New_Index_Generation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"/Users/akhil/Documents/Fire Vulnerability Project/Input Data/Assembled_Dataset/complete_dataset.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Unnamed: 0',\n",
       " 'STATEFP_x',\n",
       " 'COUNTYFP_x',\n",
       " 'TRACTCE_x',\n",
       " 'GEOID_x',\n",
       " 'NAME_x',\n",
       " 'geometry',\n",
       " 'GEOID10',\n",
       " 'pnhwhitect',\n",
       " 'pblackct',\n",
       " 'phispct',\n",
       " 'ploweconct',\n",
       " 'pelderlyct',\n",
       " 'pchildct',\n",
       " 'palonect',\n",
       " 'pnocomct',\n",
       " 'medincome',\n",
       " 'hunit',\n",
       " 'pnophonebg',\n",
       " 'poldhubg',\n",
       " 'pnohvacbg',\n",
       " 'CTGEOID',\n",
       " 'TotalPopulation',\n",
       " 'ACCESS2_CrudePrev',\n",
       " 'BPHIGH_CrudePrev',\n",
       " 'BPMED_CrudePrev',\n",
       " 'CANCER_CrudePrev',\n",
       " 'CASTHMA_CrudePrev',\n",
       " 'CHD_CrudePrev',\n",
       " 'CHECKUP_CrudePrev',\n",
       " 'COPD_CrudePrev',\n",
       " 'CSMOKING_CrudePrev',\n",
       " 'DIABETES_CrudePrev',\n",
       " 'HIGHCHOL_CrudePrev',\n",
       " 'LPA_CrudePrev',\n",
       " 'OBESITY_CrudePrev',\n",
       " 'PHLTH_CrudePrev',\n",
       " 'STROKE_CrudePrev',\n",
       " 'Join_Count_2020',\n",
       " 'Join_Count_2019',\n",
       " 'Join_Count_2018',\n",
       " 'Join_Count_2017',\n",
       " 'Join_Count_2016',\n",
       " 'CTID',\n",
       " 'total_count']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "            \n",
    "pc1_vars = ['BPHIGH_CrudePrev','CHD_CrudePrev','COPD_CrudePrev','DIABETES_CrudePrev','PHLTH_CrudePrev',\n",
    " 'STROKE_CrudePrev','OBESITY_CrudePrev','CASTHMA_CrudePrev']\n",
    "pc2_vars = ['BPMED_CrudePrev','CANCER_CrudePrev','CHECKUP_CrudePrev','CSMOKING_CrudePrev']\n",
    "pc3_vars = ['hunit','pnohvacbg','poldhubg','pelderlyct',] \n",
    "pc4_vars = ['pblackct','pchildct']\n",
    "#'TotalPopulation'\n",
    "all_vars = pc1_vars+pc2_vars+pc3_vars+pc4_vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "140"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[\"phispct\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in all_vars:\n",
    "    column_title = x+\"_rank\"\n",
    "    df[column_title] = df[x].rank(pct=True, na_option=\"bottom\", ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>STATEFP_x</th>\n",
       "      <th>COUNTYFP_x</th>\n",
       "      <th>TRACTCE_x</th>\n",
       "      <th>GEOID_x</th>\n",
       "      <th>NAME_x</th>\n",
       "      <th>geometry</th>\n",
       "      <th>GEOID10</th>\n",
       "      <th>pnhwhitect</th>\n",
       "      <th>pblackct</th>\n",
       "      <th>...</th>\n",
       "      <th>BPMED_CrudePrev_rank</th>\n",
       "      <th>CANCER_CrudePrev_rank</th>\n",
       "      <th>CHECKUP_CrudePrev_rank</th>\n",
       "      <th>CSMOKING_CrudePrev_rank</th>\n",
       "      <th>hunit_rank</th>\n",
       "      <th>pnohvacbg_rank</th>\n",
       "      <th>poldhubg_rank</th>\n",
       "      <th>pelderlyct_rank</th>\n",
       "      <th>pblackct_rank</th>\n",
       "      <th>pchildct_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>453</td>\n",
       "      <td>2110</td>\n",
       "      <td>48453002110</td>\n",
       "      <td>21.10</td>\n",
       "      <td>POLYGON ((-97.691622 30.271748, -97.6913989999...</td>\n",
       "      <td>48453002110</td>\n",
       "      <td>23.856906</td>\n",
       "      <td>29.424307</td>\n",
       "      <td>...</td>\n",
       "      <td>0.760714</td>\n",
       "      <td>0.510714</td>\n",
       "      <td>0.757143</td>\n",
       "      <td>0.767857</td>\n",
       "      <td>0.271429</td>\n",
       "      <td>0.842857</td>\n",
       "      <td>0.671429</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.978571</td>\n",
       "      <td>0.457143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>453</td>\n",
       "      <td>2111</td>\n",
       "      <td>48453002111</td>\n",
       "      <td>21.11</td>\n",
       "      <td>POLYGON ((-97.70498699999999 30.25097599999999...</td>\n",
       "      <td>48453002111</td>\n",
       "      <td>26.515420</td>\n",
       "      <td>8.277207</td>\n",
       "      <td>...</td>\n",
       "      <td>0.696429</td>\n",
       "      <td>0.453571</td>\n",
       "      <td>0.614286</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.528571</td>\n",
       "      <td>0.296429</td>\n",
       "      <td>0.414286</td>\n",
       "      <td>0.621429</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.814286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
       "      <td>453</td>\n",
       "      <td>2105</td>\n",
       "      <td>48453002105</td>\n",
       "      <td>21.05</td>\n",
       "      <td>POLYGON ((-97.71462799999999 30.315025, -97.71...</td>\n",
       "      <td>48453002105</td>\n",
       "      <td>28.624754</td>\n",
       "      <td>12.259332</td>\n",
       "      <td>...</td>\n",
       "      <td>0.385714</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>0.292857</td>\n",
       "      <td>0.814286</td>\n",
       "      <td>0.664286</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.564286</td>\n",
       "      <td>0.378571</td>\n",
       "      <td>0.814286</td>\n",
       "      <td>0.364286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>453</td>\n",
       "      <td>2106</td>\n",
       "      <td>48453002106</td>\n",
       "      <td>21.06</td>\n",
       "      <td>POLYGON ((-97.698876 30.303907, -97.698505 30....</td>\n",
       "      <td>48453002106</td>\n",
       "      <td>40.071267</td>\n",
       "      <td>10.236476</td>\n",
       "      <td>...</td>\n",
       "      <td>0.864286</td>\n",
       "      <td>0.807143</td>\n",
       "      <td>0.828571</td>\n",
       "      <td>0.289286</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.296429</td>\n",
       "      <td>0.985714</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.764286</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>48</td>\n",
       "      <td>453</td>\n",
       "      <td>2107</td>\n",
       "      <td>48453002107</td>\n",
       "      <td>21.07</td>\n",
       "      <td>POLYGON ((-97.69851799999999 30.289116, -97.69...</td>\n",
       "      <td>48453002107</td>\n",
       "      <td>33.199679</td>\n",
       "      <td>22.052927</td>\n",
       "      <td>...</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.728571</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.296429</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.578571</td>\n",
       "      <td>0.942857</td>\n",
       "      <td>0.385714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>135</td>\n",
       "      <td>48</td>\n",
       "      <td>453</td>\n",
       "      <td>1757</td>\n",
       "      <td>48453001757</td>\n",
       "      <td>17.57</td>\n",
       "      <td>POLYGON ((-97.76999099999999 30.408648, -97.76...</td>\n",
       "      <td>48453001757</td>\n",
       "      <td>74.977778</td>\n",
       "      <td>2.355556</td>\n",
       "      <td>...</td>\n",
       "      <td>0.921429</td>\n",
       "      <td>0.939286</td>\n",
       "      <td>0.942857</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.107143</td>\n",
       "      <td>0.296429</td>\n",
       "      <td>0.092857</td>\n",
       "      <td>0.992857</td>\n",
       "      <td>0.292857</td>\n",
       "      <td>0.464286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>136</td>\n",
       "      <td>48</td>\n",
       "      <td>453</td>\n",
       "      <td>1781</td>\n",
       "      <td>48453001781</td>\n",
       "      <td>17.81</td>\n",
       "      <td>POLYGON ((-97.806761 30.437469, -97.806744 30....</td>\n",
       "      <td>48453001781</td>\n",
       "      <td>70.909786</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.978571</td>\n",
       "      <td>0.985714</td>\n",
       "      <td>0.978571</td>\n",
       "      <td>0.121429</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.864286</td>\n",
       "      <td>0.764286</td>\n",
       "      <td>0.971429</td>\n",
       "      <td>0.010714</td>\n",
       "      <td>0.914286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>137</td>\n",
       "      <td>48</td>\n",
       "      <td>453</td>\n",
       "      <td>1777</td>\n",
       "      <td>48453001777</td>\n",
       "      <td>17.77</td>\n",
       "      <td>POLYGON ((-97.861156 30.208181999999997, -97.8...</td>\n",
       "      <td>48453001777</td>\n",
       "      <td>58.193061</td>\n",
       "      <td>5.324631</td>\n",
       "      <td>...</td>\n",
       "      <td>0.396429</td>\n",
       "      <td>0.510714</td>\n",
       "      <td>0.521429</td>\n",
       "      <td>0.378571</td>\n",
       "      <td>0.657143</td>\n",
       "      <td>0.296429</td>\n",
       "      <td>0.064286</td>\n",
       "      <td>0.478571</td>\n",
       "      <td>0.528571</td>\n",
       "      <td>0.678571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>138</td>\n",
       "      <td>48</td>\n",
       "      <td>453</td>\n",
       "      <td>2112</td>\n",
       "      <td>48453002112</td>\n",
       "      <td>21.12</td>\n",
       "      <td>POLYGON ((-97.691261 30.316219999999998, -97.6...</td>\n",
       "      <td>48453002112</td>\n",
       "      <td>31.699226</td>\n",
       "      <td>23.921858</td>\n",
       "      <td>...</td>\n",
       "      <td>0.564286</td>\n",
       "      <td>0.389286</td>\n",
       "      <td>0.475000</td>\n",
       "      <td>0.964286</td>\n",
       "      <td>0.564286</td>\n",
       "      <td>0.296429</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.421429</td>\n",
       "      <td>0.957143</td>\n",
       "      <td>0.992857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>139</td>\n",
       "      <td>48</td>\n",
       "      <td>453</td>\n",
       "      <td>2113</td>\n",
       "      <td>48453002113</td>\n",
       "      <td>21.13</td>\n",
       "      <td>POLYGON ((-97.681586 30.316067, -97.6815759999...</td>\n",
       "      <td>48453002113</td>\n",
       "      <td>48.582231</td>\n",
       "      <td>16.770186</td>\n",
       "      <td>...</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.775000</td>\n",
       "      <td>0.885714</td>\n",
       "      <td>0.607143</td>\n",
       "      <td>0.221429</td>\n",
       "      <td>0.296429</td>\n",
       "      <td>0.878571</td>\n",
       "      <td>0.821429</td>\n",
       "      <td>0.892857</td>\n",
       "      <td>0.578571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  STATEFP_x  COUNTYFP_x  TRACTCE_x      GEOID_x  NAME_x  \\\n",
       "0             0         48         453       2110  48453002110   21.10   \n",
       "1             1         48         453       2111  48453002111   21.11   \n",
       "2             2         48         453       2105  48453002105   21.05   \n",
       "3             3         48         453       2106  48453002106   21.06   \n",
       "4             4         48         453       2107  48453002107   21.07   \n",
       "..          ...        ...         ...        ...          ...     ...   \n",
       "135         135         48         453       1757  48453001757   17.57   \n",
       "136         136         48         453       1781  48453001781   17.81   \n",
       "137         137         48         453       1777  48453001777   17.77   \n",
       "138         138         48         453       2112  48453002112   21.12   \n",
       "139         139         48         453       2113  48453002113   21.13   \n",
       "\n",
       "                                              geometry      GEOID10  \\\n",
       "0    POLYGON ((-97.691622 30.271748, -97.6913989999...  48453002110   \n",
       "1    POLYGON ((-97.70498699999999 30.25097599999999...  48453002111   \n",
       "2    POLYGON ((-97.71462799999999 30.315025, -97.71...  48453002105   \n",
       "3    POLYGON ((-97.698876 30.303907, -97.698505 30....  48453002106   \n",
       "4    POLYGON ((-97.69851799999999 30.289116, -97.69...  48453002107   \n",
