Predicting_Food_Deserts-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e1073621-6bcc-4b94-ab8b-00a6f4a7ec9d",
   "metadata": {},
   "source": [
    "# Predicting the Emergence of Food Deserts \n",
    "\n",
    "**Term Project Group 37** \n",
    "\n",
    "- Nigele X McCoy (nmccoy9@gatech.edu), Anthony Philip Lee (alee657@gatech.edu) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f39e52b-8a6b-41ac-9fc2-b102510caae5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "45c8472c-e174-469d-a755-282ae05d7f73",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, confusion_matrix, precision_score, recall_score, f1_score, accuracy_score\n",
    "from matplotlib.colors import ListedColormap\n",
    "import scipy.io as sio\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.feature_selection import mutual_info_classif\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "655b122f-1c8c-47cd-9de7-3b06e2a12511",
   "metadata": {},
   "source": [
    "##  Data Preparation\n",
    "\n",
    "- **Crime Data** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7f5c1509-f243-487b-9a11-d92211209098",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Crime "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "91371afa-cf45-4ae4-b410-17b7717ee8eb",
   "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>county_name</th>\n",
       "      <th>crime_rate_per_100000</th>\n",
       "      <th>index</th>\n",
       "      <th>EDITION</th>\n",
       "      <th>PART</th>\n",
       "      <th>IDNO</th>\n",
       "      <th>CPOPARST</th>\n",
       "      <th>CPOPCRIM</th>\n",
       "      <th>AG_ARRST</th>\n",
       "      <th>AG_OFF</th>\n",
       "      <th>...</th>\n",
       "      <th>RAPE</th>\n",
       "      <th>ROBBERY</th>\n",
       "      <th>AGASSLT</th>\n",
       "      <th>BURGLRY</th>\n",
       "      <th>LARCENY</th>\n",
       "      <th>MVTHEFT</th>\n",
       "      <th>ARSON</th>\n",
       "      <th>population</th>\n",
       "      <th>FIPS_ST</th>\n",
       "      <th>FIPS_CTY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>St.Louiscity,MO</td>\n",
       "      <td>1791.995377</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1612</td>\n",
       "      <td>318667</td>\n",
       "      <td>318667</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>200</td>\n",
       "      <td>1778</td>\n",
       "      <td>3609</td>\n",
       "      <td>4995</td>\n",
       "      <td>13791</td>\n",
       "      <td>3543</td>\n",
       "      <td>464</td>\n",
       "      <td>318416</td>\n",
       "      <td>29</td>\n",
       "      <td>510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Crittenden,AR</td>\n",
       "      <td>1754.914968</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>130</td>\n",
       "      <td>50717</td>\n",
       "      <td>50717</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>38</td>\n",
       "      <td>165</td>\n",
       "      <td>662</td>\n",
       "      <td>1482</td>\n",
       "      <td>1753</td>\n",
       "      <td>189</td>\n",
       "      <td>28</td>\n",
       "      <td>49746</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alexander,IL</td>\n",
       "      <td>1664.700485</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>604</td>\n",
       "      <td>8040</td>\n",
       "      <td>8040</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>119</td>\n",
       "      <td>82</td>\n",
       "      <td>184</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>7629</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kenedy,TX</td>\n",
       "      <td>1456.310680</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2681</td>\n",
       "      <td>444</td>\n",
       "      <td>444</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>412</td>\n",
       "      <td>48</td>\n",
       "      <td>261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DeSotoParish,LA</td>\n",
       "      <td>1447.402430</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1137</td>\n",
       "      <td>26971</td>\n",
       "      <td>26971</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>368</td>\n",
       "      <td>149</td>\n",
       "      <td>494</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>27083</td>\n",
       "      <td>22</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       county_name  crime_rate_per_100000  index  EDITION  PART  IDNO  \\\n",
       "0  St.Louiscity,MO            1791.995377      1        1     4  1612   \n",
       "1    Crittenden,AR            1754.914968      2        1     4   130   \n",
       "2     Alexander,IL            1664.700485      3        1     4   604   \n",
       "3        Kenedy,TX            1456.310680      4        1     4  2681   \n",
       "4  DeSotoParish,LA            1447.402430      5        1     4  1137   \n",
       "\n",
       "   CPOPARST  CPOPCRIM  AG_ARRST  AG_OFF  ...  RAPE  ROBBERY  AGASSLT  BURGLRY  \\\n",
       "0    318667    318667        15      15  ...   200     1778     3609     4995   \n",
       "1     50717     50717         4       4  ...    38      165      662     1482   \n",
       "2      8040      8040         2       2  ...     2        5      119       82   \n",
       "3       444       444         1       1  ...     3        1        2        5   \n",
       "4     26971     26971         3       3  ...     4       17      368      149   \n",
       "\n",
       "   LARCENY  MVTHEFT  ARSON  population  FIPS_ST  FIPS_CTY  \n",
       "0    13791     3543    464      318416       29       510  \n",
       "1     1753      189     28       49746        5        35  \n",
       "2      184       12      2        7629       17         3  \n",
       "3        4        4      0         412       48       261  \n",
       "4      494       60      0       27083       22        31  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimeData = pd.read_csv('crime_data_w_population_and_crime_rate.csv')\n",
    "crimeData['county_name'] = crimeData['county_name'].str.replace('County','')\n",
    "crimeData['county_name'] = crimeData['county_name'].str.replace(' ','')\n",
    "crimeData.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "59dd499b-867e-4dd1-96ad-65391b2031ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Crime describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "82a86a81-eee4-45ca-b561-104b16d6b2d8",
   "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>crime_rate_per_100000</th>\n",
       "      <th>index</th>\n",
       "      <th>EDITION</th>\n",
       "      <th>PART</th>\n",
       "      <th>IDNO</th>\n",
       "      <th>CPOPARST</th>\n",
       "      <th>CPOPCRIM</th>\n",
       "      <th>AG_ARRST</th>\n",
       "      <th>AG_OFF</th>\n",
       "      <th>COVIND</th>\n",
       "      <th>...</th>\n",
       "      <th>RAPE</th>\n",
       "      <th>ROBBERY</th>\n",
       "      <th>AGASSLT</th>\n",
       "      <th>BURGLRY</th>\n",
       "      <th>LARCENY</th>\n",
       "      <th>MVTHEFT</th>\n",
       "      <th>ARSON</th>\n",
       "      <th>population</th>\n",
       "      <th>FIPS_ST</th>\n",
       "      <th>FIPS_CTY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3136.000000</td>\n",
       "      <td>3136.000000</td>\n",
       "      <td>3136.0</td>\n",
       "      <td>3136.0</td>\n",
       "      <td>3136.000000</td>\n",
       "      <td>3.136000e+03</td>\n",
       "      <td>3.136000e+03</td>\n",
       "      <td>3136.000000</td>\n",
       "      <td>3136.000000</td>\n",
       "      <td>3136.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3136.000000</td>\n",
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       "      <td>3136.000000</td>\n",
       "      <td>3136.000000</td>\n",
       "      <td>3136.000000</td>\n",
       "      <td>3136.000000</td>\n",
       "      <td>3136.000000</td>\n",
       "      <td>3.136000e+03</td>\n",
       "      <td>3136.000000</td>\n",
       "      <td>3136.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>235.085665</td>\n",
       "      <td>1568.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1590.655612</td>\n",
       "      <td>9.977578e+04</td>\n",
       "      <td>9.841005e+04</td>\n",
       "      <td>6.201212</td>\n",
       "      <td>5.400510</td>\n",
       "      <td>98.154624</td>\n",
       "      <td>...</td>\n",
       "      <td>26.324936</td>\n",
       "      <td>112.447704</td>\n",
       "      <td>238.644133</td>\n",
       "      <td>660.654337</td>\n",
       "      <td>1923.397959</td>\n",
       "      <td>227.536671</td>\n",
       "      <td>16.765625</td>\n",
       "      <td>1.007329e+05</td>\n",
       "      <td>30.339286</td>\n",
       "      <td>103.628827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>200.502872</td>\n",
       "      <td>905.429548</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>916.038464</td>\n",
       "      <td>3.198554e+05</td>\n",
       "      <td>3.189017e+05</td>\n",
       "      <td>8.104974</td>\n",
       "      <td>7.532491</td>\n",
       "      <td>8.311058</td>\n",
       "      <td>...</td>\n",
       "      <td>79.309098</td>\n",
       "      <td>666.311425</td>\n",
       "      <td>931.091864</td>\n",
       "      <td>2242.359745</td>\n",
       "      <td>6435.993731</td>\n",
       "      <td>1253.236660</td>\n",
       "      <td>67.970823</td>\n",
       "      <td>3.226566e+05</td>\n",
       "      <td>15.112433</td>\n",
       "      <td>107.987364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000e+01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>94.641916</td>\n",
       "      <td>784.750000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>797.750000</td>\n",
       "      <td>1.120325e+04</td>\n",
       "      <td>1.013975e+04</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.109275e+04</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>185.948066</td>\n",
       "      <td>1568.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1588.500000</td>\n",
       "      <td>2.596550e+04</td>\n",
       "      <td>2.478250e+04</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>131.000000</td>\n",
       "      <td>348.500000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.581250e+04</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>321.367803</td>\n",
       "      <td>2352.250000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2385.250000</td>\n",
       "      <td>6.628100e+04</td>\n",
       "      <td>6.603775e+04</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>416.500000</td>\n",
       "      <td>1205.250000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>6.758225e+04</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>133.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1791.995377</td>\n",
       "      <td>3136.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3177.000000</td>\n",
       "      <td>9.980757e+06</td>\n",
       "      <td>9.980757e+06</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1976.000000</td>\n",
       "      <td>18923.000000</td>\n",
       "      <td>23057.000000</td>\n",
       "      <td>49107.000000</td>\n",
       "      <td>142604.000000</td>\n",
       "      <td>40555.000000</td>\n",
       "      <td>2210.000000</td>\n",
       "      <td>1.001707e+07</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>840.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       crime_rate_per_100000        index  EDITION    PART         IDNO  \\\n",
       "count            3136.000000  3136.000000   3136.0  3136.0  3136.000000   \n",
       "mean              235.085665  1568.500000      1.0     4.0  1590.655612   \n",
       "std               200.502872   905.429548      0.0     0.0   916.038464   \n",
       "min                 0.000000     1.000000      1.0     4.0     1.000000   \n",
       "25%                94.641916   784.750000      1.0     4.0   797.750000   \n",
       "50%               185.948066  1568.500000      1.0     4.0  1588.500000   \n",
       "75%               321.367803  2352.250000      1.0     4.0  2385.250000   \n",
       "max              1791.995377  3136.000000      1.0     4.0  3177.000000   \n",
       "\n",
       "           CPOPARST      CPOPCRIM     AG_ARRST       AG_OFF       COVIND  ...  \\\n",
       "count  3.136000e+03  3.136000e+03  3136.000000  3136.000000  3136.000000  ...   \n",
       "mean   9.977578e+04  9.841005e+04     6.201212     5.400510    98.154624  ...   \n",
       "std    3.198554e+05  3.189017e+05     8.104974     7.532491     8.311058  ...   \n",
       "min    0.000000e+00  0.000000e+00     0.000000     0.000000     0.000000  ...   \n",
       "25%    1.120325e+04  1.013975e+04     2.000000     2.000000   100.000000  ...   \n",
       "50%    2.596550e+04  2.478250e+04     4.000000     3.000000   100.000000  ...   \n",
       "75%    6.628100e+04  6.603775e+04     7.000000     6.000000   100.000000  ...   \n",
       "max    9.980757e+06  9.980757e+06   150.000000   140.000000   100.000000  ...   \n",
       "\n",
       "              RAPE       ROBBERY       AGASSLT       BURGLRY        LARCENY  \\\n",
       "count  3136.000000   3136.000000   3136.000000   3136.000000    3136.000000   \n",
       "mean     26.324936    112.447704    238.644133    660.654337    1923.397959   \n",
       "std      79.309098    666.311425    931.091864   2242.359745    6435.993731   \n",
       "min       0.000000      0.000000      0.000000      0.000000       0.000000   \n",
       "25%       1.000000      0.000000     10.000000     37.000000      81.000000   \n",
       "50%       5.000000      4.000000     36.000000    131.000000     348.500000   \n",
       "75%      19.000000     22.000000    124.000000    416.500000    1205.250000   \n",
       "max    1976.000000  18923.000000  23057.000000  49107.000000  142604.000000   \n",
       "\n",
       "            MVTHEFT        ARSON    population      FIPS_ST     FIPS_CTY  \n",
       "count   3136.000000  3136.000000  3.136000e+03  3136.000000  3136.000000  \n",
       "mean     227.536671    16.765625  1.007329e+05    30.339286   103.628827  \n",
       "std     1253.236660    67.970823  3.226566e+05    15.112433   107.987364  \n",
       "min        0.000000     0.000000  9.000000e+01     1.000000     1.000000  \n",
       "25%        6.000000     0.000000  1.109275e+04    19.000000    35.000000  \n",
       "50%       22.000000     3.000000  2.581250e+04    29.000000    79.000000  \n",
       "75%       74.000000     9.000000  6.758225e+04    45.000000   133.000000  \n",
       "max    40555.000000  2210.000000  1.001707e+07    56.000000   840.000000  \n",
       "\n",
       "[8 rows x 23 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimeData.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "90c9aecb-180f-4e56-b0b1-1bcdb14c3429",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read 2015 Food Environment Atlas Data Download"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "12425b74-d5d4-427f-a616-e0e9f279ffcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "FoodEnvironAtlas2015 = pd.ExcelFile('2015 Food Environment Atlas Data Download.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cc9a2cbd-573b-403a-b1cb-2aaee7a3009e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['Read_Me', 'Variable List', 'Supplemental Data - County', 'Supplemental Data - State', 'ACCESS', 'ASSISTANCE', 'HEALTH', 'INSECURITY', 'LOCAL', 'PRICES_TAXES', 'RESTAURANTS', 'STORES', 'SOCIOECONOMIC'])\n"
     ]
    }
   ],
   "source": [
    "FoodAtlasDict = {tabName: pd.read_excel('2015 Food Environment Atlas Data Download.xls',sheet_name=tabName) for tabName in FoodEnvironAtlas2015.sheet_names}\n",
    "print(FoodAtlasDict.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ae892904-f438-4ccb-946d-9bd55487f600",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPS</th>\n",
       "      <th>county_name</th>\n",
       "      <th>LACCESS_POP10</th>\n",
       "      <th>PCT_LACCESS_POP10</th>\n",
       "      <th>LACCESS_LOWI10</th>\n",
       "      <th>PCT_LACCESS_LOWI10</th>\n",
       "      <th>LACCESS_CHILD10</th>\n",
       "      <th>PCT_LACCESS_CHILD10</th>\n",
       "      <th>LACCESS_SENIORS10</th>\n",
       "      <th>PCT_LACCESS_SENIORS10</th>\n",
       "      <th>LACCESS_HHNV10</th>\n",
       "      <th>PCT_LACCESS_HHNV10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>Autauga,AL</td>\n",
       "      <td>18428.439685</td>\n",
       "      <td>33.769657</td>\n",
       "      <td>5344.427472</td>\n",
       "      <td>9.793530</td>\n",
       "      <td>4822.500269</td>\n",
       "      <td>8.837112</td>\n",
       "      <td>2388.233228</td>\n",
       "      <td>4.376378</td>\n",
       "      <td>664.216487</td>\n",
       "      <td>3.284786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1003</td>\n",
       "      <td>Baldwin,AL</td>\n",
       "      <td>35210.814078</td>\n",
       "      <td>19.318473</td>\n",
       "      <td>9952.144027</td>\n",
       "      <td>5.460261</td>\n",
       "      <td>7916.131932</td>\n",
       "      <td>4.343199</td>\n",
       "      <td>6404.008603</td>\n",
       "      <td>3.513570</td>\n",
       "      <td>1571.779915</td>\n",
       "      <td>2.147827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1005</td>\n",
       "      <td>Barbour,AL</td>\n",
       "      <td>5722.305602</td>\n",
       "      <td>20.840972</td>\n",
       "      <td>3135.676086</td>\n",
       "      <td>11.420316</td>\n",
       "      <td>940.419327</td>\n",
       "      <td>3.425062</td>\n",
       "      <td>770.214550</td>\n",
       "      <td>2.805166</td>\n",
       "      <td>406.142372</td>\n",
       "      <td>4.135869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1007</td>\n",
       "      <td>Bibb,AL</td>\n",
       "      <td>1044.867327</td>\n",
       "      <td>4.559753</td>\n",
       "      <td>491.449066</td>\n",
       "      <td>2.144661</td>\n",
       "      <td>249.204753</td>\n",
       "      <td>1.087518</td>\n",
       "      <td>150.553323</td>\n",
       "      <td>0.657008</td>\n",
       "      <td>275.060875</td>\n",
       "      <td>3.458580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1009</td>\n",
       "      <td>Blount,AL</td>\n",
       "      <td>1548.175559</td>\n",
       "      <td>2.700840</td>\n",
       "      <td>609.027708</td>\n",
       "      <td>1.062468</td>\n",
       "      <td>384.911607</td>\n",
       "      <td>0.671490</td>\n",
       "      <td>195.049051</td>\n",
       "      <td>0.340269</td>\n",
       "      <td>705.466783</td>\n",
       "      <td>3.269380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FIPS county_name  LACCESS_POP10  PCT_LACCESS_POP10  LACCESS_LOWI10  \\\n",
       "0  1001  Autauga,AL   18428.439685          33.769657     5344.427472   \n",
       "1  1003  Baldwin,AL   35210.814078          19.318473     9952.144027   \n",
       "2  1005  Barbour,AL    5722.305602          20.840972     3135.676086   \n",
       "3  1007     Bibb,AL    1044.867327           4.559753      491.449066   \n",
       "4  1009   Blount,AL    1548.175559           2.700840      609.027708   \n",
       "\n",
       "   PCT_LACCESS_LOWI10  LACCESS_CHILD10  PCT_LACCESS_CHILD10  \\\n",
       "0            9.793530      4822.500269             8.837112   \n",
       "1            5.460261      7916.131932             4.343199   \n",
       "2           11.420316       940.419327             3.425062   \n",
       "3            2.144661       249.204753             1.087518   \n",
       "4            1.062468       384.911607             0.671490   \n",
       "\n",
       "   LACCESS_SENIORS10  PCT_LACCESS_SENIORS10  LACCESS_HHNV10  \\\n",
       "0        2388.233228               4.376378      664.216487   \n",
       "1        6404.008603               3.513570     1571.779915   \n",
       "2         770.214550               2.805166      406.142372   \n",
       "3         150.553323               0.657008      275.060875   \n",
       "4         195.049051               0.340269      705.466783   \n",
       "\n",
       "   PCT_LACCESS_HHNV10  \n",
       "0            3.284786  \n",
       "1            2.147827  \n",
       "2            4.135869  \n",
       "3            3.458580  \n",
       "4            3.269380  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FoodAtlasDict['ACCESS'].insert(1,'county_name','',True)\n",
    "FoodAtlasDict['ACCESS']['county_name'] = FoodAtlasDict['ACCESS']['County'] + ',' + FoodAtlasDict['ACCESS']['State']\n",
    "FoodAtlasDict['ACCESS'] = FoodAtlasDict['ACCESS'].drop(columns=['State','County'])\n",
    "FoodAtlasDict['ACCESS'].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9a093be3-20e9-4e15-8165-3c60c3198d19",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>county_name</th>\n",
       "      <th>PCT_NHWHITE10</th>\n",
       "      <th>PCT_NHBLACK10</th>\n",
       "      <th>PCT_HISP10</th>\n",
       "      <th>PCT_NHASIAN10</th>\n",
       "      <th>PCT_NHNA10</th>\n",
       "      <th>PCT_NHPI10</th>\n",
       "      <th>PCT_65OLDER10</th>\n",
       "      <th>PCT_18YOUNGER10</th>\n",
       "      <th>MEDHHINC10</th>\n",
       "      <th>POVRATE10</th>\n",
       "      <th>PERPOV10</th>\n",
       "      <th>CHILDPOVRATE10</th>\n",
       "      <th>PERCHLDPOV10</th>\n",
       "      <th>METRO13</th>\n",
       "      <th>POPLOSS00</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>Autauga,AL</td>\n",
       "      <td>77.246156</td>\n",
       "      <td>17.582599</td>\n",
       "      <td>2.400542</td>\n",
       "      <td>0.855766</td>\n",
       "      <td>0.397647</td>\n",
       "      <td>0.040314</td>\n",
