{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Geographically Weighted Regression (Part II): COVID 19 Mortality\n",
    "\n",
    "<b>Author:</b>  Naomi W. Lazarus, PhD<br>\n",
    "<b>Date Created:</b> 6-15-21\n",
    "\n",
    "This notebook provides the code for running a Geographically Weighted Regression (GWR) using COVID-19 death-case ratios as the dependent variable and independent variables representing age and underlying conditions. It is an exploratory analysis of spatial relationships between COVID-19, age demographics, and comorbidities.  It is not recommended for predictive purposes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook Outline\n",
    "\n",
    "- [Introduction](#Intro)\n",
    "    - [Install MGWR package](#Install)\n",
    "    - [Load required packages](#Load)\n",
    "- [Load and preview data for deat-case ratios for peak period 1](#Peak1)\n",
    "    - [Preprocess data for GWR](#Data1)\n",
    "    - [Review GWR Results](#Results1)\n",
    "- [Load and preview data for death-case ratios for peak period 2](#Peak2)\n",
    "    - [Preprocess data for GWR](#Data2)\n",
    "    - [Review GWR Results](#Results2)\n",
    "- [References](#Ref)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Intro'></a>\n",
    "# Introduction\n",
    "\n",
    "The notebooks consists of two sections.  The first uses covid-19 death-case ratios for peak period 1 (03/01/20 - 04/30/20).  The second uses covid-19 death-case ratios for peak period 2 (06/01/20 - 07/31/20). The following links provide information on file descriptions and metadata. \n",
    "\n",
    "[Link to Metadata](Data_Files2/Metadata.txt)<br>\n",
    "[Link to file descriptions](Data_Files2/Files.txt)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Install'></a>\n",
    "## Install MGWR package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    from mgwr.gwr import GWR\n",
    "except:\n",
    "    print('Installing MGWR')\n",
    "    ! pip install -U mgwr\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Load'></a>\n",
    "## Load required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import libpysal as ps\n",
    "from spreg import OLS\n",
    "from mgwr.gwr import GWR, MGWR\n",
    "from mgwr.sel_bw import Sel_BW\n",
    "from mgwr.utils import compare_surfaces, truncate_colormap\n",
    "import geopandas as gp\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Peak1'></a>\n",
    "# Load and preview data for death-case ratios for peak period 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_DR1 = gp.read_file('/home/jovyan/shared_data/data/geospatialfellows21/lazarus_data/Data_Files2/Layer_DR1_1.shp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>COUNTYNS</th>\n",
       "      <th>GEOID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>NAMELSAD</th>\n",
       "      <th>LSAD</th>\n",
       "      <th>CLASSFP</th>\n",
       "      <th>MTFCC</th>\n",
       "      <th>CSAFP</th>\n",
       "      <th>...</th>\n",
       "      <th>PCT_over75</th>\n",
       "      <th>DIAB_PCT</th>\n",
       "      <th>CARDIO_MR</th>\n",
       "      <th>OBESE_PCT</th>\n",
       "      <th>DR1_log</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>00835876</td>\n",
       "      <td>31109</td>\n",
       "      <td>Lancaster</td>\n",
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       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>339</td>\n",
       "      <td>...</td>\n",
       "      <td>5.4</td>\n",
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       "      <td>186.9</td>\n",
       "      <td>29.9</td>\n",
       "      <td>-0.380211</td>\n",
       "      <td>191498.450302</td>\n",
       "      <td>2.181031e+09</td>\n",
       "      <td>-5.775873e+04</td>\n",
       "      <td>198290.895484</td>\n",
       "      <td>POLYGON ((-76484.353 198568.818, -76478.478 19...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46</td>\n",
       "      <td>099</td>\n",
       "      <td>01265772</td>\n",
       "      <td>46099</td>\n",
       "      <td>Minnehaha</td>\n",
       "      <td>Minnehaha County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>5.1</td>\n",
       "      <td>8.2</td>\n",
       "      <td>229.2</td>\n",
       "      <td>31.1</td>\n",
       "      <td>-0.230023</td>\n",
       "      <td>186372.852811</td>\n",
       "      <td>2.103144e+09</td>\n",
       "      <td>-6.367993e+04</td>\n",
       "      <td>519393.038636</td>\n",
       "      <td>POLYGON ((-71439.032 538818.414, -71405.569 53...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39</td>\n",
       "      <td>063</td>\n",
       "      <td>01074044</td>\n",
       "      <td>39063</td>\n",
       "      <td>Hancock</td>\n",
       "      <td>Hancock County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>534</td>\n",
       "      <td>...</td>\n",
       "      <td>7.1</td>\n",
       "      <td>9.8</td>\n",
       "      <td>296.3</td>\n",
       "      <td>35.9</td>\n",
       "      <td>0.431798</td>\n",
       "      <td>154277.000259</td>\n",
       "      <td>1.375313e+09</td>\n",
       "      <td>1.029369e+06</td>\n",
       "      <td>291980.881448</td>\n",
       "      <td>POLYGON ((1010444.794 298225.136, 1010431.187 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>189</td>\n",
       "      <td>01383880</td>\n",
       "      <td>48189</td>\n",
       "      <td>Hale</td>\n",
       "      <td>Hale County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>352</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.7</td>\n",
       "      <td>266.0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>1.204120</td>\n",
       "      <td>204211.504278</td>\n",
       "      <td>2.597938e+09</td>\n",
       "      <td>-5.365911e+05</td>\n",
       "      <td>-529891.392654</td>\n",
       "      <td>POLYGON ((-561065.818 -536037.871, -560987.294...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01</td>\n",
       "      <td>027</td>\n",
       "      <td>00161539</td>\n",
       "      <td>01027</td>\n",
       "      <td>Clay</td>\n",
       "      <td>Clay County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>8.3</td>\n",
       "      <td>14.2</td>\n",
       "      <td>452.0</td>\n",
       "      <td>37.1</td>\n",
       "      <td>0.677781</td>\n",
       "      <td>203930.129612</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP COUNTYFP  COUNTYNS  GEOID       NAME          NAMELSAD LSAD CLASSFP  \\\n",
       "0      31      109  00835876  31109  Lancaster  Lancaster County   06      H1   \n",
       "1      46      099  01265772  46099  Minnehaha  Minnehaha County   06      H1   \n",
       "2      39      063  01074044  39063    Hancock    Hancock County   06      H1   \n",
       "3      48      189  01383880  48189       Hale       Hale County   06      H1   \n",
       "4      01      027  00161539  01027       Clay       Clay County   06      H1   \n",
