notebooks/05_Melbourne-crime-index.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "therapeutic-coordinate",
   "metadata": {},
   "source": [
    "## Melbourne crime index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rocky-times",
   "metadata": {},
   "source": [
    "In notebook 3, we have calculated the number of criminal incidents in diffrent postcodes along with its shapefiles. However, to calculate a crime index, we need to compare it with their relative population.\n",
    "\n",
    "This notebook will calculate the number of criminal incidents per 1000 households and per 1000 people.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "congressional-chance",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "massive-experiment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9001, 8)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Read crime data\n",
    "crime_gdf = gpd.read_file(\"../data/processed/poa_crime_gdf.geojson\")\n",
    "crime_gdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "geological-assistant",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Local Government Area</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Total Incidents</th>\n",
       "      <th>POA_CODE16</th>\n",
       "      <th>POA_NAME16</th>\n",
       "      <th>AREASQKM16</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>Alpine</td>\n",
       "      <td>3691</td>\n",
       "      <td>5</td>\n",
       "      <td>3691</td>\n",
       "      <td>3691</td>\n",
       "      <td>1837.4123</td>\n",
       "      <td>MULTIPOLYGON (((146.90262 -36.10540, 146.90189...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011</td>\n",
       "      <td>Alpine</td>\n",
       "      <td>3697</td>\n",
       "      <td>4</td>\n",
       "      <td>3697</td>\n",
       "      <td>3697</td>\n",
       "      <td>140.6154</td>\n",
       "      <td>POLYGON ((147.13594 -36.69941, 147.13629 -36.6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011</td>\n",
       "      <td>Alpine</td>\n",
       "      <td>3698</td>\n",
       "      <td>3</td>\n",
       "      <td>3698</td>\n",
       "      <td>3698</td>\n",
       "      <td>48.8349</td>\n",
       "      <td>POLYGON ((147.13954 -36.72241, 147.14057 -36.7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>Alpine</td>\n",
       "      <td>3699</td>\n",
       "      <td>13</td>\n",
       "      <td>3699</td>\n",
       "      <td>3699</td>\n",
       "      <td>502.9748</td>\n",
       "      <td>POLYGON ((147.16186 -36.73468, 147.16187 -36.7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011</td>\n",
       "      <td>Alpine</td>\n",
       "      <td>3737</td>\n",
       "      <td>153</td>\n",
       "      <td>3737</td>\n",
       "      <td>3737</td>\n",
       "      <td>1737.5617</td>\n",
       "      <td>POLYGON ((146.65912 -36.54841, 146.65910 -36.5...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year Local Government Area Postcode  Total Incidents POA_CODE16 POA_NAME16  \\\n",
       "0  2011                Alpine     3691                5       3691       3691   \n",
       "1  2011                Alpine     3697                4       3697       3697   \n",
       "2  2011                Alpine     3698                3       3698       3698   \n",
       "3  2011                Alpine     3699               13       3699       3699   \n",
       "4  2011                Alpine     3737              153       3737       3737   \n",
       "\n",
       "   AREASQKM16                                           geometry  \n",
       "0   1837.4123  MULTIPOLYGON (((146.90262 -36.10540, 146.90189...  \n",
       "1    140.6154  POLYGON ((147.13594 -36.69941, 147.13629 -36.6...  \n",
       "2     48.8349  POLYGON ((147.13954 -36.72241, 147.14057 -36.7...  \n",
       "3    502.9748  POLYGON ((147.16186 -36.73468, 147.16187 -36.7...  \n",
       "4   1737.5617  POLYGON ((146.65912 -36.54841, 146.65910 -36.5...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime_gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "digital-perception",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year                        int64\n",
       "Local Government Area      object\n",
       "Postcode                   object\n",
       "Total Incidents             int64\n",
       "POA_CODE16                 object\n",
       "POA_NAME16                 object\n",
       "AREASQKM16                float64\n",
       "geometry                 geometry\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime_gdf.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "rocky-education",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "697"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime_gdf.POA_CODE16.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "genuine-nightmare",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(698, 3)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Read postcode households\n",
    "poa_hh = pd.read_csv(\"../data/processed/poa_households.csv\")\n",
    "poa_hh.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "graduate-valve",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>POA_CODE16</th>\n",
       "      <th>Total Households</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3000</td>\n",
       "      <td>5804</td>\n",
       "      <td>49192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3002</td>\n",
       "      <td>1107</td>\n",