       "      <td>11.995382</td>\n",
       "      <td>26.777959</td>\n",
       "      <td>53049</td>\n",
       "      <td>11.9</td>\n",
       "      <td>0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1003</td>\n",
       "      <td>Baldwin,AL</td>\n",
       "      <td>83.504787</td>\n",
       "      <td>9.308425</td>\n",
       "      <td>4.384824</td>\n",
       "      <td>0.735193</td>\n",
       "      <td>0.628755</td>\n",
       "      <td>0.043343</td>\n",
       "      <td>16.771185</td>\n",
       "      <td>22.987408</td>\n",
       "      <td>47618</td>\n",
       "      <td>13.3</td>\n",
       "      <td>0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1005</td>\n",
       "      <td>Barbour,AL</td>\n",
       "      <td>46.753105</td>\n",
       "      <td>46.691190</td>\n",
       "      <td>5.051535</td>\n",
       "      <td>0.389700</td>\n",
       "      <td>0.218524</td>\n",
       "      <td>0.087409</td>\n",
       "      <td>14.236807</td>\n",
       "      <td>21.906982</td>\n",
       "      <td>33074</td>\n",
       "      <td>25.3</td>\n",
       "      <td>1</td>\n",
       "      <td>36.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1007</td>\n",
       "      <td>Bibb,AL</td>\n",
       "      <td>75.020729</td>\n",
       "      <td>21.924504</td>\n",
       "      <td>1.771765</td>\n",
       "      <td>0.096007</td>\n",
       "      <td>0.279293</td>\n",
       "      <td>0.030548</td>\n",
       "      <td>12.681650</td>\n",
       "      <td>22.696923</td>\n",
       "      <td>35472</td>\n",
       "      <td>20.9</td>\n",
       "      <td>0</td>\n",
       "      <td>28.6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1009</td>\n",
       "      <td>Blount,AL</td>\n",
       "      <td>88.887338</td>\n",
       "      <td>1.263040</td>\n",
       "      <td>8.070200</td>\n",
       "      <td>0.200621</td>\n",
       "      <td>0.497191</td>\n",
       "      <td>0.031402</td>\n",
       "      <td>14.722096</td>\n",
       "      <td>24.608353</td>\n",
       "      <td>42906</td>\n",
       "      <td>16.5</td>\n",
       "      <td>0</td>\n",
       "      <td>24.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FIPS county_name  PCT_NHWHITE10  PCT_NHBLACK10  PCT_HISP10  PCT_NHASIAN10  \\\n",
       "0  1001  Autauga,AL      77.246156      17.582599    2.400542       0.855766   \n",
       "1  1003  Baldwin,AL      83.504787       9.308425    4.384824       0.735193   \n",
       "2  1005  Barbour,AL      46.753105      46.691190    5.051535       0.389700   \n",
       "3  1007     Bibb,AL      75.020729      21.924504    1.771765       0.096007   \n",
       "4  1009   Blount,AL      88.887338       1.263040    8.070200       0.200621   \n",
       "\n",
       "   PCT_NHNA10  PCT_NHPI10  PCT_65OLDER10  PCT_18YOUNGER10 MEDHHINC10  \\\n",
       "0    0.397647    0.040314      11.995382        26.777959      53049   \n",
       "1    0.628755    0.043343      16.771185        22.987408      47618   \n",
       "2    0.218524    0.087409      14.236807        21.906982      33074   \n",
       "3    0.279293    0.030548      12.681650        22.696923      35472   \n",
       "4    0.497191    0.031402      14.722096        24.608353      42906   \n",
       "\n",
       "  POVRATE10  PERPOV10 CHILDPOVRATE10  PERCHLDPOV10  METRO13 POPLOSS00  \n",
       "0      11.9         0           17.5             0        1         0  \n",
       "1      13.3         0           20.2             0        1         0  \n",
       "2      25.3         1           36.2             1        0         0  \n",
       "3      20.9         0           28.6             1        1         0  \n",
       "4      16.5         0           24.1             0        1         0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FoodAtlasDict['SOCIOECONOMIC'].insert(1,'county_name','',True)\n",
    "FoodAtlasDict['SOCIOECONOMIC']['county_name'] = FoodAtlasDict['SOCIOECONOMIC']['County'] + ',' + FoodAtlasDict['SOCIOECONOMIC']['State']\n",
    "FoodAtlasDict['SOCIOECONOMIC'] = FoodAtlasDict['SOCIOECONOMIC'].drop(columns=['State','County'])\n",
    "FoodAtlasDict['SOCIOECONOMIC'].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "94242322-8191-4ac0-894e-f51e1af12c28",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>county_name</th>\n",
       "      <th>PCT_DIABETES_ADULTS09</th>\n",
       "      <th>PCT_DIABETES_ADULTS10</th>\n",
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       "      <th>PCT_OBESE_ADULTS10</th>\n",
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       "      <th>RECFAC12</th>\n",
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       "      <th>RECFACPTH12</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Autauga,AL</td>\n",
       "      <td>12.3</td>\n",
       "      <td>11.8</td>\n",
       "      <td>34.1</td>\n",
       "      <td>30.5</td>\n",
       "      <td>32.4</td>\n",
       "      <td>12.4</td>\n",
       "      <td>13.5</td>\n",
       "      <td>1.1</td>\n",
       "      <td>19.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100333</td>\n",
       "      <td>0.090067</td>\n",
       "      <td>-10.231653</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Baldwin,AL</td>\n",
       "      <td>11.4</td>\n",
       "      <td>11.8</td>\n",
       "      <td>25.5</td>\n",
       "      <td>26.6</td>\n",
       "      <td>32.4</td>\n",
       "      <td>10.8</td>\n",
       "      <td>13.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>19.4</td>\n",
       "      <td>17.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17.647059</td>\n",
       "      <td>0.098371</td>\n",
       "      <td>0.104827</td>\n",
       "      <td>6.563114</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Barbour,AL</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.2</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.3</td>\n",
       "      <td>32.4</td>\n",
       "      <td>20.8</td>\n",
       "      <td>18.6</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>19.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033629</td>\n",
       "      <td>0.036763</td>\n",
       "      <td>9.319510</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bibb,AL</td>\n",
       "      <td>11.8</td>\n",
       "      <td>11.1</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.3</td>\n",
       "      <td>32.4</td>\n",
       "      <td>15.4</td>\n",
       "      <td>13.7</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>19.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>0.093088</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Blount,AL</td>\n",
       "      <td>12.6</td>\n",
       "      <td>14.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>32.4</td>\n",
       "      <td>19.9</td>\n",
       "      <td>18.5</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>19.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-66.666667</td>\n",
       "      <td>0.052756</td>\n",
       "      <td>0.017293</td>\n",
       "      <td>-67.220051</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  county_name  PCT_DIABETES_ADULTS09  PCT_DIABETES_ADULTS10  \\\n",
       "0  Autauga,AL                   12.3                   11.8   \n",
       "1  Baldwin,AL                   11.4                   11.8   \n",
       "2  Barbour,AL                   14.0                   14.2   \n",
       "3     Bibb,AL                   11.8                   11.1   \n",
       "4   Blount,AL                   12.6                   14.0   \n",
       "\n",
       "   PCT_OBESE_ADULTS09  PCT_OBESE_ADULTS10  PCT_OBESE_ADULTS13  \\\n",
       "0                34.1                30.5                32.4   \n",
       "1                25.5                26.6                32.4   \n",
       "2                37.0                37.3                32.4   \n",
       "3                34.0                34.3                32.4   \n",
       "4                32.0                30.4                32.4   \n",
       "\n",
       "   PCT_OBESE_CHILD08  PCT_OBESE_CHILD11  PCH_OBESE_CHILD_08_11  PCT_HSPA09  \\\n",
       "0               12.4               13.5                    1.1        19.4   \n",
       "1               10.8               13.3                    2.5        19.4   \n",
       "2               20.8               18.6                   -2.2        19.4   \n",
       "3               15.4               13.7                   -1.7        19.4   \n",
       "4               19.9               18.5                   -1.4        19.4   \n",
       "\n",
       "   RECFAC07  RECFAC12  PCH_RECFAC_07_12  RECFACPTH07  RECFACPTH12  \\\n",
       "0       5.0       5.0          0.000000     0.100333     0.090067   \n",
       "1      17.0      20.0         17.647059     0.098371     0.104827   \n",
       "2       1.0       1.0          0.000000     0.033629     0.036763   \n",
       "3       2.0       0.0       -100.000000     0.093088     0.000000   \n",
       "4       3.0       1.0        -66.666667     0.052756     0.017293   \n",
       "\n",
       "   PCH_RECFACPTH_07_12  NATAMEN  \n",
       "0           -10.231653      4.0  \n",
       "1             6.563114      4.0  \n",
       "2             9.319510      4.0  \n",
       "3          -100.000000      3.0  \n",
       "4           -67.220051      4.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FoodAtlasDict['HEALTH'].insert(1,'county_name','',True)\n",
    "FoodAtlasDict['HEALTH']['county_name'] = FoodAtlasDict['HEALTH']['County'] + ',' + FoodAtlasDict['HEALTH']['State']\n",
    "FoodAtlasDict['HEALTH'] = FoodAtlasDict['HEALTH'].drop(columns=['State','County','FIPS'])\n",
    "FoodAtlasDict['HEALTH'].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "77923355-d110-474b-bb10-ad728f4635d5",
   "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>county_name</th>\n",
       "      <th>FOODINSEC_00_02</th>\n",
       "      <th>FOODINSEC_07_09</th>\n",
       "      <th>FOODINSEC_10_12</th>\n",
       "      <th>CH_FOODINSEC_02_12</th>\n",
       "      <th>CH_FOODINSEC_09_12</th>\n",
       "      <th>VLFOODSEC_00_02</th>\n",
       "      <th>VLFOODSEC_07_09</th>\n",
       "      <th>VLFOODSEC_10_12</th>\n",
       "      <th>CH_VLFOODSEC_02_12</th>\n",
       "      <th>CH_VLFOODSEC_09_12</th>\n",
       "      <th>FOODINSEC_CHILD_01_07</th>\n",
       "      <th>FOODINSEC_CHILD_03_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Autauga,AL</td>\n",
       "      <td>12.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6.8</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Baldwin,AL</td>\n",
       "      <td>12.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6.8</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Barbour,AL</td>\n",
       "      <td>12.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6.8</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bibb,AL</td>\n",
       "      <td>12.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6.8</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Blount,AL</td>\n",
       "      <td>12.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6.8</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  county_name  FOODINSEC_00_02  FOODINSEC_07_09  FOODINSEC_10_12  \\\n",
       "0  Autauga,AL             12.5             15.0             17.9   \n",
       "1  Baldwin,AL             12.5             15.0             17.9   \n",
       "2  Barbour,AL             12.5             15.0             17.9   \n",
       "3     Bibb,AL             12.5             15.0             17.9   \n",
       "4   Blount,AL             12.5             15.0             17.9   \n",
       "\n",
       "   CH_FOODINSEC_02_12  CH_FOODINSEC_09_12  VLFOODSEC_00_02  VLFOODSEC_07_09  \\\n",
       "0                 5.4                 2.9              3.7              6.8   \n",
       "1                 5.4                 2.9              3.7              6.8   \n",
       "2                 5.4                 2.9              3.7              6.8   \n",
       "3                 5.4                 2.9              3.7              6.8   \n",
       "4                 5.4                 2.9              3.7              6.8   \n",
       "\n",
       "   VLFOODSEC_10_12  CH_VLFOODSEC_02_12  CH_VLFOODSEC_09_12  \\\n",
       "0              6.8                 3.1                 0.0   \n",
       "1              6.8                 3.1                 0.0   \n",
       "2              6.8                 3.1                 0.0   \n",
       "3              6.8                 3.1                 0.0   \n",
       "4              6.8                 3.1                 0.0   \n",
       "\n",
       "   FOODINSEC_CHILD_01_07  FOODINSEC_CHILD_03_11  \n",
       "0                    8.5                    9.3  \n",
       "1                    8.5                    9.3  \n",
       "2                    8.5                    9.3  \n",
       "3                    8.5                    9.3  \n",
       "4                    8.5                    9.3  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FoodAtlasDict['INSECURITY'].insert(1,'county_name','',True)\n",
    "FoodAtlasDict['INSECURITY']['county_name'] = FoodAtlasDict['INSECURITY']['County'] + ',' + FoodAtlasDict['INSECURITY']['State']\n",
    "FoodAtlasDict['INSECURITY'] = FoodAtlasDict['INSECURITY'].drop(columns=['State','County','FIPS'])\n",
    "FoodAtlasDict['INSECURITY'].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "71e57ee9-e062-4198-9e41-d19a59374c93",
   "metadata": {},
   "outputs": [],
   "source": [
    "crimeDict = {\n",
    "    \"AGASSLT\": \"AGGRAVATED ASSAULTS (04)\",\n",
    "    \"SAGASSLT\": \"AGGRAVATED ASSAULTS (04)\",\n",
    "    \"ALLOTHR\": \"ALL OTHER OFF EXCEPT TRAFFIC (26)\",\n",
    "    \"SALLOTHR\": \"ALL OTHER OFF EXCEPT TRAFFIC (26)\",\n",
    "    \"ARSON\": \"ARSONS (09)\",\n",
    "    \"SARSON\": \"ARSONS (09)\",\n",
    "    \"BOOKMKG\": \"BOOKMAKING, HORSE AND SPORT (19A)\",\n",
    "    \"SBOOKMKG\": \"BOOKMAKING, HORSE & SPORT (19A)\",\n",
    "    \"BURGLRY\": \"BURGLARIES (05)\",\n",
    "    \"SBURGLRY\": \"BURGLARIES (05)\",\n",
    "    \"CPOPCRIM\": \"COUNTY POPULATION-AGENCIES REPORT CRIMES\",\n",
    "    \"COVIND\": \"COVERAGE INDICATOR\",\n",
    "    \"CURFEW\": \"CURFEW, LOITERING VIOL: JUV (28)\",\n",
    "    \"SCURFEW\": \"CURFEW, LOITERING VIOL: JUV (28)\",\n",
    "    \"DISORDR\": \"DISORDERLY CONDUCT (24)\",\n",
    "    \"SDISORDR\": \"DISORDERLY CONDUCT (24)\",\n",
    "    \"DUI\": \"DRIVING UNDER INFLUENCE (21)\",\n",
    "    \"SDUI\": \"DRIVING UNDER INFLUENCE (21)\",\n",
    "    \"DRUGTOT\": \"DRUG ABUSE VIOLATIONS-TOTAL (18)\",\n",
    "    \"SDRUGTOT\": \"DRUG ABUSE VIOLATIONS-TOTAL (18)\",\n",
    "    \"DRGSALE\": \"DRUG ABUSE-SALE/MANUFACTURE (180)\",\n",
    "    \"SDRGSALE\": \"DRUG ABUSE-SALE/MANUFACTURE (180)\",\n",
    "    \"DRGPOSS\": \"DRUG POSSESSION-SUBTOTAL (185)\",\n",
    "    \"SDRGPOSS\": \"DRUG POSSESSION-SUBTOTAL (185)\",\n",
    "    \"DRUNK\": \"DRUNKENNESS (23)\",\n",
    "    \"SDRUNK\": \"DRUNKENNESS (23)\",\n",
    "    \"EMBEZL\": \"EMBEZZLEMENT (12)\",\n",
    "    \"SEMBEZL\": \"EMBEZZLEMENT (12)\",\n",
    "    \"FIPS_CTY\": \"FIPS COUNTY CODE\",\n",
    "    \"FIPS_ST\": \"FIPS STATE CODE\",\n",
    "    \"FRGYCNT\": \"FORGERY AND COUNTERFEITING (10)\",\n",
    "    \"SFRGYCNT\": \"FORGERY & COUNTERFEITING (10)\",\n",
    "    \"FRAUD\": \"FRAUD (11)\",\n",
    "    \"SFRAUD\": \"FRAUD (11)\",\n",
    "    \"OTGAMBL\": \"GAMBLING-ALL OTHER (19C)\",\n",
    "    \"SOTGAMBL\": \"GAMBLING-ALL OTHER (19C)\",\n",
    "    \"GAMBLE\": \"GAMBLING-TOTAL (19)\",\n",
    "    \"SGAMBLE\": \"GAMBLING-TOTAL (19)\",\n",
    "    \"GRNDTOT\": \"GRAND TOTAL\",\n",
    "    \"SGRNDTOT\": \"GRAND TOTAL\",\n",
    "    \"STLNPRP\": \"HAVE STOLEN PROPERTY (13)\",\n",
    "    \"SSTLNPRP\": \"HAVE STOLEN PROPERTY (13)\",\n",
    "    \"EDITION\": \"ICPSR EDITION NUMBER\",\n",
    "    \"PART\": \"ICPSR PART NUMBER\",\n",
    "    \"IDNO\": \"ICPSR SEQUENTIAL CASE ID NUMBER\",\n",
    "    \"STUDYNO\": \"ICPSR STUDY NUMBER\",\n",
    "    \"LARCENY\": \"LARCENIES (06)\",\n",
    "    \"SLARCENY\": \"LARCENIES (06)\",\n",
    "    \"LIQUOR\": \"LIQUOR LAW VIOLATIONS (22)\",\n",
    "    \"SLIQUOR\": \"LIQUOR LAW VIOLATIONS (22)\",\n",
    "    \"MJPOSS\": \"MARIJUANA-POSSESSION (18F)\",\n",
    "    \"SMJPOSS\": \"MARIJUANA-POSSESSION (18F)\",\n",
    "    \"MJSALE\": \"MARIJUANA-SALE/MANUFACTURE (18B)\",\n",
    "    \"SMJSALE\": \"MARIJUANA-SALE/MANUFACTURE (18B)\",\n",
    "    \"MVTHEFT\": \"MOTOR VEHICLE THEFTS (07)\",\n",
    "    \"SMVTHEFT\": \"MOTOR VEHICLE THEFTS (07)\",\n",
    "    \"JURFLAG\": \"MULTI-COUNTY JURISDICTION FLAG\",\n",
    "    \"MURDER\": \"MURDERS (011)\",\n",
    "    \"SMURDER\": \"MURDERS (011)\",\n",
    "    \"AG_ARRST\": \"NMBR OF AGENCIES IN COUNTY REPORT ARRSTS\",\n",
    "    \"AG_OFF\": \"NMBR OF AGENCIES IN COUNTY REPORT CRIMES\",\n",
    "    \"SNUMBERS\": \"NUMBERS & LOTTERY (19B)\",\n",
    "    \"NUMBERS\": \"NUMBERS AND LOTTERY (19B)\",\n",
    "    \"SOFAGFAM\": \"OFFENSES AGAINST FAMILY & CHILD (20)\",\n",
    "    \"OFAGFAM\": \"OFFENSES AGAINST FAMILY/CHILD (20)\",\n",
    "    \"COCPOSS\": \"OPIUM/COCAINE-POSSESSION (18E)\",\n",
    "    \"SCOCPOSS\": \"OPIUM/COCAINE-POSSESSION (18E)\",\n",
    "    \"COCSALE\": \"OPIUM/COCAINE-SALE/MANUFACTURE (18A)\",\n",
    "    \"SCOCSALE\": \"OPIUM/COCAINE-SALE/MANUFACTURE (18A)\",\n",
    "    \"OTHASLT\": \"OTHER ASSAULTS (08)\",\n",
    "    \"SOTHASLT\": \"OTHER ASSAULTS (08)\",\n",
    "    \"OTHPOSS\": \"OTHER DRUG-POSSESSION (18H)\",\n",
    "    \"SOTHPOSS\": \"OTHER DRUG-POSSESSION (18H)\",\n",
    "    \"OTHSALE\": \"OTHER:DANGEROUS NON-NARCOTICS (18D)\",\n",
    "    \"SOTHSALE\": \"OTHER:DANGEROUS NON-NARCOTICS (18D)\",\n",
    "    \"P1PRPTY\": \"PART 1-PROPERTY CRIMES\",\n",
    "    \"SP1PRPTY\": \"PART 1-PROPERTY CRIMES\",\n",
    "    \"P1TOT\": \"PART 1-TOTAL\",\n",
    "    \"SP1TOT\": \"PART 1-TOTAL\",\n",
    "    \"P1VLNT\": \"PART 1-VIOLENT CRIMES\",\n",
    "    \"SP1VLNT\": \"PART 1-VIOLENT CRIMES\",\n",
    "    \"SCOMVICE\": \"PROSTITUTION & COMMERC VICE (16)\",\n",
    "    \"COMVICE\": \"PROSTITUTION AND COMMERC VICE (16)\",\n",
    "    \"RAPE\": \"RAPES (02)\",\n",
    "    \"SRAPE\": \"RAPES (02)\",\n",
    "    \"ROBBERY\": \"ROBBERIES (03)\",\n",
    "    \"SROBBERY\": \"ROBBERIES (03)\",\n",
    "    \"RUNAWAY\": \"RUNAWAYS: JUVENILES (29)\",\n",
    "    \"SRUNAWAY\": \"RUNAWAYS: JUVENILES (29)\",\n",
    "    \"SEXOFF\": \"SEX OFFENSES (17)\",\n",
    "    \"SSEXOFF\": \"SEX OFFENSES (17)\",\n",
    "    \"SPOPARST\": \"STATE POPULATION\",\n",
    "    \"SPOPCRIM\": \"STATE POPULATION-AGENCIES RPRT CRIMES\",\n",
    "    \"SUSPICN\": \"SUSPICION (27)\",\n",
    "    \"SSUSPICN\": \"SUSPICION (27)\",\n",
    "    \"SYNSALE\": \"SYNTHETIC DRUG-SALE/MANUFACTURE (18C)\",\n",
    "    \"SSYNSALE\": \"SYNTHETIC DRUG-SALE/MANUFACTURE (18C)\",\n",
    "    \"SYNPOSS\": \"SYNTHETIC NARCOTICS-POSSESSION (18G)\",\n",
    "    \"SSYNPOSS\": \"SYNTHETIC NARCOTICS-POSSESSION (18G)\",\n",
    "    \"CPOPARST\": \"TOT CNTY POPULATION-AGENCIES RPRT ARRSTS\",\n",
    "    \"PROPERTY\": \"TOTAL PROPERTY CRIMES\",\n",
    "    \"SPROPERTY\": \"TOTAL PROPERTY CRIMES\",\n",
    "    \"VIOL\": \"TOTAL VIOLENT CRIMES\",\n",
    "    \"SVIOL\": \"TOTAL VIOLENT CRIMES\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b1e17fe-7d33-47d2-9b8d-16efd1603502",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "74bb869f-7631-4e40-9757-024745d768d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://www.icpsr.umich.edu/web/ICPSR/studies/37059/variables\n",
    "# https://www.kaggle.com/datasets/mikejohnsonjr/united-states-crime-rates-by-county/data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82089bf9-77ab-4094-9088-d8d1b9672f82",
   "metadata": {},
   "source": [
    "## Food Environment Atlas & Crime Data\n",
    "\n",
    "- Crime Data\n",
    "- Food Environment Atlas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9fdbf3e2-13e3-4fd1-a912-36f2ecef9e73",
   "metadata": {},
   "outputs": [],
   "source": [
    "SocEconCrime = pd.merge(crimeData,FoodAtlasDict['SOCIOECONOMIC'],on='county_name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3fbe10ff-6da8-4ab1-be75-80c050e18844",
   "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>county_name</th>\n",
       "      <th>crime_rate_per_100000</th>\n",
       "      <th>index</th>\n",
       "      <th>EDITION</th>\n",
       "      <th>PART</th>\n",
       "      <th>IDNO</th>\n",
       "      <th>CPOPARST</th>\n",
       "      <th>CPOPCRIM</th>\n",
       "      <th>AG_ARRST</th>\n",
       "      <th>AG_OFF</th>\n",
       "      <th>...</th>\n",
       "      <th>PCT_NHPI10</th>\n",
       "      <th>PCT_65OLDER10</th>\n",
       "      <th>PCT_18YOUNGER10</th>\n",
       "      <th>MEDHHINC10</th>\n",
       "      <th>POVRATE10</th>\n",
       "      <th>PERPOV10</th>\n",
       "      <th>CHILDPOVRATE10</th>\n",
       "      <th>PERCHLDPOV10</th>\n",
       "      <th>METRO13</th>\n",
       "      <th>POPLOSS00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Crittenden,AR</td>\n",
       "      <td>1754.914968</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>130</td>\n",
       "      <td>50717</td>\n",
       "      <td>50717</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011787</td>\n",
       "      <td>10.759892</td>\n",
       "      <td>29.093159</td>\n",
       "      <td>33938</td>\n",
       "      <td>30.3</td>\n",
       "      <td>1</td>\n",
       "      <td>45.9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alexander,IL</td>\n",