       "..                                                 ...          ...   \n",
       "135  POLYGON ((-97.76999099999999 30.408648, -97.76...  48453001757   \n",
       "136  POLYGON ((-97.806761 30.437469, -97.806744 30....  48453001781   \n",
       "137  POLYGON ((-97.861156 30.208181999999997, -97.8...  48453001777   \n",
       "138  POLYGON ((-97.691261 30.316219999999998, -97.6...  48453002112   \n",
       "139  POLYGON ((-97.681586 30.316067, -97.6815759999...  48453002113   \n",
       "\n",
       "     pnhwhitect   pblackct  ...  BPMED_CrudePrev_rank  CANCER_CrudePrev_rank  \\\n",
       "0     23.856906  29.424307  ...              0.760714               0.510714   \n",
       "1     26.515420   8.277207  ...              0.696429               0.453571   \n",
       "2     28.624754  12.259332  ...              0.385714               0.325000   \n",
       "3     40.071267  10.236476  ...              0.864286               0.807143   \n",
       "4     33.199679  22.052927  ...              0.900000               0.700000   \n",
       "..          ...        ...  ...                   ...                    ...   \n",
       "135   74.977778   2.355556  ...              0.921429               0.939286   \n",
       "136   70.909786   0.000000  ...              0.978571               0.985714   \n",
       "137   58.193061   5.324631  ...              0.396429               0.510714   \n",
       "138   31.699226  23.921858  ...              0.564286               0.389286   \n",
       "139   48.582231  16.770186  ...              0.875000               0.775000   \n",
       "\n",
       "     CHECKUP_CrudePrev_rank  CSMOKING_CrudePrev_rank  hunit_rank  \\\n",
       "0                  0.757143                 0.767857    0.271429   \n",
       "1                  0.614286                 0.875000    0.528571   \n",
       "2                  0.292857                 0.814286    0.664286   \n",
       "3                  0.828571                 0.289286    0.150000   \n",
       "4                  0.875000                 0.728571    0.300000   \n",
       "..                      ...                      ...         ...   \n",
       "135                0.942857                 0.071429    0.107143   \n",
       "136                0.978571                 0.121429    0.071429   \n",
       "137                0.521429                 0.378571    0.657143   \n",
       "138                0.475000                 0.964286    0.564286   \n",
       "139                0.885714                 0.607143    0.221429   \n",
       "\n",
       "     pnohvacbg_rank  poldhubg_rank  pelderlyct_rank  pblackct_rank  \\\n",
       "0          0.842857       0.671429         0.700000       0.978571   \n",
       "1          0.296429       0.414286         0.621429       0.700000   \n",
       "2          0.642857       0.564286         0.378571       0.814286   \n",
       "3          0.296429       0.985714         0.571429       0.764286   \n",
       "4          0.296429       0.900000         0.578571       0.942857   \n",
       "..              ...            ...              ...            ...   \n",
       "135        0.296429       0.092857         0.992857       0.292857   \n",
       "136        0.864286       0.764286         0.971429       0.010714   \n",
       "137        0.296429       0.064286         0.478571       0.528571   \n",
       "138        0.296429       0.785714         0.421429       0.957143   \n",
       "139        0.296429       0.878571         0.821429       0.892857   \n",
       "\n",
       "     pchildct_rank  \n",
       "0         0.457143  \n",
       "1         0.814286  \n",
       "2         0.364286  \n",
       "3         0.400000  \n",
       "4         0.385714  \n",
       "..             ...  \n",
       "135       0.464286  \n",
       "136       0.914286  \n",
       "137       0.678571  \n",
       "138       0.992857  \n",
       "139       0.578571  \n",
       "\n",
       "[140 rows x 63 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def floor_func(input):\n",
    "    if input >= 0.9:\n",
    "        return 1\n",
    "    elif input <= 0.1:\n",
    "        return 0\n",
    "    else:\n",
    "        return input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "for y in all_vars:\n",
    "    column_title = y+\"_rank\"\n",
    "    df[column_title] = df[column_title].apply(lambda x: floor_func(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"pc1\"] = 0\n",
    "df[\"pc2\"] = 0 \n",
    "df[\"pc3\"] = 0\n",
    "df[\"pc4\"] = 0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     -70.8\n",
       "1     -69.6\n",
       "2     -66.7\n",
       "3     -72.3\n",
       "4     -72.8\n",
       "       ... \n",
       "135   -74.4\n",
       "136   -76.2\n",
       "137   -68.8\n",
       "138   -68.4\n",
       "139   -73.0\n",
       "Name: CHECKUP_CrudePrev, Length: 140, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"CHECKUP_CrudePrev\"] = -1*df[\"CHECKUP_CrudePrev\"]\n",
    "#Need to make negative for checkups\n",
    "\n",
    "df[\"CHECKUP_CrudePrev\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in pc1_vars:\n",
    "    column_title = x+\"_rank\"\n",
    "    df[\"pc1\"] += df[column_title]\n",
    "\n",
    "for x in pc2_vars:\n",
    "    column_title = x+\"_rank\"\n",
    "    df[\"pc2\"] += df[column_title]\n",
    "    \n",
    "\n",
    "for x in pc3_vars:\n",
    "    column_title = x+\"_rank\"\n",
    "    df[\"pc3\"] += df[column_title]\n",
    "\n",
    "for x in pc4_vars:\n",
    "    column_title = x+\"_rank\"\n",
    "    df[\"pc4\"] += df[column_title]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"pc1_z\"] = (df[\"pc1\"]-df[\"pc1\"].mean())/df[\"pc1\"].std()\n",
    "df[\"pc1\"] = df[\"pc1\"].rank(pct=True, na_option=\"bottom\", ascending=True)\n",
    "\n",
    "df[\"pc2_z\"] = (df[\"pc2\"]-df[\"pc2\"].mean())/df[\"pc2\"].std()\n",
    "df[\"pc2\"] = df[\"pc2\"].rank(pct=True, na_option=\"bottom\", ascending=True)\n",
    "\n",
    "df[\"pc3_z\"] = (df[\"pc3\"]-df[\"pc3\"].mean())/df[\"pc3\"].std()\n",
    "df[\"pc3\"] = df[\"pc3\"].rank(pct=True, na_option=\"bottom\", ascending=True)\n",
    "\n",
    "df[\"pc4_z\"] = (df[\"pc4\"]-df[\"pc4\"].mean())/df[\"pc4\"].std()\n",
    "df[\"pc4\"] = df[\"pc4\"].rank(pct=True, na_option=\"bottom\", ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Unnamed: 0</th>\n",
       "      <th>STATEFP_x</th>\n",
       "      <th>COUNTYFP_x</th>\n",
       "      <th>TRACTCE_x</th>\n",
       "      <th>GEOID_x</th>\n",
       "      <th>NAME_x</th>\n",
       "      <th>geometry</th>\n",
       "      <th>GEOID10</th>\n",
       "      <th>pnhwhitect</th>\n",
       "      <th>pblackct</th>\n",
       "      <th>...</th>\n",
       "      <th>pblackct_rank</th>\n",
       "      <th>pchildct_rank</th>\n",
       "      <th>pc1</th>\n",
       "      <th>pc2</th>\n",
       "      <th>pc3</th>\n",
       "      <th>pc4</th>\n",
       "      <th>pc1_z</th>\n",
       "      <th>pc2_z</th>\n",
       "      <th>pc3_z</th>\n",
       "      <th>pc4_z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>453</td>\n",
       "      <td>2110</td>\n",
       "      <td>48453002110</td>\n",
       "      <td>21.10</td>\n",
       "      <td>POLYGON ((-97.691622 30.271748, -97.6913989999...</td>\n",
       "      <td>48453002110</td>\n",
       "      <td>23.856906</td>\n",
       "      <td>29.424307</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.457143</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.807143</td>\n",
       "      <td>0.839286</td>\n",
       "      <td>0.853571</td>\n",
       "      <td>1.482898</td>\n",
       "      <td>0.942866</td>\n",
       "      <td>0.969512</td>\n",
       "      <td>1.021147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>453</td>\n",
       "      <td>2111</td>\n",
       "      <td>48453002111</td>\n",
       "      <td>21.11</td>\n",
       "      <td>POLYGON ((-97.70498699999999 30.25097599999999...</td>\n",
       "      <td>48453002111</td>\n",
       "      <td>26.515420</td>\n",
       "      <td>8.277207</td>\n",
       "      <td>...</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.814286</td>\n",
       "      <td>0.964286</td>\n",
       "      <td>0.739286</td>\n",
       "      <td>0.367857</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.702878</td>\n",
       "      <td>0.753431</td>\n",
       "      <td>-0.330600</td>\n",
       "      <td>1.150816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
       "      <td>453</td>\n",
       "      <td>2105</td>\n",
       "      <td>48453002105</td>\n",
       "      <td>21.05</td>\n",
       "      <td>POLYGON ((-97.71462799999999 30.315025, -97.71...</td>\n",
       "      <td>48453002105</td>\n",
       "      <td>28.624754</td>\n",
       "      <td>12.259332</td>\n",
       "      <td>...</td>\n",
       "      <td>0.814286</td>\n",
       "      <td>0.364286</td>\n",
       "      <td>0.764286</td>\n",
       "      <td>0.410714</td>\n",
       "      <td>0.664286</td>\n",
       "      <td>0.657143</td>\n",
       "      <td>0.803404</td>\n",
       "      <td>-0.236793</td>\n",
       "      <td>0.479184</td>\n",
       "      <td>0.389008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>453</td>\n",
       "      <td>2106</td>\n",
       "      <td>48453002106</td>\n",
       "      <td>21.06</td>\n",
       "      <td>POLYGON ((-97.698876 30.303907, -97.698505 30....</td>\n",
       "      <td>48453002106</td>\n",
       "      <td>40.071267</td>\n",
       "      <td>10.236476</td>\n",
       "      <td>...</td>\n",
       "      <td>0.764286</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.664286</td>\n",
       "      <td>0.796429</td>\n",
       "      <td>0.507143</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.516615</td>\n",
       "      <td>0.934255</td>\n",
       "      <td>-0.003715</td>\n",
       "      <td>0.356591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>48</td>\n",
       "      <td>453</td>\n",
       "      <td>2107</td>\n",
       "      <td>48453002107</td>\n",
       "      <td>21.07</td>\n",
       "      <td>POLYGON ((-97.69851799999999 30.289116, -97.69...</td>\n",
       "      <td>48453002107</td>\n",
       "      <td>33.199679</td>\n",
       "      <td>22.052927</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.385714</td>\n",
       "      <td>0.942857</td>\n",
       "      <td>0.964286</td>\n",
       "      <td>0.592857</td>\n",
       "      <td>0.792857</td>\n",
       "      <td>1.676806</td>\n",
       "      <td>1.554221</td>\n",
       "      <td>0.323171</td>\n",
       "      <td>0.859060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>135</td>\n",
       "      <td>48</td>\n",
       "      <td>453</td>\n",
       "      <td>1757</td>\n",
       "      <td>48453001757</td>\n",
       "      <td>17.57</td>\n",
       "      <td>POLYGON ((-97.76999099999999 30.408648, -97.76...</td>\n",
       "      <td>48453001757</td>\n",
       "      <td>74.977778</td>\n",
       "      <td>2.355556</td>\n",
       "      <td>...</td>\n",
       "      <td>0.292857</td>\n",
       "      <td>0.464286</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.882143</td>\n",
       "      <td>0.092857</td>\n",
       "      <td>0.307143</td>\n",
       "      <td>-0.242723</td>\n",
       "      <td>1.188269</td>\n",
       "      <td>-1.281539</td>\n",
       "      <td>-0.567304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>136</td>\n",
       "      <td>48</td>\n",
       "      <td>453</td>\n",
       "      <td>1781</td>\n",
       "      <td>48453001781</td>\n",
       "      <td>17.81</td>\n",
       "      <td>POLYGON ((-97.806761 30.437469, -97.806744 30....</td>\n",
       "      <td>48453001781</td>\n",
       "      <td>70.909786</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.678571</td>\n",
       "      <td>0.942857</td>\n",
       "      <td>0.907143</td>\n",
       "      <td>0.503571</td>\n",
       "      <td>0.534540</td>\n",
       "      <td>1.334650</td>\n",
