{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Geographically Weighted Regression (Part I): COVID 19 Incidence\n",
    "\n",
    "<b>Author:</b>  Naomi W. Lazarus, PhD<br>\n",
    "<b>Date Created:</b> 6-14-21\n",
    "\n",
    "This notebook provides the code for running a Geographically Weighted Regression (GWR) using COVID-19 incidence rates 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 incidence rates for peak period 1](#Peak1)\n",
    "    - [Preprocess data for GWR](#Data1)\n",
    "    - [Review GWR Results](#Results1)\n",
    "- [Load and preview data for incidence rates 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 incidence rates for peak period 1 (03/01/20 - 04/30/20).  The second uses covid-19 incidence rates 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_Files1/Metadata.txt)<br>\n",
    "[Link to file descriptions](Data_Files1/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 incidence rates for peak period 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "covid_IR1 = gp.read_file('/home/jovyan/shared_data/data/geospatialfellows21/lazarus_data/Data_Files1/Layer_IR1_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>IR1_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",
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       "      <td>00835841</td>\n",
       "      <td>31039</td>\n",
       "      <td>Cuming</td>\n",
       "      <td>Cuming County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>11.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>391.5</td>\n",
       "      <td>39.4</td>\n",
       "      <td>1.831404</td>\n",
       "      <td>153989.870317</td>\n",
       "      <td>1.481950e+09</td>\n",
       "      <td>-6.504090e+04</td>\n",
       "      <td>3.241203e+05</td>\n",
       "      <td>POLYGON ((-84104.520 334058.032, -84103.729 33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>069</td>\n",
       "      <td>01513275</td>\n",
       "      <td>53069</td>\n",
       "      <td>Wahkiakum</td>\n",
       "      <td>Wahkiakum County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>11.7</td>\n",
       "      <td>10.3</td>\n",
       "      <td>326.0</td>\n",
       "      <td>34.7</td>\n",
       "      <td>1.648977</td>\n",
       "      <td>127476.552039</td>\n",
       "      <td>7.446410e+08</td>\n",
       "      <td>-2.088050e+06</td>\n",
       "      <td>1.126359e+06</td>\n",
       "      <td>POLYGON ((-2090017.997 1120806.324, -2090461.7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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>1.876298</td>\n",
       "      <td>191498.450302</td>\n",
       "      <td>2.181031e+09</td>\n",
       "      <td>-5.775873e+04</td>\n",
       "      <td>1.982909e+05</td>\n",
       "      <td>POLYGON ((-76484.353 198568.818, -76478.478 19...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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>3.023345</td>\n",
       "      <td>186372.852811</td>\n",
       "      <td>2.103144e+09</td>\n",
       "      <td>-6.367993e+04</td>\n",
       "      <td>5.193930e+05</td>\n",
       "      <td>POLYGON ((-71439.032 538818.414, -71405.569 53...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>053</td>\n",
       "      <td>00516873</td>\n",
       "      <td>21053</td>\n",
       "      <td>Clinton</td>\n",
       "      <td>Clinton County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>7.8</td>\n",
       "      <td>9.4</td>\n",
       "      <td>342.9</td>\n",
       "      <td>37.7</td>\n",
       "      <td>1.467755</td>\n",
       "      <td>113442.973971</td>\n",
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       "      <td>9.637362e+05</td>\n",
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       "    </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      039  00835841  31039     Cuming     Cuming County   06      H1   \n",
       "1      53      069  01513275  53069  Wahkiakum  Wahkiakum County   06      H1   \n",
       "2      31      109  00835876  31109  Lancaster  Lancaster County   06      H1   \n",
       "3      46      099  01265772  46099  Minnehaha  Minnehaha County   06      H1   \n",
       "4      21      053  00516873  21053    Clinton    Clinton County   06      H1   \n",
       "\n",
       "   MTFCC CSAFP  ... PCT_over75 DIAB_PCT CARDIO_MR  OBESE_PCT   IR1_log  \\\n",
       "0  G4020  None  ...       11.5      7.0     391.5       39.4  1.831404   \n",
       "1  G4020  None  ...       11.7     10.3     326.0       34.7  1.648977   \n",
       "2  G4020   339  ...        5.4      8.1     186.9       29.9  1.876298   \n",
       "3  G4020  None  ...        5.1      8.2     229.2       31.1  3.023345   \n",
       "4  G4020  None  ...        7.8      9.4     342.9       37.7  1.467755   \n",
       "\n",
       "      Shape_Leng    Shape_Area             X             Y  \\\n",
       "0  153989.870317  1.481950e+09 -6.504090e+04  3.241203e+05   \n",
       "1  127476.552039  7.446410e+08 -2.088050e+06  1.126359e+06   \n",