       "      <td>1664.700485</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>604</td>\n",
       "      <td>8040</td>\n",
       "      <td>8040</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.072833</td>\n",
       "      <td>16.958000</td>\n",
       "      <td>22.881767</td>\n",
       "      <td>29278</td>\n",
       "      <td>31.1</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kenedy,TX</td>\n",
       "      <td>1456.310680</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2681</td>\n",
       "      <td>444</td>\n",
       "      <td>444</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.182692</td>\n",
       "      <td>24.519231</td>\n",
       "      <td>33502</td>\n",
       "      <td>12.5</td>\n",
       "      <td>0</td>\n",
       "      <td>17.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shelby,TN</td>\n",
       "      <td>1315.003752</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2534</td>\n",
       "      <td>942807</td>\n",
       "      <td>942807</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029537</td>\n",
       "      <td>10.265145</td>\n",
       "      <td>26.383181</td>\n",
       "      <td>43859</td>\n",
       "      <td>20.4</td>\n",
       "      <td>0</td>\n",
       "      <td>29.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Marlboro,SC</td>\n",
       "      <td>1253.437132</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2377</td>\n",
       "      <td>28780</td>\n",
       "      <td>28780</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010369</td>\n",
       "      <td>13.061210</td>\n",
       "      <td>21.853938</td>\n",
       "      <td>28630</td>\n",
       "      <td>33.1</td>\n",
       "      <td>1</td>\n",
       "      <td>43.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     county_name  crime_rate_per_100000  index  EDITION  PART  IDNO  CPOPARST  \\\n",
       "0  Crittenden,AR            1754.914968      2        1     4   130     50717   \n",
       "1   Alexander,IL            1664.700485      3        1     4   604      8040   \n",
       "2      Kenedy,TX            1456.310680      4        1     4  2681       444   \n",
       "3      Shelby,TN            1315.003752      7        1     4  2534    942807   \n",
       "4    Marlboro,SC            1253.437132      8        1     4  2377     28780   \n",
       "\n",
       "   CPOPCRIM  AG_ARRST  AG_OFF  ...  PCT_NHPI10  PCT_65OLDER10  \\\n",
       "0     50717         4       4  ...    0.011787      10.759892   \n",
       "1      8040         2       2  ...    0.072833      16.958000   \n",
       "2       444         1       1  ...    0.000000      14.182692   \n",
       "3    942807        15      14  ...    0.029537      10.265145   \n",
       "4     28780         9       8  ...    0.010369      13.061210   \n",
       "\n",
       "   PCT_18YOUNGER10  MEDHHINC10  POVRATE10  PERPOV10  CHILDPOVRATE10  \\\n",
       "0        29.093159       33938       30.3         1            45.9   \n",
       "1        22.881767       29278       31.1         1              49   \n",
       "2        24.519231       33502       12.5         0            17.6   \n",
       "3        26.383181       43859       20.4         0            29.2   \n",
       "4        21.853938       28630       33.1         1            43.2   \n",
       "\n",
       "   PERCHLDPOV10  METRO13  POPLOSS00  \n",
       "0             1        1          0  \n",
       "1             1        1          1  \n",
       "2             0        0          1  \n",
       "3             1        1          0  \n",
       "4             1        0          1  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SocEconCrime.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4001eff9-bbba-4c25-b707-e6bafcb7a4d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['county_name', 'crime_rate_per_100000', 'index', 'EDITION', 'PART',\n",
       "       'IDNO', 'CPOPARST', 'CPOPCRIM', 'AG_ARRST', 'AG_OFF', 'COVIND', 'INDEX',\n",
       "       'MODINDX', 'MURDER', 'RAPE', 'ROBBERY', 'AGASSLT', 'BURGLRY', 'LARCENY',\n",
       "       'MVTHEFT', 'ARSON', 'population', 'FIPS_ST', 'FIPS_CTY', 'FIPS',\n",
       "       'PCT_NHWHITE10', 'PCT_NHBLACK10', 'PCT_HISP10', 'PCT_NHASIAN10',\n",
       "       'PCT_NHNA10', 'PCT_NHPI10', 'PCT_65OLDER10', 'PCT_18YOUNGER10',\n",
       "       'MEDHHINC10', 'POVRATE10', 'PERPOV10', 'CHILDPOVRATE10', 'PERCHLDPOV10',\n",
       "       'METRO13', 'POPLOSS00'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see crime dict for meaning or follow link # https://www.kaggle.com/datasets/mikejohnsonjr/united-states-crime-rates-by-county/data\n",
    "SocEconCrime.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c0e51162-b90c-4235-a0ab-c514ec0f2506",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2854, 40)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SocEconCrime.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "758c9d85-1d59-44bc-8feb-111ff89722e0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a4ae5354-9ecc-43ba-87ba-0c69eba622fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# MODINDX = BURGLRY +\tLARCENY +\tMVTHEFT\n",
    "# INDEX = MURDER\t+ RAPE +\tROBBERY +\tAGASSLT\n",
    "SocEconCrime = SocEconCrime.drop(columns=['index'\t,'EDITION',\t'PART','IDNO'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6f9b1a99-d51b-44f1-bb92-25da3effbe7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "SocEconCrime.to_excel('test.xlsx',index=False)\n",
    "countyName = SocEconCrime['county_name']\n",
    "SocEconCrime = SocEconCrime.drop(columns='county_name')\n",
    "SocEconCrime = SocEconCrime.replace('<Null>', '0')\n",
    "SocEconCrime = SocEconCrime.astype(float)\n",
    "SocEconCrime = pd.concat([countyName, SocEconCrime], axis=1)\n",
    "SocEconCrime['Food Desert'] = (SocEconCrime['POVRATE10'] >= 30).astype(int) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c1e06f73-f770-430f-91bb-d082cd6f0bec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>county_name</th>\n",
       "      <th>crime_rate_per_100000</th>\n",
       "      <th>CPOPARST</th>\n",
       "      <th>CPOPCRIM</th>\n",
       "      <th>AG_ARRST</th>\n",
       "      <th>AG_OFF</th>\n",
       "      <th>COVIND</th>\n",
       "      <th>INDEX</th>\n",
       "      <th>MODINDX</th>\n",
       "      <th>MURDER</th>\n",
       "      <th>...</th>\n",
       "      <th>PCT_65OLDER10</th>\n",
       "      <th>PCT_18YOUNGER10</th>\n",
       "      <th>MEDHHINC10</th>\n",
       "      <th>POVRATE10</th>\n",
       "      <th>PERPOV10</th>\n",
       "      <th>CHILDPOVRATE10</th>\n",
       "      <th>PERCHLDPOV10</th>\n",
       "      <th>METRO13</th>\n",
       "      <th>POPLOSS00</th>\n",
       "      <th>Food Desert</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Crittenden,AR</td>\n",
       "      <td>1754.914968</td>\n",
       "      <td>50717.0</td>\n",
       "      <td>50717.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>873.0</td>\n",
       "      <td>3424.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.759892</td>\n",
       "      <td>29.093159</td>\n",
       "      <td>33938.0</td>\n",
       "      <td>30.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alexander,IL</td>\n",
       "      <td>1664.700485</td>\n",
       "      <td>8040.0</td>\n",
       "      <td>8040.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16.958000</td>\n",
       "      <td>22.881767</td>\n",
       "      <td>29278.0</td>\n",
       "      <td>31.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kenedy,TX</td>\n",
       "      <td>1456.310680</td>\n",
       "      <td>444.0</td>\n",
       "      <td>444.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.182692</td>\n",
       "      <td>24.519231</td>\n",
       "      <td>33502.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shelby,TN</td>\n",
       "      <td>1315.003752</td>\n",
       "      <td>942807.0</td>\n",
       "      <td>942807.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>12354.0</td>\n",
       "      <td>47864.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.265145</td>\n",
       "      <td>26.383181</td>\n",
       "      <td>43859.0</td>\n",
       "      <td>20.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Marlboro,SC</td>\n",
       "      <td>1253.437132</td>\n",
       "      <td>28780.0</td>\n",
       "      <td>28780.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>1549.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>13.061210</td>\n",
       "      <td>21.853938</td>\n",
       "      <td>28630.0</td>\n",
       "      <td>33.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>43.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Storey,NV</td>\n",
       "      <td>1217.656012</td>\n",
       "      <td>3947.0</td>\n",
       "      <td>3947.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>18.403990</td>\n",
       "      <td>17.481297</td>\n",
       "      <td>57763.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dillon,SC</td>\n",
       "      <td>1165.583272</td>\n",
       "      <td>32060.0</td>\n",
       "      <td>32060.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>364.0</td>\n",
       "      <td>1906.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.971742</td>\n",
       "      <td>26.741937</td>\n",
       "      <td>28665.0</td>\n",
       "      <td>26.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Scott,MO</td>\n",
       "      <td>1163.145839</td>\n",
       "      <td>40941.0</td>\n",
       "      <td>40941.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>1277.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.983032</td>\n",
       "      <td>25.133321</td>\n",
       "      <td>37457.0</td>\n",
       "      <td>18.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Philadelphia,PA</td>\n",
       "      <td>1150.296330</td>\n",
       "      <td>1538957.0</td>\n",
       "      <td>1538957.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>17866.0</td>\n",
       "      <td>57000.0</td>\n",
       "      <td>332.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.143399</td>\n",
       "      <td>22.531825</td>\n",
       "      <td>34667.0</td>\n",
       "      <td>26.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Pershing,NV</td>\n",
       "      <td>1148.756725</td>\n",
       "      <td>6822.0</td>\n",
       "      <td>6822.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.957204</td>\n",
       "      <td>19.576485</td>\n",
       "      <td>44684.0</td>\n",
       "      <td>18.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       county_name  crime_rate_per_100000   CPOPARST   CPOPCRIM  AG_ARRST  \\\n",
       "0    Crittenden,AR            1754.914968    50717.0    50717.0       4.0   \n",
       "1     Alexander,IL            1664.700485     8040.0     8040.0       2.0   \n",
       "2        Kenedy,TX            1456.310680      444.0      444.0       1.0   \n",
       "3        Shelby,TN            1315.003752   942807.0   942807.0      15.0   \n",
       "4      Marlboro,SC            1253.437132    28780.0    28780.0       9.0   \n",
       "5        Storey,NV            1217.656012     3947.0     3947.0       1.0   \n",
       "6        Dillon,SC            1165.583272    32060.0    32060.0       9.0   \n",
       "7         Scott,MO            1163.145839    40941.0    40941.0       7.0   \n",
       "8  Philadelphia,PA            1150.296330  1538957.0  1538957.0       6.0   \n",
       "9      Pershing,NV            1148.756725     6822.0     6822.0       2.0   \n",
       "\n",
       "   AG_OFF  COVIND    INDEX  MODINDX  MURDER  ...  PCT_65OLDER10  \\\n",
       "0     4.0   100.0    873.0   3424.0     8.0  ...      10.759892   \n",
       "1     2.0   100.0    127.0    278.0     1.0  ...      16.958000   \n",
       "2     1.0   100.0      6.0     13.0     0.0  ...      14.182692   \n",
       "3    14.0   100.0  12354.0  47864.0   135.0  ...      10.265145   \n",
       "4     8.0   100.0    351.0   1549.0     3.0  ...      13.061210   \n",
       "5     1.0   100.0     48.0     86.0     0.0  ...      18.403990   \n",
       "6     8.0   100.0    364.0   1906.0     8.0  ...      12.971742   \n",
       "7     8.0   100.0    457.0   1277.0     2.0  ...      14.983032   \n",
       "8     5.0   100.0  17866.0  57000.0   332.0  ...      12.143399   \n",
       "9     2.0   100.0     79.0     75.0     0.0  ...      12.957204   \n",
       "\n",
       "   PCT_18YOUNGER10  MEDHHINC10  POVRATE10  PERPOV10  CHILDPOVRATE10  \\\n",
       "0        29.093159     33938.0       30.3       1.0            45.9   \n",
       "1        22.881767     29278.0       31.1       1.0            49.0   \n",
       "2        24.519231     33502.0       12.5       0.0            17.6   \n",
       "3        26.383181     43859.0       20.4       0.0            29.2   \n",
       "4        21.853938     28630.0       33.1       1.0            43.2   \n",
       "5        17.481297     57763.0        8.7       0.0            13.6   \n",
       "6        26.741937     28665.0       26.2       1.0            37.1   \n",
       "7        25.133321     37457.0       18.5       0.0            27.6   \n",
       "8        22.531825     34667.0       26.4       1.0            36.4   \n",
       "9        19.576485     44684.0       18.5       0.0            22.0   \n",
       "\n",
       "   PERCHLDPOV10  METRO13  POPLOSS00  Food Desert  \n",
       "0           1.0      1.0        0.0            1  \n",
       "1           1.0      1.0        1.0            1  \n",
       "2           0.0      0.0        1.0            0  \n",
       "3           1.0      1.0        0.0            0  \n",
       "4           1.0      0.0        1.0            1  \n",
       "5           0.0      1.0        0.0            0  \n",
       "6           1.0      0.0        0.0            0  \n",
       "7           1.0      0.0        0.0            0  \n",
       "8           1.0      1.0        1.0            0  \n",
       "9           0.0      0.0        0.0            0  \n",
       "\n",
       "[10 rows x 37 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SocEconCrime.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5a5bd474-2f08-46d6-98a4-8df17a402be2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Food Desert\n",
       "0    2749\n",
       "1     105\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 30 as threshold\n",
    "SocEconCrime['Food Desert'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "667422eb-7778-469a-b89f-10ae731501f4",
   "metadata": {},
   "source": [
    "# MODINDX and INDEX Analysis\n",
    "\n",
    "- **MODINDX = BURGLRY +\tLARCENY +\tMVTHEFT**\n",
    "- **INDEX = MURDER\t+ RAPE +\tROBBERY +\tAGASSLT**\n",
    "- "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "61d6d1c9-b938-48e4-98ad-7df15659d87d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>county_name</th>\n",
       "      <th>INDEX</th>\n",
       "      <th>MODINDX</th>\n",
       "      <th>population</th>\n",
       "      <th>Food Desert</th>\n",
       "      <th>MODINDX_population</th>\n",
       "      <th>INDEX_population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Crittenden,AR</td>\n",
       "      <td>873.0</td>\n",
       "      <td>3424.0</td>\n",
       "      <td>49746.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.068830</td>\n",
       "      <td>0.017549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alexander,IL</td>\n",
       "      <td>127.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>7629.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.036440</td>\n",
       "      <td>0.016647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kenedy,TX</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.031553</td>\n",
       "      <td>0.014563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shelby,TN</td>\n",
       "      <td>12354.0</td>\n",
       "      <td>47864.0</td>\n",
       "      <td>939465.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.050948</td>\n",
       "      <td>0.013150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Marlboro,SC</td>\n",
       "      <td>351.0</td>\n",
       "      <td>1549.0</td>\n",
       "      <td>28003.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.055316</td>\n",
       "      <td>0.012534</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     county_name    INDEX  MODINDX  population  Food Desert  \\\n",
       "0  Crittenden,AR    873.0   3424.0     49746.0            1   \n",
       "1   Alexander,IL    127.0    278.0      7629.0            1   \n",
       "2      Kenedy,TX      6.0     13.0       412.0            0   \n",
       "3      Shelby,TN  12354.0  47864.0    939465.0            0   \n",
       "4    Marlboro,SC    351.0   1549.0     28003.0            1   \n",
       "\n",
       "   MODINDX_population  INDEX_population  \n",
       "0            0.068830          0.017549  \n",
       "1            0.036440          0.016647  \n",
       "2            0.031553          0.014563  \n",
       "3            0.050948          0.013150  \n",
       "4            0.055316          0.012534  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modindx_indx = SocEconCrime[['county_name','INDEX',\t'MODINDX','population','Food Desert']].copy()\n",
    "modindx_indx['MODINDX_population'] = modindx_indx['MODINDX'] / modindx_indx['population']\n",
    "modindx_indx['INDEX_population'] = modindx_indx['INDEX'] / modindx_indx['population']\n",
    "\n",
    "modindx_indx.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a39f1afb-8fa8-4ee0-adbf-264e9a990970",
   "metadata": {},
   "outputs": [
    {
     "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>INDEX</th>\n",
       "      <td>2854.0</td>\n",
       "      <td>346.846181</td>\n",
       "      <td>1453.836638</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>161.000000</td>\n",
       "      <td>3.126900e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MODINDX</th>\n",
       "      <td>2854.0</td>\n",
       "      <td>2629.444989</td>\n",
       "      <td>8826.942539</td>\n",
       "      <td>0.0</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>497.000000</td>\n",
       "      <td>1644.500000</td>\n",
       "      <td>1.786810e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>2854.0</td>\n",
       "      <td>94420.254380</td>\n",
       "      <td>264080.761264</td>\n",
       "      <td>90.0</td>\n",
       "      <td>11029.500000</td>\n",
       "      <td>25656.500000</td>\n",
       "      <td>65818.000000</td>\n",
       "      <td>5.240700e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food Desert</th>\n",
       "      <td>2854.0</td>\n",
       "      <td>0.036790</td>\n",
       "      <td>0.188280</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MODINDX_population</th>\n",
       "      <td>2854.0</td>\n",
       "      <td>0.019756</td>\n",
       "      <td>0.011876</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.011062</td>\n",
       "      <td>0.018622</td>\n",
       "      <td>0.026724</td>\n",
       "      <td>6.882965e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDEX_population</th>\n",
       "      <td>2854.0</td>\n",
       "      <td>0.002280</td>\n",
       "      <td>0.001934</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000913</td>\n",
       "      <td>0.001812</td>\n",
       "      <td>0.003122</td>\n",
       "      <td>1.754915e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count          mean            std   min           25%  \\\n",
       "INDEX               2854.0    346.846181    1453.836638   0.0     13.000000   \n",
       "MODINDX             2854.0   2629.444989    8826.942539   0.0    126.000000   \n",
       "population          2854.0  94420.254380  264080.761264  90.0  11029.500000   \n",
       "Food Desert         2854.0      0.036790       0.188280   0.0      0.000000   \n",
       "MODINDX_population  2854.0      0.019756       0.011876   0.0      0.011062   \n",
       "INDEX_population    2854.0      0.002280       0.001934   0.0      0.000913   \n",
       "\n",
       "                             50%           75%           max  \n",
       "INDEX                  47.000000    161.000000  3.126900e+04  \n",
       "MODINDX               497.000000   1644.500000  1.786810e+05  \n",
       "population          25656.500000  65818.000000  5.240700e+06  \n",
       "Food Desert             0.000000      0.000000  1.000000e+00  \n",
       "MODINDX_population      0.018622      0.026724  6.882965e-02  \n",
       "INDEX_population        0.001812      0.003122  1.754915e-02  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modindx_indx.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d3be6236-efc0-4dc2-b071-759508f3285f",
   "metadata": {},
   "outputs": [
    {
     "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>county_name</th>\n",
       "      <th>INDEX</th>\n",
       "      <th>MODINDX</th>\n",
       "      <th>population</th>\n",
       "      <th>Food Desert</th>\n",
       "      <th>MODINDX_population</th>\n",
       "      <th>INDEX_population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Crittenden,AR</td>\n",
       "      <td>873.0</td>\n",
       "      <td>3424.0</td>\n",
       "      <td>49746.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.068830</td>\n",
       "      <td>0.017549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Pulaski,AR</td>\n",
       "      <td>3756.0</td>\n",
       "      <td>26881.0</td>\n",
       "      <td>391284.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.068699</td>\n",
