Kmeans elbow 2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.mlab as mlab\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>name_x</th>\n",
       "      <th>fclass</th>\n",
       "      <th>osm_id</th>\n",
       "      <th>long</th>\n",
       "      <th>latt</th>\n",
       "      <th>cluster</th>\n",
       "      <th>C_PTS</th>\n",
       "      <th>D_C_PTS</th>\n",
       "      <th>C_Super</th>\n",
       "      <th>D_C_Super</th>\n",
       "      <th>C_DoctorsI</th>\n",
       "      <th>D_C_Doctor</th>\n",
       "      <th>C_SchoolsI</th>\n",
       "      <th>D_C_School</th>\n",
       "      <th>C_SportID</th>\n",
       "      <th>D_C_Sport</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Hague Tower</td>\n",
       "      <td>building</td>\n",
       "      <td>37774391</td>\n",
       "      <td>4.324224</td>\n",
       "      <td>52.071575</td>\n",
       "      <td>3603</td>\n",
       "      <td>1301378</td>\n",
       "      <td>107.161512</td>\n",
       "      <td>170</td>\n",
       "      <td>397.025030</td>\n",
       "      <td>12</td>\n",
       "      <td>644.307493</td>\n",
       "      <td>78</td>\n",
       "      <td>1093.290909</td>\n",
       "      <td>92</td>\n",
       "      <td>351.857639</td>\n",
       "      <td>POINT (4.32422 52.07158)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>building</td>\n",
       "      <td>267117842</td>\n",
       "      <td>4.324910</td>\n",
       "      <td>52.071850</td>\n",
       "      <td>3603</td>\n",
       "      <td>1301378</td>\n",
       "      <td>107.161512</td>\n",
       "      <td>170</td>\n",
       "      <td>397.025030</td>\n",
       "      <td>12</td>\n",
       "      <td>644.307493</td>\n",
       "      <td>78</td>\n",
       "      <td>1093.290909</td>\n",
       "      <td>92</td>\n",
       "      <td>351.857639</td>\n",
       "      <td>POINT (4.32491 52.07185)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>building</td>\n",
       "      <td>58350937</td>\n",
       "      <td>4.319504</td>\n",
       "      <td>52.019770</td>\n",
       "      <td>1403</td>\n",
       "      <td>16402</td>\n",
       "      <td>1401.525367</td>\n",
       "      <td>59</td>\n",
       "      <td>2545.583465</td>\n",
       "      <td>3</td>\n",
       "      <td>2830.766890</td>\n",
       "      <td>101</td>\n",
       "      <td>3064.414074</td>\n",
       "      <td>13</td>\n",
       "      <td>2781.176538</td>\n",
       "      <td>POINT (4.31950 52.01977)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>building</td>\n",
       "      <td>253797461</td>\n",
       "      <td>4.320369</td>\n",
       "      <td>52.019398</td>\n",
       "      <td>1403</td>\n",
       "      <td>16402</td>\n",
       "      <td>1401.525367</td>\n",
       "      <td>59</td>\n",
       "      <td>2545.583465</td>\n",
       "      <td>3</td>\n",
       "      <td>2830.766890</td>\n",
       "      <td>101</td>\n",
       "      <td>3064.414074</td>\n",
       "      <td>13</td>\n",
       "      <td>2781.176538</td>\n",
       "      <td>POINT (4.32037 52.01940)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>building</td>\n",
       "      <td>427468640</td>\n",
       "      <td>4.321300</td>\n",
       "      <td>52.018973</td>\n",
       "      <td>1403</td>\n",
       "      <td>16402</td>\n",
       "      <td>1401.525367</td>\n",
       "      <td>59</td>\n",
       "      <td>2545.583465</td>\n",
       "      <td>3</td>\n",
       "      <td>2830.766890</td>\n",
       "      <td>101</td>\n",
       "      <td>3064.414074</td>\n",
       "      <td>13</td>\n",
       "      <td>2781.176538</td>\n",
       "      <td>POINT (4.32130 52.01897)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93480</th>\n",
       "      <td>None</td>\n",
       "      <td>building</td>\n",
       "      <td>766622131</td>\n",
       "      <td>4.382658</td>\n",
       "      <td>52.054053</td>\n",
       "      <td>3714</td>\n",
       "      <td>651931</td>\n",
       "      <td>435.366264</td>\n",
       "      <td>67</td>\n",
       "      <td>1704.349670</td>\n",
       "      <td>29</td>\n",
       "      <td>1777.164385</td>\n",
       "      <td>9</td>\n",
       "      <td>1302.511442</td>\n",
       "      <td>6</td>\n",
       "      <td>1596.647441</td>\n",
