{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7c1e4c1e",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<h1> <center> GEOG 172: INTERMEDIATE GEOGRAPHICAL ANALYSIS </h1>\n",
    "    <h2> <center> Evgeny Noi </h2>\n",
    "        <h3> <center> Lecture 11: Local Spatial Autocorrelation </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b9bdb9f",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Review \n",
    "\n",
    "* Correlation versus Autocorrelation\n",
    "* Global Spatial Autocorrelation (positive: HH/LL; negative: HL/LH). \n",
    "* Moran's $I$, Geary's $C$ and Getis & Ord's $G$ "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "758313f9",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Global versus Local Moran's $I$ \n",
    "\n",
    "$$\n",
    "I = \\frac{\\sum_i \\sum_j w_{ij} z_iz_j}{\\sum_i z_i^2},\n",
    "$$\n",
    "\n",
    "$$\n",
    "I_i = \\frac{\\sum_j w_{ij} z_iz_j}{\\sum_i z_i^2}.\n",
    "$$\n",
    "\n",
    "where $z_i = x_i - \\bar{x}$ (standardized value of the variable)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e8e043c",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Local Moran's $I$ \n",
    "\n",
    "* Originally conceived to define local *spatial clusters* and *spatial outliers* \n",
    "    * spatial clusters (high-high, low-low)\n",
    "    * spatial outliers (high-low, low-high)\n",
    "* The sum of the local statistics is proporational to the global Moran’s I, or, alternatively, that the global Moran’s I corresponds with the average of the local statistics\n",
    "* Significance is assessed via a permutation inference (!!! Multiple Comparisons)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8667f3d1",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Multiple Comparisons \n",
    "\n",
    "> Situation when statistical analysis involves multiple simultaneous statistical tests, each of which has a potential to produce a \"discovery.\"). A stated confidence level generally applies only to each test considered individually, but often it is desirable to have a confidence level for the whole family of simultaneous tests. Failure to compensate for multiple comparisons can have important real-world consequences.\n",
    "\n",
    "Take the case of $S = 100$ hypotheses being tested at the same time, all of them being true, with the size and level of each test exactly equal to $\\alpha$. For $\\alpha = 0.05$, one expects five true hypotheses to be rejected. Further, if all tests are mutually independent, then the probability that at least one true null hypothesis will be rejected is given by $1 − 0.95^{100} = 0.994$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2069f15f",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Multiple Comparisons and LISA \n",
    "\n",
    "* Traditional choice of 0.05 is likely to lead to many false positives, i.e., rejections of the null when in fact it holds.\n",
    "* Bonferonni bounds ($\\alpha/n$) - very conservative (use to identify cluster centers) \n",
    "* False Discovery Rate - less conservative \n",
    "    * Sort $I_i$ p-values in the increasing order (variable $I$ denotes order) \n",
    "    * $FDR = i \\times \\alpha/n$ (for first observation: $FDR = 1 \\times 0.05/85 = 0.000118$ \n",
    "    * Select all observations where $p_{i_{max}} \\leq i \\times \\alpha / n$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fef4f855",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example not available: Guerry\n",
      "Downloading AirBnB to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\AirBnB\n",
      "Downloading Atlanta to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Atlanta\n",
      "Downloading Baltimore to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Baltimore\n",
      "Downloading Bostonhsg to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Bostonhsg\n",
      "Downloading Buenosaires to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Buenosaires\n",
      "Downloading Charleston1 to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Charleston1\n",
      "Downloading Charleston2 to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Charleston2\n",
      "Downloading Chicago Health to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Chicago_Health\n",
      "Downloading Chicago commpop to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Chicago_commpop\n",
      "Example not downloaded: Chicago parcels\n",
      "Downloading Chile Labor to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Chile_Labor\n",
      "Example not downloaded: Chile Migration\n",
      "Downloading Cincinnati to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Cincinnati\n",
      "Downloading Cleveland to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Cleveland\n",
      "Downloading Columbus to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Columbus\n",
      "Downloading Elections to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Elections\n",
      "Downloading Grid100 to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Grid100\n",