       "\n",
       "   MTFCC CSAFP  ... PCT_over75 DIAB_PCT CARDIO_MR  OBESE_PCT   DR1_log  \\\n",
       "0  G4020   339  ...        5.4      8.1     186.9       29.9 -0.380211   \n",
       "1  G4020  None  ...        5.1      8.2     229.2       31.1 -0.230023   \n",
       "2  G4020   534  ...        7.1      9.8     296.3       35.9  0.431798   \n",
       "3  G4020   352  ...        6.0     13.7     266.0       31.5  1.204120   \n",
       "4  G4020  None  ...        8.3     14.2     452.0       37.1  0.677781   \n",
       "\n",
       "      Shape_Leng    Shape_Area             X              Y  \\\n",
       "0  191498.450302  2.181031e+09 -5.775873e+04  198290.895484   \n",
       "1  186372.852811  2.103144e+09 -6.367993e+04  519393.038636   \n",
       "2  154277.000259  1.375313e+09  1.029369e+06  291980.881448   \n",
       "3  204211.504278  2.597938e+09 -5.365911e+05 -529891.392654   \n",
       "4  203930.129612  1.568824e+09  9.422905e+05 -583488.845178   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-76484.353 198568.818, -76478.478 19...  \n",
       "1  POLYGON ((-71439.032 538818.414, -71405.569 53...  \n",
       "2  POLYGON ((1010444.794 298225.136, 1010431.187 ...  \n",
       "3  POLYGON ((-561065.818 -536037.871, -560987.294...  \n",
       "4  POLYGON ((930647.181 -578247.838, 930643.004 -...  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_DR1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Data1'></a>\n",
    "## Preprocess data for GWR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run Ordinary Least Squares Regression\n",
    "\n",
    "y = covid_DR1['DR1_log'].values.reshape((-1, 1))\n",
    "X = covid_DR1[['PCT_50to74', 'PCT_over75', 'DIAB_PCT', 'CARDIO_MR', 'OBESE_PCT']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REGRESSION\n",
      "----------\n",
      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES\n",
      "-----------------------------------------\n",
      "Data set            :     unknown\n",
      "Weights matrix      :        None\n",
      "Dependent Variable  :     dep_var                Number of Observations:        1445\n",
      "Mean dependent var  :      0.6915                Number of Variables   :           6\n",
      "S.D. dependent var  :      0.3642                Degrees of Freedom    :        1439\n",
      "R-squared           :      0.1142\n",
      "Adjusted R-squared  :      0.1111\n",
      "Sum squared residual:     169.686                F-statistic           :     37.1065\n",
      "Sigma-square        :       0.118                Prob(F-statistic)     :   7.282e-36\n",
      "S.E. of regression  :       0.343                Log likelihood        :    -502.833\n",
      "Sigma-square ML     :       0.117                Akaike info criterion :    1017.665\n",
      "S.E of regression ML:      0.3427                Schwarz criterion     :    1049.320\n",
      "\n",
      "------------------------------------------------------------------------------------\n",
      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
      "------------------------------------------------------------------------------------\n",
      "            CONSTANT       0.1410451       0.0882119       1.5989338       0.1100548\n",
      "               var_1       0.0097492       0.0032037       3.0431016       0.0023839\n",
      "               var_2       0.0174308       0.0079537       2.1915246       0.0285737\n",
      "               var_3      -0.0043206       0.0034979      -1.2351952       0.2169595\n",
      "               var_4       0.0007210       0.0001403       5.1381415       0.0000003\n",
      "               var_5      -0.0015088       0.0020531      -0.7348922       0.4625249\n",
      "------------------------------------------------------------------------------------\n",
      "\n",
      "REGRESSION DIAGNOSTICS\n",
      "MULTICOLLINEARITY CONDITION NUMBER           33.912\n",
      "\n",
      "TEST ON NORMALITY OF ERRORS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Jarque-Bera                       2         117.196           0.0000\n",
      "\n",
      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
      "RANDOM COEFFICIENTS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Breusch-Pagan test                5          29.735           0.0000\n",
      "Koenker-Bassett test              5          18.369           0.0025\n",
      "================================ END OF REPORT =====================================\n"
     ]
    }
   ],
   "source": [
    "ols = OLS(y, X)\n",
    "print(ols.summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining variables and coordinates\n",
    "\n",
    "g_y = covid_DR1['DR1_log'].values.reshape((-1, 1))\n",
    "g_X = covid_DR1[['PCT_50to74', 'PCT_over75', 'DIAB_PCT', 'CARDIO_MR', 'OBESE_PCT']].values\n",
    "u = covid_DR1['X']\n",
    "v = covid_DR1['Y']\n",
    "g_coords = list(zip(u, v))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "g_y:\n",
      " [[-0.38021124]\n",
      " [-0.23002293]\n",
      " [ 0.43179828]\n",
      " [ 1.20411998]\n",
      " [ 0.67778071]]\n",
      "\n",
      "g_X:\n",
      " [[ 24.7   5.4   8.1 186.9  29.9]\n",
      " [ 25.7   5.1   8.2 229.2  31.1]\n",
      " [ 29.9   7.1   9.8 296.3  35.9]\n",
      " [ 23.8   6.   13.7 266.   31.5]\n",
      " [ 33.6   8.3  14.2 452.   37.1]]\n",
      "\n",
      "u:\n",
      " [-57758.7266368, -63679.9337388, 1029369.27457, -536591.069548, 942290.494088]\n",
      "\n",
      "v:\n",
      " [198290.895484, 519393.038636, 291980.881448, -529891.392654, -583488.845178]\n",
      "\n",
      "g_coords:\n",
      " [(-57758.7266368, 198290.895484), (-63679.9337388, 519393.038636), (1029369.27457, 291980.881448), (-536591.069548, -529891.392654), (942290.494088, -583488.845178)] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Inspecting the data contents\n",
    "\n",
    "print('g_y:\\n', g_y[:5])\n",
    "print('\\ng_X:\\n', g_X[:5])\n",
    "print('\\nu:\\n', list(u[:5]))\n",
    "print('\\nv:\\n', list(v[:5]))\n",
    "print('\\ng_coords:\\n', g_coords[:5], \"\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "210.0\n"
     ]
    }
   ],
   "source": [
    "# Testing suitable bandwidth prior to specifying the model\n",
    "\n",
    "gwr_selector = Sel_BW(g_coords, g_y, g_X)\n",
    "gwr_bw = gwr_selector.search()\n",
    "print(gwr_bw)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specifying the GWR model - bandwidth set at 210 neighbors using an adaptive Gaussian kernal function\n",
    "\n",
    "gwr_model = GWR(g_coords, g_y, g_X, bw=210, fixed=False, kernel='gaussian')\n",
    "gwr_results = gwr_model.fit()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Results1'></a>\n",
    "## Review GWR Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "156.37939932960043\n",
      "936.4351388419129\n",
      "0.18366972233771262\n",