       "      <td>1.266681</td>\n",
       "      <td>-0.016209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>137</td>\n",
       "      <td>48</td>\n",
       "      <td>453</td>\n",
       "      <td>1777</td>\n",
       "      <td>48453001777</td>\n",
       "      <td>17.77</td>\n",
       "      <td>POLYGON ((-97.861156 30.208181999999997, -97.8...</td>\n",
       "      <td>48453001777</td>\n",
       "      <td>58.193061</td>\n",
       "      <td>5.324631</td>\n",
       "      <td>...</td>\n",
       "      <td>0.528571</td>\n",
       "      <td>0.678571</td>\n",
       "      <td>0.257143</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.707143</td>\n",
       "      <td>-0.759268</td>\n",
       "      <td>-0.249709</td>\n",
       "      <td>-1.222106</td>\n",
       "      <td>0.453843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>138</td>\n",
       "      <td>48</td>\n",
       "      <td>453</td>\n",
       "      <td>2112</td>\n",
       "      <td>48453002112</td>\n",
       "      <td>21.12</td>\n",
       "      <td>POLYGON ((-97.691261 30.316219999999998, -97.6...</td>\n",
       "      <td>48453002112</td>\n",
       "      <td>31.699226</td>\n",
       "      <td>23.921858</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.992857</td>\n",
       "      <td>0.621429</td>\n",
       "      <td>0.535714</td>\n",
       "      <td>0.982143</td>\n",
       "      <td>1.759910</td>\n",
       "      <td>0.499417</td>\n",
       "      <td>0.100294</td>\n",
       "      <td>2.253006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>139</td>\n",
       "      <td>48</td>\n",
       "      <td>453</td>\n",
       "      <td>2113</td>\n",
       "      <td>48453002113</td>\n",
       "      <td>21.13</td>\n",
       "      <td>POLYGON ((-97.681586 30.316067, -97.6815759999...</td>\n",
       "      <td>48453002113</td>\n",
       "      <td>48.582231</td>\n",
       "      <td>16.770186</td>\n",
       "      <td>...</td>\n",
       "      <td>0.892857</td>\n",
       "      <td>0.578571</td>\n",
       "      <td>0.821429</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.635714</td>\n",
       "      <td>0.871429</td>\n",
       "      <td>1.111376</td>\n",
       "      <td>1.360482</td>\n",
       "      <td>0.412321</td>\n",
       "      <td>1.053564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  STATEFP_x  COUNTYFP_x  TRACTCE_x      GEOID_x  NAME_x  \\\n",
       "0             0         48         453       2110  48453002110   21.10   \n",
       "1             1         48         453       2111  48453002111   21.11   \n",
       "2             2         48         453       2105  48453002105   21.05   \n",
       "3             3         48         453       2106  48453002106   21.06   \n",
       "4             4         48         453       2107  48453002107   21.07   \n",
       "..          ...        ...         ...        ...          ...     ...   \n",
       "135         135         48         453       1757  48453001757   17.57   \n",
       "136         136         48         453       1781  48453001781   17.81   \n",
       "137         137         48         453       1777  48453001777   17.77   \n",
       "138         138         48         453       2112  48453002112   21.12   \n",
       "139         139         48         453       2113  48453002113   21.13   \n",
       "\n",
       "                                              geometry      GEOID10  \\\n",
       "0    POLYGON ((-97.691622 30.271748, -97.6913989999...  48453002110   \n",
       "1    POLYGON ((-97.70498699999999 30.25097599999999...  48453002111   \n",
       "2    POLYGON ((-97.71462799999999 30.315025, -97.71...  48453002105   \n",
       "3    POLYGON ((-97.698876 30.303907, -97.698505 30....  48453002106   \n",
       "4    POLYGON ((-97.69851799999999 30.289116, -97.69...  48453002107   \n",
       "..                                                 ...          ...   \n",
       "135  POLYGON ((-97.76999099999999 30.408648, -97.76...  48453001757   \n",
       "136  POLYGON ((-97.806761 30.437469, -97.806744 30....  48453001781   \n",
       "137  POLYGON ((-97.861156 30.208181999999997, -97.8...  48453001777   \n",
       "138  POLYGON ((-97.691261 30.316219999999998, -97.6...  48453002112   \n",
       "139  POLYGON ((-97.681586 30.316067, -97.6815759999...  48453002113   \n",
       "\n",
       "     pnhwhitect   pblackct  ...  pblackct_rank  pchildct_rank       pc1  \\\n",
       "0     23.856906  29.424307  ...       1.000000       0.457143  0.900000   \n",
       "1     26.515420   8.277207  ...       0.700000       0.814286  0.964286   \n",
       "2     28.624754  12.259332  ...       0.814286       0.364286  0.764286   \n",
       "3     40.071267  10.236476  ...       0.764286       0.400000  0.664286   \n",
       "4     33.199679  22.052927  ...       1.000000       0.385714  0.942857   \n",
       "..          ...        ...  ...            ...            ...       ...   \n",
       "135   74.977778   2.355556  ...       0.292857       0.464286  0.428571   \n",
       "136   70.909786   0.000000  ...       0.000000       1.000000  0.678571   \n",
       "137   58.193061   5.324631  ...       0.528571       0.678571  0.257143   \n",
       "138   31.699226  23.921858  ...       1.000000       1.000000  0.992857   \n",
       "139   48.582231  16.770186  ...       0.892857       0.578571  0.821429   \n",
       "\n",
       "          pc2       pc3       pc4     pc1_z     pc2_z     pc3_z     pc4_z  \n",
       "0    0.807143  0.839286  0.853571  1.482898  0.942866  0.969512  1.021147  \n",
       "1    0.739286  0.367857  0.900000  1.702878  0.753431 -0.330600  1.150816  \n",
       "2    0.410714  0.664286  0.657143  0.803404 -0.236793  0.479184  0.389008  \n",
       "3    0.796429  0.507143  0.650000  0.516615  0.934255 -0.003715  0.356591  \n",
       "4    0.964286  0.592857  0.792857  1.676806  1.554221  0.323171  0.859060  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "135  0.882143  0.092857  0.307143 -0.242723  1.188269 -1.281539 -0.567304  \n",
       "136  0.942857  0.907143  0.503571  0.534540  1.334650  1.266681 -0.016209  \n",
       "137  0.400000  0.100000  0.707143 -0.759268 -0.249709 -1.222106  0.453843  \n",
       "138  0.621429  0.535714  0.982143  1.759910  0.499417  0.100294  2.253006  \n",
       "139  0.950000  0.635714  0.871429  1.111376  1.360482  0.412321  1.053564  \n",
       "\n",
       "[140 rows x 71 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"health_index\"] = df[\"pc1\"] + df[\"pc2\"]\n",
    "df[\"health_index_z\"]=(df[\"health_index\"]-df[\"health_index\"].mean())/df[\"health_index\"].std()\n",
    "df[\"health_index\"] = df[\"health_index\"].rank(pct=True, na_option=\"bottom\", ascending=True)\n",
    "\n",
    "\n",
    "\n",
    "df[\"soc_env_index\"] = df[\"pc3\"] + df[\"pc4\"]\n",
    "df[\"soc_env_index_z\"]=(df[\"soc_env_index\"]-df[\"soc_env_index\"].mean())/df[\"soc_env_index\"].std()\n",
    "df[\"soc_env_index\"] = df[\"soc_env_index\"].rank(pct=True, na_option=\"bottom\", ascending=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"vulnerability_index\"]= df[\"health_index\"] + df[\"soc_env_index\"]\n",
    "df[\"vulnerability_index_z\"]=(df[\"vulnerability_index\"]-df[\"vulnerability_index\"].mean())/df[\"vulnerability_index\"].std()\n",
    "df[\"vulnerability_index\"] = df[\"vulnerability_index\"].rank(pct=True, na_option=\"bottom\", ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot: title={'center': 'vulnerability_index_z'}>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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apKioKH366afq16+fLMtSRkaGHnnkEQ0bNkyS9PLLLys6OlpLlizRPffcU+ZxMzIylJiYqLS0NElSWlqacnJylJGRoddee606dQEAgCDiVWC5UEFBgSSpWbNmkqQ9e/YoPz9fSUlJ7j4Oh0P9+/fX5s2byw0subm5mjx5skfboEGDlJGRUe51O51OOZ1O93ZhYaEkyeVyyeVyVaueEiXja3oc01FncAn2Oh0NLDlCrJ//+///NV111yLY17IEdQaPmtRY1TE2y7Kq9ci3LEtDhw7V8ePHtWnTJknS5s2b9Ytf/EIHDhxQXFycu+/48eO1b98+vffee2Ueq2HDhsrMzFRKSoq7bcmSJRo3bpxHKDlfenq6Zs6cWap9yZIlCgsLq05JAACglhUVFSklJUUFBQWKiIgot1+1z7BMmjRJX3zxhT788MNS+2w2m8e2ZVml2mo6Ji0tTVOmTHFvFxYWqlWrVkpKSqqw4KpwuVzKyspSYmKi7HZ7jY5lMur0jU7pZQfx2uYIsfR4j2LN2BoiZ3HFj7ev0gfV0qx8p1P6e17VaILq3s51+diszfuzr9bT9PtzffhbW5MaS14hqUy1Asv999+vN998Uxs3btSll17qbo+JiZEk5efnKzY21t1+5MgRRUdHl3u8mJgY5efne7RVNsbhcMjhcJRqt9vtPrtD+PJYJqPOmnGeM+uJ01lsq3ROgbje59dUlRpNUNPbuS4em3Vxu9Z0PQPl/lwf/tZWp8aq9vfqU0KWZWnSpElasWKFPvjgA8XHx3vsj4+PV0xMjLKystxtZ86cUU5Ojvr27VvucRMSEjzGSNK6desqHAMAAOoPr86wTJw4UUuWLNHq1asVHh7uPisSGRmpxo0by2az6aGHHtITTzyh9u3bq3379nriiScUFhbm8f6UMWPGqGXLlpo1a5Yk6cEHH1S/fv00e/ZsDR06VKtXr9b69evLfLkJAADUP14FlgULFkiSBgwY4NG+aNEijR07VpI0bdo0/fTTT7rvvvt0/Phx9e7dW+vWrVN4eLi7f15enkJC/ntyp2/fvlq6dKkeffRRzZgxQ+3atdOyZcvUu3fvapYFAACCiVeBpSofKLLZbEpPT1d6enq5fbKzs0u1jRgxQiNGjPBmOgAAoJ7gt4QAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMbzOrBs3LhRQ4YMUVxcnGw2m1atWuWx32azlXn5y1/+Uu4xMzMzyxxz+vRprwsCAADBx+vAcurUKXXp0kVz584tc/+hQ4c8Li+99JJsNpuGDx9e4XEjIiJKjW3UqJG30wMAAEEo1NsBycnJSk5OLnd/TEyMx/bq1as1cOBAtW3btsLj2my2UmMBAACkagQWbxw+fFhr1qzRyy+/XGnfH3/8Ua1bt9a5c+d07bXX6vHHH1fXrl3L7e90OuV0Ot3bhYWFkiSXyyWXy1WjeZeMr+lxTEedvuFoYPnluN5yhFge/1YkENfc0cDyqkYTVPd2rsvHZm3en321nqbfn+vD39qa1FjVMTbLsqp9T7HZbFq5cqVuvvnmMvfPmTNHTz75pA4ePFjhyztbtmzR7t271blzZxUWFupvf/ub1q5dq88//1zt27cvc0x6erpmzpxZqn3JkiUKCwurVj0AAKB2FRUVKSUlRQUFBYqIiCi3n18Dy1VXXaXExEQ999xzXh23uLhY3bp1U79+/fTss8+W2aesMyytWrXS0aNHKyy4Klwul7KyspSYmCi73V6jY5mMOn2jU/p7Pj9mdThCLD3eo1gztobIWWyrsO9X6YNqaVa+0yn9Pa9qNEF1b+e6fGzW5v3ZV+tp+v25PvytrUmNhYWFatGiRaWBxW8vCW3atEk7d+7UsmXLvB4bEhKinj17ateuXeX2cTgccjgcpdrtdrvP7hC+PJbJqLNmnOfMeuJ0FtsqnVMgrvf5NVWlRhPU9Haui8dmXdyuNV3PQLk/14e/tdWpsar9/fY9LC+++KK6d++uLl26eD3Wsixt375dsbGxfpgZAAAINF6fYfnxxx+1e/du9/aePXu0fft2NWvWTJdddpmkn0/v/POf/9Rf//rXMo8xZswYtWzZUrNmzZIkzZw5U3369FH79u1VWFioZ599Vtu3b9e8efOqUxMAAAgyXgeWrVu3auDAge7tKVOmSJJSU1OVmZkpSVq6dKksy9Jtt91W5jHy8vIUEvLfkzsnTpzQ+PHjlZ+fr8jISHXt2lUbN25Ur169vJ0eAAAIQl4HlgEDBqiy9+mOHz9e48ePL3d/dna2x/YzzzyjZ555xtupAACAeoLfEgIAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGM/rwLJx40YNGTJEcXFxstlsWrVqlcf+sWPHymazeVz69OlT6XGXL1+uDh06yOFwqEOHDlq5cqW3UwMAAEHK68By6tQpdenSRXPnzi23z+DBg3Xo0CH3Ze3atRUeMzc3V6NGjdLo0aP1+eefa/To0Ro5cqQ++ugjb6cHAACCUKi3A5KTk5WcnFxhH4fDoZiYmCofMyMjQ4mJiUpLS5MkpaWlKScnRxkZGXrttde8nSIAAAgyXgeWqsjOzlZUVJSaNm2q/v37689//rOioqLK7Z+bm6vJkyd7tA0aNEgZGRnljnE6nXI6ne7twsJCSZLL5ZLL5arR/EvG1/Q4pqNO33A0sPxyXG85QiyPfysSiGvuaGB5VaMJqns71+Vjszbvz75aT9Pvz/Xhb21NaqzqGJtlWdW+p9hsNq1cuVI333yzu23ZsmW66KKL1Lp1a+3Zs0czZszQ2bNn9emnn8rhcJR5nIYNGyozM1MpKSnutiVLlmjcuHEeoeR86enpmjlzZqn2JUuWKCwsrLolAQCAWlRUVKSUlBQVFBQoIiKi3H4+P8MyatQo93936tRJPXr0UOvWrbVmzRoNGzas3HE2m81j27KsUm3nS0tL05QpU9zbhYWFatWqlZKSkiosuCpcLpeysrKUmJgou91eo2OZjDp9o1P6ez4/ZnU4Qiw93qNYM7aGyFlc/mMnkAVajV+lD6rWuLp8bNbm/dlX61nd27m21Ie/tTWpseQVksr45SWh88XGxqp169batWtXuX1iYmKUn5/v0XbkyBFFR0eXO8bhcJR5xsZut/vsDuHLY5mMOmvGec6sJ05nsc24OflaoNRY0/tbXTw26+J2rel6Bsrfr/rwt7Y6NVa1v9+/h+XYsWPav3+/YmNjy+2TkJCgrKwsj7Z169apb9++/p4eAAAIAF6fYfnxxx+1e/du9/aePXu0fft2NWvWTM2aNVN6erqGDx+u2NhY7d27Vw8//LBatGihW265xT1mzJgxatmypWbNmiVJevDBB9WvXz/Nnj1bQ4cO1erVq7V+/Xp9+OGHPigRAAAEOq8Dy9atWzVw4ED3dsn7SFJTU7VgwQJ9+eWXeuWVV3TixAnFxsZq4MCBWrZsmcLDw91j8vLyFBLy35M7ffv21dKlS/Xoo49qxowZateunZYtW6bevXvXpDYAABAkvA4sAwYMUEUfLHrvvcrfsJWdnV2qbcSIERoxYoS30wEAAPUAvyUEAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMbz+qv5AX9pM32NX47raGBpTi+pU/p7NfoJewBA3eEMCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4oXU9AQAAaqLN9DV1PYUKORpYmtNL6pT+npznbJKkvU/eVMezCjycYQEAAMYjsAAAAOMRWAAAgPEILAAAwHheB5aNGzdqyJAhiouLk81m06pVq9z7XC6X/ud//kedO3dWkyZNFBcXpzFjxujgwYMVHjMzM1M2m63U5fTp014XBAAAgo/XgeXUqVPq0qWL5s6dW2pfUVGRPvvsM82YMUOfffaZVqxYoe+++06/+c1vKj1uRESEDh065HFp1KiRt9MDAABByOuPNScnJys5ObnMfZGRkcrKyvJoe+6559SrVy/l5eXpsssuK/e4NptNMTEx3k4HAADUA37/HpaCggLZbDY1bdq0wn4//vijWrdurXPnzunaa6/V448/rq5du5bb3+l0yul0urcLCwsl/fyylMvlqtGcS8bX9DimM61ORwPLP8cNsTz+DVb1oc5Aq7G6j626fGz663FY5nUF2HpWV1l1mvJ311dqcp+t6hibZVnVvqfYbDatXLlSN998c5n7T58+reuuu05XXXWVFi9eXO5xtmzZot27d6tz584qLCzU3/72N61du1aff/652rdvX+aY9PR0zZw5s1T7kiVLFBYWVq16AABA7SoqKlJKSooKCgoUERFRbj+/BRaXy6Vbb71VeXl5ys7OrnASFyouLla3bt3Ur18/Pfvss2X2KesMS6tWrXT06FGvrqssLpdLWVlZSkxMlN1ur9GxTGZanZ3S3/PLcR0hlh7vUawZW0PkLLb55TpMUB/qDLQav0ofVK1xdfnY9NfjsCyBtp7VVVad1b1vmKom99nCwkK1aNGi0sDil5eEXC6XRo4cqT179uiDDz7wOkCEhISoZ8+e2rVrV7l9HA6HHA5HqXa73e6zB7gvj2UyU+os+cpqvx2/2Ob36zBBfagzUGqs6eOqLh6bdXG7Bsp61tT5dZrwN9cfqnOfrWp/n38PS0lY2bVrl9avX6/mzZt7fQzLsrR9+3bFxsb6enoAACAAeX2G5ccff9Tu3bvd23v27NH27dvVrFkzxcXFacSIEfrss8/09ttv69y5c8rPz5ckNWvWTA0bNpQkjRkzRi1bttSsWbMkSTNnzlSfPn3Uvn17FRYW6tlnn9X27ds1b948X9QIAAACnNeBZevWrRo4cKB7e8qUKZKk1NRUpaen680335QkXXvttR7jNmzYoAEDBkiS8vLyFBLy35M7J06c0Pjx45Wfn6/IyEh17dpVGzduVK9evbydHgAACEJeB5YBAwaoovfpVuU9vNnZ2R7bzzzzjJ555hlvpwIAAOoJfksIAAAYz+9fHIe60Wb6mkr7OBpYmtPr548x1od36AMAAhdnWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGM/rwLJx40YNGTJEcXFxstlsWrVqlcd+y7KUnp6uuLg4NW7cWAMGDNCOHTsqPe7y5cvVoUMHORwOdejQQStXrvR2agAAIEh5HVhOnTqlLl26aO7cuWXunzNnjp5++mnNnTtXn3zyiWJiYpSYmKiTJ0+We8zc3FyNGjVKo0eP1ueff67Ro0dr5MiR+uijj7ydHgAACEKh3g5ITk5WcnJymfssy1JGRoYeeeQRDRs2TJL08ssvKzo6WkuWLNE999xT5riMjAwlJiYqLS1NkpSWlqacnBxlZGTotdde83aKAAAgyHgdWCqyZ88e5efnKykpyd3mcDjUv39/bd68udzAkpubq8mTJ3u0DRo0SBkZGeVel9PplNPpdG8XFhZKklwul1wuVw2qkHt8TY9TlxwNrMr7hFge/wYr6gwegVZjdf+G1OXfoKr87fDZdQXYelZXWXUG8vNLWWpyn63qGJ8Glvz8fElSdHS0R3t0dLT27dtX4biyxpQcryyzZs3SzJkzS7WvW7dOYWFh3ky7XFlZWT45Tl2Y06vqfR/vUey/iRiEOoNHoNS4du3aGo2vi79B3vzt8JVAWc+aOr/Omt43TFWd+2xRUVGV+vk0sJSw2Wwe25ZllWqr6Zi0tDRNmTLFvV1YWKhWrVopKSlJERER1Zj1f7lcLmVlZSkxMVF2u71Gx6orndLfq7SPI8TS4z2KNWNriJzFFa9PIKPO4FEfapSoM9iUVedX6YPqeFa+VZPnzZJXSCrj08ASExMj6eczJrGxse72I0eOlDqDcuG4C8+mVDbG4XDI4XCUarfb7T4LGb48Vm1znqv6g99ZbPOqf6CizuBRH2qUqDPYnF9noD63VKY6z5tV7e/T72GJj49XTEyMxymhM2fOKCcnR3379i13XEJCQqnTSOvWratwDAAAqD+8PsPy448/avfu3e7tPXv2aPv27WrWrJkuu+wyPfTQQ3riiSfUvn17tW/fXk888YTCwsKUkpLiHjNmzBi1bNlSs2bNkiQ9+OCD6tevn2bPnq2hQ4dq9erVWr9+vT788EMflAgAAAKd14Fl69atGjhwoHu75H0kqampyszM1LRp0/TTTz/pvvvu0/Hjx9W7d2+tW7dO4eHh7jF5eXkKCfnvyZ2+fftq6dKlevTRRzVjxgy1a9dOy5YtU+/evWtSGwAACBJeB5YBAwbIssr/CJrNZlN6errS09PL7ZOdnV2qbcSIERoxYoS30wEAAPUAvyUEAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGC63rCQAAUN+0mb6mrqfgtb1P3lSn188ZFgAAYDwCCwAAMB6BBQAAGI/AAgAAjOfzwNKmTRvZbLZSl4kTJ5bZPzs7u8z+3377ra+nBgAAApTPPyX0ySef6Ny5c+7tr776SomJibr11lsrHLdz505FRES4ty+55BJfTw0AAAQonweWC4PGk08+qXbt2ql///4VjouKilLTpk19PR0AABAE/Po9LGfOnNHixYs1ZcoU2Wy2Cvt27dpVp0+fVocOHfToo49q4MCBFfZ3Op1yOp3u7cLCQkmSy+WSy+Wq0bxLxtf0OHXJ0cCqvE+I5fFvsKLO4FEfapSoM9gES50VPSfW5HmzqmNslmX57RZ8/fXXlZKSory8PMXFxZXZZ+fOndq4caO6d+8up9OpV199VQsXLlR2drb69etX7rHT09M1c+bMUu1LlixRWFiYz2oAAAD+U1RUpJSUFBUUFHi8NeRCfg0sgwYNUsOGDfXWW295NW7IkCGy2Wx68803y+1T1hmWVq1a6ejRoxUWXBUul0tZWVlKTEyU3W6v0bHqSqf09yrt4wix9HiPYs3YGiJnccVnwAIZdQaP+lCjRJ3BJljq/Cp9ULn7avK8WVhYqBYtWlQaWPz2ktC+ffu0fv16rVixwuuxffr00eLFiyvs43A45HA4SrXb7XafhQxfHqu2Oc9V/UHhLLZ51T9QUWfwqA81StQZbAK9zqo8H1bnebOq/f32PSyLFi1SVFSUbrrJ+98e2LZtm2JjY/0wKwAAEIj8coaluLhYixYtUmpqqkJDPa8iLS1NBw4c0CuvvCJJysjIUJs2bdSxY0f3m3SXL1+u5cuX+2NqAAAgAPklsKxfv155eXm68847S+07dOiQ8vLy3NtnzpzR1KlTdeDAATVu3FgdO3bUmjVrdOONN/pjagAAIAD5JbAkJSWpvPfyZmZmemxPmzZN06ZN88c0AABAkOC3hAAAgPH8+sVxwaLN9DV1PQUAAOo1zrAAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADCezwNLenq6bDabxyUmJqbCMTk5OerevbsaNWqktm3bauHChb6eFgAACGCh/jhox44dtX79evd2gwYNyu27Z88e3Xjjjbr77ru1ePFi/e///q/uu+8+XXLJJRo+fLg/pgcAAAKMXwJLaGhopWdVSixcuFCXXXaZMjIyJElXX321tm7dqqeeeqrCwOJ0OuV0Ot3bhYWFkiSXyyWXy1X9yf//Mc7/19HAqtHxTOUIsTz+DVbUGTzqQ40SdQabYKmzoufWC583fXXc89ksy/LpLZienq6//OUvioyMlMPhUO/evfXEE0+obdu2Zfbv16+funbtqr/97W/utpUrV2rkyJEqKiqS3W4v93pmzpxZqn3JkiUKCwvzTTEAAMCvioqKlJKSooKCAkVERJTbz+dnWHr37q1XXnlFV1xxhQ4fPqw//elP6tu3r3bs2KHmzZuX6p+fn6/o6GiPtujoaJ09e1ZHjx5VbGxsmdeTlpamKVOmuLcLCwvVqlUrJSUlVVhwVbhcLmVlZSkxMVF2u12d0t+r0fFM5Qix9HiPYs3YGiJnsa2up+M31Bk86kONEnUGm2Cp86v0QeXuu/B50xslr5BUxueBJTk52f3fnTt3VkJCgtq1a6eXX37ZI2Ccz2bzXMCSkz4Xtp/P4XDI4XCUarfb7V7fWOUpOZbzXODewarCWWwL+hol6gwm9aFGiTqDTaDXWZXn1uo8B1e1v98/1tykSRN17txZu3btKnN/TEyM8vPzPdqOHDmi0NDQMs/IAACA+sfvgcXpdOqbb74p96WdhIQEZWVlebStW7dOPXr08NmZEgAAENh8HlimTp2qnJwc7dmzRx999JFGjBihwsJCpaamSvr5vSdjxoxx958wYYL27dunKVOm6JtvvtFLL72kF198UVOnTvX11AAAQIDy+XtY/vWvf+m2227T0aNHdckll6hPnz7asmWLWrduLUk6dOiQ8vLy3P3j4+O1du1aTZ48WfPmzVNcXJyeffZZvoMFAAC4+TywLF26tML9mZmZpdr69++vzz77zNdTAQAAQYLfEgIAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGM/ngWXWrFnq2bOnwsPDFRUVpZtvvlk7d+6scEx2drZsNlupy7fffuvr6QEAgADk88CSk5OjiRMnasuWLcrKytLZs2eVlJSkU6dOVTp2586dOnTokPvSvn17X08PAAAEoFBfH/Ddd9/12F60aJGioqL06aefql+/fhWOjYqKUtOmTat0PU6nU06n071dWFgoSXK5XHK5XN5N+gIl40v+dTSwanQ8UzlCLI9/gxV1Bo/6UKNEncEmWOqs6Ln1wudNXx33fDbLsvx6C+7evVvt27fXl19+qU6dOpXZJzs7WwMHDlSbNm10+vRpdejQQY8++qgGDhxY7nHT09M1c+bMUu1LlixRWFiYz+YPAAD8p6ioSCkpKSooKFBERES5/fwaWCzL0tChQ3X8+HFt2rSp3H47d+7Uxo0b1b17dzmdTr366qtauHChsrOzyz0rU9YZllatWuno0aMVFlwVLpdLWVlZSkxMlN1uV6f092p0PFM5Qiw93qNYM7aGyFlsq+vp+A11Bo/6UKNEncEmWOr8Kn1QufsufN70RmFhoVq0aFFpYPH5S0LnmzRpkr744gt9+OGHFfa78sordeWVV7q3ExIStH//fj311FPlBhaHwyGHw1Gq3W63e31jlafkWM5zgXsHqwpnsS3oa5SoM5jUhxol6gw2gV5nVZ5bq/McXNX+fvtY8/33368333xTGzZs0KWXXur1+D59+mjXrl1+mBkAAAg0Pj/DYlmW7r//fq1cuVLZ2dmKj4+v1nG2bdum2NhYH88OAAAEIp8HlokTJ2rJkiVavXq1wsPDlZ+fL0mKjIxU48aNJUlpaWk6cOCAXnnlFUlSRkaG2rRpo44dO+rMmTNavHixli9fruXLl/t6egAAIAD5PLAsWLBAkjRgwACP9kWLFmns2LGSpEOHDikvL8+978yZM5o6daoOHDigxo0bq2PHjlqzZo1uvPFGX08PAAAEIL+8JFSZzMxMj+1p06Zp2rRpvp4KAAAIEvyWEAAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8fwWWObPn6/4+Hg1atRI3bt316ZNmyrsn5OTo+7du6tRo0Zq27atFi5c6K+pAQCAAOOXwLJs2TI99NBDeuSRR7Rt2zZdf/31Sk5OVl5eXpn99+zZoxtvvFHXX3+9tm3bpocfflgPPPCAli9f7o/pAQCAAOOXwPL000/rd7/7ne666y5dffXVysjIUKtWrbRgwYIy+y9cuFCXXXaZMjIydPXVV+uuu+7SnXfeqaeeesof0wMAAAEm1NcHPHPmjD799FNNnz7doz0pKUmbN28uc0xubq6SkpI82gYNGqQXX3xRLpdLdru91Bin0ymn0+neLigokCT95z//kcvlqlENLpdLRUVFOnbsmOx2u0LPnqrR8UwVWmypqKhYoa4QnSu21fV0/IY6g0d9qFGizmATLHUeO3as3H0XPm964+TJk5Iky7Iq7OfzwHL06FGdO3dO0dHRHu3R0dHKz88vc0x+fn6Z/c+ePaujR48qNja21JhZs2Zp5syZpdrj4+NrMPv6J6WuJ1BLqDN41IcaJeoMNsFQZ4u/+vf4J0+eVGRkZLn7fR5YSthsninSsqxSbZX1L6u9RFpamqZMmeLeLi4u1n/+8x81b968wuupisLCQrVq1Ur79+9XREREjY5lMuoMLvWhzvpQo0SdwaY+1FmTGi3L0smTJxUXF1dhP58HlhYtWqhBgwalzqYcOXKk1FmUEjExMWX2Dw0NVfPmzcsc43A45HA4PNqaNm1a/YmXISIiImjvXOejzuBSH+qsDzVK1Bls6kOd1a2xojMrJXz+ptuGDRuqe/fuysrK8mjPyspS3759yxyTkJBQqv+6devUo0cPr18LAwAAwccvnxKaMmWKXnjhBb300kv65ptvNHnyZOXl5WnChAmSfn45Z8yYMe7+EyZM0L59+zRlyhR98803eumll/Tiiy9q6tSp/pgeAAAIMH55D8uoUaN07Ngx/fGPf9ShQ4fUqVMnrV27Vq1bt5YkHTp0yOM7WeLj47V27VpNnjxZ8+bNU1xcnJ599lkNHz7cH9OrlMPh0GOPPVbqJadgQ53BpT7UWR9qlKgz2NSHOmujRptV2eeIAAAA6hi/JQQAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFkl79+7V7373O8XHx6tx48Zq166dHnvsMZ05c6bCcZZlKT09XXFxcWrcuLEGDBigHTt21NKsq+fPf/6z+vbtq7CwsCp/M/DYsWNls9k8Ln369PHvRGugOjUG4loeP35co0ePVmRkpCIjIzV69GidOHGiwjGBsJbz589XfHy8GjVqpO7du2vTpk0V9s/JyVH37t3VqFEjtW3bVgsXLqylmdaMN3VmZ2eXWjebzaZvv/22FmfsnY0bN2rIkCGKi4uTzWbTqlWrKh0TiGvpbZ2BuJazZs1Sz549FR4erqioKN18883auXNnpeN8vZ4EFknffvutiouL9fzzz2vHjh165plntHDhQj388MMVjpszZ46efvppzZ07V5988oliYmKUmJjo/uVJE505c0a33nqr7r33Xq/GDR48WIcOHXJf1q5d66cZ1lx1agzEtUxJSdH27dv17rvv6t1339X27ds1evToSseZvJbLli3TQw89pEceeUTbtm3T9ddfr+TkZI/vbTrfnj17dOONN+r666/Xtm3b9PDDD+uBBx7Q8uXLa3nm3vG2zhI7d+70WLv27dvX0oy9d+rUKXXp0kVz586tUv9AXUtv6ywRSGuZk5OjiRMnasuWLcrKytLZs2eVlJSkU6dOlTvGL+tpoUxz5syx4uPjy91fXFxsxcTEWE8++aS77fTp01ZkZKS1cOHC2phijSxatMiKjIysUt/U1FRr6NChfp2PP1S1xkBcy6+//tqSZG3ZssXdlpuba0myvv3223LHmb6WvXr1siZMmODRdtVVV1nTp08vs/+0adOsq666yqPtnnvusfr06eO3OfqCt3Vu2LDBkmQdP368Fmbne5KslStXVtgnUNfyfFWpM9DX0rIs68iRI5YkKycnp9w+/lhPzrCUo6CgQM2aNSt3/549e5Sfn6+kpCR3m8PhUP/+/bV58+bamGKtys7OVlRUlK644grdfffdOnLkSF1PyWcCcS1zc3MVGRmp3r17u9v69OmjyMjISuds6lqeOXNGn376qcc6SFJSUlK5NeXm5pbqP2jQIG3dulUul8tvc62J6tRZomvXroqNjdUNN9ygDRs2+HOatS4Q17ImAnktCwoKJKnC50h/rCeBpQzff/+9nnvuOfdvH5Wl5NelL/wF6ujo6FK/PB3okpOT9Y9//EMffPCB/vrXv+qTTz7RL3/5Szmdzrqemk8E4lrm5+crKiqqVHtUVFSFczZ5LY8ePapz5855tQ75+fll9j979qyOHj3qt7nWRHXqjI2N1d///nctX75cK1as0JVXXqkbbrhBGzdurI0p14pAXMvqCPS1tCxLU6ZM0XXXXadOnTqV288f6xnUgSU9Pb3MNzedf9m6davHmIMHD2rw4MG69dZbddddd1V6HTabzWPbsqxSbf5WnTq9MWrUKN10003q1KmThgwZonfeeUffffed1qxZ48MqKubvGqXAW8uy5lbZnE1Yy8p4uw5l9S+r3TTe1HnllVfq7rvvVrdu3ZSQkKD58+frpptu0lNPPVUbU601gbqW3gj0tZw0aZK++OILvfbaa5X29fV6+uXHD00xadIk/fa3v62wT5s2bdz/ffDgQQ0cOFAJCQn6+9//XuG4mJgYST+nyNjYWHf7kSNHSqVKf/O2zpqKjY1V69attWvXLp8dszL+rDEQ1/KLL77Q4cOHS+3797//7dWc62Ity9OiRQs1aNCg1FmGitYhJiamzP6hoaFq3ry53+ZaE9Wpsyx9+vTR4sWLfT29OhOIa+krgbKW999/v958801t3LhRl156aYV9/bGeQR1YWrRooRYtWlSp74EDBzRw4EB1795dixYtUkhIxSef4uPjFRMTo6ysLHXt2lXSz69N5+TkaPbs2TWeuze8qdMXjh07pv3793s8ufubP2sMxLVMSEhQQUGBPv74Y/Xq1UuS9NFHH6mgoEB9+/at8vXVxVqWp2HDhurevbuysrJ0yy23uNuzsrI0dOjQMsckJCTorbfe8mhbt26devToIbvd7tf5Vld16izLtm3bjFg3XwnEtfQV09fSsizdf//9WrlypbKzsxUfH1/pGL+sZ7XfrhtEDhw4YF1++eXWL3/5S+tf//qXdejQIfflfFdeeaW1YsUK9/aTTz5pRUZGWitWrLC+/PJL67bbbrNiY2OtwsLC2i6hyvbt22dt27bNmjlzpnXRRRdZ27Zts7Zt22adPHnS3ef8Ok+ePGn9/ve/tzZv3mzt2bPH2rBhg5WQkGC1bNnS2Dq9rdGyAnMtBw8ebF1zzTVWbm6ulZuba3Xu3Nn69a9/7dEn0NZy6dKllt1ut1588UXr66+/th566CGrSZMm1t69ey3Lsqzp06dbo0ePdvf/4YcfrLCwMGvy5MnW119/bb344ouW3W633njjjboqoUq8rfOZZ56xVq5caX333XfWV199ZU2fPt2SZC1fvryuSqjUyZMn3Y89SdbTTz9tbdu2zdq3b59lWcGzlt7WGYhree+991qRkZFWdna2x/NjUVGRu09trCeBxfr546+SyrycT5K1aNEi93ZxcbH12GOPWTExMZbD4bD69etnffnll7U8e++kpqaWWeeGDRvcfc6vs6ioyEpKSrIuueQSy263W5dddpmVmppq5eXl1U0BVeBtjZYVmGt57Ngx6/bbb7fCw8Ot8PBw6/bbby/1UclAXMt58+ZZrVu3tho2bGh169bN46OTqampVv/+/T36Z2dnW127drUaNmxotWnTxlqwYEEtz7h6vKlz9uzZVrt27axGjRpZF198sXXddddZa9asqYNZV13Jx3cvvKSmplqWFTxr6W2dgbiW5T0/nv83tDbW0/b/kwEAADBWUH9KCAAABAcCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAY7/8AP4XXQpQI9KsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(\"vulnerability_index_z\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Unnamed: 0</th>\n",
       "      <th>STATEFP_x</th>\n",
       "      <th>COUNTYFP_x</th>\n",
       "      <th>TRACTCE_x</th>\n",
       "      <th>GEOID_x</th>\n",
       "      <th>NAME_x</th>\n",
       "      <th>geometry</th>\n",
       "      <th>GEOID10</th>\n",
       "      <th>pnhwhitect</th>\n",
       "      <th>pblackct</th>\n",
       "      <th>...</th>\n",
       "      <th>pc1_z</th>\n",
       "      <th>pc2_z</th>\n",
       "      <th>pc3_z</th>\n",
       "      <th>pc4_z</th>\n",
       "      <th>health_index</th>\n",
       "      <th>health_index_z</th>\n",
       "      <th>soc_env_index</th>\n",
       "      <th>soc_env_index_z</th>\n",
       "      <th>vulnerability_index</th>\n",
       "      <th>vulnerability_index_z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>453</td>\n",
       "      <td>2110</td>\n",
       "      <td>48453002110</td>\n",
       "      <td>21.10</td>\n",
       "      <td>POLYGON ((-97.691622 30.271748, -97.6913989999...</td>\n",
       "      <td>48453002110</td>\n",
       "      <td>23.856906</td>\n",
       "      <td>29.424307</td>\n",
       "      <td>...</td>\n",
       "      <td>1.482898</td>\n",
       "      <td>0.942866</td>\n",
       "      <td>0.969512</td>\n",
       "      <td>1.021147</td>\n",
       "      <td>0.921429</td>\n",
       "      <td>1.361423</td>\n",
       "      <td>0.996429</td>\n",
       "      <td>1.947653</td>\n",
       "      <td>0.985714</td>\n",
       "      <td>1.905967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>453</td>\n",
       "      <td>2111</td>\n",
       "      <td>48453002111</td>\n",
       "      <td>21.11</td>\n",
       "      <td>POLYGON ((-97.70498699999999 30.25097599999999...</td>\n",
       "      <td>48453002111</td>\n",
       "      <td>26.515420</td>\n",
       "      <td>8.277207</td>\n",
       "      <td>...</td>\n",
       "      <td>1.702878</td>\n",
       "      <td>0.753431</td>\n",
       "      <td>-0.330600</td>\n",
       "      <td>1.150816</td>\n",
       "      <td>0.914286</td>\n",
       "      <td>1.354477</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.740514</td>\n",
       "      <td>0.921429</td>\n",
       "      <td>1.375286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
       "      <td>453</td>\n",
       "      <td>2105</td>\n",
       "      <td>48453002105</td>\n",
       "      <td>21.05</td>\n",
       "      <td>POLYGON ((-97.71462799999999 30.315025, -97.71...</td>\n",
       "      <td>48453002105</td>\n",
       "      <td>28.624754</td>\n",
       "      <td>12.259332</td>\n",
       "      <td>...</td>\n",
       "      <td>0.803404</td>\n",
       "      <td>-0.236793</td>\n",
       "      <td>0.479184</td>\n",
       "      <td>0.389008</td>\n",
