{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5e6bd310",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<h1> <center> GEOG 172: INTERMEDIATE GEOGRAPHICAL ANALYSIS </h1>\n",
    "    <h2> <center> Evgeny Noi </h2>\n",
    "        <h3> <center> Lecture 18: Multi-scale Geographically Weighted Regression </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "158a7d00",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Regression Modeling for Spatial Relations\n",
    "\n",
    "1. Spatial heterogeneity \n",
    "    1. Fixed-effect model\n",
    "    2. Spatial regimes \n",
    "2. Spatial Dependence \n",
    "    1. SLX (spatial feature engineering) \n",
    "    2. Spatial error model\n",
    "    3. Spatial lag model \n",
    "3. Local Modeling Frameworks (GWR and MGWR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "947e0246",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<img src=\"https://gistbok.ucgis.org/sites/default/files/AM34_Fig1.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc0cd2b5",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# (M)GWR \n",
    "\n",
    "* estimate location-dependent relationship between dependent and independent variables \n",
    "* place-based analytic technique\n",
    "* GWR models borrow data from neighboring observations and weight these data according to a smooth decay function based on either a physical distance or the number of nearest neighbors (higher weight for nearby locations) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45d07135",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# (M)GWR \n",
    "\n",
    "* Small bandwidths denote more local processes; large bandwidths indicate regional or global processes.  \n",
    "* As long as an **optimal bandwidth** is determined in the calibration of the GWR model and some continuous smooth function of distance is used, the **specific kernel function chosen is not critical**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b161865b",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"https://gistbok.ucgis.org/sites/default/files/AM34_Fig2.png\" width=\"500px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72e92c16",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"https://gistbok.ucgis.org/sites/default/files/AM34_Fig3.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f640bad",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Selecting Optimal Bandwidth \n",
    "\n",
    "* Trade-off between bias and variance. \n",
    "* As bw increases, the bias increases, because we are borrowing from locations that could have been generated by increasingly different processes \n",
    "* As bw decreases, the local parameter estimation uncertainty rises (fewer data points)  \n",
    "* Statistical optimization: trade-off between model fit and model complexity (e.g. AIC) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22bd678c",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# GWR Model Specification \n",
    "\n",
    "$$\n",
    "Y_i = \\beta_0(u_i, v_i) + \\sum_{j}\\beta_j(u_i, v_i)X_{ji} + \\epsilon_i\n",
    "$$\n",
    "\n",
    "where $j$ is the number of dependent variables and $\\beta$ vary across space (**surface estimation**)\n",
    "\n",
    "* overlapping sets of points are used, classical t-tests are not appropriate, thus $\\alpha$ needs to be corrected and MC appliedd\n",
    "\n",
    "$$\n",
    "\\alpha = \\frac{\\xi}{\\frac{ENP}{p}}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63b30272",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# MGWR Model Specification \n",
    "\n",
    "$$\n",
    "Y_i = \\beta_0(u_i, v_i) + \\sum_{j}\\beta_{bwj}(u_i, v_i)X_{ji} + \\epsilon_i\n",
    "$$\n",
    "\n",
    "where $\\beta_{bwj}$ indicates the bandwidth used for calibration of the $j$th relationship. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7618e9c8",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"https://gistbok.ucgis.org/sites/default/files/AM34_Fig4.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c71f996c",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# (M)GWR Limitations\n",
    "\n",
    "* More computationally intensive (if you have >500 observations might take too long on desktop computers) \n",
    "* Limited interpretation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cbb9073e",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\barguzin\\anaconda3\\envs\\geo_env\\lib\\site-packages\\spaghetti\\network.py:36: FutureWarning: The next major release of pysal/spaghetti (2.0.0) will drop support for all ``libpysal.cg`` geometries. This change is a first step in refactoring ``spaghetti`` that is expected to result in dramatically reduced runtimes for network instantiation and operations. Users currently requiring network and point pattern input as ``libpysal.cg`` geometries should prepare for this simply by converting to ``shapely`` geometries.\n",