       "      <td>POINT (4.38266 52.05405)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93481</th>\n",
       "      <td>None</td>\n",
       "      <td>building</td>\n",
       "      <td>697411648</td>\n",
       "      <td>4.381724</td>\n",
       "      <td>52.054196</td>\n",
       "      <td>600</td>\n",
       "      <td>651931</td>\n",
       "      <td>628.828303</td>\n",
       "      <td>67</td>\n",
       "      <td>1897.811709</td>\n",
       "      <td>29</td>\n",
       "      <td>1970.626425</td>\n",
       "      <td>9</td>\n",
       "      <td>1495.973482</td>\n",
       "      <td>6</td>\n",
       "      <td>1790.109481</td>\n",
       "      <td>POINT (4.38172 52.05420)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93482</th>\n",
       "      <td>None</td>\n",
       "      <td>building</td>\n",
       "      <td>766622106</td>\n",
       "      <td>4.382214</td>\n",
       "      <td>52.054351</td>\n",
       "      <td>600</td>\n",
       "      <td>651931</td>\n",
       "      <td>628.828303</td>\n",
       "      <td>67</td>\n",
       "      <td>1897.811709</td>\n",
       "      <td>29</td>\n",
       "      <td>1970.626425</td>\n",
       "      <td>9</td>\n",
       "      <td>1495.973482</td>\n",
       "      <td>6</td>\n",
       "      <td>1790.109481</td>\n",
       "      <td>POINT (4.38221 52.05435)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93483</th>\n",
       "      <td>None</td>\n",
       "      <td>building</td>\n",
       "      <td>745899409</td>\n",
       "      <td>4.218895</td>\n",
       "      <td>52.041474</td>\n",
       "      <td>4934</td>\n",
       "      <td>1547770</td>\n",
       "      <td>1639.160926</td>\n",
       "      <td>163</td>\n",
       "      <td>2204.908802</td>\n",
       "      <td>65</td>\n",
       "      <td>4893.801925</td>\n",
       "      <td>90</td>\n",
       "      <td>4589.109529</td>\n",
       "      <td>47</td>\n",
       "      <td>2636.794572</td>\n",
       "      <td>POINT (4.21889 52.04147)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93484</th>\n",
       "      <td>None</td>\n",
       "      <td>building</td>\n",
       "      <td>745899513</td>\n",
       "      <td>4.218672</td>\n",
       "      <td>52.041785</td>\n",
       "      <td>4934</td>\n",
       "      <td>1547770</td>\n",
       "      <td>1639.160926</td>\n",
       "      <td>163</td>\n",
       "      <td>2204.908802</td>\n",
       "      <td>65</td>\n",
       "      <td>4893.801925</td>\n",
       "      <td>90</td>\n",
       "      <td>4589.109529</td>\n",
       "      <td>47</td>\n",
       "      <td>2636.794572</td>\n",
       "      <td>POINT (4.21867 52.04179)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93485 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                name_x    fclass     osm_id      long       latt  cluster  \\\n",
       "0      The Hague Tower  building   37774391  4.324224  52.071575     3603   \n",
       "1                 None  building  267117842  4.324910  52.071850     3603   \n",
       "2                 None  building   58350937  4.319504  52.019770     1403   \n",
       "3                 None  building  253797461  4.320369  52.019398     1403   \n",
       "4                 None  building  427468640  4.321300  52.018973     1403   \n",
       "...                ...       ...        ...       ...        ...      ...   \n",
       "93480             None  building  766622131  4.382658  52.054053     3714   \n",
       "93481             None  building  697411648  4.381724  52.054196      600   \n",
       "93482             None  building  766622106  4.382214  52.054351      600   \n",
       "93483             None  building  745899409  4.218895  52.041474     4934   \n",
       "93484             None  building  745899513  4.218672  52.041785     4934   \n",
       "\n",
       "         C_PTS      D_C_PTS  C_Super    D_C_Super  C_DoctorsI   D_C_Doctor  \\\n",
       "0      1301378   107.161512      170   397.025030          12   644.307493   \n",
       "1      1301378   107.161512      170   397.025030          12   644.307493   \n",
       "2        16402  1401.525367       59  2545.583465           3  2830.766890   \n",
       "3        16402  1401.525367       59  2545.583465           3  2830.766890   \n",