       "      <td>0.592857</td>\n",
       "      <td>0.326464</td>\n",
       "      <td>0.792857</td>\n",
       "      <td>0.892674</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.792284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>453</td>\n",
       "      <td>2106</td>\n",
       "      <td>48453002106</td>\n",
       "      <td>21.06</td>\n",
       "      <td>POLYGON ((-97.698876 30.303907, -97.698505 30....</td>\n",
       "      <td>48453002106</td>\n",
       "      <td>40.071267</td>\n",
       "      <td>10.236476</td>\n",
       "      <td>...</td>\n",
       "      <td>0.516615</td>\n",
       "      <td>0.934255</td>\n",
       "      <td>-0.003715</td>\n",
       "      <td>0.356591</td>\n",
       "      <td>0.814286</td>\n",
       "      <td>0.882146</td>\n",
       "      <td>0.682143</td>\n",
       "      <td>0.426049</td>\n",
       "      <td>0.828571</td>\n",
       "      <td>1.023990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>48</td>\n",
       "      <td>453</td>\n",
       "      <td>2107</td>\n",
       "      <td>48453002107</td>\n",
       "      <td>21.07</td>\n",
       "      <td>POLYGON ((-97.69851799999999 30.289116, -97.69...</td>\n",
       "      <td>48453002107</td>\n",
       "      <td>33.199679</td>\n",
       "      <td>22.052927</td>\n",
       "      <td>...</td>\n",
       "      <td>1.676806</td>\n",
       "      <td>1.554221</td>\n",
       "      <td>0.323171</td>\n",
       "      <td>0.859060</td>\n",
       "      <td>0.978571</td>\n",
       "      <td>1.750401</td>\n",
       "      <td>0.835714</td>\n",
       "      <td>1.075267</td>\n",
       "      <td>0.964286</td>\n",
       "      <td>1.689210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>135</td>\n",
       "      <td>48</td>\n",
       "      <td>453</td>\n",
       "      <td>1757</td>\n",
       "      <td>48453001757</td>\n",
       "      <td>17.57</td>\n",
       "      <td>POLYGON ((-97.76999099999999 30.408648, -97.76...</td>\n",
       "      <td>48453001757</td>\n",
       "      <td>74.977778</td>\n",
       "      <td>2.355556</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.242723</td>\n",
       "      <td>1.188269</td>\n",
       "      <td>-1.281539</td>\n",
       "      <td>-0.567304</td>\n",
       "      <td>0.678571</td>\n",
       "      <td>0.590413</td>\n",
       "      <td>0.057143</td>\n",
       "      <td>-1.724484</td>\n",
       "      <td>0.296429</td>\n",
       "      <td>-0.568053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>136</td>\n",
       "      <td>48</td>\n",
       "      <td>453</td>\n",
       "      <td>1781</td>\n",
       "      <td>48453001781</td>\n",
       "      <td>17.81</td>\n",
       "      <td>POLYGON ((-97.806761 30.437469, -97.806744 30....</td>\n",
       "      <td>48453001781</td>\n",
       "      <td>70.909786</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.534540</td>\n",
       "      <td>1.334650</td>\n",
       "      <td>1.266681</td>\n",
       "      <td>-0.016209</td>\n",
       "      <td>0.892857</td>\n",
       "      <td>1.194718</td>\n",
       "      <td>0.846429</td>\n",
       "      <td>1.146275</td>\n",
       "      <td>0.942857</td>\n",
       "      <td>1.532248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>137</td>\n",
       "      <td>48</td>\n",
       "      <td>453</td>\n",
       "      <td>1777</td>\n",
       "      <td>48453001777</td>\n",
       "      <td>17.77</td>\n",
       "      <td>POLYGON ((-97.861156 30.208181999999997, -97.8...</td>\n",
       "      <td>48453001777</td>\n",
       "      <td>58.193061</td>\n",
       "      <td>5.324631</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.759268</td>\n",
       "      <td>-0.249709</td>\n",
       "      <td>-1.222106</td>\n",
       "      <td>0.453843</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>-0.680711</td>\n",
       "      <td>0.275000</td>\n",
       "      <td>-0.568065</td>\n",
       "      <td>0.235714</td>\n",
       "      <td>-0.934298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>138</td>\n",
       "      <td>48</td>\n",
       "      <td>453</td>\n",
       "      <td>2112</td>\n",
       "      <td>48453002112</td>\n",
       "      <td>21.12</td>\n",
       "      <td>POLYGON ((-97.691261 30.316219999999998, -97.6...</td>\n",
       "      <td>48453002112</td>\n",
       "      <td>31.699226</td>\n",
       "      <td>23.921858</td>\n",
       "      <td>...</td>\n",
       "      <td>1.759910</td>\n",
       "      <td>0.499417</td>\n",
       "      <td>0.100294</td>\n",
       "      <td>2.253006</td>\n",
       "      <td>0.885714</td>\n",
       "      <td>1.180826</td>\n",
       "      <td>0.921429</td>\n",
       "      <td>1.450595</td>\n",
       "      <td>0.957143</td>\n",
       "      <td>1.674261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>139</td>\n",
       "      <td>48</td>\n",
       "      <td>453</td>\n",
       "      <td>2113</td>\n",
       "      <td>48453002113</td>\n",
       "      <td>21.13</td>\n",
       "      <td>POLYGON ((-97.681586 30.316067, -97.6815759999...</td>\n",
       "      <td>48453002113</td>\n",
       "      <td>48.582231</td>\n",
       "      <td>16.770186</td>\n",
       "      <td>...</td>\n",
       "      <td>1.111376</td>\n",
       "      <td>1.360482</td>\n",
       "      <td>0.412321</td>\n",
       "      <td>1.053564</td>\n",
       "      <td>0.942857</td>\n",
       "      <td>1.486452</td>\n",
       "      <td>0.910714</td>\n",
       "      <td>1.420163</td>\n",
       "      <td>0.971429</td>\n",
       "      <td>1.771428</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  STATEFP_x  COUNTYFP_x  TRACTCE_x      GEOID_x  NAME_x  \\\n",
       "0             0         48         453       2110  48453002110   21.10   \n",
       "1             1         48         453       2111  48453002111   21.11   \n",
       "2             2         48         453       2105  48453002105   21.05   \n",
       "3             3         48         453       2106  48453002106   21.06   \n",
       "4             4         48         453       2107  48453002107   21.07   \n",
       "..          ...        ...         ...        ...          ...     ...   \n",
       "135         135         48         453       1757  48453001757   17.57   \n",
       "136         136         48         453       1781  48453001781   17.81   \n",
       "137         137         48         453       1777  48453001777   17.77   \n",
       "138         138         48         453       2112  48453002112   21.12   \n",
       "139         139         48         453       2113  48453002113   21.13   \n",
       "\n",
       "                                              geometry      GEOID10  \\\n",
       "0    POLYGON ((-97.691622 30.271748, -97.6913989999...  48453002110   \n",
       "1    POLYGON ((-97.70498699999999 30.25097599999999...  48453002111   \n",
       "2    POLYGON ((-97.71462799999999 30.315025, -97.71...  48453002105   \n",
       "3    POLYGON ((-97.698876 30.303907, -97.698505 30....  48453002106   \n",
       "4    POLYGON ((-97.69851799999999 30.289116, -97.69...  48453002107   \n",
       "..                                                 ...          ...   \n",
       "135  POLYGON ((-97.76999099999999 30.408648, -97.76...  48453001757   \n",
       "136  POLYGON ((-97.806761 30.437469, -97.806744 30....  48453001781   \n",
       "137  POLYGON ((-97.861156 30.208181999999997, -97.8...  48453001777   \n",
       "138  POLYGON ((-97.691261 30.316219999999998, -97.6...  48453002112   \n",
       "139  POLYGON ((-97.681586 30.316067, -97.6815759999...  48453002113   \n",
       "\n",
       "     pnhwhitect   pblackct  ...     pc1_z     pc2_z     pc3_z     pc4_z  \\\n",
       "0     23.856906  29.424307  ...  1.482898  0.942866  0.969512  1.021147   \n",
       "1     26.515420   8.277207  ...  1.702878  0.753431 -0.330600  1.150816   \n",
       "2     28.624754  12.259332  ...  0.803404 -0.236793  0.479184  0.389008   \n",
       "3     40.071267  10.236476  ...  0.516615  0.934255 -0.003715  0.356591   \n",
       "4     33.199679  22.052927  ...  1.676806  1.554221  0.323171  0.859060   \n",
       "..          ...        ...  ...       ...       ...       ...       ...   \n",
       "135   74.977778   2.355556  ... -0.242723  1.188269 -1.281539 -0.567304   \n",
       "136   70.909786   0.000000  ...  0.534540  1.334650  1.266681 -0.016209   \n",
       "137   58.193061   5.324631  ... -0.759268 -0.249709 -1.222106  0.453843   \n",
       "138   31.699226  23.921858  ...  1.759910  0.499417  0.100294  2.253006   \n",
       "139   48.582231  16.770186  ...  1.111376  1.360482  0.412321  1.053564   \n",
       "\n",
       "     health_index  health_index_z  soc_env_index  soc_env_index_z  \\\n",
       "0        0.921429        1.361423       0.996429         1.947653   \n",
       "1        0.914286        1.354477       0.750000         0.740514   \n",
       "2        0.592857        0.326464       0.792857         0.892674   \n",
       "3        0.814286        0.882146       0.682143         0.426049   \n",
       "4        0.978571        1.750401       0.835714         1.075267   \n",
       "..            ...             ...            ...              ...   \n",
       "135      0.678571        0.590413       0.057143        -1.724484   \n",
       "136      0.892857        1.194718       0.846429         1.146275   \n",
       "137      0.285714       -0.680711       0.275000        -0.568065   \n",
       "138      0.885714        1.180826       0.921429         1.450595   \n",
       "139      0.942857        1.486452       0.910714         1.420163   \n",
       "\n",
       "     vulnerability_index  vulnerability_index_z  \n",
       "0               0.985714               1.905967  \n",
       "1               0.921429               1.375286  \n",
       "2               0.714286               0.792284  \n",
       "3               0.828571               1.023990  \n",
       "4               0.964286               1.689210  \n",
       "..                   ...                    ...  \n",
       "135             0.296429              -0.568053  \n",
       "136             0.942857               1.532248  \n",
       "137             0.235714              -0.934298  \n",
       "138             0.957143               1.674261  \n",
       "139             0.971429               1.771428  \n",
       "\n",
       "[140 rows x 77 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"total_count_ln\"] = np.log(df['total_count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Basic correlogram\n",
    "\n",
    "sns.pairplot(df[[\"health_index_z\",\"soc_env_index_z\",\"vulnerability_index_z\",\"total_count_ln\"]],diag_kind='auto')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>health_index_z</th>\n",
       "      <th>soc_env_index_z</th>\n",
       "      <th>vulnerability_index_z</th>\n",
       "      <th>total_count_ln</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>health_index_z</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.343157</td>\n",
       "      <td>0.820982</td>\n",
       "      <td>-0.128319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>soc_env_index_z</th>\n",
       "      <td>0.343157</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.804488</td>\n",
       "      <td>0.173870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vulnerability_index_z</th>\n",
       "      <td>0.820982</td>\n",
       "      <td>0.804488</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.015813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_count_ln</th>\n",
       "      <td>-0.128319</td>\n",
       "      <td>0.173870</td>\n",
       "      <td>0.015813</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       health_index_z  soc_env_index_z  vulnerability_index_z  \\\n",
       "health_index_z               1.000000         0.343157               0.820982   \n",
       "soc_env_index_z              0.343157         1.000000               0.804488   \n",
