKpKynFQdZV7updNT6KTpH2kD/14KXdNvj7knqmawEnWHbduS9uw3L09u8bl+uOKCjhZbFMUEblXRIq8QI+vAztF5FPZtjcK3jChFFID/rkU5l2BWMMhBjR1fCUyUajThTqdQ4paNTam9umjcDdc+LE4jlJdtYzls95JZeE8lCShoJ+IvxDHcZVqIOAnPtBFf6qf2EAfXc3P4ThxAv5hU01RsJSA7xQ9GP0rh9wzRYLEkz3097eQTNmo3XRE1aqKQpoGXDfPouJVHOqagovQprcNfpU3Yn8L8HtgHvCX2TY2Ct4wYaRSNn946FVEBxCneahcNYadyByHqb8/nrHO0bzQ2MAn//QHtrY2Z6yrLRvQlvMYTGgvEqY/GaU7fiKTssexm0sCx0mSdCJYEga7E8cewEkOYFnDbcL55RQECgj1NVNctf6IRVKqp8YfXlU50Nsx9KBSVR5+bReRvDUEgklauhuPCOqmqsQH9lOTV0ki6co0u2R8TGvjyvRW8AFxX3HfAvzai0OTtcRGwRsmDL/fx1tuOBvxz0OCw37qImH8eW/P2L6utYOd+3+KY/dlrHtwZyMAW5obeWDnNhr6sjFbDnqaD/9+8gMRikPZT8oOhyMQVI/1XLckhN8fJBT0I5JEfBVYvjwGHIdY6tiwBYGapViWxcAR3jeufD2JOIlUikOdnfQNdOE4J6epRIQFhWVHHF++yl1wFgzMJi/Sc8Sbya5Dh9m1pdU7P/Vs70NMbwX/P7hxaPKBp0RkPmBs8IapR1vLNr72nT+iA/eiiVfG3H7ZvGriiRX8+u4Xeep3x2/fGR9g3jJ3pLm/u5P5RSXU5BVk7F9Kvo6UfJMxrAQ/hqGRtkZhlLkwEUHEXamqTi8FoQJCvvRVq4M2cKV3oJtwmidO0tOxRcEQClQWFJKMt+CkRl9dO0h3zwAAjtrYThJVm1QqieM4xKJHmpYsN1mRK3OqCJEgjibpjrcRix1i1TlZhSWfPLJP+DHl8CZVm1V1tqq+UV3bYR1wWbZ9GAVvmDDKKhZy63suQvLefcQIPlt8lsVpy07n8MFOdr5yrM1+06b9OI5DUTA0ZGb43IbL+OON72Nt9ayM/Uv4SncbhwiQYuUjVslxz8fsAcBzhRTXW8b9PbvYvS/gJFpJ2Sle2/4EmhhW3vvb6uns2k9Xxz5Cfj8Bv4WVV8PrHd0AdHf3jypbfcsmuvr3UNe0l4e3PExLyy4s+mhuv5/2wy8NzT2kbJtoIi3rnFVGe3c/O9tfo72li5rypfgCUz8ksmjmbSqi7ivgh48qUx2Djc4oeMOE4ThRCgvCHOrK+g3zGESE2z//F3zwjusAsD0ziKYa2LOnmaef2UlvcljBhf2BUf3IB1Ens9lnLAxOmPb2uAuWbNvxyh36ugcI+/IQERL2yCGFQ6Xn0x9ziDtJ5i86lwN93UPnSsL5HG7qIBguYCAapa7laZob+6G/gYde30zPwOiTw3GNYw8UM6uykGvXXkZZaSmOJiktfjOzF69HxFXa/YkEz9XvY6C/EzvZR8CXojDcR6JzJylfFwX+aRIKeXqbaB4VkU+KyFwRKRvcsm08hQ1nhlyjd2AbpUUXs7n+ELsbW7l4+SI64gMELB/FobGNBAdH6IcHOpmbXw5WGTfdNOeEZRMrswlnLFiWD9UUhUWub/ygv3rSdgjnD0+Y7murY0X1QmDQ88ZGJELSsWmOxpA+2LNnB6uXVwy1qSippKwkQttAnJKgQ3HBQjQ/yJ6mLirtp5hTc/xUob09Uc5eetURZeLrw07sIppaTCgQARQRH8WRCCv8C0j4LQJODJ/dTWd3F/g6iXY9gr/i1MX7GU+m6gg9S97vfd6eVqa4ScAzYhS8YcIoKbwIgOtPW0ki1Y9IjIFUkr5E/5gV/CBz893UeWKdfLam8SKZsgn4fUcs+lFVHEcJBfyo0w0U09LexeKKubS1HqRJfaypch9QSSeGT0I4frB795DM20jYWgKchqrS3dlPfV8L1RUlWL4gQaeArmiK2YX5SOEHRnwjUI2j6iev0J1f6E72UhxwJ48VH/7IhTid+wAZMhVF4110O6XMtprpT1oc2r+NeEkVm+vyWVLVTSLWSMCqwvJPcTPNFLWxZ4OqLjyZ9sZEY5gw9ne0YjuuqSLoz0ckwrzCElaVV2VoOXY0tWdc+okls4sdk05gBNOF4+jQKH5/12FiyRRV5SUEfH4KS/KYW+i2UVUCVphorJ9l5fMJFSwhECmkrXcn/Qk31WGcVuaW1VAWjJCI2UTCxZQX5VNdVMCs6mE3xlQySWdn66BUWJYPwYeqDil3AMvnvh2Uly4adpG0DxFLWsyqDDKQmkVX5x8ZCBayuqqKM2fFicc28MxLW+jvPTDm72dCycY8M4VH+CKSJyL/R0Tu8o6Xish12bY3Ct4wYSwqr8oYEjcdtdtxeu5E48+M6Tqv7zjMvrqxjyobB14jZh85PxAOBI9T+/ikuxLaSVfBpocUyE8kCaQdB/2FFIYqUKefeGyHWydSQH+8G3/KIuRfTF54JTsb3Bj3JQVhLF8jgUAQX8DGdiDaN0B/cg69vQMcbm/ktcYXeGbnPTi+fjp7+4AE/X0xLJFjwiKkvGBmjp3kUEcz6vShDjR17uTV/Tt5peEhDraHWTL/bDo79hBKlLF+9lzWLl9GfsG8MX8/E840VvDA93EXZlzgHTcA/5ptY2OiMUxZtP9/YeB7aOw3UPlEViENmnr7eGnndjacHgTGZpPP85fRGY1R7B8gL5w2EnYc/Ec9mBLJ1DG+36oOIpYb5MyLL9PTf5BQXjF5weEHRXXN0a6FPq9dPuHIyqHSSKCQ8qoUVXIF8YTNvFr3oRUOVaGpLpJ2P+KzGEilsPIKKPRDNC5U5lUSHCigSJL09zezq7OJi5edTV6+P22VrmJ59xRNDuB3QoimKIqkSAw0cKA9xKGGbZRXdBMML2RO8Wr8vTsoKFhD5Sw3Tn8hHBEJdKoiUzuhRyYWq+qNIvIuAFWNSqa402lkPZwSEZ+IvCwiv/WOy0TkURHZ7X2OngHBMOOpq2vjvrufGfIs0dS+4y7pB5C8tyEl30SKvkC2/6p1XV1sbGynqnArmjqQsf6+vnp+feiPABQHZ7O3rZtn67uOqHO0cocjF/a49nUHdXpRe9AkkkRT+ygpPvMI5T5YPx33wWWRSMvY5Nhd9MeT5AeDhAMBivOH30iSSWhoGCDoLyHV56MwGKSnfwCxIgSDQn93E+FAkM7uWfQkC7hy1Xo03ood7/D871390J/oxtEEPisBCI6jxO0AXQmLaPQwh6N7idhbSXT+kqb4L2iyypCCI2Pti3VkFNApyfQewSdExJ35BkRkMZD1ku6xjOA/BmxnOK7rp4HHVPVOEfm0d/yPY+jPMMOYN6+CqhvPIxHzVmwmt4JVDjKyGUT8S2CMgcKqCwq48CyH12LtrHGKyeQbE/GFsNL8z7t9TTT1RulLLqAgSx/vYaVZnFYWBP/Ijg7KsVlARYSAz0cilSJg9ZNSH5bVzUCqgMMdffgiwsLCSuzYy0R1GbPKQ7T29FFRnE99Rxc1hX5StoNqjOLSakQCnLZkLjHbxrF7ScSb2N36ImuX3oqIQqqdvEAlAAFfAYJDW28nPg0Q1CKCwYeZXVPK4tICujoupnLu+Vl9F1ONqeznniX/DDwEzBWRHwMbgL/KtnFWwyIRmQO8CfjftOIbgLu9/btxYyUYDKMSDgcoKvEW9kSuR6xidvTsPaLOySSanl9awjvPPJumHXORzr/LGOGxxlfPm2svHzqeV9DP25csIN8fGqXVidMdde3gAB1tvfT3uT7rgyadoN+PWMUE/YU4FJPocRAnTkkigqqyvXsWe1q30my3kR9q50BjB76BKB1tMbo6e/ntE//MQMc2WlqbsSRKJOBnINlHPDyf2qo3e2YZCytYhYigGiUe76Kjd4C8QAHJZJzX6h4kJcsIW6VQ/nEqV09P5T7ENF3JCqCqjwB/gavUfwKsU9Unsm2frYnma8A/cGRayGpVdx2z9zmiK4SI3CYim0RkU2tr60hVDDOYxw/vYFPHa0eUHer8HInUoRPuUwJLOXf5FeTlr4dRvGlUE2jvV9l74EPsbd+Fow4DsW4O1P0vqdTYvWdGI5myOdzZQ3Fk+K2grKKQ/AL3OJ6WtUlVcVINSApKygpYXDOX0vICRITlFZWkBvYz0B0iaVezoNqGSJKe/mcpKIpw+uJL2dkWB+lGrCIs8dPY/AwVeeWUF7j3lPBi3ji2TXdPC8FgOeVFRRTk59Gf6AS7g4RdTrRnHoHgqXnQTSjT2EQjIj/CVfB7VfW3qto2lvYZFbznktOiqlln8k5HVe9S1XWquq6ysvJEujDkEE70eb6948ekvJWel85azo3zrmPPlv3Ub/0J0b6f0WNvwGcVZ+hpdEprqtmffAcSWDpKLQH/IqzQOdyzdwu7e/dzTuXFNDgVBAIjKzbHHnskR9u2ScX7qQlvxUkdG9VSNXFErBkRwfLPocBbEKVOFwB93e0EfD7ml5zJ0po5FEVC9NiF9HZtprRqHSlR5s29gLOWn0de0QJsdbCdASorrqOrbwt4K1R9XtTKmB3jQIvS2LWFVw5+hb6uzVRWVmPVvBWr7wAXz5n8EM7jwXQNVeDxfWAW8HUR2SsiPxeRj2XbOJsR/AbgehE5ANwHXC4i9wDNIjILwPtsGbPohpmHU8wF4RiHe9wQvAK82riDn/9sI8VzridhXUN9K/hOcmXp5sbD/Hz7tpFFSO72TCIBrOIvUFn+Xt61+HKWFy0Gp50BJ8S3Xt7Iod5jPUQsX3bTVi2NXfTF3KBe4rTQPtBHY/cBSAuLrKp0dfUTTw6/ZTR2DYckSDkOPbEYzTFXIRcUu4u6amqW0+P0Ex0YoLfjIIf6XsbREsK+AOAQjccJ+/1Y2o8lYUryI4TDKwj4Xc8gHx0k7QQh+wV6e57Hit+P34GCorMo4lVOL61h2cLTCM55X1b3OqVR14sm0zZVUdXHgS8Cn8U1ka8DPpRt+4wKXlXvUNU5qroAuAl4XFXfCzwI3OJVuwX49dhEN8xErPyVrJj9Dp5+YTvReBLVAayugyx6U5hXGlt4eX8rC/1nAvDk1n20dJ9YjJgL583nUxdcNLIMgaVH+IJH/H6+tfkFmvv6kMBqzqydzU0rTqMqL3/E9kcTSwvl6zgOHd29lFcVUhDOw3EcoAJbU+yL2ji+JTR29JCwB2iL9eOP+LDFnUh+qe5p9h9+gW31L9MdjRFPpSgKhykNRYj2u9mfVJVD3T0UpPLIy8+nqHQBaxd9kqrCGD4RbMoIBgI46vDKc/VDq1LD3mpTVQX7EIiF+CrxWa9zuDdFv385JPfSnSogbNUTDGe1En56ML1NNI8BzwI3AjuBc1R1RbbtT2ah053AVSKyG7jKOzYYMhIIFPHG9QPs7/gpD25/mKrqV6ko9dE1EGPF7CqWLauhP5bgG7/7M00dJ5JsY2z4LIuvXv1GqgsKeGzLHsKBJSSTqayClAGE09wgRYSSwgg+r20ilUL8fuZXzeO8OTfiD61kVlkRQV8elZEC8n0t5Pld//6KwpUUlQaJtSUojoSxk+7QMuTzY3vhg0WE2cVFYB2goelJCoMhygqLgCQ4LXR1RXGSbdR17qZo9vZjslIlkp089cphOpv7aIzOpbj8CuZWvZnTSwvpj81nwF5Cd8pHMHTq0ihOONNYwQOv4i50WgOcDqzx3CazYkwKXlWfUNXrvP12Vb1CVZd6n1MwV5dhqvLU7xWnv5mrqh+jb2AOs3yVXHv6MioK3VFzXijAJ264iNMXZg7zO8hPtr9Cyjm59+0LVy/k2w+1ZEyHd9zkGhrFsobNOOFgcOhtITjCqljxzxs6P6+0gjWzLmHJCjeIV1Ga77u/wCEeG04I0t17gOK8GDsP/YFXW14FtSG5jfJiH/5gJYvLFzC/EpL995JM7R/yJgoFy6i3Lfp9LzG7uJg51edSW3YZkdAqCkvzKYlAzB7fwGuTzXS2wavqJ1T1YuCtQDuuTb4r2/YmVIFhUnjD9RtYVrSIpwfW8ExPD032i3z/9xuJxRKo08uOzlbOXzF/TH0+Wb+fpHPiLpYA4aCfL7ztKkrzRx8kJVMpEokUtn3k9cTKw+l7iVjnvqyup8ld9HYPJ9XoTzYPedokE8NzCKJhUHd9izp91JRY1LWcTlFxFWFrAJUyJHw5ibhNU9NrNLcn2bv3bHa8vIx4rJ/dO4av8Z7zrmFhlesaGrBd85IVqnaPpYRY+/h6EBlOHBH5sIj8FNiC64r+PeAN2bY3oQoMk0IwL0KvE6Am1UlB6WkU+pey9qwgecnP8EjDYn5aV8P3rnpbVn1pag+O+jm/dh59iQQRf2bzwi83buVASyefeLNrp08mbX70wPPc/I71+LOIcx4KjnwNVSVhF6PB4YXdTjKJ4/NhaQLLF3Zt6eDGhQksozDNYSg/4CralJMgEFzt9un04UiAZMAijBfaOHQO8ypCdDphygNNuIsbC/AHwpQUryCSF6Smooi+5CwKAiGWrQTHSXCob4BZBYX4xYedOkxdz/dYGPo4IgFCvhDBYJClSxZnvP9pxRQeoWdBBPgKsHksiT4GMSN4w6SRH1pBY/8ylhfXEraeoSb0bVpjbyFhXcod51yafUepg0jnLdy8ejWVaROjtuMcM6J31MFRh+qiPG659Oyh8kDAx/vftSEr5T4aIkK4eDG+4HC0RvH78YkQtTtpi/WiQH/09VH7iXkJSNrjjSBBetKSmLjJSXzsr2+jtqCYtp5yBpIFqKYI+lqI5LmxcFLxF/Elm4n3v8ZAshWcbuYUFuO3fDiOgwCz8i9FCNHVsTEtn2wOMf29aP4fEAP+xhvNjykIv1HwhknDCizhgtqLONj+KJv787ADK+kfaMdxnmJ2wfE9WFLxPTg9/4rT8y84PXeC041V9SQiw77rTf29nP+zb3HDb384VGY7Dg80/Ir/3vFZzpz1A0rzj01ybavDF7bey5V/uoPHmrac0H0lbJu97cNTUoM29rxANRXhQmIDCRxreJT8Yl3D0L4mtwPgT7p28FRHnM6OGBWBYgo47K7y1T7QFKetmYdFF4trArQe3IrjWIh/Do7j8EL7RjqTs0FqCOWfRl6gkliiiMGFvZZlIVYBJUUX4rN8bG5rYWf9P53Q/U55pvEkq4h8FPgx7kLSKuAeEflItu2NgjdMKoWBHoLRqzgveDUhaaG0aiGLnWUc7Bg5qXZ/T5R/v/0BGPghDPzIjTYZ/8Mx9e7Z+TIt0T7Cnt/6Ky2NvPVXP6YzGqW24BwiJV9AjlpMlUzZ7Dnczj+sfDt3rLqRM0qPn2tBY38asfz3h15G1aGmyEum4bk2qt0MiWeoO9SECES73IBrA7EEiyvKhzvwzcVRm3DEHYVXVs8jr0SxgQRz6EkM0JcoRXzl7oIoXxn+QD5zFpyGJQOoKtGBeiqTRVQW1BKJBGnq7UVViWPT0dWHY7cTG0iARFCN0h2PUVu6EJvVGUM7TDeE6T3JCnwQOE9V/0lV/wlYD9yabWOj4A2TihVcSnnxIkqL8jhsz+JAbDfbn4cXG3y0tR/rA59fFGHRyko3UXVgLQQvA9+xijg/4P5qb1tzHgC/3LWd7a2tpLrKmZ8/95j66vRzON7GstoKwr4gV1SvpSJ0/NW0mnxtxPINlcvxawPFqf9A1Sbe8zkONtez/TBI6GLKSlJE8kKUlhUOKdOyvCMndLsTwxO0IhD2FROwLHq7+vHFDhDxOUNePOr0ofjwBSyQfNRuIhIuQqgmZsfdCdnCQpobn6Sv4WGisRYa2vqwfEIq6SASoSw4wNqas1k97z3YrQ3kHNN4BI/7jEq3M9ocG6vuuBgFb5h0amtKONSynaaDZ3O4eSnh9bO5YtFSykpdM83WnYePGFm+82M3gm8RpA5A3nuwij5zRH9xu5ekPk1NXj75AXcytKQrwAU6m1BXKeeUnc3RvFzfxT1PZh+Nwyr86IjlRYEIIhEk//1gNxC1/4JIqItA1F09m5/nxlIPBf2ICHnhYddJdXpBY5SGlqKq2I7NoNeniFBRUUbcqsby+Rlcp9XT+zQN7XvZfmgvhzraEasYhxALagrYub+O+qZOHLuTspKlJHQOVaV+fMF6uvs30t9bj2qUmF3sXcMHzstZfwfTgixG72MZwY8lbLqI3CEie0Rkp4hcc4J38H1go4j8s4j8M/A88N1sGxsFb5h0RIRZxR08vulVfv38Xn778nb2t9ZRF3Xt0f9zz1PYaXHjff4CpOx/wSpGnKZj+gv5Clla2sFbl7TwaJ0bBuCSlYt4ZdthNG1CbSj5hd3C2to4n7n24mOyHY2VfT3PYfd+hScaD9DUU0zQN4DPmsOchTW0xXaA9tEW7x7RFCJWIeKrIJmyUaeP6EAUy/JS6KkNpCgIFSCWH/VcJvMLriGvsJzVc5czp7yCA02dbKvfiW33UR/sIxof4GDXRgKRWhYuWU4wPIdobC5BCVFUOoeerv209w0MyeCvuf6k7n9K4mSxZc9g2PRBBsOmLwUe844RkVW4K/9XA9cC35SRkuVmQFW/ArwP6AA6gfep6teybW8UvGFKUFJyKW+6optzS9qx98V45E/72fX7dg43d7Fz72GePnQ9B3t+OlRfrDKk9LugMVSVnliMxp5euhJRT3naFIUOEva7D4Cq4gI+/56rueS0Rexr7eDKr32Xv/3JgwBo/93gtLnuh6OgTjdOhiQiC4vOo8e6nh09PXR3xMgvvoCSvGLygkspCsxBNU5FqJi26ACaqsNJbDmmD5+9D5wmEkk3znwqmULEh61+goEItu3Q1pOis/cAz+3YS0Vo2IY/v7qY2pKlQACfD3YPPIMv8RyHmtsAH5qqI9negT/vDFJxwR9YTG1R7BgZconxGsGPMWz6DcB9qhpX1f3AHuDcrGV23wzKRKQMOADcA/wIOOiVZYVR8IYpgd8XoCZ6KUGW8pn3X8Wc8nJ0TYi7n3yEdRcV4hc/XdE/059MDI1+xT+PePBdvO+en3PhV+7inl0vcsFvvsK9ezaz77F5RAJ1rKp8HMdR/Ag3rF/NstmVLKwoJeDz4fdZbkYp7QP/ygwSgljFWP4Fx5QPZ3ECS3zEOlfwhsAZzJrnZ9+uJna/fhiAkL9wKMH1kDtn/w+PSHunmiAaewakkLISONy4G3/Az4HeV9n6Sj2PPHU3Kj0UhIOoEyavvIeEnSCZSNEU7aRlIMmh7t/R3flHrqhewAXVSzh8eA1hn7CjfitJSqmYu4j8UJB4LEUk0svhQw+f4F9tmpCdDb5iMKy5t902Qk9fI/uw6bOB+rR6DV5ZtmwGNnmfg/ub0vazwix0MkwZVq9Zzq6BJAe7unnjJcLL3Ts4NFtJVsY517eHF7trWP/4NzizcjZXzFnMzcvPImHbbD3cQjxl83JXPWtKZxH0+7DaV5GX2k1Sotz8iR9QWpzH179wI+BGafzEFRs4fXaNm3mp6LPASfi/e2/erT19PNd5gIsK5uPz51EUzKN42chN+qJxfL5Z+IJXE/D0hapNQ1sbhUU3kecLu6arKjc5yvyClRyu2s+5y67DbxURCPvw+SsItEWxqny0tHRS53Qzv6QMv7+SbT3zubA8QlnFJazyDxApgo7uubT0QFWRa95xHIcD22O0J9cwZ2yLhqcP2U+itqnquuOdTA+bLiKXZtHfSLa+rK39qnp8F64xYBS8YcoQjcV4w9plxFM2dd1NXFi4ndWhAf5t51l85+C/8eQP27Av7Of5pjqeb6oj5Ti8f+U6fnHru9nZ0saGRfPJCwZcRXnjfL7639381ftP52Pvz2d//XCehIDPx9WrhuPEixz/Z9AafQUHm+rIWaNI7iazLi6IcH3RGjRVB6k9NMeWUh0+0mNH7XawysiL3o4UfQ4i59KV7KU0VIKIj4JIKSWhCE78JQgsB6scRxVLApxZtZCOZA/FqZ3s6QtQ19aNpUXs3fc7ukIpTi/zkVdwAz3J05idtxOSbTS1dxMonkfQqWbRnAP4QucxYA9w+HA72//8AlddfxWLgmNLTj7dGCc3yMGw6W8EwkBReth0VW08Kmx6A5D+x58DHM5aZpHR/uFQ1Zey6mci/V7XrVunmzZl/XZhmIEcbO8k5PdTkR/B57N4sf672Mm9bO5dwPxuH+cv7SBS9gnyPe8TR20sbwT94Y33sKunma+uTrKy7BziXEQkPHK+1xMhZkdJOnEKAyXHnGuP9VPX38mZ5cPK8vk9z3D2onMJWEHau/pp7epj+fwSREKu3T94DhJYNZSuL53BspeaDjOQTHLh3PkcONROYSmUBpP09m/CLzX0923HZgu7OpZRUXYxy2qWEfASeqgqOvAwkncpjT1RivP6yBMbjf6Kl9vOo+lgH2+69Kpx+37GGxHZPNqoOhsiNXN18V/+XcZ62778d1lfyxvBf1JVrxOR/we0p+WmLlPVfxCR1cC9uHb3WtwJ2KWaZT5KERl5oYWLqurlo5wfwozgDVOK+eWul5njODjAylnvpbPjSfY2d7DuTIdI4N386vmtvPOiMwj4fOzre40lhWsBuHPxi/jt7ViSD/3bqQvkM9e/ijx/0fEveBxspwefdWS7LV3PURuZP6KCLw/nUx7OJ253E7SKaO5u5rUHOqn+iyYWLptHeUk+5SX57Og+xIri2Uj+LUNt9/c3sKhgLrbjcLCpk3nVpe78gN3E6pIOAsFVOI7D7KoCNu35d4KRdzEnv4Ty0rU8s3sPZ9f6WFZhU1NRgFg+bMfhj/XPcXEVRPKvRWN/ZMBaQYHGUE1C+C2cvXABjIsRYOpzikMR3AncLyIfAOqAdwCo6jYRuR94HUgBt2er3L32l42HcGaS1TDliKVSKO6rtSXCvOprqSqcx9bmSvIjc3nPpWeRSri/lUHlDpBndRIMX4C/9BvEg1fxVMv9vN7z7ND5lJ39L92SPHcRUfS3JKMH6WzpZn355czLGz0Q186uB9jatYtfb/0N574pQpO1f+ickzrIksKaoePXmt30fYsK3Df5TfWHWFRbjt/n/izFV0MovAbLstjb9yyJVIy24JWcOW8xhUXnsru+jYXzLqGk7O94Zd+FKEHU6cRnWaytrEL86+iNxSF0GXODNoWBBYh/DlZgQdbfw7QnmwnWMRoxsg2brqpfVNXFqrpcVY9dbp0FIhIQkY+KyAPe9mERyTpYv1HwhilH2O/HZ1k4KAkvB+pVy/O4ouK3gGt6+PpDvyeRSrC9rXXYq6bkG0jR/0c89QqPHC7h1iVfYW3JFQA8v7+ed37nJ9jeyqFohqTaIn5I7SHV80Wad38JJzq8CGpPdzvf3+6aGjVxpCnU33cZs+0f88bFr6DhLawoOchAzPWS0VQd/jTzyZrKwiPanjd/7tC5fc3tR5xbWnQReeEizp61mqQ65IWCLJpbREjDtPTlccbyOUACxV20ZFkBoskUffEEIj42NTWQGPgDYo39bWY6I1luU5hvAWcD3/S2s72yrDAmGsOUxW/5KA15vumB05H8DwLuwqi/uvZKAr4AtuOQchwCPh+P728h6Thcs+QablhuMZAa4Ln256i0ZrO8ah75oSCHu3qYW1ZCxJ++grQfsYaDm2nsT2jiSZQyYqE3MFAZpiPxK7S/lor8FVRHCrhmnuse024LkXiClt5+5pUW8/zORlZWlzO/opte/wqi4bU8W/8Ya6oupDY8m7buPgrzhLxAPgkV/IlDoG1YITdIoKb20BlrYlH1hQC0DPyZysj5ro0+tZ1waoDGnteJdq5kwSI/dmcXc5acT8KJ8OKBPqxkA+tWzKVY4zy1r5ErVi8mlTzAhvkbSDnRifizTT2mdiiCTJyjqukRJB8XkZEDNY2AUfCGaYGIH4LD/+dVEVfxr6mqHir79qZNLC0v59qlrodMezTGg4d/Q6R9KW9fcAM/et87julX48+jyW1IwQeGC1M7IfZHrIoHKbTK+E7d4ywKXk3pQDUPP/MqCxdWsmyZm2mqMLgAxxacpM3PfvEiV164kl0HKygtjbP7oWba8x/mwvWnEwiG8Ot2qouHc50GrQgEZ5PuHm3Hd+OkhhcuVUbWD0/A+pdQUuCjKFyGr3ouIgGq5j+HECXs8zO/ppSaAvetIBBezGUrbfp7vsOW/vlcWLsAv5V1precYooHE8uELSKLVXUvgIgs4sjYNKNiTDSGnOFL11zNWbNcxWs7Dm/66Y8ptmrxaYiHXt45YhuN/QHsg+zc9jX6o57ZJu9dSPkvEctdMPg3Szbw229spqO7n2uuOX1IuQOEfKWEA362bt7NaWvmMm9WGVeuX04k73TWL67igrVvgfg+GtqjdHMWmw4dQlVxnASqqeFwCaqo083BjnMoLkiLlZN8dWhXJIhl+fAFFoLtrtAtCJ/vreaNDSl3t64fK3Y/ddE8NtScc9Lf7bRmegcb+xTwJxF5QkSeBB4H/j7bxmYEb8gZFpSUsqDE9cLpSyY4c1YNnf0+rigo5PTFzwJXHNPGKv48AC899RzVc1MQCR4TRjiQ+Caf+ug7WbJ4WLHH7RTRVJKSUATs/Vx3hY0VnkVbczcD1DO3uJUVKwrA2YwEL6FMKvn31/7AHWe8yVPqiqqA04j4a1GnG9uOEYsqAS/pyEAsQSS0aoQ7TVDfmY/f7qK2pgSkEFKvg68S8OYFAmci+X/JioiDWDN4HKdTO6HH8RARv6qmVPUxEVkKLMedLtihg4GIssAoeENOUhwK8903vI26vk4W5DkIS0b0Nx/kXW8+f8Ry1SS+og+xrLAbkWFFGfL5CfncBU6kDuCLuG7JdqSDV1o6mFt18ZB/Pri/zI+uWk1zaw9FhWF80ksgWE5PPEixHyxfCZYPVi11cJIHiCcP0x5dwdxw2bFyJ1+hrb+GhtRurucKLF8BLYmVpAa6qc0rRoJnoeoQTfVT39zNstljWSGfg0ztEfrxeAEYXOz0ZVXNOslHOhkf7SISFpEXROQVEdkmIp/3yo8bJtNgmAr4LYtFReVY/krEv/DEIkX2fwfab0Bbr0RTIyXSTpIemqQoXMuFc1eyt7ULANUYGt+Ixv5IOPk6Tz+/m/aOfqAEEaE4v+KI3kQsxD8bn382GokSSyVpafvGkdEn/Us4c/4s3rxwxXCZswUYoC9RjzodpDpuZXdbIwtqapjpTNOEH+n/rBtOtJNs3t3iwOXeTO5a4FoRWc9xwmQaDLmCagzFB04nIG6SEY8Xnt6F4ziQeBF8runGGfgNu9p/RV7/v7C4stD1o1cLjT6IWrVEY/tZd/4i5tSWEgy5L8+OoyRTNsl4NwM927HtKC831NOTjOAM/IiOvmYs5wIOHfoD2zY+xu7mevp7fwv4EN9sT06b0vBpFAWL2PRiL+2dAXz5t7DA90t8emxawhnH9LTBj4tUGRW8ugym1gl4m3L8MJkGQ26Q2oUgSNmPIP9mxDc8Gj73omVYlgX4wMsFq+E3UZqsozm1joO9/eztiENyG+3xfto6H6fffzPb6nfx1PZtbK93Fzk1tW4n2f0bhL10OJ2Ic4iVVaV0xHopCrwPiUNpxRk0yFJWnL6MRRW1hPOvIZXYT390I+Am6niuuZNvb93Mxeev5OHnd4D/DAJOI2IfmvCvbaoxTUfwK0TkVRF5LW3/VRF5TURezdjaIysbvBeofjOwBPiGqm4UkSPCZIpI1XHa3gbcBjBv3rxs5TIYpgCCJl9B8m9FCkZ2XJDQegBSqRR3/e4xbrtEcIILCERcE4wT30+5v4VfHaqmo2sH161bQML2UZvneuiE8xfQ1eXDN7CT4tI5vPBaivlzf8vSine7GZa8+d71s5emXbUa/NX4nOHf0+riCi6ctQBLhLUbkqDdkP9Ook4Xx09fPgNQxprQY6qQOX51FmQ1va6qtqquxY2Idq6IrMn2Aqp6l6quU9V1lZWVJyimwTAZWBB/FO39l2ODgUUfRPu+PnRcN/As167biBVay4tba4fKO2JhXu5bSXFhiA9ctI6avGrmFVYMhSMoK8ijpnYZ4ZIrCQcXsnaZRZcUkU3yH8saXrFenl+I5cm4NH8+WPn0+lfyeFvWLtM5yXRNuq2qB0fbsu1nTP5TqtoFPIGbgqrZC4/JUWEyDYbcwCoHqxrSEnoMoomXUC8bk+NEmR+Isjd5Ia26niVLq+jsj6KpOqLtdSyb9SFao40kU/0jX8YSigvzCARLCRcsw9e1FKfpSziJrhMSOxiYzea27ZT78+jr3oXT05O5US4zPW3w40I2XjSVIlLi7UeAK4EdwIPAYEi8W4BfnyIZDYZJQXw1SMWDiOcrf8S5os9glXm5j3u/QCyWoqi3hapIPuVFBXz9z88j/nnMWXApDnD1kr8nGBg9JeAgy5asAtZDMuuYUsew8Z4eXqzbjG21ER94+wn3kwuIasYtV8lmBD8LdyXVq8CLwKOq+lvcMJlXichu4Crv2GDIKcQqHVrReiSK9v0PAN95+o08+TpUFl+BiOD3WXz2qst4vbUFsfIoDlZQFhybu6JVczFW/olZz/fsfJKBgM2LO14jnlLi4Q+fUD85wSmIJjmdyMaL5lVVPVNVT1fVNar6Ba/8uGEyxwvHntn2Q8PURSSE+hah0V8ztyLA3tYSFs0bnmOyRFhVOaLfwSklHo/TmHqWW970LG2Wn7MqKjnY1pa5YQ4zHW3woyEi/5xt3Sm9ktXyHTvR5CQSWMHxy9JjMJwoEr4cbXsT1y7qhdU3AW4EyOcbtnJW1XyCwcLROzgFOMkkNeFD1CeqKAoqpQXraK13OBCpY8HsmenFNh1DFWRgc+YqLtMuSIUETtwuaTCMJyJ+pOTrSP4HkYJbAdD+H9DVdtekKHeASEEBqhuIxdZyTsGFpOoKaNrVwbYdz0+KPFOCHDPRqOpvsq07pUfwI3FCy80NhlOEBJZBYNlwQfiNXF1TcfwGE8Dc/GIOWz62NnVwWnUtzpwiCmtmqJlmGppg0hGR/xqhuBvYpKoZHVum3QjeYJiKqBcSQHxVWDXXTaosgarLeWTfb4kV76e0spDSSIQDO/JJJmdo2ILpPYIP44aI2e1tpwNlwAdE5GuZGk+7EbzBMNVQpwPif6ZPriIvFMCSyR03+X0R3rugm0dbCuno6OPi9UtZsnMjvugABIozd5BDDC50msYswY0FlgIQkW8Bj+B6Lr6WqbEZwRsMJ4sUQ/hKfr71Je6v+zU3f+RO+vpGXtQ0UaxYdDetO/t46KHX6O+PU1acwCqaWcp9EHE04zaFmQ1HRJvIB2pV1cYNBDkq034EP1KM79HifhsM440bVsDHjWvPpiPZxvJbwsTjCQoKJi8KjGVZvPeSdaxY5Mawefjgdq6ZiZGDp74JJhNfAraIyBO4LyQXA/8mIvnAHzM1nvYKfjRFbhS9YSIJ+4PU+mupXVebufIEMKjcbdtmfcnySZZm8pjObpKq+l0R+T1wLq6C/4yqHvZOfypT+2mv4EdiUKkb5W4wgBW9i6Q1g2NKTuMRvIg8CPwEeFBVx2z3m5E2eM3h2BMGw9G09L+VkoqbJluMSWOar2T9D+Ai4HUR+ZmIvF1Ewtk2zskRfCbMyN6QqzjJJOL3o62t4LxOf/7ZlJaW4ffP0NXfCkzjAZ2qPgk86eXkuBy4FfgeUJRN+xmp4A2GXMXyVnpLVRVQxeSsp51aTGcbPAxF8X0zcCNuIu67R28xjFHwuEHNRop7YzAYpjfT3Q9eRH4KnAc8BHwDeEJVs35kGQUPR7zCGc8bgyGHUJ3WJhrg+8C7Pb93RGSDiLxbVW/PprFR8IDlH/4ajHI3GHKL6TyCV9WHRGStiLwL10SzH/hFtu2Ngs8SM7I3GKYp01DBi8gy4CbgXUA78FNAVPWysfRjFHyWGOVuMExPpukIfgfwNPBmVd0DICKfGGsnM9IPfrw4vL8Fx5nmU/QGQy6jgK2Zt6nH24Am3HSp3xGRK3DnjMeEUfAnQUlF4TSfvzEYcp/puNBJVX+pqjcCK4AngE8A1SLyLRG5Ott+jII/CfIKI/h87lfY3NbF4YamSZbIYDAcw6AnzWhbBkRkroj8SUS2i8g2EfmYV14mIo+KyG7vszStzR0iskdEdorINScmuvar6o9V9TpgDrAF+HS27Y2CHydapY+Uf/tki2EwGI5inEbwKeDvVXUlsB64XURW4Srbx1R1KfCYd4x37iZgNXAt8E1vNeoJo6odqvptVb082zYZFfyJPLlmImvK5zCvxp3gVvswrc2NkyyRwWDIKptTFgpeVRtV9SVvvxfYjhur/QaGV5beDbzF278BuE9V46q6H9iDGxFyQslmBD+mJ5cBsGYRKZiZyRUMhqmEAGJrxg2oEJFNadttx+1TZAFwJrARqFbVRnAfAkCVV202UJ/WrMErm1Ayukl6Qg/eQK+IpD+5LvWq3Y07EfCPp0TKaYaIUJCfN9liGAwGQLLzhGhT1XUZ+xIpAH4OfFxVe0Zxnx7pxIRP547JBp/lk+voNrcNPhVbW1tPUtzphR39HWq3T7YYBsPMZZxMNAAiEsBV7j9W1cHVpM0iMss7Pwto8cobgLlpzecAh5lgslbwRz+5sm2nqnep6jpVXVdZWXkiMk5b9vQ8wbOtTezpaclc2WAwnAKy8KDJzotGgO8C21X1K2mnHgRu8fZvAX6dVn6TiIREZCGwFHhh3G4rS7JayTrak0tVG496chk8dm39C95wydKZG4vbYJgCjJOf+wbgL4HXRGSLV/YZ4E7gfhH5AFAHvANAVbeJyP3A67jzmLcPBgybSDIq+CyeXHdy5JPL4HHlhrONcjcYJptxWI2oqs9w/JWkVxynzReBL570xU+CbEbwY3pyGYaJhI1yNxgmFWXQS2ZGko0XzZifXAaDYWZj2zbdff0k+w8yEE1SO3cVoWDWqUTHl5mr3000SYPBMP7EU0nyAha9gRqqigsQJi9jWpZukjmJUfAGg2HcyQu5o/VwXsEkS8J0z+h0UhgFbzAYchcFZnBEb6PgDQZDziKoMdEYDAZDzjKDk/IYBW8wGHIXY6IxGAyG3MWYaAwGgyFXMQp+Yti8eXObiBzMUK0CaJsIecYBI+upwch6aphuss4/+W6yCyaWq0yoglfVjOEkRWRTNnGZpwJG1lODkfXUMA1lXXDSHSlgQhUYDAZDbmJs8AaDwZCrGAU/pbhrsgUYA0bWU4OR9dQw82RVwJm5Cl50Bj/dDAZDblMcrtEL5t2Ssd5Du7+0ebrMT4yFqTiCNxgMhvFjBg9ijYI3GAy5iwL2zF3KmnXS7fFGRN4hIttExBGRdWnlARG5W0ReE5HtInJH2rmzvfI9IvJfXjrBSZPVO3e6iDznnX9NRMJTVVbv/DwR6RORT6aVTSlZReQqEdnsybRZRC6fqrJ65+7w5NkpItdMtqxHIyJrReR5EdkiIptE5NxMsk8mIvIRT55tIvKltPITkFVBncxbjjJpCh7YCvwF8NRR5e8AQqp6GnA28NcissA79y3gNtwM5UuBaydG1JFlFRE/cA/wN6q6GrgUSE5FWdP4KvCHo8qmmqxtwJu9/4FbgB+lnZtSsorIKuAmYLUnyzdFZDC7xWTJejRfAj6vqmuBf/KOM8k+KYjIZcANwOneb+rLXvmJy6qaectRJk3Bq+p2Vd050ikg31OeESAB9IjILKBIVZ9Td2b4h8BbJlnWq4FXVfUVr167qtpTVFZE5C3APmBbWtmUk1VVX1bVw97hNiAsIqGpKCuuMrpPVeOquh/YA5w7mbKOgAJF3n4xMPjdjij7JMiXzoeAO1U1DqCqLV75ick66EWTactRJnMEfzweAPqBRtxk3l9W1Q5gNtCQVq/BK5tMlgEqIg+LyEsi8g9e+ZSTVUTygX8EPn/UqSkn61G8DXjZ+8FPRVlnA/Vpx4MyTSVZPw78PxGpxx0RD5o9jyf7ZLIMuEhENorIkyJyjld+4rLO4BH8KZ1kFZE/AjUjnPr/VPXXx2l2LmADtUAp8LTXz0j2y3H7y5ygrH7gQuAcYAB4TEQ2Az0j1J1sWT8PfFVV+44yBU/F73Ww7Wrg33HflGBqyno8mU6prMcIMYrswBXAJ1T15yLyTuC7wJUTLeMgGWT14/7u1+P+ru4XkUWcjKw5rMAzcUoVvKpeeQLN3g08pKpJoEVEngXWAU8Dc9LqzWH4VfOkOUFZG4AnVbUNQER+D5yFa5efarKeB7zdm7QqARwRiQE/Z+rJiojMAX4J3Kyqe73iBqaerA3A3LTjQZlOqaxHM5rsIvJD4GPe4c+A//X2jyf7KSWDrB8CfuGZtV4QEQc38NiJyaoKtn1yAk9jpqKJpg64XFzycZ/kO1S1EegVkfWeN8LNwKgjwAngYeB0Ecnz5gwuAV6firKq6kWqusAL4PQ14N9U9b+noqwiUgL8DrhDVZ8dLJ+KsgIPAjd5cwQLcSdTX5hish7G/d8EuBzY7e2PKPskyJfOr3BlRESWAUHcSfcTl3UGm2gm003yrSLSAJwP/E5EHvZOfQMowPVaeBH4vqq+6p37EO7oYw+wl2O9QSZUVlXtBL7iybkFeElVfzcVZc3AVJP1w8AS4LOea98WEamairKq6jbgfuB14CHgdlUdHDJOiqwjcCvwHyLyCvBvuJ49mWSfLL4HLBKRrcB9wC3qcuKyzmAFb0IVGAyGnKU4UKkXlLwtY72H2r5tQhUYDAbDtEJBc3ghUyaMgjcYDLnNDA5VYBS8wWDIXVTBMQreYDAYcpMZPM9oFLzBYMhp1IzgDQaDIRfJbTfITBgFbzAYcpcZnrLPKHiDwZCzKKAmVIHBYDDkIDp+CT9E5Fov2cgeEfn0KZZ8XDAjeIPBkNPoOJhovOQi3wCuwg189qKIPKiqr59056cQM4I3GAy5zfiM4M8F9qjqPlVN4MbJueGUyj0OmBG8wWDIWXrpfPiP+kBFFlXDIrIp7fguVb0r7XikhCPnjYeMpxKj4A0GQ86iquOVB3dSkqOcLMZEYzAYDJmZlOQoJ4tR8AaDwZCZF4GlIrJQRILATbhJSKY0xkRjMBgMGVDVlIh8GDeLmw/4npeEZEpjEn4YDAZDjmJMNAaDwZCjGAVvMBgMOYpR8AaDwZCjGAVvMBgMOYpR8AaDwZCjGAVvMBgMOYpR8AaDwZCj/P/ft8x+aUoRigAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "ax.set_title(\"Avg. Friends by Subdivision\")\n",
    "county_subd_gdf.merge(subdivs_by_followers, how=\"left\", on=\"Geo_FIPS\").plot(\n",
    "    ax=ax,\n",
    "    column='Following', \n",
    "    legend=True, \n",
    "    vmax=1000,\n",
    "    legend_kwds={'label': \"Avg. Followers of Subdiv\"}\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad5634bf",
   "metadata": {},
   "source": [
    "## K-Means Clustering\n",
    "\n",
    "Inertia is calculated by measuring the distance between each data point and its centroid, squaring this distance, and summing these squares across one cluster. \n",
    "\n",
    "A good model is one with low inertia AND a low number of clusters ( K ).\n",
    "\n",
    "First, we determine the ideal number of clusters with the \"elbow\" method -- plotting inertia x number of clusters and seeing where the benefit of added k tapers off."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "2f0219b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_cluster_coeffs(cluster_centers, X_cols, k, inertia):\n",
    "    print()\n",
    "    print(f\"_______ COEFFS at K={k} ______\")\n",
    "    print(f\"Inertia: {inertia}\")\n",
    "    print()\n",
    "    \n",
    "    for idx in range(len(cluster_centers)):\n",
    "        \n",
    "        cluster_coeffs = cluster_centers[idx]   \n",
    "        coeffs = list(zip(cluster_coeffs, X.columns))\n",
    "        print(f\"_______ Cluster ______\")\n",
    "        for tup in coeffs:\n",
    "            print(f\"{tup}\") \n",
    "            \n",
    "        print()\n",
    "        \n",
    "    print(\"____________\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "564127be",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = locs_gdf[[\n",
    "    'WhitePopPerc',\n",
    "    'BlackPopPerc',\n",
    "    'OtherRacePopPerc',\n",
    "    'PctLatinoPop',\n",
    "    'WhitePerCap',\n",
    "    'BlackPerCap',\n",
    "    'LatinoPerCap',\n",
    "    'PerCap',\n",
    "    'PKPerSqMile',\n",
    "    'Blck_Wht_PerCap_Ratio',\n",
    "    'Pop_Dens'\n",
    "]]\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "2df1b944",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f96783f5790>]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "k_vals = range(1, 20)\n",
    "inertias = []\n",
    "\n",
    "for k in k_vals:\n",
    "    \n",
    "    km = make_pipeline(\n",
    "        SimpleImputer(missing_values=np.nan, strategy='median'), \n",
    "        StandardScaler(), \n",
    "        KMeans(n_clusters=k, init='k-means++' )\n",
    "    )\n",
    "    km.fit(X)  \n",
    "    inertias.append(km[-1].inertia_)\n",
    "    \n",
    "    \n",
    "plt.ylabel(\"Inertia\")\n",
    "plt.xlabel(\"K Clusters\")\n",
    "plt.title(\"Number of Clusters x Inertia\")\n",
    "plt.plot(k_vals, inertias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "37ec0a4f",
   "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>mean-mode dev.</th>\n",
       "      <th>Geo_FIPS</th>\n",
       "      <th>Geo_STATE</th>\n",
       "      <th>Geo_COUNTY</th>\n",
       "      <th>Geo_COUSUB</th>\n",
       "      <th>Total_Pop</th>\n",
       "      <th>Pop_Dens</th>\n",
       "      <th>Land_Area</th>\n",
       "      <th>WhitePop</th>\n",
       "      <th>BlackPop</th>\n",
       "      <th>OtherRacePop</th>\n",
       "      <th>WhitePopPerc</th>\n",
       "      <th>BlackPopPerc</th>\n",
       "      <th>OtherRacePopPerc</th>\n",
       "      <th>PctLatinoPop</th>\n",
       "      <th>WhitePerCap</th>\n",
       "      <th>BlackPerCap</th>\n",
       "      <th>LatinoPerCap</th>\n",
       "      <th>PerCap</th>\n",
       "      <th>PoliceKillings</th>\n",
       "      <th>PKPerCap</th>\n",
       "      <th>PKPerSqMile</th>\n",
       "      <th>Blck_Wht_PerCap_Ratio</th>\n",
       "      <th>Following</th>\n",
       "      <th>Followers</th>\n",
       "      <th>Tweets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.437900e+04</td>\n",
       "      <td>3.437900e+04</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>3.437900e+04</td>\n",
       "      <td>34300.000000</td>\n",
       "      <td>34300.000000</td>\n",
       "      <td>3.437900e+04</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>3.437900e+04</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34050.000000</td>\n",
       "      <td>34194.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34300.000000</td>\n",
       "      <td>34050.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>3.437900e+04</td>\n",
       "      <td>3.437900e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.579819e+03</td>\n",
       "      <td>2.598706e+09</td>\n",
       "      <td>25.902382</td>\n",
       "      <td>83.953693</td>\n",
       "      <td>71933.772506</td>\n",
       "      <td>8.556566e+05</td>\n",
       "      <td>8036.880040</td>\n",
       "      <td>192.025560</td>\n",
       "      <td>4.641589e+05</td>\n",
       "      <td>156743.689229</td>\n",
       "      <td>2.347540e+05</td>\n",
       "      <td>61.736517</td>\n",
       "      <td>18.027244</td>\n",
       "      <td>20.236247</td>\n",
       "      <td>23.307587</td>\n",
       "      <td>45324.986096</td>\n",
       "      <td>26451.629134</td>\n",
       "      <td>22605.882348</td>\n",
       "      <td>37659.224439</td>\n",
       "      <td>25.720033</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.160133</td>\n",
       "      <td>0.630862</td>\n",
       "      <td>1196.417231</td>\n",
       "      <td>3.282625e+03</td>\n",
       "      <td>1.658021e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.955855e+03</td>\n",
       "      <td>1.652377e+09</td>\n",
       "      <td>16.489786</td>\n",
       "      <td>108.017975</td>\n",
       "      <td>29841.414150</td>\n",
       "      <td>9.993318e+05</td>\n",
       "      <td>13604.529302</td>\n",
       "      <td>466.710048</td>\n",
       "      <td>5.485587e+05</td>\n",
       "      <td>221619.030296</td>\n",
       "      <td>3.245024e+05</td>\n",
       "      <td>18.226162</td>\n",
       "      <td>16.546325</td>\n",
       "      <td>13.338321</td>\n",
       "      <td>18.519561</td>\n",
       "      <td>18702.669909</td>\n",
       "      <td>9201.380373</td>\n",
       "      <td>8103.623555</td>\n",
       "      <td>13530.812948</td>\n",
       "      <td>36.069957</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.161441</td>\n",
       "      <td>0.235241</td>\n",
       "      <td>4387.541000</td>\n",
       "      <td>5.706542e+04</td>\n",
       "      <td>3.645231e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.001926e+08</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.660000e+02</td>\n",
       "      <td>0.434984</td>\n",
       "      <td>0.032625</td>\n",
       "      <td>1.630000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.380000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4226.000000</td>\n",
       "      <td>602.000000</td>\n",
       "      <td>1611.000000</td>\n",
       "      <td>4725.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020028</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.001336e+08</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>8.099300e+04</td>\n",
       "      <td>1933.357000</td>\n",
       "      <td>36.339705</td>\n",
       "      <td>5.553000e+04</td>\n",
       "      <td>6863.500000</td>\n",
       "      <td>9.244500e+03</td>\n",
       "      <td>47.780000</td>\n",
       "      <td>5.780000</td>\n",
       "      <td>9.550000</td>\n",
       "      <td>8.210000</td>\n",
       "      <td>33715.000000</td>\n",
       "      <td>20324.000000</td>\n",
       "      <td>18142.000000</td>\n",
       "      <td>29147.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.023944</td>\n",
       "      <td>0.498853</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>1.900000e+02</td>\n",
       "      <td>2.039000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.328306e-10</td>\n",
       "      <td>2.605938e+09</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>90884.000000</td>\n",
       "      <td>3.957770e+05</td>\n",
       "      <td>3570.421000</td>\n",
       "      <td>87.658840</td>\n",
       "      <td>2.094240e+05</td>\n",
       "      <td>45888.000000</td>\n",
       "      <td>6.624500e+04</td>\n",
       "      <td>60.300000</td>\n",
       "      <td>11.880000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>18.750000</td>\n",
       "      <td>41883.000000</td>\n",
       "      <td>25557.000000</td>\n",
       "      <td>20597.000000</td>\n",
       "      <td>35563.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.107292</td>\n",
       "      <td>0.611203</td>\n",
       "      <td>597.000000</td>\n",
       "      <td>4.600000e+02</td>\n",
       "      <td>6.379000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.275834e+02</td>\n",
       "      <td>3.906102e+09</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>92522.000000</td>\n",
       "      <td>1.579075e+06</td>\n",
       "      <td>9067.710000</td>\n",
       "      <td>235.938329</td>\n",
       "      <td>7.563910e+05</td>\n",
       "      <td>240993.000000</td>\n",
       "      <td>4.233490e+05</td>\n",
       "      <td>76.380000</td>\n",
       "      <td>28.280000</td>\n",
       "      <td>28.450000</td>\n",
       "      <td>33.035000</td>\n",
       "      <td>49305.000000</td>\n",
       "      <td>30095.000000</td>\n",
       "      <td>26033.000000</td>\n",
       "      <td>41569.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.272115</td>\n",
       "      <td>0.726091</td>\n",
       "      <td>1227.000000</td>\n",
       "      <td>1.169000e+03</td>\n",
       "      <td>1.745500e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.993679e+04</td>\n",
       "      <td>7.214985e+09</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>840.000000</td>\n",
       "      <td>98000.000000</td>\n",
       "      <td>3.433961e+06</td>\n",
       "      <td>71488.690000</td>\n",
       "      <td>24607.898341</td>\n",
       "      <td>2.556502e+06</td>\n",
       "      <td>833683.000000</td>\n",
       "      <td>1.050342e+06</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>94.340000</td>\n",
       "      <td>98.430000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>156737.000000</td>\n",
       "      <td>192355.000000</td>\n",
       "      <td>238039.000000</td>\n",
       "      <td>164307.000000</td>\n",
       "      <td>204.000000</td>\n",
       "      <td>0.004762</td>\n",
       "      <td>2.579073</td>\n",
       "      <td>5.684942</td>\n",
       "      <td>362676.000000</td>\n",
       "      <td>6.848484e+06</td>\n",
       "      <td>2.355436e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean-mode dev.      Geo_FIPS     Geo_STATE    Geo_COUNTY    Geo_COUSUB     Total_Pop      Pop_Dens     Land_Area      WhitePop       BlackPop  OtherRacePop  WhitePopPerc  BlackPopPerc  OtherRacePopPerc  PctLatinoPop    WhitePerCap    BlackPerCap   LatinoPerCap         PerCap  PoliceKillings      PKPerCap   PKPerSqMile  Blck_Wht_PerCap_Ratio      Following     Followers        Tweets\n",
       "count    3.437900e+04  3.437900e+04  34379.000000  34379.000000  34379.000000  3.437900e+04  34300.000000  34300.000000  3.437900e+04   34379.000000  3.437900e+04  34379.000000  34379.000000      34379.000000  34379.000000   34379.000000   34050.000000   34194.000000   34379.000000    34379.000000  34379.000000  34300.000000           34050.000000   34379.000000  3.437900e+04  3.437900e+04\n",
       "mean     1.579819e+03  2.598706e+09     25.902382     83.953693  71933.772506  8.556566e+05   8036.880040    192.025560  4.641589e+05  156743.689229  2.347540e+05     61.736517     18.027244         20.236247     23.307587   45324.986096   26451.629134   22605.882348   37659.224439       25.720033      0.000028      0.160133               0.630862    1196.417231  3.282625e+03  1.658021e+04\n",