       "2  191498.450302  2.181031e+09 -5.775873e+04  1.982909e+05   \n",
       "3  186372.852811  2.103144e+09 -6.367993e+04  5.193930e+05   \n",
       "4  113442.973971  5.295929e+08  9.637362e+05 -1.947744e+05   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-84104.520 334058.032, -84103.729 33...  \n",
       "1  POLYGON ((-2090017.997 1120806.324, -2090461.7...  \n",
       "2  POLYGON ((-76484.353 198568.818, -76478.478 19...  \n",
       "3  POLYGON ((-71439.032 538818.414, -71405.569 53...  \n",
       "4  POLYGON ((954080.057 -191188.032, 954275.742 -...  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_IR1.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_IR1['IR1_log'].values.reshape((-1, 1))\n",
    "X = covid_IR1[['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:        2807\n",
      "Mean dependent var  :      1.8743                Number of Variables   :           6\n",
      "S.D. dependent var  :      0.5202                Degrees of Freedom    :        2801\n",
      "R-squared           :      0.0555\n",
      "Adjusted R-squared  :      0.0538\n",
      "Sum squared residual:     717.126                F-statistic           :     32.9234\n",
      "Sigma-square        :       0.256                Prob(F-statistic)     :   9.676e-33\n",
      "S.E. of regression  :       0.506                Log likelihood        :   -2067.717\n",
      "Sigma-square ML     :       0.255                Akaike info criterion :    4147.434\n",
      "S.E of regression ML:      0.5054                Schwarz criterion     :    4183.074\n",
      "\n",
      "------------------------------------------------------------------------------------\n",
      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
      "------------------------------------------------------------------------------------\n",
      "            CONSTANT       2.3281208       0.0893977      26.0422895       0.0000000\n",
      "               var_1      -0.0027373       0.0029429      -0.9301303       0.3523838\n",
      "               var_2      -0.0448017       0.0069800      -6.4185440       0.0000000\n",
      "               var_3       0.0088904       0.0032918       2.7008176       0.0069586\n",
      "               var_4      -0.0001715       0.0001255      -1.3669691       0.1717447\n",
      "               var_5      -0.0018299       0.0019887      -0.9201381       0.3575799\n",
      "------------------------------------------------------------------------------------\n",
      "\n",
      "REGRESSION DIAGNOSTICS\n",
      "MULTICOLLINEARITY CONDITION NUMBER           31.066\n",
      "\n",
      "TEST ON NORMALITY OF ERRORS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Jarque-Bera                       2          34.266           0.0000\n",
      "\n",
      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
      "RANDOM COEFFICIENTS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Breusch-Pagan test                5          12.696           0.0264\n",
      "Koenker-Bassett test              5          11.436           0.0434\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_IR1['IR1_log'].values.reshape((-1, 1))\n",
    "g_X = covid_IR1[['PCT_50to74', 'PCT_over75', 'DIAB_PCT', 'CARDIO_MR', 'OBESE_PCT']].values\n",
    "u = covid_IR1['X']\n",
    "v = covid_IR1['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",
      " [[1.83140432]\n",
      " [1.64897715]\n",
      " [1.87629805]\n",
      " [3.02334544]\n",
      " [1.46775536]]\n",
      "\n",
      "g_X:\n",
      " [[ 31.9  11.5   7.  391.5  39.4]\n",
      " [ 44.3  11.7  10.3 326.   34.7]\n",
      " [ 24.7   5.4   8.1 186.9  29.9]\n",
      " [ 25.7   5.1   8.2 229.2  31.1]\n",
      " [ 32.    7.8   9.4 342.9  37.7]]\n",
      "\n",
      "u:\n",
      " [-65040.9000187, -2088050.14619, -57758.7266368, -63679.9337388, 963736.215444]\n",
      "\n",
      "v:\n",
      " [324120.335166, 1126359.47541, 198290.895484, 519393.038636, -194774.396232]\n",
      "\n",
      "g_coords:\n",
      " [(-65040.9000187, 324120.335166), (-2088050.14619, 1126359.47541), (-57758.7266368, 198290.895484), (-63679.9337388, 519393.038636), (963736.215444, -194774.396232)] \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": [
      "102.0\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 102 neighbors using an adaptive Gaussian kernal function\n",
    "\n",
    "gwr_model = GWR(g_coords, g_y, g_X, bw=102, 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": [
      "497.5393077277695\n",
      "3274.3466280487974\n",
      "0.3447157830553744\n",
      "0.325102789515628\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_IR1['R2'] = gwr_results.localR2   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>COUNTYNS</th>\n",
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       "      <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>IR1_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>039</td>\n",
       "      <td>00835841</td>\n",