       "      <td>0.009599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>Vance,NC</td>\n",
       "      <td>234.0</td>\n",
       "      <td>2996.0</td>\n",
       "      <td>44808.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.066863</td>\n",
       "      <td>0.005222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Macon,AL</td>\n",
       "      <td>181.0</td>\n",
       "      <td>1266.0</td>\n",
       "      <td>19688.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.064303</td>\n",
       "      <td>0.009193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>Washington,MS</td>\n",
       "      <td>151.0</td>\n",
       "      <td>3078.0</td>\n",
       "      <td>49688.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.061947</td>\n",
       "      <td>0.003039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Dougherty,GA</td>\n",
       "      <td>865.0</td>\n",
       "      <td>5710.0</td>\n",
       "      <td>92969.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.061418</td>\n",
       "      <td>0.009304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dillon,SC</td>\n",
       "      <td>364.0</td>\n",
       "      <td>1906.0</td>\n",
       "      <td>31229.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.061033</td>\n",
       "      <td>0.011656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>Bibb,GA</td>\n",
       "      <td>811.0</td>\n",
       "      <td>9385.0</td>\n",
       "      <td>154721.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.060658</td>\n",
       "      <td>0.005242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Greene,MO</td>\n",
       "      <td>1762.0</td>\n",
       "      <td>17117.0</td>\n",
       "      <td>283870.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.060299</td>\n",
       "      <td>0.006207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>Richmond,NC</td>\n",
       "      <td>213.0</td>\n",
       "      <td>2784.0</td>\n",
       "      <td>46405.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.059994</td>\n",
       "      <td>0.004590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Marion,SC</td>\n",
       "      <td>302.0</td>\n",
       "      <td>1923.0</td>\n",
       "      <td>32072.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.059959</td>\n",
       "      <td>0.009416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Jackson,MO</td>\n",
       "      <td>6981.0</td>\n",
       "      <td>40642.0</td>\n",
       "      <td>679996.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.059768</td>\n",
       "      <td>0.010266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>Richmond,GA</td>\n",
       "      <td>919.0</td>\n",
       "      <td>11971.0</td>\n",
       "      <td>202003.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.059261</td>\n",
       "      <td>0.004549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Cumberland,NC</td>\n",
       "      <td>1834.0</td>\n",
       "      <td>19300.0</td>\n",
       "      <td>325871.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.059226</td>\n",
       "      <td>0.005628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Robeson,NC</td>\n",
       "      <td>1092.0</td>\n",
       "      <td>7958.0</td>\n",
       "      <td>134841.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.059018</td>\n",
       "      <td>0.008098</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       county_name   INDEX  MODINDX  population  Food Desert  \\\n",
       "0    Crittenden,AR   873.0   3424.0     49746.0            1   \n",
       "17      Pulaski,AR  3756.0  26881.0    391284.0            0   \n",
       "227       Vance,NC   234.0   2996.0     44808.0            0   \n",
       "26        Macon,AL   181.0   1266.0     19688.0            1   \n",
       "746  Washington,MS   151.0   3078.0     49688.0            1   \n",
       "23    Dougherty,GA   865.0   5710.0     92969.0            1   \n",
       "6        Dillon,SC   364.0   1906.0     31229.0            0   \n",
       "225        Bibb,GA   811.0   9385.0    154721.0            0   \n",
       "119      Greene,MO  1762.0  17117.0    283870.0            0   \n",
       "315    Richmond,NC   213.0   2784.0     46405.0            0   \n",
       "22       Marion,SC   302.0   1923.0     32072.0            0   \n",
       "15      Jackson,MO  6981.0  40642.0    679996.0            0   \n",
       "321    Richmond,GA   919.0  11971.0    202003.0            0   \n",
       "174  Cumberland,NC  1834.0  19300.0    325871.0            0   \n",
       "44      Robeson,NC  1092.0   7958.0    134841.0            1   \n",
       "\n",
       "     MODINDX_population  INDEX_population  \n",
       "0              0.068830          0.017549  \n",
       "17             0.068699          0.009599  \n",
       "227            0.066863          0.005222  \n",
       "26             0.064303          0.009193  \n",
       "746            0.061947          0.003039  \n",
       "23             0.061418          0.009304  \n",
       "6              0.061033          0.011656  \n",
       "225            0.060658          0.005242  \n",
       "119            0.060299          0.006207  \n",
       "315            0.059994          0.004590  \n",
       "22             0.059959          0.009416  \n",
       "15             0.059768          0.010266  \n",
       "321            0.059261          0.004549  \n",
       "174            0.059226          0.005628  \n",
       "44             0.059018          0.008098  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modindx_indx.sort_values(by='MODINDX_population',ascending=False).head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a2e147f3-8c7c-4c4e-b525-5795fe1e67ed",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>county_name</th>\n",
       "      <th>INDEX</th>\n",
       "      <th>MODINDX</th>\n",
       "      <th>population</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Crittenden,AR</td>\n",
       "      <td>873.0</td>\n",
       "      <td>3424.0</td>\n",
       "      <td>49746.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.017549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alexander,IL</td>\n",
       "      <td>127.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>7629.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.016647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kenedy,TX</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.031553</td>\n",
       "      <td>0.014563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shelby,TN</td>\n",
       "      <td>12354.0</td>\n",
       "      <td>47864.0</td>\n",
       "      <td>939465.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.050948</td>\n",
       "      <td>0.013150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Marlboro,SC</td>\n",
       "      <td>351.0</td>\n",
       "      <td>1549.0</td>\n",
       "      <td>28003.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.055316</td>\n",
       "      <td>0.012534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Storey,NV</td>\n",
       "      <td>48.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>3942.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.021816</td>\n",
       "      <td>0.012177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dillon,SC</td>\n",
       "      <td>364.0</td>\n",
       "      <td>1906.0</td>\n",
       "      <td>31229.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.061033</td>\n",
       "      <td>0.011656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Scott,MO</td>\n",
       "      <td>457.0</td>\n",
       "      <td>1277.0</td>\n",
       "      <td>39290.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.032502</td>\n",
       "      <td>0.011631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Philadelphia,PA</td>\n",
       "      <td>17866.0</td>\n",
       "      <td>57000.0</td>\n",
       "      <td>1553165.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.036699</td>\n",
       "      <td>0.011503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Pershing,NV</td>\n",
       "      <td>79.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>6877.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.010906</td>\n",
       "      <td>0.011488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Greenwood,SC</td>\n",
       "      <td>795.0</td>\n",
       "      <td>3651.0</td>\n",
       "      <td>69723.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.052364</td>\n",
       "      <td>0.011402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Madison,TN</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>4084.0</td>\n",
       "      <td>98733.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.041364</td>\n",
       "      <td>0.011384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Davidson,TN</td>\n",
       "      <td>7439.0</td>\n",
       "      <td>27666.0</td>\n",
       "      <td>658602.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.042007</td>\n",
       "      <td>0.011295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Marion,IN</td>\n",
       "      <td>10058.0</td>\n",
       "      <td>49004.0</td>\n",
       "      <td>928281.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.052790</td>\n",
       "      <td>0.010835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Wayne,MI</td>\n",
       "      <td>18910.0</td>\n",
       "      <td>69839.0</td>\n",
       "      <td>1775273.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.039340</td>\n",
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      "text/plain": [
       "        county_name    INDEX  MODINDX  population  Food Desert  \\\n",
       "0     Crittenden,AR    873.0   3424.0     49746.0            1   \n",
       "1      Alexander,IL    127.0    278.0      7629.0            1   \n",
       "2         Kenedy,TX      6.0     13.0       412.0            0   \n",
       "3         Shelby,TN  12354.0  47864.0    939465.0            0   \n",
       "4       Marlboro,SC    351.0   1549.0     28003.0            1   \n",
       "5         Storey,NV     48.0     86.0      3942.0            0   \n",
       "6         Dillon,SC    364.0   1906.0     31229.0            0   \n",
       "7          Scott,MO    457.0   1277.0     39290.0            0   \n",
       "8   Philadelphia,PA  17866.0  57000.0   1553165.0            0   \n",
       "9       Pershing,NV     79.0     75.0      6877.0            0   \n",
       "10     Greenwood,SC    795.0   3651.0     69723.0            0   \n",
       "11       Madison,TN   1124.0   4084.0     98733.0            0   \n",
       "12      Davidson,TN   7439.0  27666.0    658602.0            0   \n",
       "13        Marion,IN  10058.0  49004.0    928281.0            0   \n",
       "14         Wayne,MI  18910.0  69839.0   1775273.0            0   \n",
       "\n",
       "    MODINDX_population  INDEX_population  \n",
       "0             0.068830          0.017549  \n",
       "1             0.036440          0.016647  \n",
       "2             0.031553          0.014563  \n",
       "3             0.050948          0.013150  \n",
       "4             0.055316          0.012534  \n",
       "5             0.021816          0.012177  \n",
       "6             0.061033          0.011656  \n",
       "7             0.032502          0.011631  \n",
       "8             0.036699          0.011503  \n",
       "9             0.010906          0.011488  \n",
       "10            0.052364          0.011402  \n",
       "11            0.041364          0.011384  \n",
       "12            0.042007          0.011295  \n",
       "13            0.052790          0.010835  \n",
       "14            0.039340          0.010652  "
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     },
     "execution_count": 26,
     "metadata": {},
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    }
   ],
   "source": [
    "modindx_indx.sort_values(by='INDEX_population',ascending=False).head(15)"
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  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fe78c422-f1e1-4e85-948a-31ed5726ed3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Food Desert  MODINDX_population\n",
       "0            0.000000              67\n",
       "             0.000119               1\n",
       "             0.000137               1\n",
       "             0.000145               1\n",
       "             0.000176               1\n",
       "                                   ..\n",
       "1            0.059018               1\n",
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       "             0.061947               1\n",
       "             0.064303               1\n",
       "             0.068830               1\n",
       "Name: Food Desert, Length: 2768, dtype: int64"
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     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modindx_indx.groupby(['Food Desert','MODINDX_population'])['Food Desert'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "68c58173-d7c7-4b5b-aebd-6cb5fdaa3441",
   "metadata": {},
   "outputs": [
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.068830</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    county_name  INDEX  MODINDX  population  Food Desert  \\\n",
       "MODINDX_population                                                         \n",
       "0.000000                      9      9        9           9            9   \n",
       "0.000376                      1      1        1           1            1   \n",
       "0.000657                      1      1        1           1            1   \n",
       "0.000895                      1      1        1           1            1   \n",
       "0.001194                      1      1        1           1            1   \n",
       "...                         ...    ...      ...         ...          ...   \n",
       "0.059018                      1      1        1           1            1   \n",
       "0.061418                      1      1        1           1            1   \n",
       "0.061947                      1      1        1           1            1   \n",
       "0.064303                      1      1        1           1            1   \n",
       "0.068830                      1      1        1           1            1   \n",
       "\n",
       "                    INDEX_population  \n",
       "MODINDX_population                    \n",
       "0.000000                           9  \n",
       "0.000376                           1  \n",
       "0.000657                           1  \n",
       "0.000895                           1  \n",
       "0.001194                           1  \n",
       "...                              ...  \n",
       "0.059018                           1  \n",
       "0.061418                           1  \n",
       "0.061947                           1  \n",
       "0.064303                           1  \n",
       "0.068830                           1  \n",
       "\n",
       "[97 rows x 6 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modindx_indx[modindx_indx['Food Desert'] == 1].groupby('MODINDX_population').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d606ee72-ae92-4559-9d24-e41665ee6caf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Playing around with a potential example\n",
    "\n",
    "# Step 1: Filter rows where 'Food Desert' == 1\n",
    "filtered_data = modindx_indx[modindx_indx['Food Desert'] == 1]\n",
    "\n",
    "# Step 2: Extract the 'MODINDX_population' column\n",
    "modindx_population = filtered_data['MODINDX_population']\n",
    "\n",
    "# Step 3: Plot the distribution\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Histogram with KDE overlay\n",
    "sns.histplot(modindx_population, kde=True, bins=30, color='green')\n",
    "\n",
    "# Add labels and title\n",
    "plt.title('Distribution of MODINDX_population (Food Desert = 1)', fontsize=16)\n",
    "plt.xlabel('MODINDX_population', fontsize=14)\n",
    "plt.ylabel('Frequency', fontsize=14)\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b09e1620-458f-444d-8ae8-aa243e091bb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9661610268378062\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.98       826\n",
      "           1       0.62      0.16      0.26        31\n",
      "\n",
      "    accuracy                           0.97       857\n",
      "   macro avg       0.80      0.58      0.62       857\n",
      "weighted avg       0.96      0.97      0.96       857\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "target = 'Food Desert'\n",
    "features = SocEconCrime.drop(columns=['county_name', target])\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, SocEconCrime[target], test_size=0.3, random_state=42)\n",
    "\n",
    "# Logistic Regression Model\n",
    "log_reg = LogisticRegression(max_iter=500)\n",
    "log_reg.fit(X_train, y_train)\n",
    "\n",
    "# Predictions\n",
    "y_pred = log_reg.predict(X_test)\n",
    "\n",
    "# Report\n",
    "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "78057d3d-8b9b-4e3c-b43f-b2b0825d7306",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN Accuracy: 0.9661610268378062\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.98       826\n",
      "           1       0.62      0.16      0.26        31\n",
      "\n",
      "    accuracy                           0.97       857\n",
      "   macro avg       0.80      0.58      0.62       857\n",
      "weighted avg       0.96      0.97      0.96       857\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# KNN Model\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "# Predictions\n",
    "y_pred_knn = knn.predict(X_test)\n",
    "\n",
    "# Report\n",
    "print(\"KNN Accuracy:\", accuracy_score(y_test, y_pred_knn))\n",
    "print(classification_report(y_test, y_pred_knn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6dedc40b-e12c-4928-a34a-489d8a528685",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9894982497082847\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99       826\n",
      "           1       0.96      0.74      0.84        31\n",
      "\n",
      "    accuracy                           0.99       857\n",
      "   macro avg       0.97      0.87      0.92       857\n",
      "weighted avg       0.99      0.99      0.99       857\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "target = 'Food Desert'\n",
    "features = SocEconCrime.drop(columns=['county_name', target])\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, SocEconCrime[target], test_size=0.3, random_state=42)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# SVM classifier\n",
    "svm_model = SVC(kernel='rbf', random_state=42) \n",
    "svm_model.fit(X_train_scaled, y_train)\n",
    "\n",
    "# \n",
    "y_pred = svm_model.predict(X_test_scaled)\n",
    "\n",
    "# Report\n",
    "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24fcc10b-cfb4-46cd-a2f7-e3c622e8f632",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3ee5fdf-fd92-45de-b337-06ad86dff3ca",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "16d30bef-8dcf-4e3f-91a2-cce13c283cad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "18877b47-3e89-477c-a51d-28ee97562543",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Read_Me',\n",
       " 'Variable List',\n",
       " 'Supplemental Data - County',\n",
       " 'Supplemental Data - State',\n",
       " 'ACCESS',\n",
       " 'ASSISTANCE',\n",
       " 'HEALTH',\n",
       " 'INSECURITY',\n",
       " 'LOCAL',\n",
       " 'PRICES_TAXES',\n",
       " 'RESTAURANTS',\n",
       " 'STORES',\n",
       " 'SOCIOECONOMIC']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FoodResearchAtlas2015 = pd.ExcelFile('2015 Food Environment Atlas Data Download.xls')\n",
    "FoodResearchAtlas2015.sheet_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "36dee2a1-9c30-4dc7-a218-066a19bb735d",
   "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>FIPS</th>\n",
       "      <th>State</th>\n",
       "      <th>County</th>\n",
       "      <th>LACCESS_POP10</th>\n",
       "      <th>PCT_LACCESS_POP10</th>\n",
       "      <th>LACCESS_LOWI10</th>\n",
       "      <th>PCT_LACCESS_LOWI10</th>\n",
       "      <th>LACCESS_CHILD10</th>\n",
       "      <th>PCT_LACCESS_CHILD10</th>\n",
       "      <th>LACCESS_SENIORS10</th>\n",
       "      <th>...</th>\n",
       "      <th>PCT_65OLDER10</th>\n",
       "      <th>PCT_18YOUNGER10</th>\n",
       "      <th>MEDHHINC10</th>\n",
       "      <th>POVRATE10</th>\n",
       "      <th>PERPOV10</th>\n",
       "      <th>CHILDPOVRATE10</th>\n",
       "      <th>PERCHLDPOV10</th>\n",
       "      <th>METRO13</th>\n",
       "      <th>POPLOSS00</th>\n",
       "      <th>2010 Census population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>AL</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>18428.439685</td>\n",
       "      <td>33.769657</td>\n",
       "      <td>5344.427472</td>\n",
       "      <td>9.793530</td>\n",
       "      <td>4822.500269</td>\n",
       "      <td>8.837112</td>\n",
       "      <td>2388.233228</td>\n",
       "      <td>...</td>\n",
       "      <td>11.995382</td>\n",
       "      <td>26.777959</td>\n",
       "      <td>53049</td>\n",
       "      <td>11.9</td>\n",
       "      <td>0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>54571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1003</td>\n",
       "      <td>AL</td>\n",
       "      <td>Baldwin</td>\n",
       "      <td>35210.814078</td>\n",
       "      <td>19.318473</td>\n",
       "      <td>9952.144027</td>\n",