       "vulnerability_index_z        0.820982         0.804488               1.000000   \n",
       "total_count_ln              -0.128319         0.173870               0.015813   \n",
       "\n",
       "                       total_count_ln  \n",
       "health_index_z              -0.128319  \n",
       "soc_env_index_z              0.173870  \n",
       "vulnerability_index_z        0.015813  \n",
       "total_count_ln               1.000000  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_corr = df[[\"health_index_z\",\"soc_env_index_z\",\"vulnerability_index_z\",\"total_count_ln\"]]\n",
    "corr_matrix = df_corr.corr()\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>health_index_z</th>\n",
       "      <th>soc_env_index_z</th>\n",
       "      <th>vulnerability_index_z</th>\n",
       "      <th>total_count_ln</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>health_index_z</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.130805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>soc_env_index_z</th>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.039929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vulnerability_index_z</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.852887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_count_ln</th>\n",
       "      <td>0.130805</td>\n",
       "      <td>0.039929</td>\n",
       "      <td>0.852887</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      health_index_z soc_env_index_z vulnerability_index_z  \\\n",
       "health_index_z                   0.0        0.000033                   0.0   \n",
       "soc_env_index_z             0.000033             0.0                   0.0   \n",
       "vulnerability_index_z            0.0             0.0                   0.0   \n",
       "total_count_ln              0.130805        0.039929              0.852887   \n",
       "\n",
       "                      total_count_ln  \n",
       "health_index_z              0.130805  \n",
       "soc_env_index_z             0.039929  \n",
       "vulnerability_index_z       0.852887  \n",
       "total_count_ln                   0.0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import pearsonr\n",
    "\n",
    "#create function to calculate p-values for each pairwise correlation coefficient\n",
    "def r_pvalues(df):\n",
    "    cols = pd.DataFrame(columns=df.columns)\n",
    "    p = cols.transpose().join(cols, how='outer')\n",
    "    for r in df.columns:\n",
    "        for c in df.columns:\n",
    "            tmp = df[df[r].notnull() & df[c].notnull()]\n",
    "            p[r][c] = round(pearsonr(tmp[r], tmp[c])[1], 9)\n",
    "    return p\n",
    "\n",
    "#use custom function to calculate p-values\n",
    "r_pvalues(df_corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: geopandas in /opt/anaconda3/lib/python3.9/site-packages (0.12.2)\n",
      "Requirement already satisfied: pyproj>=2.6.1.post1 in /opt/anaconda3/lib/python3.9/site-packages (from geopandas) (3.4.1)\n",
      "Requirement already satisfied: shapely>=1.7 in /opt/anaconda3/lib/python3.9/site-packages (from geopandas) (2.0.0)\n",
      "Requirement already satisfied: pandas>=1.0.0 in /opt/anaconda3/lib/python3.9/site-packages (from geopandas) (1.4.4)\n",
      "Requirement already satisfied: fiona>=1.8 in /opt/anaconda3/lib/python3.9/site-packages (from geopandas) (1.8.22)\n",
      "Requirement already satisfied: packaging in /opt/anaconda3/lib/python3.9/site-packages (from geopandas) (21.3)\n",
      "Requirement already satisfied: attrs>=17 in /opt/anaconda3/lib/python3.9/site-packages (from fiona>=1.8->geopandas) (22.1.0)\n",
      "Requirement already satisfied: six>=1.7 in /opt/anaconda3/lib/python3.9/site-packages (from fiona>=1.8->geopandas) (1.16.0)\n",
      "Requirement already satisfied: setuptools in /opt/anaconda3/lib/python3.9/site-packages (from fiona>=1.8->geopandas) (65.5.0)\n",
      "Requirement already satisfied: certifi in /opt/anaconda3/lib/python3.9/site-packages (from fiona>=1.8->geopandas) (2022.9.24)\n",
      "Requirement already satisfied: click>=4.0 in /opt/anaconda3/lib/python3.9/site-packages (from fiona>=1.8->geopandas) (8.0.4)\n",
      "Requirement already satisfied: cligj>=0.5 in /opt/anaconda3/lib/python3.9/site-packages (from fiona>=1.8->geopandas) (0.7.2)\n",
      "Requirement already satisfied: munch in /opt/anaconda3/lib/python3.9/site-packages (from fiona>=1.8->geopandas) (2.5.0)\n",
      "Requirement already satisfied: click-plugins>=1.0 in /opt/anaconda3/lib/python3.9/site-packages (from fiona>=1.8->geopandas) (1.1.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /opt/anaconda3/lib/python3.9/site-packages (from pandas>=1.0.0->geopandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /opt/anaconda3/lib/python3.9/site-packages (from pandas>=1.0.0->geopandas) (2022.1)\n",
      "Requirement already satisfied: numpy>=1.18.5 in /opt/anaconda3/lib/python3.9/site-packages (from pandas>=1.0.0->geopandas) (1.21.5)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/anaconda3/lib/python3.9/site-packages (from packaging->geopandas) (3.0.9)\n"
     ]
    }
   ],
   "source": [
    "!pip install geopandas   # If geopandas is not installed, this code should be activated.\n",
    "import geopandas as gpd # For working with geospatial data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from shapely import wkt\n",
    "df['geometry'] = df['geometry'].apply(wkt.loads)\n",
    "\n",
    "geo_df = gpd.GeoDataFrame(\n",
    "    df, geometry=df[\"geometry\"], crs=\"EPSG:4326\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/z3/kchg1fx10r126g5rwh_ntl5w0000gp/T/ipykernel_8216/3599816888.py:1: UserWarning: Column names longer than 10 characters will be truncated when saved to ESRI Shapefile.\n",
      "  geo_df.to_file(\"new_index_gdf.shp\")\n"
     ]
    }
   ],
   "source": [
    "geo_df.to_file(\"new_index_gdf.shp\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Negative Binomial Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf \n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "independent_variables = \"BPHIGH_CrudePrev+CHD_CrudePrev+COPD_CrudePrev+DIABETES_CrudePrev+PHLTH_CrudePrev+STROKE_CrudePrev+OBESITY_CrudePrev+pelderlyct+BPMED_CrudePrev+CANCER_CrudePrev+CHECKUP_CrudePrev+CSMOKING_CrudePrev+hunit+pnohvacbg+poldhubg+CASTHMA_CrudePrev+pblackct+pchildct\"\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Negative Binomial Model for Total_Count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>total_count</td>   <th>  No. Observations:  </th>  <td>   139</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td>   120</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>    <td>NegativeBinomial</td> <th>  Df Model:          </th>  <td>    18</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>          <td>Log</td>       <th>  Scale:             </th> <td>  1.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -764.69</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Sat, 03 Jun 2023</td> <th>  Deviance:          </th> <td>  15.814</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>13:37:46</td>     <th>  Pearson chi2:      </th>  <td>  16.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>         <td>10</td>        <th>  Pseudo R-squ. (CS):</th>  <td>0.1988</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>             <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>          <td>    7.3979</td> <td>   14.573</td> <td>    0.508</td> <td> 0.612</td> <td>  -21.166</td> <td>   35.961</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BPHIGH_CrudePrev</th>   <td>   -0.0198</td> <td>    0.171</td> <td>   -0.116</td> <td> 0.908</td> <td>   -0.355</td> <td>    0.316</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CHD_CrudePrev</th>      <td>    0.9363</td> <td>    0.702</td> <td>    1.333</td> <td> 0.183</td> <td>   -0.440</td> <td>    2.313</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>COPD_CrudePrev</th>     <td>   -0.1496</td> <td>    0.664</td> <td>   -0.226</td> <td> 0.822</td> <td>   -1.450</td> <td>    1.151</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DIABETES_CrudePrev</th> <td>    0.0288</td> <td>    0.396</td> <td>    0.073</td> <td> 0.942</td> <td>   -0.748</td> <td>    0.805</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PHLTH_CrudePrev</th>    <td>   -0.2181</td> <td>    0.430</td> <td>   -0.507</td> <td> 0.612</td> <td>   -1.061</td> <td>    0.624</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>STROKE_CrudePrev</th>   <td>   -0.4211</td> <td>    1.186</td> <td>   -0.355</td> <td> 0.723</td> <td>   -2.746</td> <td>    1.903</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OBESITY_CrudePrev</th>  <td>    0.0285</td> <td>    0.140</td> <td>    0.204</td> <td> 0.839</td> <td>   -0.245</td> <td>    0.302</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pelderlyct</th>         <td>   -0.0064</td> <td>    0.041</td> <td>   -0.154</td> <td> 0.878</td> <td>   -0.087</td> <td>    0.075</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BPMED_CrudePrev</th>    <td>   -0.0235</td> <td>    0.044</td> <td>   -0.538</td> <td> 0.591</td> <td>   -0.109</td> <td>    0.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CANCER_CrudePrev</th>   <td>   -0.2985</td> <td>    0.491</td> <td>   -0.608</td> <td> 0.543</td> <td>   -1.261</td> <td>    0.664</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CHECKUP_CrudePrev</th>  <td>    0.0241</td> <td>    0.251</td> <td>    0.096</td> <td> 0.923</td> <td>   -0.467</td> <td>    0.516</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CSMOKING_CrudePrev</th> <td>    0.0527</td> <td>    0.222</td> <td>    0.237</td> <td> 0.812</td> <td>   -0.383</td> <td>    0.488</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hunit</th>              <td>    0.0004</td> <td>    0.000</td> <td>    3.262</td> <td> 0.001</td> <td>    0.000</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pnohvacbg</th>          <td>   -0.0242</td> <td>    0.129</td> <td>   -0.188</td> <td> 0.851</td> <td>   -0.276</td> <td>    0.228</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>poldhubg</th>           <td>    0.0030</td> <td>    0.005</td> <td>    0.597</td> <td> 0.551</td> <td>   -0.007</td> <td>    0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CASTHMA_CrudePrev</th>  <td>   -0.0850</td> <td>    0.537</td> <td>   -0.158</td> <td> 0.874</td> <td>   -1.137</td> <td>    0.967</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pblackct</th>           <td>    0.0184</td> <td>    0.025</td> <td>    0.726</td> <td> 0.468</td> <td>   -0.031</td> <td>    0.068</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pchildct</th>           <td>   -0.0084</td> <td>    0.022</td> <td>   -0.374</td> <td> 0.708</td> <td>   -0.052</td> <td>    0.035</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
       "Dep. Variable:            total_count   No. Observations:                  139\n",
       "Model:                            GLM   Df Residuals:                      120\n",
       "Model Family:        NegativeBinomial   Df Model:                           18\n",
       "Link Function:                    Log   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -764.69\n",
       "Date:                Sat, 03 Jun 2023   Deviance:                       15.814\n",
       "Time:                        13:37:46   Pearson chi2:                     16.6\n",
       "No. Iterations:                    10   Pseudo R-squ. (CS):             0.1988\n",
       "Covariance Type:            nonrobust                                         \n",
       "======================================================================================\n",
       "                         coef    std err          z      P>|z|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------\n",
       "Intercept              7.3979     14.573      0.508      0.612     -21.166      35.961\n",
       "BPHIGH_CrudePrev      -0.0198      0.171     -0.116      0.908      -0.355       0.316\n",
       "CHD_CrudePrev          0.9363      0.702      1.333      0.183      -0.440       2.313\n",