      "0.17023045708720108\n"
     ]
    }
   ],
   "source": [
    "# Print Global regression results\n",
    "\n",
    "print(gwr_results.resid_ss)\n",
    "print(gwr_results.aic)\n",
    "print(gwr_results.R2)\n",
    "print(gwr_results.adj_R2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a column to store R2 values in the dataframe\n",
    "\n",
    "covid_DR1['R2'] = gwr_results.localR2   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
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       "      <th>...</th>\n",
       "      <th>DIAB_PCT</th>\n",
       "      <th>CARDIO_MR</th>\n",
       "      <th>OBESE_PCT</th>\n",
       "      <th>DR1_log</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>geometry</th>\n",
       "      <th>R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31</td>\n",
       "      <td>109</td>\n",
       "      <td>00835876</td>\n",
       "      <td>31109</td>\n",
       "      <td>Lancaster</td>\n",
       "      <td>Lancaster County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>339</td>\n",
       "      <td>...</td>\n",
       "      <td>8.1</td>\n",
       "      <td>186.9</td>\n",
       "      <td>29.9</td>\n",
       "      <td>-0.380211</td>\n",
       "      <td>191498.450302</td>\n",
       "      <td>2.181031e+09</td>\n",
       "      <td>-5.775873e+04</td>\n",
       "      <td>198290.895484</td>\n",
       "      <td>POLYGON ((-76484.353 198568.818, -76478.478 19...</td>\n",
       "      <td>0.215712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46</td>\n",
       "      <td>099</td>\n",
       "      <td>01265772</td>\n",
       "      <td>46099</td>\n",
       "      <td>Minnehaha</td>\n",
       "      <td>Minnehaha County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>8.2</td>\n",
       "      <td>229.2</td>\n",
       "      <td>31.1</td>\n",
       "      <td>-0.230023</td>\n",
       "      <td>186372.852811</td>\n",
       "      <td>2.103144e+09</td>\n",
       "      <td>-6.367993e+04</td>\n",
       "      <td>519393.038636</td>\n",
       "      <td>POLYGON ((-71439.032 538818.414, -71405.569 53...</td>\n",
       "      <td>0.215420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39</td>\n",
       "      <td>063</td>\n",
       "      <td>01074044</td>\n",
       "      <td>39063</td>\n",
       "      <td>Hancock</td>\n",
       "      <td>Hancock County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>534</td>\n",
       "      <td>...</td>\n",
       "      <td>9.8</td>\n",
       "      <td>296.3</td>\n",
       "      <td>35.9</td>\n",
       "      <td>0.431798</td>\n",
       "      <td>154277.000259</td>\n",
       "      <td>1.375313e+09</td>\n",
       "      <td>1.029369e+06</td>\n",
       "      <td>291980.881448</td>\n",
       "      <td>POLYGON ((1010444.794 298225.136, 1010431.187 ...</td>\n",
       "      <td>0.103319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>189</td>\n",
       "      <td>01383880</td>\n",
       "      <td>48189</td>\n",
       "      <td>Hale</td>\n",
       "      <td>Hale County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>352</td>\n",
       "      <td>...</td>\n",
       "      <td>13.7</td>\n",
       "      <td>266.0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>1.204120</td>\n",
       "      <td>204211.504278</td>\n",
       "      <td>2.597938e+09</td>\n",
       "      <td>-5.365911e+05</td>\n",
       "      <td>-529891.392654</td>\n",
       "      <td>POLYGON ((-561065.818 -536037.871, -560987.294...</td>\n",
       "      <td>0.240960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01</td>\n",
       "      <td>027</td>\n",
       "      <td>00161539</td>\n",
       "      <td>01027</td>\n",
       "      <td>Clay</td>\n",
       "      <td>Clay County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>14.2</td>\n",
       "      <td>452.0</td>\n",
       "      <td>37.1</td>\n",
       "      <td>0.677781</td>\n",
       "      <td>203930.129612</td>\n",
       "      <td>1.568824e+09</td>\n",
       "      <td>9.422905e+05</td>\n",
       "      <td>-583488.845178</td>\n",
       "      <td>POLYGON ((930647.181 -578247.838, 930643.004 -...</td>\n",
       "      <td>0.089116</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP COUNTYFP  COUNTYNS  GEOID       NAME          NAMELSAD LSAD CLASSFP  \\\n",
       "0      31      109  00835876  31109  Lancaster  Lancaster County   06      H1   \n",
       "1      46      099  01265772  46099  Minnehaha  Minnehaha County   06      H1   \n",
       "2      39      063  01074044  39063    Hancock    Hancock County   06      H1   \n",
       "3      48      189  01383880  48189       Hale       Hale County   06      H1   \n",
       "4      01      027  00161539  01027       Clay       Clay County   06      H1   \n",
       "\n",
       "   MTFCC CSAFP  ... DIAB_PCT CARDIO_MR OBESE_PCT   DR1_log     Shape_Leng  \\\n",
       "0  G4020   339  ...      8.1     186.9      29.9 -0.380211  191498.450302   \n",
       "1  G4020  None  ...      8.2     229.2      31.1 -0.230023  186372.852811   \n",
       "2  G4020   534  ...      9.8     296.3      35.9  0.431798  154277.000259   \n",
       "3  G4020   352  ...     13.7     266.0      31.5  1.204120  204211.504278   \n",
       "4  G4020  None  ...     14.2     452.0      37.1  0.677781  203930.129612   \n",
       "\n",
       "     Shape_Area             X              Y  \\\n",
       "0  2.181031e+09 -5.775873e+04  198290.895484   \n",
       "1  2.103144e+09 -6.367993e+04  519393.038636   \n",
       "2  1.375313e+09  1.029369e+06  291980.881448   \n",
       "3  2.597938e+09 -5.365911e+05 -529891.392654   \n",
       "4  1.568824e+09  9.422905e+05 -583488.845178   \n",
       "\n",
       "                                            geometry        R2  \n",
       "0  POLYGON ((-76484.353 198568.818, -76478.478 19...  0.215712  \n",
       "1  POLYGON ((-71439.032 538818.414, -71405.569 53...  0.215420  \n",
       "2  POLYGON ((1010444.794 298225.136, 1010431.187 ...  0.103319  \n",
       "3  POLYGON ((-561065.818 -536037.871, -560987.294...  0.240960  \n",
       "4  POLYGON ((930647.181 -578247.838, 930643.004 -...  0.089116  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_DR1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing local R2 values on a map\n",
    "\n",
    "covid_DR1['R2'] = gwr_results.localR2\n",
    "covid_DR1.plot('R2', legend = True)\n",
    "ax = plt.gca()\n",
    "ax.get_xaxis().set_visible(False)\n",
    "ax.get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a column to store standardized residual values in the dataframe\n",
    "\n",
    "covid_DR1['SR'] = gwr_results.std_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>COUNTYNS</th>\n",
       "      <th>GEOID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>NAMELSAD</th>\n",
       "      <th>LSAD</th>\n",
       "      <th>CLASSFP</th>\n",