       "      <td>5.460261</td>\n",
       "      <td>7916.131932</td>\n",
       "      <td>4.343199</td>\n",
       "      <td>6404.008603</td>\n",
       "      <td>...</td>\n",
       "      <td>16.771185</td>\n",
       "      <td>22.987408</td>\n",
       "      <td>47618</td>\n",
       "      <td>13.3</td>\n",
       "      <td>0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>182265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1005</td>\n",
       "      <td>AL</td>\n",
       "      <td>Barbour</td>\n",
       "      <td>5722.305602</td>\n",
       "      <td>20.840972</td>\n",
       "      <td>3135.676086</td>\n",
       "      <td>11.420316</td>\n",
       "      <td>940.419327</td>\n",
       "      <td>3.425062</td>\n",
       "      <td>770.214550</td>\n",
       "      <td>...</td>\n",
       "      <td>14.236807</td>\n",
       "      <td>21.906982</td>\n",
       "      <td>33074</td>\n",
       "      <td>25.3</td>\n",
       "      <td>1</td>\n",
       "      <td>36.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1007</td>\n",
       "      <td>AL</td>\n",
       "      <td>Bibb</td>\n",
       "      <td>1044.867327</td>\n",
       "      <td>4.559753</td>\n",
       "      <td>491.449066</td>\n",
       "      <td>2.144661</td>\n",
       "      <td>249.204753</td>\n",
       "      <td>1.087518</td>\n",
       "      <td>150.553323</td>\n",
       "      <td>...</td>\n",
       "      <td>12.681650</td>\n",
       "      <td>22.696923</td>\n",
       "      <td>35472</td>\n",
       "      <td>20.9</td>\n",
       "      <td>0</td>\n",
       "      <td>28.6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>22915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1009</td>\n",
       "      <td>AL</td>\n",
       "      <td>Blount</td>\n",
       "      <td>1548.175559</td>\n",
       "      <td>2.700840</td>\n",
       "      <td>609.027708</td>\n",
       "      <td>1.062468</td>\n",
       "      <td>384.911607</td>\n",
       "      <td>0.671490</td>\n",
       "      <td>195.049051</td>\n",
       "      <td>...</td>\n",
       "      <td>14.722096</td>\n",
       "      <td>24.608353</td>\n",
       "      <td>42906</td>\n",
       "      <td>16.5</td>\n",
       "      <td>0</td>\n",
       "      <td>24.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>57322</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   FIPS State   County  LACCESS_POP10  PCT_LACCESS_POP10  LACCESS_LOWI10  \\\n",
       "0  1001    AL  Autauga   18428.439685          33.769657     5344.427472   \n",
       "1  1003    AL  Baldwin   35210.814078          19.318473     9952.144027   \n",
       "2  1005    AL  Barbour    5722.305602          20.840972     3135.676086   \n",
       "3  1007    AL     Bibb    1044.867327           4.559753      491.449066   \n",
       "4  1009    AL   Blount    1548.175559           2.700840      609.027708   \n",
       "\n",
       "   PCT_LACCESS_LOWI10  LACCESS_CHILD10  PCT_LACCESS_CHILD10  \\\n",
       "0            9.793530      4822.500269             8.837112   \n",
       "1            5.460261      7916.131932             4.343199   \n",
       "2           11.420316       940.419327             3.425062   \n",
       "3            2.144661       249.204753             1.087518   \n",
       "4            1.062468       384.911607             0.671490   \n",
       "\n",
       "   LACCESS_SENIORS10  ...  PCT_65OLDER10  PCT_18YOUNGER10  MEDHHINC10  \\\n",
       "0        2388.233228  ...      11.995382        26.777959       53049   \n",
       "1        6404.008603  ...      16.771185        22.987408       47618   \n",
       "2         770.214550  ...      14.236807        21.906982       33074   \n",
       "3         150.553323  ...      12.681650        22.696923       35472   \n",
       "4         195.049051  ...      14.722096        24.608353       42906   \n",
       "\n",
       "   POVRATE10  PERPOV10  CHILDPOVRATE10  PERCHLDPOV10  METRO13  POPLOSS00  \\\n",
       "0       11.9         0            17.5             0        1          0   \n",
       "1       13.3         0            20.2             0        1          0   \n",
       "2       25.3         1            36.2             1        0          0   \n",
       "3       20.9         0            28.6             1        1          0   \n",
       "4       16.5         0            24.1             0        1          0   \n",
       "\n",
       "   2010 Census population  \n",
       "0                   54571  \n",
       "1                  182265  \n",
       "2                   27457  \n",
       "3                   22915  \n",
       "4                   57322  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Food Access Data\n",
    "food_atlas_access_df=FoodResearchAtlas2015.parse('ACCESS')\n",
    "\n",
    "# Socio Economic Data\n",
    "food_atlas_socioeconomic_df=FoodResearchAtlas2015.parse('SOCIOECONOMIC')\n",
    "food_atlas_socioeconomic_df=food_atlas_socioeconomic_df.drop(['State', 'County'], axis=1)\n",
    "\n",
    "# Population Data - Choose only 2010\n",
    "food_atlas_population_df=FoodResearchAtlas2015.parse('Supplemental Data - County')\n",
    "food_atlas_population_df=food_atlas_population_df[['FIPS Code','State', 'County name', '2010 Census population']]\n",
    "food_atlas_population_df.rename(columns={'FIPS Code': 'FIPS'}, inplace=True)\n",
    "food_atlas_population_df=food_atlas_population_df.drop(['State', 'County name'], axis=1)\n",
    "\n",
    "# Merge the data frames\n",
    "food_atlas_df = pd.merge(food_atlas_access_df, food_atlas_socioeconomic_df, on='FIPS')\n",
    "food_atlas_df = pd.merge(food_atlas_df, food_atlas_population_df, on='FIPS')\n",
    "\n",
    "food_atlas_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2f0a9a34-0942-401a-9951-e0397a1b1bc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['FIPS', 'State', 'County', 'LACCESS_POP10', 'PCT_LACCESS_POP10',\n",
       "       'LACCESS_LOWI10', 'PCT_LACCESS_LOWI10', 'LACCESS_CHILD10',\n",
       "       'PCT_LACCESS_CHILD10', 'LACCESS_SENIORS10', 'PCT_LACCESS_SENIORS10',\n",
       "       'LACCESS_HHNV10', 'PCT_LACCESS_HHNV10'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_atlas_access_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2a1e3f31-f465-4a3e-923c-7939dc908d28",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hn/d_xtpgtj16x0lv7mgcy_jfqh0000gn/T/ipykernel_1843/628899762.py:10: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  food_atlas_df = food_atlas_df.replace('<Null>', 0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "FIPS                        int64\n",
       "State                      object\n",
       "County                     object\n",
       "LACCESS_POP10             float64\n",
       "PCT_LACCESS_POP10         float64\n",
       "LACCESS_LOWI10            float64\n",
       "PCT_LACCESS_LOWI10        float64\n",
       "LACCESS_CHILD10           float64\n",
       "PCT_LACCESS_CHILD10       float64\n",
       "LACCESS_SENIORS10         float64\n",
       "PCT_LACCESS_SENIORS10     float64\n",
       "LACCESS_HHNV10            float64\n",
       "PCT_LACCESS_HHNV10        float64\n",
       "PCT_NHWHITE10             float64\n",
       "PCT_NHBLACK10             float64\n",
       "PCT_HISP10                float64\n",
       "PCT_NHASIAN10             float64\n",
       "PCT_NHNA10                float64\n",
       "PCT_NHPI10                float64\n",
       "PCT_65OLDER10             float64\n",
       "PCT_18YOUNGER10           float64\n",
       "MEDHHINC10                  int64\n",
       "POVRATE10                 float64\n",
       "PERPOV10                    int64\n",
       "CHILDPOVRATE10            float64\n",
       "PERCHLDPOV10                int64\n",
       "METRO13                     int64\n",
       "POPLOSS00                   int64\n",
       "2010 Census population      int64\n",
       "LACCESS_POP10_FLAG          int64\n",
       "LACCESS_LOWI10_FLAG         int64\n",
       "LACCESS_CHILD10_FLAG        int64\n",
       "LACCESS_SENIORS10_FLAG      int64\n",
       "LACCESS_HHNV10_FLAG         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FOOD_DESERT_ACCCESS_THRESHOLD=30\n",
    "# Add a LACCESS_POP10_FLAG column and set to 1 if PCT_LACCESS_POP10 > 0.3\n",
    "food_atlas_df['LACCESS_POP10_FLAG'] = food_atlas_df['PCT_LACCESS_POP10'].apply(lambda x: 1 if x > FOOD_DESERT_ACCCESS_THRESHOLD else 0)\n",
    "food_atlas_df['LACCESS_LOWI10_FLAG'] = food_atlas_df['PCT_LACCESS_LOWI10'].apply(lambda x: 1 if x > FOOD_DESERT_ACCCESS_THRESHOLD else 0)\n",
    "food_atlas_df['LACCESS_CHILD10_FLAG'] = food_atlas_df['PCT_LACCESS_CHILD10'].apply(lambda x: 1 if x > FOOD_DESERT_ACCCESS_THRESHOLD else 0)\n",
    "food_atlas_df['LACCESS_SENIORS10_FLAG'] = food_atlas_df['PCT_LACCESS_SENIORS10'].apply(lambda x: 1 if x > FOOD_DESERT_ACCCESS_THRESHOLD else 0)\n",
    "food_atlas_df['LACCESS_HHNV10_FLAG'] = food_atlas_df['PCT_LACCESS_HHNV10'].apply(lambda x: 1 if x > FOOD_DESERT_ACCCESS_THRESHOLD else 0)\n",
    "\n",
    "# Replace all '<Null>' with 0\n",
    "food_atlas_df = food_atlas_df.replace('<Null>', 0)\n",
    "\n",
    "# Show all the column types\n",
    "food_atlas_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "32ddf002-3539-4e48-a441-9ad63417cc20",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load crime data\n",
    "crime_df = pd.read_csv('crime_data_w_population_and_crime_rate.csv')\n",
    "\n",
    "if 'index' in crime_df.columns:\n",
    "    crime_df = crime_df.drop(['index', 'EDITION', 'PART', 'population', 'FIPS_ST', 'FIPS_CTY', 'IDNO', 'COVIND', 'INDEX', 'MODINDX'], axis=1)\n",
    "\n",
    "if 'county_name' in crime_df.columns:\n",
    "    # Split county and state from county_name\n",
    "    crime_df['County'] = crime_df['county_name'].apply(lambda x: x.split(',')[0])\n",
    "    crime_df['State'] = crime_df['county_name'].apply(lambda x: x.split(',')[1])\n",
    "\n",
    "    # Drop the word 'County' from the county name\n",
    "    crime_df['County'] = crime_df['County'].apply(lambda x: x.replace(' County', ''))\n",
    "    crime_df['County'] = crime_df['County'].apply(lambda x: x.replace(' city', ''))\n",
    "\n",
    "    # Strip leading and trailing spaces\n",
    "    crime_df['County'] = crime_df['County'].apply(lambda x: x.strip())\n",
    "    crime_df['State'] = crime_df['State'].apply(lambda x: x.strip())\n",
    "\n",
    "    crime_df = crime_df.drop(['county_name'], axis=1)\n",
    "\n",
    "food_atlas_crime_df = pd.merge(food_atlas_df, crime_df, on=['State', 'County'])\n",
    "food_atlas_crime_df = food_atlas_crime_df.dropna()\n",
    "food_atlas_crime_df.to_excel('foodATLAS.xlsx',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "db92d4dc-de42-4482-beeb-de462dbc8140",
   "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>FIPS</th>\n",
       "      <th>State</th>\n",
       "      <th>County</th>\n",
       "      <th>LACCESS_POP10</th>\n",
       "      <th>PCT_LACCESS_POP10</th>\n",
       "      <th>LACCESS_LOWI10</th>\n",
       "      <th>PCT_LACCESS_LOWI10</th>\n",
       "      <th>LACCESS_CHILD10</th>\n",
       "      <th>PCT_LACCESS_CHILD10</th>\n",
       "      <th>LACCESS_SENIORS10</th>\n",
       "      <th>...</th>\n",
       "      <th>AG_ARRST</th>\n",
       "      <th>AG_OFF</th>\n",
       "      <th>MURDER</th>\n",
       "      <th>RAPE</th>\n",
       "      <th>ROBBERY</th>\n",
       "      <th>AGASSLT</th>\n",
       "      <th>BURGLRY</th>\n",
       "      <th>LARCENY</th>\n",
       "      <th>MVTHEFT</th>\n",
       "      <th>ARSON</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>AL</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>18428.439685</td>\n",
       "      <td>33.769657</td>\n",
       "      <td>5344.427472</td>\n",
       "      <td>9.793530</td>\n",
       "      <td>4822.500269</td>\n",
       "      <td>8.837112</td>\n",
       "      <td>2388.233228</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>34</td>\n",
       "      <td>87</td>\n",
       "      <td>447</td>\n",
       "      <td>1233</td>\n",
       "      <td>85</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1003</td>\n",
       "      <td>AL</td>\n",
       "      <td>Baldwin</td>\n",
       "      <td>35210.814078</td>\n",
       "      <td>19.318473</td>\n",
       "      <td>9952.144027</td>\n",
       "      <td>5.460261</td>\n",
       "      <td>7916.131932</td>\n",
       "      <td>4.343199</td>\n",
       "      <td>6404.008603</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>76</td>\n",
       "      <td>332</td>\n",
       "      <td>967</td>\n",
       "      <td>3829</td>\n",
       "      <td>192</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1005</td>\n",
       "      <td>AL</td>\n",
       "      <td>Barbour</td>\n",
       "      <td>5722.305602</td>\n",
       "      <td>20.840972</td>\n",
       "      <td>3135.676086</td>\n",
       "      <td>11.420316</td>\n",
       "      <td>940.419327</td>\n",
       "      <td>3.425062</td>\n",
       "      <td>770.214550</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>36</td>\n",
       "      <td>90</td>\n",
       "      <td>362</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1007</td>\n",
       "      <td>AL</td>\n",
       "      <td>Bibb</td>\n",
       "      <td>1044.867327</td>\n",
       "      <td>4.559753</td>\n",
       "      <td>491.449066</td>\n",
       "      <td>2.144661</td>\n",
       "      <td>249.204753</td>\n",
       "      <td>1.087518</td>\n",
       "      <td>150.553323</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>36</td>\n",
       "      <td>122</td>\n",
       "      <td>251</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1009</td>\n",
       "      <td>AL</td>\n",
       "      <td>Blount</td>\n",
       "      <td>1548.175559</td>\n",
       "      <td>2.700840</td>\n",
       "      <td>609.027708</td>\n",
       "      <td>1.062468</td>\n",
       "      <td>384.911607</td>\n",
       "      <td>0.671490</td>\n",
       "      <td>195.049051</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>101</td>\n",
       "      <td>397</td>\n",
       "      <td>865</td>\n",
       "      <td>86</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1011</td>\n",
       "      <td>AL</td>\n",
       "      <td>Bullock</td>\n",
       "      <td>4089.983548</td>\n",
       "      <td>37.474652</td>\n",
       "      <td>2199.359828</td>\n",
       "      <td>20.151730</td>\n",
       "      <td>607.097868</td>\n",
       "      <td>5.562561</td>\n",
       "      <td>554.388021</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>43</td>\n",
       "      <td>98</td>\n",
       "      <td>55</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1013</td>\n",
       "      <td>AL</td>\n",
       "      <td>Butler</td>\n",
       "      <td>1307.819706</td>\n",
       "      <td>6.243470</td>\n",
       "      <td>593.225139</td>\n",
       "      <td>2.832029</td>\n",
       "      <td>264.826077</td>\n",
       "      <td>1.264267</td>\n",
       "      <td>254.954428</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>87</td>\n",
       "      <td>182</td>\n",
       "      <td>408</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1015</td>\n",
       "      <td>AL</td>\n",
       "      <td>Calhoun</td>\n",
       "      <td>30901.150585</td>\n",
       "      <td>26.061086</td>\n",
       "      <td>11909.162858</td>\n",
       "      <td>10.043824</td>\n",
       "      <td>6959.887106</td>\n",
       "      <td>5.869756</td>\n",
       "      <td>4974.957133</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>131</td>\n",
       "      <td>504</td>\n",
       "      <td>1710</td>\n",
       "      <td>3482</td>\n",
       "      <td>147</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1017</td>\n",
       "      <td>AL</td>\n",
       "      <td>Chambers</td>\n",
       "      <td>6748.213223</td>\n",
       "      <td>19.722967</td>\n",
       "      <td>3018.284747</td>\n",
       "      <td>8.821525</td>\n",
       "      <td>1531.676677</td>\n",
       "      <td>4.476623</td>\n",
       "      <td>1170.805467</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>160</td>\n",
       "      <td>374</td>\n",
       "      <td>875</td>\n",
       "      <td>96</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1019</td>\n",
       "      <td>AL</td>\n",
       "      <td>Cherokee</td>\n",
       "      <td>79.410235</td>\n",
       "      <td>0.305553</td>\n",
       "      <td>34.483862</td>\n",
       "      <td>0.132686</td>\n",
       "      <td>17.326047</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>9.222340</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>102</td>\n",
       "      <td>329</td>\n",
       "      <td>610</td>\n",
       "      <td>75</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   FIPS State    County  LACCESS_POP10  PCT_LACCESS_POP10  LACCESS_LOWI10  \\\n",
       "0  1001    AL   Autauga   18428.439685          33.769657     5344.427472   \n",
       "1  1003    AL   Baldwin   35210.814078          19.318473     9952.144027   \n",
       "2  1005    AL   Barbour    5722.305602          20.840972     3135.676086   \n",
       "3  1007    AL      Bibb    1044.867327           4.559753      491.449066   \n",
       "4  1009    AL    Blount    1548.175559           2.700840      609.027708   \n",
       "5  1011    AL   Bullock    4089.983548          37.474652     2199.359828   \n",
       "6  1013    AL    Butler    1307.819706           6.243470      593.225139   \n",
       "7  1015    AL   Calhoun   30901.150585          26.061086    11909.162858   \n",
       "8  1017    AL  Chambers    6748.213223          19.722967     3018.284747   \n",
       "9  1019    AL  Cherokee      79.410235           0.305553       34.483862   \n",
       "\n",
       "   PCT_LACCESS_LOWI10  LACCESS_CHILD10  PCT_LACCESS_CHILD10  \\\n",
       "0            9.793530      4822.500269             8.837112   \n",
       "1            5.460261      7916.131932             4.343199   \n",
       "2           11.420316       940.419327             3.425062   \n",
       "3            2.144661       249.204753             1.087518   \n",
       "4            1.062468       384.911607             0.671490   \n",
       "5           20.151730       607.097868             5.562561   \n",
       "6            2.832029       264.826077             1.264267   \n",
       "7           10.043824      6959.887106             5.869756   \n",
       "8            8.821525      1531.676677             4.476623   \n",
       "9            0.132686        17.326047             0.066667   \n",
       "\n",
       "   LACCESS_SENIORS10  ...  AG_ARRST  AG_OFF  MURDER  RAPE  ROBBERY  AGASSLT  \\\n",
       "0        2388.233228  ...         3       3       3    15       34       87   \n",
       "1        6404.008603  ...        14      14       8    30       76      332   \n",
       "2         770.214550  ...         6       4       0     4        8       36   \n",
       "3         150.553323  ...         5       6       1     4        8       36   \n",
       "4         195.049051  ...         6       6       1    11        9      101   \n",
       "5         554.388021  ...         2       2       0     2       12       43   \n",
       "6         254.954428  ...         4       4       6     5       11       87   \n",
       "7        4974.957133  ...         8      10       6    37      131      504   \n",
       "8        1170.805467  ...         4       4       1     7       24      160   \n",
       "9           9.222340  ...         4       5       1     4        2      102   \n",
       "\n",
       "   BURGLRY  LARCENY  MVTHEFT  ARSON  \n",
       "0      447     1233       85    108  \n",
       "1      967     3829      192     31  \n",
       "2       90      362       21      0  \n",
       "3      122      251       27      0  \n",
       "4      397      865       86      9  \n",
       "5       98       55        3     23  \n",
       "6      182      408       30      3  \n",
       "7     1710     3482      147     34  \n",
       "8      374      875       96      5  \n",
       "9      329      610       75     12  \n",
       "\n",
       "[10 rows x 47 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_atlas_crime_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e9358bfa-f9a8-431b-b7bb-cffcb6b04939",
   "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>PCT_NHWHITE10</th>\n",
       "      <th>PCT_NHBLACK10</th>\n",
       "      <th>PCT_HISP10</th>\n",
       "      <th>PCT_NHASIAN10</th>\n",
       "      <th>PCT_NHNA10</th>\n",
       "      <th>PCT_NHPI10</th>\n",
       "      <th>PCT_65OLDER10</th>\n",
       "      <th>PCT_18YOUNGER10</th>\n",
       "      <th>MEDHHINC10</th>\n",
       "      <th>POVRATE10</th>\n",
       "      <th>...</th>\n",
       "      <th>AG_ARRST</th>\n",
       "      <th>AG_OFF</th>\n",
       "      <th>MURDER</th>\n",
       "      <th>RAPE</th>\n",
       "      <th>ROBBERY</th>\n",
       "      <th>AGASSLT</th>\n",
       "      <th>BURGLRY</th>\n",
       "      <th>LARCENY</th>\n",
       "      <th>MVTHEFT</th>\n",
       "      <th>ARSON</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.677225</td>\n",
       "      <td>-0.432612</td>\n",
       "      <td>-0.537841</td>\n",
       "      <td>0.669964</td>\n",
       "      <td>-0.212250</td>\n",
       "      <td>-0.049559</td>\n",
       "      <td>-1.578648</td>\n",
       "      <td>1.162121</td>\n",
       "      <td>1.849144</td>\n",
       "      <td>-1.363323</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.399399</td>\n",
       "      <td>-0.064163</td>\n",