       "4        16402  1401.525367       59  2545.583465           3  2830.766890   \n",
       "...        ...          ...      ...          ...         ...          ...   \n",
       "93480   651931   435.366264       67  1704.349670          29  1777.164385   \n",
       "93481   651931   628.828303       67  1897.811709          29  1970.626425   \n",
       "93482   651931   628.828303       67  1897.811709          29  1970.626425   \n",
       "93483  1547770  1639.160926      163  2204.908802          65  4893.801925   \n",
       "93484  1547770  1639.160926      163  2204.908802          65  4893.801925   \n",
       "\n",
       "       C_SchoolsI   D_C_School  C_SportID    D_C_Sport  \\\n",
       "0              78  1093.290909         92   351.857639   \n",
       "1              78  1093.290909         92   351.857639   \n",
       "2             101  3064.414074         13  2781.176538   \n",
       "3             101  3064.414074         13  2781.176538   \n",
       "4             101  3064.414074         13  2781.176538   \n",
       "...           ...          ...        ...          ...   \n",
       "93480           9  1302.511442          6  1596.647441   \n",
       "93481           9  1495.973482          6  1790.109481   \n",
       "93482           9  1495.973482          6  1790.109481   \n",
       "93483          90  4589.109529         47  2636.794572   \n",
       "93484          90  4589.109529         47  2636.794572   \n",
       "\n",
       "                       geometry  \n",
       "0      POINT (4.32422 52.07158)  \n",
       "1      POINT (4.32491 52.07185)  \n",
       "2      POINT (4.31950 52.01977)  \n",
       "3      POINT (4.32037 52.01940)  \n",
       "4      POINT (4.32130 52.01897)  \n",
       "...                         ...  \n",
       "93480  POINT (4.38266 52.05405)  \n",
       "93481  POINT (4.38172 52.05420)  \n",
       "93482  POINT (4.38221 52.05435)  \n",
       "93483  POINT (4.21889 52.04147)  \n",
       "93484  POINT (4.21867 52.04179)  \n",
       "\n",
       "[93485 rows x 17 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf = gpd.read_file(r\"C:\\Users\\bruno\\Documents\\J3Q3-4 BEP\\repositories\\allhousesclustered1.shp\")\n",
    "gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[254911013608.71408,\n",
       " 162414559350.43085,\n",
       " 113921573981.06815,\n",
       " 88621059911.20073,\n",
       " 69462614590.65262,\n",
       " 57634206346.71695,\n",
       " 51460558107.59728,\n",
       " 47131561168.46438,\n",
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       " 40124901211.648735,\n",
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       " 34615850686.99275,\n",
       " 32454854142.28035,\n",
       " 30321160760.887283]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_squared_distances = []\n",
    "X =  gdf[['D_C_PTS', 'D_C_Super', 'D_C_Doctor', 'D_C_Sport', 'D_C_School']]\n",
    "for i in range(1,15):\n",
    "    kmeans = KMeans(n_clusters=i)\n",
    "    kmeans = kmeans.fit(X)\n",
    "    sum_squared_distances.append(kmeans.inertia_)\n",
    "\n",
    "sum_squared_distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2400x1600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = range(1,15)\n",
    "y = sum_squared_distances\n",
    "fig, ax = plt.subplots(figsize=(12,8), dpi = 200)\n",
    "ax.plot(x, y, 'bx-')\n",
    "ax.set(xlabel = 'k', ylabel = 'Sum of squared distances', title = 'Elbow method for optimal K')\n",
    "fig.savefig(r\"C:\\Users\\bruno\\Documents\\J3Q3-4 BEP\\Onderzoeksrapport\\kaarten\\Elbowplot3.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name_x</th>\n",
       "      <th>fclass</th>\n",
       "      <th>osm_id</th>\n",
       "      <th>long</th>\n",
       "      <th>latt</th>\n",
       "      <th>cluster</th>\n",
       "      <th>C_PTS</th>\n",
       "      <th>D_C_PTS</th>\n",
       "      <th>C_Super</th>\n",
       "      <th>D_C_Super</th>\n",
       "      <th>C_DoctorsI</th>\n",
       "      <th>D_C_Doctor</th>\n",
       "      <th>C_SchoolsI</th>\n",
       "      <th>D_C_School</th>\n",
       "      <th>C_SportID</th>\n",
       "      <th>D_C_Sport</th>\n",
       "      <th>geometry</th>\n",
       "      <th>accs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Hague Tower</td>\n",