      "Downloading Groceries to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Groceries\n",
      "Downloading Guerry to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Guerry\n",
      "Downloading Health Indicators to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Health_Indicators\n",
      "Downloading Health+ to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Health+\n",
      "Downloading Hickory1 to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Hickory1\n",
      "Downloading Hickory2 to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Hickory2\n",
      "Downloading Home Sales to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Home_Sales\n",
      "Downloading Houston to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Houston\n",
      "Downloading Juvenile to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Juvenile\n",
      "Downloading Lansing1 to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Lansing1\n",
      "Downloading Lansing2 to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Lansing2\n",
      "Downloading Laozone to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Laozone\n",
      "Downloading LasRosas to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\LasRosas\n",
      "Downloading Liquor Stores to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Liquor_Stores\n",
      "Downloading Malaria to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Malaria\n",
      "Downloading Milwaukee1 to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Milwaukee1\n",
      "Downloading Milwaukee2 to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Milwaukee2\n",
      "Downloading NCOVR to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\NCOVR\n",
      "Downloading NDVI to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\NDVI\n",
      "Downloading NYC to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\NYC\n",
      "Downloading NYC Earnings to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\NYC_Earnings\n",
      "Downloading NYC Education to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\NYC_Education\n",
      "Downloading NYC Neighborhoods to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\NYC_Neighborhoods\n",
      "Downloading NYC Socio-Demographics to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\NYC_Socio-Demographics\n",
      "Downloading Natregimes to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Natregimes\n",
      "Downloading Nepal to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Nepal\n",
      "Downloading Ohiolung to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Ohiolung\n",
      "Downloading Orlando1 to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Orlando1\n",
      "Downloading Orlando2 to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Orlando2\n",
      "Downloading Oz9799 to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Oz9799\n",
      "Downloading Phoenix ACS to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Phoenix_ACS\n",
      "Downloading Pittsburgh to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Pittsburgh\n",
      "Downloading Police to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Police\n",
      "Downloading Rio Grande do Sul to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Rio_Grande_do_Sul\n",
      "Downloading SIDS to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\SIDS\n",
      "Downloading SIDS2 to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\SIDS2\n",
      "Downloading Sacramento1 to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Sacramento1\n",
      "Downloading Sacramento2 to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Sacramento2\n",
      "Downloading SanFran Crime to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\SanFran_Crime\n",
      "Downloading Savannah1 to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Savannah1\n",
      "Downloading Savannah2 to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Savannah2\n",
      "Downloading Scotlip to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Scotlip\n",
      "Downloading Seattle1 to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Seattle1\n",
      "Downloading Seattle2 to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Seattle2\n",
      "Downloading Snow to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Snow\n",
      "Downloading South to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\South\n",
      "Example not downloaded: Spirals\n",
      "Downloading StLouis to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\StLouis\n",
      "Downloading Tampa1 to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\Tampa1\n",
      "Downloading US SDOH to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\US_SDOH\n",
      "Downloading clearwater to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\clearwater\n",
      "Downloading newHaven to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\newHaven\n",
      "Downloading nyc_bikes to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\nyc_bikes\n",