       "      <td>31039</td>\n",
       "      <td>Cuming</td>\n",
       "      <td>Cuming County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>391.5</td>\n",
       "      <td>39.4</td>\n",
       "      <td>1.831404</td>\n",
       "      <td>153989.870317</td>\n",
       "      <td>1.481950e+09</td>\n",
       "      <td>-6.504090e+04</td>\n",
       "      <td>3.241203e+05</td>\n",
       "      <td>POLYGON ((-84104.520 334058.032, -84103.729 33...</td>\n",
       "      <td>0.107070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>069</td>\n",
       "      <td>01513275</td>\n",
       "      <td>53069</td>\n",
       "      <td>Wahkiakum</td>\n",
       "      <td>Wahkiakum County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>10.3</td>\n",
       "      <td>326.0</td>\n",
       "      <td>34.7</td>\n",
       "      <td>1.648977</td>\n",
       "      <td>127476.552039</td>\n",
       "      <td>7.446410e+08</td>\n",
       "      <td>-2.088050e+06</td>\n",
       "      <td>1.126359e+06</td>\n",
       "      <td>POLYGON ((-2090017.997 1120806.324, -2090461.7...</td>\n",
       "      <td>0.206945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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>1.876298</td>\n",
       "      <td>191498.450302</td>\n",
       "      <td>2.181031e+09</td>\n",
       "      <td>-5.775873e+04</td>\n",
       "      <td>1.982909e+05</td>\n",
       "      <td>POLYGON ((-76484.353 198568.818, -76478.478 19...</td>\n",
       "      <td>0.103294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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>3.023345</td>\n",
       "      <td>186372.852811</td>\n",
       "      <td>2.103144e+09</td>\n",
       "      <td>-6.367993e+04</td>\n",
       "      <td>5.193930e+05</td>\n",
       "      <td>POLYGON ((-71439.032 538818.414, -71405.569 53...</td>\n",
       "      <td>0.124902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>053</td>\n",
       "      <td>00516873</td>\n",
       "      <td>21053</td>\n",
       "      <td>Clinton</td>\n",
       "      <td>Clinton County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>9.4</td>\n",
       "      <td>342.9</td>\n",
       "      <td>37.7</td>\n",
       "      <td>1.467755</td>\n",
       "      <td>113442.973971</td>\n",
       "      <td>5.295929e+08</td>\n",
       "      <td>9.637362e+05</td>\n",
       "      <td>-1.947744e+05</td>\n",
       "      <td>POLYGON ((954080.057 -191188.032, 954275.742 -...</td>\n",
       "      <td>0.202185</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      039  00835841  31039     Cuming     Cuming County   06      H1   \n",
       "1      53      069  01513275  53069  Wahkiakum  Wahkiakum County   06      H1   \n",
       "2      31      109  00835876  31109  Lancaster  Lancaster County   06      H1   \n",
       "3      46      099  01265772  46099  Minnehaha  Minnehaha County   06      H1   \n",
       "4      21      053  00516873  21053    Clinton    Clinton County   06      H1   \n",
       "\n",
       "   MTFCC CSAFP  ... DIAB_PCT CARDIO_MR OBESE_PCT   IR1_log     Shape_Leng  \\\n",
       "0  G4020  None  ...      7.0     391.5      39.4  1.831404  153989.870317   \n",
       "1  G4020  None  ...     10.3     326.0      34.7  1.648977  127476.552039   \n",
       "2  G4020   339  ...      8.1     186.9      29.9  1.876298  191498.450302   \n",
       "3  G4020  None  ...      8.2     229.2      31.1  3.023345  186372.852811   \n",
       "4  G4020  None  ...      9.4     342.9      37.7  1.467755  113442.973971   \n",
       "\n",
       "     Shape_Area             X             Y  \\\n",
       "0  1.481950e+09 -6.504090e+04  3.241203e+05   \n",
       "1  7.446410e+08 -2.088050e+06  1.126359e+06   \n",
       "2  2.181031e+09 -5.775873e+04  1.982909e+05   \n",
       "3  2.103144e+09 -6.367993e+04  5.193930e+05   \n",
       "4  5.295929e+08  9.637362e+05 -1.947744e+05   \n",
       "\n",
       "                                            geometry        R2  \n",
       "0  POLYGON ((-84104.520 334058.032, -84103.729 33...  0.107070  \n",
       "1  POLYGON ((-2090017.997 1120806.324, -2090461.7...  0.206945  \n",
       "2  POLYGON ((-76484.353 198568.818, -76478.478 19...  0.103294  \n",
       "3  POLYGON ((-71439.032 538818.414, -71405.569 53...  0.124902  \n",
       "4  POLYGON ((954080.057 -191188.032, 954275.742 -...  0.202185  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_IR1.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_IR1['R2'] = gwr_results.localR2\n",
    "covid_IR1.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_IR1['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>IR1_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>039</td>\n",
       "      <td>00835841</td>\n",
       "      <td>31039</td>\n",
       "      <td>Cuming</td>\n",
       "      <td>Cuming County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>391.5</td>\n",
       "      <td>39.4</td>\n",
       "      <td>1.831404</td>\n",