       "std      3.955855e+03  1.652377e+09     16.489786    108.017975  29841.414150  9.993318e+05  13604.529302    466.710048  5.485587e+05  221619.030296  3.245024e+05     18.226162     16.546325         13.338321     18.519561   18702.669909    9201.380373    8103.623555   13530.812948       36.069957      0.000037      0.161441               0.235241    4387.541000  5.706542e+04  3.645231e+04\n",
       "min      0.000000e+00  1.001926e+08      1.000000      1.000000     70.000000  1.660000e+02      0.434984      0.032625  1.630000e+02       0.000000  0.000000e+00      1.380000      0.000000          0.000000      0.000000    4226.000000     602.000000    1611.000000    4725.000000        0.000000      0.000000      0.000000               0.020028       0.000000  0.000000e+00  1.000000e+00\n",
       "25%      0.000000e+00  9.001336e+08      9.000000     31.000000  50000.000000  8.099300e+04   1933.357000     36.339705  5.553000e+04    6863.500000  9.244500e+03     47.780000      5.780000          9.550000      8.210000   33715.000000   20324.000000   18142.000000   29147.000000        1.000000      0.000011      0.023944               0.498853     290.000000  1.900000e+02  2.039000e+03\n",
       "50%      2.328306e-10  2.605938e+09     26.000000     55.000000  90884.000000  3.957770e+05   3570.421000     87.658840  2.094240e+05   45888.000000  6.624500e+04     60.300000     11.880000         17.200000     18.750000   41883.000000   25557.000000   20597.000000   35563.000000       10.000000      0.000027      0.107292               0.611203     597.000000  4.600000e+02  6.379000e+03\n",
       "75%      9.275834e+02  3.906102e+09     39.000000     97.000000  92522.000000  1.579075e+06   9067.710000    235.938329  7.563910e+05  240993.000000  4.233490e+05     76.380000     28.280000         28.450000     33.035000   49305.000000   30095.000000   26033.000000   41569.000000       34.000000      0.000037      0.272115               0.726091    1227.000000  1.169000e+03  1.745500e+04\n",
       "max      2.993679e+04  7.214985e+09     72.000000    840.000000  98000.000000  3.433961e+06  71488.690000  24607.898341  2.556502e+06  833683.000000  1.050342e+06    100.000000     94.340000         98.430000    100.000000  156737.000000  192355.000000  238039.000000  164307.000000      204.000000      0.004762      2.579073               5.684942  362676.000000  6.848484e+06  2.355436e+06"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "locs_gdf.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a87e2f0",
   "metadata": {},
   "source": [
    "We want to simplify our feature space, so from now on using `load_X_for_clustering()` to grab a few salient chars. from `locs_gdf`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "4f4b09e0",
   "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>Pop_Dens</th>\n",
       "      <th>PKPerSqMile</th>\n",
       "      <th>WhitePopPerc</th>\n",
       "      <th>BlackPopPerc</th>\n",
       "      <th>WhitePerCap</th>\n",
       "      <th>BlackPerCap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.089974</td>\n",
       "      <td>-0.018430</td>\n",
       "      <td>0.010804</td>\n",
       "      <td>-0.040242</td>\n",
       "      <td>-0.053404</td>\n",
       "      <td>-0.035377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.632849</td>\n",
       "      <td>0.931282</td>\n",
       "      <td>0.974335</td>\n",
       "      <td>0.885940</td>\n",
       "      <td>0.853503</td>\n",
       "      <td>0.818921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.590578</td>\n",
       "      <td>-0.992178</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-1.089517</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10%</th>\n",
       "      <td>-0.541993</td>\n",
       "      <td>-0.992178</td>\n",
       "      <td>-1.156405</td>\n",
       "      <td>-0.961995</td>\n",
       "      <td>-0.994032</td>\n",
       "      <td>-1.010324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20%</th>\n",
       "      <td>-0.477622</td>\n",
       "      <td>-0.929009</td>\n",
       "      <td>-0.840920</td>\n",
       "      <td>-0.809088</td>\n",
       "      <td>-0.740546</td>\n",
       "      <td>-0.758291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30%</th>\n",
       "      <td>-0.415446</td>\n",
       "      <td>-0.761938</td>\n",
       "      <td>-0.669186</td>\n",
       "      <td>-0.690631</td>\n",
       "      <td>-0.548731</td>\n",
       "      <td>-0.533448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40%</th>\n",
       "      <td>-0.364628</td>\n",
       "      <td>-0.520250</td>\n",
       "      <td>-0.287312</td>\n",
       "      <td>-0.485145</td>\n",
       "      <td>-0.412727</td>\n",
       "      <td>-0.321928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.327894</td>\n",
       "      <td>-0.326895</td>\n",
       "      <td>-0.078817</td>\n",
       "      <td>-0.371523</td>\n",
       "      <td>-0.184040</td>\n",
       "      <td>-0.096758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60%</th>\n",
       "      <td>-0.259673</td>\n",
       "      <td>-0.003575</td>\n",
       "      <td>0.288791</td>\n",
       "      <td>-0.097863</td>\n",
       "      <td>0.000482</td>\n",
       "      <td>0.112141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70%</th>\n",
       "      <td>-0.095369</td>\n",
       "      <td>0.370320</td>\n",
       "      <td>0.657498</td>\n",
       "      <td>0.224993</td>\n",
       "      <td>0.209279</td>\n",
       "      <td>0.380445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80%</th>\n",
       "      <td>0.127554</td>\n",
       "      <td>1.262515</td>\n",
       "      <td>0.930955</td>\n",
       "      <td>0.700634</td>\n",
       "      <td>0.364777</td>\n",
       "      <td>0.437862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90%</th>\n",
       "      <td>0.452482</td>\n",
       "      <td>1.452376</td>\n",
       "      <td>1.393812</td>\n",
       "      <td>1.456704</td>\n",
       "      <td>1.264598</td>\n",
       "      <td>1.175310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Pop_Dens   PKPerSqMile  WhitePopPerc  BlackPopPerc   WhitePerCap   BlackPerCap\n",
       "count  34379.000000  34379.000000  34379.000000  34379.000000  34379.000000  34379.000000\n",
       "mean      -0.089974     -0.018430      0.010804     -0.040242     -0.053404     -0.035377\n",
       "std        0.632849      0.931282      0.974335      0.885940      0.853503      0.818921\n",
       "min       -0.590578     -0.992178     -2.000000     -1.089517     -2.000000     -2.000000\n",
       "10%       -0.541993     -0.992178     -1.156405     -0.961995     -0.994032     -1.010324\n",
       "20%       -0.477622     -0.929009     -0.840920     -0.809088     -0.740546     -0.758291\n",
       "30%       -0.415446     -0.761938     -0.669186     -0.690631     -0.548731     -0.533448\n",
       "40%       -0.364628     -0.520250     -0.287312     -0.485145     -0.412727     -0.321928\n",
       "50%       -0.327894     -0.326895     -0.078817     -0.371523     -0.184040     -0.096758\n",
       "60%       -0.259673     -0.003575      0.288791     -0.097863      0.000482      0.112141\n",
       "70%       -0.095369      0.370320      0.657498      0.224993      0.209279      0.380445\n",
       "80%        0.127554      1.262515      0.930955      0.700634      0.364777      0.437862\n",
       "90%        0.452482      1.452376      1.393812      1.456704      1.264598      1.175310\n",
       "max        2.000000      2.000000      2.000000      2.000000      2.000000      2.000000"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib qt\n",
    "\n",
    "# CONVERT DATA to CORRECT format!\n",
    "\n",
    "def load_X_for_clustering():\n",
    "    \n",
    "    X = locs_gdf[\n",
    "        [\n",
    "        'Pop_Dens',\n",
    "        'PKPerSqMile',\n",
    "        'WhitePopPerc',\n",
    "        'BlackPopPerc',\n",
    "        'WhitePerCap',\n",
    "        'BlackPerCap',\n",
    "        ]\n",
    "    ]\n",
    "    cols = X.columns\n",
    "    clean_data = make_pipeline(\n",
    "            SimpleImputer(missing_values=np.nan, strategy='median'), \n",
    "            StandardScaler(), \n",
    "        )\n",
    "    clean_data.fit(X)  \n",
    "    X = pd.DataFrame(\n",
    "        data=clean_data.transform(X), \n",
    "        index=None, \n",
    "        columns=cols\n",
    "    )\n",
    "    X = X.clip(lower=-2.0, upper=2.0)\n",
    "    return X\n",
    "\n",
    "X = load_X_for_clustering()\n",
    "X.describe(percentiles=[0.1*x for x in range(1,10)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "d7592bc9",
   "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>Pop_Dens</th>\n",
       "      <th>PKPerSqMile</th>\n",
       "      <th>WhitePopPerc</th>\n",
       "      <th>BlackPopPerc</th>\n",
       "      <th>WhitePerCap</th>\n",
       "      <th>BlackPerCap</th>\n",
       "      <th>Adopters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3686.000000</td>\n",
       "      <td>3686.000000</td>\n",
       "      <td>3686.000000</td>\n",
       "      <td>3686.000000</td>\n",
       "      <td>3686.000000</td>\n",
       "      <td>3686.000000</td>\n",
       "      <td>3686.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.451091</td>\n",
       "      <td>-0.802536</td>\n",
       "      <td>0.890210</td>\n",
       "      <td>-0.460599</td>\n",
       "      <td>-0.442265</td>\n",
       "      <td>-0.153763</td>\n",
       "      <td>9.326913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.202724</td>\n",
       "      <td>0.459097</td>\n",
       "      <td>0.960602</td>\n",
       "      <td>0.796513</td>\n",
       "      <td>0.730121</td>\n",
       "      <td>1.078307</td>\n",
       "      <td>58.836478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.590578</td>\n",
       "      <td>-0.992178</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-1.089517</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.565694</td>\n",
       "      <td>-0.992178</td>\n",
       "      <td>0.455038</td>\n",
       "      <td>-0.988436</td>\n",
       "      <td>-0.941710</td>\n",
       "      <td>-0.956079</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.514485</td>\n",
       "      <td>-0.992178</td>\n",
       "      <td>1.175716</td>\n",
       "      <td>-0.797001</td>\n",
       "      <td>-0.589683</td>\n",
       "      <td>-0.212073</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-0.409946</td>\n",
       "      <td>-0.828229</td>\n",
       "      <td>1.607519</td>\n",
       "      <td>-0.287515</td>\n",
       "      <td>-0.093477</td>\n",
       "      <td>0.519647</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2499.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Pop_Dens  PKPerSqMile  WhitePopPerc  BlackPopPerc  WhitePerCap  BlackPerCap     Adopters\n",
       "count  3686.000000  3686.000000   3686.000000   3686.000000  3686.000000  3686.000000  3686.000000\n",
       "mean     -0.451091    -0.802536      0.890210     -0.460599    -0.442265    -0.153763     9.326913\n",
       "std       0.202724     0.459097      0.960602      0.796513     0.730121     1.078307    58.836478\n",
       "min      -0.590578    -0.992178     -2.000000     -1.089517    -2.000000    -2.000000     1.000000\n",
       "25%      -0.565694    -0.992178      0.455038     -0.988436    -0.941710    -0.956079     1.000000\n",
       "50%      -0.514485    -0.992178      1.175716     -0.797001    -0.589683    -0.212073     2.000000\n",
       "75%      -0.409946    -0.828229      1.607519     -0.287515    -0.093477     0.519647     5.000000\n",
       "max       2.000000     2.000000      2.000000      2.000000     2.000000     2.000000  2499.000000"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_agg_df(X):\n",
    "\n",
    "    X_counts = X\n",
    "    X_counts[\"num_users\"] = 1\n",
    "    X_counts = X_counts.groupby([X_counts[col] for col in X_counts.columns]).agg({\n",
    "        'num_users': 'count'\n",
    "    })\n",
    "    X_counts.rename({'num_users': 'Adopters'}, axis=1, inplace=True)\n",
    "    X_counts.reset_index(inplace=True)\n",
    "    X_counts.drop(\"num_users\", axis=1, inplace=True)\n",
    "    return X_counts\n",
    "\n",
    "get_agg_df(load_X_for_clustering()).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "eaac0279",
   "metadata": {},
   "outputs": [],
   "source": [
    "subdiv_counts_df = get_agg_df(load_X_for_clustering())\n",
    "\n",
    "fig = plt.figure(figsize = (16, 9))\n",
    "ax = plt.axes(projection =\"3d\")\n",
    "sctt = ax.scatter3D(\n",
    "    xs=subdiv_counts_df['Pop_Dens'].clip(upper=0.6), \n",
    "    ys=subdiv_counts_df['BlackPopPerc'].clip(upper=1.5), \n",
    "    zs=subdiv_counts_df['WhitePerCap'], \n",
    "    s=(subdiv_counts_df['Adopters']**1/2)\n",
    ")\n",
    "ax.set_xlabel('Population Density')\n",
    "ax.set_zlabel('White PerCap Income')\n",
    "ax.set_ylabel('% Black')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc3588b2",
   "metadata": {},
   "source": [
    "Now I examine inertia at varying values of K to determine how many groups to form."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "2a5a5c01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Only Index(['Pop_Dens', 'PKPerSqMile', 'WhitePopPerc', 'BlackPopPerc', 'WhitePerCap', 'BlackPerCap'], dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f95fbe43d60>]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "X = load_X_for_clustering()\n",
    "\n",
    "k_vals = range(1, 20)\n",
    "inertias = []\n",
    "\n",
    "for k in k_vals:\n",
    "    \n",
    "    km = KMeans(n_clusters=k, init='k-means++')\n",
    "    km.fit(X)  \n",
    "    inertias.append(km.inertia_)  \n",
    "    \n",
    "print(f\"Using Only {X.columns}\")\n",
    "plt.title(\"Number of Clusters x Inertia\")\n",
    "plt.plot(k_vals, inertias)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61596b2b",
   "metadata": {},
   "source": [
    "Given this information, I decide to go with K=5 (5 clusters)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "03e42fdf",
   "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>Pop_Dens</th>\n",
       "      <th>PKPerSqMile</th>\n",
       "      <th>WhitePopPerc</th>\n",
       "      <th>BlackPopPerc</th>\n",
       "      <th>WhitePerCap</th>\n",
       "      <th>BlackPerCap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7457</td>\n",
       "      <td>7457</td>\n",
       "      <td>7457</td>\n",
       "      <td>7457</td>\n",
       "      <td>7457</td>\n",
       "      <td>7457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7937</td>\n",
       "      <td>7937</td>\n",
       "      <td>7937</td>\n",
       "      <td>7937</td>\n",
       "      <td>7937</td>\n",
       "      <td>7937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7050</td>\n",
       "      <td>7050</td>\n",
       "      <td>7050</td>\n",
       "      <td>7050</td>\n",
       "      <td>7050</td>\n",
       "      <td>7050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7814</td>\n",
       "      <td>7814</td>\n",
       "      <td>7814</td>\n",
       "      <td>7814</td>\n",
       "      <td>7814</td>\n",
       "      <td>7814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4121</td>\n",
       "      <td>4121</td>\n",
       "      <td>4121</td>\n",
       "      <td>4121</td>\n",
       "      <td>4121</td>\n",
