Lesson4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6c91687d-90f8-4ffb-8a40-36fec94bcf94",
   "metadata": {},
   "source": [
    "# Lesson 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c09e824c-e7e0-4f50-9beb-bcdfbb46bd61",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Paths\n",
    "import pathlib\n",
    "\n",
    "MAIN_PATH = pathlib.Path().resolve()\n",
    "DATA_DIRECTORY = MAIN_PATH / \"notebooks\" / \"lessons\" / \"lesson-4\" / \"data\"\n",
    "SAVE_DIRECTORY = MAIN_PATH / \"lesson-4\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e79220d4-7d98-420d-9603-fc84d6ae1603",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import data \n",
    "import geopandas\n",
    "\n",
    "# data grid on travel time to the Helsinki railway station\n",
    "grid = geopandas.read_file(\n",
    "    DATA_DIRECTORY \n",
    "    / \"helsinki_region_travel_times_to_railway_station\"\n",
    "    / \"helsinki_region_travel_times_to_railway_station.gpkg\"\n",
    ")\n",
    "\n",
    "# Helsinki municipality geography\n",
    "helsinki = geopandas.read_file(\n",
    "    DATA_DIRECTORY \n",
    "    / \"helsinki_municipality\" \n",
    "    / \"helsinki_municipality.gpkg\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f6ab76e1-fb4d-45d5-bf79-851923bec82b",
   "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>car_m_d</th>\n",
       "      <th>car_m_t</th>\n",
       "      <th>car_r_d</th>\n",
       "      <th>car_r_t</th>\n",
       "      <th>from_id</th>\n",
       "      <th>pt_m_d</th>\n",
       "      <th>pt_m_t</th>\n",
       "      <th>pt_m_tt</th>\n",
       "      <th>pt_r_d</th>\n",
       "      <th>pt_r_t</th>\n",
       "      <th>pt_r_tt</th>\n",
       "      <th>to_id</th>\n",
       "      <th>walk_d</th>\n",
       "      <th>walk_t</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32297</td>\n",
       "      <td>43</td>\n",
       "      <td>32260</td>\n",
       "      <td>48</td>\n",
       "      <td>5785640</td>\n",
       "      <td>32616</td>\n",
       "      <td>116</td>\n",
       "      <td>147</td>\n",
       "      <td>32616</td>\n",
       "      <td>108</td>\n",
       "      <td>139</td>\n",
       "      <td>5975375</td>\n",
       "      <td>32164</td>\n",
       "      <td>459</td>\n",
       "      <td>POLYGON ((382000.000 6697750.000, 381750.000 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32508</td>\n",
       "      <td>43</td>\n",
       "      <td>32471</td>\n",
       "      <td>49</td>\n",
       "      <td>5785641</td>\n",
       "      <td>32822</td>\n",
       "      <td>119</td>\n",
       "      <td>145</td>\n",
       "      <td>32822</td>\n",
       "      <td>111</td>\n",
       "      <td>133</td>\n",
       "      <td>5975375</td>\n",
       "      <td>29547</td>\n",
       "      <td>422</td>\n",
       "      <td>POLYGON ((382250.000 6697750.000, 382000.000 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30133</td>\n",
       "      <td>50</td>\n",
       "      <td>31872</td>\n",
       "      <td>56</td>\n",
       "      <td>5785642</td>\n",
       "      <td>32940</td>\n",
       "      <td>121</td>\n",
       "      <td>146</td>\n",
       "      <td>32940</td>\n",
       "      <td>113</td>\n",
       "      <td>133</td>\n",
       "      <td>5975375</td>\n",
       "      <td>29626</td>\n",
       "      <td>423</td>\n",
       "      <td>POLYGON ((382500.000 6697750.000, 382250.000 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32690</td>\n",
       "      <td>54</td>\n",
       "      <td>34429</td>\n",
       "      <td>60</td>\n",
       "      <td>5785643</td>\n",
       "      <td>33233</td>\n",
       "      <td>125</td>\n",
       "      <td>150</td>\n",
       "      <td>33233</td>\n",
       "      <td>117</td>\n",
       "      <td>144</td>\n",
       "      <td>5975375</td>\n",
       "      <td>29919</td>\n",
       "      <td>427</td>\n",
       "      <td>POLYGON ((382750.000 6697750.000, 382500.000 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31872</td>\n",
       "      <td>42</td>\n",
       "      <td>31834</td>\n",
       "      <td>48</td>\n",
       "      <td>5787544</td>\n",
       "      <td>32127</td>\n",
       "      <td>109</td>\n",
       "      <td>126</td>\n",
       "      <td>32127</td>\n",
       "      <td>101</td>\n",
       "      <td>121</td>\n",
       "      <td>5975375</td>\n",
       "      <td>31674</td>\n",
       "      <td>452</td>\n",
       "      <td>POLYGON ((381250.000 6697500.000, 381000.000 6...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   car_m_d  car_m_t  car_r_d  car_r_t  from_id  pt_m_d  pt_m_t  pt_m_tt  \\\n",
       "0    32297       43    32260       48  5785640   32616     116      147   \n",
       "1    32508       43    32471       49  5785641   32822     119      145   \n",
       "2    30133       50    31872       56  5785642   32940     121      146   \n",
       "3    32690       54    34429       60  5785643   33233     125      150   \n",
       "4    31872       42    31834       48  5787544   32127     109      126   \n",
       "\n",
       "   pt_r_d  pt_r_t  pt_r_tt    to_id  walk_d  walk_t  \\\n",
       "0   32616     108      139  5975375   32164     459   \n",
       "1   32822     111      133  5975375   29547     422   \n",
       "2   32940     113      133  5975375   29626     423   \n",
       "3   33233     117      144  5975375   29919     427   \n",
       "4   32127     101      121  5975375   31674     452   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((382000.000 6697750.000, 381750.000 6...  \n",
       "1  POLYGON ((382250.000 6697750.000, 382000.000 6...  \n",
       "2  POLYGON ((382500.000 6697750.000, 382250.000 6...  \n",
       "3  POLYGON ((382750.000 6697750.000, 382500.000 6...  \n",
       "4  POLYGON ((381250.000 6697500.000, 381000.000 6...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ac0fb585-24f7-4311-b7f1-152ee48b09ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the layers\n",
    "ax = grid.plot(facecolor=\"gray\")\n",
    "helsinki.plot(ax=ax, facecolor=\"None\", edgecolor=\"blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e8023c0a-346f-41f5-8b66-fe67e7125aac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epsg:3067\n"
     ]
    }
   ],
   "source": [
    "# Check the crs of the municipality polygon\n",
    "print(helsinki.crs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "16290ad0-e785-4bda-b19f-9dede5bf8cca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ensure that the CRS matches, if not raise an AssertionError\n",
    "assert helsinki.crs == grid.crs, \"CRS differs between layers!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d6cdbe48-9b17-4677-9fae-ac260b61460f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# select grid cells that intersect (are inside) the Helsinki municiplality area\n",
    "intersection = grid.overlay(helsinki, how=\"intersection\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ba5c8789-34ec-4e12-961f-5c17b0e357e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "intersection.plot(color=\"b\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7d9e2f8d-095c-48bf-ad5a-869291ad7ed6",
   "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>car_m_d</th>\n",
       "      <th>car_m_t</th>\n",
       "      <th>car_r_d</th>\n",
       "      <th>car_r_t</th>\n",
       "      <th>from_id</th>\n",
       "      <th>pt_m_d</th>\n",
       "      <th>pt_m_t</th>\n",
       "      <th>pt_m_tt</th>\n",
       "      <th>pt_r_d</th>\n",
       "      <th>pt_r_t</th>\n",
       "      <th>pt_r_tt</th>\n",
       "      <th>to_id</th>\n",
       "      <th>walk_d</th>\n",
       "      <th>walk_t</th>\n",
       "      <th>GML_ID</th>\n",
       "      <th>NAMEFIN</th>\n",
       "      <th>NAMESWE</th>\n",
       "      <th>NATCODE</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29476</td>\n",
       "      <td>41</td>\n",
       "      <td>29483</td>\n",
       "      <td>46</td>\n",
       "      <td>5876274</td>\n",
       "      <td>29990</td>\n",
       "      <td>76</td>\n",
       "      <td>95</td>\n",
       "      <td>24984</td>\n",
       "      <td>77</td>\n",
       "      <td>99</td>\n",
       "      <td>5975375</td>\n",
       "      <td>25532</td>\n",
       "      <td>365</td>\n",
       "      <td>27517366</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Helsingfors</td>\n",
       "      <td>091</td>\n",
       "      <td>POLYGON ((402024.224 6685750.000, 402003.328 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29456</td>\n",
       "      <td>41</td>\n",
       "      <td>29462</td>\n",
       "      <td>46</td>\n",
       "      <td>5876275</td>\n",
       "      <td>29866</td>\n",
       "      <td>74</td>\n",
       "      <td>95</td>\n",
       "      <td>24860</td>\n",
       "      <td>75</td>\n",
       "      <td>93</td>\n",
       "      <td>5975375</td>\n",
       "      <td>25408</td>\n",
       "      <td>363</td>\n",
       "      <td>27517366</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Helsingfors</td>\n",
       "      <td>091</td>\n",
       "      <td>POLYGON ((402250.000 6685750.000, 402250.000 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36772</td>\n",
       "      <td>50</td>\n",
       "      <td>36778</td>\n",
       "      <td>56</td>\n",
       "      <td>5876278</td>\n",
       "      <td>33541</td>\n",
       "      <td>116</td>\n",
       "      <td>137</td>\n",
       "      <td>44265</td>\n",
       "      <td>130</td>\n",
       "      <td>146</td>\n",
       "      <td>5975375</td>\n",
       "      <td>31110</td>\n",
       "      <td>444</td>\n",
       "      <td>27517366</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Helsingfors</td>\n",
       "      <td>091</td>\n",
       "      <td>POLYGON ((403148.515 6685750.000, 403243.781 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36898</td>\n",
       "      <td>49</td>\n",
       "      <td>36904</td>\n",
       "      <td>56</td>\n",
       "      <td>5876279</td>\n",
       "      <td>33720</td>\n",
       "      <td>119</td>\n",
       "      <td>141</td>\n",