       "      <td>153989.870317</td>\n",
       "      <td>1.481950e+09</td>\n",
       "      <td>-6.504090e+04</td>\n",
       "      <td>3.241203e+05</td>\n",
       "      <td>POLYGON ((-84104.520 334058.032, -84103.729 33...</td>\n",
       "      <td>0.107070</td>\n",
       "      <td>0.186783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>069</td>\n",
       "      <td>01513275</td>\n",
       "      <td>53069</td>\n",
       "      <td>Wahkiakum</td>\n",
       "      <td>Wahkiakum County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>326.0</td>\n",
       "      <td>34.7</td>\n",
       "      <td>1.648977</td>\n",
       "      <td>127476.552039</td>\n",
       "      <td>7.446410e+08</td>\n",
       "      <td>-2.088050e+06</td>\n",
       "      <td>1.126359e+06</td>\n",
       "      <td>POLYGON ((-2090017.997 1120806.324, -2090461.7...</td>\n",
       "      <td>0.206945</td>\n",
       "      <td>0.733777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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>1.876298</td>\n",
       "      <td>191498.450302</td>\n",
       "      <td>2.181031e+09</td>\n",
       "      <td>-5.775873e+04</td>\n",
       "      <td>1.982909e+05</td>\n",
       "      <td>POLYGON ((-76484.353 198568.818, -76478.478 19...</td>\n",
       "      <td>0.103294</td>\n",
       "      <td>-0.187736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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>3.023345</td>\n",
       "      <td>186372.852811</td>\n",
       "      <td>2.103144e+09</td>\n",
       "      <td>-6.367993e+04</td>\n",
       "      <td>5.193930e+05</td>\n",
       "      <td>POLYGON ((-71439.032 538818.414, -71405.569 53...</td>\n",
       "      <td>0.124902</td>\n",
       "      <td>2.692081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>053</td>\n",
       "      <td>00516873</td>\n",
       "      <td>21053</td>\n",
       "      <td>Clinton</td>\n",
       "      <td>Clinton County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>342.9</td>\n",
       "      <td>37.7</td>\n",
       "      <td>1.467755</td>\n",
       "      <td>113442.973971</td>\n",
       "      <td>5.295929e+08</td>\n",
       "      <td>9.637362e+05</td>\n",
       "      <td>-1.947744e+05</td>\n",
       "      <td>POLYGON ((954080.057 -191188.032, 954275.742 -...</td>\n",
       "      <td>0.202185</td>\n",
       "      <td>-0.739007</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      039  00835841  31039     Cuming     Cuming County   06      H1   \n",
       "1      53      069  01513275  53069  Wahkiakum  Wahkiakum County   06      H1   \n",
       "2      31      109  00835876  31109  Lancaster  Lancaster County   06      H1   \n",
       "3      46      099  01265772  46099  Minnehaha  Minnehaha County   06      H1   \n",
       "4      21      053  00516873  21053    Clinton    Clinton County   06      H1   \n",
       "\n",
       "   MTFCC CSAFP  ... CARDIO_MR OBESE_PCT   IR1_log     Shape_Leng  \\\n",
       "0  G4020  None  ...     391.5      39.4  1.831404  153989.870317   \n",
       "1  G4020  None  ...     326.0      34.7  1.648977  127476.552039   \n",
       "2  G4020   339  ...     186.9      29.9  1.876298  191498.450302   \n",
       "3  G4020  None  ...     229.2      31.1  3.023345  186372.852811   \n",
       "4  G4020  None  ...     342.9      37.7  1.467755  113442.973971   \n",
       "\n",
       "     Shape_Area             X             Y  \\\n",
       "0  1.481950e+09 -6.504090e+04  3.241203e+05   \n",
       "1  7.446410e+08 -2.088050e+06  1.126359e+06   \n",
       "2  2.181031e+09 -5.775873e+04  1.982909e+05   \n",
       "3  2.103144e+09 -6.367993e+04  5.193930e+05   \n",
       "4  5.295929e+08  9.637362e+05 -1.947744e+05   \n",
       "\n",
       "                                            geometry        R2        SR  \n",
       "0  POLYGON ((-84104.520 334058.032, -84103.729 33...  0.107070  0.186783  \n",
       "1  POLYGON ((-2090017.997 1120806.324, -2090461.7...  0.206945  0.733777  \n",
       "2  POLYGON ((-76484.353 198568.818, -76478.478 19...  0.103294 -0.187736  \n",
       "3  POLYGON ((-71439.032 538818.414, -71405.569 53...  0.124902  2.692081  \n",
       "4  POLYGON ((954080.057 -191188.032, 954275.742 -...  0.202185 -0.739007  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_IR1.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_IR1['SR'] = gwr_results.std_res\n",
    "covid_IR1.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": [
      "[[ 2.44308643e+00 -1.67268326e-02 -1.18612977e-02 -1.20809756e-02\n",
      "  -4.43367197e-04  6.03536058e-03]\n",
      " [ 2.84501495e+00 -1.30931699e-02 -1.30846267e-02 -1.91091199e-02\n",
      "  -8.20776487e-04 -8.77443204e-03]\n",
      " [ 2.22257986e+00 -9.94843892e-03 -1.31924239e-02 -1.06409647e-02\n",
      "  -7.13865665e-04  9.00354726e-03]\n",
      " ...\n",
      " [ 1.98114323e+00 -9.70352695e-04 -6.97662067e-02 -5.41841873e-03\n",