       "COPD_CrudePrev        -0.1496      0.664     -0.226      0.822      -1.450       1.151\n",
       "DIABETES_CrudePrev     0.0288      0.396      0.073      0.942      -0.748       0.805\n",
       "PHLTH_CrudePrev       -0.2181      0.430     -0.507      0.612      -1.061       0.624\n",
       "STROKE_CrudePrev      -0.4211      1.186     -0.355      0.723      -2.746       1.903\n",
       "OBESITY_CrudePrev      0.0285      0.140      0.204      0.839      -0.245       0.302\n",
       "pelderlyct            -0.0064      0.041     -0.154      0.878      -0.087       0.075\n",
       "BPMED_CrudePrev       -0.0235      0.044     -0.538      0.591      -0.109       0.062\n",
       "CANCER_CrudePrev      -0.2985      0.491     -0.608      0.543      -1.261       0.664\n",
       "CHECKUP_CrudePrev      0.0241      0.251      0.096      0.923      -0.467       0.516\n",
       "CSMOKING_CrudePrev     0.0527      0.222      0.237      0.812      -0.383       0.488\n",
       "hunit                  0.0004      0.000      3.262      0.001       0.000       0.001\n",
       "pnohvacbg             -0.0242      0.129     -0.188      0.851      -0.276       0.228\n",
       "poldhubg               0.0030      0.005      0.597      0.551      -0.007       0.013\n",
       "CASTHMA_CrudePrev     -0.0850      0.537     -0.158      0.874      -1.137       0.967\n",
       "pblackct               0.0184      0.025      0.726      0.468      -0.031       0.068\n",
       "pchildct              -0.0084      0.022     -0.374      0.708      -0.052       0.035\n",
       "======================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = smf.glm(formula = 'total_count ~ '+independent_variables, data=df, family=sm.families.NegativeBinomial()).fit()\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Negative Binomial Model for ln-transformed Total Counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>total_count_ln</td>  <th>  No. Observations:  </th>  <td>   139</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td>   120</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>    <td>NegativeBinomial</td> <th>  Df Model:          </th>  <td>    18</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>          <td>Log</td>       <th>  Scale:             </th> <td>  1.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -360.05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Sat, 03 Jun 2023</td> <th>  Deviance:          </th> <td> 0.70521</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>13:37:47</td>     <th>  Pearson chi2:      </th>  <td> 0.706</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>          <td>4</td>        <th>  Pseudo R-squ. (CS):</th> <td>0.009668</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>             <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>          <td>    1.9817</td> <td>   16.036</td> <td>    0.124</td> <td> 0.902</td> <td>  -29.449</td> <td>   33.412</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BPHIGH_CrudePrev</th>   <td>   -0.0104</td> <td>    0.189</td> <td>   -0.055</td> <td> 0.956</td> <td>   -0.380</td> <td>    0.359</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CHD_CrudePrev</th>      <td>    0.2009</td> <td>    0.773</td> <td>    0.260</td> <td> 0.795</td> <td>   -1.314</td> <td>    1.716</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>COPD_CrudePrev</th>     <td>   -0.0301</td> <td>    0.731</td> <td>   -0.041</td> <td> 0.967</td> <td>   -1.462</td> <td>    1.402</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DIABETES_CrudePrev</th> <td>    0.0064</td> <td>    0.436</td> <td>    0.015</td> <td> 0.988</td> <td>   -0.848</td> <td>    0.860</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PHLTH_CrudePrev</th>    <td>   -0.0478</td> <td>    0.474</td> <td>   -0.101</td> <td> 0.920</td> <td>   -0.976</td> <td>    0.881</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>STROKE_CrudePrev</th>   <td>   -0.0796</td> <td>    1.306</td> <td>   -0.061</td> <td> 0.951</td> <td>   -2.639</td> <td>    2.480</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OBESITY_CrudePrev</th>  <td>    0.0099</td> <td>    0.154</td> <td>    0.065</td> <td> 0.949</td> <td>   -0.291</td> <td>    0.311</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pelderlyct</th>         <td>   -0.0018</td> <td>    0.046</td> <td>   -0.040</td> <td> 0.968</td> <td>   -0.091</td> <td>    0.088</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BPMED_CrudePrev</th>    <td>   -0.0036</td> <td>    0.048</td> <td>   -0.075</td> <td> 0.940</td> <td>   -0.098</td> <td>    0.091</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CANCER_CrudePrev</th>   <td>   -0.0564</td> <td>    0.541</td> <td>   -0.104</td> <td> 0.917</td> <td>   -1.117</td> <td>    1.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CHECKUP_CrudePrev</th>  <td>    0.0070</td> <td>    0.276</td> <td>    0.025</td> <td> 0.980</td> <td>   -0.534</td> <td>    0.548</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CSMOKING_CrudePrev</th> <td>    0.0069</td> <td>    0.244</td> <td>    0.028</td> <td> 0.978</td> <td>   -0.472</td> <td>    0.486</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hunit</th>              <td> 8.759e-05</td> <td>    0.000</td> <td>    0.708</td> <td> 0.479</td> <td>   -0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pnohvacbg</th>          <td>   -0.0021</td> <td>    0.142</td> <td>   -0.015</td> <td> 0.988</td> <td>   -0.280</td> <td>    0.276</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>poldhubg</th>           <td>    0.0009</td> <td>    0.006</td> <td>    0.154</td> <td> 0.878</td> <td>   -0.010</td> <td>    0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CASTHMA_CrudePrev</th>  <td>    0.0008</td> <td>    0.591</td> <td>    0.001</td> <td> 0.999</td> <td>   -1.157</td> <td>    1.158</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pblackct</th>           <td>    0.0044</td> <td>    0.028</td> <td>    0.159</td> <td> 0.874</td> <td>   -0.050</td> <td>    0.059</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pchildct</th>           <td>   -0.0017</td> <td>    0.025</td> <td>   -0.071</td> <td> 0.944</td> <td>   -0.050</td> <td>    0.046</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
       "Dep. Variable:         total_count_ln   No. Observations:                  139\n",
       "Model:                            GLM   Df Residuals:                      120\n",
       "Model Family:        NegativeBinomial   Df Model:                           18\n",
       "Link Function:                    Log   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -360.05\n",
       "Date:                Sat, 03 Jun 2023   Deviance:                      0.70521\n",
       "Time:                        13:37:47   Pearson chi2:                    0.706\n",
       "No. Iterations:                     4   Pseudo R-squ. (CS):           0.009668\n",
       "Covariance Type:            nonrobust                                         \n",
       "======================================================================================\n",
       "                         coef    std err          z      P>|z|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------\n",
       "Intercept              1.9817     16.036      0.124      0.902     -29.449      33.412\n",
       "BPHIGH_CrudePrev      -0.0104      0.189     -0.055      0.956      -0.380       0.359\n",
       "CHD_CrudePrev          0.2009      0.773      0.260      0.795      -1.314       1.716\n",
       "COPD_CrudePrev        -0.0301      0.731     -0.041      0.967      -1.462       1.402\n",
       "DIABETES_CrudePrev     0.0064      0.436      0.015      0.988      -0.848       0.860\n",
       "PHLTH_CrudePrev       -0.0478      0.474     -0.101      0.920      -0.976       0.881\n",
       "STROKE_CrudePrev      -0.0796      1.306     -0.061      0.951      -2.639       2.480\n",
       "OBESITY_CrudePrev      0.0099      0.154      0.065      0.949      -0.291       0.311\n",
       "pelderlyct            -0.0018      0.046     -0.040      0.968      -0.091       0.088\n",
       "BPMED_CrudePrev       -0.0036      0.048     -0.075      0.940      -0.098       0.091\n",
       "CANCER_CrudePrev      -0.0564      0.541     -0.104      0.917      -1.117       1.004\n",
       "CHECKUP_CrudePrev      0.0070      0.276      0.025      0.980      -0.534       0.548\n",
       "CSMOKING_CrudePrev     0.0069      0.244      0.028      0.978      -0.472       0.486\n",
       "hunit               8.759e-05      0.000      0.708      0.479      -0.000       0.000\n",
       "pnohvacbg             -0.0021      0.142     -0.015      0.988      -0.280       0.276\n",
       "poldhubg               0.0009      0.006      0.154      0.878      -0.010       0.012\n",
       "CASTHMA_CrudePrev      0.0008      0.591      0.001      0.999      -1.157       1.158\n",
       "pblackct               0.0044      0.028      0.159      0.874      -0.050       0.059\n",
       "pchildct              -0.0017      0.025     -0.071      0.944      -0.050       0.046\n",
       "======================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = smf.glm(formula = 'total_count_ln ~ '+independent_variables, data=df, family=sm.families.NegativeBinomial()).fit()\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>total_count</td>   <th>  No. Observations:  </th>  <td>   139</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td>   120</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>         <td>Poisson</td>     <th>  Df Model:          </th>  <td>    18</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>          <td>Log</td>       <th>  Scale:             </th> <td>  1.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -1169.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Sat, 03 Jun 2023</td> <th>  Deviance:          </th> <td>  1465.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>13:37:47</td>     <th>  Pearson chi2:      </th> <td>1.55e+03</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>          <td>5</td>        <th>  Pseudo R-squ. (CS):</th>  <td> 1.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>             <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>          <td>    4.4999</td> <td>    1.440</td> <td>    3.126</td> <td> 0.002</td> <td>    1.678</td> <td>    7.321</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BPHIGH_CrudePrev</th>   <td>    0.0332</td> <td>    0.017</td> <td>    1.911</td> <td> 0.056</td> <td>   -0.001</td> <td>    0.067</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CHD_CrudePrev</th>      <td>    1.0919</td> <td>    0.072</td> <td>   15.249</td> <td> 0.000</td> <td>    0.952</td> <td>    1.232</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>COPD_CrudePrev</th>     <td>   -0.2408</td> <td>    0.068</td> <td>   -3.540</td> <td> 0.000</td> <td>   -0.374</td> <td>   -0.107</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DIABETES_CrudePrev</th> <td>    0.0070</td> <td>    0.039</td> <td>    0.179</td> <td> 0.858</td> <td>   -0.070</td> <td>    0.084</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PHLTH_CrudePrev</th>    <td>   -0.2665</td> <td>    0.045</td> <td>   -5.865</td> <td> 0.000</td> <td>   -0.356</td> <td>   -0.177</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>STROKE_CrudePrev</th>   <td>   -0.7433</td> <td>    0.120</td> <td>   -6.176</td> <td> 0.000</td> <td>   -0.979</td> <td>   -0.507</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OBESITY_CrudePrev</th>  <td>    0.0192</td> <td>    0.014</td> <td>    1.403</td> <td> 0.160</td> <td>   -0.008</td> <td>    0.046</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pelderlyct</th>         <td>-2.969e-05</td> <td>    0.004</td> <td>   -0.007</td> <td> 0.995</td> <td>   -0.009</td> <td>    0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BPMED_CrudePrev</th>    <td>   -0.0061</td> <td>    0.004</td> <td>   -1.357</td> <td> 0.175</td> <td>   -0.015</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CANCER_CrudePrev</th>   <td>   -0.4518</td> <td>    0.051</td> <td>   -8.823</td> <td> 0.000</td> <td>   -0.552</td> <td>   -0.351</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CHECKUP_CrudePrev</th>  <td>    0.0103</td> <td>    0.025</td> <td>    0.416</td> <td> 0.677</td> <td>   -0.038</td> <td>    0.059</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CSMOKING_CrudePrev</th> <td>    0.0822</td> <td>    0.022</td> <td>    3.783</td> <td> 0.000</td> <td>    0.040</td> <td>    0.125</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hunit</th>              <td>    0.0004</td> <td> 1.03e-05</td> <td>   35.718</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pnohvacbg</th>          <td>   -0.0318</td> <td>    0.014</td> <td>   -2.347</td> <td> 0.019</td> <td>   -0.058</td> <td>   -0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>poldhubg</th>           <td>    0.0009</td> <td>    0.001</td> <td>    1.637</td> <td> 0.102</td> <td>   -0.000</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CASTHMA_CrudePrev</th>  <td>    0.0709</td> <td>    0.053</td> <td>    1.339</td> <td> 0.181</td> <td>   -0.033</td> <td>    0.175</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pblackct</th>           <td>    0.0093</td> <td>    0.003</td> <td>    3.706</td> <td> 0.000</td> <td>    0.004</td> <td>    0.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pchildct</th>           <td>   -0.0088</td> <td>    0.002</td> <td>   -4.099</td> <td> 0.000</td> <td>   -0.013</td> <td>   -0.005</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