       "      <td>44444</td>\n",
       "      <td>132</td>\n",
       "      <td>155</td>\n",
       "      <td>5975375</td>\n",
       "      <td>31289</td>\n",
       "      <td>447</td>\n",
       "      <td>27517366</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Helsingfors</td>\n",
       "      <td>091</td>\n",
       "      <td>POLYGON ((403250.000 6685750.000, 403250.000 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29411</td>\n",
       "      <td>40</td>\n",
       "      <td>29418</td>\n",
       "      <td>44</td>\n",
       "      <td>5878128</td>\n",
       "      <td>29944</td>\n",
       "      <td>75</td>\n",
       "      <td>95</td>\n",
       "      <td>24938</td>\n",
       "      <td>76</td>\n",
       "      <td>99</td>\n",
       "      <td>5975375</td>\n",
       "      <td>25486</td>\n",
       "      <td>364</td>\n",
       "      <td>27517366</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Helsingfors</td>\n",
       "      <td>091</td>\n",
       "      <td>POLYGON ((401900.425 6685500.000, 402000.000 6...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   car_m_d  car_m_t  car_r_d  car_r_t  from_id  pt_m_d  pt_m_t  pt_m_tt  \\\n",
       "0    29476       41    29483       46  5876274   29990      76       95   \n",
       "1    29456       41    29462       46  5876275   29866      74       95   \n",
       "2    36772       50    36778       56  5876278   33541     116      137   \n",
       "3    36898       49    36904       56  5876279   33720     119      141   \n",
       "4    29411       40    29418       44  5878128   29944      75       95   \n",
       "\n",
       "   pt_r_d  pt_r_t  pt_r_tt    to_id  walk_d  walk_t    GML_ID   NAMEFIN  \\\n",
       "0   24984      77       99  5975375   25532     365  27517366  Helsinki   \n",
       "1   24860      75       93  5975375   25408     363  27517366  Helsinki   \n",
       "2   44265     130      146  5975375   31110     444  27517366  Helsinki   \n",
       "3   44444     132      155  5975375   31289     447  27517366  Helsinki   \n",
       "4   24938      76       99  5975375   25486     364  27517366  Helsinki   \n",
       "\n",
       "       NAMESWE NATCODE                                           geometry  \n",
       "0  Helsingfors     091  POLYGON ((402024.224 6685750.000, 402003.328 6...  \n",
       "1  Helsingfors     091  POLYGON ((402250.000 6685750.000, 402250.000 6...  \n",
       "2  Helsingfors     091  POLYGON ((403148.515 6685750.000, 403243.781 6...  \n",
       "3  Helsingfors     091  POLYGON ((403250.000 6685750.000, 403250.000 6...  \n",
       "4  Helsingfors     091  POLYGON ((401900.425 6685500.000, 402000.000 6...  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intersection.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "20162f3e-cf20-4a0d-86a0-8b7813946024",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the output\n",
    "intersection.to_file(\n",
    "    SAVE_DIRECTORY / \"intersection.gpkg\",\n",
    "    layer=\"travel_time_matrix_helsinki_region\"\n",
    ") "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87ecf5b6-69ba-41b1-8e62-95edb5da663a",
   "metadata": {},
   "source": [
    "## Aggregating data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b514f579-7567-468c-a0c6-505f4c2fedf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas\n",
    "\n",
    "intersection = geopandas.read_file(SAVE_DIRECTORY / \"intersection.gpkg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "99c9adcf-1881-4d88-baca-ec443ec9bd2b",
   "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>geometry</th>\n",
       "      <th>car_m_d</th>\n",
       "      <th>car_m_t</th>\n",
       "      <th>car_r_d</th>\n",
       "      <th>from_id</th>\n",
       "      <th>pt_m_d</th>\n",
       "      <th>pt_m_t</th>\n",
       "      <th>pt_m_tt</th>\n",
       "      <th>pt_r_d</th>\n",
       "      <th>pt_r_t</th>\n",
       "      <th>pt_r_tt</th>\n",
       "      <th>to_id</th>\n",
       "      <th>walk_d</th>\n",
       "      <th>walk_t</th>\n",
       "      <th>GML_ID</th>\n",
       "      <th>NAMEFIN</th>\n",
       "      <th>NAMESWE</th>\n",
       "      <th>NATCODE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>car_r_t</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-1</th>\n",
       "      <td>MULTIPOLYGON (((384750.000 6670000.000, 384500...</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>5913094</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>27517366</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Helsingfors</td>\n",
       "      <td>091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POLYGON ((385750.000 6672000.000, 385750.000 6...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5975375</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5975375</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27517366</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Helsingfors</td>\n",
       "      <td>091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>POLYGON ((386250.000 6671750.000, 386000.000 6...</td>\n",
       "      <td>1051</td>\n",
       "      <td>7</td>\n",
       "      <td>1051</td>\n",
       "      <td>5973739</td>\n",
       "      <td>617</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>617</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5975375</td>\n",
       "      <td>448</td>\n",
       "      <td>6</td>\n",
       "      <td>27517366</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Helsingfors</td>\n",
       "      <td>091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MULTIPOLYGON (((386000.000 6671500.000, 385750...</td>\n",
       "      <td>1286</td>\n",
       "      <td>8</td>\n",
       "      <td>1286</td>\n",
       "      <td>5973736</td>\n",
       "      <td>706</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>706</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5975375</td>\n",
       "      <td>706</td>\n",
       "      <td>10</td>\n",
       "      <td>27517366</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Helsingfors</td>\n",
       "      <td>091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MULTIPOLYGON (((385000.000 6671250.000, 385000...</td>\n",
       "      <td>1871</td>\n",
       "      <td>9</td>\n",
       "      <td>1871</td>\n",
       "      <td>5970457</td>\n",
       "      <td>1384</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>1394</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>5975375</td>\n",
       "      <td>1249</td>\n",
       "      <td>18</td>\n",
       "      <td>27517366</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Helsingfors</td>\n",
       "      <td>091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  geometry  car_m_d  car_m_t  \\\n",
       "car_r_t                                                                        \n",
       "-1       MULTIPOLYGON (((384750.000 6670000.000, 384500...       -1       -1   \n",
       " 0       POLYGON ((385750.000 6672000.000, 385750.000 6...        0        0   \n",
       " 7       POLYGON ((386250.000 6671750.000, 386000.000 6...     1051        7   \n",
       " 8       MULTIPOLYGON (((386000.000 6671500.000, 385750...     1286        8   \n",
       " 9       MULTIPOLYGON (((385000.000 6671250.000, 385000...     1871        9   \n",
       "\n",
       "         car_r_d  from_id  pt_m_d  pt_m_t  pt_m_tt  pt_r_d  pt_r_t  pt_r_tt  \\\n",
       "car_r_t                                                                       \n",
       "-1            -1  5913094      -1      -1       -1      -1      -1       -1   \n",
       " 0             0  5975375       0       0        0       0       0        0   \n",
       " 7          1051  5973739     617       5        6     617       5        6   \n",
       " 8          1286  5973736     706      10       10     706      10       10   \n",
       " 9          1871  5970457    1384      11       13    1394      11       12   \n",
       "\n",
       "           to_id  walk_d  walk_t    GML_ID   NAMEFIN      NAMESWE NATCODE  \n",
       "car_r_t                                                                    \n",
       "-1            -1      -1      -1  27517366  Helsinki  Helsingfors     091  \n",
       " 0       5975375       0       0  27517366  Helsinki  Helsingfors     091  \n",
       " 7       5975375     448       6  27517366  Helsinki  Helsingfors     091  \n",
       " 8       5975375     706      10  27517366  Helsinki  Helsingfors     091  \n",
       " 9       5975375    1249      18  27517366  Helsinki  Helsingfors     091  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Conduct the aggregation\n",
    "dissolved = intersection.dissolve(by=\"car_r_t\")\n",
    "\n",
    "# What did we get\n",
    "dissolved.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "63befa6f-2e07-4c44-b64f-661383fb1efd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows in original intersection GeoDataFrame: 3826\n",
      "Rows in dissolved layer: 51\n"
     ]
    }
   ],
   "source": [
    "# comparing the number of cells in the layers before and after the aggregation\n",
    "print(f\"Rows in original intersection GeoDataFrame: {len(intersection)}\")\n",
    "print(f\"Rows in dissolved layer: {len(dissolved)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ef9d689b-3243-4ce8-aad0-266f25c41a1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['geometry', 'car_m_d', 'car_m_t', 'car_r_d', 'from_id', 'pt_m_d',\n",
       "       'pt_m_t', 'pt_m_tt', 'pt_r_d', 'pt_r_t', 'pt_r_tt', 'to_id', 'walk_d',\n",
       "       'walk_t', 'GML_ID', 'NAMEFIN', 'NAMESWE', 'NATCODE'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the column car_r_t was removed...\n",
    "dissolved.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c66f9bfc-80e0-4215-814b-744ccb4410dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([-1,  0,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,\n",
       "            22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n",
       "            39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,\n",
       "            56],\n",