       "      <td>4121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Pop_Dens  PKPerSqMile  WhitePopPerc  BlackPopPerc  WhitePerCap  BlackPerCap\n",
       "cluster_label                                                                             \n",
       "0                  7457         7457          7457          7457         7457         7457\n",
       "1                  7937         7937          7937          7937         7937         7937\n",
       "2                  7050         7050          7050          7050         7050         7050\n",
       "3                  7814         7814          7814          7814         7814         7814\n",
       "4                  4121         4121          4121          4121         4121         4121"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = load_X_for_clustering()\n",
    "\n",
    "km = KMeans(n_clusters=5, init='k-means++')\n",
    "km.fit(X)  \n",
    "\n",
    "cluster_centers = km.cluster_centers_\n",
    "X[\"cluster_label\"] = km.predict(X)   # this should work because X has the same number of rows, same user_ids represented as locs_gdf\n",
    "\n",
    "X.groupby(\"cluster_label\").count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd0d52cb",
   "metadata": {},
   "source": [
    "In order to see what these clusters represent, I display in 2 projections of feature-space, each using 3 of 6 salient dimensions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "924d66d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib qt\n",
    "# Then plot!\n",
    "\n",
    "X_c = get_agg_df(X)\n",
    "\n",
    "fig = plt.figure(figsize = (16, 9))\n",
    "ax = plt.axes(projection =\"3d\")\n",
    "sctt = ax.scatter3D(\n",
    "    xs=X_c['BlackPopPerc'], \n",
    "    ys=X_c['BlackPerCap'], \n",
    "    zs=X_c['PKPerSqMile'], \n",
    "    s=(X_c['Adopters']**1/2),\n",
    "    c=X_c['cluster_label'],\n",
    "    cmap='nipy_spectral'\n",
    ")\n",
    "ax.set_xlabel('Percent Black')\n",
    "ax.set_ylabel('Black Income (Per Cap.)')\n",
    "ax.set_zlabel('Police Killings (Per Sq. Mile)')\n",
    "\n",
    "cbar = fig.colorbar(sctt, ax = ax, shrink = 0.5, aspect = 5)\n",
    "cbar.set_label('Cluster Label')\n",
    "\n",
    "colors = [\"black\", \"powderblue\", \"lightgreen\", \"orange\", \"grey\"]\n",
    "\n",
    "for idx in range(len(km.cluster_centers_)):\n",
    "    center = km.cluster_centers_[idx]\n",
    "    x, y, z = center[0:3]\n",
    "    ax.plot3D([0, x], [0, y], [0, z], colors[idx])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "81607b09",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib qt\n",
    "# Then plot!\n",
    "\n",
    "X_c = get_agg_df(X)\n",
    "\n",
    "fig = plt.figure(figsize = (16, 9))\n",
    "ax = plt.axes(projection =\"3d\")\n",
    "sctt = ax.scatter3D(\n",
    "    xs=X_c['WhitePopPerc'], #.clip(upper=0.6), \n",
    "    ys=X_c['WhitePerCap'], #.clip(upper=1.5), \n",
    "    zs=X_c['Pop_Dens'],\n",
    "    s=(X_c['Adopters']**1/2),\n",
    "    c=X_c['cluster_label'],\n",
    "    cmap='nipy_spectral'\n",
    ")\n",
    "ax.set_xlabel('Percent White')\n",
    "ax.set_ylabel('White Income (Per Cap.)')\n",
    "ax.set_zlabel('Pop. (Per Sq. Mile)')\n",
    "\n",
    "cbar = fig.colorbar(sctt, ax = ax, shrink = 0.5, aspect = 5)\n",
    "cbar.set_label('Cluster Label')\n",
    "\n",
    "colors = [\"black\", \"blue\", \"powderblue\", \"lightgreen\", \"orange\", \"grey\"]\n",
    "\n",
    "for idx in range(len(km.cluster_centers_)):\n",
    "    center = km.cluster_centers_[idx]\n",
    "    x, y, z = center[3:6]\n",
    "    ax.plot3D([0, x], [0, y], [0, z], colors[idx])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "800b2180",
   "metadata": {},
   "source": [
    "Now I convert the centroid labels from standardized values to real values, to see if they're meaningful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "804505c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pop_Dens:\t2160.72\t-0.43\n",
      "PKPerSqMile:\t0.05\t-0.67\n",
      "WhitePopPerc:\t82.95\t1.16\n",
      "BlackPopPerc:\t6.14\t-0.72\n",
      "WhitePerCap:\t32908.95\t-0.66\n",
      "BlackPerCap:\t20783.36\t-0.62\n",
      "\n",
      "Pop_Dens:\t18372.37\t0.76\n",
      "PKPerSqMile:\t0.38\t1.33\n",
      "WhitePopPerc:\t47.07\t-0.8\n",
      "BlackPopPerc:\t20.68\t0.16\n",
      "WhitePerCap:\t57022.63\t0.63\n",
      "BlackPerCap:\t28852.86\t0.26\n",
      "\n",
      "Pop_Dens:\t3237.58\t-0.35\n",
      "PKPerSqMile:\t0.05\t-0.7\n",
      "WhitePopPerc:\t70.6\t0.49\n",
      "BlackPopPerc:\t6.41\t-0.7\n",
      "WhitePerCap:\t50861.37\t0.3\n",
      "BlackPerCap:\t35861.8\t1.02\n",
      "\n",
      "Pop_Dens:\t3984.92\t-0.3\n",
      "PKPerSqMile:\t0.14\t-0.14\n",
      "WhitePopPerc:\t61.32\t-0.02\n",
      "BlackPopPerc:\t19.43\t0.08\n",
      "WhitePerCap:\t37670.41\t-0.41\n",
      "BlackPerCap:\t21926.11\t-0.49\n",
      "\n",
      "Pop_Dens:\t4443.09\t-0.26\n",
      "PKPerSqMile:\t0.15\t-0.05\n",
      "WhitePopPerc:\t38.9\t-1.25\n",
      "BlackPopPerc:\t46.07\t1.69\n",
      "WhitePerCap:\t41921.72\t-0.18\n",
      "BlackPerCap:\t21807.01\t-0.5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ftr_names = km.feature_names_in_\n",
    "describe_locs_gdf = locs_gdf.describe()\n",
    "\n",
    "for idx in range(len(km.cluster_centers_)):\n",
    "    \n",
    "    center = km.cluster_centers_[idx]\n",
    "    \n",
    "    for ftr in range(len(center)):\n",
    "        val = center[ftr]*describe_locs_gdf.loc[\"std\",ftr_names[ftr]]\n",
    "        val += describe_locs_gdf.loc[\"mean\",ftr_names[ftr]]\n",
    "        \n",
    "        print(f\"{ftr_names[ftr]}:\\t{round(val, 2)}\\t{round(center[ftr], 2)}\")\n",
    "        \n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "3262d554",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>mean-mode dev.</th>\n",
       "      <th>Geo_FIPS</th>\n",
       "      <th>Geo_STATE</th>\n",
       "      <th>Geo_COUNTY</th>\n",
       "      <th>Geo_COUSUB</th>\n",
       "      <th>Total_Pop</th>\n",
       "      <th>Pop_Dens</th>\n",
       "      <th>Land_Area</th>\n",
       "      <th>WhitePop</th>\n",
       "      <th>BlackPop</th>\n",
       "      <th>OtherRacePop</th>\n",
       "      <th>WhitePopPerc</th>\n",
       "      <th>BlackPopPerc</th>\n",
       "      <th>OtherRacePopPerc</th>\n",
       "      <th>PctLatinoPop</th>\n",
       "      <th>WhitePerCap</th>\n",
       "      <th>BlackPerCap</th>\n",
       "      <th>LatinoPerCap</th>\n",
       "      <th>PerCap</th>\n",
       "      <th>PoliceKillings</th>\n",
       "      <th>PKPerCap</th>\n",
       "      <th>PKPerSqMile</th>\n",
       "      <th>Blck_Wht_PerCap_Ratio</th>\n",
       "      <th>Following</th>\n",
       "      <th>Followers</th>\n",
       "      <th>Tweets</th>\n",
       "      <th>Pop_Dens_std</th>\n",
       "      <th>PKPerSqMile_std</th>\n",
       "      <th>WhitePopPerc_std</th>\n",
       "      <th>BlackPopPerc_std</th>\n",
       "      <th>WhitePerCap_std</th>\n",
       "      <th>BlackPerCap_std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1797.649992</td>\n",
       "      <td>2.990379e+09</td>\n",
       "      <td>29.822046</td>\n",
       "      <td>81.008583</td>\n",
       "      <td>73658.550221</td>\n",
       "      <td>3.229688e+05</td>\n",
       "      <td>2188.402347</td>\n",
       "      <td>209.989912</td>\n",
       "      <td>252286.681776</td>\n",
       "      <td>19455.863618</td>\n",
       "      <td>51226.298109</td>\n",
       "      <td>82.952788</td>\n",
       "      <td>6.137866</td>\n",
       "      <td>10.909772</td>\n",
       "      <td>20.860889</td>\n",
       "      <td>32936.376023</td>\n",
       "      <td>20558.053911</td>\n",
       "      <td>20216.061715</td>\n",
       "      <td>31038.275312</td>\n",
       "      <td>15.992088</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.052553</td>\n",
       "      <td>0.653378</td>\n",
       "      <td>1129.983237</td>\n",
       "      <td>1827.080595</td>\n",
       "      <td>16106.177551</td>\n",
       "      <td>-0.431864</td>\n",
       "      <td>-0.665871</td>\n",
       "      <td>1.163811</td>\n",
       "      <td>-0.718561</td>\n",
       "      <td>-0.663120</td>\n",
       "      <td>-0.614242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1090.498467</td>\n",
       "      <td>1.871319e+09</td>\n",
       "      <td>18.670908</td>\n",
       "      <td>41.711982</td>\n",
       "      <td>57008.392592</td>\n",
       "      <td>1.922605e+06</td>\n",
       "      <td>23579.627162</td>\n",
       "      <td>137.467407</td>\n",
       "      <td>909738.373441</td>\n",
       "      <td>381732.324430</td>\n",
       "      <td>631134.316870</td>\n",
       "      <td>46.989055</td>\n",
       "      <td>20.683126</td>\n",
       "      <td>32.328155</td>\n",
       "      <td>32.379682</td>\n",
       "      <td>60955.426106</td>\n",
       "      <td>28832.856243</td>\n",
       "      <td>24045.503843</td>\n",
       "      <td>45089.554996</td>\n",
       "      <td>50.265970</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.380390</td>\n",
       "      <td>0.528553</td>\n",
       "      <td>1276.630087</td>\n",
       "      <td>7047.594431</td>\n",
       "      <td>15969.399899</td>\n",
       "      <td>0.759709</td>\n",
       "      <td>1.331634</td>\n",
       "      <td>-0.804548</td>\n",
       "      <td>0.160514</td>\n",
       "      <td>0.625453</td>\n",
       "      <td>0.260964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1839.010605</td>\n",
       "      <td>2.394234e+09</td>\n",
       "      <td>23.850922</td>\n",
       "      <td>90.635177</td>\n",
       "      <td>77814.391915</td>\n",
       "      <td>4.879922e+05</td>\n",
       "      <td>3272.920829</td>\n",
       "      <td>229.938996</td>\n",
       "      <td>308961.951631</td>\n",
       "      <td>26257.112340</td>\n",
       "      <td>152773.100851</td>\n",
       "      <td>70.473974</td>\n",
       "      <td>6.417186</td>\n",
       "      <td>23.107316</td>\n",
       "      <td>19.815389</td>\n",
       "      <td>51309.319716</td>\n",
       "      <td>37472.679994</td>\n",
       "      <td>28141.444334</td>\n",
       "      <td>46932.137305</td>\n",
       "      <td>10.744823</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.046564</td>\n",
       "      <td>0.772556</td>\n",
       "      <td>1227.007943</td>\n",
       "      <td>2450.117163</td>\n",
       "      <td>15611.921277</td>\n",
       "      <td>-0.352692</td>\n",
       "      <td>-0.701527</td>\n",
       "      <td>0.485266</td>\n",
       "      <td>-0.701680</td>\n",
       "      <td>0.296958</td>\n",
       "      <td>1.023792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1658.875417</td>\n",
       "      <td>2.981259e+09</td>\n",
       "      <td>29.717558</td>\n",
       "      <td>94.254287</td>\n",
       "      <td>78222.668416</td>\n",
       "      <td>8.263296e+05</td>\n",
       "      <td>3979.656197</td>\n",
       "      <td>237.623636</td>\n",
       "      <td>504005.038521</td>\n",
       "      <td>148079.916944</td>\n",
       "      <td>174244.643588</td>\n",
       "      <td>61.300340</td>\n",
       "      <td>19.431400</td>\n",
       "      <td>19.268723</td>\n",
       "      <td>26.167272</td>\n",
       "      <td>37667.185052</td>\n",
       "      <td>21929.774090</td>\n",
       "      <td>19722.875768</td>\n",
       "      <td>32035.771180</td>\n",
       "      <td>29.510878</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.139732</td>\n",
       "      <td>0.613238</td>\n",
       "      <td>1140.697082</td>\n",
       "      <td>2268.416560</td>\n",
       "      <td>16677.471206</td>\n",
       "      <td>-0.297866</td>\n",
       "      <td>-0.140229</td>\n",
       "      <td>-0.022920</td>\n",
       "      <td>0.084863</td>\n",
       "      <td>-0.409392</td>\n",
       "      <td>-0.492023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1534.761852</td>\n",
       "      <td>2.915329e+09</td>\n",
       "      <td>29.012861</td>\n",
       "      <td>139.678233</td>\n",
       "      <td>75573.974763</td>\n",
       "      <td>4.492213e+05</td>\n",
       "      <td>4437.824194</td>\n",
       "      <td>113.802548</td>\n",
       "      <td>179312.506916</td>\n",
       "      <td>211499.719728</td>\n",
       "      <td>58409.043921</td>\n",
       "      <td>37.628314</td>\n",
       "      <td>51.625445</td>\n",
       "      <td>10.746615</td>\n",
       "      <td>10.814069</td>\n",
       "      <td>41920.853191</td>\n",
       "      <td>21872.840087</td>\n",
       "      <td>20077.944526</td>\n",
       "      <td>30128.409609</td>\n",
       "      <td>14.478525</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.162068</td>\n",
       "      <td>0.580518</td>\n",
       "      <td>1215.461538</td>\n",
       "      <td>2012.455957</td>\n",
       "      <td>20086.444310</td>\n",
       "      <td>-0.264162</td>\n",
       "      <td>-0.047530</td>\n",
       "      <td>-1.252958</td>\n",
       "      <td>1.694870</td>\n",
       "      <td>-0.181967</td>\n",
       "      <td>-0.504774</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               mean-mode dev.      Geo_FIPS  Geo_STATE  Geo_COUNTY    Geo_COUSUB     Total_Pop      Pop_Dens   Land_Area       WhitePop       BlackPop   OtherRacePop  WhitePopPerc  BlackPopPerc  OtherRacePopPerc  PctLatinoPop   WhitePerCap   BlackPerCap  LatinoPerCap        PerCap  PoliceKillings  PKPerCap  PKPerSqMile  Blck_Wht_PerCap_Ratio    Following    Followers        Tweets  Pop_Dens_std  PKPerSqMile_std  WhitePopPerc_std  BlackPopPerc_std  WhitePerCap_std  BlackPerCap_std\n",
       "cluster_label                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       \n",
       "0                 1797.649992  2.990379e+09  29.822046   81.008583  73658.550221  3.229688e+05   2188.402347  209.989912  252286.681776   19455.863618   51226.298109     82.952788      6.137866         10.909772     20.860889  32936.376023  20558.053911  20216.061715  31038.275312       15.992088  0.000027     0.052553               0.653378  1129.983237  1827.080595  16106.177551     -0.431864        -0.665871          1.163811         -0.718561        -0.663120        -0.614242\n",
       "1                 1090.498467  1.871319e+09  18.670908   41.711982  57008.392592  1.922605e+06  23579.627162  137.467407  909738.373441  381732.324430  631134.316870     46.989055     20.683126         32.328155     32.379682  60955.426106  28832.856243  24045.503843  45089.554996       50.265970  0.000027     0.380390               0.528553  1276.630087  7047.594431  15969.399899      0.759709         1.331634         -0.804548          0.160514         0.625453         0.260964\n",
       "2                 1839.010605  2.394234e+09  23.850922   90.635177  77814.391915  4.879922e+05   3272.920829  229.938996  308961.951631   26257.112340  152773.100851     70.473974      6.417186         23.107316     19.815389  51309.319716  37472.679994  28141.444334  46932.137305       10.744823  0.000016     0.046564               0.772556  1227.007943  2450.117163  15611.921277     -0.352692        -0.701527          0.485266         -0.701680         0.296958         1.023792\n",
       "3                 1658.875417  2.981259e+09  29.717558   94.254287  78222.668416  8.263296e+05   3979.656197  237.623636  504005.038521  148079.916944  174244.643588     61.300340     19.431400         19.268723     26.167272  37667.185052  21929.774090  19722.875768  32035.771180       29.510878  0.000036     0.139732               0.613238  1140.697082  2268.416560  16677.471206     -0.297866        -0.140229         -0.022920          0.084863        -0.409392        -0.492023\n",