       "      <td>-0.201644</td>\n",
       "      <td>-0.259093</td>\n",
       "      <td>-0.169771</td>\n",
       "      <td>-0.250661</td>\n",
       "      <td>-0.267100</td>\n",
       "      <td>-0.272088</td>\n",
       "      <td>-0.177352</td>\n",
       "      <td>-0.221274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.843154</td>\n",
       "      <td>1.876114</td>\n",
       "      <td>-0.527057</td>\n",
       "      <td>-0.354801</td>\n",
       "      <td>-0.224933</td>\n",
       "      <td>0.047816</td>\n",
       "      <td>-0.484615</td>\n",
       "      <td>0.898097</td>\n",
       "      <td>-0.766983</td>\n",
       "      <td>0.838819</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.399399</td>\n",
       "      <td>-0.323867</td>\n",
       "      <td>-0.201644</td>\n",
       "      <td>-0.321397</td>\n",
       "      <td>-0.163866</td>\n",
       "      <td>-0.220035</td>\n",
       "      <td>-0.237142</td>\n",
       "      <td>-0.270555</td>\n",
       "      <td>-0.166334</td>\n",
       "      <td>-0.221274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.202141</td>\n",
       "      <td>0.065442</td>\n",
       "      <td>-0.183201</td>\n",
       "      <td>-0.199462</td>\n",
       "      <td>-0.196312</td>\n",
       "      <td>-0.050209</td>\n",
       "      <td>6.598595</td>\n",
       "      <td>-4.264994</td>\n",
       "      <td>0.184185</td>\n",
       "      <td>-0.591770</td>\n",
       "      <td>...</td>\n",
       "      <td>1.172778</td>\n",
       "      <td>0.065689</td>\n",
       "      <td>-0.201644</td>\n",
       "      <td>-0.134484</td>\n",
       "      <td>-0.146150</td>\n",
       "      <td>-0.131325</td>\n",
       "      <td>-0.154317</td>\n",
       "      <td>-0.214904</td>\n",
       "      <td>-0.149019</td>\n",
       "      <td>-0.235693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.908423</td>\n",
       "      <td>-0.499341</td>\n",
       "      <td>-0.597185</td>\n",
       "      <td>-0.402427</td>\n",
       "      <td>-0.199686</td>\n",
       "      <td>-0.068820</td>\n",
       "      <td>0.048470</td>\n",
       "      <td>-0.876622</td>\n",
       "      <td>-0.803751</td>\n",
       "      <td>0.629856</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.036589</td>\n",
       "      <td>-0.323867</td>\n",
       "      <td>-0.201644</td>\n",
       "      <td>-0.333858</td>\n",
       "      <td>-0.171247</td>\n",
       "      <td>-0.248549</td>\n",
       "      <td>-0.290449</td>\n",
       "      <td>-0.296923</td>\n",
       "      <td>-0.183648</td>\n",
       "      <td>-0.235693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.801160</td>\n",
       "      <td>-0.574923</td>\n",
       "      <td>-0.545890</td>\n",
       "      <td>-0.351549</td>\n",
       "      <td>0.148969</td>\n",
       "      <td>-0.046733</td>\n",
       "      <td>0.743951</td>\n",
       "      <td>-0.712347</td>\n",
       "      <td>-0.268454</td>\n",
       "      <td>-0.463178</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.278462</td>\n",
       "      <td>-0.323867</td>\n",
       "      <td>-0.201644</td>\n",
       "      <td>-0.159405</td>\n",
       "      <td>-0.168294</td>\n",
       "      <td>-0.228484</td>\n",
       "      <td>-0.226128</td>\n",
       "      <td>-0.242500</td>\n",
       "      <td>-0.174991</td>\n",
       "      <td>-0.235693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.288164</td>\n",
       "      <td>-0.594907</td>\n",
       "      <td>1.019384</td>\n",
       "      <td>-0.474422</td>\n",
       "      <td>0.500456</td>\n",
       "      <td>-0.081581</td>\n",
       "      <td>-0.329679</td>\n",
       "      <td>-3.707152</td>\n",
       "      <td>0.647052</td>\n",
       "      <td>-0.350659</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.641272</td>\n",
       "      <td>-0.583570</td>\n",
       "      <td>-0.201644</td>\n",
       "      <td>-0.333858</td>\n",
       "      <td>-0.171247</td>\n",
       "      <td>-0.256998</td>\n",
       "      <td>-0.295736</td>\n",
       "      <td>-0.300296</td>\n",
       "      <td>-0.184435</td>\n",
       "      <td>-0.250111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.937568</td>\n",
       "      <td>-0.589189</td>\n",
       "      <td>-0.559836</td>\n",
       "      <td>-0.389040</td>\n",
       "      <td>-0.188729</td>\n",
       "      <td>-0.081581</td>\n",
       "      <td>1.041427</td>\n",
       "      <td>-0.521881</td>\n",
       "      <td>1.193960</td>\n",
       "      <td>-1.347249</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.520336</td>\n",
       "      <td>-0.453718</td>\n",
       "      <td>-0.201644</td>\n",
       "      <td>-0.333858</td>\n",
       "      <td>-0.171247</td>\n",
       "      <td>-0.255942</td>\n",
       "      <td>-0.294855</td>\n",
       "      <td>-0.297843</td>\n",
       "      <td>-0.184435</td>\n",
       "      <td>-0.250111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.925872</td>\n",
       "      <td>-0.569766</td>\n",
       "      <td>-0.533363</td>\n",
       "      <td>-0.344088</td>\n",
       "      <td>-0.217183</td>\n",
       "      <td>-0.056972</td>\n",
       "      <td>0.159981</td>\n",
       "      <td>0.926771</td>\n",
       "      <td>0.174417</td>\n",
       "      <td>-0.318511</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.036589</td>\n",
       "      <td>-0.323867</td>\n",
       "      <td>-0.201644</td>\n",
       "      <td>-0.271554</td>\n",
       "      <td>-0.171247</td>\n",
       "      <td>-0.246437</td>\n",
       "      <td>-0.269743</td>\n",
       "      <td>-0.269941</td>\n",
       "      <td>-0.176565</td>\n",
       "      <td>-0.235693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.794191</td>\n",
       "      <td>-0.447572</td>\n",
       "      <td>-0.521570</td>\n",
       "      <td>-0.237471</td>\n",
       "      <td>-0.207463</td>\n",
       "      <td>-0.062892</td>\n",
       "      <td>0.633064</td>\n",
       "      <td>-0.829349</td>\n",
       "      <td>-0.629312</td>\n",
       "      <td>0.211932</td>\n",
       "      <td>...</td>\n",
       "      <td>0.205284</td>\n",
       "      <td>0.455244</td>\n",
       "      <td>-0.075159</td>\n",
       "      <td>0.363953</td>\n",
       "      <td>-0.067907</td>\n",
       "      <td>0.563571</td>\n",
       "      <td>0.178746</td>\n",
       "      <td>0.332400</td>\n",
       "      <td>0.050885</td>\n",
       "      <td>0.283371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.711691</td>\n",
       "      <td>1.701511</td>\n",
       "      <td>-0.520366</td>\n",
       "      <td>-0.394782</td>\n",
       "      <td>-0.222187</td>\n",
       "      <td>-0.081581</td>\n",
       "      <td>-0.566146</td>\n",
       "      <td>0.453928</td>\n",
       "      <td>-1.138715</td>\n",
       "      <td>2.076519</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.520336</td>\n",
       "      <td>-0.583570</td>\n",
       "      <td>-0.201644</td>\n",
       "      <td>-0.333858</td>\n",
       "      <td>-0.171247</td>\n",
       "      <td>-0.252773</td>\n",
       "      <td>-0.284281</td>\n",
       "      <td>-0.292937</td>\n",
       "      <td>-0.184435</td>\n",
       "      <td>-0.250111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-1.474233</td>\n",
       "      <td>-0.574878</td>\n",
       "      <td>2.918871</td>\n",
       "      <td>-0.327180</td>\n",
       "      <td>-0.100004</td>\n",
       "      <td>-0.035907</td>\n",
       "      <td>0.977398</td>\n",
       "      <td>-0.812810</td>\n",
       "      <td>-0.617240</td>\n",
       "      <td>0.195858</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.157526</td>\n",
       "      <td>-0.194015</td>\n",
       "      <td>-0.201644</td>\n",
       "      <td>-0.296475</td>\n",
       "      <td>-0.166818</td>\n",
       "      <td>-0.243269</td>\n",
       "      <td>-0.221722</td>\n",
       "      <td>-0.277147</td>\n",
       "      <td>-0.172630</td>\n",
       "      <td>-0.192437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-2.359600</td>\n",
       "      <td>-0.507120</td>\n",
       "      <td>3.930419</td>\n",
       "      <td>0.880469</td>\n",
       "      <td>-0.129618</td>\n",
       "      <td>0.006004</td>\n",
       "      <td>-1.579324</td>\n",
       "      <td>2.762380</td>\n",
       "      <td>-0.072728</td>\n",
       "      <td>1.272818</td>\n",
       "      <td>...</td>\n",
       "      <td>0.930905</td>\n",
       "      <td>0.844799</td>\n",
       "      <td>1.189691</td>\n",
       "      <td>1.099147</td>\n",
       "      <td>0.436986</td>\n",
       "      <td>1.369355</td>\n",
       "      <td>1.626863</td>\n",
       "      <td>1.108898</td>\n",
       "      <td>1.259756</td>\n",
       "      <td>0.312208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.185675</td>\n",
       "      <td>0.074949</td>\n",
       "      <td>-0.343296</td>\n",
       "      <td>0.441983</td>\n",
       "      <td>-0.227018</td>\n",
       "      <td>-0.022126</td>\n",
       "      <td>-0.170680</td>\n",
       "      <td>-3.957466</td>\n",
       "      <td>-0.172158</td>\n",
       "      <td>0.758449</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.520336</td>\n",
       "      <td>-0.453718</td>\n",
       "      <td>-0.201644</td>\n",
       "      <td>-0.333858</td>\n",
       "      <td>-0.171247</td>\n",
       "      <td>-0.255942</td>\n",
       "      <td>-0.279435</td>\n",
       "      <td>-0.292477</td>\n",
       "      <td>-0.184435</td>\n",
       "      <td>-0.250111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.811579</td>\n",
       "      <td>-0.563774</td>\n",
       "      <td>-0.368810</td>\n",
       "      <td>-0.299623</td>\n",
       "      <td>-0.230901</td>\n",
       "      <td>-0.072352</td>\n",
       "      <td>0.746218</td>\n",
       "      <td>0.022070</td>\n",
       "      <td>0.463398</td>\n",
       "      <td>-1.170435</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.520336</td>\n",
       "      <td>-0.453718</td>\n",
       "      <td>-0.201644</td>\n",
       "      <td>-0.333858</td>\n",
       "      <td>-0.171247</td>\n",
       "      <td>-0.250661</td>\n",
       "      <td>-0.288246</td>\n",
       "      <td>-0.298303</td>\n",
       "      <td>-0.184435</td>\n",
       "      <td>-0.250111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-1.887956</td>\n",
       "      <td>-0.584643</td>\n",
       "      <td>3.579315</td>\n",
       "      <td>-0.382463</td>\n",
       "      <td>-0.157441</td>\n",
       "      <td>-0.030868</td>\n",
       "      <td>-0.195982</td>\n",
       "      <td>1.475721</td>\n",
       "      <td>-1.005558</td>\n",
       "      <td>0.742375</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.641272</td>\n",
       "      <td>-0.583570</td>\n",
       "      <td>-0.201644</td>\n",
       "      <td>-0.333858</td>\n",
       "      <td>-0.171247</td>\n",
       "      <td>-0.253830</td>\n",
       "      <td>-0.285603</td>\n",
       "      <td>-0.297537</td>\n",
       "      <td>-0.178926</td>\n",
       "      <td>-0.206856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-0.216142</td>\n",
       "      <td>0.195978</td>\n",
       "      <td>0.022133</td>\n",
       "      <td>0.959967</td>\n",
       "      <td>-0.215749</td>\n",
       "      <td>-0.039331</td>\n",
       "      <td>-1.607844</td>\n",
       "      <td>1.459286</td>\n",
       "      <td>0.850058</td>\n",
       "      <td>-0.752510</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.278462</td>\n",
       "      <td>-0.194015</td>\n",
       "      <td>-0.201644</td>\n",
       "      <td>-0.196788</td>\n",
       "      <td>-0.138769</td>\n",
       "      <td>0.055600</td>\n",
       "      <td>-0.115547</td>\n",
       "      <td>-0.124453</td>\n",
       "      <td>-0.110455</td>\n",
       "      <td>-0.149182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.828741</td>\n",
       "      <td>-0.594907</td>\n",
       "      <td>-0.349335</td>\n",
       "      <td>-0.474422</td>\n",
       "      <td>-0.177978</td>\n",
       "      <td>-0.081581</td>\n",
       "      <td>0.893529</td>\n",
       "      <td>-0.312577</td>\n",
       "      <td>-0.554302</td>\n",
       "      <td>-0.559622</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.520336</td>\n",
       "      <td>-0.583570</td>\n",
       "      <td>-0.201644</td>\n",
       "      <td>-0.333858</td>\n",
       "      <td>-0.171247</td>\n",
       "      <td>-0.256998</td>\n",
       "      <td>-0.295736</td>\n",
       "      <td>-0.300449</td>\n",
       "      <td>-0.184435</td>\n",
       "      <td>-0.250111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.599112</td>\n",
       "      <td>-0.563455</td>\n",
       "      <td>-0.215826</td>\n",
       "      <td>-0.194435</td>\n",
       "      <td>-0.135161</td>\n",
       "      <td>0.008073</td>\n",
       "      <td>-0.736373</td>\n",
       "      <td>-0.865534</td>\n",
       "      <td>1.548091</td>\n",
       "      <td>-1.282953</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.399399</td>\n",
       "      <td>-0.194015</td>\n",
       "      <td>-0.201644</td>\n",
       "      <td>-0.271554</td>\n",
       "      <td>-0.171247</td>\n",
       "      <td>-0.211587</td>\n",
       "      <td>-0.272827</td>\n",
       "      <td>-0.273161</td>\n",
       "      <td>-0.174991</td>\n",
       "      <td>-0.206856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.770478</td>\n",
       "      <td>-0.462314</td>\n",
       "      <td>-0.532303</td>\n",
       "      <td>-0.115132</td>\n",
       "      <td>-0.220884</td>\n",
       "      <td>0.020076</td>\n",
       "      <td>-0.754186</td>\n",
       "      <td>1.183850</td>\n",
       "      <td>0.266843</td>\n",
       "      <td>-0.720362</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.036589</td>\n",
       "      <td>-0.194015</td>\n",
       "      <td>-0.201644</td>\n",
       "      <td>-0.209249</td>\n",
       "      <td>-0.144674</td>\n",
       "      <td>-0.208418</td>\n",
       "      <td>-0.136254</td>\n",
       "      <td>-0.129053</td>\n",
       "      <td>-0.154528</td>\n",
       "      <td>-0.048253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.529320</td>\n",
       "      <td>0.120486</td>\n",
       "      <td>0.618292</td>\n",
       "      <td>0.355037</td>\n",
       "      <td>-0.118150</td>\n",
       "      <td>-0.013923</td>\n",
       "      <td>-0.708446</td>\n",
       "      <td>-0.071411</td>\n",
       "      <td>-0.356365</td>\n",
       "      <td>0.067265</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.036589</td>\n",
       "      <td>0.065689</td>\n",
       "      <td>0.051326</td>\n",
       "      <td>0.239344</td>\n",
       "      <td>0.056102</td>\n",
       "      <td>0.125301</td>\n",
       "      <td>0.338228</td>\n",
       "      <td>0.308484</td>\n",
       "      <td>0.023340</td>\n",
       "      <td>-0.091508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-3.535203</td>\n",
       "      <td>-0.583152</td>\n",
       "      <td>-0.455207</td>\n",
       "      <td>-0.395435</td>\n",
       "      <td>12.935551</td>\n",
       "      <td>-0.081581</td>\n",
       "      <td>-2.369509</td>\n",
       "      <td>5.016671</td>\n",
       "      <td>-1.545647</td>\n",
       "      <td>5.210955</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.641272</td>\n",
       "      <td>-0.583570</td>\n",
       "      <td>-0.201644</td>\n",
       "      <td>-0.333858</td>\n",
       "      <td>-0.171247</td>\n",
       "      <td>-0.256998</td>\n",
       "      <td>-0.295736</td>\n",
       "      <td>-0.300603</td>\n",
       "      <td>-0.184435</td>\n",
       "      <td>-0.250111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-1.587452</td>\n",
       "      <td>2.895460</td>\n",
       "      <td>-0.516045</td>\n",
       "      <td>-0.332651</td>\n",
       "      <td>-0.197204</td>\n",
       "      <td>-0.062564</td>\n",
       "      <td>0.064524</td>\n",
       "      <td>0.894854</td>\n",
       "      <td>-1.160554</td>\n",
       "      <td>1.755038</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.399399</td>\n",
       "      <td>-0.323867</td>\n",
       "      <td>-0.159482</td>\n",
       "      <td>-0.296475</td>\n",
       "      <td>-0.159437</td>\n",
       "      <td>-0.198914</td>\n",
       "      <td>-0.249477</td>\n",
       "      <td>-0.270555</td>\n",
       "      <td>-0.179713</td>\n",
       "      <td>-0.250111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.867964</td>\n",
       "      <td>-0.417464</td>\n",
       "      <td>-0.606249</td>\n",
       "      <td>-0.416820</td>\n",
       "      <td>-0.200575</td>\n",
       "      <td>-0.060140</td>\n",
       "      <td>1.149315</td>\n",
       "      <td>-1.276245</td>\n",
       "      <td>-0.474869</td>\n",
       "      <td>0.099413</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.520336</td>\n",
       "      <td>-0.583570</td>\n",
       "      <td>-0.201644</td>\n",
       "      <td>-0.333858</td>\n",
       "      <td>-0.171247</td>\n",
       "      <td>-0.256998</td>\n",
       "      <td>-0.295736</td>\n",
       "      <td>-0.300603</td>\n",
       "      <td>-0.184435</td>\n",
       "      <td>-0.250111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.377788</td>\n",
       "      <td>0.063281</td>\n",
       "      <td>-0.491786</td>\n",
       "      <td>-0.204218</td>\n",
       "      <td>-0.212640</td>\n",
       "      <td>-0.057418</td>\n",
       "      <td>-0.419604</td>\n",
       "      <td>0.379683</td>\n",
       "      <td>-0.208372</td>\n",
       "      <td>-0.077401</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.399399</td>\n",
       "      <td>-0.453718</td>\n",
       "      <td>-0.159482</td>\n",
       "      <td>-0.196788</td>\n",
       "      <td>-0.153532</td>\n",
       "      <td>-0.242213</td>\n",
       "      <td>-0.230093</td>\n",
       "      <td>-0.252771</td>\n",
       "      <td>-0.168695</td>\n",
       "      <td>-0.221274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.836562</td>\n",
       "      <td>-0.450533</td>\n",
       "      <td>-0.521988</td>\n",
       "      <td>-0.393505</td>\n",
       "      <td>-0.217583</td>\n",
       "      <td>-0.064369</td>\n",
       "      <td>0.407557</td>\n",
       "      <td>-1.476957</td>\n",
       "      <td>-1.285232</td>\n",
       "      <td>1.562150</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.520336</td>\n",
       "      <td>-0.453718</td>\n",
       "      <td>-0.117321</td>\n",
       "      <td>-0.271554</td>\n",
       "      <td>-0.168294</td>\n",
       "      <td>-0.172512</td>\n",
       "      <td>-0.253442</td>\n",
       "      <td>-0.274694</td>\n",
       "      <td>-0.154528</td>\n",
       "      <td>-0.221274</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    PCT_NHWHITE10  PCT_NHBLACK10  PCT_HISP10  PCT_NHASIAN10  PCT_NHNA10  \\\n",
       "0        0.677225      -0.432612   -0.537841       0.669964   -0.212250   \n",
       "1       -0.843154       1.876114   -0.527057      -0.354801   -0.224933   \n",
       "2        0.202141       0.065442   -0.183201      -0.199462   -0.196312   \n",
       "3        0.908423      -0.499341   -0.597185      -0.402427   -0.199686   \n",
       "4        0.801160      -0.574923   -0.545890      -0.351549    0.148969   \n",
       "5       -0.288164      -0.594907    1.019384      -0.474422    0.500456   \n",
       "6        0.937568      -0.589189   -0.559836      -0.389040   -0.188729   \n",
       "7        0.925872      -0.569766   -0.533363      -0.344088   -0.217183   \n",
       "8        0.794191      -0.447572   -0.521570      -0.237471   -0.207463   \n",
       "9       -0.711691       1.701511   -0.520366      -0.394782   -0.222187   \n",
       "10      -1.474233      -0.574878    2.918871      -0.327180   -0.100004   \n",
       "11      -2.359600      -0.507120    3.930419       0.880469   -0.129618   \n",
       "12       0.185675       0.074949   -0.343296       0.441983   -0.227018   \n",
       "13       0.811579      -0.563774   -0.368810      -0.299623   -0.230901   \n",
       "14      -1.887956      -0.584643    3.579315      -0.382463   -0.157441   \n",
       "15      -0.216142       0.195978    0.022133       0.959967   -0.215749   \n",
       "16       0.828741      -0.594907   -0.349335      -0.474422   -0.177978   \n",
       "17       0.599112      -0.563455   -0.215826      -0.194435   -0.135161   \n",
       "18       0.770478      -0.462314   -0.532303      -0.115132   -0.220884   \n",
       "19      -0.529320       0.120486    0.618292       0.355037   -0.118150   \n",
       "20      -3.535203      -0.583152   -0.455207      -0.395435   12.935551   \n",
       "21      -1.587452       2.895460   -0.516045      -0.332651   -0.197204   \n",
       "22       0.867964      -0.417464   -0.606249      -0.416820   -0.200575   \n",
       "23       0.377788       0.063281   -0.491786      -0.204218   -0.212640   \n",
       "24       0.836562      -0.450533   -0.521988      -0.393505   -0.217583   \n",
       "\n",
       "    PCT_NHPI10  PCT_65OLDER10  PCT_18YOUNGER10  MEDHHINC10  POVRATE10  ...  \\\n",
       "0    -0.049559      -1.578648         1.162121    1.849144  -1.363323  ...   \n",
       "1     0.047816      -0.484615         0.898097   -0.766983   0.838819  ...   \n",
       "2    -0.050209       6.598595        -4.264994    0.184185  -0.591770  ...   \n",
       "3    -0.068820       0.048470        -0.876622   -0.803751   0.629856  ...   \n",