       "           dtype='int64', name='car_r_t')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ...and is being used as the index\n",
    "dissolved.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f2583427-16fb-464b-8726-25ee15927dd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "geometry    MULTIPOLYGON (((384000.00013620744 6670750.000...\n",
       "car_m_d                                                  7458\n",
       "car_m_t                                                    13\n",
       "car_r_d                                                  7458\n",
       "from_id                                               5934913\n",
       "pt_m_d                                                   6858\n",
       "pt_m_t                                                     26\n",
       "pt_m_tt                                                    30\n",
       "pt_r_d                                                   6858\n",
       "pt_r_t                                                     27\n",
       "pt_r_tt                                                    32\n",
       "to_id                                                 5975375\n",
       "walk_d                                                   6757\n",
       "walk_t                                                     97\n",
       "GML_ID                                               27517366\n",
       "NAMEFIN                                              Helsinki\n",
       "NAMESWE                                           Helsingfors\n",
       "NATCODE                                                   091\n",
       "Name: 15, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecting geometries that are 15 min away\n",
    "dissolved.loc[15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cfe38301-4f61-42b2-9e60-661b3776d199",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See the type\n",
    "type(dissolved.loc[15])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d43067fa-5495-4db2-b3eb-61c772d133e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "geometry    MULTIPOLYGON (((384000.00013620744 6670750.000...\n",
       "car_m_d                                                  7458\n",
       "car_m_t                                                    13\n",
       "car_r_d                                                  7458\n",
       "from_id                                               5934913\n",
       "Name: 15, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See the data\n",
    "dissolved.loc[15].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6d3e7cd0-f71b-498d-9c4b-50b6b332f07b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Geodataframe with the grid cells that are 15min away\n",
    "selection = geopandas.GeoDataFrame([dissolved.loc[15]], crs=dissolved.crs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fb5f155e-c654-4157-b084-452079ad9667",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot all the grid cells, and then the grid cells that are 15 min away\n",
    "ax = dissolved.plot(facecolor=\"gray\")\n",
    "selection.plot(ax=ax, facecolor=\"r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b1c2b156-39f9-449d-a611-760ae29e0121",
   "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>index</th>\n",
       "      <th>car_r_t</th>\n",
       "      <th>geometry</th>\n",
       "      <th>car_m_d</th>\n",
       "      <th>car_m_t</th>\n",
       "      <th>car_r_d</th>\n",
       "      <th>from_id</th>\n",
       "      <th>pt_m_d</th>\n",
       "      <th>pt_m_t</th>\n",
       "      <th>pt_m_tt</th>\n",
       "      <th>pt_r_d</th>\n",
       "      <th>pt_r_t</th>\n",
       "      <th>pt_r_tt</th>\n",
       "      <th>to_id</th>\n",
       "      <th>walk_d</th>\n",
       "      <th>walk_t</th>\n",
       "      <th>GML_ID</th>\n",
       "      <th>NAMEFIN</th>\n",
       "      <th>NAMESWE</th>\n",
       "      <th>NATCODE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>MULTIPOLYGON (((384750.000 6670000.000, 384500...</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>5913094</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>27517366</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Helsingfors</td>\n",
       "      <td>091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((385750.000 6672000.000, 385750.000 6...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5975375</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5975375</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27517366</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Helsingfors</td>\n",
       "      <td>091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>POLYGON ((386250.000 6671750.000, 386000.000 6...</td>\n",
       "      <td>1051</td>\n",
       "      <td>7</td>\n",
       "      <td>1051</td>\n",
       "      <td>5973739</td>\n",
       "      <td>617</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>617</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5975375</td>\n",
       "      <td>448</td>\n",
       "      <td>6</td>\n",
       "      <td>27517366</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Helsingfors</td>\n",
       "      <td>091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>MULTIPOLYGON (((386000.000 6671500.000, 385750...</td>\n",
       "      <td>1286</td>\n",
       "      <td>8</td>\n",
       "      <td>1286</td>\n",
       "      <td>5973736</td>\n",
       "      <td>706</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>706</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5975375</td>\n",
       "      <td>706</td>\n",
       "      <td>10</td>\n",
       "      <td>27517366</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Helsingfors</td>\n",
       "      <td>091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>MULTIPOLYGON (((385000.000 6671250.000, 385000...</td>\n",
       "      <td>1871</td>\n",
       "      <td>9</td>\n",
       "      <td>1871</td>\n",
       "      <td>5970457</td>\n",
       "      <td>1384</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>1394</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>5975375</td>\n",
       "      <td>1249</td>\n",
       "      <td>18</td>\n",
       "      <td>27517366</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Helsingfors</td>\n",
       "      <td>091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  car_r_t                                           geometry  car_m_d  \\\n",
       "0      0       -1  MULTIPOLYGON (((384750.000 6670000.000, 384500...       -1   \n",
       "1      1        0  POLYGON ((385750.000 6672000.000, 385750.000 6...        0   \n",
       "2      2        7  POLYGON ((386250.000 6671750.000, 386000.000 6...     1051   \n",
       "3      3        8  MULTIPOLYGON (((386000.000 6671500.000, 385750...     1286   \n",
       "4      4        9  MULTIPOLYGON (((385000.000 6671250.000, 385000...     1871   \n",
       "\n",
       "   car_m_t  car_r_d  from_id  pt_m_d  pt_m_t  pt_m_tt  pt_r_d  pt_r_t  \\\n",
       "0       -1       -1  5913094      -1      -1       -1      -1      -1   \n",
       "1        0        0  5975375       0       0        0       0       0   \n",
       "2        7     1051  5973739     617       5        6     617       5   \n",
       "3        8     1286  5973736     706      10       10     706      10   \n",
       "4        9     1871  5970457    1384      11       13    1394      11   \n",
       "\n",
       "   pt_r_tt    to_id  walk_d  walk_t    GML_ID   NAMEFIN      NAMESWE NATCODE  \n",
       "0       -1       -1      -1      -1  27517366  Helsinki  Helsingfors     091  \n",
       "1        0  5975375       0       0  27517366  Helsinki  Helsingfors     091  \n",
       "2        6  5975375     448       6  27517366  Helsinki  Helsingfors     091  \n",
       "3       10  5975375     706      10  27517366  Helsinki  Helsingfors     091  \n",
       "4       12  5975375    1249      18  27517366  Helsinki  Helsingfors     091  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reseting the index\n",
    "dissolved = dissolved.reset_index()\n",
    "dissolved.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c9e88773-63fa-4be7-a064-2d6900db9c8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use one of the columns as a variable to be ploted (with colors)\n",
    "dissolved.plot(column=\"car_r_t\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cda41a1-27de-48cd-84fe-8be7bd717d2d",
   "metadata": {},
   "source": [
    "## Simplifying geometries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "370f7d60-4369-4820-9fbf-a3b791f2e0e4",
   "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>bbox_east</th>\n",
       "      <th>bbox_north</th>\n",
       "      <th>bbox_south</th>\n",
       "      <th>bbox_west</th>\n",
       "      <th>place_name</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-49.10927</td>\n",
       "      <td>0.70653</td>\n",
       "      <td>-4.44219</td>\n",
       "      <td>-73.450126</td>\n",
       "      <td>Rio Amazonas, Microrregião de Manaus, Mesorreg...</td>\n",
       "      <td>MULTILINESTRING ((1612358.589 9508606.173, 161...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bbox_east  bbox_north  bbox_south  bbox_west  \\\n",
       "0  -49.10927     0.70653    -4.44219 -73.450126   \n",
       "\n",
       "                                          place_name  \\\n",
       "0  Rio Amazonas, Microrregião de Manaus, Mesorreg...   \n",
       "\n",
       "                                            geometry  \n",
       "0  MULTILINESTRING ((1612358.589 9508606.173, 161...  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import geopandas\n",
    "\n",
    "amazon = geopandas.read_file(DATA_DIRECTORY / \"amazon_river\" / \"amazon_river.gpkg\")\n",
    "amazon.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9d26209a-4ac3-4aea-af8d-007e52d44508",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Derived Projected CRS: PROJCS[\"Mercator_2SP\",GEOGCS[\"GCS_GRS 1980(IUGG, 1 ...>\n",
       "Name: Mercator_2SP\n",
       "Axis Info [cartesian]:\n",
       "- [east]: Easting (metre)\n",