       "4                 1534.761852  2.915329e+09  29.012861  139.678233  75573.974763  4.492213e+05   4437.824194  113.802548  179312.506916  211499.719728   58409.043921     37.628314     51.625445         10.746615     10.814069  41920.853191  21872.840087  20077.944526  30128.409609       14.478525  0.000041     0.162068               0.580518  1215.461538  2012.455957  20086.444310     -0.264162        -0.047530         -1.252958          1.694870        -0.181967        -0.504774"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "locs_gdf[\"cluster_label\"] = X[\"cluster_label\"]\n",
    "for col in ['Pop_Dens','PKPerSqMile','WhitePopPerc','BlackPopPerc','WhitePerCap','BlackPerCap']:\n",
    "    locs_gdf[f\"{col}_std\"] = X[col]\n",
    "locs_gdf.groupby(\"cluster_label\").mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "889147e8",
   "metadata": {},
   "source": [
    "## K-Means (Take 2, by Subdiv.)\n",
    "\n",
    "I realize that my initial approach to K means had the side effect of ignoring those county subdivisions which did not have much adoption at all, which are still analytically relevant. This take to examines K means across all subdivisions, using a weighted medium this time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "991a2bec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# FROM HERE: https://stackoverflow.com/questions/26102867/python-weighted-median-algorithm-with-pandas\n",
    "\n",
    "def wtd_median(df, val, weight):\n",
    "    df_sorted = df.sort_values(val)\n",
    "    cumsum = df_sorted[weight].cumsum()\n",
    "    cutoff = df_sorted[weight].sum() / 2.\n",
    "    return df_sorted[cumsum >= cutoff][val].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "347af954",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Pop_Dens_std', 'PKPerSqMile_std', 'WhitePopPerc_std', 'BlackPopPerc_std', 'WhitePerCap_std', 'BlackPerCap_std']\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Geo_FIPS</th>\n",
       "      <th>Geo_STATE</th>\n",
       "      <th>Geo_COUNTY</th>\n",
       "      <th>Geo_COUSUB</th>\n",
       "      <th>Total_Pop</th>\n",
       "      <th>Pop_Dens</th>\n",
       "      <th>Land_Area</th>\n",
       "      <th>WhitePop</th>\n",
       "      <th>BlackPop</th>\n",
       "      <th>OtherRacePop</th>\n",
       "      <th>WhitePopPerc</th>\n",
       "      <th>BlackPopPerc</th>\n",
       "      <th>OtherRacePopPerc</th>\n",
       "      <th>PctLatinoPop</th>\n",
       "      <th>WhitePerCap</th>\n",
       "      <th>BlackPerCap</th>\n",
       "      <th>LatinoPerCap</th>\n",
       "      <th>PerCap</th>\n",
       "      <th>PoliceKillings</th>\n",
       "      <th>PKPerCap</th>\n",
       "      <th>PKPerSqMile</th>\n",
       "      <th>Blck_Wht_PerCap_Ratio</th>\n",
       "      <th>Adopters</th>\n",
       "      <th>AdoptersPerCap</th>\n",
       "      <th>Pop_Dens_std</th>\n",
       "      <th>PKPerSqMile_std</th>\n",
       "      <th>WhitePopPerc_std</th>\n",
       "      <th>BlackPopPerc_std</th>\n",
       "      <th>WhitePerCap_std</th>\n",
       "      <th>BlackPerCap_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.609900e+04</td>\n",
       "      <td>36099.000000</td>\n",
       "      <td>36099.000000</td>\n",
       "      <td>36099.000000</td>\n",
       "      <td>3.609900e+04</td>\n",
       "      <td>35935.000000</td>\n",
       "      <td>35935.000000</td>\n",
       "      <td>3.609900e+04</td>\n",
       "      <td>36099.000000</td>\n",
       "      <td>3.609900e+04</td>\n",
       "      <td>36099.000000</td>\n",
       "      <td>36099.000000</td>\n",
       "      <td>36099.000000</td>\n",
       "      <td>36099.000000</td>\n",
       "      <td>35499.000000</td>\n",
       "      <td>12917.000000</td>\n",
       "      <td>17121.000000</td>\n",
       "      <td>35572.000000</td>\n",
       "      <td>36099.000000</td>\n",
       "      <td>36099.000000</td>\n",
       "      <td>35935.000000</td>\n",
       "      <td>12897.000000</td>\n",
       "      <td>36099.000000</td>\n",
       "      <td>36099.000000</td>\n",
       "      <td>36099.000000</td>\n",
       "      <td>36099.000000</td>\n",
       "      <td>36099.000000</td>\n",
       "      <td>36099.000000</td>\n",
       "      <td>36099.000000</td>\n",
       "      <td>36099.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.204561e+09</td>\n",
       "      <td>31.962077</td>\n",
       "      <td>82.928087</td>\n",
       "      <td>60152.618106</td>\n",
       "      <td>9.086574e+03</td>\n",
       "      <td>456.789764</td>\n",
       "      <td>96.973384</td>\n",
       "      <td>6.580949e+03</td>\n",
       "      <td>1152.977035</td>\n",
       "      <td>1.352648e+03</td>\n",
       "      <td>88.917793</td>\n",
       "      <td>5.160535</td>\n",
       "      <td>5.921656</td>\n",
       "      <td>7.274752</td>\n",
       "      <td>32055.453309</td>\n",
       "      <td>22914.766819</td>\n",
       "      <td>20948.759243</td>\n",
       "      <td>30843.422242</td>\n",
       "      <td>0.223801</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.006402</td>\n",
       "      <td>0.738738</td>\n",
       "      <td>0.952353</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>-0.405122</td>\n",
       "      <td>-0.538002</td>\n",
       "      <td>0.848320</td>\n",
       "      <td>-0.521132</td>\n",
       "      <td>-0.422155</td>\n",
       "      <td>-0.155978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.474881e+09</td>\n",
       "      <td>14.749915</td>\n",
       "      <td>67.613777</td>\n",
       "      <td>30977.136060</td>\n",
       "      <td>5.807135e+04</td>\n",
       "      <td>1404.784462</td>\n",
       "      <td>818.072410</td>\n",
       "      <td>3.510661e+04</td>\n",
       "      <td>12133.266138</td>\n",
       "      <td>1.631819e+04</td>\n",
       "      <td>16.906969</td>\n",
       "      <td>12.941793</td>\n",
       "      <td>10.566261</td>\n",
       "      <td>17.709235</td>\n",
       "      <td>13834.467362</td>\n",
       "      <td>17076.419762</td>\n",
       "      <td>18096.346440</td>\n",
       "      <td>13627.507547</td>\n",
       "      <td>2.061354</td>\n",
       "      <td>0.000599</td>\n",
       "      <td>0.055775</td>\n",
       "      <td>0.505072</td>\n",
       "      <td>19.009482</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>0.174888</td>\n",
       "      <td>0.234600</td>\n",
       "      <td>0.784024</td>\n",
       "      <td>0.643753</td>\n",
       "      <td>0.759227</td>\n",
       "      <td>0.627455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.001902e+08</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.001521</td>\n",
       "      <td>0.004915</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>875.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>832.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001229</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.470305</td>\n",
       "      <td>-0.579824</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-0.812009</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.014351e+09</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>32906.000000</td>\n",
       "      <td>4.090000e+02</td>\n",
       "      <td>10.798565</td>\n",
       "      <td>21.654164</td>\n",
       "      <td>3.650000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>87.335000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24775.000000</td>\n",
       "      <td>13438.000000</td>\n",
       "      <td>12062.000000</td>\n",
       "      <td>23617.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.477688</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.468743</td>\n",
       "      <td>-0.579824</td>\n",
       "      <td>0.751820</td>\n",
       "      <td>-0.812009</td>\n",
       "      <td>-0.898791</td>\n",
       "      <td>-0.035321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.101190e+09</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>65503.000000</td>\n",
       "      <td>1.497000e+03</td>\n",
       "      <td>43.909340</td>\n",
       "      <td>35.600690</td>\n",
       "      <td>1.292000e+03</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.100000e+01</td>\n",
       "      <td>95.550000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>2.880000</td>\n",
       "      <td>1.760000</td>\n",
       "      <td>29755.000000</td>\n",
       "      <td>19441.000000</td>\n",
       "      <td>17495.000000</td>\n",
       "      <td>28703.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.661769</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.463947</td>\n",
       "      <td>-0.579824</td>\n",
       "      <td>1.166054</td>\n",
       "      <td>-0.794090</td>\n",
       "      <td>-0.542383</td>\n",
       "      <td>-0.035321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.204576e+09</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>91170.000000</td>\n",
       "      <td>4.757500e+03</td>\n",
       "      <td>238.155600</td>\n",
       "      <td>54.086595</td>\n",
       "      <td>4.037500e+03</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>2.430000e+02</td>\n",
       "      <td>98.910000</td>\n",
       "      <td>2.920000</td>\n",
       "      <td>6.570000</td>\n",
       "      <td>5.430000</td>\n",
       "      <td>36423.000000</td>\n",
       "      <td>28164.000000</td>\n",
       "      <td>25079.000000</td>\n",
       "      <td>35284.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.878522</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.434850</td>\n",
       "      <td>-0.579824</td>\n",
       "      <td>1.335480</td>\n",
       "      <td>-0.625146</td>\n",
       "      <td>-0.095375</td>\n",
       "      <td>-0.035321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.215388e+09</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>840.000000</td>\n",
       "      <td>98000.000000</td>\n",
       "      <td>3.433961e+06</td>\n",
       "      <td>71488.690000</td>\n",
       "      <td>88695.410024</td>\n",
       "      <td>2.556502e+06</td>\n",
       "      <td>833683.000000</td>\n",
       "      <td>1.050342e+06</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>449605.000000</td>\n",
       "      <td>417268.000000</td>\n",
       "      <td>904414.000000</td>\n",
       "      <td>449605.000000</td>\n",
       "      <td>204.000000</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>2.594289</td>\n",
       "      <td>12.321448</td>\n",
       "      <td>2499.000000</td>\n",
       "      <td>0.010080</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.390442</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Geo_FIPS     Geo_STATE    Geo_COUNTY    Geo_COUSUB     Total_Pop      Pop_Dens     Land_Area      WhitePop       BlackPop  OtherRacePop  WhitePopPerc  BlackPopPerc  OtherRacePopPerc  PctLatinoPop    WhitePerCap    BlackPerCap   LatinoPerCap         PerCap  PoliceKillings      PKPerCap   PKPerSqMile  Blck_Wht_PerCap_Ratio      Adopters  AdoptersPerCap  Pop_Dens_std  PKPerSqMile_std  WhitePopPerc_std  BlackPopPerc_std  WhitePerCap_std  BlackPerCap_std\n",
       "count  3.609900e+04  36099.000000  36099.000000  36099.000000  3.609900e+04  35935.000000  35935.000000  3.609900e+04   36099.000000  3.609900e+04  36099.000000  36099.000000      36099.000000  36099.000000   35499.000000   12917.000000   17121.000000   35572.000000    36099.000000  36099.000000  35935.000000           12897.000000  36099.000000    36099.000000  36099.000000     36099.000000      36099.000000      36099.000000     36099.000000     36099.000000\n",
       "mean   3.204561e+09     31.962077     82.928087  60152.618106  9.086574e+03    456.789764     96.973384  6.580949e+03    1152.977035  1.352648e+03     88.917793      5.160535          5.921656      7.274752   32055.453309   22914.766819   20948.759243   30843.422242        0.223801      0.000026      0.006402               0.738738      0.952353        0.000017     -0.405122        -0.538002          0.848320         -0.521132        -0.422155        -0.155978\n",
       "std    1.474881e+09     14.749915     67.613777  30977.136060  5.807135e+04   1404.784462    818.072410  3.510661e+04   12133.266138  1.631819e+04     16.906969     12.941793         10.566261     17.709235   13834.467362   17076.419762   18096.346440   13627.507547        2.061354      0.000599      0.055775               0.505072     19.009482        0.000118      0.174888         0.234600          0.784024          0.643753         0.759227         0.627455\n",
       "min    1.001902e+08      1.000000      1.000000     70.000000  1.000000e+00      0.001521      0.004915  0.000000e+00       0.000000  0.000000e+00      0.000000      0.000000          0.000000      0.000000     875.000000      23.000000      52.000000     832.000000        0.000000      0.000000      0.000000               0.001229      0.000000        0.000000     -0.470305        -0.579824         -2.000000         -0.812009        -2.000000        -2.000000\n",
       "25%    2.014351e+09     20.000000     31.000000  32906.000000  4.090000e+02     10.798565     21.654164  3.650000e+02       0.000000  4.000000e+00     87.335000      0.000000          0.590000      0.000000   24775.000000   13438.000000   12062.000000   23617.000000        0.000000      0.000000      0.000000               0.477688      0.000000        0.000000     -0.468743        -0.579824          0.751820         -0.812009        -0.898791        -0.035321\n",
       "50%    3.101190e+09     31.000000     73.000000  65503.000000  1.497000e+03     43.909340     35.600690  1.292000e+03       5.000000  4.100000e+01     95.550000      0.280000          2.880000      1.760000   29755.000000   19441.000000   17495.000000   28703.500000        0.000000      0.000000      0.000000               0.661769      0.000000        0.000000     -0.463947        -0.579824          1.166054         -0.794090        -0.542383        -0.035321\n",
       "75%    4.204576e+09     42.000000    121.000000  91170.000000  4.757500e+03    238.155600     54.086595  4.037500e+03     138.000000  2.430000e+02     98.910000      2.920000          6.570000      5.430000   36423.000000   28164.000000   25079.000000   35284.000000        0.000000      0.000000      0.000000               0.878522      0.000000        0.000000     -0.434850        -0.579824          1.335480         -0.625146        -0.095375        -0.035321\n",
       "max    7.215388e+09     72.000000    840.000000  98000.000000  3.433961e+06  71488.690000  88695.410024  2.556502e+06  833683.000000  1.050342e+06    100.000000    100.000000        100.000000    100.000000  449605.000000  417268.000000  904414.000000  449605.000000      204.000000      0.076923      2.594289              12.321448   2499.000000        0.010080      2.000000         2.000000          1.390442          2.000000         2.000000         2.000000"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols =  ['Pop_Dens', 'PKPerSqMile', 'WhitePopPerc', 'BlackPopPerc','WhitePerCap', 'BlackPerCap']\n",
    "for col in cols:\n",
    "    county_subd_gdf[f\"{col}_std\"] = county_subd_gdf[col]\n",
    "    county_subd_gdf.fillna(\n",
    "        value={\n",