      "  -2.22340329e-04  2.29916676e-02]\n",
      " [ 1.94957145e+00  4.25418331e-03 -3.35436249e-02 -5.56654204e-03\n",
      "  -5.70721906e-04  4.46066728e-03]\n",
      " [ 2.59221745e+00 -2.00587208e-02  2.85294747e-02 -1.39577140e-02\n",
      "  -9.08008198e-04 -1.26467092e-04]]\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 running OLS\n",
    "\n",
    "print(gwr_results.params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Peak2'></a>\n",
    "# Load and preview data for incidence rates for peak period 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_IR2 = gp.read_file('/home/jovyan/shared_data/data/geospatialfellows21/lazarus_data/Data_Files1/Layer_IR2_1.shp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>IR2_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>039</td>\n",
       "      <td>00835841</td>\n",
       "      <td>31039</td>\n",
       "      <td>Cuming</td>\n",
       "      <td>Cuming County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>11.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>391.5</td>\n",
       "      <td>39.4</td>\n",
       "      <td>2.451193</td>\n",
       "      <td>153989.870317</td>\n",
       "      <td>1.481950e+09</td>\n",
       "      <td>-65040.900019</td>\n",
       "      <td>324120.335166</td>\n",
       "      <td>POLYGON ((-84104.520 334058.032, -84103.729 33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>2.752805</td>\n",
       "      <td>191498.450302</td>\n",
       "      <td>2.181031e+09</td>\n",
       "      <td>-57758.726637</td>\n",
       "      <td>198290.895484</td>\n",
       "      <td>POLYGON ((-76484.353 198568.818, -76478.478 19...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31</td>\n",
       "      <td>129</td>\n",
       "      <td>00835886</td>\n",
       "      <td>31129</td>\n",
       "      <td>Nuckolls</td>\n",
       "      <td>Nuckolls County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>548.3</td>\n",
       "      <td>35.6</td>\n",
       "      <td>1.984221</td>\n",
       "      <td>154088.963652</td>\n",
       "      <td>1.483452e+09</td>\n",
       "      <td>-173426.738663</td>\n",
       "      <td>132548.110644</td>\n",
       "      <td>POLYGON ((-192849.850 123355.389, -192849.816 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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>2.614508</td>\n",
       "      <td>186372.852811</td>\n",
       "      <td>2.103144e+09</td>\n",
       "      <td>-63679.933739</td>\n",
       "      <td>519393.038636</td>\n",
       "      <td>POLYGON ((-71439.032 538818.414, -71405.569 53...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48</td>\n",
       "      <td>327</td>\n",
       "      <td>01383949</td>\n",
       "      <td>48327</td>\n",
       "      <td>Menard</td>\n",
       "      <td>Menard County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>11.2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>342.1</td>\n",
       "      <td>29.1</td>\n",
       "      <td>2.874112</td>\n",
       "      <td>204909.652555</td>\n",
       "      <td>2.346750e+09</td>\n",
       "      <td>-366739.340990</td>\n",
       "      <td>-892066.944835</td>\n",
       "      <td>POLYGON ((-365946.769 -870192.658, -365650.326...</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      039  00835841  31039     Cuming     Cuming County   06      H1   \n",
       "1      31      109  00835876  31109  Lancaster  Lancaster County   06      H1   \n",
       "2      31      129  00835886  31129   Nuckolls   Nuckolls County   06      H1   \n",
       "3      46      099  01265772  46099  Minnehaha  Minnehaha County   06      H1   \n",
       "4      48      327  01383949  48327     Menard     Menard County   06      H1   \n",
       "\n",
       "   MTFCC CSAFP  ... PCT_over75 DIAB_PCT CARDIO_MR  OBESE_PCT   IR2_log  \\\n",
       "0  G4020  None  ...       11.5      7.0     391.5       39.4  2.451193   \n",
       "1  G4020   339  ...        5.4      8.1     186.9       29.9  2.752805   \n",
       "2  G4020  None  ...       14.0      5.6     548.3       35.6  1.984221   \n",
       "3  G4020  None  ...        5.1      8.2     229.2       31.1  2.614508   \n",
       "4  G4020  None  ...       11.2      7.0     342.1       29.1  2.874112   \n",
       "\n",
       "      Shape_Leng    Shape_Area              X              Y  \\\n",
       "0  153989.870317  1.481950e+09  -65040.900019  324120.335166   \n",
       "1  191498.450302  2.181031e+09  -57758.726637  198290.895484   \n",
       "2  154088.963652  1.483452e+09 -173426.738663  132548.110644   \n",
       "3  186372.852811  2.103144e+09  -63679.933739  519393.038636   \n",
       "4  204909.652555  2.346750e+09 -366739.340990 -892066.944835   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-84104.520 334058.032, -84103.729 33...  \n",
       "1  POLYGON ((-76484.353 198568.818, -76478.478 19...  \n",
       "2  POLYGON ((-192849.850 123355.389, -192849.816 ...  \n",
       "3  POLYGON ((-71439.032 538818.414, -71405.569 53...  \n",