       "- [north]: Northing (metre)\n",
       "Area of Use:\n",
       "- undefined\n",
       "Coordinate Operation:\n",
       "- name: unnamed\n",
       "- method: Mercator (variant B)\n",
       "Datum: D_unknown\n",
       "- Ellipsoid: GRS80\n",
       "- Prime Meridian: Unknown"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amazon.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "530efbfd-e42c-473f-8805-538e60cfebf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "amazon.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e4af9408-1e52-415d-8284-1348dc5992c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generalize geometry\n",
    "amazon['simplegeom'] = amazon.simplify(tolerance=20000)\n",
    "\n",
    "# Set geometry to be our new simplified geometry\n",
    "amazon = amazon.set_geometry('simplegeom')\n",
    "\n",
    "# Plot\n",
    "amazon.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21b8b00b-5d52-467c-b03e-142f266fb202",
   "metadata": {},
   "source": [
    "## Reclassifying data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0e0d6193-a9b8-48b9-8b6e-121cb20cc5bf",
   "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>car_m_d</th>\n",
       "      <th>car_m_t</th>\n",
       "      <th>car_r_d</th>\n",
       "      <th>car_r_t</th>\n",
       "      <th>from_id</th>\n",
       "      <th>pt_m_d</th>\n",
       "      <th>pt_m_t</th>\n",
       "      <th>pt_m_tt</th>\n",
       "      <th>pt_r_d</th>\n",
       "      <th>pt_r_t</th>\n",
       "      <th>pt_r_tt</th>\n",
       "      <th>to_id</th>\n",
       "      <th>walk_d</th>\n",
       "      <th>walk_t</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32297</td>\n",
       "      <td>43</td>\n",
       "      <td>32260</td>\n",
       "      <td>48</td>\n",
       "      <td>5785640</td>\n",
       "      <td>32616</td>\n",
       "      <td>116</td>\n",
       "      <td>147</td>\n",
       "      <td>32616</td>\n",
       "      <td>108</td>\n",
       "      <td>139</td>\n",
       "      <td>5975375</td>\n",
       "      <td>32164</td>\n",
       "      <td>459</td>\n",
       "      <td>POLYGON ((382000.000 6697750.000, 381750.000 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32508</td>\n",
       "      <td>43</td>\n",
       "      <td>32471</td>\n",
       "      <td>49</td>\n",
       "      <td>5785641</td>\n",
       "      <td>32822</td>\n",
       "      <td>119</td>\n",
       "      <td>145</td>\n",
       "      <td>32822</td>\n",
       "      <td>111</td>\n",
       "      <td>133</td>\n",
       "      <td>5975375</td>\n",
       "      <td>29547</td>\n",
       "      <td>422</td>\n",
       "      <td>POLYGON ((382250.000 6697750.000, 382000.000 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30133</td>\n",
       "      <td>50</td>\n",
       "      <td>31872</td>\n",
       "      <td>56</td>\n",
       "      <td>5785642</td>\n",
       "      <td>32940</td>\n",
       "      <td>121</td>\n",
       "      <td>146</td>\n",
       "      <td>32940</td>\n",
       "      <td>113</td>\n",
       "      <td>133</td>\n",
       "      <td>5975375</td>\n",
       "      <td>29626</td>\n",
       "      <td>423</td>\n",
       "      <td>POLYGON ((382500.000 6697750.000, 382250.000 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32690</td>\n",
       "      <td>54</td>\n",
       "      <td>34429</td>\n",
       "      <td>60</td>\n",
       "      <td>5785643</td>\n",
       "      <td>33233</td>\n",
       "      <td>125</td>\n",
       "      <td>150</td>\n",
       "      <td>33233</td>\n",
       "      <td>117</td>\n",
       "      <td>144</td>\n",
       "      <td>5975375</td>\n",
       "      <td>29919</td>\n",
       "      <td>427</td>\n",
       "      <td>POLYGON ((382750.000 6697750.000, 382500.000 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31872</td>\n",
       "      <td>42</td>\n",
       "      <td>31834</td>\n",
       "      <td>48</td>\n",
       "      <td>5787544</td>\n",
       "      <td>32127</td>\n",
       "      <td>109</td>\n",
       "      <td>126</td>\n",
       "      <td>32127</td>\n",
       "      <td>101</td>\n",
       "      <td>121</td>\n",
       "      <td>5975375</td>\n",
       "      <td>31674</td>\n",
       "      <td>452</td>\n",
       "      <td>POLYGON ((381250.000 6697500.000, 381000.000 6...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   car_m_d  car_m_t  car_r_d  car_r_t  from_id  pt_m_d  pt_m_t  pt_m_tt  \\\n",
       "0    32297       43    32260       48  5785640   32616     116      147   \n",
       "1    32508       43    32471       49  5785641   32822     119      145   \n",
       "2    30133       50    31872       56  5785642   32940     121      146   \n",
       "3    32690       54    34429       60  5785643   33233     125      150   \n",
       "4    31872       42    31834       48  5787544   32127     109      126   \n",
       "\n",
       "   pt_r_d  pt_r_t  pt_r_tt    to_id  walk_d  walk_t  \\\n",
       "0   32616     108      139  5975375   32164     459   \n",
       "1   32822     111      133  5975375   29547     422   \n",
       "2   32940     113      133  5975375   29626     423   \n",
       "3   33233     117      144  5975375   29919     427   \n",
       "4   32127     101      121  5975375   31674     452   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((382000.000 6697750.000, 381750.000 6...  \n",
       "1  POLYGON ((382250.000 6697750.000, 382000.000 6...  \n",
       "2  POLYGON ((382500.000 6697750.000, 382250.000 6...  \n",
       "3  POLYGON ((382750.000 6697750.000, 382500.000 6...  \n",
       "4  POLYGON ((381250.000 6697500.000, 381000.000 6...  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import geopandas\n",
    "accessibility_grid = geopandas.read_file(\n",
    "    DATA_DIRECTORY\n",
    "    / \"helsinki_region_travel_times_to_railway_station\"\n",
    "    / \"helsinki_region_travel_times_to_railway_station.gpkg\"\n",
    ")\n",
    "accessibility_grid.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8ca25e0a-136d-4b78-9482-e65ceb8e2388",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove NoData values (-1 values)\n",
    "accessibility_grid = accessibility_grid.loc[accessibility_grid[\"pt_r_tt\"] >= 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c3acb128-3e9e-4f17-ad39-fb1aa06e354b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot using 9 classes and classify the values using \"Natural Breaks\" classification\n",
    "accessibility_grid.plot(column=\"pt_r_tt\", scheme=\"Natural_Breaks\", k=9, cmap=\"RdYlBu\", linewidth=0, legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d27423c4-4eb1-4a34-b964-ed8a07906452",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot walking distance\n",
    "accessibility_grid.plot(column=\"walk_d\", scheme=\"Natural_Breaks\", k=9, cmap=\"RdYlBu\", linewidth=0, legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "18b2add6-b7e9-4836-b408-d9a64a557f33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaturalBreaks           \n",
       "\n",
       "    Interval       Count\n",
       "------------------------\n",
       "[  0.00,  31.00] |   926\n",
       "( 31.00,  43.00] |  2272\n",
       "( 43.00,  54.00] |  2453\n",
       "( 54.00,  66.00] |  2222\n",
       "( 66.00,  77.00] |  1548\n",
       "( 77.00,  89.00] |  1483\n",
       "( 89.00, 103.00] |  1079\n",
       "(103.00, 125.00] |   811\n",
       "(125.00, 199.00] |   226"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import mapclassify\n",
    "\n",
    "mapclassify.NaturalBreaks(y=accessibility_grid[\"pt_r_tt\"], k=9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8474d0c8-70e6-43ae-b40b-c7ee43c20ec2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Quantiles               \n",
       "\n",
       "    Interval       Count\n",
       "------------------------\n",
       "[  0.00,  41.00] |  2674\n",
       "( 41.00,  52.00] |  2576\n",
       "( 52.00,  66.00] |  2623\n",
       "( 66.00,  85.00] |  2616\n",
       "( 85.00, 199.00] |  2531"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# in quantiles (default is 5 classes)\n",
    "mapclassify.Quantiles(y=accessibility_grid[\"pt_r_tt\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4d4c0517-4d86-41cc-942a-c2f2e29a43cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 30.,  41.,  52.,  64.,  77.,  90., 104., 126., 199.])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract threshold values\n",
    "classifier = mapclassify.NaturalBreaks(y=accessibility_grid[\"pt_r_tt\"], k=9)\n",
    "classifier.bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c115a953-206b-40bb-a40c-947b55cf0beb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using a Pysal classifiers\n",
    "# create a Natural Breaks classifier\n",
    "classifier = mapclassify.NaturalBreaks.make(k=9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "26411f20-78da-49a9-8ea4-525722426736",
   "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>pt_r_tt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pt_r_tt\n",
       "0        8\n",
       "1        8\n",
       "2        8\n",
       "3        8\n",
       "4        7"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Classify the data\n",
    "classifications = accessibility_grid[[\"pt_r_tt\"]].apply(classifier)\n",
    "classifications.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "eb383efb-51e5-447c-8d62-3d64fabc98cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(classifications)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "fec7a3f0-a955-4b06-b63f-5ff42a3b4125",
   "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>pt_r_tt</th>\n",
       "      <th>nb_pt_r_tt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>139</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>133</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>133</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>121</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pt_r_tt  nb_pt_r_tt\n",
       "0      139           8\n",
       "1      133           8\n",
       "2      133           8\n",