    "            f\"{col}_std\": wtd_median(county_subd_gdf, col, 'Total_Pop')\n",
    "        }, \n",
    "        inplace=True\n",
    "    )\n",
    "    \n",
    "std_cols = [f\"{col}_std\" for col in cols]\n",
    "print(std_cols)\n",
    "\n",
    "s = StandardScaler()\n",
    "county_subd_gdf.loc[:, std_cols] = s.fit_transform(\n",
    "    X = county_subd_gdf.loc[:, std_cols], \n",
    "    sample_weight = county_subd_gdf.loc[:, \"Total_Pop\"]\n",
    ")\n",
    "\n",
    "csub_std_df = pd.DataFrame(county_subd_gdf.loc[:, std_cols])\n",
    "csub_std_df.clip(lower=-2.0, upper=2.0, inplace=True)\n",
    "\n",
    "county_subd_gdf.loc[:, std_cols] = csub_std_df\n",
    "county_subd_gdf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "2a214dd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f96ba614d30>]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "inertias = []\n",
    "k_vals = range(2, 20)\n",
    "for n in k_vals:\n",
    "\n",
    "    km = KMeans(n_clusters=n, init='k-means++')\n",
    "    km.fit(X=county_subd_gdf.loc[:, std_cols], sample_weight=county_subd_gdf.loc[:, \"Total_Pop\"])\n",
    "    inertias.append(km.inertia_)  \n",
    "    \n",
    "plt.title(\"Number of Clusters x Inertia\")\n",
    "plt.plot(k_vals, inertias)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd78e876",
   "metadata": {},
   "source": [
    "Now, the tricky part, adding this to locs_gdf:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "265aa64b",
   "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>mean-mode dev.</th>\n",
       "      <th>Geo_FIPS</th>\n",
       "      <th>Geo_STATE</th>\n",
       "      <th>Geo_COUNTY</th>\n",
       "      <th>Geo_COUSUB</th>\n",
       "      <th>Total_Pop</th>\n",
       "      <th>Pop_Dens</th>\n",
       "      <th>Land_Area</th>\n",
       "      <th>WhitePop</th>\n",
       "      <th>BlackPop</th>\n",
       "      <th>OtherRacePop</th>\n",
       "      <th>WhitePopPerc</th>\n",
       "      <th>BlackPopPerc</th>\n",
       "      <th>OtherRacePopPerc</th>\n",
       "      <th>PctLatinoPop</th>\n",
       "      <th>WhitePerCap</th>\n",
       "      <th>BlackPerCap</th>\n",
       "      <th>LatinoPerCap</th>\n",
       "      <th>PerCap</th>\n",
       "      <th>PoliceKillings</th>\n",
       "      <th>PKPerCap</th>\n",
       "      <th>PKPerSqMile</th>\n",
       "      <th>Blck_Wht_PerCap_Ratio</th>\n",
       "      <th>Following</th>\n",
       "      <th>Followers</th>\n",
       "      <th>Tweets</th>\n",
       "      <th>cluster_label</th>\n",
       "      <th>Pop_Dens_std</th>\n",
       "      <th>PKPerSqMile_std</th>\n",
       "      <th>WhitePopPerc_std</th>\n",
       "      <th>BlackPopPerc_std</th>\n",
       "      <th>WhitePerCap_std</th>\n",
       "      <th>BlackPerCap_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.437900e+04</td>\n",
       "      <td>3.437900e+04</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>3.437900e+04</td>\n",
       "      <td>34300.000000</td>\n",
       "      <td>34300.000000</td>\n",
       "      <td>3.437900e+04</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>3.437900e+04</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34050.000000</td>\n",
       "      <td>34194.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34300.000000</td>\n",
       "      <td>34050.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>3.437900e+04</td>\n",
       "      <td>3.437900e+04</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.579819e+03</td>\n",
       "      <td>2.598706e+09</td>\n",
       "      <td>25.902382</td>\n",
       "      <td>83.953693</td>\n",
       "      <td>71933.772506</td>\n",
       "      <td>8.556566e+05</td>\n",
       "      <td>8036.880040</td>\n",
       "      <td>192.025560</td>\n",
       "      <td>4.641589e+05</td>\n",
       "      <td>156743.689229</td>\n",
       "      <td>2.347540e+05</td>\n",
       "      <td>61.736517</td>\n",
       "      <td>18.027244</td>\n",
       "      <td>20.236247</td>\n",
       "      <td>23.307587</td>\n",
       "      <td>45324.986096</td>\n",
       "      <td>26451.629134</td>\n",
       "      <td>22605.882348</td>\n",
       "      <td>37659.224439</td>\n",
       "      <td>25.720033</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.160133</td>\n",
       "      <td>0.630862</td>\n",
       "      <td>1196.417231</td>\n",
       "      <td>3.282625e+03</td>\n",
       "      <td>1.658021e+04</td>\n",
       "      <td>1.802350</td>\n",
       "      <td>0.680125</td>\n",
       "      <td>0.770593</td>\n",
       "      <td>-0.538963</td>\n",
       "      <td>0.341629</td>\n",
       "      <td>0.542345</td>\n",
       "      <td>0.036051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.955855e+03</td>\n",
       "      <td>1.652377e+09</td>\n",
       "      <td>16.489786</td>\n",
       "      <td>108.017975</td>\n",
       "      <td>29841.414150</td>\n",
       "      <td>9.993318e+05</td>\n",
       "      <td>13604.529302</td>\n",
       "      <td>466.710048</td>\n",
       "      <td>5.485587e+05</td>\n",
       "      <td>221619.030296</td>\n",
       "      <td>3.245024e+05</td>\n",
       "      <td>18.226162</td>\n",
       "      <td>16.546325</td>\n",
       "      <td>13.338321</td>\n",
       "      <td>18.519561</td>\n",
       "      <td>18702.669909</td>\n",
       "      <td>9201.380373</td>\n",
       "      <td>8103.623555</td>\n",
       "      <td>13530.812948</td>\n",
       "      <td>36.069957</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.161441</td>\n",
       "      <td>0.235241</td>\n",
       "      <td>4387.541000</td>\n",
       "      <td>5.706542e+04</td>\n",
       "      <td>3.645231e+04</td>\n",
       "      <td>1.329001</td>\n",
       "      <td>1.949278</td>\n",
       "      <td>1.363909</td>\n",
       "      <td>0.919040</td>\n",
       "      <td>1.058868</td>\n",
       "      <td>1.318129</td>\n",
       "      <td>0.796388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.001926e+08</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.660000e+02</td>\n",
       "      <td>0.434984</td>\n",
       "      <td>0.032625</td>\n",
       "      <td>1.630000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.380000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4226.000000</td>\n",
       "      <td>602.000000</td>\n",
       "      <td>1611.000000</td>\n",
       "      <td>4725.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020028</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.470243</td>\n",
       "      <td>-0.579824</td>\n",
       "      <td>-3.582392</td>\n",
       "      <td>-0.812009</td>\n",
       "      <td>-2.354235</td>\n",
       "      <td>-2.211264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.001336e+08</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>8.099300e+04</td>\n",
       "      <td>1933.357000</td>\n",
       "      <td>36.339705</td>\n",
       "      <td>5.553000e+04</td>\n",
       "      <td>6863.500000</td>\n",
       "      <td>9.244500e+03</td>\n",
       "      <td>47.780000</td>\n",
       "      <td>5.780000</td>\n",
       "      <td>9.550000</td>\n",
       "      <td>8.210000</td>\n",
       "      <td>33715.000000</td>\n",
       "      <td>20324.000000</td>\n",
       "      <td>18142.000000</td>\n",
       "      <td>29147.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.023944</td>\n",
       "      <td>0.498853</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>1.900000e+02</td>\n",
       "      <td>2.039000e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.193044</td>\n",
       "      <td>-0.380999</td>\n",
       "      <td>-1.242709</td>\n",
       "      <td>-0.442123</td>\n",
       "      <td>-0.275905</td>\n",
       "      <td>-0.490059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.328306e-10</td>\n",
       "      <td>2.605938e+09</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>90884.000000</td>\n",
       "      <td>3.957770e+05</td>\n",
       "      <td>3570.421000</td>\n",
       "      <td>87.658840</td>\n",
       "      <td>2.094240e+05</td>\n",
       "      <td>45888.000000</td>\n",
       "      <td>6.624500e+04</td>\n",
       "      <td>60.300000</td>\n",
       "      <td>11.880000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>18.750000</td>\n",
       "      <td>41883.000000</td>\n",
       "      <td>25557.000000</td>\n",
       "      <td>20597.000000</td>\n",
       "      <td>35563.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.107292</td>\n",
       "      <td>0.611203</td>\n",
       "      <td>597.000000</td>\n",
       "      <td>4.600000e+02</td>\n",
       "      <td>6.379000e+03</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.041726</td>\n",
       "      <td>0.326842</td>\n",
       "      <td>-0.611399</td>\n",
       "      <td>-0.051758</td>\n",
       "      <td>0.299761</td>\n",
       "      <td>-0.035321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.275834e+02</td>\n",
       "      <td>3.906102e+09</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>92522.000000</td>\n",
       "      <td>1.579075e+06</td>\n",
       "      <td>9067.710000</td>\n",
       "      <td>235.938329</td>\n",
       "      <td>7.563910e+05</td>\n",
       "      <td>240993.000000</td>\n",
       "      <td>4.233490e+05</td>\n",
       "      <td>76.380000</td>\n",
       "      <td>28.280000</td>\n",
       "      <td>28.450000</td>\n",
       "      <td>33.035000</td>\n",
       "      <td>49305.000000</td>\n",
       "      <td>30095.000000</td>\n",
       "      <td>26033.000000</td>\n",
       "      <td>41569.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.272115</td>\n",
       "      <td>0.726091</td>\n",
       "      <td>1227.000000</td>\n",
       "      <td>1.169000e+03</td>\n",
       "      <td>1.745500e+04</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.830088</td>\n",
       "      <td>1.719667</td>\n",
       "      <td>0.199423</td>\n",
       "      <td>0.997745</td>\n",
       "      <td>0.822849</td>\n",
       "      <td>0.353585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.993679e+04</td>\n",
       "      <td>7.214985e+09</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>840.000000</td>\n",
       "      <td>98000.000000</td>\n",
       "      <td>3.433961e+06</td>\n",
       "      <td>71488.690000</td>\n",
       "      <td>24607.898341</td>\n",
       "      <td>2.556502e+06</td>\n",
       "      <td>833683.000000</td>\n",
       "      <td>1.050342e+06</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>94.340000</td>\n",
       "      <td>98.430000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>156737.000000</td>\n",
       "      <td>192355.000000</td>\n",
       "      <td>238039.000000</td>\n",
       "      <td>164307.000000</td>\n",
       "      <td>204.000000</td>\n",
       "      <td>0.004762</td>\n",
       "      <td>2.579073</td>\n",
       "      <td>5.684942</td>\n",
       "      <td>362676.000000</td>\n",
       "      <td>6.848484e+06</td>\n",
       "      <td>2.355436e+06</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.781831</td>\n",
       "      <td>21.214474</td>\n",
       "      <td>1.390442</td>\n",
       "      <td>5.225198</td>\n",
       "      <td>8.394457</td>\n",
       "      <td>14.464471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean-mode dev.      Geo_FIPS     Geo_STATE    Geo_COUNTY    Geo_COUSUB     Total_Pop      Pop_Dens     Land_Area      WhitePop       BlackPop  OtherRacePop  WhitePopPerc  BlackPopPerc  OtherRacePopPerc  PctLatinoPop    WhitePerCap    BlackPerCap   LatinoPerCap         PerCap  PoliceKillings      PKPerCap   PKPerSqMile  Blck_Wht_PerCap_Ratio      Following     Followers        Tweets  cluster_label  Pop_Dens_std  PKPerSqMile_std  WhitePopPerc_std  BlackPopPerc_std  WhitePerCap_std  BlackPerCap_std\n",
       "count    3.437900e+04  3.437900e+04  34379.000000  34379.000000  34379.000000  3.437900e+04  34300.000000  34300.000000  3.437900e+04   34379.000000  3.437900e+04  34379.000000  34379.000000      34379.000000  34379.000000   34379.000000   34050.000000   34194.000000   34379.000000    34379.000000  34379.000000  34300.000000           34050.000000   34379.000000  3.437900e+04  3.437900e+04   34379.000000  34379.000000     34379.000000      34379.000000      34379.000000     34379.000000     34379.000000\n",
       "mean     1.579819e+03  2.598706e+09     25.902382     83.953693  71933.772506  8.556566e+05   8036.880040    192.025560  4.641589e+05  156743.689229  2.347540e+05     61.736517     18.027244         20.236247     23.307587   45324.986096   26451.629134   22605.882348   37659.224439       25.720033      0.000028      0.160133               0.630862    1196.417231  3.282625e+03  1.658021e+04       1.802350      0.680125         0.770593         -0.538963          0.341629         0.542345         0.036051\n",
       "std      3.955855e+03  1.652377e+09     16.489786    108.017975  29841.414150  9.993318e+05  13604.529302    466.710048  5.485587e+05  221619.030296  3.245024e+05     18.226162     16.546325         13.338321     18.519561   18702.669909    9201.380373    8103.623555   13530.812948       36.069957      0.000037      0.161441               0.235241    4387.541000  5.706542e+04  3.645231e+04       1.329001      1.949278         1.363909          0.919040          1.058868         1.318129         0.796388\n",
       "min      0.000000e+00  1.001926e+08      1.000000      1.000000     70.000000  1.660000e+02      0.434984      0.032625  1.630000e+02       0.000000  0.000000e+00      1.380000      0.000000          0.000000      0.000000    4226.000000     602.000000    1611.000000    4725.000000        0.000000      0.000000      0.000000               0.020028       0.000000  0.000000e+00  1.000000e+00       0.000000     -0.470243        -0.579824         -3.582392         -0.812009        -2.354235        -2.211264\n",
       "25%      0.000000e+00  9.001336e+08      9.000000     31.000000  50000.000000  8.099300e+04   1933.357000     36.339705  5.553000e+04    6863.500000  9.244500e+03     47.780000      5.780000          9.550000      8.210000   33715.000000   20324.000000   18142.000000   29147.000000        1.000000      0.000011      0.023944               0.498853     290.000000  1.900000e+02  2.039000e+03       1.000000     -0.193044        -0.380999         -1.242709         -0.442123        -0.275905        -0.490059\n",
       "50%      2.328306e-10  2.605938e+09     26.000000     55.000000  90884.000000  3.957770e+05   3570.421000     87.658840  2.094240e+05   45888.000000  6.624500e+04     60.300000     11.880000         17.200000     18.750000   41883.000000   25557.000000   20597.000000   35563.000000       10.000000      0.000027      0.107292               0.611203     597.000000  4.600000e+02  6.379000e+03       2.000000      0.041726         0.326842         -0.611399         -0.051758         0.299761        -0.035321\n",
       "75%      9.275834e+02  3.906102e+09     39.000000     97.000000  92522.000000  1.579075e+06   9067.710000    235.938329  7.563910e+05  240993.000000  4.233490e+05     76.380000     28.280000         28.450000     33.035000   49305.000000   30095.000000   26033.000000   41569.000000       34.000000      0.000037      0.272115               0.726091    1227.000000  1.169000e+03  1.745500e+04       3.000000      0.830088         1.719667          0.199423          0.997745         0.822849         0.353585\n",