       "4  POLYGON ((-365946.769 -870192.658, -365650.326...  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_IR2.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_IR2['IR2_log'].values.reshape((-1, 1))\n",
    "X = covid_IR2[['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:        3061\n",
      "Mean dependent var  :      2.6156                Number of Variables   :           6\n",
      "S.D. dependent var  :      0.4425                Degrees of Freedom    :        3055\n",
      "R-squared           :      0.2311\n",
      "Adjusted R-squared  :      0.2298\n",
      "Sum squared residual:     460.654                F-statistic           :    183.6234\n",
      "Sigma-square        :       0.151                Prob(F-statistic)     :  2.407e-171\n",
      "S.E. of regression  :       0.388                Log likelihood        :   -1444.835\n",
      "Sigma-square ML     :       0.150                Akaike info criterion :    2901.670\n",
      "S.E of regression ML:      0.3879                Schwarz criterion     :    2937.829\n",
      "\n",
      "------------------------------------------------------------------------------------\n",
      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
      "------------------------------------------------------------------------------------\n",
      "            CONSTANT       3.3050449       0.0658427      50.1960803       0.0000000\n",
      "               var_1      -0.0322776       0.0021048     -15.3351981       0.0000000\n",
      "               var_2      -0.0195059       0.0047664      -4.0924030       0.0000438\n",
      "               var_3       0.0250955       0.0024248      10.3495114       0.0000000\n",
      "               var_4       0.0004145       0.0000865       4.7901228       0.0000017\n",
      "               var_5       0.0021492       0.0014464       1.4858631       0.1374185\n",
      "------------------------------------------------------------------------------------\n",
      "\n",
      "REGRESSION DIAGNOSTICS\n",
      "MULTICOLLINEARITY CONDITION NUMBER           30.649\n",
      "\n",
      "TEST ON NORMALITY OF ERRORS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Jarque-Bera                       2          20.705           0.0000\n",
      "\n",
      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
      "RANDOM COEFFICIENTS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Breusch-Pagan test                5          94.715           0.0000\n",
      "Koenker-Bassett test              5          87.249           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_IR2['IR2_log'].values.reshape((-1, 1))\n",
    "g_X = covid_IR2[['PCT_50to74', 'PCT_over75', 'DIAB_PCT', 'CARDIO_MR', 'OBESE_PCT']].values\n",
    "u = covid_IR2['X']\n",
    "v = covid_IR2['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",
      " [[2.45119307]\n",
      " [2.75280455]\n",
      " [1.98422124]\n",
      " [2.61450839]\n",
      " [2.87411228]]\n",
      "\n",
      "g_X:\n",
      " [[ 31.9  11.5   7.  391.5  39.4]\n",
      " [ 24.7   5.4   8.1 186.9  29.9]\n",
      " [ 35.6  14.    5.6 548.3  35.6]\n",
      " [ 25.7   5.1   8.2 229.2  31.1]\n",
      " [ 38.1  11.2   7.  342.1  29.1]]\n",
      "\n",
      "u:\n",
      " [-65040.9000187, -57758.7266368, -173426.738663, -63679.9337388, -366739.34099]\n",
      "\n",
      "v:\n",
      " [324120.335166, 198290.895484, 132548.110644, 519393.038636, -892066.944835]\n",
      "\n",
      "g_coords:\n",
      " [(-65040.9000187, 324120.335166), (-57758.7266368, 198290.895484), (-173426.738663, 132548.110644), (-63679.9337388, 519393.038636), (-366739.34099, -892066.944835)] \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": [
      "111.0\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 111 neighbors using an adaptive Gaussian kernal function\n",
    "\n",
    "gwr_model = GWR(g_coords, g_y, g_X, bw=111, 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": [
      "244.77789104048077\n",
      "1118.6395502102937\n",
      "0.5914202638307168\n",
      "0.5802805630659\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_IR2['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>IR2_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>039</td>\n",
       "      <td>00835841</td>\n",
       "      <td>31039</td>\n",
       "      <td>Cuming</td>\n",
       "      <td>Cuming County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>391.5</td>\n",
       "      <td>39.4</td>\n",
       "      <td>2.451193</td>\n",
       "      <td>153989.870317</td>\n",
       "      <td>1.481950e+09</td>\n",
       "      <td>-65040.900019</td>\n",
       "      <td>324120.335166</td>\n",
       "      <td>POLYGON ((-84104.520 334058.032, -84103.729 33...</td>\n",
       "      <td>0.303681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>2.752805</td>\n",
       "      <td>191498.450302</td>\n",
       "      <td>2.181031e+09</td>\n",
       "      <td>-57758.726637</td>\n",