       "      <td>building</td>\n",
       "      <td>37774391</td>\n",
       "      <td>4.324224</td>\n",
       "      <td>52.071575</td>\n",
       "      <td>3603</td>\n",
       "      <td>1301378</td>\n",
       "      <td>107.161512</td>\n",
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       "      <td>397.025030</td>\n",
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       "      <td>78</td>\n",
       "      <td>1093.290909</td>\n",
       "      <td>92</td>\n",
       "      <td>351.857639</td>\n",
       "      <td>POINT (4.32422 52.07158)</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>building</td>\n",
       "      <td>267117842</td>\n",
       "      <td>4.324910</td>\n",
       "      <td>52.071850</td>\n",
       "      <td>3603</td>\n",
       "      <td>1301378</td>\n",
       "      <td>107.161512</td>\n",
       "      <td>170</td>\n",
       "      <td>397.025030</td>\n",
       "      <td>12</td>\n",
       "      <td>644.307493</td>\n",
       "      <td>78</td>\n",
       "      <td>1093.290909</td>\n",
       "      <td>92</td>\n",
       "      <td>351.857639</td>\n",
       "      <td>POINT (4.32491 52.07185)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>building</td>\n",
       "      <td>58350937</td>\n",
       "      <td>4.319504</td>\n",
       "      <td>52.019770</td>\n",
       "      <td>1403</td>\n",
       "      <td>16402</td>\n",
       "      <td>1401.525367</td>\n",
       "      <td>59</td>\n",
       "      <td>2545.583465</td>\n",
       "      <td>3</td>\n",
       "      <td>2830.766890</td>\n",
       "      <td>101</td>\n",
       "      <td>3064.414074</td>\n",
       "      <td>13</td>\n",
       "      <td>2781.176538</td>\n",
       "      <td>POINT (4.31950 52.01977)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>building</td>\n",
       "      <td>253797461</td>\n",
       "      <td>4.320369</td>\n",
       "      <td>52.019398</td>\n",
       "      <td>1403</td>\n",
       "      <td>16402</td>\n",
       "      <td>1401.525367</td>\n",
       "      <td>59</td>\n",
       "      <td>2545.583465</td>\n",
       "      <td>3</td>\n",
       "      <td>2830.766890</td>\n",
       "      <td>101</td>\n",
       "      <td>3064.414074</td>\n",
       "      <td>13</td>\n",
       "      <td>2781.176538</td>\n",
       "      <td>POINT (4.32037 52.01940)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>building</td>\n",
       "      <td>427468640</td>\n",
       "      <td>4.321300</td>\n",
       "      <td>52.018973</td>\n",
       "      <td>1403</td>\n",
       "      <td>16402</td>\n",
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       "      <td>59</td>\n",
       "      <td>2545.583465</td>\n",
       "      <td>3</td>\n",
       "      <td>2830.766890</td>\n",
       "      <td>101</td>\n",
       "      <td>3064.414074</td>\n",
       "      <td>13</td>\n",
       "      <td>2781.176538</td>\n",
       "      <td>POINT (4.32130 52.01897)</td>\n",
       "      <td>0</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93480</th>\n",
       "      <td>None</td>\n",
       "      <td>building</td>\n",
       "      <td>766622131</td>\n",
       "      <td>4.382658</td>\n",
       "      <td>52.054053</td>\n",
       "      <td>3714</td>\n",
       "      <td>651931</td>\n",
       "      <td>435.366264</td>\n",
       "      <td>67</td>\n",
       "      <td>1704.349670</td>\n",
       "      <td>29</td>\n",
       "      <td>1777.164385</td>\n",
       "      <td>9</td>\n",
       "      <td>1302.511442</td>\n",
       "      <td>6</td>\n",
       "      <td>1596.647441</td>\n",
       "      <td>POINT (4.38266 52.05405)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93481</th>\n",
       "      <td>None</td>\n",
       "      <td>building</td>\n",
       "      <td>697411648</td>\n",
       "      <td>4.381724</td>\n",
       "      <td>52.054196</td>\n",
       "      <td>600</td>\n",
       "      <td>651931</td>\n",
       "      <td>628.828303</td>\n",
       "      <td>67</td>\n",
       "      <td>1897.811709</td>\n",
       "      <td>29</td>\n",
       "      <td>1970.626425</td>\n",
       "      <td>9</td>\n",
       "      <td>1495.973482</td>\n",
       "      <td>6</td>\n",
       "      <td>1790.109481</td>\n",