       "max      2.993679e+04  7.214985e+09     72.000000    840.000000  98000.000000  3.433961e+06  71488.690000  24607.898341  2.556502e+06  833683.000000  1.050342e+06    100.000000     94.340000         98.430000    100.000000  156737.000000  192355.000000  238039.000000  164307.000000      204.000000      0.004762      2.579073               5.684942  362676.000000  6.848484e+06  2.355436e+06       4.000000      9.781831        21.214474          1.390442          5.225198         8.394457        14.464471"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in cols:\n",
    "    locs_gdf[f\"{col}_std\"] = locs_gdf[col]\n",
    "    locs_gdf.fillna(\n",
    "        value={\n",
    "            f\"{col}_std\": wtd_median(county_subd_gdf, col, 'Total_Pop')\n",
    "        }, \n",
    "        inplace=True\n",
    "    )\n",
    "    \n",
    "locs_gdf[std_cols] = s.transform(locs_gdf.loc[:, std_cols])\n",
    "locs_gdf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "9ef17a9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=6)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km = KMeans(n_clusters=6, init='k-means++')\n",
    "km.fit(X=county_subd_gdf.loc[:, std_cols], sample_weight=county_subd_gdf.loc[:, \"Total_Pop\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "949955e5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['3146 in 0', '4930 in 1', '7897 in 2', '3472 in 3', '10467 in 4', '4467 in 5']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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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>mean-mode dev.</th>\n",
       "      <th>Geo_FIPS</th>\n",
       "      <th>Geo_STATE</th>\n",
       "      <th>Geo_COUNTY</th>\n",
       "      <th>Geo_COUSUB</th>\n",
       "      <th>Total_Pop</th>\n",
       "      <th>Pop_Dens</th>\n",
       "      <th>Land_Area</th>\n",
       "      <th>WhitePop</th>\n",
       "      <th>BlackPop</th>\n",
       "      <th>OtherRacePop</th>\n",
       "      <th>WhitePopPerc</th>\n",
       "      <th>BlackPopPerc</th>\n",
       "      <th>OtherRacePopPerc</th>\n",
       "      <th>PctLatinoPop</th>\n",
       "      <th>WhitePerCap</th>\n",
       "      <th>BlackPerCap</th>\n",
       "      <th>LatinoPerCap</th>\n",
       "      <th>PerCap</th>\n",
       "      <th>PoliceKillings</th>\n",
       "      <th>PKPerCap</th>\n",
       "      <th>PKPerSqMile</th>\n",
       "      <th>Blck_Wht_PerCap_Ratio</th>\n",
       "      <th>Following</th>\n",
       "      <th>Followers</th>\n",
       "      <th>Tweets</th>\n",
       "      <th>Pop_Dens_std</th>\n",
       "      <th>PKPerSqMile_std</th>\n",
       "      <th>WhitePopPerc_std</th>\n",
       "      <th>BlackPopPerc_std</th>\n",
       "      <th>WhitePerCap_std</th>\n",
       "      <th>BlackPerCap_std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1996.854593</td>\n",
       "      <td>2.949673e+09</td>\n",
       "      <td>29.404959</td>\n",
       "      <td>91.081373</td>\n",
       "      <td>69038.194851</td>\n",
       "      <td>1.023946e+05</td>\n",
       "      <td>1572.299372</td>\n",
       "      <td>154.252852</td>\n",
       "      <td>86667.785760</td>\n",
       "      <td>3832.077877</td>\n",
       "      <td>11894.695486</td>\n",
       "      <td>87.522495</td>\n",
       "      <td>3.808334</td>\n",
       "      <td>8.669488</td>\n",
       "      <td>15.312540</td>\n",
       "      <td>36541.303878</td>\n",
       "      <td>27967.694715</td>\n",
       "      <td>23552.126561</td>\n",
       "      <td>35290.297521</td>\n",
       "      <td>2.208837</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.019168</td>\n",
       "      <td>0.800584</td>\n",
       "      <td>1210.844882</td>\n",
       "      <td>1795.046726</td>\n",
       "      <td>15981.930070</td>\n",
       "      <td>-0.244825</td>\n",
       "      <td>-0.417884</td>\n",
       "      <td>0.761274</td>\n",
       "      <td>-0.568298</td>\n",
       "      <td>-0.076712</td>\n",
       "      <td>0.151085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1622.769566</td>\n",
       "      <td>2.800738e+09</td>\n",
       "      <td>27.893915</td>\n",
       "      <td>112.728600</td>\n",
       "      <td>73543.613590</td>\n",
       "      <td>3.091773e+05</td>\n",
       "      <td>2959.099236</td>\n",
       "      <td>119.632077</td>\n",
       "      <td>144198.564503</td>\n",
       "      <td>129875.945233</td>\n",
       "      <td>35102.796755</td>\n",
       "      <td>44.715712</td>\n",
       "      <td>44.842507</td>\n",
       "      <td>10.442266</td>\n",
       "      <td>11.941625</td>\n",
       "      <td>39026.962880</td>\n",
       "      <td>21600.859606</td>\n",
       "      <td>19226.783833</td>\n",
       "      <td>29586.979310</td>\n",
       "      <td>11.342799</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.103088</td>\n",
       "      <td>0.607865</td>\n",
       "      <td>1231.013590</td>\n",
       "      <td>2092.861866</td>\n",
       "      <td>19104.503245</td>\n",
       "      <td>-0.046672</td>\n",
       "      <td>0.288660</td>\n",
       "      <td>-1.397224</td>\n",
       "      <td>2.057649</td>\n",
       "      <td>0.098473</td>\n",
       "      <td>-0.385034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1723.417907</td>\n",
       "      <td>2.668516e+09</td>\n",
       "      <td>26.581740</td>\n",
       "      <td>102.564392</td>\n",
       "      <td>85807.426364</td>\n",
       "      <td>1.107236e+06</td>\n",
       "      <td>3383.025336</td>\n",
       "      <td>341.007142</td>\n",
       "      <td>721405.772572</td>\n",
       "      <td>134406.638090</td>\n",
       "      <td>251424.051032</td>\n",
       "      <td>65.566738</td>\n",
       "      <td>12.341655</td>\n",
       "      <td>22.091130</td>\n",
       "      <td>29.834849</td>\n",
       "      <td>37680.826263</td>\n",
       "      <td>24913.234742</td>\n",
       "      <td>20499.742177</td>\n",
       "      <td>33400.608459</td>\n",
       "      <td>40.076485</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.107417</td>\n",
       "      <td>0.675923</td>\n",
       "      <td>1083.642016</td>\n",
       "      <td>2168.597062</td>\n",
       "      <td>17064.078004</td>\n",
       "      <td>0.014786</td>\n",
       "      <td>0.327701</td>\n",
       "      <td>-0.345828</td>\n",
       "      <td>-0.022215</td>\n",
       "      <td>0.003599</td>\n",
       "      <td>-0.096920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1820.452609</td>\n",
       "      <td>3.235087e+09</td>\n",
       "      <td>32.262385</td>\n",
       "      <td>87.736463</td>\n",
       "      <td>74548.868088</td>\n",
       "      <td>1.503770e+05</td>\n",
       "      <td>1777.689602</td>\n",
       "      <td>187.692021</td>\n",
       "      <td>121412.580069</td>\n",
       "      <td>9695.862039</td>\n",
       "      <td>19268.515265</td>\n",
       "      <td>81.791210</td>\n",
       "      <td>7.723629</td>\n",
       "      <td>10.484781</td>\n",
       "      <td>25.308122</td>\n",
       "      <td>27922.853111</td>\n",
       "      <td>16463.443513</td>\n",
       "      <td>17467.655433</td>\n",
       "      <td>26217.923387</td>\n",
       "      <td>6.310484</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.040557</td>\n",
       "      <td>0.617357</td>\n",
       "      <td>1128.728399</td>\n",
       "      <td>1843.517281</td>\n",
       "      <td>16330.353399</td>\n",
       "      <td>-0.215464</td>\n",
       "      <td>-0.237729</td>\n",
       "      <td>0.472278</td>\n",
       "      <td>-0.317742</td>\n",
       "      <td>-0.684124</td>\n",
       "      <td>-0.829355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1126.568944</td>\n",
       "      <td>2.195091e+09</td>\n",
       "      <td>21.892424</td>\n",
       "      <td>57.884590</td>\n",
       "      <td>59814.252126</td>\n",
       "      <td>1.636705e+06</td>\n",
       "      <td>19955.945161</td>\n",
       "      <td>126.627040</td>\n",
       "      <td>780603.593580</td>\n",
       "      <td>342018.894908</td>\n",
       "      <td>514082.740422</td>\n",
       "      <td>48.113876</td>\n",
       "      <td>22.459145</td>\n",
       "      <td>29.427582</td>\n",
       "      <td>29.419089</td>\n",
       "      <td>56985.151715</td>\n",
       "      <td>27052.528709</td>\n",
       "      <td>23480.883241</td>\n",
       "      <td>42560.522404</td>\n",
       "      <td>44.208178</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.360810</td>\n",
       "      <td>0.526397</td>\n",
       "      <td>1258.890035</td>\n",
       "      <td>5891.861756</td>\n",
       "      <td>15951.859559</td>\n",
       "      <td>2.391561</td>\n",
       "      <td>2.469179</td>\n",
       "      <td>-1.225874</td>\n",
       "      <td>0.625245</td>\n",
       "      <td>1.364132</td>\n",
       "      <td>0.088998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1859.860058</td>\n",
       "      <td>2.456253e+09</td>\n",
       "      <td>24.489366</td>\n",
       "      <td>72.419969</td>\n",
       "      <td>74035.436758</td>\n",
       "      <td>2.625685e+05</td>\n",
       "      <td>3265.701642</td>\n",
       "      <td>191.456292</td>\n",
       "      <td>153279.554959</td>\n",
       "      <td>13736.766958</td>\n",
       "      <td>95552.203716</td>\n",
       "      <td>71.922532</td>\n",
       "      <td>6.121710</td>\n",
       "      <td>21.954786</td>\n",
       "      <td>14.067799</td>\n",
       "      <td>58179.676293</td>\n",
       "      <td>39966.692065</td>\n",
       "      <td>31327.245957</td>\n",
       "      <td>53173.308708</td>\n",
       "      <td>4.530781</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.036377</td>\n",
       "      <td>0.723350</td>\n",
       "      <td>1253.670248</td>\n",
       "      <td>2617.442355</td>\n",
       "      <td>15026.742109</td>\n",
       "      <td>-0.004211</td>\n",
       "      <td>-0.273913</td>\n",
       "      <td>-0.025342</td>\n",
       "      <td>-0.420255</td>\n",
       "      <td>1.448320</td>\n",
       "      <td>1.203417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               mean-mode dev.      Geo_FIPS  Geo_STATE  Geo_COUNTY    Geo_COUSUB     Total_Pop      Pop_Dens   Land_Area       WhitePop       BlackPop   OtherRacePop  WhitePopPerc  BlackPopPerc  OtherRacePopPerc  PctLatinoPop   WhitePerCap   BlackPerCap  LatinoPerCap        PerCap  PoliceKillings  PKPerCap  PKPerSqMile  Blck_Wht_PerCap_Ratio    Following    Followers        Tweets  Pop_Dens_std  PKPerSqMile_std  WhitePopPerc_std  BlackPopPerc_std  WhitePerCap_std  BlackPerCap_std\n",
       "cluster_label                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       \n",
       "0                 1996.854593  2.949673e+09  29.404959   91.081373  69038.194851  1.023946e+05   1572.299372  154.252852   86667.785760    3832.077877   11894.695486     87.522495      3.808334          8.669488     15.312540  36541.303878  27967.694715  23552.126561  35290.297521        2.208837  0.000019     0.019168               0.800584  1210.844882  1795.046726  15981.930070     -0.244825        -0.417884          0.761274         -0.568298        -0.076712         0.151085\n",
       "1                 1622.769566  2.800738e+09  27.893915  112.728600  73543.613590  3.091773e+05   2959.099236  119.632077  144198.564503  129875.945233   35102.796755     44.715712     44.842507         10.442266     11.941625  39026.962880  21600.859606  19226.783833  29586.979310       11.342799  0.000038     0.103088               0.607865  1231.013590  2092.861866  19104.503245     -0.046672         0.288660         -1.397224          2.057649         0.098473        -0.385034\n",
       "2                 1723.417907  2.668516e+09  26.581740  102.564392  85807.426364  1.107236e+06   3383.025336  341.007142  721405.772572  134406.638090  251424.051032     65.566738     12.341655         22.091130     29.834849  37680.826263  24913.234742  20499.742177  33400.608459       40.076485  0.000033     0.107417               0.675923  1083.642016  2168.597062  17064.078004      0.014786         0.327701         -0.345828         -0.022215         0.003599        -0.096920\n",
       "3                 1820.452609  3.235087e+09  32.262385   87.736463  74548.868088  1.503770e+05   1777.689602  187.692021  121412.580069    9695.862039   19268.515265     81.791210      7.723629         10.484781     25.308122  27922.853111  16463.443513  17467.655433  26217.923387        6.310484  0.000026     0.040557               0.617357  1128.728399  1843.517281  16330.353399     -0.215464        -0.237729          0.472278         -0.317742        -0.684124        -0.829355\n",
       "4                 1126.568944  2.195091e+09  21.892424   57.884590  59814.252126  1.636705e+06  19955.945161  126.627040  780603.593580  342018.894908  514082.740422     48.113876     22.459145         29.427582     29.419089  56985.151715  27052.528709  23480.883241  42560.522404       44.208178  0.000031     0.360810               0.526397  1258.890035  5891.861756  15951.859559      2.391561         2.469179         -1.225874          0.625245         1.364132         0.088998\n",
       "5                 1859.860058  2.456253e+09  24.489366   72.419969  74035.436758  2.625685e+05   3265.701642  191.456292  153279.554959   13736.766958   95552.203716     71.922532      6.121710         21.954786     14.067799  58179.676293  39966.692065  31327.245957  53173.308708        4.530781  0.000012     0.036377               0.723350  1253.670248  2617.442355  15026.742109     -0.004211        -0.273913         -0.025342         -0.420255         1.448320         1.203417"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "locs_gdf[\"cluster_label\"] = km.predict(X=locs_gdf.loc[:, std_cols])\n",
    "\n",
    "print([f\"{len(c)} in {c_l}\" for (c_l, c) in locs_gdf.groupby([\"cluster_label\"])])\n",
    "locs_gdf.groupby([\"cluster_label\"]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "de75beb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "county_subd_gdf[\"cluster_label\"] = km.predict(X=county_subd_gdf.loc[:, std_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "6d2ec64b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib qt\n",
    "\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "county_subd_gdf.boundary.plot(\n",
    "    ax=ax,\n",
    "    edgecolor=\"grey\",\n",
    "    linewidth=0.1\n",
    ")\n",
    "county_subd_gdf.loc[county_subd_gdf[\"Adopters\"] > 0, :].plot(\n",
    "    ax=ax,\n",
    "    legend=True,\n",
    "    column=\"cluster_label\",\n",
    "    cmap='nipy_spectral',\n",
    "    edgecolor=\"black\",\n",
    "    linewidth=0.1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "299a7e9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "locs_count = locs_gdf.groupby([\"cluster_label\"]).agg({\"U_ID\": \"count\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "da2a754e",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib qt\n",
    "# Then plot!\n",
    "\n",
    "fig = plt.figure(figsize = (16, 9))\n",
    "ax = plt.axes(projection =\"3d\")\n",
    "sctt = ax.scatter3D(\n",
    "    xs=county_subd_gdf['WhitePopPerc_std'],\n",
    "    ys=county_subd_gdf['WhitePerCap_std'],\n",
    "    zs=county_subd_gdf['Pop_Dens_std'],\n",
    "    s=(county_subd_gdf['Adopters']**1/2),\n",
    "    c=county_subd_gdf[\"cluster_label\"],\n",
    "    cmap='nipy_spectral'\n",
    ")\n",
    "ax.set_xlabel('Percent White')\n",