       "3      144           8\n",
       "4      121           7"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add the classification to our dataframe\n",
    "accessibility_grid[\"nb_pt_r_tt\"] = classifications\n",
    "accessibility_grid[[\"pt_r_tt\", \"nb_pt_r_tt\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "dea954f9-1625-4caa-8bb5-28f498af0cef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "accessibility_grid.plot(column=\"nb_pt_r_tt\", linewidth=0, legend=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af14c97b-9695-4281-8523-702ccfce8fb1",
   "metadata": {},
   "source": [
    "### Ploting a histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "11aff186-d61c-4b53-9d7b-ee35a71053a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: ylabel='Frequency'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ploting a Histogram\n",
    "accessibility_grid[\"pt_r_tt\"].plot.hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "80d58742-1716-4f1f-96d6-36595ae1a103",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add Natural Breaks\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Define the classifier\n",
    "classifier = mapclassify.NaturalBreaks(y=accessibility_grid[\"pt_r_tt\"], k=9)\n",
    "\n",
    "#Plot the histogram\n",
    "accessibility_grid[\"pt_r_tt\"].plot.hist(bins=50)\n",
    "\n",
    "# Add the vertical lines for class breaks\n",
    "for break_point in classifier.bins:\n",
    "    plt.axvline(break_point, color=\"k\", linestyle=\"dashed\", linewidth=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2d5fb1cd-8237-429f-aaa9-0728c14c7cb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MWGaugclyrWIZBoNB0YyOHD58GOnp6cjJycGwYcO6PF8OkWolImVoYR7Q9Ftm//Zv/4YlS5ZgypQpSEtLw4wZM/DEE09g+fLlAACLxQIAbc70VFdXe88aWSwWOJ1O1NXVXXZMe5YuXQqbzeZ9lJaWduauERERkYZouiFqampCUJBvicHBwd5l98nJybBYLNi1a5f3eafTiX379mHkyJEAgPT0dBiNRp8xFRUVyM/P945pj8lkQnR0tM+DiIiIuidNv2V2zz334E9/+hP69euHwYMHIzc3F9nZ2Xj44YcB/PRWWWZmJpYtW4aUlBSkpKRg2bJliIiIwNSpUwEAZrMZs2fPxuLFixETE4PevXvjySefRFpamnfVGREREembphuitWvX4tlnn8X8+fNRXV0Nq9WKefPm4T//8z+9Y5566ik0Nzdj/vz5qKurw0033YSdO3ciKirKO2b16tUICQnB5MmT0dzcjDFjxmDz5s0IDg5WY7eIiIhIY3i3ez/xbvfdw4Aln0JqdaK1vgYhUbEwhIQqktOZGYHc7b6lpQVlZWVITExEWJhy11vqDCLVSkTKUHIe8Pf1W9NniIiUYAgJhbGXVfiMjoSFheHaa7t+dVsgRKqViJShhXlA0x+qJlKC62Ilaj75L7guXv46VCJkdKSoqAjTp09HUVGRKvlyiFQrESlDC/MAGyLSHU9LAxqP74WnpUHojI7U1dXhnXfeaXO5CS0SqVYiUoYW5gE2RERERKR7bIiIiIhI99gQERERke6xISLdCe7RG+Zb/oDgHr2FzuhIQkICnnvuOSQkJKiSL4dItRKRMrQwD/A6RH7idYi6hwFLPlW7BNkCuQ4RERH9xN/Xb54hIt3xOJrQfCYHHkeT0Bkdsdvt+OKLL2C321XJl0OkWolIGVqYB9gQke646s6h+oPn4Ko7J3RGR06fPo277roLp0+fViVfDpFqJSJlaGEeYENEREREuseGiIiIiHSPDRERERHpHhsi0h1DsBEhPRNgCDYKndERk8mEa665BiaTSZV8OUSqlYiUoYV5gMvu/cRl990Dl90TEekLl90TERER+YkNEemOs7oIpa9MhbO6SOiMjhw7dgx9+vTBsWPHVMmXQ6RaiUgZWpgH2BCR7kgeNzzNdkget9AZHWltbUVNTQ1aW1tVyZdDpFqJSBlamAfYEBEREZHusSEiIiIi3WNDRERERLrHhoh0x9i7LyzTX4axd1+hMzoyaNAgHDhwAIMGDVIlXw6RaiUiZWhhHghRLZlIJUGh4TD1vUH4jI706NEDI0aMUC1fDpFqJSJlaGEe4Bki0p1Wew0ufPkGWu01Qmd0pKysDIsWLUJZWZkq+XKIVCsRKUML8wDPEFG3IOcK1O6mi6j/4W+IHHw7QqJjFamnKzI6Ul1djdWrV2P69OlITEzs8nw5RKqViJShhXmAZ4iIiIhI99gQERERke7xLTMijfPn7UDeAJaI6OrwDBHpTnBENHoMvRvBEZe/67EIGR2JjY3F/PnzERvb9Z9fkkukWolIGVqYBwySJEmqpQvEbrfDbDbDZrMhOlqdFzm6PDkfqu6OeIaIiKh9/r5+8wwR6Y7H1QJH5Wl4XC1CZ3SkqakJhw8fRlNTkyr5cohUKxEpQwvzABsi0h1XbRkqt2TCVavc9S66IqMj//jHP5Ceno5//OMfquTLIVKtRKQMLcwDbIiIiIhI99gQERERke6xISIiIiLdY0NEumMwBMEQGg6DQblf/67I6EhQUBCioqIQFKT9P3GRaiUiZWhhHuCyez9x2b22cdk9l90TEbWHy+6JiIiI/MSGiHTHWVOCc3+ZD2dNidAZHTl+/DgGDx6M48ePq5Ivh0i1EpEytDAPsCEi3ZFanXDVlkBqdQqd0ZGWlhYcP34cLS3qXBhSDpFqJSJlaGEeYENEREREuseGiIiIiHSPDRERERHpHhsi0h1jTwv6THoWxp4WoTM6MnDgQPztb3/DwIEDVcmXQ6RaiUgZWpgHeB0iP/E6RNrG6xDxOkRERO3hdYiILsPdUAfbd+/D3VAndEZHKisrsXz5clRWVqqSL4dItRKRMrQwD7AhIt1pbajFxa/fQmtDrdAZHTl37hz+/d//HefOnVMlXw6RaiUiZWhhHmBDRERERLrHhoiIiIh0jw0RERER6R4bItKdoLAeiLjuFgSF9RA6oyM9e/bE7373O/Ts2VOVfDlEqpWIlKGFeYDL7v3EZffaxmX3XHZPRNQeLrsnugzJ7UKrvQaS2yV0RkecTifKysrgdKpzc1k5RKqViJShhXmADRHpjvN8McpfnQXn+WKhMzqSn5+PpKQk5Ofnq5Ivh0i1EpEytDAPBNQQFRUVdXYdRERERKoJCeSbrr32Wtx2222YPXs2fve73yEsLKyz6yIiGX7+GSpH5WkAwN2vfAOTpQIAP2dERNSRgM4QHT16FEOHDsXixYthsVgwb948fP/9951dGxEREVGXCKghSk1NRXZ2NsrLy7Fp0yZUVlbi1ltvxeDBg5GdnY3z5893dp1EREREiumUZfcOhwPr16/H0qVL4XQ6YTQa8cADD+Cll15CQkJCZ9SpOi671zY5y+4lyQO43UBwMAwGZdYVdEWG3HytvmXm8XjgcrlgNBoRFMR1HkR6pOQ80CXL7n/44QfMnz8fCQkJyM7OxpNPPokff/wRX331FcrLy3HvvfdezY8HAJSXl2P69OmIiYlBREQEfvnLXyInJ8f7vCRJyMrKgtVqRXh4OEaPHo2CggKfn+FwOLBw4ULExsYiMjISEydORFlZ2VXXRmIyGIJgCDEq2qh0RYaW8+UICgqCyWRiM0SkY1qYBwJKzs7ORlpaGkaOHIlz587hrbfeQnFxMV588UUkJyfjlltuwYYNG3D48OGrKq6urg633HILjEYjPvvsMxw/fhyrVq3yuZLlypUrkZ2djXXr1uHQoUOwWCwYN24c6uvrvWMyMzOxY8cObN++Hfv370dDQwMmTJgAt9t9VfWRmFwXylG5bQlcF8qFztByvhynTp3C6NGjcerUKbVLISKVaGEeCGiV2auvvoqHH34YDz30ECwWS7tj+vXrh40bN15VcS+99BKSkpKwadMm77YBAwZ4/12SJKxZswbPPPMMJk2aBADYsmUL4uPjsW3bNsybNw82mw0bN27E22+/jbFjxwIAtm7diqSkJOzevRt33nnnVdVI4vE4m+EozYfH2Sx0hpbz5WhoaMC+ffvQ0NCgdilEpBItzAMBnSEqLCzE0qVLL9sMAUBoaChmzpwZcGEA8PHHH2P48OH4/e9/j7i4OAwdOhRvvPGG9/mioiJUVlYiIyPDu81kMmHUqFE4cOAAACAnJwcul8tnjNVqRWpqqndMexwOB+x2u8+DiIiIuqeAGqJNmzbhgw8+aLP9gw8+wJYtW666qEvOnDmDV199FSkpKfjiiy/wyCOP4LHHHsNbb70FAKisrAQAxMfH+3xffHy897nKykqEhoaiV69elx3TnuXLl8NsNnsfSUlJnbZfREREpC0BNUQrVqxAbGxsm+1xcXFYtmzZVRd1icfjwbBhw7Bs2TIMHToU8+bNw9y5c/Hqq6/6jDMYDD5fS5LUZtvPXWnM0qVLYbPZvI/S0tLAd4SIiIg0LaCGqLi4GMnJyW229+/fHyUlJVdd1CUJCQm48cYbfbbdcMMN3oxLb9n9/ExPdXW196yRxWKB0+lEXV3dZce0x2QyITo62udB3UNIdB/0vmshQqL7CJ2h5Xw5+vXrhzfeeAP9+vVTuxQiUokW5oGAGqK4uDgcO3aszfajR48iJibmqou65JZbbsHJkyd9tp06dQr9+/cHACQnJ8NisWDXrl3e551OJ/bt24eRI0cCANLT02E0Gn3GVFRUID8/3zuG9CU4woyoIXciOMIsdIaW8+WIjY3FnDlz2j3rTET6oIV5IKCGaMqUKXjsscewZ88euN1uuN1ufPXVV3j88ccxZcqUTivuiSeewMGDB7Fs2TKcPn0a27Ztw+uvv44FCxYA+OmtsszMTCxbtgw7duxAfn4+Zs2ahYiICEydOhUAYDabMXv2bCxevBhffvklcnNzMX36dKSlpXlXnZG+uJtsqD/6BdxNNqEztJwvR01NDf7yl7+gpqZG7VKISCVamAcCWnb/4osvori4GGPGjEFIyE8/wuPx4MEHH+zUzxD96le/wo4dO7B06VK88MILSE5Oxpo1azBt2jTvmKeeegrNzc2YP38+6urqcNNNN2Hnzp2Iioryjlm9ejVCQkIwefJkNDc3Y8yYMdi8eTOCg4M7rVYSR6v9PC58vhah8dcodgalKzK0nC9HSUkJ5s6di2HDhvEsEZFOaWEeCKghCg0NxXvvvYc//vGPOHr0KMLDw5GWluZ9K6szTZgwARMmTLjs8waDAVlZWcjKyrrsmLCwMKxduxZr167t9PqIiIhIfAE1RJcMGjQIgwYN6qxaiIiIiFQRUEPkdruxefNmfPnll6iurobH4/F5/quvvuqU4oiIiIi6QkAN0eOPP47Nmzfj7rvvRmpq6hWv+UOkJUGh4TAlpSIoNFzoDLn5A5Z8esXvO7vi7iuO6ayfc0mPHj0watQo9OjRw+/vIaLuRQvzgEGSJEnuN8XGxuKtt97Cb3/7WyVq0iS73Q6z2QybzcZrEmmQPy/SdGVqNEREREry9/U7oGX3oaGhuPbaawMujkhNkuSB1OqCJHmuPFjDGVrOl8Pj8cDhcLR5652I9EML80BADdHixYvx3//93wjg5BKR6pxVZ1Cy6n44q84InaHlfDmOHDmCsLAwHDlyRO1SiEglWpgHAvoM0f79+7Fnzx589tlnGDx4MIxGo8/zH330UacUR0RERNQVAmqIevbsifvvv7+zayEiIiJSRUAN0aZNmzq7DiIiIiLVBPQZIgBobW3F7t27sWHDBtTX1wMAzp07h4aGhk4rjoiIiKgrBHSGqLi4GHfddRdKSkrgcDgwbtw4REVFYeXKlWhpacFrr73W2XUSdZrQPv3R9183IzhSuXt8dUWGlvPlSE1NRWlpKeLi4tQuhYhUooV5IOALMw4fPhxHjx5FTEyMd/v999+POXPmdFpxREowBBsREq3szQO7IkPL+f7g9YyI6JLQ0FAkJiaqWkPAq8y+/fZbhIaG+mzv378/ysvLO6Uwoks6+6KLrouVuLh3E3qOfgjGnpZO/dldmaHlfDlEqpWIlHHmzBk8/fTTeOmllzBw4EBVagjoM0Qejwdut7vN9rKyMkRFRV11UURK8rQ0oOnkt/C0KPd5t67I0HK+HCLVSkTKuHjxIj788ENcvHhRtRoCaojGjRuHNWvWeL82GAxoaGjAc889p6vbeRAREVH3ENBbZqtXr8btt9+OG2+8ES0tLZg6dSoKCwsRGxuLd999t7NrJCIiIlJUQA2R1WrFkSNH8O677+Lw4cPweDyYPXs2pk2bhvBwde7uTURERBSogBoiAAgPD8fDDz+Mhx9+uDPrIVJcSI8Y9LztQYT0iLnyYA1naDlfDpFqJSJlWK1WLFu2DFarVbUaAmqI3nrrrQ6ff/DBBwMqhqgrBPfoBfOIycJnaDlfDpFqJSJlWCwWLF26VNUaAr4O0T9zuVxoampCaGgoIiIi2BCRpnlaGtBSWoCwpMEICushbIaW8+UQqVYiUsbFixfx9ddf47bbbkPPnj1VqSGgVWZ1dXU+j4aGBpw8eRK33norP1RNmue6WInzH/0RrouVQmdoOV8OkWolImWcOXMG9957L86cOaNaDQHfy+znUlJSsGLFijZnj4iIiIi0rtMaIgAIDg7GuXPnOvNHEhERESkuoM8Qffzxxz5fS5KEiooKrFu3DrfcckunFEZEXa+zb5NCRCSKgBqi++67z+drg8GAPn364I477sCqVas6oy4ixRhCQmGM6QdDSOiVB2s4Q8v5cohUKxEpIywsDDfeeCPCwsJUq8EgSZKkWrpA7HY7zGYzbDYboqOj1S5HV3jWQjz+3KWed7snoq7g7+t3p36GiIiIiEhEAb1ltmjRIr/HZmdnBxJBpBhn1RlUbnsalqkvITR+oLAZWs6XQ6RaiUgZR44cwW233Yavv/4av/zlL1WpIaCGKDc3F4cPH0Zrayuuu+46AMCpU6cQHByMYcOGeccZDIbOqZKoE0mSB5KzGZLkETpDy/lyiFQrESnD4/Ggvr4eHo9680BADdE999yDqKgobNmyBb169QLw08UaH3roIfzmN7/B4sWLO7VIIiIiIiUF9BmiVatWYfny5d5mCAB69eqFF198kavMiIiISDgBNUR2ux1VVVVttldXV6O+vv6qiyIiIiLqSgE1RPfffz8eeughfPjhhygrK0NZWRk+/PBDzJ49G5MmTersGok6lTEmEZaZa2CMSRQ6Q8v5cohUKxEp4/rrr0dOTg6uv/561WoI6DpETU1NePLJJ/Hmm2/C5XIBAEJCQjB79my8/PLLiIyM7PRC1cbrEKmH1yHSL16HiIiulqLXIYqIiMD69etRW1vrXXF24cIFrF+/vls2Q9S9tNqrUbvzVbTaq4XO0HK+HCLVSkTKKCkpwYIFC1BSUqJaDVd1YcaKigpUVFRg0KBBiIyMBC96TSJwN9nRkPsp3E12oTO0nC+HSLUSkTJqamqwfv161NTUqFZDQA1RbW0txowZg0GDBuG3v/0tKioqAABz5szhknsiIiISTkAN0RNPPAGj0YiSkhJERER4tz/wwAP4/PPPO604IiIioq4Q0IUZd+7ciS+++AKJib6rQlJSUlBcXNwphRERERF1lYDOEDU2NvqcGbqkpqYGJpPpqosiUlJwRE9EDb8XwRE9hc7Qcr4cItVKRMqIi4vDE088gbi4ONVqCGjZ/d13341hw4bhj3/8I6KionDs2DH0798fU6ZMgcfjwYcffqhErarisnv1cNm9fnHZPRFdLX9fvwN6y+zll1/G6NGj8cMPP8DpdOKpp55CQUEBLly4gG+//Tbgoom6gsfZDNf5szD2GYCg0HBhM7ScL4dItRKRMhoaGpCXl4e0tDT06NFDlRoCesvsxhtvxLFjx/DrX/8a48aNQ2NjIyZNmoTc3Fxcc801nV0jUadyXShH5dZ/g+tCudAZWs6XQ6RaiUgZp06dwsiRI3Hq1CnVapB9hsjlciEjIwMbNmzA888/r0RNRERERF1K9hkio9GI/Px8GAwGJeohIiIi6nIBvWX24IMPYuPGjZ1dCxEREZEqAvpQtdPpxF/+8hfs2rULw4cPb3P/suzs7E4pjkgJhqBgBIVHwxAULHSGlvPlEKlWIlJGSEgIYmNjERISUFvSKWQtuz9z5gwGDBiAMWPGXP4HGgz46quvOqU4LeGye/Vw2b1+cdk9EV0tRZbdp6SkoKKiAnv27AHw0606XnnlFcTHx19dtUREREQqkvUZop+fTPrss8/Q2NjYqQURKc15vhjlG+bCeV6528x0RYaW8+UQqVYiUkZBQQGuvfZaFBQUqFZDQB+qviSAi1wTqU5yu9B6sQKS2yV0hpbz5RCpViJShsPhwI8//giHw6FaDbIaIoPB0Ga5PZffExERkehkfYZIkiTMmjXLewPXlpYWPPLII21WmX300UedVyERERGRwmQ1RDNnzvT5evr06Z1aDBEREZEaArrbvR5x2b16OnvZvcfRBEf5CZj63oAgU0Sn/uyuzNByvhwd1cpl90T6YLfb8d1332HEiBGd/hqr6N3uiUQWZIpA+MB04TO0nC+HSLUSkTKio6Nx5513qloDGyLSndaGC2g48hl6/HI8Qnr0FjZDy/lydFSrP2cHeRaJSHwVFRXYsGED5s2bh4SEBFVquKpl90QicjdcgO3bd+FuuCB0hpbz5RCpViJSRkVFBZ5//nlUVFSoVoNQDdHy5cthMBiQmZnp3SZJErKysmC1WhEeHo7Ro0e3ubCTw+HAwoULERsbi8jISEycOBFlZWVdXD0RERFplTAN0aFDh/D666/jF7/4hc/2lStXIjs7G+vWrcOhQ4dgsVgwbtw41NfXe8dkZmZix44d2L59O/bv34+GhgZMmDABbre7q3eDiIiINEiIhqihoQHTpk3DG2+8gV69enm3S5KENWvW4JlnnsGkSZOQmpqKLVu2oKmpCdu2bQMA2Gw2bNy4EatWrcLYsWMxdOhQbN26FXl5edi9e7dau0REREQaIkRDtGDBAtx9990YO3asz/aioiJUVlYiIyPDu81kMmHUqFE4cOAAACAnJwcul8tnjNVqRWpqqndMexwOB+x2u8+DuoegsB6IvHE0gsJ6CJ2h5Xw5RKqViJTRq1cvTJs2zeekR1fT/Cqz7du34/Dhwzh06FCb5yorKwEA8fHxPtvj4+NRXFzsHRMaGtrmP3J8fLz3+9uzfPlyPP/881dbPmmQsacFsfc8KXyGlvPlEKlWIlJGcnIytm7dqmoNmj5DVFpaiscffxxbt25FWFjYZcf9/H5qkiRd8R5rVxqzdOlS2Gw276O0tFRe8aRZUqsTrrpzkFqdQmdoOV8OkWolImW0tLTg9OnTaGlpUa0GTTdEOTk5qK6uRnp6OkJCQhASEoJ9+/bhlVdeQUhIiPfM0M/P9FRXV3ufs1gscDqdqKuru+yY9phMJkRHR/s8qHtw1pTg3Ov/B86aEqEztJwvh0i1EpEyjh8/jpSUFBw/fly1GjTdEI0ZMwZ5eXk4cuSI9zF8+HBMmzYNR44cwcCBA2GxWLBr1y7v9zidTuzbtw8jR44EAKSnp8NoNPqMqaioQH5+vncMERER6ZumP0MUFRWF1NRUn22RkZGIiYnxbs/MzMSyZcuQkpKClJQULFu2DBEREZg6dSoAwGw2Y/bs2Vi8eDFiYmLQu3dvPPnkk0hLS2vzIW0iIiLSJ003RP546qmn0NzcjPnz56Ourg433XQTdu7ciaioKO+Y1atXIyQkBJMnT0ZzczPGjBmDzZs3Izg4WMXKiYiISCuEa4j27t3r87XBYEBWVhaysrIu+z1hYWFYu3Yt1q5dq2xxREREJCSDJEmS2kWIwG63w2w2w2az8QPWXcyfG3ySfvHmrkTUEX9fvzX9oWoiIiKirsCGiHTHVVuGircXw1Wr3A1+uyJDy/lyiFQrESnj5MmTGDFiBE6ePKlaDWyISHc8rhY4z52Ex6XcBcC6IkPL+XKIVCsRKaOxsREHDx5EY2OjajWwISIiIiLdY0NEREREuseGiIiIiHSPDRHpTog5HjETFiPEfPl72YmQoeV8OUSqlYiUMWDAALz99tsYMGCAajXwOkR+4nWI1MPrEFFHeB0iIuoIr0NEdBnuJhvqD/8P3E02oTO0nC+HSLUSkTLOnz+PP//5zzh//rxqNQh36w7qXtQ4+9NqP48Lu15DqPV6BEeYhc3Qcr4cItVKRMooLS3Fo48+ihEjRqBPnz6q1MAzRERERKR7bIiIiIhI99gQERERke6xISLdCQoNR9iAoQgKDRc6Q8v5cohUKxEpIyoqChkZGYiKilKtBi679xOX3SuDS+rpavmz7N6f3zMu3yfqnrjsnugyJI8bHkcTJI9b6Awt58shUq1EpAy32w273Q63W715gA0R6Y6zugilaybDWV0kdIaW8+UQqVYiUsbRo0dhNptx9OhR1WpgQ0RERES6x4aIiIiIdI8NEREREekeGyIiIiLSPd7LjHQntM8AJC58B0GmSKEztJwvh0i1EpEy0tLSUF1djZ49e6pWAxsi0h1DcIjiNxHtigwt58shUq1EpAyj0ajaTV0v4VtmpDuuugpU/98X4KqrEDpDy/lyiFQrESnjxx9/xMSJE/Hjjz+qVgMbItIdj6MRzae/h8fRKHSGlvPlEKlWIlKGzWbDJ598ApvNploNbIiIiIhI99gQERERke6xISIiIiLdY0NEuhMSFYNet89GSFSM0BlazpdDpFqJSBl9+/bFqlWr0LdvX9Vq4LJ70p3gyF6I/vX9wmdoOV8OkWolImXEx8dj0aJFqtbAM0SkO+6WBjT+Yz/cLQ1CZ2g5Xw6RaiUiZdTV1eGDDz5AXV2dajXwDBHpTuvFStT8bQUsM9cg2HKtsBlazpfjamsdsORTBaoioq5UVFSEyZMnIycnB7169VKlBp4hIiIiIt1jQ0RERES6x4aIiIiIdI8NEelOUIgJofHXICjEJHSGlvPlEKlWIlJGeHg4hg4divDwcNVqMEiSJKmWLhC73Q6z2QybzYbo6Gi1y+k2+IFY0oqzK+5WuwQiUoC/r99cZUZE5Cd/Gng2VkRi4ltmpDvOqh9R/F/3wVn1o9AZWs6XQ6RaiUgZubm5MJlMyM3NVa0GNkSkO5IkAe5WKPlucVdkaDlfDpFqJSJlSJIEp9Op6jzAhoiIiIh0jw0RERER6R4bIiIiItI9rjIj3THGJCHh4T8jpKdF6Awt58shUq1EpIwbbrgB+fn5GDhwoGo1sCEi3QkymhDap7/wGVrOl0OkWolIGeHh4Rg8eLCqNfAtM9KdVls1aj97Ba22aqEztJwvh0i1EpEyiouLMWfOHBQXF6tWAxsi0h13sx0Nx3bC3WwXOkPL+XKIVCsRKaO2thYbN25EbW2tajWwISIiIiLdY0NEREREuseGiIiIiHSPq8xIMVq9k31wZE9E3/w7BEf2FDpDy/lyiFQrESkjPj4eS5YsQXx8vGo1GCTeQMgvdrsdZrMZNpsN0dHRapcjBK02RETt8ecu9bzbPZF4/H395ltmpDseRxNaSo7B42gSOkPL+XKIVCsRKaO+vh579+5FfX29ajWwISLdcdWdQ9W7/w5X3TmhM7ScL4dItRKRMgoLC3H77bejsLBQtRrYEBEREZHusSEiIiIi3WNDRERERLrHZfekO4bgEAT3iIEhWLlf/67I0HK+HFqplasiidRjNBrRt29fGI1G1WrQ9Bmi5cuX41e/+hWioqIQFxeH++67DydPnvQZI0kSsrKyYLVaER4ejtGjR6OgoMBnjMPhwMKFCxEbG4vIyEhMnDgRZWVlXbkrpCGhfQYgccEWhPYZIHSGlvPlEKlWIlJGWloaysrKkJaWploNmm6I9u3bhwULFuDgwYPYtWsXWltbkZGRgcbGRu+YlStXIjs7G+vWrcOhQ4dgsVgwbtw4n6V7mZmZ2LFjB7Zv3479+/ejoaEBEyZMgNvtVmO3iIiISGM03RB9/vnnmDVrFgYPHowhQ4Zg06ZNKCkpQU5ODoCfzg6tWbMGzzzzDCZNmoTU1FRs2bIFTU1N2LZtGwDAZrNh48aNWLVqFcaOHYuhQ4di69atyMvLw+7du9XcPVKJ8/xZlP15JpznzwqdoeV8OUSqlYiUkZeXh8TEROTl5alWg6Ybop+z2WwAgN69ewMAioqKUFlZiYyMDO8Yk8mEUaNG4cCBAwCAnJwcuFwunzFWqxWpqaneMe1xOByw2+0+D+oeJHcr3A21kNytQmdoOV8OkWolImW4XC6Ul5fD5XKpVoMwDZEkSVi0aBFuvfVWpKamAgAqKysBoM29T+Lj473PVVZWIjQ0FL169brsmPYsX74cZrPZ+0hKSurM3SEiIiINEaYhevTRR3Hs2DG8++67bZ4zGAw+X0uS1Gbbz11pzNKlS2Gz2byP0tLSwAonIiIizROiIVq4cCE+/vhj7NmzB4mJid7tFosFANqc6amurvaeNbJYLHA6nairq7vsmPaYTCZER0f7PIiIiKh70nRDJEkSHn30UXz00Uf46quvkJyc7PN8cnIyLBYLdu3a5d3mdDqxb98+jBw5EgCQnp4Oo9HoM6aiogL5+fneMaQvxl5WxP9hGYy9rEJnaDlfDpFqJSJlpKSkYM+ePUhJSVGtBk1ftW3BggXYtm0b/va3vyEqKsp7JshsNiM8PBwGgwGZmZlYtmwZUlJSkJKSgmXLliEiIgJTp071jp09ezYWL16MmJgY9O7dG08++STS0tIwduxYNXePVBJkikBYv18In6HlfDlEqpWIlBEVFYXRo0erWoOmzxC9+uqrsNlsGD16NBISEryP9957zzvmqaeeQmZmJubPn4/hw4ejvLwcO3fuRFRUlHfM6tWrcd9992Hy5Mm45ZZbEBERgU8++QTBwcFq7BaprLW+BnX7NqO1vkboDC3nyyFSrUSkjPLycixduhTl5eWq1aDphkiSpHYfs2bN8o4xGAzIyspCRUUFWlpasG/fPu8qtEvCwsKwdu1a1NbWoqmpCZ988glXjemYu/Ei7Ac/hLvxotAZWs6XQ6RaiUgZVVVVWLFiBaqqqlSrQdMNEREREVFXYENEREREuseGiIiIiHRP06vMiJQQHB6NHr/IQHC4cteW6ooMLefLIVKt/hiw5NMrjjm74u4uqIRIHDExMZg9ezZiYmJUq8EgSZKkWrpA7HY7zGYzbDYbL9LoJ39eGIj0iA0RUdfx9/Wbb5mR7nhcDjjPF8PjcgidoeV8OUSqlYiU0dzcjIKCAjQ3N6tWAxsi0h1XbSkq3lwAV61y96frigwt58shUq1EpIwTJ04gNTUVJ06cUK0GNkRERESke2yIiIiISPfYEBEREZHusSEi3TEYDEBwyE//FDhDy/lyiFQrESnDYDAgNDRU1XmAy+79xGX38nHZPVH7uOyeqOv4+/rNCzNSQNjsEBFRd8K3zEh3XDWlqNj8OFw1Ci6774IMLefLIVKtRKSMEydOYNiwYVx2T9SVPK0OOKt+hKdVwQszdkGGlvPlEKlWIlJGc3MzcnNzeWFGIiIiIjWxISIiIiLdY0NEREREusdVZqQ7IT0tiL13CUJ6WoTO0HK+HCLV2ln8WaXJpfmkJ8nJyXj//feRnJysWg28DpGfeB0iX1x2T6QsNkREncPf12++ZUa6426sg/37HXA31gmdoeV8OUSqlYiUUVVVhezsbFRVValWAxsi0p3W+lrU7dmI1vpaoTO0nC+HSLUSkTLKy8uxePFilJeXq1YDGyIiIiLSPTZEREREpHtsiIiIiEj3uOyedCfIFInwa3+NIFOk0BlazpdDpFq1hsv3qbswm8245557YDabVauBy+79xGX3vrjsnkhZ/jQybIiIrszf12+eIaI2unuzI7lb4XE0IsgUCUOwMn8CXZGh5Xw5RKqViJThcrlw8eJF9OzZE0ajUZUa+Bki0h3n+bMoWzsNzvNnhc7Qcr4cItVKRMrIy8tDXFwc8vLyVKuB/ztGRKRB3f1MLZHW8AwRERER6R4bIiIiItI9NkRERESke1x27yc9Lbvv7p9dkDxuSC4HDEYTDEHBwmZoOV8OkWoVEZfdkwjcbjcaGxsRGRmJ4ODOnQe47J7oMgxBwTCYIoTP0HK+HCLVSkTKCA4OVv1kA98yI91xXShH1XvPwnVBubsqd0WGlvPlEKlWIlJGYWEh7rzzThQWFqpWAxsi0h2PsxktZ3PhcTYLnaHlfDlEqpWIlFFfX4+dO3eivr5etRrYEBEREZHusSEiIiIi3WNDRERERLrHVWakOyHRfdB73CMIie4jdIaW8+UQqVYR+XMZjc5amt+VWdS9JCUlYd26dUhKSlKtBl6HyE/d5TpE3f0aQ0QkHxsi6s78ff3mW2akO+7mejQU7IG7WbnVDF2RoeV8OUSqlYiUceHCBWzduhUXLlxQrQY2RKQ7rbYq1P7PKrTaqoTO0HK+HCLVSkTKOHv2LGbMmIGzZ8+qVgMbIiIiItI9NkRERESke2yIiIiISPfYEJHuBBnDEGq9DkHGMKEztJwvh0i1EpEyIiMjcfPNNyMyMlK1Grjs3k9cdk9E3ZU/S+E7a+7gsnvqalx2T0REROQnNkSkO47K0yh+aQIclaeFztByvhwi1UpEyjh8+DAMBgMOHz6sWg1siIiIiEj32BARERGR7vHmrt0IPzBNRFrH+52RVrEhIiLSOf7PFBGX3ftNhGX3nNT8I7U60Vpfg5CoWBhCQoXN0HK+HCLVSl2DZ4j0p6WlBWVlZUhMTERYWOdek8zf12+eISLdMYSEwtjLKnyGlvPlEKlWIlJGWFgYrr32WlVr4IeqSXdcFytR88l/wXWxUugMLefLIVKtRKSMoqIiTJ8+HUVFRarVoKuGaP369UhOTkZYWBjS09PxzTffqF0SqcDT0oDG43vhaWkQOkPL+XKIVCsRKaOurg7vvPMO6urqVKtBN2+Zvffee8jMzMT69etxyy23YMOGDRg/fjyOHz+Ofv36qV0eERF1Mq2taNNaPeRLNw1RdnY2Zs+ejTlz5gAA1qxZgy+++AKvvvoqli9frnJ1V8YPTBORXrBxIDXooiFyOp3IycnBkiVLfLZnZGTgwIED7X6Pw+GAw+Hwfm2z2QD89Gl1NXgcTarkdkceZ4v3n0r9d+2KDC3nyyFSraQd/szF/vw+deWcrrV6Okvqc19ccUz+83d2+HxDQ4P3n5393+DSz7vionpJB8rLyyUA0rfffuuz/U9/+pM0aNCgdr/nueeekwDwwQcffPDBBx/d4FFaWtphr6CLM0SXGAwGn68lSWqz7ZKlS5di0aJF3q89Hg8uXLiAmJiYy35PIOx2O5KSklBaWqrZ6xtdLe6j+Lr7/gHcx+6gu+8f0P33UYn9kyQJ9fX1sFo7vryHLhqi2NhYBAcHo7LSd1lvdXU14uPj2/0ek8kEk8nks61nz55KlYjo6Ohu+cv9z7iP4uvu+wdwH7uD7r5/QPffx87eP7PZfMUxulh2HxoaivT0dOzatctn+65duzBy5EiVqiIiIiKt0MUZIgBYtGgRZsyYgeHDh2PEiBF4/fXXUVJSgkceeUTt0oiIiEhlummIHnjgAdTW1uKFF15ARUUFUlNT8fe//x39+/dXtS6TyYTnnnuuzdtz3Qn3UXzdff8A7mN30N33D+j++6jm/vHmrkRERKR7uvgMEREREVFH2BARERGR7rEhIiIiIt1jQ0RERES6x4ZIZevXr0dycjLCwsKQnp6Ob775Ru2SArJ8+XL86le/QlRUFOLi4nDffffh5MmTPmNmzZoFg8Hg87j55ptVqli+rKysNvVbLBbv85IkISsrC1arFeHh4Rg9ejQKCgpUrFi+AQMGtNlHg8GABQsWABDvGH799de45557YLVaYTAY8Ne//tXneX+OmcPhwMKFCxEbG4vIyEhMnDgRZWVlXbgXHetoH10uF55++mmkpaUhMjISVqsVDz74IM6dO+fzM0aPHt3muE6ZMqWL96R9VzqG/vxOinwMAbT7N2kwGPDyyy97x2j5GPrz+qCFv0U2RCp67733kJmZiWeeeQa5ubn4zW9+g/Hjx6OkpETt0mTbt28fFixYgIMHD2LXrl1obW1FRkYGGhsbfcbdddddqKio8D7+/ve/q1RxYAYPHuxTf15enve5lStXIjs7G+vWrcOhQ4dgsVgwbtw41NfXq1ixPIcOHfLZv0sXM/3973/vHSPSMWxsbMSQIUOwbt26dp/355hlZmZix44d2L59O/bv34+GhgZMmDABbre7q3ajQx3tY1NTEw4fPoxnn30Whw8fxkcffYRTp05h4sSJbcbOnTvX57hu2LChK8q/oisdQ+DKv5MiH0MAPvtWUVGBN998EwaDAf/yL//iM06rx9Cf1wdN/C1e/a1TKVC//vWvpUceecRn2/XXXy8tWbJEpYo6T3V1tQRA2rdvn3fbzJkzpXvvvVe9oq7Sc889Jw0ZMqTd5zwej2SxWKQVK1Z4t7W0tEhms1l67bXXuqjCzvf4449L11xzjeTxeCRJEvsYApB27Njh/dqfY3bx4kXJaDRK27dv944pLy+XgoKCpM8//7zLavfXz/exPd9//70EQCouLvZuGzVqlPT4448rW1wnaG//rvQ72R2P4b333ivdcccdPttEOYaS1Pb1QSt/izxDpBKn04mcnBxkZGT4bM/IyMCBAwdUqqrz2Gw2AEDv3r19tu/duxdxcXEYNGgQ5s6di+rqajXKC1hhYSGsViuSk5MxZcoUnDlzBgBQVFSEyspKn+NpMpkwatQoYY+n0+nE1q1b8fDDD/vc0Fj0Y3iJP8csJycHLpfLZ4zVakVqaqqwx9Vms8FgMLS5N+M777yD2NhYDB48GE8++aRQZzY7+p3sbsewqqoKn376KWbPnt3mOVGO4c9fH7Tyt6ibK1VrTU1NDdxud5uby8bHx7e5Ca1oJEnCokWLcOuttyI1NdW7ffz48fj973+P/v37o6ioCM8++yzuuOMO5OTkCHHV1ZtuuglvvfUWBg0ahKqqKrz44osYOXIkCgoKvMesveNZXFysRrlX7a9//SsuXryIWbNmebeJfgz/mT/HrLKyEqGhoejVq1ebMSL+nba0tGDJkiWYOnWqz40zp02bhuTkZFgsFuTn52Pp0qU4evRom/s/atGVfie72zHcsmULoqKiMGnSJJ/tohzD9l4ftPK3yIZIZf/8f97AT78sP98mmkcffRTHjh3D/v37fbY/8MAD3n9PTU3F8OHD0b9/f3z66adt/ri1aPz48d5/T0tLw4gRI3DNNddgy5Yt3g9xdqfjuXHjRowfPx5Wq9W7TfRj2J5AjpmIx9XlcmHKlCnweDxYv369z3Nz5871/ntqaipSUlIwfPhwHD58GMOGDevqUmUJ9HdSxGMIAG+++SamTZuGsLAwn+2iHMPLvT4A6v8t8i0zlcTGxiI4OLhNZ1tdXd2mSxbJwoUL8fHHH2PPnj1ITEzscGxCQgL69++PwsLCLqquc0VGRiItLQ2FhYXe1Wbd5XgWFxdj9+7dmDNnTofjRD6G/hwzi8UCp9OJurq6y44RgcvlwuTJk1FUVIRdu3b5nB1qz7Bhw2A0GoU8rj//newuxxAAvvnmG5w8efKKf5eANo/h5V4ftPK3yIZIJaGhoUhPT29zOnPXrl0YOXKkSlUFTpIkPProo/joo4/w1VdfITk5+YrfU1tbi9LSUiQkJHRBhZ3P4XDgxIkTSEhI8J6q/ufj6XQ6sW/fPiGP56ZNmxAXF4e77767w3EiH0N/jll6ejqMRqPPmIqKCuTn5wtzXC81Q4WFhdi9ezdiYmKu+D0FBQVwuVxCHtef/052h2N4ycaNG5Geno4hQ4ZccayWjuGVXh8087fYKR/NpoBs375dMhqN0saNG6Xjx49LmZmZUmRkpHT27Fm1S5PtX//1XyWz2Szt3btXqqio8D6ampokSZKk+vp6afHixdKBAwekoqIiac+ePdKIESOkvn37Sna7XeXq/bN48WJp79690pkzZ6SDBw9KEyZMkKKiorzHa8WKFZLZbJY++ugjKS8vT/rDH/4gJSQkCLN/l7jdbqlfv37S008/7bNdxGNYX18v5ebmSrm5uRIAKTs7W8rNzfWusPLnmD3yyCNSYmKitHv3bunw4cPSHXfcIQ0ZMkRqbW1Va7d8dLSPLpdLmjhxopSYmCgdOXLE52/T4XBIkiRJp0+flp5//nnp0KFDUlFRkfTpp59K119/vTR06FBN7GNH++fv76TIx/ASm80mRURESK+++mqb79f6MbzS64MkaeNvkQ2Ryv785z9L/fv3l0JDQ6Vhw4b5LFMXCYB2H5s2bZIkSZKampqkjIwMqU+fPpLRaJT69esnzZw5UyopKVG3cBkeeOABKSEhQTIajZLVapUmTZokFRQUeJ/3eDzSc889J1ksFslkMkm33XablJeXp2LFgfniiy8kANLJkyd9tot4DPfs2dPu7+XMmTMlSfLvmDU3N0uPPvqo1Lt3byk8PFyaMGGCpva5o30sKiq67N/mnj17JEmSpJKSEum2226TevfuLYWGhkrXXHON9Nhjj0m1tbXq7tj/19H++fs7KfIxvGTDhg1SeHi4dPHixTbfr/VjeKXXB0nSxt+i4f8XS0RERKRb/AwRERER6R4bIiIiItI9NkRERESke2yIiIiISPfYEBEREZHusSEiIiIi3WNDRERERLrHhoiIiIh0jw0RERER6R4bIiIiItI9NkRERESke2yIiIiISPf+HxUFA2HTo7gcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# or divide by quantiles\n",
    "# Define classifier\n",
    "classifier = mapclassify.Quantiles(y=accessibility_grid['pt_r_tt'])\n",
    "\n",
    "# Plot histogram for public transport rush hour travel time\n",
    "accessibility_grid[\"pt_r_tt\"].plot.hist(bins=50)\n",
    "\n",
    "for break_point in classifier.bins:\n",
    "    plt.axvline(break_point, color=\"k\", linestyle=\"dashed\", linewidth=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "101f2300-e493-4b3e-80b7-b811a18fcdcd",
   "metadata": {},
   "source": [
    "### Custom classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "25c76da1-b150-4fce-a4ad-1fcfb2dfdf79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How to classify one row???\n",