      "Downloading taz to C:\\Users\\barguzin\\AppData\\Local\\pysal\\pysal\\taz\n",
      "(85, 24)\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>dept</th>\n",
       "      <th>Region</th>\n",
       "      <th>Dprtmnt</th>\n",
       "      <th>Crm_prs</th>\n",
       "      <th>Crm_prp</th>\n",
       "      <th>Litercy</th>\n",
       "      <th>Donatns</th>\n",
       "      <th>Infants</th>\n",
       "      <th>Suicids</th>\n",
       "      <th>MainCty</th>\n",
       "      <th>...</th>\n",
       "      <th>Infntcd</th>\n",
       "      <th>Dntn_cl</th>\n",
       "      <th>Lottery</th>\n",
       "      <th>Desertn</th>\n",
       "      <th>Instrct</th>\n",
       "      <th>Prsttts</th>\n",
       "      <th>Distanc</th>\n",
       "      <th>Area</th>\n",
       "      <th>Pop1831</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>E</td>\n",
       "      <td>Ain</td>\n",
       "      <td>28870</td>\n",
       "      <td>15890</td>\n",
       "      <td>37</td>\n",
       "      <td>5098</td>\n",
       "      <td>33120</td>\n",
       "      <td>35039</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>60</td>\n",
       "      <td>69</td>\n",
       "      <td>41</td>\n",
       "      <td>55</td>\n",
       "      <td>46</td>\n",
       "      <td>13</td>\n",
       "      <td>218.372</td>\n",
       "      <td>5762</td>\n",
       "      <td>346.03</td>\n",
       "      <td>MULTIPOLYGON (((801150.00000 2092615.00000, 80...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>Aisne</td>\n",
       "      <td>26226</td>\n",
       "      <td>5521</td>\n",
       "      <td>51</td>\n",
       "      <td>8901</td>\n",
       "      <td>14572</td>\n",
       "      <td>12831</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>82</td>\n",
       "      <td>36</td>\n",
       "      <td>38</td>\n",
       "      <td>82</td>\n",
       "      <td>24</td>\n",
       "      <td>327</td>\n",
       "      <td>65.945</td>\n",
       "      <td>7369</td>\n",
       "      <td>513.00</td>\n",
       "      <td>MULTIPOLYGON (((729326.00000 2521619.00000, 72...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>C</td>\n",
       "      <td>Allier</td>\n",
       "      <td>26747</td>\n",
       "      <td>7925</td>\n",
       "      <td>13</td>\n",
       "      <td>10973</td>\n",
       "      <td>17044</td>\n",
       "      <td>114121</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>42</td>\n",
       "      <td>76</td>\n",
       "      <td>66</td>\n",
       "      <td>16</td>\n",
       "      <td>85</td>\n",
       "      <td>34</td>\n",
       "      <td>161.927</td>\n",
       "      <td>7340</td>\n",
       "      <td>298.26</td>\n",
       "      <td>MULTIPOLYGON (((710830.00000 2137350.00000, 71...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>E</td>\n",
       "      <td>Basses-Alpes</td>\n",
       "      <td>12935</td>\n",
       "      <td>7289</td>\n",
       "      <td>46</td>\n",
       "      <td>2733</td>\n",
       "      <td>23018</td>\n",
       "      <td>14238</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>37</td>\n",
       "      <td>80</td>\n",
       "      <td>32</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>351.399</td>\n",
       "      <td>6925</td>\n",
       "      <td>155.90</td>\n",
       "      <td>MULTIPOLYGON (((882701.00000 1920024.00000, 88...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>E</td>\n",
       "      <td>Hautes-Alpes</td>\n",
       "      <td>17488</td>\n",
       "      <td>8174</td>\n",
       "      <td>69</td>\n",
       "      <td>6962</td>\n",
       "      <td>23076</td>\n",
       "      <td>16171</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>23</td>\n",
       "      <td>64</td>\n",
       "      <td>79</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>320.280</td>\n",
       "      <td>5549</td>\n",
       "      <td>129.10</td>\n",
       "      <td>MULTIPOLYGON (((886504.00000 1922890.00000, 88...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   dept Region       Dprtmnt  Crm_prs  Crm_prp  Litercy  Donatns  Infants  \\\n",
       "0     1      E           Ain    28870    15890       37     5098    33120   \n",
       "1     2      N         Aisne    26226     5521       51     8901    14572   \n",
       "2     3      C        Allier    26747     7925       13    10973    17044   \n",
       "3     4      E  Basses-Alpes    12935     7289       46     2733    23018   \n",
       "4     5      E  Hautes-Alpes    17488     8174       69     6962    23076   \n",
       "\n",
       "   Suicids  MainCty  ...  Infntcd  Dntn_cl  Lottery  Desertn  Instrct  \\\n",
       "0    35039        2  ...       60       69       41       55       46   \n",
       "1    12831        2  ...       82       36       38       82       24   \n",
       "2   114121        2  ...       42       76       66       16       85   \n",
       "3    14238        1  ...       12       37       80       32       29   \n",
       "4    16171        1  ...       23       64       79       35        7   \n",
       "\n",
       "   Prsttts  Distanc  Area  Pop1831  \\\n",