    "ax.set_ylabel('White Income (per cap.)')\n",
    "ax.set_zlabel('Pop. (per sq. mi.)')\n",
    "\n",
    "cbar = fig.colorbar(sctt, ax = ax, shrink = 0.5, aspect = 5)\n",
    "cbar.set_label('Cluster Label')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "944b302b",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib qt\n",
    "# Then plot!\n",
    "\n",
    "fig = plt.figure(figsize = (16, 9))\n",
    "ax = plt.axes(projection =\"3d\")\n",
    "sctt = ax.scatter3D(\n",
    "    xs=county_subd_gdf['BlackPopPerc_std'],\n",
    "    ys=county_subd_gdf['BlackPerCap_std'],\n",
    "    zs=county_subd_gdf['PKPerSqMile_std'],\n",
    "    s=(county_subd_gdf['Adopters']**1/2),\n",
    "    c=county_subd_gdf[\"cluster_label\"],\n",
    "    cmap='nipy_spectral'\n",
    ")\n",
    "ax.set_xlabel('Percent Black')\n",
    "ax.set_ylabel('Black Income (per cap.)')\n",
    "ax.set_zlabel('Police Killings (per sq. mi.)')\n",
    "\n",
    "cbar = fig.colorbar(sctt, ax = ax, shrink = 0.5, aspect = 5)\n",
    "cbar.set_label('Cluster Label')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f0ba2b3",
   "metadata": {},
   "source": [
    "## Non deterministic algorithm\n",
    "\n",
    "Note that the key means clustering algorithm will produce different results every time. It is a probabilistic algorithm. However, before defining my clusters for my research, I've ran this method several times, as you can see in my methods section, to ensure that my results were consistent not a fluke. However, the latest run of the algorithm as saved in the Jupiter notebook is more recent than the cluster results that I use for the remainder of my research, so please see those details for the final definitions of the clusters."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f24e9d5",
   "metadata": {},
   "source": [
    "## Combining Network Data, Saving the Dataframe\n",
    "\n",
    "Once you have generated the final group-ed dataframes and saved them the below code will not overwrite unless you explicitly delete the old copy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40249815",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "locs_gdf = locs_gdf.drop([\"Geo_COUSUB\",\"Geo_COUNTY\", \"PKPerCap\"], axis=1)\n",
    "locs_gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "4c120028",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aborting! Found existing saved dataframe at this location. Rename or delete existing copy first.\n"
     ]
    }
   ],
   "source": [
    "    try:\n",
    "        check_for_df = gpd.read_feather(\"utils/data_analysis/feathers/geo_users_w_info_gdf.ftr\")\n",
    "        check_for_df_2 = gpd.read_feather(\"utils/data_analysis/feathers/csubs_w_info_gdf.ftr\")\n",
    "        print(\"Aborting! Found existing saved dataframe at this location. Rename or delete existing copy first.\")\n",
    "    except Exception as e:\n",
    "        print(f\"No existing files at save locations. Saving now.\")\n",
    "        locs_gdf.to_feather(\"utils/data_analysis/feathers/geo_users_w_info_gdf.ftr\")\n",
    "        county_subd_gdf.to_feather(\"utils/data_analysis/feathers/csubs_w_info_gdf.ftr\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8987ced8",
   "metadata": {},
   "source": [
    ".<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Roads Not Taken:\n",
    "\n",
    "I also tried 2 other forms of clustering, just to see what happens.\n",
    "\n",
    "\n",
    "## AgglomerativeClustering:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "e7d1e4ba",
   "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>Pop_Dens</th>\n",
       "      <th>PKPerSqMile</th>\n",
       "      <th>WhitePopPerc</th>\n",
       "      <th>BlackPopPerc</th>\n",
       "      <th>WhitePerCap</th>\n",
       "      <th>BlackPerCap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9596</td>\n",
       "      <td>9596</td>\n",
       "      <td>9596</td>\n",
       "      <td>9596</td>\n",
       "      <td>9596</td>\n",
       "      <td>9596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5432</td>\n",
       "      <td>5432</td>\n",
       "      <td>5432</td>\n",
       "      <td>5432</td>\n",
       "      <td>5432</td>\n",
       "      <td>5432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6753</td>\n",
       "      <td>6753</td>\n",
       "      <td>6753</td>\n",
       "      <td>6753</td>\n",
       "      <td>6753</td>\n",
       "      <td>6753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2525</td>\n",
       "      <td>2525</td>\n",
       "      <td>2525</td>\n",
       "      <td>2525</td>\n",
       "      <td>2525</td>\n",
       "      <td>2525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3939</td>\n",
       "      <td>3939</td>\n",
       "      <td>3939</td>\n",
       "      <td>3939</td>\n",
       "      <td>3939</td>\n",
       "      <td>3939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6134</td>\n",
       "      <td>6134</td>\n",
       "      <td>6134</td>\n",
       "      <td>6134</td>\n",
       "      <td>6134</td>\n",
       "      <td>6134</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Pop_Dens  PKPerSqMile  WhitePopPerc  BlackPopPerc  WhitePerCap  BlackPerCap\n",
       "cluster_label                                                                             \n",
       "0                  9596         9596          9596          9596         9596         9596\n",
       "1                  5432         5432          5432          5432         5432         5432\n",
       "2                  6753         6753          6753          6753         6753         6753\n",
       "3                  2525         2525          2525          2525         2525         2525\n",
       "4                  3939         3939          3939          3939         3939         3939\n",
       "5                  6134         6134          6134          6134         6134         6134"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = load_X_for_clustering()\n",
    "X['cluster_label'] = AgglomerativeClustering(n_clusters=6).fit_predict(X)\n",
    "X.groupby('cluster_label').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "9174271d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Pop_Dens</th>\n",
       "      <th>PKPerSqMile</th>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.379271</td>\n",
       "      <td>-0.760212</td>\n",
       "      <td>0.705890</td>\n",
       "      <td>-0.690808</td>\n",
       "      <td>0.123671</td>\n",
       "      <td>0.719837</td>\n",
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       "      <td>1.246626</td>\n",
       "      <td>-0.635298</td>\n",
       "      <td>-0.292179</td>\n",
       "      <td>0.770049</td>\n",
       "      <td>0.393397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.234520</td>\n",
       "      <td>-0.149607</td>\n",
       "      <td>-0.183666</td>\n",
       "      <td>0.127530</td>\n",
       "      <td>-0.452273</td>\n",
       "      <td>-0.412874</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.362350</td>\n",
       "      <td>-0.364997</td>\n",
       "      <td>-1.302004</td>\n",
       "      <td>1.704258</td>\n",
       "      <td>-0.502286</td>\n",
       "      <td>-0.567112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.644107</td>\n",
       "      <td>1.230602</td>\n",
       "      <td>-1.079449</td>\n",
       "      <td>1.272526</td>\n",
       "      <td>0.434962</td>\n",
       "      <td>-0.215571</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.410869</td>\n",
       "      <td>-0.493269</td>\n",
       "      <td>0.950188</td>\n",
       "      <td>-0.545207</td>\n",
       "      <td>-0.749345</td>\n",
       "      <td>-0.846348</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "               Pop_Dens  PKPerSqMile  WhitePopPerc  BlackPopPerc  WhitePerCap  BlackPerCap\n",
       "cluster_label                                                                             \n",
       "0             -0.379271    -0.760212      0.705890     -0.690808     0.123671     0.719837\n",
       "1              0.557448     1.246626     -0.635298     -0.292179     0.770049     0.393397\n",
       "2             -0.234520    -0.149607     -0.183666      0.127530    -0.452273    -0.412874\n",
       "3             -0.362350    -0.364997     -1.302004      1.704258    -0.502286    -0.567112\n",
       "4              0.644107     1.230602     -1.079449      1.272526     0.434962    -0.215571\n",
       "5             -0.410869    -0.493269      0.950188     -0.545207    -0.749345    -0.846348"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.groupby('cluster_label').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "ecddd8e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize = (16, 9))\n",
    "ax = plt.axes(projection =\"3d\")\n",
    "sctt = ax.scatter3D(\n",
    "    X['Pop_Dens'], \n",
    "    X['BlackPopPerc'], \n",
    "    X['WhitePerCap'], \n",
    "    s=((X['PKPerSqMile']+5)**2), \n",
    "    c=X['cluster_label'], \n",
    "    cmap='nipy_spectral'\n",
    ")\n",
    "\n",
    "ax.set_xlim(-0.3,1.5)\n",
    "ax.set_xlabel('Population Density')\n",
    "\n",
    "ax.set_ylim(-0.5,1.5)\n",
    "ax.set_ylabel('% Black')\n",
    "\n",
    "ax.set_zlim(-1.0,1.5)\n",
    "ax.set_zlabel('White PerCap Income')\n",
    "\n",
    "cbar = fig.colorbar(sctt, ax = ax, shrink = 0.5, aspect = 5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb5d6a52",
   "metadata": {},
   "source": [
    "# DBScan Clustering\n",
    "\n",
    "Use the DBSCan algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31971eeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "X['cluster_label'] = DBSCAN(eps=0.05).fit_predict(X.drop(\"cluster_label\", axis=1))\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "5263fd83",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Pop_Dens</th>\n",
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       "      <th>-1</th>\n",
       "      <td>4658</td>\n",
       "      <td>4658</td>\n",
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       "      <td>12</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>330</td>\n",
       "      <td>330</td>\n",
       "      <td>330</td>\n",
       "      <td>330</td>\n",
       "      <td>330</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2499</td>\n",
       "      <td>2499</td>\n",
       "      <td>2499</td>\n",
       "      <td>2499</td>\n",
       "      <td>2499</td>\n",
       "      <td>2499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>459</td>\n",
       "      <td>459</td>\n",
       "      <td>459</td>\n",
       "      <td>459</td>\n",
       "      <td>459</td>\n",
       "      <td>459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1109</td>\n",
       "      <td>1109</td>\n",
       "      <td>1109</td>\n",
       "      <td>1109</td>\n",
       "      <td>1109</td>\n",
       "      <td>1109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>378</td>\n",
       "      <td>378</td>\n",
       "      <td>378</td>\n",
       "      <td>378</td>\n",
       "      <td>378</td>\n",
       "      <td>378</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>941</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>942</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>943</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>944</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>945</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>946</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>948</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>949</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>950</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>952 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Pop_Dens  PKPerSqMile  WhitePopPerc  BlackPopPerc  WhitePerCap  BlackPerCap\n",
       "cluster_label                                                                             \n",
       "-1                 4658         4658          4658          4658         4658         4658\n",
       " 0                   12           12            12            12           12           12\n",
       " 1                  330          330           330           330          330          330\n",
       " 2                   10           10            10            10           10           10\n",
       " 3                 2499         2499          2499          2499         2499         2499\n",
       " 4                   16           16            16            16           16           16\n",
       " 5                   58           58            58            58           58           58\n",
       " 6                  459          459           459           459          459          459\n",
       " 7                 1109         1109          1109          1109         1109         1109\n",
       " 8                  378          378           378           378          378          378\n",
       "...                 ...          ...           ...           ...          ...          ...\n",
       " 941                  5            5             5             5            5            5\n",
       " 942                  5            5             5             5            5            5\n",
       " 943                  8            8             8             8            8            8\n",
       " 944                  5            5             5             5            5            5\n",
       " 945                  7            7             7             7            7            7\n",
       " 946                  5            5             5             5            5            5\n",
       " 947                  5            5             5             5            5            5\n",
       " 948                  5            5             5             5            5            5\n",
       " 949                  7            7             7             7            7            7\n",
       " 950                  5            5             5             5            5            5\n",
       "\n",
       "[952 rows x 6 columns]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.groupby('cluster_label').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "6d91d733",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/raycrist/opt/anaconda3/lib/python3.9/site-packages/matplotlib/collections.py:1003: RuntimeWarning: invalid value encountered in sqrt\n",
      "  scale = np.sqrt(self._sizes) * dpi / 72.0 * self._factor\n"
     ]
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (16, 9))\n",
    "ax = plt.axes(projection =\"3d\")\n",
    "sctt = ax.scatter3D(\n",
    "    xs=X['Pop_Dens'], \n",
    "    ys=X['BlackPopPerc'], \n",
    "    zs=X['WhitePerCap'], \n",
    "    s=X['PKPerSqMile']**3, \n",
    "    c=X['cluster_label'], \n",
    "    cmap='nipy_spectral'\n",
    ")\n",
    "\n",
    "ax.set_xlabel('Population Density')\n",
    "ax.set_ylabel('% Black')\n",
    "ax.set_zlabel('White PerCap Income')\n",
    "\n",
    "cbar = fig.colorbar(sctt, ax = ax, shrink = 0.5, aspect = 5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70b2bc47",
   "metadata": {},
   "source": [
    "I like K-Means clustering -- it gives me relatively evenly sized clusters and is intuitive, especially for social scientists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a41042d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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