       "      <td>POINT (4.38172 52.05420)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93482</th>\n",
       "      <td>None</td>\n",
       "      <td>building</td>\n",
       "      <td>766622106</td>\n",
       "      <td>4.382214</td>\n",
       "      <td>52.054351</td>\n",
       "      <td>600</td>\n",
       "      <td>651931</td>\n",
       "      <td>628.828303</td>\n",
       "      <td>67</td>\n",
       "      <td>1897.811709</td>\n",
       "      <td>29</td>\n",
       "      <td>1970.626425</td>\n",
       "      <td>9</td>\n",
       "      <td>1495.973482</td>\n",
       "      <td>6</td>\n",
       "      <td>1790.109481</td>\n",
       "      <td>POINT (4.38221 52.05435)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93483</th>\n",
       "      <td>None</td>\n",
       "      <td>building</td>\n",
       "      <td>745899409</td>\n",
       "      <td>4.218895</td>\n",
       "      <td>52.041474</td>\n",
       "      <td>4934</td>\n",
       "      <td>1547770</td>\n",
       "      <td>1639.160926</td>\n",
       "      <td>163</td>\n",
       "      <td>2204.908802</td>\n",
       "      <td>65</td>\n",
       "      <td>4893.801925</td>\n",
       "      <td>90</td>\n",
       "      <td>4589.109529</td>\n",
       "      <td>47</td>\n",
       "      <td>2636.794572</td>\n",
       "      <td>POINT (4.21889 52.04147)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93484</th>\n",
       "      <td>None</td>\n",
       "      <td>building</td>\n",
       "      <td>745899513</td>\n",
       "      <td>4.218672</td>\n",
       "      <td>52.041785</td>\n",
       "      <td>4934</td>\n",
       "      <td>1547770</td>\n",
       "      <td>1639.160926</td>\n",
       "      <td>163</td>\n",
       "      <td>2204.908802</td>\n",
       "      <td>65</td>\n",
       "      <td>4893.801925</td>\n",
       "      <td>90</td>\n",
       "      <td>4589.109529</td>\n",
       "      <td>47</td>\n",
       "      <td>2636.794572</td>\n",
       "      <td>POINT (4.21867 52.04179)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93485 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                name_x    fclass     osm_id      long       latt  cluster  \\\n",
       "0      The Hague Tower  building   37774391  4.324224  52.071575     3603   \n",
       "1                 None  building  267117842  4.324910  52.071850     3603   \n",
       "2                 None  building   58350937  4.319504  52.019770     1403   \n",
       "3                 None  building  253797461  4.320369  52.019398     1403   \n",
       "4                 None  building  427468640  4.321300  52.018973     1403   \n",
       "...                ...       ...        ...       ...        ...      ...   \n",
       "93480             None  building  766622131  4.382658  52.054053     3714   \n",
       "93481             None  building  697411648  4.381724  52.054196      600   \n",
       "93482             None  building  766622106  4.382214  52.054351      600   \n",
       "93483             None  building  745899409  4.218895  52.041474     4934   \n",
       "93484             None  building  745899513  4.218672  52.041785     4934   \n",
       "\n",
       "         C_PTS      D_C_PTS  C_Super    D_C_Super  C_DoctorsI   D_C_Doctor  \\\n",
       "0      1301378   107.161512      170   397.025030          12   644.307493   \n",
       "1      1301378   107.161512      170   397.025030          12   644.307493   \n",
       "2        16402  1401.525367       59  2545.583465           3  2830.766890   \n",
       "3        16402  1401.525367       59  2545.583465           3  2830.766890   \n",
       "4        16402  1401.525367       59  2545.583465           3  2830.766890   \n",
       "...        ...          ...      ...          ...         ...          ...   \n",
       "93480   651931   435.366264       67  1704.349670          29  1777.164385   \n",
       "93481   651931   628.828303       67  1897.811709          29  1970.626425   \n",
       "93482   651931   628.828303       67  1897.811709          29  1970.626425   \n",
       "93483  1547770  1639.160926      163  2204.908802          65  4893.801925   \n",
       "93484  1547770  1639.160926      163  2204.908802          65  4893.801925   \n",
       "\n",
       "       C_SchoolsI   D_C_School  C_SportID    D_C_Sport  \\\n",