       "4    -0.046733       0.743951        -0.712347   -0.268454  -0.463178  ...   \n",
       "5    -0.081581      -0.329679        -3.707152    0.647052  -0.350659  ...   \n",
       "6    -0.081581       1.041427        -0.521881    1.193960  -1.347249  ...   \n",
       "7    -0.056972       0.159981         0.926771    0.174417  -0.318511  ...   \n",
       "8    -0.062892       0.633064        -0.829349   -0.629312   0.211932  ...   \n",
       "9    -0.081581      -0.566146         0.453928   -1.138715   2.076519  ...   \n",
       "10   -0.035907       0.977398        -0.812810   -0.617240   0.195858  ...   \n",
       "11    0.006004      -1.579324         2.762380   -0.072728   1.272818  ...   \n",
       "12   -0.022126      -0.170680        -3.957466   -0.172158   0.758449  ...   \n",
       "13   -0.072352       0.746218         0.022070    0.463398  -1.170435  ...   \n",
       "14   -0.030868      -0.195982         1.475721   -1.005558   0.742375  ...   \n",
       "15   -0.039331      -1.607844         1.459286    0.850058  -0.752510  ...   \n",
       "16   -0.081581       0.893529        -0.312577   -0.554302  -0.559622  ...   \n",
       "17    0.008073      -0.736373        -0.865534    1.548091  -1.282953  ...   \n",
       "18    0.020076      -0.754186         1.183850    0.266843  -0.720362  ...   \n",
       "19   -0.013923      -0.708446        -0.071411   -0.356365   0.067265  ...   \n",
       "20   -0.081581      -2.369509         5.016671   -1.545647   5.210955  ...   \n",
       "21   -0.062564       0.064524         0.894854   -1.160554   1.755038  ...   \n",
       "22   -0.060140       1.149315        -1.276245   -0.474869   0.099413  ...   \n",
       "23   -0.057418      -0.419604         0.379683   -0.208372  -0.077401  ...   \n",
       "24   -0.064369       0.407557        -1.476957   -1.285232   1.562150  ...   \n",
       "\n",
       "    AG_ARRST    AG_OFF    MURDER      RAPE   ROBBERY   AGASSLT   BURGLRY  \\\n",
       "0  -0.399399 -0.064163 -0.201644 -0.259093 -0.169771 -0.250661 -0.267100   \n",
       "1  -0.399399 -0.323867 -0.201644 -0.321397 -0.163866 -0.220035 -0.237142   \n",
       "2   1.172778  0.065689 -0.201644 -0.134484 -0.146150 -0.131325 -0.154317   \n",
       "3  -0.036589 -0.323867 -0.201644 -0.333858 -0.171247 -0.248549 -0.290449   \n",
       "4  -0.278462 -0.323867 -0.201644 -0.159405 -0.168294 -0.228484 -0.226128   \n",
       "5  -0.641272 -0.583570 -0.201644 -0.333858 -0.171247 -0.256998 -0.295736   \n",
       "6  -0.520336 -0.453718 -0.201644 -0.333858 -0.171247 -0.255942 -0.294855   \n",
       "7  -0.036589 -0.323867 -0.201644 -0.271554 -0.171247 -0.246437 -0.269743   \n",
       "8   0.205284  0.455244 -0.075159  0.363953 -0.067907  0.563571  0.178746   \n",
       "9  -0.520336 -0.583570 -0.201644 -0.333858 -0.171247 -0.252773 -0.284281   \n",
       "10 -0.157526 -0.194015 -0.201644 -0.296475 -0.166818 -0.243269 -0.221722   \n",
       "11  0.930905  0.844799  1.189691  1.099147  0.436986  1.369355  1.626863   \n",
       "12 -0.520336 -0.453718 -0.201644 -0.333858 -0.171247 -0.255942 -0.279435   \n",
       "13 -0.520336 -0.453718 -0.201644 -0.333858 -0.171247 -0.250661 -0.288246   \n",
       "14 -0.641272 -0.583570 -0.201644 -0.333858 -0.171247 -0.253830 -0.285603   \n",
       "15 -0.278462 -0.194015 -0.201644 -0.196788 -0.138769  0.055600 -0.115547   \n",
       "16 -0.520336 -0.583570 -0.201644 -0.333858 -0.171247 -0.256998 -0.295736   \n",
       "17 -0.399399 -0.194015 -0.201644 -0.271554 -0.171247 -0.211587 -0.272827   \n",
       "18 -0.036589 -0.194015 -0.201644 -0.209249 -0.144674 -0.208418 -0.136254   \n",
       "19 -0.036589  0.065689  0.051326  0.239344  0.056102  0.125301  0.338228   \n",
       "20 -0.641272 -0.583570 -0.201644 -0.333858 -0.171247 -0.256998 -0.295736   \n",
       "21 -0.399399 -0.323867 -0.159482 -0.296475 -0.159437 -0.198914 -0.249477   \n",
       "22 -0.520336 -0.583570 -0.201644 -0.333858 -0.171247 -0.256998 -0.295736   \n",
       "23 -0.399399 -0.453718 -0.159482 -0.196788 -0.153532 -0.242213 -0.230093   \n",
       "24 -0.520336 -0.453718 -0.117321 -0.271554 -0.168294 -0.172512 -0.253442   \n",
       "\n",
       "     LARCENY   MVTHEFT     ARSON  \n",
       "0  -0.272088 -0.177352 -0.221274  \n",
       "1  -0.270555 -0.166334 -0.221274  \n",
       "2  -0.214904 -0.149019 -0.235693  \n",
       "3  -0.296923 -0.183648 -0.235693  \n",
       "4  -0.242500 -0.174991 -0.235693  \n",
       "5  -0.300296 -0.184435 -0.250111  \n",
       "6  -0.297843 -0.184435 -0.250111  \n",
       "7  -0.269941 -0.176565 -0.235693  \n",
       "8   0.332400  0.050885  0.283371  \n",
       "9  -0.292937 -0.184435 -0.250111  \n",
       "10 -0.277147 -0.172630 -0.192437  \n",
       "11  1.108898  1.259756  0.312208  \n",
       "12 -0.292477 -0.184435 -0.250111  \n",
       "13 -0.298303 -0.184435 -0.250111  \n",
       "14 -0.297537 -0.178926 -0.206856  \n",
       "15 -0.124453 -0.110455 -0.149182  \n",
       "16 -0.300449 -0.184435 -0.250111  \n",
       "17 -0.273161 -0.174991 -0.206856  \n",
       "18 -0.129053 -0.154528 -0.048253  \n",
       "19  0.308484  0.023340 -0.091508  \n",
       "20 -0.300603 -0.184435 -0.250111  \n",
       "21 -0.270555 -0.179713 -0.250111  \n",
       "22 -0.300603 -0.184435 -0.250111  \n",
       "23 -0.252771 -0.168695 -0.221274  \n",
       "24 -0.274694 -0.154528 -0.221274  \n",
       "\n",
       "[25 rows x 29 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split the data into training and test data\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "X = food_atlas_crime_df\n",
    "# Scale the data\n",
    "X = X.drop(['LACCESS_POP10', 'PCT_LACCESS_POP10', 'LACCESS_POP10_FLAG', \n",
    "            'LACCESS_LOWI10', 'PCT_LACCESS_LOWI10', 'LACCESS_LOWI10_FLAG', \n",
    "            'LACCESS_CHILD10', 'PCT_LACCESS_CHILD10', 'LACCESS_CHILD10_FLAG', \n",
    "            'LACCESS_SENIORS10', 'PCT_LACCESS_SENIORS10', 'LACCESS_SENIORS10_FLAG', \n",
    "            'LACCESS_HHNV10', 'PCT_LACCESS_HHNV10', 'LACCESS_HHNV10_FLAG', \n",
    "            'FIPS', 'State', 'County', ], axis=1)\n",
    "            #'CPOPCRIM', 'CPOPARST', 'crime_rate_per_100000', 'PCT_NHBLACK10', 'PCT_HISP10'], axis=1)\n",
    "column_names = X.columns\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X)\n",
    "y = food_atlas_crime_df['LACCESS_POP10_FLAG']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "\n",
    "# Convert X_rain to data frames\n",
    "X_train_df = pd.DataFrame(X_train, columns=column_names)\n",
    "X_train_df.head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "75938ebb-8a59-4746-984b-d36d79eef6f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7699836867862969"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform logistic regression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, y_train)\n",
    "y_pred = logreg.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c0740d85-b1a4-44ff-86c9-fb102215ec70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                        Coefficient\n",
      "BURGLRY                    1.233865\n",
      "PCT_65OLDER10              0.577234\n",
      "POPLOSS00                  0.278644\n",
      "LARCENY                    0.277463\n",
      "PERPOV10                   0.192877\n",
      "RAPE                       0.162991\n",
      "POVRATE10                  0.150776\n",
      "CPOPARST                   0.131338\n",
      "AG_OFF                     0.109888\n",
      "PCT_18YOUNGER10            0.078581\n",
      "MEDHHINC10                 0.077333\n",
      "2010 Census population     0.055256\n",
      "AGASSLT                    0.039961\n",
      "METRO13                    0.006301\n",
      "crime_rate_per_100000     -0.008657\n",
      "PCT_NHASIAN10             -0.009835\n",
      "PCT_NHNA10                -0.011316\n",
      "PCT_NHPI10                -0.107692\n",
      "PERCHLDPOV10              -0.119286\n",
      "MURDER                    -0.228596\n",
      "AG_ARRST                  -0.230721\n",
      "CPOPCRIM                  -0.239472\n",
      "PCT_HISP10                -0.295119\n",
      "ARSON                     -0.325513\n",
      "CHILDPOVRATE10            -0.526663\n",
      "PCT_NHBLACK10             -0.627934\n",
      "ROBBERY                   -0.746618\n",
      "PCT_NHWHITE10             -1.119785\n",
      "MVTHEFT                   -1.453830\n",
      "                        Standardized Coefficient\n",
      "BURGLRY                                 1.233865\n",
      "PCT_65OLDER10                           0.577234\n",
      "POPLOSS00                               0.278644\n",
      "LARCENY                                 0.277463\n",
      "PERPOV10                                0.192877\n",
      "RAPE                                    0.162991\n",
      "POVRATE10                               0.150776\n",
      "CPOPARST                                0.131338\n",
      "AG_OFF                                  0.109888\n",
      "PCT_18YOUNGER10                         0.078581\n",
      "MEDHHINC10                              0.077333\n",
      "2010 Census population                  0.055256\n",
      "AGASSLT                                 0.039961\n",
      "METRO13                                 0.006301\n",
      "crime_rate_per_100000                  -0.008657\n",
      "PCT_NHASIAN10                          -0.009835\n",
      "PCT_NHNA10                             -0.011316\n",
      "PCT_NHPI10                             -0.107692\n",
      "PERCHLDPOV10                           -0.119286\n",
      "MURDER                                 -0.228596\n",
      "AG_ARRST                               -0.230721\n",
      "CPOPCRIM                               -0.239472\n",
      "PCT_HISP10                             -0.295119\n",
      "ARSON                                  -0.325513\n",
      "CHILDPOVRATE10                         -0.526663\n",
      "PCT_NHBLACK10                          -0.627934\n",
      "ROBBERY                                -0.746618\n",
      "PCT_NHWHITE10                          -1.119785\n",
      "MVTHEFT                                -1.453830\n"
     ]
    }
   ],
   "source": [
    "# Display coefficients\n",
    "coefficients = pd.DataFrame(logreg.coef_[0], column_names, columns=['Coefficient'])\n",
    "print(coefficients.sort_values(by='Coefficient', ascending=False))\n",
    "\n",
    "coefficients = pd.DataFrame(logreg.coef_[0], column_names, columns=['Standardized Coefficient'])\n",
    "print(coefficients.sort_values(by='Standardized Coefficient', ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6f3feb04-dbe7-4653-ae97-1afa96253034",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.483636\n",
      "         Iterations 8\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:     LACCESS_POP10_FLAG   No. Observations:                 2451\n",
      "Model:                          Logit   Df Residuals:                     2421\n",
      "Method:                           MLE   Df Model:                           29\n",
      "Date:                Sat, 07 Dec 2024   Pseudo R-squ.:                  0.1050\n",
      "Time:                        16:11:40   Log-Likelihood:                -1185.4\n",
      "converged:                       True   LL-Null:                       -1324.5\n",
      "Covariance Type:            nonrobust   LLR p-value:                 1.589e-42\n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -1.4612      0.067    -21.856      0.000      -1.592      -1.330\n",
      "x1            -2.9947      1.012     -2.959      0.003      -4.978      -1.011\n",
      "x2            -1.9742      0.728     -2.713      0.007      -3.401      -0.548\n",
      "x3            -1.5505      0.679     -2.283      0.022      -2.882      -0.219\n",
      "x4            -0.2836      0.176     -1.610      0.107      -0.629       0.062\n",
      "x5            -0.6733      0.361     -1.865      0.062      -1.381       0.034\n",
      "x6            -0.2354      0.096     -2.451      0.014      -0.424      -0.047\n",
      "x7             0.6085      0.093      6.572      0.000       0.427       0.790\n",
      "x8             0.0920      0.077      1.198      0.231      -0.059       0.243\n",
      "x9             0.0973      0.122      0.797      0.426      -0.142       0.337\n",
      "x10            0.2267      0.220      1.032      0.302      -0.204       0.657\n",
      "x11            0.2045      0.079      2.596      0.009       0.050       0.359\n",
      "x12           -0.5987      0.202     -2.968      0.003      -0.994      -0.203\n",
      "x13           -0.1234      0.084     -1.476      0.140      -0.287       0.040\n",
      "x14           -0.0076      0.069     -0.111      0.912      -0.143       0.127\n",
      "x15            0.2901      0.053      5.485      0.000       0.186       0.394\n",
      "x16            0.0878      0.451      0.195      0.846      -0.796       0.972\n",
      "x17           -0.0207      0.074     -0.279      0.780      -0.166       0.125\n",
      "x18            3.5429      2.637      1.344      0.179      -1.626       8.711\n",
      "x19           -3.5904      2.614     -1.373      0.170      -8.714       1.533\n",
      "x20           -0.3707      0.275     -1.348      0.178      -0.910       0.168\n",
      "x21            0.2235      0.271      0.826      0.409      -0.307       0.754\n",
      "x22           -0.1777      0.424     -0.419      0.675      -1.010       0.654\n",
      "x23            0.1483      0.219      0.677      0.498      -0.281       0.578\n",
      "x24           -0.9753      0.575     -1.697      0.090      -2.101       0.151\n",
      "x25            0.0591      0.378      0.156      0.876      -0.682       0.801\n",
      "x26            1.5934      0.449      3.548      0.000       0.713       2.474\n",
      "x27            0.2996      0.426      0.703      0.482      -0.536       1.135\n",
      "x28           -1.9542      0.557     -3.510      0.000      -3.045      -0.863\n",
      "x29           -0.4232      0.251     -1.687      0.092      -0.915       0.069\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "# Add intercept term\n",
    "X_train_sm = sm.add_constant(X_train)\n",
    "logit_model = sm.Logit(y_train, X_train_sm)\n",
    "result = logit_model.fit(maxiter=1000)\n",
    "\n",
    "# Print summary with p-values\n",
    "print(result.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ed3d2136-7395-4dde-b56d-c3810867a6a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['PCT_NHWHITE10', 'PCT_NHBLACK10', 'PCT_HISP10', 'PCT_NHASIAN10',\n",
      "       'PCT_NHNA10', 'PCT_NHPI10', 'PCT_65OLDER10', 'PCT_18YOUNGER10',\n",
      "       'MEDHHINC10', 'POVRATE10', 'PERPOV10', 'CHILDPOVRATE10', 'PERCHLDPOV10',\n",
      "       'METRO13', 'POPLOSS00', '2010 Census population',\n",
      "       'crime_rate_per_100000', 'CPOPARST', 'CPOPCRIM', 'AG_ARRST', 'AG_OFF',\n",
      "       'MURDER', 'RAPE', 'ROBBERY', 'AGASSLT', 'BURGLRY', 'LARCENY', 'MVTHEFT',\n",
      "       'ARSON'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "907c0915-f68b-4d12-97db-e3ad5a39bb38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Variable          VIF\n",
      "0            PCT_NHWHITE10   477.623822\n",
      "1            PCT_NHBLACK10   244.966642\n",
      "2               PCT_HISP10   219.510520\n",
      "3            PCT_NHASIAN10    12.955199\n",
      "4               PCT_NHNA10    64.391294\n",
      "5               PCT_NHPI10     3.691304\n",
      "6            PCT_65OLDER10     3.354323\n",
      "7          PCT_18YOUNGER10     2.165424\n",
      "8               MEDHHINC10     6.038424\n",
      "9                POVRATE10    17.924634\n",
      "10                PERPOV10     2.158179\n",
      "11          CHILDPOVRATE10    14.853912\n",
      "12            PERCHLDPOV10     2.358690\n",
      "13                 METRO13     1.666028\n",
      "14               POPLOSS00     1.234993\n",
      "15  2010 Census population    53.264274\n",
      "16   crime_rate_per_100000     1.822020\n",
      "17                CPOPARST  1422.227453\n",
      "18                CPOPCRIM  1406.104840\n",
      "19                AG_ARRST    22.735114\n",
      "20                  AG_OFF    22.521603\n",
      "21                  MURDER    11.416183\n",
      "22                    RAPE     8.476807\n",
      "23                 ROBBERY    12.590472\n",
      "24                 AGASSLT    15.618620\n",
      "25                 BURGLRY    23.726991\n",
      "26                 LARCENY    24.869699\n",
      "27                 MVTHEFT     7.647715\n",
      "28                   ARSON     4.993192\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "data = X_train_df\n",
    "# data = data.drop(['CPOPCRIM', 'CPOPARST', 'crime_rate_per_100000', 'PCT_NHBLACK10', 'PCT_HISP10' ], axis=1)\n",
    "vif = [variance_inflation_factor(data, i) for i in range(data.shape[1])]\n",
    "\n",
    "# Display VIF values\n",
    "vif_df = pd.DataFrame({'Variable': data.columns, 'VIF': vif})\n",
    "print(vif_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a243000b-96d2-446a-b7ed-8477f2a0823f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c60f4cc-2972-4636-b8e8-969db6f5b355",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6237ac24-f5ac-4af8-b96b-2f4185516d55",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "bda77222-e4a9-48aa-9c24-e254e7346827",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.74      1.00      0.85       454\n",
      "           1       0.00      0.00      0.00       159\n",
      "\n",
      "    accuracy                           0.74       613\n",
      "   macro avg       0.37      0.50      0.43       613\n",
      "weighted avg       0.55      0.74      0.63       613\n",
      "\n",
      "Accuracy: 0.74\n",
      "\n",
      "Feature Importance:\n",
      "                   Feature  Coefficient  Importance\n",
      "0            PCT_NHWHITE10     0.005512    0.005512\n",
      "1            PCT_NHBLACK10     0.003968    0.003968\n",
      "2               PCT_HISP10     0.003701    0.003701\n",
      "4               PCT_NHNA10     0.003467    0.003467\n",
      "27                 MVTHEFT    -0.000819    0.000819\n",
      "3            PCT_NHASIAN10     0.000801    0.000801\n",
      "25                 BURGLRY     0.000632    0.000632\n",
      "17                CPOPARST     0.000566    0.000566\n",
      "18                CPOPCRIM    -0.000514    0.000514\n",
      "23                 ROBBERY    -0.000483    0.000483\n",
      "5               PCT_NHPI10     0.000348    0.000348\n",
      "26                 LARCENY     0.000229    0.000229\n",
      "19                AG_ARRST     0.000105    0.000105\n",
      "11          CHILDPOVRATE10    -0.000083    0.000083\n",
      "22                    RAPE    -0.000080    0.000080\n",
      "20                  AG_OFF    -0.000071    0.000071\n",
      "21                  MURDER    -0.000066    0.000066\n",
      "13                 METRO13     0.000064    0.000064\n",
      "15  2010 Census population    -0.000042    0.000042\n",
      "9                POVRATE10     0.000037    0.000037\n",
      "24                 AGASSLT    -0.000035    0.000035\n",
      "14               POPLOSS00     0.000029    0.000029\n",
      "7          PCT_18YOUNGER10    -0.000026    0.000026\n",
      "28                   ARSON     0.000026    0.000026\n",
      "8               MEDHHINC10     0.000022    0.000022\n",
      "12            PERCHLDPOV10    -0.000020    0.000020\n",
      "6            PCT_65OLDER10     0.000011    0.000011\n",
      "10                PERPOV10     0.000009    0.000009\n",
      "16   crime_rate_per_100000    -0.000007    0.000007\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "\n",
    "# Train the SVM model\n",
    "\n",
    "svm_model = SVC(kernel='linear', random_state=42)  # Linear kernel for coefficients\n",
    "svm_model.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions\n",
    "y_pred = svm_model.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "# Get the accuracy score\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy: {accuracy:.2f}\")\n",
    "\n",
    "# Extract coefficients (feature importance)\n",
    "coefficients = svm_model.coef_.flatten()\n",
    "\n",
    "# Rank features by importance\n",
    "feature_importance = pd.DataFrame({\n",
    "    'Feature': column_names,\n",
    "    'Coefficient': coefficients,\n",
    "    'Importance': abs(coefficients)  # Absolute value for importance\n",
    "}).sort_values(by='Importance', ascending=False)\n",
    "\n",
    "print(\"\\nFeature Importance:\")\n",
    "print(feature_importance)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6f38fc5-b55f-4251-9f83-79c20f38b510",
   "metadata": {},
   "source": [
    "## County Breakdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d16cb762-2860-4976-a654-cf595525fd9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"600\"\n",
       "            src=\"Food_Desert_Map_0.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x17cb66d20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correctly define the path to the GeoJSON file\n",
    "geojson_path = 'geojson-counties-fips.json'\n",
    "from folium.features import GeoJsonTooltip\n",
    "import folium\n",
    "from IPython.display import display, IFrame\n",
    "\n",
    "# Create the map\n",
    "m = folium.Map(location=[37.8, -96.9], zoom_start=5)\n",
    "\n",
    "# Add GeoJSON to the map with LACCESS_POP10_FLAG as the color field and county names\n",
    "choropleth = folium.Choropleth(\n",
    "    geo_data=geojson_path,               # GeoJSON file path\n",