      "  warnings.warn(f\"{dep_msg}\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "import pingouin as pg\n",
    "import statsmodels\n",
    "import statsmodels.formula.api as smf\n",
    "from pysal.model import spreg\n",
    "from libpysal.weights import Queen, Rook, KNN\n",
    "from esda.moran import Moran\n",
    "from pysal.explore import esda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "12c3f0ec",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6110, 20)\n"
     ]
    },
    {
     "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>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>pool</th>\n",
       "      <th>d2balboa</th>\n",
       "      <th>coastal</th>\n",
       "      <th>price</th>\n",
       "      <th>log_price</th>\n",
       "      <th>id</th>\n",
       "      <th>pg_Apartment</th>\n",
       "      <th>pg_Condominium</th>\n",
       "      <th>pg_House</th>\n",
       "      <th>pg_Other</th>\n",
       "      <th>pg_Townhouse</th>\n",
       "      <th>rt_Entire_home/apt</th>\n",
       "      <th>rt_Private_room</th>\n",
       "      <th>rt_Shared_room</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>North Hills</td>\n",
       "      <td>0</td>\n",
       "      <td>2.972077</td>\n",
       "      <td>0</td>\n",
       "      <td>425.0</td>\n",
       "      <td>6.052089</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POINT (-117.12971 32.75399)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Mission Bay</td>\n",
       "      <td>0</td>\n",
       "      <td>11.501385</td>\n",
       "      <td>1</td>\n",
       "      <td>205.0</td>\n",
       "      <td>5.323010</td>\n",
       "      <td>5570</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POINT (-117.25253 32.78421)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North Hills</td>\n",
       "      <td>0</td>\n",
       "      <td>2.493893</td>\n",
       "      <td>0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>4.595120</td>\n",
       "      <td>9553</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>POINT (-117.14121 32.75327)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mira Mesa</td>\n",
       "      <td>0</td>\n",
       "      <td>22.293757</td>\n",
       "      <td>0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>4.276666</td>\n",
       "      <td>14668</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>POINT (-117.15269 32.93110)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Roseville</td>\n",
       "      <td>0</td>\n",
       "      <td>6.829451</td>\n",
       "      <td>0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>4.007333</td>\n",
       "      <td>38245</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>POINT (-117.21870 32.74202)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   accommodates  bathrooms  bedrooms  beds neighborhood  pool   d2balboa  \\\n",
       "0             5        2.0       2.0   2.0  North Hills     0   2.972077   \n",
       "1             6        1.0       2.0   4.0  Mission Bay     0  11.501385   \n",
       "2             2        1.0       1.0   1.0  North Hills     0   2.493893   \n",
       "3             2        1.0       1.0   1.0    Mira Mesa     0  22.293757   \n",
       "4             2        1.0       1.0   1.0    Roseville     0   6.829451   \n",
       "\n",
       "   coastal  price  log_price     id  pg_Apartment  pg_Condominium  pg_House  \\\n",
       "0        0  425.0   6.052089      6             0               0         1   \n",
       "1        1  205.0   5.323010   5570             0               1         0   \n",
       "2        0   99.0   4.595120   9553             1               0         0   \n",
       "3        0   72.0   4.276666  14668             0               0         1   \n",
       "4        0   55.0   4.007333  38245             0               0         1   \n",
       "\n",
       "   pg_Other  pg_Townhouse  rt_Entire_home/apt  rt_Private_room  \\\n",
       "0         0             0                   1                0   \n",
       "1         0             0                   1                0   \n",
       "2         0             0                   0                1   \n",
       "3         0             0                   0                1   \n",
       "4         0             0                   0                1   \n",
       "\n",
       "   rt_Shared_room                     geometry  \n",
       "0               0  POINT (-117.12971 32.75399)  \n",
       "1               0  POINT (-117.25253 32.78421)  \n",