       "0              78  1093.290909         92   351.857639   \n",
       "1              78  1093.290909         92   351.857639   \n",
       "2             101  3064.414074         13  2781.176538   \n",
       "3             101  3064.414074         13  2781.176538   \n",
       "4             101  3064.414074         13  2781.176538   \n",
       "...           ...          ...        ...          ...   \n",
       "93480           9  1302.511442          6  1596.647441   \n",
       "93481           9  1495.973482          6  1790.109481   \n",
       "93482           9  1495.973482          6  1790.109481   \n",
       "93483          90  4589.109529         47  2636.794572   \n",
       "93484          90  4589.109529         47  2636.794572   \n",
       "\n",
       "                       geometry  accs  \n",
       "0      POINT (4.32422 52.07158)     1  \n",
       "1      POINT (4.32491 52.07185)     1  \n",
       "2      POINT (4.31950 52.01977)     0  \n",
       "3      POINT (4.32037 52.01940)     0  \n",
       "4      POINT (4.32130 52.01897)     0  \n",
       "...                         ...   ...  \n",
       "93480  POINT (4.38266 52.05405)     4  \n",
       "93481  POINT (4.38172 52.05420)     4  \n",
       "93482  POINT (4.38221 52.05435)     4  \n",
       "93483  POINT (4.21889 52.04147)     0  \n",
       "93484  POINT (4.21867 52.04179)     0  \n",
       "\n",
       "[93485 rows x 18 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X =  gdf[['D_C_PTS', 'D_C_Super', 'D_C_Doctor', 'D_C_Sport', 'D_C_School']]\n",
    "kmeans = KMeans(n_clusters=6)\n",
    "kmeans = kmeans.fit(X)\n",
    "gdf['accs'] =  kmeans.labels_\n",
    "gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    38237\n",
       "4    29350\n",
       "3    10239\n",
       "5     9880\n",
       "0     5666\n",
       "2      113\n",
       "Name: accs, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq = gdf['accs'].value_counts()\n",
    "freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.n_iter_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "freq.to_excel('frequentiesclusters.xlsx')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>0</th>\n",
       "      <td>1426.003484</td>\n",
       "      <td>2717.020510</td>\n",
       "      <td>3446.325553</td>\n",
       "      <td>2832.446494</td>\n",
       "      <td>3037.165558</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>234.833425</td>\n",
       "      <td>851.373174</td>\n",
       "      <td>1013.435907</td>\n",
       "      <td>1227.056996</td>\n",
       "      <td>1058.477409</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10072.953335</td>\n",
       "      <td>11184.318130</td>\n",
       "      <td>11127.239322</td>\n",
       "      <td>12307.348673</td>\n",
       "      <td>11725.724732</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>483.477360</td>\n",
       "      <td>1241.649006</td>\n",
       "      <td>2938.797072</td>\n",
       "      <td>1558.639985</td>\n",
       "      <td>2462.787946</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>344.967763</td>\n",
       "      <td>1212.805192</td>\n",
       "      <td>1796.631459</td>\n",
       "      <td>1702.466009</td>\n",
       "      <td>1556.410739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>224.210719</td>\n",
       "      <td>951.478383</td>\n",
       "      <td>1104.323620</td>\n",
       "      <td>2895.872666</td>\n",
       "      <td>1291.331383</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              0             1             2             3             4\n",
       "0   1426.003484   2717.020510   3446.325553   2832.446494   3037.165558\n",
       "1    234.833425    851.373174   1013.435907   1227.056996   1058.477409\n",
       "2  10072.953335  11184.318130  11127.239322  12307.348673  11725.724732\n",
       "3    483.477360   1241.649006   2938.797072   1558.639985   2462.787946\n",
       "4    344.967763   1212.805192   1796.631459   1702.466009   1556.410739\n",
       "5    224.210719    951.478383   1104.323620   2895.872666   1291.331383"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(kmeans.cluster_centers_)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel('clustercentres.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf[gdf['accs'] == 4].to_file('outliers.shp')"