       "      <td>198290.895484</td>\n",
       "      <td>POLYGON ((-76484.353 198568.818, -76478.478 19...</td>\n",
       "      <td>0.323487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31</td>\n",
       "      <td>129</td>\n",
       "      <td>00835886</td>\n",
       "      <td>31129</td>\n",
       "      <td>Nuckolls</td>\n",
       "      <td>Nuckolls County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>5.6</td>\n",
       "      <td>548.3</td>\n",
       "      <td>35.6</td>\n",
       "      <td>1.984221</td>\n",
       "      <td>154088.963652</td>\n",
       "      <td>1.483452e+09</td>\n",
       "      <td>-173426.738663</td>\n",
       "      <td>132548.110644</td>\n",
       "      <td>POLYGON ((-192849.850 123355.389, -192849.816 ...</td>\n",
       "      <td>0.314053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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>2.614508</td>\n",
       "      <td>186372.852811</td>\n",
       "      <td>2.103144e+09</td>\n",
       "      <td>-63679.933739</td>\n",
       "      <td>519393.038636</td>\n",
       "      <td>POLYGON ((-71439.032 538818.414, -71405.569 53...</td>\n",
       "      <td>0.269650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48</td>\n",
       "      <td>327</td>\n",
       "      <td>01383949</td>\n",
       "      <td>48327</td>\n",
       "      <td>Menard</td>\n",
       "      <td>Menard County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>342.1</td>\n",
       "      <td>29.1</td>\n",
       "      <td>2.874112</td>\n",
       "      <td>204909.652555</td>\n",
       "      <td>2.346750e+09</td>\n",
       "      <td>-366739.340990</td>\n",
       "      <td>-892066.944835</td>\n",
       "      <td>POLYGON ((-365946.769 -870192.658, -365650.326...</td>\n",
       "      <td>0.414429</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      039  00835841  31039     Cuming     Cuming County   06      H1   \n",
       "1      31      109  00835876  31109  Lancaster  Lancaster County   06      H1   \n",
       "2      31      129  00835886  31129   Nuckolls   Nuckolls County   06      H1   \n",
       "3      46      099  01265772  46099  Minnehaha  Minnehaha County   06      H1   \n",
       "4      48      327  01383949  48327     Menard     Menard County   06      H1   \n",
       "\n",
       "   MTFCC CSAFP  ... DIAB_PCT CARDIO_MR OBESE_PCT   IR2_log     Shape_Leng  \\\n",
       "0  G4020  None  ...      7.0     391.5      39.4  2.451193  153989.870317   \n",
       "1  G4020   339  ...      8.1     186.9      29.9  2.752805  191498.450302   \n",
       "2  G4020  None  ...      5.6     548.3      35.6  1.984221  154088.963652   \n",
       "3  G4020  None  ...      8.2     229.2      31.1  2.614508  186372.852811   \n",
       "4  G4020  None  ...      7.0     342.1      29.1  2.874112  204909.652555   \n",
       "\n",
       "     Shape_Area              X              Y  \\\n",
       "0  1.481950e+09  -65040.900019  324120.335166   \n",
       "1  2.181031e+09  -57758.726637  198290.895484   \n",
       "2  1.483452e+09 -173426.738663  132548.110644   \n",
       "3  2.103144e+09  -63679.933739  519393.038636   \n",
       "4  2.346750e+09 -366739.340990 -892066.944835   \n",
       "\n",
       "                                            geometry        R2  \n",
       "0  POLYGON ((-84104.520 334058.032, -84103.729 33...  0.303681  \n",
       "1  POLYGON ((-76484.353 198568.818, -76478.478 19...  0.323487  \n",
       "2  POLYGON ((-192849.850 123355.389, -192849.816 ...  0.314053  \n",
       "3  POLYGON ((-71439.032 538818.414, -71405.569 53...  0.269650  \n",
       "4  POLYGON ((-365946.769 -870192.658, -365650.326...  0.414429  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_IR2.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_IR2['R2'] = gwr_results.localR2\n",
    "covid_IR2.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_IR2['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>IR2_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>039</td>\n",
       "      <td>00835841</td>\n",
       "      <td>31039</td>\n",
       "      <td>Cuming</td>\n",
       "      <td>Cuming County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>391.5</td>\n",
       "      <td>39.4</td>\n",
       "      <td>2.451193</td>\n",
       "      <td>153989.870317</td>\n",
       "      <td>1.481950e+09</td>\n",
       "      <td>-65040.900019</td>\n",
       "      <td>324120.335166</td>\n",
       "      <td>POLYGON ((-84104.520 334058.032, -84103.729 33...</td>\n",
       "      <td>0.303681</td>\n",
       "      <td>-0.219855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>2.752805</td>\n",
       "      <td>191498.450302</td>\n",
       "      <td>2.181031e+09</td>\n",
       "      <td>-57758.726637</td>\n",
       "      <td>198290.895484</td>\n",
       "      <td>POLYGON ((-76484.353 198568.818, -76478.478 19...</td>\n",
       "      <td>0.323487</td>\n",