       "      <th>MTFCC</th>\n",
       "      <th>CSAFP</th>\n",
       "      <th>...</th>\n",
       "      <th>CARDIO_MR</th>\n",
       "      <th>OBESE_PCT</th>\n",
       "      <th>DR1_log</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>geometry</th>\n",
       "      <th>R2</th>\n",
       "      <th>SR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31</td>\n",
       "      <td>109</td>\n",
       "      <td>00835876</td>\n",
       "      <td>31109</td>\n",
       "      <td>Lancaster</td>\n",
       "      <td>Lancaster County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>339</td>\n",
       "      <td>...</td>\n",
       "      <td>186.9</td>\n",
       "      <td>29.9</td>\n",
       "      <td>-0.380211</td>\n",
       "      <td>191498.450302</td>\n",
       "      <td>2.181031e+09</td>\n",
       "      <td>-5.775873e+04</td>\n",
       "      <td>198290.895484</td>\n",
       "      <td>POLYGON ((-76484.353 198568.818, -76478.478 19...</td>\n",
       "      <td>0.215712</td>\n",
       "      <td>-2.678272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46</td>\n",
       "      <td>099</td>\n",
       "      <td>01265772</td>\n",
       "      <td>46099</td>\n",
       "      <td>Minnehaha</td>\n",
       "      <td>Minnehaha County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>229.2</td>\n",
       "      <td>31.1</td>\n",
       "      <td>-0.230023</td>\n",
       "      <td>186372.852811</td>\n",
       "      <td>2.103144e+09</td>\n",
       "      <td>-6.367993e+04</td>\n",
       "      <td>519393.038636</td>\n",
       "      <td>POLYGON ((-71439.032 538818.414, -71405.569 53...</td>\n",
       "      <td>0.215420</td>\n",
       "      <td>-2.302788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39</td>\n",
       "      <td>063</td>\n",
       "      <td>01074044</td>\n",
       "      <td>39063</td>\n",
       "      <td>Hancock</td>\n",
       "      <td>Hancock County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>534</td>\n",
       "      <td>...</td>\n",
       "      <td>296.3</td>\n",
       "      <td>35.9</td>\n",
       "      <td>0.431798</td>\n",
       "      <td>154277.000259</td>\n",
       "      <td>1.375313e+09</td>\n",
       "      <td>1.029369e+06</td>\n",
       "      <td>291980.881448</td>\n",
       "      <td>POLYGON ((1010444.794 298225.136, 1010431.187 ...</td>\n",
       "      <td>0.103319</td>\n",
       "      <td>-0.732327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>189</td>\n",
       "      <td>01383880</td>\n",
       "      <td>48189</td>\n",
       "      <td>Hale</td>\n",
       "      <td>Hale County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>352</td>\n",
       "      <td>...</td>\n",
       "      <td>266.0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>1.204120</td>\n",
       "      <td>204211.504278</td>\n",
       "      <td>2.597938e+09</td>\n",
       "      <td>-5.365911e+05</td>\n",
       "      <td>-529891.392654</td>\n",
       "      <td>POLYGON ((-561065.818 -536037.871, -560987.294...</td>\n",
       "      <td>0.240960</td>\n",
       "      <td>1.929299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01</td>\n",
       "      <td>027</td>\n",
       "      <td>00161539</td>\n",
       "      <td>01027</td>\n",
       "      <td>Clay</td>\n",
       "      <td>Clay County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>452.0</td>\n",
       "      <td>37.1</td>\n",
       "      <td>0.677781</td>\n",
       "      <td>203930.129612</td>\n",
       "      <td>1.568824e+09</td>\n",
       "      <td>9.422905e+05</td>\n",
       "      <td>-583488.845178</td>\n",
       "      <td>POLYGON ((930647.181 -578247.838, 930643.004 -...</td>\n",
       "      <td>0.089116</td>\n",
       "      <td>-0.208833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP COUNTYFP  COUNTYNS  GEOID       NAME          NAMELSAD LSAD CLASSFP  \\\n",
       "0      31      109  00835876  31109  Lancaster  Lancaster County   06      H1   \n",
       "1      46      099  01265772  46099  Minnehaha  Minnehaha County   06      H1   \n",
       "2      39      063  01074044  39063    Hancock    Hancock County   06      H1   \n",
       "3      48      189  01383880  48189       Hale       Hale County   06      H1   \n",
       "4      01      027  00161539  01027       Clay       Clay County   06      H1   \n",
       "\n",
       "   MTFCC CSAFP  ... CARDIO_MR OBESE_PCT   DR1_log     Shape_Leng  \\\n",
       "0  G4020   339  ...     186.9      29.9 -0.380211  191498.450302   \n",
       "1  G4020  None  ...     229.2      31.1 -0.230023  186372.852811   \n",
       "2  G4020   534  ...     296.3      35.9  0.431798  154277.000259   \n",
       "3  G4020   352  ...     266.0      31.5  1.204120  204211.504278   \n",
       "4  G4020  None  ...     452.0      37.1  0.677781  203930.129612   \n",
       "\n",
       "     Shape_Area             X              Y  \\\n",
       "0  2.181031e+09 -5.775873e+04  198290.895484   \n",
       "1  2.103144e+09 -6.367993e+04  519393.038636   \n",
       "2  1.375313e+09  1.029369e+06  291980.881448   \n",
       "3  2.597938e+09 -5.365911e+05 -529891.392654   \n",
       "4  1.568824e+09  9.422905e+05 -583488.845178   \n",
       "\n",
       "                                            geometry        R2        SR  \n",
       "0  POLYGON ((-76484.353 198568.818, -76478.478 19...  0.215712 -2.678272  \n",
       "1  POLYGON ((-71439.032 538818.414, -71405.569 53...  0.215420 -2.302788  \n",
       "2  POLYGON ((1010444.794 298225.136, 1010431.187 ...  0.103319 -0.732327  \n",
       "3  POLYGON ((-561065.818 -536037.871, -560987.294...  0.240960  1.929299  \n",
       "4  POLYGON ((930647.181 -578247.838, 930643.004 -...  0.089116 -0.208833  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_DR1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing standardized residuals on a map\n",
    "\n",
    "covid_DR1['SR'] = gwr_results.std_res\n",
    "covid_DR1.plot('SR', legend = True)\n",
    "ax = plt.gca()\n",
    "ax.get_xaxis().set_visible(False)\n",
    "ax.get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.02055622  0.0123105   0.0463624  -0.0043402   0.00085611 -0.00654612]\n",
      " [ 0.03526271  0.01245452  0.04679122 -0.00439508  0.00076873 -0.0065212 ]\n",
      " [ 0.29912958  0.01347439 -0.00420925 -0.00249183  0.0008089  -0.00594659]\n",
      " ...\n",
      " [ 0.20903462  0.00425762  0.02809669 -0.00662472  0.00051505  0.00184494]\n",
      " [ 0.05717097  0.00867551  0.03830898 -0.00782702  0.00091113 -0.00184074]\n",
      " [ 0.20592389  0.01314867 -0.00787042 -0.00202249  0.00072701 -0.00238278]]\n"
     ]
    }
   ],
   "source": [
    "# Reviewing parameter estimates associated with the predictors starting with the constant.\n",
    "# B coefficients follow the sequence of predictors listed in code sample for OLS regression\n",