   ]
  },
  {
   "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>D_C_PTS</th>\n",
       "      <th>D_C_Super</th>\n",
       "      <th>D_C_Doctor</th>\n",
       "      <th>D_C_Sport</th>\n",
       "      <th>D_C_School</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>93485.000000</td>\n",
       "      <td>93485.000000</td>\n",
       "      <td>93485.000000</td>\n",
       "      <td>93485.000000</td>\n",
       "      <td>93485.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>379.839750</td>\n",
       "      <td>1144.268912</td>\n",
       "      <td>1641.217926</td>\n",
       "      <td>1700.298211</td>\n",
       "      <td>1527.240997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>479.104459</td>\n",
       "      <td>640.615360</td>\n",
       "      <td>911.746990</td>\n",
       "      <td>789.160625</td>\n",
       "      <td>795.488170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.020916</td>\n",
       "      <td>302.503422</td>\n",
       "      <td>392.360565</td>\n",
       "      <td>269.219828</td>\n",
       "      <td>251.718036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>186.846928</td>\n",
       "      <td>796.621352</td>\n",
       "      <td>993.291031</td>\n",
       "      <td>1215.501201</td>\n",
       "      <td>1042.161592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>273.548008</td>\n",
       "      <td>1002.774926</td>\n",
       "      <td>1363.760710</td>\n",
       "      <td>1539.215285</td>\n",
       "      <td>1329.309952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>405.924926</td>\n",
       "      <td>1304.502105</td>\n",
       "      <td>2017.920334</td>\n",
       "      <td>2001.326875</td>\n",
       "      <td>1808.625566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12475.934829</td>\n",
       "      <td>13587.299624</td>\n",
       "      <td>13530.220816</td>\n",
       "      <td>14710.330166</td>\n",
       "      <td>14128.706226</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            D_C_PTS     D_C_Super    D_C_Doctor     D_C_Sport    D_C_School\n",
       "count  93485.000000  93485.000000  93485.000000  93485.000000  93485.000000\n",
       "mean     379.839750   1144.268912   1641.217926   1700.298211   1527.240997\n",
       "std      479.104459    640.615360    911.746990    789.160625    795.488170\n",
       "min        0.020916    302.503422    392.360565    269.219828    251.718036\n",
       "25%      186.846928    796.621352    993.291031   1215.501201   1042.161592\n",
       "50%      273.548008   1002.774926   1363.760710   1539.215285   1329.309952\n",
       "75%      405.924926   1304.502105   2017.920334   2001.326875   1808.625566\n",
       "max    12475.934829  13587.299624  13530.220816  14710.330166  14128.706226"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Describe = gdf[['D_C_PTS', 'D_C_Super', 'D_C_Doctor', 'D_C_Sport', 'D_C_School']].describe()\n",
    "Describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "Describe.to_excel('descriptives.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "x = gdf[gdf['D_C_PTS'] < 2000].D_C_PTS\n",
    "num_bins = 200\n",
    "n, bins, patches = plt.hist(x, num_bins, facecolor='blue', alpha=0.5)\n",
    "plt.title('Distance to Public transport stops')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "x = gdf[gdf['D_C_Super'] < 5000].D_C_Super\n",
    "num_bins = 200\n",
    "n, bins, patches = plt.hist(x, num_bins, facecolor='blue', alpha=0.5)\n",
    "plt.title('Distance to Supermarkets')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "x = gdf[gdf['D_C_School'] < 5000].D_C_School\n",
    "num_bins = 200\n",
    "n, bins, patches = plt.hist(x, num_bins, facecolor='blue', alpha=0.5)\n",