       "0       13  218.372  5762   346.03   \n",
       "1      327   65.945  7369   513.00   \n",
       "2       34  161.927  7340   298.26   \n",
       "3        2  351.399  6925   155.90   \n",
       "4        1  320.280  5549   129.10   \n",
       "\n",
       "                                            geometry  \n",
       "0  MULTIPOLYGON (((801150.00000 2092615.00000, 80...  \n",
       "1  MULTIPOLYGON (((729326.00000 2521619.00000, 72...  \n",
       "2  MULTIPOLYGON (((710830.00000 2137350.00000, 71...  \n",
       "3  MULTIPOLYGON (((882701.00000 1920024.00000, 88...  \n",
       "4  MULTIPOLYGON (((886504.00000 1922890.00000, 88...  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from libpysal.examples import load_example\n",
    "import geopandas as gpd\n",
    "guerry_loc = load_example('Guerry')\n",
    "guerry = gpd.read_file(guerry_loc.get_path('guerry.geojson'))\n",
    "print(guerry.shape)\n",
    "guerry.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5e708bbb",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "from pysal.explore import esda  # Exploratory Spatial analytics\n",
    "from pysal.lib import weights  # Spatial weights\n",
    "from splot.esda import moran_scatterplot, lisa_cluster\n",
    "from esda.moran import Moran, Moran_Local\n",
    "from splot.esda import plot_local_autocorrelation\n",
    "from splot.esda import plot_moran\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "77a84fbe",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moran's I: 0.26355334031776523\n",
      "Moran's p-val: 0.001\n"
     ]
    }
   ],
   "source": [
    "y = guerry['Crm_prp'].values\n",
    "w = weights.Queen.from_dataframe(guerry)\n",
    "w.transform = 'r'\n",
    "\n",
    "# calculate global\n",
    "moran = Moran(y, w)\n",
    "print(\"Moran's I:\", moran.I)\n",
    "print(\"Moran's p-val:\", moran.p_sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4b41750a",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_moran(moran, zstandard=True, figsize=(10,4))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1722c5a",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"https://geographicdata.science/book/_images/07_local_autocorrelation_21_0.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "accde1a7",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# calculate Moran_Local and plot\n",
    "moran_loc = Moran_Local(y, w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9902b1cf",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = moran_scatterplot(moran_loc, p=0.05)\n",
    "ax.set_xlabel('Crimes Againts Property')\n",
    "ax.set_ylabel('Spatial Lag of Crimes Against Property')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ac0254e6",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lisa_cluster(moran_loc, guerry, p=0.05, figsize = (5,5), aspect=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8959723d",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0017647058823529415\n",
      "0.010588235294117647\n"
     ]
    }
   ],
   "source": [
    "# Calculate the p-value cut-off to control \n",
    "# for the false discovery rate (FDR) for multiple testing.\n",
    "print(esda.fdr(moran_loc.p_sim, 0.05)) # Bonferonni\n",
    "print(esda.fdr(moran_loc.p_sim, 0.1)) # FDF cut-off"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fb2adb2",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Extensions to Local Moran's $I$ \n",
    "\n",
    "* Bivariate Local Moran's $I$ \n",
    "* Differential Local Moran's $I$ \n",
    "* Local Moran's $I$ with EB rates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c538813",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Local Geary's $c$ \n",
    "\n",
    "$$\n",
    "LG_i = \\sum_j w_{ij}(x_i - x_j)^2\n",
    "$$\n",
    "\n",
    "While calculating Geary's c is possible in Python the plots are not yet automated. Use GeoDa for comparisons! "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4183887a",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Local Getis & Ord's $G$\n",
    "\n",
    "$$\n",
    "G_i = \\frac{\\sum_{j \\neq i} w_{ij} x_j}{\\sum_{j \\neq i} x_j} \\quad \\text{not including value at location i} \n",
    "$$\n",
    "\n",
    "$$\n",
    "G_i^* = \\frac{\\sum_j w_{ij} x_j}{\\sum_j x_j}\n",
    "$$\n",
    "\n",
    "In contrast to the Local Moran and Local Geary statistics, the Getis-Ord approach does not consider spatial outliers. Calculations similar to Local Geary's $C$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57f9870a",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Interim Data Report\n",
    "\n",
    "## Class exercise \n",
    "\n",
    "> Work in pairs. Tell one another what data you are working in and what questions you hope to answer with the methods we've learned in class. \n",
    "\n",
    "### Due: Sunday (Nov 6, 2022) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44149bbb",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Questions? "
   ]
  }
 ],
 "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"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}