       "2               0  POINT (-117.14121 32.75327)  \n",
       "3               0  POINT (-117.15269 32.93110)  \n",
       "4               0  POINT (-117.21870 32.74202)  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db = gpd.read_file(\"regression_db.geojson\")\n",
    "print(db.shape) \n",
    "db.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f95c2f1b",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "variable_names = [\n",
    "    \"accommodates\",  # Number of people it accommodates\n",
    "    \"bathrooms\",  # Number of bathrooms\n",
    "    \"bedrooms\",  # Number of bedrooms\n",
    "    \"beds\",  # Number of beds\n",
    "    # Below are binary variables, 1 True, 0 False\n",
    "    \"rt_Private_room\",  # Room type: private room\n",
    "    \"rt_Shared_room\",  # Room type: shared room\n",
    "    \"pg_Condominium\",  # Property group: condo\n",
    "    \"pg_House\",  # Property group: house\n",
    "    \"pg_Other\",  # Property group: other\n",
    "    \"pg_Townhouse\",  # Property group: townhouse\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dcecc4dd",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'AirBnb listing price in San Diego \\n log(price)')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax = plt.subplots(figsize=(5,5))\n",
    "db.plot(column='log_price', ax=ax, legend=True, markersize=5, alpha=.5);\n",
    "ax.set_title('AirBnb listing price in San Diego \\n log(price)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dcf209b0",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Fit OLS model\n",
    "m1 = spreg.OLS(\n",
    "    # Dependent variable\n",
    "    db[[\"log_price\"]].values,\n",
    "    # Independent variables\n",
    "    db[variable_names].values,\n",
    "    # Dependent variable name\n",
    "    name_y=\"log_price\",\n",
    "    # Independent variable name\n",
    "    name_x=variable_names,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ae85600f",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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  :   log_price                Number of Observations:        6110\n",
      "Mean dependent var  :      4.9958                Number of Variables   :          11\n",
      "S.D. dependent var  :      0.8072                Degrees of Freedom    :        6099\n",
      "R-squared           :      0.6683\n",
      "Adjusted R-squared  :      0.6678\n",
      "Sum squared residual:    1320.148                F-statistic           :   1229.0564\n",
      "Sigma-square        :       0.216                Prob(F-statistic)     :           0\n",
      "S.E. of regression  :       0.465                Log likelihood        :   -3988.895\n",
      "Sigma-square ML     :       0.216                Akaike info criterion :    7999.790\n",
      "S.E of regression ML:      0.4648                Schwarz criterion     :    8073.685\n",
      "\n",
      "------------------------------------------------------------------------------------\n",
      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
      "------------------------------------------------------------------------------------\n",
      "            CONSTANT       4.3883830       0.0161147     272.3217773       0.0000000\n",
      "        accommodates       0.0834523       0.0050781      16.4336318       0.0000000\n",
      "           bathrooms       0.1923790       0.0109668      17.5419773       0.0000000\n",
      "            bedrooms       0.1525221       0.0111323      13.7009195       0.0000000\n",
      "                beds      -0.0417231       0.0069383      -6.0134430       0.0000000\n",
      "     rt_Private_room      -0.5506868       0.0159046     -34.6244758       0.0000000\n",
      "      rt_Shared_room      -1.2383055       0.0384329     -32.2198992       0.0000000\n",
      "      pg_Condominium       0.1436347       0.0221499       6.4846529       0.0000000\n",
      "            pg_House      -0.0104894       0.0145315      -0.7218393       0.4704209\n",
      "            pg_Other       0.1411546       0.0228016       6.1905633       0.0000000\n",
      "        pg_Townhouse      -0.0416702       0.0342758      -1.2157316       0.2241342\n",
      "------------------------------------------------------------------------------------\n",
      "\n",
      "REGRESSION DIAGNOSTICS\n",
      "MULTICOLLINEARITY CONDITION NUMBER           11.964\n",
      "\n",
      "TEST ON NORMALITY OF ERRORS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Jarque-Bera                       2        2671.611           0.0000\n",
      "\n",
      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
      "RANDOM COEFFICIENTS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Breusch-Pagan test               10         322.532           0.0000\n",