       "      <td>6196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3003</td>\n",
       "      <td>1087</td>\n",
       "      <td>5588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3004</td>\n",
       "      <td>2223</td>\n",
       "      <td>10861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3005</td>\n",
       "      <td>99</td>\n",
       "      <td>879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   POA_CODE16  Total Households  Population\n",
       "0        3000              5804       49192\n",
       "1        3002              1107        6196\n",
       "2        3003              1087        5588\n",
       "3        3004              2223       10861\n",
       "4        3005                99         879"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poa_hh.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "pressed-savage",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "POA_CODE16          int64\n",
       "Total Households    int64\n",
       "Population          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poa_hh.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "rising-jersey",
   "metadata": {},
   "outputs": [],
   "source": [
    "poa_hh.POA_CODE16 = poa_hh.POA_CODE16.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "electoral-arlington",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "698"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poa_hh.POA_CODE16.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "outstanding-usage",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Join data\n",
    "crime_gdf = crime_gdf.merge(poa_hh, how=\"left\", left_on=\"POA_CODE16\", right_on=\"POA_CODE16\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "imperial-alexander",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Local Government Area</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Total Incidents</th>\n",
       "      <th>POA_CODE16</th>\n",
       "      <th>POA_NAME16</th>\n",
       "      <th>AREASQKM16</th>\n",
       "      <th>geometry</th>\n",
       "      <th>Total Households</th>\n",
       "      <th>Population</th>\n",
       "      <th>Incidents by 10,000 households</th>\n",
       "      <th>Incidents by 10,000 population</th>\n",
       "      <th>Incidents by 1000 households</th>\n",
       "      <th>Incidents by 1000 population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>Alpine</td>\n",
       "      <td>3691</td>\n",
       "      <td>5</td>\n",
       "      <td>3691</td>\n",
       "      <td>3691</td>\n",
       "      <td>1837.4123</td>\n",
       "      <td>MULTIPOLYGON (((146.90262 -36.10540, 146.90189...</td>\n",
       "      <td>2550</td>\n",
       "      <td>9600</td>\n",
       "      <td>1.960016</td>\n",
       "      <td>0.520779</td>\n",
       "      <td>1.960016</td>\n",
       "      <td>0.520779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011</td>\n",
       "      <td>Alpine</td>\n",
       "      <td>3697</td>\n",
       "      <td>4</td>\n",
       "      <td>3697</td>\n",
       "      <td>3697</td>\n",
       "      <td>140.6154</td>\n",
       "      <td>POLYGON ((147.13594 -36.69941, 147.13629 -36.6...</td>\n",
       "      <td>158</td>\n",
       "      <td>541</td>\n",
       "      <td>25.157233</td>\n",
       "      <td>7.380074</td>\n",
       "      <td>25.157233</td>\n",
       "      <td>7.380074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011</td>\n",
       "      <td>Alpine</td>\n",
       "      <td>3698</td>\n",
       "      <td>3</td>\n",
       "      <td>3698</td>\n",
       "      <td>3698</td>\n",
       "      <td>48.8349</td>\n",
       "      <td>POLYGON ((147.13954 -36.72241, 147.14057 -36.7...</td>\n",
       "      <td>234</td>\n",
       "      <td>936</td>\n",
       "      <td>12.765957</td>\n",
       "      <td>3.201708</td>\n",
       "      <td>12.765957</td>\n",
       "      <td>3.201708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>Alpine</td>\n",
       "      <td>3699</td>\n",
       "      <td>13</td>\n",
       "      <td>3699</td>\n",
       "      <td>3699</td>\n",
       "      <td>502.9748</td>\n",
       "      <td>POLYGON ((147.16186 -36.73468, 147.16187 -36.7...</td>\n",
       "      <td>225</td>\n",
       "      <td>4169</td>\n",
       "      <td>57.522124</td>\n",
       "      <td>3.117506</td>\n",
       "      <td>57.522124</td>\n",
       "      <td>3.117506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011</td>\n",
       "      <td>Alpine</td>\n",
       "      <td>3737</td>\n",
       "      <td>153</td>\n",
       "      <td>3737</td>\n",
       "      <td>3737</td>\n",
       "      <td>1737.5617</td>\n",
       "      <td>POLYGON ((146.65912 -36.54841, 146.65910 -36.5...</td>\n",
       "      <td>1145</td>\n",
       "      <td>4136</td>\n",
       "      <td>133.507853</td>\n",
       "      <td>36.983321</td>\n",
       "      <td>133.507853</td>\n",
       "      <td>36.983321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year Local Government Area Postcode  Total Incidents POA_CODE16 POA_NAME16  \\\n",
       "0  2011                Alpine     3691                5       3691       3691   \n",
       "1  2011                Alpine     3697                4       3697       3697   \n",
       "2  2011                Alpine     3698                3       3698       3698   \n",
       "3  2011                Alpine     3699               13       3699       3699   \n",