    "plt.title('Distance to Schools')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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O4gu5Gemk8B2tSyfVySwH7ulnzqFrlheHvrWMDyhmncehb2aWEYe+mVlGHPpmZhlx6JuZZcShbxPyxViz3uLQt7bzgcRs6vjLWZly8JrlyT19ayofTMw6m3v61nQOfrPOVXfoS3ot8PVS0QnAx4AjgT8CRlL5RyLizrTOcuC9wAHgjyPi7nr3b+1TzXNyHPRm3aHu0I+IrcBCAEnTgKeA24A/AD4TEZ8qLy/pZGApcAowG/iWpJMi4kC9dTAzs9o0a0z/LGB7RDw5wTJLgDURsS8ingC2AYuatH8zM6tCs0J/KbC69P5SSQ9JWiXpqFQ2B9hZWmYolZmZWZs0HPqSDgXeCfxNKroWOJFi6GcXcPXoohVWj3G2uUzSoKTBkZGRSotYC/gHT8x6XzN6+m8DfhARuwEiYndEHIiIXwJf4uAQzhAwt7RePzBcaYMRsTIiBiJioK+vrwlVtKnmg4lZZ2hG6J9PaWhH0qzSvHcBm9P0WmCppOmS5gMLgA1N2L+ZmVWpofv0Jf0KcDbwvlLxJyUtpBi62TE6LyK2SLoZeBjYD1ziO3c6l3vmZr2podCPiJ8Drx5TdsEEy68AVjSyTzMzq5+/kWs19ep9BmDW3fzsHTOzjLinP45qHj1gtXFbmk099/TNzDLi0Dczy4hD38wsIw59ewmPvZv1Loe+mVlGHPpmZhlx6FvVPOxj1v0c+mZmGXHoT8K929Zy+5q1l0PfzCwjDn0zs4w49Ev8c4Fm1usc+mZmGXHo25TzGZZZ+zj0zcwy4tA3M8uIQz/x8IKZ5aCh0Je0Q9ImSRslDaayoyWtk/RY+ntUafnlkrZJ2irpnEYrb2ZmtWlGT//XI2JhRAyk95cD90bEAuDe9B5JJwNLgVOAxcAXJU1rwv7NzKxKrRjeWQLcmKZvBM4rla+JiH0R8QSwDVjUgv1bj/HdPWbN02joB3CPpAckLUtlx0bELoD095hUPgfYWVp3KJW9hKRlkgYlDY6MjDRYRTMzG/XyBtc/IyKGJR0DrJP06ATLqkJZVFowIlYCKwEGBgYqLmM2ntGzAp8dmL1UQz39iBhOf/cAt1EM1+yWNAsg/d2TFh8C5pZW7weGG9l/qzgszKxX1R36kl4p6YjRaeA3gc3AWuCitNhFwO1pei2wVNJ0SfOBBcCGevdvvc3j+Gat0UhP/1jge5IepAjvb0TEN4GrgLMlPQacnd4TEVuAm4GHgW8Cl0TEgUYqb/nyAcGsPnWP6UfE48DrKpT/GDhrnHVWACvq3adZmcfuzWrnb+SamWXEoW9ZqPZswNcSrNc1esumWVM5cM1ayz196xgO/MrcLtZMDn3raA48s+Zy6Js1Ua0HKR/UrN0c+tY1HJBmjXPom42j2jt5fDCybuK7d6yrOGDNGuOevmWtFQcR3+tvncyhb1anTgz3TquPdR6HvnU9B51Z9Rz6Zi3UrLOBTjyrsO7k0DerUjND1yFuU8Whb9lx2FrOfMumZcnBb7lyT9/MLCMOfbMaeCzeup1D33pCu8PYD1azbpX9mL7/Z7Rm8H9H1i3q7ulLmivp7yU9ImmLpA+k8islPSVpY3q9vbTOcknbJG2VdE4zPoBZtWo5Gygv1+n32vunIK0WjfT09wMfiogfSDoCeEDSujTvMxHxqfLCkk4GlgKnALOBb0k6KSIONFAHs55Rayi3alnrbXWHfkTsAnal6b2SHgHmTLDKEmBNROwDnpC0DVgErK+3DmaTqeXRyFMVjJX26165tUpTLuRKmge8Hrg/FV0q6SFJqyQdlcrmADtLqw0xzkFC0jJJg5IGR0ZGmlFFs+z4oGGVNBz6kg4HbgE+GBHPA9cCJwILKc4Erh5dtMLqUWmbEbEyIgYiYqCvr6/RKo7L/1P0tl7+923VZ/MZRu9r6O4dSYdQBP5XI+JWgIjYXZr/JeCO9HYImFtavR8YbmT/Zs3UDWHXDXW0ztbI3TsCrgceiYhPl8pnlRZ7F7A5Ta8FlkqaLmk+sADYUO/+zcysdo309M8ALgA2SdqYyj4CnC9pIcXQzQ7gfQARsUXSzcDDFHf+XOI7d8xezD15a7VG7t75HpXH6e+cYJ0VwIp692nWTcYL8E7+5rD1Pj+GwXqKQ25ybqO8OfTNzDLi0DfrUJ1y++TYR1JYd8v+gWtmNjmHfe9w6Jv1GAe0TSTL4Z1OOW026wVj/1/y/1+dLcvQN+s19Tyhc6LlHdq9y8M7ZlYXHxi6k3v6ZmYZceibZa6ZY/CTDRn57GDqOfTNzDKS1Zi+exlmljv39M0MaKxT1IyhG3fK2sOhb2aWkayGd8zsxdp1r361+/FzflrPPX0za4lOC+123T3U6XcpuadvZjVp1xnAeMuPrlPprKDSIyFapdPDfTwOfTNrmUqh2IqgrOfgMfp3vANGr8pmeCeXf1CzXjHVPelq9j/VdaxHNqFvZtaoRoajOkXbh3ckLQb+GpgGfDkirmrVvjqtsc2sPo3e1VPNOs0e6pno+sJUZlNbQ1/SNOALwNnAEPCPktZGxMPtrIeZdY9GvzRWy/zxrkE0O7ArXVNo1rYn0+7hnUXAtoh4PCL+BVgDLGlzHcwsY/WeKTTynYZ6LzS3giKidVsfuzPpt4HFEfGf0/sLgDdGxKVjllsGLEtvXwtsbVsl22sm8MxUV6JDuC0OclsU3A4H1doWzwBExOKxM9o9pq8KZS856kTESmBl66sztSQNRsTAVNejE7gtDnJbFNwOBzWzLdo9vDMEzC297weG21wHM7NstTv0/xFYIGm+pEOBpcDaNtfBzCxbbR3eiYj9ki4F7qa4ZXNVRGxpZx06TM8PYdXAbXGQ26LgdjioaW3R1gu5ZmY2tfyNXDOzjDj0zcwy4tBvMkmrJO2RtLlUdrSkdZIeS3+PKs1bLmmbpK2SzimVnyZpU5p3jaRKt7t2LElzJf29pEckbZH0gVSeY1u8QtIGSQ+mtvjTVJ5dW0DxzXxJP5R0R3qfazvsSJ9ho6TBVNb6togIv5r4At4C/BqwuVT2SeDyNH058D/T9MnAg8B0YD6wHZiW5m0A3kTx3Ya7gLdN9WersR1mAb+Wpo8A/il93hzbQsDhafoQ4H7g9BzbIn2Gy4CvAXek97m2ww5g5piylreFe/pNFhH3Ac+OKV4C3JimbwTOK5WviYh9EfEEsA1YJGkWMCMi1kfxr3pTaZ2uEBG7IuIHaXov8AgwhzzbIiLip+ntIekVZNgWkvqB3wK+XCrOrh0m0PK2cOi3x7ERsQuKMASOSeVzgJ2l5YZS2Zw0Pba8K0maB7yeooebZVukIY2NwB5gXUTk2hafBT4M/LJUlmM7QHHgv0fSA+nRM9CGtvAvZ02t8R5LUdXjKrqBpMOBW4APRsTzEww39nRbRMQBYKGkI4HbJJ06weI92RaSzgX2RMQDks6sZpUKZV3fDiVnRMSwpGOAdZIenWDZprWFe/rtsTudhpH+7knl4z2WYihNjy3vKpIOoQj8r0bErak4y7YYFRHPAd8GFpNfW5wBvFPSDoon7P6GpK+QXzsAEBHD6e8e4DaKpxC3vC0c+u2xFrgoTV8E3F4qXyppuqT5wAJgQzqt2yvp9HQl/sLSOl0h1ft64JGI+HRpVo5t0Zd6+Eg6DHgr8CiZtUVELI+I/oiYR/EIlr+LiN8ns3YAkPRKSUeMTgO/CWymHW0x1Vewe+0FrAZ2Ab+gOAq/F3g1cC/wWPp7dGn5KyiuxG+ldNUdGEj/EWwHPk/69nS3vIB/T3Ga+RCwMb3enmlb/Crww9QWm4GPpfLs2qL0Oc7k4N072bUDcALF3TgPAluAK9rVFn4Mg5lZRjy8Y2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhn5/9XscDAQYPW1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = gdf[gdf['D_C_Doctor'] < 5000].D_C_Doctor\n",
    "num_bins = 200\n",
    "n, bins, patches = plt.hist(x, num_bins, facecolor='blue', alpha=0.5)\n",
    "plt.title('Distance to Doctors')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "x = gdf[gdf['D_C_Sport'] < 5000].D_C_Sport\n",
    "num_bins = 200\n",
    "n, bins, patches = plt.hist(x, num_bins, facecolor='blue', alpha=0.5)\n",
    "plt.title('Distance to Sport Centres')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = gdf[gdf['D_C_School'] < 5000].D_C_School\n",
    "num_bins = 200\n",
    "n, bins, patches = plt.hist(x, num_bins, facecolor='blue', alpha=0.5)\n",
    "plt.title('Distance to Schools')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf.to_file('FINALDATASET3.shp')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}