       "      <td>0.005572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31</td>\n",
       "      <td>129</td>\n",
       "      <td>00835886</td>\n",
       "      <td>31129</td>\n",
       "      <td>Nuckolls</td>\n",
       "      <td>Nuckolls County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>548.3</td>\n",
       "      <td>35.6</td>\n",
       "      <td>1.984221</td>\n",
       "      <td>154088.963652</td>\n",
       "      <td>1.483452e+09</td>\n",
       "      <td>-173426.738663</td>\n",
       "      <td>132548.110644</td>\n",
       "      <td>POLYGON ((-192849.850 123355.389, -192849.816 ...</td>\n",
       "      <td>0.314053</td>\n",
       "      <td>-0.643062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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>2.614508</td>\n",
       "      <td>186372.852811</td>\n",
       "      <td>2.103144e+09</td>\n",
       "      <td>-63679.933739</td>\n",
       "      <td>519393.038636</td>\n",
       "      <td>POLYGON ((-71439.032 538818.414, -71405.569 53...</td>\n",
       "      <td>0.269650</td>\n",
       "      <td>-0.392223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48</td>\n",
       "      <td>327</td>\n",
       "      <td>01383949</td>\n",
       "      <td>48327</td>\n",
       "      <td>Menard</td>\n",
       "      <td>Menard County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>342.1</td>\n",
       "      <td>29.1</td>\n",
       "      <td>2.874112</td>\n",
       "      <td>204909.652555</td>\n",
       "      <td>2.346750e+09</td>\n",
       "      <td>-366739.340990</td>\n",
       "      <td>-892066.944835</td>\n",
       "      <td>POLYGON ((-365946.769 -870192.658, -365650.326...</td>\n",
       "      <td>0.414429</td>\n",
       "      <td>1.072694</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      039  00835841  31039     Cuming     Cuming County   06      H1   \n",
       "1      31      109  00835876  31109  Lancaster  Lancaster County   06      H1   \n",
       "2      31      129  00835886  31129   Nuckolls   Nuckolls County   06      H1   \n",
       "3      46      099  01265772  46099  Minnehaha  Minnehaha County   06      H1   \n",
       "4      48      327  01383949  48327     Menard     Menard County   06      H1   \n",
       "\n",
       "   MTFCC CSAFP  ... CARDIO_MR OBESE_PCT   IR2_log     Shape_Leng  \\\n",
       "0  G4020  None  ...     391.5      39.4  2.451193  153989.870317   \n",
       "1  G4020   339  ...     186.9      29.9  2.752805  191498.450302   \n",
       "2  G4020  None  ...     548.3      35.6  1.984221  154088.963652   \n",
       "3  G4020  None  ...     229.2      31.1  2.614508  186372.852811   \n",
       "4  G4020  None  ...     342.1      29.1  2.874112  204909.652555   \n",
       "\n",
       "     Shape_Area              X              Y  \\\n",
       "0  1.481950e+09  -65040.900019  324120.335166   \n",
       "1  2.181031e+09  -57758.726637  198290.895484   \n",
       "2  1.483452e+09 -173426.738663  132548.110644   \n",
       "3  2.103144e+09  -63679.933739  519393.038636   \n",
       "4  2.346750e+09 -366739.340990 -892066.944835   \n",
       "\n",
       "                                            geometry        R2        SR  \n",
       "0  POLYGON ((-84104.520 334058.032, -84103.729 33...  0.303681 -0.219855  \n",
       "1  POLYGON ((-76484.353 198568.818, -76478.478 19...  0.323487  0.005572  \n",
       "2  POLYGON ((-192849.850 123355.389, -192849.816 ...  0.314053 -0.643062  \n",
       "3  POLYGON ((-71439.032 538818.414, -71405.569 53...  0.269650 -0.392223  \n",
       "4  POLYGON ((-365946.769 -870192.658, -365650.326...  0.414429  1.072694  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_IR2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "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_IR2['SR'] = gwr_results.std_res\n",
    "covid_IR2.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": [
      "[[ 3.42051640e+00 -2.91233040e-02 -1.92100095e-02 -7.08721266e-03\n",
      "  -1.27870941e-06  7.43637734e-03]\n",
      " [ 3.38296963e+00 -2.45975765e-02 -3.10116738e-02 -3.14546459e-03\n",
      "  -1.86382645e-05  5.76076721e-03]\n",
      " [ 3.35100421e+00 -2.04769184e-02 -3.70347392e-02  2.99649115e-03\n",
      "  -3.51703339e-05  1.81009171e-03]\n",
      " ...\n",
      " [ 3.18425934e+00 -1.42676098e-02 -6.21984067e-02  7.77708432e-03\n",
      "   5.66626305e-04  1.11885576e-02]\n",
      " [ 3.69959106e+00 -2.31228731e-02 -2.72156817e-02  9.67155363e-03\n",
      "   7.65445917e-05 -5.28147719e-03]\n",
      " [ 3.83916259e+00 -2.50772245e-02 -1.25253623e-02  2.83855556e-03\n",
      "   2.04014912e-04 -1.65326376e-02]]\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 running OLS\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",
    "Spatial Regression Models (spreg). (2018). https://pysal.org/spreg/generated/spreg.OLS.html#spreg.OLS. Accessed Jun 15th, 2021\n",
    "\n",
    "\n"
   ]
  }
 ],
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