    "\n",
    "print(gwr_results.params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Peak2'></a>\n",
    "# Load and preview data for death-case ratios for peak period 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_DR2 = gp.read_file('/home/jovyan/shared_data/data/geospatialfellows21/lazarus_data/Data_Files2/Layer_DR2_1.shp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>COUNTYNS</th>\n",
       "      <th>GEOID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>NAMELSAD</th>\n",
       "      <th>LSAD</th>\n",
       "      <th>CLASSFP</th>\n",
       "      <th>MTFCC</th>\n",
       "      <th>CSAFP</th>\n",
       "      <th>...</th>\n",
       "      <th>PCT_over75</th>\n",
       "      <th>DIAB_PCT</th>\n",
       "      <th>CARDIO_MR</th>\n",
       "      <th>OBESE_PCT</th>\n",
       "      <th>DR2_log</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31</td>\n",
       "      <td>109</td>\n",
       "      <td>00835876</td>\n",
       "      <td>31109</td>\n",
       "      <td>Lancaster</td>\n",
       "      <td>Lancaster County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>339</td>\n",
       "      <td>...</td>\n",
       "      <td>5.4</td>\n",
       "      <td>8.1</td>\n",
       "      <td>186.9</td>\n",
       "      <td>29.9</td>\n",
       "      <td>-0.411620</td>\n",
       "      <td>191498.450302</td>\n",
       "      <td>2.181031e+09</td>\n",
       "      <td>-5.775873e+04</td>\n",
       "      <td>198290.895484</td>\n",
       "      <td>POLYGON ((-76484.353 198568.818, -76478.478 19...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46</td>\n",
       "      <td>099</td>\n",
       "      <td>01265772</td>\n",
       "      <td>46099</td>\n",
       "      <td>Minnehaha</td>\n",
       "      <td>Minnehaha County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>5.1</td>\n",
       "      <td>8.2</td>\n",
       "      <td>229.2</td>\n",
       "      <td>31.1</td>\n",
       "      <td>0.245761</td>\n",
       "      <td>186372.852811</td>\n",
       "      <td>2.103144e+09</td>\n",
       "      <td>-6.367993e+04</td>\n",
       "      <td>519393.038636</td>\n",
       "      <td>POLYGON ((-71439.032 538818.414, -71405.569 53...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39</td>\n",
       "      <td>063</td>\n",
       "      <td>01074044</td>\n",
       "      <td>39063</td>\n",
       "      <td>Hancock</td>\n",
       "      <td>Hancock County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>534</td>\n",
       "      <td>...</td>\n",
       "      <td>7.1</td>\n",
       "      <td>9.8</td>\n",
       "      <td>296.3</td>\n",
       "      <td>35.9</td>\n",
       "      <td>-0.403121</td>\n",
       "      <td>154277.000259</td>\n",
       "      <td>1.375313e+09</td>\n",
       "      <td>1.029369e+06</td>\n",
       "      <td>291980.881448</td>\n",
       "      <td>POLYGON ((1010444.794 298225.136, 1010431.187 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>189</td>\n",
       "      <td>01383880</td>\n",
       "      <td>48189</td>\n",
       "      <td>Hale</td>\n",
       "      <td>Hale County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>352</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.7</td>\n",
       "      <td>266.0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>0.207998</td>\n",
       "      <td>204211.504278</td>\n",
       "      <td>2.597938e+09</td>\n",
       "      <td>-5.365911e+05</td>\n",
       "      <td>-529891.392654</td>\n",
       "      <td>POLYGON ((-561065.818 -536037.871, -560987.294...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01</td>\n",
       "      <td>027</td>\n",
       "      <td>00161539</td>\n",
       "      <td>01027</td>\n",
       "      <td>Clay</td>\n",
       "      <td>Clay County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>8.3</td>\n",
       "      <td>14.2</td>\n",
       "      <td>452.0</td>\n",
       "      <td>37.1</td>\n",
       "      <td>0.246672</td>\n",
       "      <td>203930.129612</td>\n",
       "      <td>1.568824e+09</td>\n",
       "      <td>9.422905e+05</td>\n",
       "      <td>-583488.845178</td>\n",
       "      <td>POLYGON ((930647.181 -578247.838, 930643.004 -...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP COUNTYFP  COUNTYNS  GEOID       NAME          NAMELSAD LSAD CLASSFP  \\\n",
       "0      31      109  00835876  31109  Lancaster  Lancaster County   06      H1   \n",
       "1      46      099  01265772  46099  Minnehaha  Minnehaha County   06      H1   \n",
       "2      39      063  01074044  39063    Hancock    Hancock County   06      H1   \n",
       "3      48      189  01383880  48189       Hale       Hale County   06      H1   \n",
       "4      01      027  00161539  01027       Clay       Clay County   06      H1   \n",
       "\n",
       "   MTFCC CSAFP  ... PCT_over75 DIAB_PCT CARDIO_MR  OBESE_PCT   DR2_log  \\\n",
       "0  G4020   339  ...        5.4      8.1     186.9       29.9 -0.411620   \n",
       "1  G4020  None  ...        5.1      8.2     229.2       31.1  0.245761   \n",
       "2  G4020   534  ...        7.1      9.8     296.3       35.9 -0.403121   \n",
       "3  G4020   352  ...        6.0     13.7     266.0       31.5  0.207998   \n",
       "4  G4020  None  ...        8.3     14.2     452.0       37.1  0.246672   \n",
       "\n",
       "      Shape_Leng    Shape_Area             X              Y  \\\n",
       "0  191498.450302  2.181031e+09 -5.775873e+04  198290.895484   \n",
       "1  186372.852811  2.103144e+09 -6.367993e+04  519393.038636   \n",
       "2  154277.000259  1.375313e+09  1.029369e+06  291980.881448   \n",
       "3  204211.504278  2.597938e+09 -5.365911e+05 -529891.392654   \n",
       "4  203930.129612  1.568824e+09  9.422905e+05 -583488.845178   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-76484.353 198568.818, -76478.478 19...  \n",
       "1  POLYGON ((-71439.032 538818.414, -71405.569 53...  \n",
       "2  POLYGON ((1010444.794 298225.136, 1010431.187 ...  \n",
       "3  POLYGON ((-561065.818 -536037.871, -560987.294...  \n",
       "4  POLYGON ((930647.181 -578247.838, 930643.004 -...  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_DR2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Data2'></a>\n",
    "## Preprocess data for GWR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run Ordinary Least Squares Regression\n",
    "\n",
    "y = covid_DR2['DR2_log'].values.reshape((-1, 1))\n",
    "X = covid_DR2[['PCT_50to74', 'PCT_over75', 'DIAB_PCT', 'CARDIO_MR', 'OBESE_PCT']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REGRESSION\n",
      "----------\n",
      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES\n",
      "-----------------------------------------\n",
      "Data set            :     unknown\n",
      "Weights matrix      :        None\n",