       "4  2011                Alpine     3737              153       3737       3737   \n",
       "\n",
       "   AREASQKM16                                           geometry  \\\n",
       "0   1837.4123  MULTIPOLYGON (((146.90262 -36.10540, 146.90189...   \n",
       "1    140.6154  POLYGON ((147.13594 -36.69941, 147.13629 -36.6...   \n",
       "2     48.8349  POLYGON ((147.13954 -36.72241, 147.14057 -36.7...   \n",
       "3    502.9748  POLYGON ((147.16186 -36.73468, 147.16187 -36.7...   \n",
       "4   1737.5617  POLYGON ((146.65912 -36.54841, 146.65910 -36.5...   \n",
       "\n",
       "   Total Households  Population  Incidents by 10,000 households  \\\n",
       "0              2550        9600                        1.960016   \n",
       "1               158         541                       25.157233   \n",
       "2               234         936                       12.765957   \n",
       "3               225        4169                       57.522124   \n",
       "4              1145        4136                      133.507853   \n",
       "\n",
       "   Incidents by 10,000 population  Incidents by 1000 households  \\\n",
       "0                        0.520779                      1.960016   \n",
       "1                        7.380074                     25.157233   \n",
       "2                        3.201708                     12.765957   \n",
       "3                        3.117506                     57.522124   \n",
       "4                       36.983321                    133.507853   \n",
       "\n",
       "   Incidents by 1000 population  \n",
       "0                      0.520779  \n",
       "1                      7.380074  \n",
       "2                      3.201708  \n",
       "3                      3.117506  \n",
       "4                     36.983321  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime_gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "multiple-relation",
   "metadata": {},
   "outputs": [],
   "source": [
    "crime_gdf[\"Incidents by 1000 households\"] = 1_000 * crime_gdf[\"Total Incidents\"] / (crime_gdf[\"Total Households\"] + 1)\n",
    "crime_gdf[\"Incidents by 1000 population\"] = 1_000 * crime_gdf[\"Total Incidents\"] / (crime_gdf[\"Population\"] + 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fallen-destination",
   "metadata": {},
   "source": [
    "### Explore the crime index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "different-diving",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      9001.000000\n",
       "mean       1412.337846\n",
       "std       21779.785129\n",
       "min           0.126534\n",
       "25%          48.560136\n",
       "50%         112.537899\n",
       "75%         194.774347\n",
       "max      620000.000000\n",
       "Name: Incidents by 10,000 households, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime_gdf[\"Incidents by 10,000 households\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ultimate-federal",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22, 14)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime_gdf[crime_gdf[\"Incidents by 1000 households\"] >= 2e5].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "experimental-india",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram\n",
    "crime_gdf[crime_gdf[\"Incidents by 1000 households\"] <= 500][\"Incidents by 1000 households\"].plot(kind=\"hist\", bins=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "working-secretariat",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/danph/anaconda3/envs/pysal-workshop/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Incidents by 1000 households', ylabel='Density'>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sbn.distplot(crime_gdf[crime_gdf[\"Incidents by 1000 households\"] <= 500][\"Incidents by 1000 households\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "organic-shopping",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      9001.000000\n",
       "mean        253.708380\n",
       "std        6321.600042\n",
       "min           0.031883\n",
       "25%          12.987013\n",
       "50%          30.726257\n",
       "75%          51.622419\n",
       "max      239000.000000\n",
       "Name: Incidents by 1000 population, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime_gdf[\"Incidents by 1000 population\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "sustainable-throat",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram\n",
    "crime_gdf[crime_gdf[\"Incidents by 1000 population\"] <= 200][\"Incidents by 1000 population\"].plot(kind=\"hist\", bins=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "closing-monkey",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sbn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "nearby-bangkok",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/danph/anaconda3/envs/pysal-workshop/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Incidents by 1000 population', ylabel='Density'>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sbn.distplot(crime_gdf[crime_gdf[\"Incidents by 1000 population\"] <= 200][\"Incidents by 1000 population\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "affected-relief",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save data\n",
    "crime_gdf.to_file(\"../data/processed/poa_crime_index.geojson\", driver=\"GeoJSON\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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