      "Koenker-Bassett test             10         135.581           0.0000\n",
      "================================ END OF REPORT =====================================\n"
     ]
    }
   ],
   "source": [
    "print(m1.summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3bc9692e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>pool</th>\n",
       "      <th>d2balboa</th>\n",
       "      <th>coastal</th>\n",
       "      <th>price</th>\n",
       "      <th>log_price</th>\n",
       "      <th>id</th>\n",
       "      <th>pg_Apartment</th>\n",
       "      <th>pg_Condominium</th>\n",
       "      <th>pg_House</th>\n",
       "      <th>pg_Other</th>\n",
       "      <th>pg_Townhouse</th>\n",
       "      <th>rt_Entire_home/apt</th>\n",
       "      <th>rt_Private_room</th>\n",
       "      <th>rt_Shared_room</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>North Hills</td>\n",
       "      <td>0</td>\n",
       "      <td>2.972077</td>\n",
       "      <td>0</td>\n",
       "      <td>425.0</td>\n",
       "      <td>6.052089</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>POINT (-117.12971 32.75399)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Mission Bay</td>\n",
       "      <td>0</td>\n",
       "      <td>11.501385</td>\n",
       "      <td>1</td>\n",
       "      <td>205.0</td>\n",
       "      <td>5.323010</td>\n",
       "      <td>5570</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>POINT (-117.25253 32.78421)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North Hills</td>\n",
       "      <td>0</td>\n",
       "      <td>2.493893</td>\n",
       "      <td>0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>4.595120</td>\n",
       "      <td>9553</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>POINT (-117.14121 32.75327)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mira Mesa</td>\n",
       "      <td>0</td>\n",
       "      <td>22.293757</td>\n",
       "      <td>0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>4.276666</td>\n",
       "      <td>14668</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>POINT (-117.15269 32.93110)</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Roseville</td>\n",
       "      <td>0</td>\n",
       "      <td>6.829451</td>\n",
       "      <td>0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>4.007333</td>\n",
       "      <td>38245</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   accommodates  bathrooms  bedrooms  beds neighborhood  pool   d2balboa  \\\n",
       "0             5        2.0       2.0   2.0  North Hills     0   2.972077   \n",
       "1             6        1.0       2.0   4.0  Mission Bay     0  11.501385   \n",
       "2             2        1.0       1.0   1.0  North Hills     0   2.493893   \n",
       "3             2        1.0       1.0   1.0    Mira Mesa     0  22.293757   \n",
       "4             2        1.0       1.0   1.0    Roseville     0   6.829451   \n",
       "\n",
       "   coastal  price  log_price     id  pg_Apartment  pg_Condominium  pg_House  \\\n",
       "0        0  425.0   6.052089      6             0               0         1   \n",
       "1        1  205.0   5.323010   5570             0               1         0   \n",
       "2        0   99.0   4.595120   9553             1               0         0   \n",
       "3        0   72.0   4.276666  14668             0               0         1   \n",
       "4        0   55.0   4.007333  38245             0               0         1   \n",
       "\n",
       "   pg_Other  pg_Townhouse  rt_Entire_home/apt  rt_Private_room  \\\n",
       "0         0             0                   1                0   \n",
       "1         0             0                   1                0   \n",
       "2         0             0                   0                1   \n",
       "3         0             0                   0                1   \n",
       "4         0             0                   0                1   \n",
       "\n",
       "   rt_Shared_room                     geometry  \n",
       "0               0  POINT (-117.12971 32.75399)  \n",
       "1               0  POINT (-117.25253 32.78421)  \n",
       "2               0  POINT (-117.14121 32.75327)  \n",
       "3               0  POINT (-117.15269 32.93110)  \n",
       "4               0  POINT (-117.21870 32.74202)  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dadcb812",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "#Prepare Georgia dataset inputs\n",
    "g_y = db['log_price'].values.reshape((-1,1))\n",
    "g_X = db[variable_names].values\n",
    "\n",
    "# reproject to get meters \n",