      "Dependent Variable  :     dep_var                Number of Observations:        1964\n",
      "Mean dependent var  :      0.2395                Number of Variables   :           6\n",
      "S.D. dependent var  :      0.4085                Degrees of Freedom    :        1958\n",
      "R-squared           :      0.0725\n",
      "Adjusted R-squared  :      0.0701\n",
      "Sum squared residual:     303.829                F-statistic           :     30.5928\n",
      "Sigma-square        :       0.155                Prob(F-statistic)     :   4.785e-30\n",
      "S.E. of regression  :       0.394                Log likelihood        :    -954.115\n",
      "Sigma-square ML     :       0.155                Akaike info criterion :    1920.231\n",
      "S.E of regression ML:      0.3933                Schwarz criterion     :    1953.727\n",
      "\n",
      "------------------------------------------------------------------------------------\n",
      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
      "------------------------------------------------------------------------------------\n",
      "            CONSTANT      -0.2514553       0.0854269      -2.9435137       0.0032833\n",
      "               var_1       0.0116541       0.0030962       3.7640305       0.0001721\n",
      "               var_2       0.0249133       0.0073201       3.4033959       0.0006789\n",
      "               var_3      -0.0018865       0.0031581      -0.5973612       0.5503353\n",
      "               var_4       0.0002216       0.0001286       1.7233358       0.0849857\n",
      "               var_5      -0.0028913       0.0019306      -1.4976637       0.1343818\n",
      "------------------------------------------------------------------------------------\n",
      "\n",
      "REGRESSION DIAGNOSTICS\n",
      "MULTICOLLINEARITY CONDITION NUMBER           33.255\n",
      "\n",
      "TEST ON NORMALITY OF ERRORS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Jarque-Bera                       2          38.494           0.0000\n",
      "\n",
      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
      "RANDOM COEFFICIENTS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Breusch-Pagan test                5          51.699           0.0000\n",
      "Koenker-Bassett test              5          44.499           0.0000\n",
      "================================ END OF REPORT =====================================\n"
     ]
    }
   ],
   "source": [
    "ols = OLS(y, X)\n",
    "print(ols.summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining variables and coordinates\n",
    "\n",
    "g_y = covid_DR2['DR2_log'].values.reshape((-1, 1))\n",
    "g_X = covid_DR2[['PCT_50to74', 'PCT_over75', 'DIAB_PCT', 'CARDIO_MR', 'OBESE_PCT']].values\n",
    "u = covid_DR2['X']\n",
    "v = covid_DR2['Y']\n",
    "g_coords = list(zip(u, v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "g_y:\n",
      " [[-0.41161971]\n",
      " [ 0.24576091]\n",
      " [-0.40312052]\n",
      " [ 0.20799764]\n",
      " [ 0.24667233]]\n",
      "\n",
      "g_X:\n",
      " [[ 24.7   5.4   8.1 186.9  29.9]\n",
      " [ 25.7   5.1   8.2 229.2  31.1]\n",
      " [ 29.9   7.1   9.8 296.3  35.9]\n",
      " [ 23.8   6.   13.7 266.   31.5]\n",
      " [ 33.6   8.3  14.2 452.   37.1]]\n",
      "\n",
      "u:\n",
      " [-57758.7266368, -63679.9337388, 1029369.27457, -536591.069548, 942290.494088]\n",
      "\n",
      "v:\n",
      " [198290.895484, 519393.038636, 291980.881448, -529891.392654, -583488.845178]\n",
      "\n",
      "g_coords:\n",
      " [(-57758.7266368, 198290.895484), (-63679.9337388, 519393.038636), (1029369.27457, 291980.881448), (-536591.069548, -529891.392654), (942290.494088, -583488.845178)] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Inspecting the data contents\n",
    "\n",
    "print('g_y:\\n', g_y[:5])\n",
    "print('\\ng_X:\\n', g_X[:5])\n",
    "print('\\nu:\\n', list(u[:5]))\n",
    "print('\\nv:\\n', list(v[:5]))\n",
    "print('\\ng_coords:\\n', g_coords[:5], \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "137.0\n"
     ]
    }
   ],
   "source": [
    "# Testing suitable bandwidth prior to specifying the model\n",
    "\n",
    "gwr_selector = Sel_BW(g_coords, g_y, g_X)\n",
    "gwr_bw = gwr_selector.search()\n",
    "print(gwr_bw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specifying the GWR model - bandwidth set at 137 neighbors using an adaptive Gaussian kernal function\n",
    "\n",
    "gwr_model = GWR(g_coords, g_y, g_X, bw=137, fixed=False, kernel='gaussian')\n",
    "gwr_results = gwr_model.fit()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Results2'></a>\n",
    "## Review GWR Results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "238.80146905675383\n",
      "1527.909375355621\n",
      "0.2709801868733682\n",
      "0.2537446704171984\n"
     ]
    }
   ],
   "source": [
    "# Print Global regression results\n",
    "\n",
    "print(gwr_results.resid_ss)\n",
    "print(gwr_results.aic)\n",
    "print(gwr_results.R2)\n",
    "print(gwr_results.adj_R2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a column to store R2 values in the dataframe\n",
    "\n",
    "covid_DR2['R2'] = gwr_results.localR2   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>COUNTYNS</th>\n",
       "      <th>GEOID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>NAMELSAD</th>\n",
       "      <th>LSAD</th>\n",
       "      <th>CLASSFP</th>\n",
       "      <th>MTFCC</th>\n",
       "      <th>CSAFP</th>\n",
       "      <th>...</th>\n",
       "      <th>DIAB_PCT</th>\n",
       "      <th>CARDIO_MR</th>\n",
       "      <th>OBESE_PCT</th>\n",
       "      <th>DR2_log</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>geometry</th>\n",
       "      <th>R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31</td>\n",
       "      <td>109</td>\n",
       "      <td>00835876</td>\n",
       "      <td>31109</td>\n",
       "      <td>Lancaster</td>\n",
       "      <td>Lancaster County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>339</td>\n",
       "      <td>...</td>\n",
       "      <td>8.1</td>\n",
       "      <td>186.9</td>\n",
       "      <td>29.9</td>\n",
       "      <td>-0.411620</td>\n",
       "      <td>191498.450302</td>\n",
       "      <td>2.181031e+09</td>\n",
       "      <td>-5.775873e+04</td>\n",
       "      <td>198290.895484</td>\n",
       "      <td>POLYGON ((-76484.353 198568.818, -76478.478 19...</td>\n",
       "      <td>0.198936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46</td>\n",
       "      <td>099</td>\n",
       "      <td>01265772</td>\n",
       "      <td>46099</td>\n",
       "      <td>Minnehaha</td>\n",
       "      <td>Minnehaha County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>8.2</td>\n",
       "      <td>229.2</td>\n",
       "      <td>31.1</td>\n",
       "      <td>0.245761</td>\n",