    "    name=\"choropleth\",\n",
    "    data=food_atlas_crime_df,               # Data containing LACCESS_POP10_FLAG\n",
    "    columns=[\"FIPS\", \"LACCESS_POP10_FLAG\"],  # Columns to match FIPS and value\n",
    "    key_on=\"feature.id\",                # Key in GeoJSON to match FIPS\n",
    "    fill_color=\"YlOrRd\",                # Color scale\n",
    "    fill_opacity=0.7,\n",
    "    line_opacity=0.2,\n",
    "    legend_name=\"LACCESS_POP10_FLAG\",\n",
    ").add_to(m)\n",
    "\n",
    "# Add county names to the choropleth as tooltips\n",
    "choropleth.geojson.add_child(\n",
    "    folium.features.GeoJsonTooltip(\n",
    "        fields=[\"NAME\"],  # Include county name and food desert flag\n",
    "        aliases=[\"County:\"],  # Label for each field\n",
    "        localize=True\n",
    "    )\n",
    ")\n",
    "\n",
    "# Add layer control to toggle layers\n",
    "folium.LayerControl().add_to(m)\n",
    "\n",
    "\n",
    "\n",
    "# Save and display the map\n",
    "map_file_path = \"Food_Desert_Map_0.html\"\n",
    "m.save(map_file_path)\n",
    "map_file_path\n",
    "\n",
    "# Display the map in the notebook\n",
    "display(IFrame(map_file_path, width=800, height=600))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1738a26f-fa25-41e3-bf18-052dabbb67fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"600\"\n",
       "            src=\"Food_Desert_Map_1.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x17f303110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correctly define the path to the GeoJSON file\n",
    "geojson_path = 'geojson-counties-fips.json'\n",
    "from folium.features import GeoJsonTooltip\n",
    "import folium\n",
    "from IPython.display import display, IFrame\n",
    "\n",
    "# Create the map\n",
    "m = folium.Map(location=[37.8, -96.9], zoom_start=5)\n",
    "\n",
    "# Add GeoJSON to the map with LACCESS_POP10_FLAG as the color field and county names\n",
    "choropleth = folium.Choropleth(\n",
    "    geo_data=geojson_path,               # GeoJSON file path\n",
    "    name=\"choropleth\",\n",
    "    data=food_atlas_crime_df,               # Data containing LACCESS_POP10_FLAG\n",
    "    columns=[\"FIPS\", \"PCT_LACCESS_POP10\"],  # Columns to match FIPS and value\n",
    "    key_on=\"feature.id\",                # Key in GeoJSON to match FIPS\n",
    "    fill_color=\"YlOrRd\",                # Color scale\n",
    "    fill_opacity=0.7,\n",
    "    line_opacity=0.2,\n",
    "    legend_name=\"PCT_LACCESS_POP10\",\n",
    ").add_to(m)\n",
    "\n",
    "# Add county names to the choropleth as tooltips\n",
    "choropleth.geojson.add_child(\n",
    "    folium.features.GeoJsonTooltip(\n",
    "        fields=[\"NAME\"],  # Include county name and food desert flag\n",
    "        aliases=[\"County:\"],  # Label for each field\n",
    "        localize=True\n",
    "    )\n",
    ")\n",
    "\n",
    "# Add layer control to toggle layers\n",
    "folium.LayerControl().add_to(m)\n",
    "\n",
    "\n",
    "\n",
    "# Save and display the map\n",
    "map_file_path = \"Food_Desert_Map_1.html\"\n",
    "m.save(map_file_path)\n",
    "map_file_path\n",
    "\n",
    "# Display the map in the notebook\n",
    "display(IFrame(map_file_path, width=800, height=600))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "85d793a8-76bd-434e-a442-33a38b694099",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='PCT_LACCESS_POP10', ylabel='Count'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(food_atlas_crime_df['PCT_LACCESS_POP10'], kde=True, bins=30, color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "86a350e3-c0d4-4807-9a89-2a2ebf934c47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='POVRATE10', ylabel='Count'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(food_atlas_crime_df['POVRATE10'], kde=True, bins=30, color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "83cf8628-f143-468e-aaec-2c8e87e43847",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='CHILDPOVRATE10', ylabel='Count'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#CHILDPOVRATE10\n",
    "sns.histplot(food_atlas_crime_df['CHILDPOVRATE10'], kde=True, bins=30, color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "cbd9245c-6ac3-4e43-90ce-5eaebf663bcc",
   "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>FIPS</th>\n",
       "      <th>LACCESS_POP10</th>\n",
       "      <th>PCT_LACCESS_POP10</th>\n",
       "      <th>LACCESS_LOWI10</th>\n",
       "      <th>PCT_LACCESS_LOWI10</th>\n",
       "      <th>LACCESS_CHILD10</th>\n",
       "      <th>PCT_LACCESS_CHILD10</th>\n",
       "      <th>LACCESS_SENIORS10</th>\n",
       "      <th>PCT_LACCESS_SENIORS10</th>\n",
       "      <th>LACCESS_HHNV10</th>\n",
       "      <th>...</th>\n",
       "      <th>AG_ARRST</th>\n",
       "      <th>AG_OFF</th>\n",
       "      <th>MURDER</th>\n",
       "      <th>RAPE</th>\n",
       "      <th>ROBBERY</th>\n",
       "      <th>AGASSLT</th>\n",
       "      <th>BURGLRY</th>\n",
       "      <th>LARCENY</th>\n",
       "      <th>MVTHEFT</th>\n",
       "      <th>ARSON</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3064.000000</td>\n",
       "      <td>3064.000000</td>\n",
       "      <td>3064.000000</td>\n",
       "      <td>3064.000000</td>\n",
       "      <td>3064.000000</td>\n",
       "      <td>3064.000000</td>\n",
       "      <td>3064.000000</td>\n",
       "      <td>3064.000000</td>\n",
       "      <td>3064.000000</td>\n",
       "      <td>3064.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3064.000000</td>\n",
       "      <td>3064.000000</td>\n",
       "      <td>3064.000000</td>\n",
       "      <td>3064.000000</td>\n",
       "      <td>3064.000000</td>\n",
       "      <td>3064.000000</td>\n",
       "      <td>3064.000000</td>\n",
       "      <td>3064.000000</td>\n",
       "      <td>3064.000000</td>\n",
       "      <td>3064.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>30886.573433</td>\n",
       "      <td>20466.704337</td>\n",
       "      <td>23.383292</td>\n",
       "      <td>5565.862686</td>\n",
       "      <td>8.266181</td>\n",
       "      <td>5027.775780</td>\n",
       "      <td>5.446103</td>\n",
       "      <td>2736.077287</td>\n",
       "      <td>3.937280</td>\n",
       "      <td>660.550246</td>\n",
       "      <td>...</td>\n",
       "      <td>6.302546</td>\n",
       "      <td>5.494125</td>\n",
       "      <td>4.782637</td>\n",
       "      <td>26.792428</td>\n",
       "      <td>115.998042</td>\n",
       "      <td>243.352154</td>\n",
       "      <td>671.274478</td>\n",
       "      <td>1960.794386</td>\n",
       "      <td>234.344648</td>\n",
       "      <td>17.346606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15108.790414</td>\n",
       "      <td>52157.462255</td>\n",
       "      <td>20.211529</td>\n",
       "      <td>13973.845224</td>\n",
       "      <td>8.129816</td>\n",
       "      <td>13380.908116</td>\n",
       "      <td>4.816022</td>\n",
       "      <td>6646.521627</td>\n",
       "      <td>4.256727</td>\n",
       "      <td>1128.634848</td>\n",
       "      <td>...</td>\n",
       "      <td>8.270140</td>\n",
       "      <td>7.702345</td>\n",
       "      <td>23.722105</td>\n",
       "      <td>80.264031</td>\n",
       "      <td>677.482948</td>\n",
       "      <td>947.058386</td>\n",
       "      <td>2270.214791</td>\n",
       "      <td>6523.940965</td>\n",
       "      <td>1270.815109</td>\n",
       "      <td>69.366943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1001.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>19010.500000</td>\n",
       "      <td>1655.598922</td>\n",
       "      <td>10.660073</td>\n",
       "      <td>592.097753</td>\n",
       "      <td>3.385565</td>\n",
       "      <td>362.496398</td>\n",
       "      <td>2.368949</td>\n",
       "      <td>279.603607</td>\n",
       "      <td>1.573819</td>\n",
       "      <td>116.729779</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>30044.000000</td>\n",
       "      <td>4090.041291</td>\n",
       "      <td>19.515439</td>\n",
       "      <td>1492.851376</td>\n",
       "      <td>6.108552</td>\n",
       "      <td>936.473185</td>\n",
       "      <td>4.416584</td>\n",
       "      <td>678.866117</td>\n",
       "      <td>2.824881</td>\n",
       "      <td>314.463149</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>350.500000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>46051.500000</td>\n",
       "      <td>12962.155450</td>\n",
       "      <td>29.428079</td>\n",
       "      <td>4555.537288</td>\n",
       "      <td>10.192647</td>\n",
       "      <td>3008.919496</td>\n",
       "      <td>6.920742</td>\n",
       "      <td>1885.576353</td>\n",
       "      <td>4.539879</td>\n",
       "      <td>702.654702</td>\n",
       "      <td>...</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>418.250000</td>\n",
       "      <td>1212.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>56045.000000</td>\n",
       "      <td>886068.668386</td>\n",
       "      <td>100.000001</td>\n",
       "      <td>292541.789025</td>\n",
       "      <td>72.274456</td>\n",
       "      <td>260308.794094</td>\n",
       "      <td>34.015595</td>\n",
       "      <td>78922.918719</td>\n",
       "      <td>29.208633</td>\n",
       "      <td>16334.423176</td>\n",
       "      <td>...</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>1976.000000</td>\n",
       "      <td>18923.000000</td>\n",
       "      <td>23057.000000</td>\n",
       "      <td>49107.000000</td>\n",
       "      <td>142604.000000</td>\n",
       "      <td>40555.000000</td>\n",
       "      <td>2210.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               FIPS  LACCESS_POP10  PCT_LACCESS_POP10  LACCESS_LOWI10  \\\n",
       "count   3064.000000    3064.000000        3064.000000     3064.000000   \n",
       "mean   30886.573433   20466.704337          23.383292     5565.862686   \n",
       "std    15108.790414   52157.462255          20.211529    13973.845224   \n",
       "min     1001.000000       0.000000           0.000000        0.000000   \n",
       "25%    19010.500000    1655.598922          10.660073      592.097753   \n",
       "50%    30044.000000    4090.041291          19.515439     1492.851376   \n",
       "75%    46051.500000   12962.155450          29.428079     4555.537288   \n",
       "max    56045.000000  886068.668386         100.000001   292541.789025   \n",
       "\n",
       "       PCT_LACCESS_LOWI10  LACCESS_CHILD10  PCT_LACCESS_CHILD10  \\\n",
       "count         3064.000000      3064.000000          3064.000000   \n",
       "mean             8.266181      5027.775780             5.446103   \n",
       "std              8.129816     13380.908116             4.816022   \n",
       "min              0.000000         0.000000             0.000000   \n",
       "25%              3.385565       362.496398             2.368949   \n",
       "50%              6.108552       936.473185             4.416584   \n",
       "75%             10.192647      3008.919496             6.920742   \n",
       "max             72.274456    260308.794094            34.015595   \n",
       "\n",
       "       LACCESS_SENIORS10  PCT_LACCESS_SENIORS10  LACCESS_HHNV10  ...  \\\n",
       "count        3064.000000            3064.000000     3064.000000  ...   \n",
       "mean         2736.077287               3.937280      660.550246  ...   \n",
       "std          6646.521627               4.256727     1128.634848  ...   \n",
       "min             0.000000               0.000000        0.000000  ...   \n",
       "25%           279.603607               1.573819      116.729779  ...   \n",
       "50%           678.866117               2.824881      314.463149  ...   \n",
       "75%          1885.576353               4.539879      702.654702  ...   \n",
       "max         78922.918719              29.208633    16334.423176  ...   \n",
       "\n",
       "          AG_ARRST       AG_OFF       MURDER         RAPE       ROBBERY  \\\n",
       "count  3064.000000  3064.000000  3064.000000  3064.000000   3064.000000   \n",
       "mean      6.302546     5.494125     4.782637    26.792428    115.998042   \n",
       "std       8.270140     7.702345    23.722105    80.264031    677.482948   \n",
       "min       0.000000     0.000000     0.000000     0.000000      0.000000   \n",
       "25%       2.000000     2.000000     0.000000     1.000000      0.000000   \n",
       "50%       4.000000     3.000000     0.000000     5.000000      4.000000   \n",
       "75%       7.000000     6.000000     2.000000    19.000000     22.000000   \n",
       "max     150.000000   140.000000   600.000000  1976.000000  18923.000000   \n",
       "\n",
       "            AGASSLT       BURGLRY        LARCENY       MVTHEFT        ARSON  \n",
       "count   3064.000000   3064.000000    3064.000000   3064.000000  3064.000000  \n",
       "mean     243.352154    671.274478    1960.794386    234.344648    17.346606  \n",
       "std      947.058386   2270.214791    6523.940965   1270.815109    69.366943  \n",
       "min        0.000000      0.000000       0.000000      0.000000     0.000000  \n",
       "25%       10.000000     37.000000      81.000000      6.000000     0.000000  \n",
       "50%       36.000000    133.000000     350.500000     22.000000     3.000000  \n",
       "75%      124.000000    418.250000    1212.000000     74.000000    10.000000  \n",
       "max    23057.000000  49107.000000  142604.000000  40555.000000  2210.000000  \n",
       "\n",
       "[8 rows x 45 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_atlas_crime_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "311ece1f-fa95-4900-9b59-3b919ea63b96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['FIPS', 'State', 'County', 'LACCESS_POP10', 'PCT_LACCESS_POP10',\n",
       "       'LACCESS_LOWI10', 'PCT_LACCESS_LOWI10', 'LACCESS_CHILD10',\n",
       "       'PCT_LACCESS_CHILD10', 'LACCESS_SENIORS10', 'PCT_LACCESS_SENIORS10',\n",
       "       'LACCESS_HHNV10', 'PCT_LACCESS_HHNV10', 'PCT_NHWHITE10',\n",
       "       'PCT_NHBLACK10', 'PCT_HISP10', 'PCT_NHASIAN10', 'PCT_NHNA10',\n",
       "       'PCT_NHPI10', 'PCT_65OLDER10', 'PCT_18YOUNGER10', 'MEDHHINC10',\n",
       "       'POVRATE10', 'PERPOV10', 'CHILDPOVRATE10', 'PERCHLDPOV10', 'METRO13',\n",
       "       'POPLOSS00', '2010 Census population', 'LACCESS_POP10_FLAG',\n",
       "       'LACCESS_LOWI10_FLAG', 'LACCESS_CHILD10_FLAG', 'LACCESS_SENIORS10_FLAG',\n",
       "       'LACCESS_HHNV10_FLAG', 'crime_rate_per_100000', 'CPOPARST', 'CPOPCRIM',\n",
       "       'AG_ARRST', 'AG_OFF', 'MURDER', 'RAPE', 'ROBBERY', 'AGASSLT', 'BURGLRY',\n",
       "       'LARCENY', 'MVTHEFT', 'ARSON'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_atlas_crime_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "807cac3d-ca97-4f9f-b828-58820b641774",
   "metadata": {},
   "outputs": [],
   "source": [
    "countAccess=food_atlas_crime_df[['LACCESS_POP10_FLAG','BURGLRY','ROBBERY']].value_counts().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "10a40b0d-174c-4a3b-a2b9-b03c73e7ac94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['LACCESS_POP10_FLAG', 'BURGLRY', 'ROBBERY', 'count'], dtype='object')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countAccess.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "83556e96-dcd5-4b75-8e6f-dd85490ffc07",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_count = countAccess['count'].sum()  # Sum of all counts\n",
    "countAccess['percentage'] = (countAccess['count'] / total_count) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "c951bdf6-b76c-424a-b32f-c63665074ab9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BURGLRY</th>\n",
       "      <th>ROBBERY</th>\n",
       "      <th>count</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LACCESS_POP10_FLAG</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>1625803</td>\n",
       "      <td>304946</td>\n",
       "      <td>2339</td>\n",
       "      <td>76.33812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>368531</td>\n",
       "      <td>41169</td>\n",
       "      <td>725</td>\n",
       "      <td>23.66188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    BURGLRY  ROBBERY  count  percentage\n",
       "LACCESS_POP10_FLAG                                     \n",
       "0                   1625803   304946   2339    76.33812\n",
       "1                    368531    41169    725    23.66188"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countAccess.groupby(by='LACCESS_POP10_FLAG').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "1af2f80e-0a62-49bb-b9e6-e41362a4f73c",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_count = countAccess['BURGLRY'].sum()  # Sum of all counts\n",
    "countAccess['BURGLRY percentage'] = (countAccess['BURGLRY'] / total_count) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "a557299a-4f9f-4d2b-aed0-63958cc4cb88",
   "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>LACCESS_POP10_FLAG</th>\n",
       "      <th>BURGLRY</th>\n",
       "      <th>ROBBERY</th>\n",
       "      <th>count</th>\n",
       "      <th>percentage</th>\n",
       "      <th>BURGLRY percentage</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>2.121410</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>1.338120</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>0.718016</td>\n",
       "      <td>0.000150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0.620104</td>\n",
       "      <td>0.000301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0.587467</td>\n",
       "      <td>0.000050</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>2113</th>\n",
       "      <td>0</td>\n",
       "      <td>263</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.032637</td>\n",
       "      <td>0.013187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2114</th>\n",
       "      <td>0</td>\n",
       "      <td>262</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0.032637</td>\n",
       "      <td>0.013137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2115</th>\n",
       "      <td>0</td>\n",
       "      <td>262</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0.032637</td>\n",
       "      <td>0.013137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2116</th>\n",
       "      <td>0</td>\n",
       "      <td>261</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.032637</td>\n",
       "      <td>0.013087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2117</th>\n",
       "      <td>1</td>\n",
       "      <td>18580</td>\n",
       "      <td>2038</td>\n",
       "      <td>1</td>\n",
       "      <td>0.032637</td>\n",
       "      <td>0.931639</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2118 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      LACCESS_POP10_FLAG  BURGLRY  ROBBERY  count  percentage  \\\n",
       "0                      1        0        0     65    2.121410   \n",
       "1                      0        0        0     41    1.338120   \n",
       "2                      1        3        0     22    0.718016   \n",
       "3                      1        6        0     19    0.620104   \n",
       "4                      1        1        0     18    0.587467   \n",
       "...                  ...      ...      ...    ...         ...   \n",
       "2113                   0      263        6      1    0.032637   \n",
       "2114                   0      262       18      1    0.032637   \n",
       "2115                   0      262        9      1    0.032637   \n",
       "2116                   0      261        6      1    0.032637   \n",
       "2117                   1    18580     2038      1    0.032637   \n",
       "\n",
       "      BURGLRY percentage  \n",
       "0               0.000000  \n",
       "1               0.000000  \n",
       "2               0.000150  \n",
       "3               0.000301  \n",
       "4               0.000050  \n",
       "...                  ...  \n",
       "2113            0.013187  \n",
       "2114            0.013137  \n",
       "2115            0.013137  \n",
       "2116            0.013087  \n",
       "2117            0.931639  \n",
       "\n",
       "[2118 rows x 6 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countAccess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "cf926a8c-be8a-4bb3-bfff-7675e10399b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BURGLRY</th>\n",
       "      <th>ROBBERY</th>\n",
       "      <th>count</th>\n",
       "      <th>percentage</th>\n",
       "      <th>BURGLRY percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LACCESS_POP10_FLAG</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>1625803</td>\n",
       "      <td>304946</td>\n",
       "      <td>2339</td>\n",
       "      <td>76.33812</td>\n",
       "      <td>81.521099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>368531</td>\n",
       "      <td>41169</td>\n",
       "      <td>725</td>\n",
       "      <td>23.66188</td>\n",
       "      <td>18.478901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    BURGLRY  ROBBERY  count  percentage  BURGLRY percentage\n",
       "LACCESS_POP10_FLAG                                                         \n",
       "0                   1625803   304946   2339    76.33812           81.521099\n",
       "1                    368531    41169    725    23.66188           18.478901"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countAccess.groupby(by='LACCESS_POP10_FLAG').sum()"
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