    "db = db.to_crs('EPSG:32611')\n",
    "\n",
    "u = db.geometry.centroid.x\n",
    "v = db.geometry.centroid.y\n",
    "g_coords = list(zip(u,v))\n",
    "\n",
    "g_X = (g_X - g_X.mean(axis=0)) / g_X.std(axis=0)\n",
    "\n",
    "# this rescales variables\n",
    "g_y = g_y.reshape((-1,1))\n",
    "g_y = (g_y - g_y.mean(axis=0)) / g_y.std(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c88a2268",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "#This might be needed to turn off the OpenMP multi-threading\n",
    "# %env OMP_NUM_THREADS = 1\n",
    "\n",
    "import multiprocessing as mp\n",
    "# here is how to use parallalization (works better on local machine) \n",
    "n_proc = 4 #two processors\n",
    "pool = mp.Pool(n_proc) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "003199ea",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "384.0\n"
     ]
    }
   ],
   "source": [
    "from mgwr.gwr import GWR, MGWR\n",
    "from mgwr.sel_bw import Sel_BW\n",
    "\n",
    "#Calibrate GWR model\n",
    "gwr_selector = Sel_BW(g_coords, g_y, g_X)\n",
    "gwr_bw = gwr_selector.search()\n",
    "print(gwr_bw)\n",
    "gwr_results = GWR(g_coords, g_y, g_X, gwr_bw).fit() #bw=384"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "31f3f35a",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.64594203],\n",
       "       [0.69396307],\n",
       "       [0.59707561],\n",
       "       [0.76902883],\n",
       "       [0.68488587],\n",
       "       [0.57839382],\n",
       "       [0.65114373],\n",
       "       [0.84073123],\n",
       "       [0.7878669 ],\n",
       "       [0.75561498]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gwr_results.localR2[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ed099d22",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===========================================================================\n",
      "Model type                                                         Gaussian\n",
      "Number of observations:                                                6110\n",
      "Number of covariates:                                                    11\n",
      "\n",
      "Global Regression Results\n",
      "---------------------------------------------------------------------------\n",
      "Residual sum of squares:                                           2026.415\n",
      "Log-likelihood:                                                   -5298.038\n",
      "AIC:                                                              10618.075\n",
      "AICc:                                                             10620.127\n",
      "BIC:                                                             -51142.728\n",
      "R2:                                                                   0.668\n",
      "Adj. R2:                                                              0.668\n",
      "\n",
      "Variable                              Est.         SE  t(Est/SE)    p-value\n",
      "------------------------------- ---------- ---------- ---------- ----------\n",
      "X0                                  -0.000      0.007     -0.000      1.000\n",
      "X1                                   0.294      0.018     16.434      0.000\n",
      "X2                                   0.206      0.012     17.542      0.000\n",
      "X3                                   0.215      0.016     13.701      0.000\n",
      "X4                                  -0.089      0.015     -6.013      0.000\n",
      "X5                                  -0.312      0.009    -34.624      0.000\n",
      "X6                                  -0.253      0.008    -32.220      0.000\n",
      "X7                                   0.051      0.008      6.485      0.000\n",
      "X8                                  -0.006      0.009     -0.722      0.470\n",
      "X9                                   0.049      0.008      6.191      0.000\n",
      "X10                                 -0.009      0.008     -1.216      0.224\n",
      "\n",
      "Geographically Weighted Regression (GWR) Results\n",
      "---------------------------------------------------------------------------\n",
      "Spatial kernel:                                           Adaptive bisquare\n",
      "Bandwidth used:                                                     384.000\n",
      "\n",
      "Diagnostic information\n",
      "---------------------------------------------------------------------------\n",
      "Residual sum of squares:                                           1452.044\n",
      "Effective number of parameters (trace(S)):                          428.246\n",
      "Degree of freedom (n - trace(S)):                                  5681.754\n",