       "      <td>186372.852811</td>\n",
       "      <td>2.103144e+09</td>\n",
       "      <td>-6.367993e+04</td>\n",
       "      <td>519393.038636</td>\n",
       "      <td>POLYGON ((-71439.032 538818.414, -71405.569 53...</td>\n",
       "      <td>0.178228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39</td>\n",
       "      <td>063</td>\n",
       "      <td>01074044</td>\n",
       "      <td>39063</td>\n",
       "      <td>Hancock</td>\n",
       "      <td>Hancock County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>534</td>\n",
       "      <td>...</td>\n",
       "      <td>9.8</td>\n",
       "      <td>296.3</td>\n",
       "      <td>35.9</td>\n",
       "      <td>-0.403121</td>\n",
       "      <td>154277.000259</td>\n",
       "      <td>1.375313e+09</td>\n",
       "      <td>1.029369e+06</td>\n",
       "      <td>291980.881448</td>\n",
       "      <td>POLYGON ((1010444.794 298225.136, 1010431.187 ...</td>\n",
       "      <td>0.122082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>189</td>\n",
       "      <td>01383880</td>\n",
       "      <td>48189</td>\n",
       "      <td>Hale</td>\n",
       "      <td>Hale County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>352</td>\n",
       "      <td>...</td>\n",
       "      <td>13.7</td>\n",
       "      <td>266.0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>0.207998</td>\n",
       "      <td>204211.504278</td>\n",
       "      <td>2.597938e+09</td>\n",
       "      <td>-5.365911e+05</td>\n",
       "      <td>-529891.392654</td>\n",
       "      <td>POLYGON ((-561065.818 -536037.871, -560987.294...</td>\n",
       "      <td>0.195827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01</td>\n",
       "      <td>027</td>\n",
       "      <td>00161539</td>\n",
       "      <td>01027</td>\n",
       "      <td>Clay</td>\n",
       "      <td>Clay County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>14.2</td>\n",
       "      <td>452.0</td>\n",
       "      <td>37.1</td>\n",
       "      <td>0.246672</td>\n",
       "      <td>203930.129612</td>\n",
       "      <td>1.568824e+09</td>\n",
       "      <td>9.422905e+05</td>\n",
       "      <td>-583488.845178</td>\n",
       "      <td>POLYGON ((930647.181 -578247.838, 930643.004 -...</td>\n",
       "      <td>0.118513</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP COUNTYFP  COUNTYNS  GEOID       NAME          NAMELSAD LSAD CLASSFP  \\\n",
       "0      31      109  00835876  31109  Lancaster  Lancaster County   06      H1   \n",
       "1      46      099  01265772  46099  Minnehaha  Minnehaha County   06      H1   \n",
       "2      39      063  01074044  39063    Hancock    Hancock County   06      H1   \n",
       "3      48      189  01383880  48189       Hale       Hale County   06      H1   \n",
       "4      01      027  00161539  01027       Clay       Clay County   06      H1   \n",
       "\n",
       "   MTFCC CSAFP  ... DIAB_PCT CARDIO_MR OBESE_PCT   DR2_log     Shape_Leng  \\\n",
       "0  G4020   339  ...      8.1     186.9      29.9 -0.411620  191498.450302   \n",
       "1  G4020  None  ...      8.2     229.2      31.1  0.245761  186372.852811   \n",
       "2  G4020   534  ...      9.8     296.3      35.9 -0.403121  154277.000259   \n",
       "3  G4020   352  ...     13.7     266.0      31.5  0.207998  204211.504278   \n",
       "4  G4020  None  ...     14.2     452.0      37.1  0.246672  203930.129612   \n",
       "\n",
       "     Shape_Area             X              Y  \\\n",
       "0  2.181031e+09 -5.775873e+04  198290.895484   \n",
       "1  2.103144e+09 -6.367993e+04  519393.038636   \n",
       "2  1.375313e+09  1.029369e+06  291980.881448   \n",
       "3  2.597938e+09 -5.365911e+05 -529891.392654   \n",
       "4  1.568824e+09  9.422905e+05 -583488.845178   \n",
       "\n",
       "                                            geometry        R2  \n",
       "0  POLYGON ((-76484.353 198568.818, -76478.478 19...  0.198936  \n",
       "1  POLYGON ((-71439.032 538818.414, -71405.569 53...  0.178228  \n",
       "2  POLYGON ((1010444.794 298225.136, 1010431.187 ...  0.122082  \n",
       "3  POLYGON ((-561065.818 -536037.871, -560987.294...  0.195827  \n",
       "4  POLYGON ((930647.181 -578247.838, 930643.004 -...  0.118513  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_DR2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing local R2 values on a map\n",
    "\n",
    "covid_DR2['R2'] = gwr_results.localR2\n",
    "covid_DR2.plot('R2', legend = True)\n",
    "ax = plt.gca()\n",
    "ax.get_xaxis().set_visible(False)\n",
    "ax.get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a column to store standardized residual values in the dataframe\n",
    "\n",
    "covid_DR2['SR'] = gwr_results.std_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>COUNTYNS</th>\n",
       "      <th>GEOID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>NAMELSAD</th>\n",
       "      <th>LSAD</th>\n",
       "      <th>CLASSFP</th>\n",
       "      <th>MTFCC</th>\n",
       "      <th>CSAFP</th>\n",
       "      <th>...</th>\n",
       "      <th>CARDIO_MR</th>\n",
       "      <th>OBESE_PCT</th>\n",
       "      <th>DR2_log</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>geometry</th>\n",
       "      <th>R2</th>\n",
       "      <th>SR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31</td>\n",
       "      <td>109</td>\n",
       "      <td>00835876</td>\n",
       "      <td>31109</td>\n",
       "      <td>Lancaster</td>\n",
       "      <td>Lancaster County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>339</td>\n",
       "      <td>...</td>\n",
       "      <td>186.9</td>\n",
       "      <td>29.9</td>\n",
       "      <td>-0.411620</td>\n",
       "      <td>191498.450302</td>\n",
       "      <td>2.181031e+09</td>\n",
       "      <td>-5.775873e+04</td>\n",
       "      <td>198290.895484</td>\n",
       "      <td>POLYGON ((-76484.353 198568.818, -76478.478 19...</td>\n",
       "      <td>0.198936</td>\n",
       "      <td>-1.306488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46</td>\n",
       "      <td>099</td>\n",
       "      <td>01265772</td>\n",
       "      <td>46099</td>\n",
       "      <td>Minnehaha</td>\n",
       "      <td>Minnehaha County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>229.2</td>\n",
       "      <td>31.1</td>\n",
       "      <td>0.245761</td>\n",
       "      <td>186372.852811</td>\n",
       "      <td>2.103144e+09</td>\n",
       "      <td>-6.367993e+04</td>\n",
       "      <td>519393.038636</td>\n",
       "      <td>POLYGON ((-71439.032 538818.414, -71405.569 53...</td>\n",
       "      <td>0.178228</td>\n",
       "      <td>0.591529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39</td>\n",
       "      <td>063</td>\n",
       "      <td>01074044</td>\n",
       "      <td>39063</td>\n",