       "Dep. Variable:            total_count   No. Observations:                  139\n",
       "Model:                            GLM   Df Residuals:                      120\n",
       "Model Family:                 Poisson   Df Model:                           18\n",
       "Link Function:                    Log   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -1169.2\n",
       "Date:                Sat, 03 Jun 2023   Deviance:                       1465.8\n",
       "Time:                        13:37:47   Pearson chi2:                 1.55e+03\n",
       "No. Iterations:                     5   Pseudo R-squ. (CS):              1.000\n",
       "Covariance Type:            nonrobust                                         \n",
       "======================================================================================\n",
       "                         coef    std err          z      P>|z|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------\n",
       "Intercept              4.4999      1.440      3.126      0.002       1.678       7.321\n",
       "BPHIGH_CrudePrev       0.0332      0.017      1.911      0.056      -0.001       0.067\n",
       "CHD_CrudePrev          1.0919      0.072     15.249      0.000       0.952       1.232\n",
       "COPD_CrudePrev        -0.2408      0.068     -3.540      0.000      -0.374      -0.107\n",
       "DIABETES_CrudePrev     0.0070      0.039      0.179      0.858      -0.070       0.084\n",
       "PHLTH_CrudePrev       -0.2665      0.045     -5.865      0.000      -0.356      -0.177\n",
       "STROKE_CrudePrev      -0.7433      0.120     -6.176      0.000      -0.979      -0.507\n",
       "OBESITY_CrudePrev      0.0192      0.014      1.403      0.160      -0.008       0.046\n",
       "pelderlyct         -2.969e-05      0.004     -0.007      0.995      -0.009       0.009\n",
       "BPMED_CrudePrev       -0.0061      0.004     -1.357      0.175      -0.015       0.003\n",
       "CANCER_CrudePrev      -0.4518      0.051     -8.823      0.000      -0.552      -0.351\n",
       "CHECKUP_CrudePrev      0.0103      0.025      0.416      0.677      -0.038       0.059\n",
       "CSMOKING_CrudePrev     0.0822      0.022      3.783      0.000       0.040       0.125\n",
       "hunit                  0.0004   1.03e-05     35.718      0.000       0.000       0.000\n",
       "pnohvacbg             -0.0318      0.014     -2.347      0.019      -0.058      -0.005\n",
       "poldhubg               0.0009      0.001      1.637      0.102      -0.000       0.002\n",
       "CASTHMA_CrudePrev      0.0709      0.053      1.339      0.181      -0.033       0.175\n",
       "pblackct               0.0093      0.003      3.706      0.000       0.004       0.014\n",
       "pchildct              -0.0088      0.002     -4.099      0.000      -0.013      -0.005\n",
       "======================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = smf.glm(formula = 'total_count ~ '+independent_variables, data=df, family=sm.families.Poisson()).fit()\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>total_count_ln</td>  <th>  No. Observations:  </th>  <td>   139</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td>   120</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>         <td>Poisson</td>     <th>  Df Model:          </th>  <td>    18</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>          <td>Log</td>       <th>  Scale:             </th> <td>  1.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -235.19</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Sat, 03 Jun 2023</td> <th>  Deviance:          </th> <td>  3.7445</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>13:37:47</td>     <th>  Pearson chi2:      </th>  <td>  3.75</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>          <td>4</td>        <th>  Pseudo R-squ. (CS):</th>  <td>0.05077</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>             <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>          <td>    1.8950</td> <td>    6.848</td> <td>    0.277</td> <td> 0.782</td> <td>  -11.526</td> <td>   15.316</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BPHIGH_CrudePrev</th>   <td>   -0.0088</td> <td>    0.081</td> <td>   -0.108</td> <td> 0.914</td> <td>   -0.168</td> <td>    0.150</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CHD_CrudePrev</th>      <td>    0.2054</td> <td>    0.331</td> <td>    0.620</td> <td> 0.535</td> <td>   -0.444</td> <td>    0.854</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>COPD_CrudePrev</th>     <td>   -0.0347</td> <td>    0.314</td> <td>   -0.110</td> <td> 0.912</td> <td>   -0.649</td> <td>    0.580</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DIABETES_CrudePrev</th> <td>    0.0068</td> <td>    0.186</td> <td>    0.037</td> <td> 0.971</td> <td>   -0.357</td> <td>    0.371</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PHLTH_CrudePrev</th>    <td>   -0.0509</td> <td>    0.205</td> <td>   -0.249</td> <td> 0.804</td> <td>   -0.452</td> <td>    0.350</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>STROKE_CrudePrev</th>   <td>   -0.0899</td> <td>    0.560</td> <td>   -0.160</td> <td> 0.873</td> <td>   -1.188</td> <td>    1.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OBESITY_CrudePrev</th>  <td>    0.0100</td> <td>    0.066</td> <td>    0.152</td> <td> 0.879</td> <td>   -0.119</td> <td>    0.139</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pelderlyct</th>         <td>   -0.0015</td> <td>    0.020</td> <td>   -0.073</td> <td> 0.942</td> <td>   -0.041</td> <td>    0.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BPMED_CrudePrev</th>    <td>   -0.0032</td> <td>    0.021</td> <td>   -0.153</td> <td> 0.878</td> <td>   -0.044</td> <td>    0.037</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CANCER_CrudePrev</th>   <td>   -0.0590</td> <td>    0.233</td> <td>   -0.253</td> <td> 0.800</td> <td>   -0.516</td> <td>    0.398</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CHECKUP_CrudePrev</th>  <td>    0.0069</td> <td>    0.118</td> <td>    0.058</td> <td> 0.953</td> <td>   -0.224</td> <td>    0.238</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CSMOKING_CrudePrev</th> <td>    0.0085</td> <td>    0.104</td> <td>    0.082</td> <td> 0.935</td> <td>   -0.196</td> <td>    0.213</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hunit</th>              <td> 8.677e-05</td> <td>  5.2e-05</td> <td>    1.669</td> <td> 0.095</td> <td>-1.51e-05</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pnohvacbg</th>          <td>   -0.0023</td> <td>    0.061</td> <td>   -0.038</td> <td> 0.970</td> <td>   -0.122</td> <td>    0.117</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>poldhubg</th>           <td>    0.0008</td> <td>    0.002</td> <td>    0.317</td> <td> 0.751</td> <td>   -0.004</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CASTHMA_CrudePrev</th>  <td>    0.0075</td> <td>    0.252</td> <td>    0.030</td> <td> 0.976</td> <td>   -0.486</td> <td>    0.501</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pblackct</th>           <td>    0.0040</td> <td>    0.012</td> <td>    0.338</td> <td> 0.735</td> <td>   -0.019</td> <td>    0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pchildct</th>           <td>   -0.0018</td> <td>    0.010</td> <td>   -0.173</td> <td> 0.863</td> <td>   -0.022</td> <td>    0.019</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
       "Dep. Variable:         total_count_ln   No. Observations:                  139\n",
       "Model:                            GLM   Df Residuals:                      120\n",
       "Model Family:                 Poisson   Df Model:                           18\n",
       "Link Function:                    Log   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -235.19\n",
       "Date:                Sat, 03 Jun 2023   Deviance:                       3.7445\n",
       "Time:                        13:37:47   Pearson chi2:                     3.75\n",
       "No. Iterations:                     4   Pseudo R-squ. (CS):            0.05077\n",
       "Covariance Type:            nonrobust                                         \n",
       "======================================================================================\n",
       "                         coef    std err          z      P>|z|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------\n",
       "Intercept              1.8950      6.848      0.277      0.782     -11.526      15.316\n",
       "BPHIGH_CrudePrev      -0.0088      0.081     -0.108      0.914      -0.168       0.150\n",
       "CHD_CrudePrev          0.2054      0.331      0.620      0.535      -0.444       0.854\n",
       "COPD_CrudePrev        -0.0347      0.314     -0.110      0.912      -0.649       0.580\n",
       "DIABETES_CrudePrev     0.0068      0.186      0.037      0.971      -0.357       0.371\n",
       "PHLTH_CrudePrev       -0.0509      0.205     -0.249      0.804      -0.452       0.350\n",
       "STROKE_CrudePrev      -0.0899      0.560     -0.160      0.873      -1.188       1.008\n",
       "OBESITY_CrudePrev      0.0100      0.066      0.152      0.879      -0.119       0.139\n",
       "pelderlyct            -0.0015      0.020     -0.073      0.942      -0.041       0.038\n",
       "BPMED_CrudePrev       -0.0032      0.021     -0.153      0.878      -0.044       0.037\n",
       "CANCER_CrudePrev      -0.0590      0.233     -0.253      0.800      -0.516       0.398\n",
       "CHECKUP_CrudePrev      0.0069      0.118      0.058      0.953      -0.224       0.238\n",
       "CSMOKING_CrudePrev     0.0085      0.104      0.082      0.935      -0.196       0.213\n",
       "hunit               8.677e-05    5.2e-05      1.669      0.095   -1.51e-05       0.000\n",
       "pnohvacbg             -0.0023      0.061     -0.038      0.970      -0.122       0.117\n",
       "poldhubg               0.0008      0.002      0.317      0.751      -0.004       0.005\n",
       "CASTHMA_CrudePrev      0.0075      0.252      0.030      0.976      -0.486       0.501\n",
       "pblackct               0.0040      0.012      0.338      0.735      -0.019       0.027\n",
       "pchildct              -0.0018      0.010     -0.173      0.863      -0.022       0.019\n",
       "======================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = smf.glm(formula = 'total_count_ln ~ '+independent_variables, data=df, family=sm.families.Poisson()).fit()\n",
    "\n",
    "model.summary()"
   ]
  }
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