      "Sigma estimate:                                                       0.506\n",
      "Log-likelihood:                                                   -4279.817\n",
      "AIC:                                                               9418.126\n",
      "AICc:                                                              9483.157\n",
      "BIC:                                                              12301.661\n",
      "R2:                                                                   0.762\n",
      "Adjusted R2:                                                          0.744\n",
      "Adj. alpha (95%):                                                     0.001\n",
      "Adj. critical t value (95%):                                          3.221\n",
      "\n",
      "Summary Statistics For GWR Parameter Estimates\n",
      "---------------------------------------------------------------------------\n",
      "Variable                   Mean        STD        Min     Median        Max\n",
      "-------------------- ---------- ---------- ---------- ---------- ----------\n",
      "X0                   225137641741.179 9249623383133.199 -2753748900385.411      0.004 624978556242947.625\n",
      "X1                        0.221      0.129     -0.259      0.215      0.654\n",
      "X2                        0.184      0.118     -0.396      0.186      0.569\n",
      "X3                        0.228      0.136     -0.156      0.255      0.745\n",
      "X4                       -0.048      0.117     -0.415     -0.063      0.356\n",
      "X5                       -0.274      0.054     -0.440     -0.282     -0.089\n",
      "X6                   1326803840988.813 54510812752279.562 -16228670558569.268     -0.198 3683186616621919.500\n",
      "X7                        0.048      0.061     -0.301      0.051      0.179\n",
      "X8                        0.044      0.079     -0.190      0.040      0.438\n",
      "X9                        0.037      0.076     -0.179      0.030      0.263\n",
      "X10                       0.001      0.042     -0.171     -0.000      0.198\n",
      "===========================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gwr_results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "700ff136",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# # THIS CODE TAKES TOO LONG TO RUN ON MY LAPTOP \n",
    "# #Calibrate MGWR model\n",
    "# mgwr_selector = Sel_BW(g_coords, g_y, g_X, multi=True)\n",
    "# mgwr_bw = mgwr_selector.search(multi_bw_min=[2])\n",
    "# print(mgwr_bw)\n",
    "# mgwr_results = MGWR(g_coords, g_y, g_X, mgwr_selector).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a80a1402",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "pool.close() # Close the pool when you finish\n",
    "pool.join()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3b236457",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add R2 to GeoDataframe \n",
    "db['gwr_R2'] = gwr_results.localR2\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "db.plot(column='gwr_R2', cmap = 'coolwarm', linewidth=0.01, scheme = 'FisherJenks', k=5, legend=True, legend_kwds={'bbox_to_anchor':(1.10, 0.96)},  ax=ax)\n",
    "ax.set_title('Local R2', fontsize=12)\n",
    "ax.axis(\"off\")\n",
    "#plt.savefig('myMap.png',dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5f12ee19",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "#Add GWR parameters to GeoDataframe\n",
    "db['gwr_intercept'] = gwr_results.params[:,0]\n",
    "db['gwr_accom'] = gwr_results.params[:,1]\n",
    "db['gwr_bathroom'] = gwr_results.params[:,2]\n",
    "db['gwr_beddroom'] = gwr_results.params[:,3]\n",
    "\n",
    "#Obtain t-vals filtered based on multiple testing correction\n",
    "gwr_filtered_t = gwr_results.filter_tvals()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "57c3e790",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Coefficients for Accommodation')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots()\n",
    "\n",
    "db.plot('gwr_accom',ax=ax, cmap='coolwarm', legend=True);\n",
    "\n",
    "ax.set_title('Coefficients for Accommodation');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfa4c3b1",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Questions? \n",
    "\n",
    "## Thank you for taking this class! \n",
    "\n",
    "### It was different from previous years, hopefully it has gotten you started on coding and programmatic spatial analysis! "
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python (geo_env)",
   "language": "python",
   "name": "geo_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
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   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}