       "      <td>Hancock</td>\n",
       "      <td>Hancock County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>534</td>\n",
       "      <td>...</td>\n",
       "      <td>296.3</td>\n",
       "      <td>35.9</td>\n",
       "      <td>-0.403121</td>\n",
       "      <td>154277.000259</td>\n",
       "      <td>1.375313e+09</td>\n",
       "      <td>1.029369e+06</td>\n",
       "      <td>291980.881448</td>\n",
       "      <td>POLYGON ((1010444.794 298225.136, 1010431.187 ...</td>\n",
       "      <td>0.122082</td>\n",
       "      <td>-1.881861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>189</td>\n",
       "      <td>01383880</td>\n",
       "      <td>48189</td>\n",
       "      <td>Hale</td>\n",
       "      <td>Hale County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>352</td>\n",
       "      <td>...</td>\n",
       "      <td>266.0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>0.207998</td>\n",
       "      <td>204211.504278</td>\n",
       "      <td>2.597938e+09</td>\n",
       "      <td>-5.365911e+05</td>\n",
       "      <td>-529891.392654</td>\n",
       "      <td>POLYGON ((-561065.818 -536037.871, -560987.294...</td>\n",
       "      <td>0.195827</td>\n",
       "      <td>0.179054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01</td>\n",
       "      <td>027</td>\n",
       "      <td>00161539</td>\n",
       "      <td>01027</td>\n",
       "      <td>Clay</td>\n",
       "      <td>Clay County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>452.0</td>\n",
       "      <td>37.1</td>\n",
       "      <td>0.246672</td>\n",
       "      <td>203930.129612</td>\n",
       "      <td>1.568824e+09</td>\n",
       "      <td>9.422905e+05</td>\n",
       "      <td>-583488.845178</td>\n",
       "      <td>POLYGON ((930647.181 -578247.838, 930643.004 -...</td>\n",
       "      <td>0.118513</td>\n",
       "      <td>0.036173</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP COUNTYFP  COUNTYNS  GEOID       NAME          NAMELSAD LSAD CLASSFP  \\\n",
       "0      31      109  00835876  31109  Lancaster  Lancaster County   06      H1   \n",
       "1      46      099  01265772  46099  Minnehaha  Minnehaha County   06      H1   \n",
       "2      39      063  01074044  39063    Hancock    Hancock County   06      H1   \n",
       "3      48      189  01383880  48189       Hale       Hale County   06      H1   \n",
       "4      01      027  00161539  01027       Clay       Clay County   06      H1   \n",
       "\n",
       "   MTFCC CSAFP  ... CARDIO_MR OBESE_PCT   DR2_log     Shape_Leng  \\\n",
       "0  G4020   339  ...     186.9      29.9 -0.411620  191498.450302   \n",
       "1  G4020  None  ...     229.2      31.1  0.245761  186372.852811   \n",
       "2  G4020   534  ...     296.3      35.9 -0.403121  154277.000259   \n",
       "3  G4020   352  ...     266.0      31.5  0.207998  204211.504278   \n",
       "4  G4020  None  ...     452.0      37.1  0.246672  203930.129612   \n",
       "\n",
       "     Shape_Area             X              Y  \\\n",
       "0  2.181031e+09 -5.775873e+04  198290.895484   \n",
       "1  2.103144e+09 -6.367993e+04  519393.038636   \n",
       "2  1.375313e+09  1.029369e+06  291980.881448   \n",
       "3  2.597938e+09 -5.365911e+05 -529891.392654   \n",
       "4  1.568824e+09  9.422905e+05 -583488.845178   \n",
       "\n",
       "                                            geometry        R2        SR  \n",
       "0  POLYGON ((-76484.353 198568.818, -76478.478 19...  0.198936 -1.306488  \n",
       "1  POLYGON ((-71439.032 538818.414, -71405.569 53...  0.178228  0.591529  \n",
       "2  POLYGON ((1010444.794 298225.136, 1010431.187 ...  0.122082 -1.881861  \n",
       "3  POLYGON ((-561065.818 -536037.871, -560987.294...  0.195827  0.179054  \n",
       "4  POLYGON ((930647.181 -578247.838, 930643.004 -...  0.118513  0.036173  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_DR2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing standardized residuals on a map\n",
    "\n",
    "covid_DR2['SR'] = gwr_results.std_res\n",
    "covid_DR2.plot('SR', legend = True)\n",
    "ax = plt.gca()\n",
    "ax.get_xaxis().set_visible(False)\n",
    "ax.get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-5.02611094e-01  5.24705809e-03  7.32579820e-02  1.13759636e-02\n",
      "  -2.42140122e-04 -7.82225838e-04]\n",
      " [-6.19709399e-01  1.13934678e-02  5.86066821e-02  2.33113008e-02\n",
      "  -2.75864098e-04 -1.97789575e-03]\n",
      " [-2.49159254e-01  9.35977020e-03  2.37851084e-02  5.76797632e-03\n",
      "   6.29925513e-04 -5.16324797e-03]\n",
      " ...\n",
      " [-4.24822295e-01  1.28331385e-02 -6.95080871e-03 -1.92003250e-03\n",
      "   4.21660311e-04  2.30771390e-03]\n",
      " [-7.43754824e-01  5.77516666e-03  3.53089758e-02 -2.00991505e-03\n",
      "   2.89997506e-04  1.11455559e-02]\n",
      " [-1.47399103e-01  1.18450569e-03  5.65793421e-02 -1.65446961e-03\n",
      "   9.48437676e-05 -2.50506640e-03]]\n"
     ]
    }
   ],
   "source": [
    "# Reviewing parameter estimates associated with the predictors starting with the constant.\n",
    "# B coefficients follow the sequence of predictors listed in code sample for OLS regression\n",
    "\n",
    "print(gwr_results.params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Ref'></a>\n",
    "### References\n",
    "\n",
    "Environmental Systems Research Institute (ESRI). (2021). How Geographically Weighted Regression (GWR) works. https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/how-geographicallyweightedregression-works.htm#GUID-A5307DAE-12AF-41C2-831B-10192ECB6CE4. Accessed Jun 15th, 2021\n",
    "\n",
    "Fotheringham, A.S., Brunsdon, C., and Charlton, M. (2002). Geographically Weighted Regression. West Sussex, U.K.: John Wiley & Sons Ltd. \n",
    "\n",
    "Multiscale Geographically Weighted Regression (MGWR). (2018). https://mgwr.readthedocs.io/en/latest/generated/mgwr.gwr.GWRResults.html#mgwr.gwr.GWRResults. Accessed Jun 15th, 2021\n",
    "\n",
    "Ndiath, M.M., Cisse, B., Ndiaye, J.L., Gomis, J.F., Bathiery, O., Dia, A. T., Gaye, O., and Faye, B. (2015).Application of geographically‑weighted regression analysis to assess risk factors for malaria hotspots in Keur Soce health and demographic surveillance site. Malaria Journal, 14:463. doi:10.1186/s12936-015-0976-9\n",
    "\n",
    "Oshan, T.M., Li, Z., Kang, W., Wolf, L.J., and Fotheringham, A.S. (2019). MGWR: A Python Implementation of Multiscale\n",
    "GeographicallyWeighted Regression for Investigating Process Spatial Heterogeneity and Scale. International Journal of Geo-Information, 8:269. doi:10.3390/ijgi8060269  \n",
    "\n",
    "\n",
    "Spatial Regression Models (spreg). (2018). https://pysal.org/spreg/generated/spreg.OLS.html#spreg.OLS. Accessed Jun 15th, 2021\n"
   ]
  }
 ],
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