    "int(accessibility_grid.iloc[0][\"pt_r_tt\"] < 20 and accessibility_grid.iloc[0][\"walk_d\"] > 4000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "a63fb878-9d98-4b7a-8801-b65ed20b128a",
   "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>car_m_d</th>\n",
       "      <th>car_m_t</th>\n",
       "      <th>car_r_d</th>\n",
       "      <th>car_r_t</th>\n",
       "      <th>from_id</th>\n",
       "      <th>pt_m_d</th>\n",
       "      <th>pt_m_t</th>\n",
       "      <th>pt_m_tt</th>\n",
       "      <th>pt_r_d</th>\n",
       "      <th>pt_r_t</th>\n",
       "      <th>pt_r_tt</th>\n",
       "      <th>to_id</th>\n",
       "      <th>walk_d</th>\n",
       "      <th>walk_t</th>\n",
       "      <th>geometry</th>\n",
       "      <th>nb_pt_r_tt</th>\n",
       "      <th>suitible_area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32297</td>\n",
       "      <td>43</td>\n",
       "      <td>32260</td>\n",
       "      <td>48</td>\n",
       "      <td>5785640</td>\n",
       "      <td>32616</td>\n",
       "      <td>116</td>\n",
       "      <td>147</td>\n",
       "      <td>32616</td>\n",
       "      <td>108</td>\n",
       "      <td>139</td>\n",
       "      <td>5975375</td>\n",
       "      <td>32164</td>\n",
       "      <td>459</td>\n",
       "      <td>POLYGON ((382000.000 6697750.000, 381750.000 6...</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32508</td>\n",
       "      <td>43</td>\n",
       "      <td>32471</td>\n",
       "      <td>49</td>\n",
       "      <td>5785641</td>\n",
       "      <td>32822</td>\n",
       "      <td>119</td>\n",
       "      <td>145</td>\n",
       "      <td>32822</td>\n",
       "      <td>111</td>\n",
       "      <td>133</td>\n",
       "      <td>5975375</td>\n",
       "      <td>29547</td>\n",
       "      <td>422</td>\n",
       "      <td>POLYGON ((382250.000 6697750.000, 382000.000 6...</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30133</td>\n",
       "      <td>50</td>\n",
       "      <td>31872</td>\n",
       "      <td>56</td>\n",
       "      <td>5785642</td>\n",
       "      <td>32940</td>\n",
       "      <td>121</td>\n",
       "      <td>146</td>\n",
       "      <td>32940</td>\n",
       "      <td>113</td>\n",
       "      <td>133</td>\n",
       "      <td>5975375</td>\n",
       "      <td>29626</td>\n",
       "      <td>423</td>\n",
       "      <td>POLYGON ((382500.000 6697750.000, 382250.000 6...</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32690</td>\n",
       "      <td>54</td>\n",
       "      <td>34429</td>\n",
       "      <td>60</td>\n",
       "      <td>5785643</td>\n",
       "      <td>33233</td>\n",
       "      <td>125</td>\n",
       "      <td>150</td>\n",
       "      <td>33233</td>\n",
       "      <td>117</td>\n",
       "      <td>144</td>\n",
       "      <td>5975375</td>\n",
       "      <td>29919</td>\n",
       "      <td>427</td>\n",
       "      <td>POLYGON ((382750.000 6697750.000, 382500.000 6...</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31872</td>\n",
       "      <td>42</td>\n",
       "      <td>31834</td>\n",
       "      <td>48</td>\n",
       "      <td>5787544</td>\n",
       "      <td>32127</td>\n",
       "      <td>109</td>\n",
       "      <td>126</td>\n",
       "      <td>32127</td>\n",
       "      <td>101</td>\n",
       "      <td>121</td>\n",
       "      <td>5975375</td>\n",
       "      <td>31674</td>\n",
       "      <td>452</td>\n",
       "      <td>POLYGON ((381250.000 6697500.000, 381000.000 6...</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   car_m_d  car_m_t  car_r_d  car_r_t  from_id  pt_m_d  pt_m_t  pt_m_tt  \\\n",
       "0    32297       43    32260       48  5785640   32616     116      147   \n",
       "1    32508       43    32471       49  5785641   32822     119      145   \n",
       "2    30133       50    31872       56  5785642   32940     121      146   \n",
       "3    32690       54    34429       60  5785643   33233     125      150   \n",
       "4    31872       42    31834       48  5787544   32127     109      126   \n",
       "\n",
       "   pt_r_d  pt_r_t  pt_r_tt    to_id  walk_d  walk_t  \\\n",
       "0   32616     108      139  5975375   32164     459   \n",
       "1   32822     111      133  5975375   29547     422   \n",
       "2   32940     113      133  5975375   29626     423   \n",
       "3   33233     117      144  5975375   29919     427   \n",
       "4   32127     101      121  5975375   31674     452   \n",
       "\n",
       "                                            geometry  nb_pt_r_tt  \\\n",
       "0  POLYGON ((382000.000 6697750.000, 381750.000 6...           8   \n",
       "1  POLYGON ((382250.000 6697750.000, 382000.000 6...           8   \n",
       "2  POLYGON ((382500.000 6697750.000, 382250.000 6...           8   \n",
       "3  POLYGON ((382750.000 6697750.000, 382500.000 6...           8   \n",
       "4  POLYGON ((381250.000 6697500.000, 381000.000 6...           7   \n",
       "\n",
       "   suitible_area  \n",
       "0              0  \n",
       "1              0  \n",
       "2              0  \n",
       "3              0  \n",
       "4              0  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now apply to each row in the df\n",
    "accessibility_grid[\"suitible_area\"] = accessibility_grid.apply(lambda row: int(row[\"pt_r_tt\"] < 20 and row[\"walk_d\"] > 4000), axis=1)\n",
    "accessibility_grid.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "988cdcab-6e64-410d-9abc-14a49131a0cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    13011\n",
       "1        9\n",
       "Name: suitible_area, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get values counts\n",
    "accessibility_grid[\"suitible_area\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2349c2fb-1564-4ee2-b92d-cbb289995e6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4+flp+fLluvvuuzVw4EAtXLhQ77//viMr8pvf/EadOnXS7bffrsDAQE2aNEljxoxRQECA07L8/Pyc3htjbpp2o/T0dJWXlzteR44ccWVzAQCAm7mUEYmKilLXrl2dpnXp0kWrVq2qsU5MTIzCwsKc6hhjdPToUXXq1Elt27bVp59+qkuXLun06dOKjo7WzJkzlZCQIEm69dZbFRAQcFP2o6ys7KYsyY1sNptsNpsrm+gxyJKgMXA23viq+h7WdM8QBgy7lzc9cBBVcykjkpaWpv379ztNKyoqUnx8fI11jh8/rvPnzzvV8ff3V2xsrFPZoKAgxcTE6OrVq1q1apUee+wxSVJgYKB69uyp/Px8p/L5+fnq06ePK5sAAAA8iEuByNSpU7V9+3ZlZGTo4MGDWrFihRYvXqyJEyc6yqSnpzuNFxkxYoQiIiI0ZswY7d27V5s2bdKMGTM0duxYBQcHS5J27Nih1atXq7i4WJs3b9aAAQNUWVmpl19+2bGcadOm6b333lNubq727dunqVOnqqSkROPHj2/oPgAAABZxqWumV69eysvLU3p6uubOnauEhARlZWVp5MiRjjKlpaUqKSlxvA8JCVF+fr4mT56s1NRURUREaOjQoZo3b56jzKVLlzRnzhwVFxcrJCREAwcO1AcffOB0f5KnnnpKp0+f1ty5c1VaWqrk5GStXbu2xmyMr/HU+5GQevYM7Pum1dB7VdBejYvubO/lZ4wxVq9EU6moqFBYWJjKy8sVGhrq1s+y4kfGE750/LjC19V0NYwr30G+K+7nCb+JzZUr/2956J0PcfWHsbofQr68QP2+B7VdVoqmRXt4Bx56BwAALEMgAgAALEPXjI+r6hr72vqm6btGXfjSwEt3PETNncuB67jfiOciIwIAACxDRgRAg3nrc1XqM5i7rrxtXzQXDGD1PGREAACAZQhEAACAZeia8XGkHuEu3tD1UNWA2poeWofmhbuxegYyIgAAwDIEIgAAwDJ0zfg4T0g5+9L9JuBd3HnPHLp4fBPdNU2PjAgAALAMT99tAr5wluSt94kAGps77z0Cz0d2pG5c+X9LRgQAAFiGQAQAAFiGwapwCQNPge9w/DdPDGZtfGREAACAZciIoE54hDbwHTIh+D4epNcwZEQAAIBlCEQAAIBl6JpBnZBu9G0MQgZgFTIiAADAMgQiAJzkV35MBgyop5/5DyGr6CICEQAAYBkCEQAAYBkGq6JOSDUCANyBjAgAALAMGRE3IYMAb8WxC6ApkREBAACWIRABAACWoWumkZHWhjfiuAUaFw/CqzsyIgAAwDIEIgAAwDJ0zcDtqkpL0hUAoLm4/nvnji6aqn5L6/qb6yldRmREAACAZfyMMcbqlWgqFRUVCgsLU3l5uUJDQy1Zh+aYCeAR8wDgrCHZCHf8jjZ2dsSV/7dkRAAAgGUIRAAAgGUYrAoAgBdorC4ZTxmkep3LGZFjx45p1KhRioiIUMuWLdWjRw8VFhbWWMdut2v27NmKj4+XzWZTYmKicnNzncpkZWWpc+fOCg4OVlxcnKZOnapLly455r/66qvy8/NzerVv397V1QcAAB7EpYzI2bNnlZaWpn79+mndunWKjIzUoUOH1KZNmxrrDR06VCdPnlROTo46duyosrIyXb161TF/+fLlmjlzpnJzc9WnTx8VFRXp2WeflST9+te/dpS744479PnnnzveBwQEuLL6lmGAJgBfcf1smt8192pO+9elQGT+/PmKi4vT0qVLHdM6dOhQY53169eroKBAxcXFCg8Pr7LOtm3blJaWphEjRjjmDx8+XDt37nRe2RYtyIIAAOBDXOqaWbNmjVJTUzVkyBBFRkYqJSVFS5YsqVOdBQsWKCYmRklJSZo+fbouXrzoKNO3b18VFhY6Ao/i4mKtXbtWDz/8sNOyDhw4oOjoaCUkJGjYsGEqLi6u8bPtdrsqKiqcXgAAwHO4lBEpLi5Wdna2pk2bplmzZmnnzp2aMmWKbDabRo8eXW2dLVu2KCgoSHl5eTp16pQmTJigM2fOOMaJDBs2TF9//bX69u0rY4yuXr2qF154QTNnznQsp3fv3lq2bJmSkpJ08uRJzZs3T3369NFXX32liIiIKj87MzNTr732miub2GiaU1qtNuwLwHfwfW5c1e3P5nRHapduaBYYGKjU1FRt3brVMW3KlCn68ssvtW3btirr9O/fX5s3b9aJEycUFhYmSVq9erUGDx6sCxcuKDg4WH/96181bNgwzZs3T71799bBgwf14osv6vnnn9crr7xS5XIvXLigxMREvfzyy5o2bVqVZex2u+x2u+N9RUWF4uLimuSGZr56wAAAGq62sTbuDESa4qoZV25o5lJGJCoqSl27dnWa1qVLF61atarGOjExMY4g5HodY4yOHj2qTp066ZVXXtHTTz+tcePGSZK6deumCxcu6Oc//7lmz54tf/+be5BatWqlbt266cCBA9V+ts1mk81mc2UTG4wABADQUO78X+LOZ9/Uh0tjRNLS0rR//36naUVFRYqPj6+xzvHjx3X+/HmnOv7+/oqNjZUkffvttzcFGwEBATLGqLqEjd1u1759+xQVFeXKJgAAAA/iUiAydepUbd++XRkZGTp48KBWrFihxYsXa+LEiY4y6enpTuNFRowYoYiICI0ZM0Z79+7Vpk2bNGPGDI0dO1bBwcGSpEGDBik7O1srV67U4cOHlZ+fr1deeUWPPvqo4xLd6dOnq6CgQIcPH9aOHTs0ePBgVVRU6JlnnmmM/QAAACzgUtdMr169lJeXp/T0dM2dO1cJCQnKysrSyJEjHWVKS0tVUlLieB8SEqL8/HxNnjxZqampioiI0NChQzVv3jxHmTlz5sjPz09z5szRsWPH1LZtWw0aNEhvvPGGo8zRo0c1fPhwnTp1Sm3bttU999yj7du315iNQePjAXYA0HBW/n56SpfMdTx9t5H5+j9nAhEA8G6eNliVh94BAADL8NC7Zqw+2Y0by3GrZwBAQ5ERAQAAliEjgnojEwIAaCgyIgAAwDIEIgAAwDJ0zTRjdK0AAKxGRgQAAFiGQAQ+Lb/yY4+7iyAAWOln/kM8KiNOIAIAACxDIAIAACzDYFX4HJ6HAwDeg4wIAACwDBkRD8VzXOqmtoGo7EcAaJon7tYXGREAAGAZAhEAAGAZumY8HAMvAaB+muvvpyd3w1SFjAgAALAMgQgAALAMXTPwSt6WegRgLV+/gs6bfxPJiAAAAMuQEWlkjRV1X69/Y5RbVcTrq9F9baraP9+fd+P85jpoDQA8HRkRAABgGQIRAABgGbpm3KSxugLoRqi7qvZVVdPo4gKaB77X3oGMCAAAsAwZkSbAQEnPUtVgVgCANciIAAAAyxCIAAAAy9A108R8/e5+3oZ2AOALvLnLmYwIAACwDBkReDUyGgDgrKY7T3siMiIAAMAyBCIAAMAyBCIAALfKr/zYa7oJ0PQIRAAAgGUYrAoAcCsGlTctb8s+kREBAACWIRABAACWIRDxcQwSAwB4MpcDkWPHjmnUqFGKiIhQy5Yt1aNHDxUWFtZYx263a/bs2YqPj5fNZlNiYqJyc3OdymRlZalz584KDg5WXFycpk6dqkuXLjmVWbRokRISEhQUFKSePXtq8+bNrq4+AADwIC4NVj179qzS0tLUr18/rVu3TpGRkTp06JDatGlTY72hQ4fq5MmTysnJUceOHVVWVqarV6865i9fvlwzZ85Ubm6u+vTpo6KiIj377LOSpF//+teSpN///vd66aWXtGjRIqWlpendd9/VQw89pL179+q2225zbasBAIBH8DPGmLoWnjlzpv72t7+5lIlYv369hg0bpuLiYoWHh1dZZtKkSdq3b5/+8pe/OKb953/+p3bu3On4rN69e+uuu+5Sdna2o0yXLl30+OOPKzMzs07rUlFRobCwMJWXlys0NLTO2+AO7hxFXlVXjKeNWr9xHT1t3QDAW3hq17sr/29d6ppZs2aNUlNTNWTIEEVGRiolJUVLliypU50FCxYoJiZGSUlJmj59ui5evOgo07dvXxUWFmrnzp2SpOLiYq1du1YPP/ywJOny5csqLCxU//79nZbdv39/bd26tdrPttvtqqiocHoBAADP4VLXTHFxsbKzszVt2jTNmjVLO3fu1JQpU2Sz2TR69Ohq62zZskVBQUHKy8vTqVOnNGHCBJ05c8YxTmTYsGH6+uuv1bdvXxljdPXqVb3wwguaOXOmJOnUqVO6du2a2rVr57Tsdu3a6cSJE9Wub2Zmpl577TVXNtHreGo0DABoXL76e+9SRqSyslJ33XWXMjIylJKSol/84hd6/vnnnbpLqqrj5+en5cuX6+6779bAgQO1cOFCvf/++46syF//+le98cYbWrRokXbt2qXVq1frz3/+s15//XWnZfn5+Tm9N8bcNO1G6enpKi8vd7yOHDniyuYCAAA3cykjEhUVpa5duzpN69Kli1atWlVjnZiYGIWFhTnVMcbo6NGj6tSpk1555RU9/fTTGjdunCSpW7duunDhgn7+859r9uzZuvXWWxUQEHBT9qOsrOymLMmNbDabbDabK5sIAACakEuBSFpamvbv3+80raioSPHx8TXW+fjjj3X+/HmFhIQ46vj7+ys2NlaS9O2338rf3zk5ExAQIGOMjDEKDAxUz549lZ+fryeeeMJRJj8/X4899pgrm+DTvGnQpzetKwDAfVzqmpk6daq2b9+ujIwMHTx4UCtWrNDixYs1ceJER5n09HSn8SIjRoxQRESExowZo71792rTpk2aMWOGxo4dq+DgYEnSoEGDlJ2drZUrV+rw4cPKz8/XK6+8okcffVQBAQGSpGnTpum9995Tbm6u9u3bp6lTp6qkpETjx49vjP0AAAAs4FJGpFevXsrLy1N6errmzp2rhIQEZWVlaeTIkY4ypaWlKikpcbwPCQlRfn6+Jk+erNTUVEVERGjo0KGaN2+eo8ycOXPk5+enOXPm6NixY2rbtq0GDRqkN954w1Hmqaee0unTpzV37lyVlpYqOTlZa9eurTEb44kaOxNAZgEAmofrv/e+NmjVpfuIeDtPuI8IgQMAoCG8IRBx231EAAAAGpNLXTOoH7IgaErctRbwfte/xzd+h70hE1IfZEQAAIBlyIi4CWeisArHHuD9fHVgalXIiAAAAMsQiAAAAMsQiAAA0MTyKz9uFt0udUEgAgAALEMgAgAALMNVM42MKxYAAFWprSumuXbVkBEBAACWISPSCMiCAADqo7lmQW5ERgQAAFiGQAQAAFiGrhkAABoZXS51R0YEAABYhoxII+Cx6wBQvaoeaQ9cR0YEAABYhkAEAABYxs8YY6xeiaZSUVGhsLAwlZeXKzQ01K2fRQoSAFAdXx/M6sr/WzIiAADAMgQiAADAMgQiAADAMgQiAADAMtxHxE2qG4jEIFYAwPX/Bb4+aLUuyIgAAADLkBFpYjVFv2RLALiqqt8UfkvgTciIAAAAyxCIAAAAy9A1A/gwHsjoXerzcDgGPXon2uvfyIgAAADLEIgAAADL0DUD+DB3dMfUp/sA7kOK33vQVlUjIwIAACxDRqQJcOYIX8T9K9zHyjPnDcf/P8ffD0Z3t2w9vBVZD9eREQEAAJYhEAEAAJaha6YJVHUvB+7vAF9Ed43384TuGH4fmxcyIgAAwDJkRJoYA5ngLRrrWOXs1r2q2qfefom1t663xJ1u68PljMixY8c0atQoRUREqGXLlurRo4cKCwtrrGO32zV79mzFx8fLZrMpMTFRubm5jvkPPPCA/Pz8bno9/PDDjjKvvvrqTfPbt2/v6uoDAAAP4lJG5OzZs0pLS1O/fv20bt06RUZG6tChQ2rTpk2N9YYOHaqTJ08qJydHHTt2VFlZma5eveqYv3r1al2+fNnx/vTp0+revbuGDHGOiu+44w59/vnnjvcBAQGurD4AAPAwLgUi8+fPV1xcnJYuXeqY1qFDhxrrrF+/XgUFBSouLlZ4eHiVda5Pv27lypVq2bLlTYFIixYtfDIL4u1pVPgWd6aUGcxas5rS+vV5EJ6nqe3Y8tT1ro8bt4Vumpq51DWzZs0apaamasiQIYqMjFRKSoqWLFlSpzoLFixQTEyMkpKSNH36dF28eLHaOjk5ORo2bJhatWrlNP3AgQOKjo5WQkKChg0bpuLi4ho/2263q6KiwukFAAA8h0sZkeLiYmVnZ2vatGmaNWuWdu7cqSlTpshms2n06NHV1tmyZYuCgoKUl5enU6dOacKECTpz5ozTOJHrdu7cqT179ignJ8dpeu/evbVs2TIlJSXp5MmTmjdvnvr06aOvvvpKERERVX52ZmamXnvtNVc20VK1DeojcwKr1HbMccbnPdx559SGHgdN8Rvnyjo2ZD34TtSdS4FIZWWlUlNTlZGRIUlKSUnRV199pezs7GoDkcrKSvn5+Wn58uUKCwuTJC1cuFCDBw/Wf//3fys4ONipfE5OjpKTk3X33Xc7TX/ooYccf3fr1k333nuvEhMT9bvf/U7Tpk2r8rPT09Od5lVUVCguLs6VTQYAAG7kUtdMVFSUunbt6jStS5cuKikpqbFOTEyMIwi5XscYo6NHjzqV/fbbb7Vy5UqNGzeu1nVp1aqVunXrpgMHDlRbxmazKTQ01OkFAAA8h0sZkbS0NO3fv99pWlFRkeLj42us8/HHH+v8+fMKCQlx1PH391dsbKxT2T/84Q+y2+0aNWpUretit9u1b98+3Xfffa5sgtdgUB+sUp/jjHsnuF9D78dyY5eMu9RngGZj/a7Vdf+40s1Yn64ivgOucykjMnXqVG3fvl0ZGRk6ePCgVqxYocWLF2vixImOMunp6U7dNCNGjFBERITGjBmjvXv3atOmTZoxY4bGjh1bZbfM448/XuWYj+nTp6ugoECHDx/Wjh07NHjwYFVUVOiZZ55xdZsBAICHcCkQ6dWrl/Ly8vTRRx8pOTlZr7/+urKysjRy5EhHmdLSUqeumpCQEOXn5+vcuXNKTU3VyJEjNWjQIL3zzjtOyy4qKtKWLVv03HPPVfnZR48e1fDhw9W5c2c9+eSTCgwM1Pbt22vMxgAAAM/mZ4wxVq9EU6moqFBYWJjKy8u9erwIXTTwVM3pPhHuVFvXbFXdLJ7wsLobueNYqKqrpLG7sRvapUTXzHdc+X/LQ+8AAIBleOgdgAar6SywIWe+DV2Ot/H27XbHgxLd+TnuXibqhowIAACwDIEIAACwDF0zXqi6FKI3pXDhmxp6DNaUHm/ofTQ8QW3bUJ/t8oRBqk3VJeNJvGldPR0ZEQAAYBkyIj6Eh+LBKq4ec+54OJq3D/R0hSdkQRrKnRmF5nQs+AIyIgAAwDIEIgAAwDJ0zQBoMo2djve2AYNN3T1AF8W/1bYveHCjdciIAAAAy5AR8UGcBaGp1TRQmjNM6/C9rxnHpmcgIwIAACxDIAIAACxD1wwAt6K7pmHqs6/okoE3ISMCAAAsQyACAAAsQ9cMAJc0xoPpqpvXkC6b5t4d0dy3H96LjAgAALAMGZFmoqozVV94rDo8V1M8hNHbjtu6fudcyQx52z4Avo+MCAAAsAyBCAAAsAxdM80Q93BAQzTGfUEa6xj0hu7F6taxKbqumkp9toHfIVxHRgQAAFiGjEgzVttZjDecbaJhajordaXNG/v4qGvWxRuOy+rWsbk/dr65bz/+jYwIAACwDIEIAACwDF0zzVhtg+iqKusNqXA0jupS5o1xDNRnGa7U4XgFvAcZEQAAYBk/Y4yxeiWaSkVFhcLCwlReXq7Q0FCrV8fjcTbpm9wxOJBj5TuuDPBurHbwpYG9DFz1Ha78vyUjAgAALEMgAgAALMNgVVTLW9O7qJo7095WHSuefK8buhmAuiEjAgAALENGBC6p7SzP085KmyPOxFFXnnCZM8cryIgAAADLEIgAAADL0DWDRuXJgwd9EWltz1LbHYrdoa6fU1M5vrewEhkRAABgGQIRAABgGbpm4DaeMCLfF9D94pl8qTujPg8UrE99jmVUxeWMyLFjxzRq1ChFRESoZcuW6tGjhwoLC2usY7fbNXv2bMXHx8tmsykxMVG5ubmO+Q888ID8/Pxuej388MNOy1m0aJESEhIUFBSknj17avPmza6uPgAA8CAuZUTOnj2rtLQ09evXT+vWrVNkZKQOHTqkNm3a1Fhv6NChOnnypHJyctSxY0eVlZXp6tWrjvmrV6/W5cuXHe9Pnz6t7t27a8iQf0fZv//97/XSSy9p0aJFSktL07vvvquHHnpIe/fu1W233ebKZgBoAlVlxNx5B9a6ZihqK1efs3ZvPdOvz773pYfswTO4FIjMnz9fcXFxWrp0qWNahw4daqyzfv16FRQUqLi4WOHh4VXWuT79upUrV6ply5ZOgcjChQv13HPPady4cZKkrKwsbdiwQdnZ2crMzHRlMwAAgIdwqWtmzZo1Sk1N1ZAhQxQZGamUlBQtWbKkTnUWLFigmJgYJSUlafr06bp48WK1dXJycjRs2DC1atVKknT58mUVFhaqf//+TuX69++vrVu3Vrscu92uiooKpxcAAPAcLmVEiouLlZ2drWnTpmnWrFnauXOnpkyZIpvNptGjR1dbZ8uWLQoKClJeXp5OnTqlCRMm6MyZM07jRK7buXOn9uzZo5ycHMe0U6dO6dq1a2rXrp1T2Xbt2unEiRPVrm9mZqZee+01VzYRqFF1KfiGpKG9Na1fV7VtnzsHfVa17KbuMmjoQFBvUNs2eut2oWm4lBGprKzUXXfdpYyMDKWkpOgXv/iFnn/+eWVnZ9dYx8/PT8uXL9fdd9+tgQMHauHChXr//ferzIrk5OQoOTlZd999903z/Pz8nN4bY26adqP09HSVl5c7XkeOHHFhawEAgLu5lBGJiopS165dnaZ16dJFq1atqrFOTEyMwsLCnOoYY3T06FF16tTJMf3bb7/VypUrNXfuXKdl3HrrrQoICLgp+1FWVnZTluRGNptNNputTtsG9/GFyxwb+4yOM8R/q88x4UqWpTGW11Qaa/BsXZcNeAKXMiJpaWnav3+/07SioiLFx8fXWOf48eM6f/68Ux1/f3/FxsY6lf3DH/4gu92uUaNGOU0PDAxUz549lZ+f7zQ9Pz9fffr0cWUTAACAB3EpEJk6daq2b9+ujIwMHTx4UCtWrNDixYs1ceJER5n09HSn8SIjRoxQRESExowZo71792rTpk2aMWOGxo4dq+DgYKfl5+Tk6PHHH1dERMRNnz1t2jS99957ys3N1b59+zR16lSVlJRo/Pjxrm4zAADwEC51zfTq1Ut5eXlKT0/X3LlzlZCQoKysLI0cOdJRprS0VCUlJY73ISEhys/P1+TJk5WamqqIiAgNHTpU8+bNc1p2UVGRtmzZoo0bN1b52U899ZROnz6tuXPnqrS0VMnJyVq7dm2N2RigIVxJidf1nhmwDu1hnevfC9oAVXH5Fu+PPPKIHnnkkWrnv//++zdNu/3222/qVvm+pKQkGWNqLDNhwgRNmDChTusJAAA8H8+aQbPjzrMyzvhc5+v7zMrB2gxQhTfg6bsAAMAyBCIAAMAyfqa2gRk+pKKiQmFhYSovL1doaKjVq4P/x8r0sa93C8DzufPeIXX9PFfwnUFduPL/lowIAACwDIEIAACwDFfNwGNYcXVBXe9vwEO94O0a6zvFPUHQ2MiIAAAAy5ARgeWqOrOq6k6l7tTQz+EsEc0NdxFGYyEjAgAALEMgAgAALEPXDABYyFu7Nbx1veF5yIgAAADLkBEBgGaKrAY8ARkRAABgGQIRAABgGR56B69Rn3t9NPX9SGpaBwBoLnjoHQAA8AoMVkWz0FjPkrEyswIAvoiMCAAAsAyBCAAAsAxdM2h26tO9QpcMALgHGREAAGAZAhEAAGAZumbgNW688qWuXSXcPwQAPBsZEQAAYBkyIkAjIgsCAK4hIwIAACxDIAIAACxD1wy8Uk1dIFYMUKVLBgDqh4wIAACwDBkR+Jz6XOYLALAGGREAAGAZAhEAAGAZumbQLFQ3mNTVrhsGpQJA4yIjAgAALENGBD6ttgzG9fm1ZUbIhACAe5ARAQAAliEQAQAAlqFrBqgG3TEA4H4uZ0SOHTumUaNGKSIiQi1btlSPHj1UWFhYYx273a7Zs2crPj5eNptNiYmJys3NdSpz7tw5TZw4UVFRUQoKClKXLl20du1ax/xXX31Vfn5+Tq/27du7uvoAAMCDuJQROXv2rNLS0tSvXz+tW7dOkZGROnTokNq0aVNjvaFDh+rkyZPKyclRx44dVVZWpqtXrzrmX758WT/72c8UGRmpTz75RLGxsTpy5Ihat27ttJw77rhDn3/+ueN9QECAK6sPVIvsBwBYw6VAZP78+YqLi9PSpUsd0zp06FBjnfXr16ugoEDFxcUKDw+vsk5ubq7OnDmjrVu36pZbbpEkxcfH37yyLVqQBQEAwIe41DWzZs0apaamasiQIYqMjFRKSoqWLFlSpzoLFixQTEyMkpKSNH36dF28eNGpzL333quJEyeqXbt2Sk5OVkZGhq5du+a0rAMHDig6OloJCQkaNmyYiouLa/xsu92uiooKpxcAAPAcLgUixcXFys7OVqdOnbRhwwaNHz9eU6ZM0bJly2qss2XLFu3Zs0d5eXnKysrSJ598ookTJzqV+eSTT3Tt2jWtXbtWc+bM0dtvv6033njDUaZ3795atmyZNmzYoCVLlujEiRPq06ePTp8+Xe1nZ2ZmKiwszPGKi4tzZXMBAICb+RljTF0LBwYGKjU1VVu3bnVMmzJlir788ktt27atyjr9+/fX5s2bdeLECYWFhUmSVq9ercGDB+vChQsKDg5WUlKSLl26pMOHDzvGfSxcuFC/+tWvVFpaWuVyL1y4oMTERL388suaNm1alWXsdrvsdrvjfUVFheLi4lReXq7Q0NC6bjYAAHBBRUWFwsLC6vT/1qWMSFRUlLp27eo0rUuXLiopKamxTkxMjCMIuV7HGKOjR486yiQlJTkNPu3SpYtOnDihy5cvV7ncVq1aqVu3bjpw4EC1n22z2RQaGur0AgAAnsOlQCQtLU379+93mlZUVFTlwNIb6xw/flznz593quPv76/Y2FhHmYMHD6qystKpTFRUlAIDA6tcrt1u1759+xQVFeXKJgAAAA/iUiAydepUbd++XRkZGTp48KBWrFihxYsXO433SE9P1+jRox3vR4wYoYiICI0ZM0Z79+7Vpk2bNGPGDI0dO1bBwcGSpBdeeEGnT5/Wiy++qKKiIn322WfKyMhwWu706dNVUFCgw4cPa8eOHRo8eLAqKir0zDPPNHQfAAAAi7gUiPTq1Ut5eXn66KOPlJycrNdff11ZWVkaOXKko0xpaalTV01ISIjy8/N17tw5paamauTIkRo0aJDeeecdR5m4uDht3LhRX375pe68805NmTJFL774ombOnOkoc/ToUQ0fPlydO3fWk08+qcDAQG3fvr3GbAwAAPBsLg1W9XauDJ4BAAD147bBqgAAAI2JQAQAAFiGQAQAAFiGQAQAAFiGQAQAAFiGQAQAAFimhdUr0JSuX6nMU3gBAHCf6/9n63KHkGYViHzzzTeSxFN4AQBoAt98843Ts+aq0qxuaFZZWanjx4+rdevW8vPzc0y//lTeI0eOcKMzD0K7eC7axnPRNp6pubWLMUbffPONoqOj5e9f8yiQZpURufFBe1XhCb2eiXbxXLSN56JtPFNzapfaMiHXMVgVAABYhkAEAABYhkBEks1m0y9/+UvZbDarVwU3oF08F23juWgbz0S7VK9ZDVYFAACehYwIAACwDIEIAACwDIEIAACwDIEIAACwjMcHItnZ2brzzjsdN4G59957tW7dOqcy+/bt06OPPqqwsDC1bt1a99xzj0pKShzz7Xa7Jk+erFtvvVWtWrXSo48+qqNHjzot4+zZs3r66acVFhamsLAwPf300zp37pxTmZKSEg0aNEitWrXSrbfeqilTpujy5ctOZXbv3q37779fwcHBiomJ0dy5c+t0r31vVFvb+Pn5Vfn61a9+5ShD27hHbW1z/vx5TZo0SbGxsQoODlaXLl2UnZ3ttAzapvHV1i4nT57Us88+q+joaLVs2VIDBgzQgQMHnJZBu7hfZmam/Pz89NJLLzmmGWP06quvKjo6WsHBwXrggQf01VdfOdWjberJeLg1a9aYzz77zOzfv9/s37/fzJo1y9xyyy1mz549xhhjDh48aMLDw82MGTPMrl27zKFDh8yf//xnc/LkSccyxo8fb2JiYkx+fr7ZtWuX6devn+nevbu5evWqo8yAAQNMcnKy2bp1q9m6datJTk42jzzyiGP+1atXTXJysunXr5/ZtWuXyc/PN9HR0WbSpEmOMuXl5aZdu3Zm2LBhZvfu3WbVqlWmdevW5q233mqCPdX0amub0tJSp1dubq7x8/Mzhw4dciyDtnGP2tpm3LhxJjEx0XzxxRfm8OHD5t133zUBAQHm008/dSyDtml8NbVLZWWlueeee8x9991ndu7caf75z3+an//85+a2224z58+fdyyDdnGvnTt3mg4dOpg777zTvPjii47pb775pmndurVZtWqV2b17t3nqqadMVFSUqaiocJShberH4wORqvzgBz8w7733njHGmKeeesqMGjWq2rLnzp0zt9xyi1m5cqVj2rFjx4y/v79Zv369McaYvXv3Gklm+/btjjLbtm0zksw///lPY4wxa9euNf7+/ubYsWOOMh999JGx2WymvLzcGGPMokWLTFhYmLl06ZKjTGZmpomOjjaVlZWNsOWe78a2+b7HHnvM/PjHP3a8p22a1o1tc8cdd5i5c+c6zb/rrrvMnDlzjDG0TVO63i779+83khzBojHf/VMKDw83S5YsMcbQLu72zTffmE6dOpn8/Hxz//33OwKRyspK0759e/Pmm286yl66dMmEhYWZ3/72t8YY2qYhPL5r5kbXrl3TypUrdeHCBd17772qrKzUZ599pqSkJD344IOKjIxU79699emnnzrqFBYW6sqVK+rfv79jWnR0tJKTk7V161ZJ0rZt2xQWFqbevXs7ytxzzz0KCwtzKpOcnKzo6GhHmQcffFB2u12FhYWOMvfff7/TDWsefPBBHT9+XP/3f//njl3iMb7fNt938uRJffbZZ3ruuecc02ibplFV2/Tt21dr1qzRsWPHZIzRF198oaKiIj344IOSaJum8P12sdvtkqSgoCBHmYCAAAUGBmrLli2SaBd3mzhxoh5++GH99Kc/dZp++PBhnThxwmm/22w23X///Y59StvUn1cEIrt371ZISIhsNpvGjx+vvLw8de3aVWVlZTp//rzefPNNDRgwQBs3btQTTzyhJ598UgUFBZKkEydOKDAwUD/4wQ+cltmuXTudOHHCUSYyMvKmz42MjHQq065dO6f5P/jBDxQYGFhjmevvr5fxNdW1zff97ne/U+vWrfXkk086ptE27lVT27zzzjvq2rWrYmNjFRgYqAEDBmjRokXq27evJNrGnaprl9tvv13x8fFKT0/X2bNndfnyZb355ps6ceKESktLJdEu7rRy5Urt2rVLmZmZN827vr1V7Y8b9xdtUz9e8fTdzp076+9//7vOnTunVatW6ZlnnlFBQYHatGkjSXrsscc0depUSVKPHj20detW/fa3v9X9999f7TKNMfLz83O8v/Hvxixj/t/goarq+oLq2ub7wUhubq5GjhzpdLZXHdqmcdTUNu+88462b9+uNWvWKD4+Xps2bdKECRMUFRV109ngjWibhqupXVatWqXnnntO4eHhCggI0E9/+lM99NBDtS6TdmmYI0eO6MUXX9TGjRtr/I2qan/Uti9om9p5RUYkMDBQHTt2VGpqqjIzM9W9e3f95je/0a233qoWLVrc9E+vS5cujqtm2rdvr8uXL+vs2bNOZcrKyhwRZPv27XXy5MmbPvfrr792KvP9SPPs2bO6cuVKjWXKysok3RxJ+4rq2uZGmzdv1v79+zVu3Din6bSNe1XXNhcvXtSsWbO0cOFCDRo0SHfeeacmTZqkp556Sm+99ZYk2sadavrO9OzZ0xGklJaWav369Tp9+rQSEhIk0S7uUlhYqLKyMvXs2VMtWrRQixYtVFBQoHfeeUctWrSoNtvw/f1O29SPVwQi32eMkd1uV2BgoHr16qX9+/c7zS8qKlJ8fLyk777Yt9xyi/Lz8x3zS0tLtWfPHvXp00eSdO+996q8vFw7d+50lNmxY4fKy8udyuzZs8eRIpWkjRs3ymazqWfPno4ymzZtcrrMauPGjYqOjlaHDh0adyd4qOttc6OcnBz17NlT3bt3d5pO2zSt621z5coVXblyRf7+zl//gIAAVVZWSqJtmlJV35mwsDC1bdtWBw4c0P/+7//qsccek0S7uMtPfvIT7d69W3//+98dr9TUVI0cOVJ///vf9cMf/lDt27d32u+XL19WQUGBY5/SNg3QVKNi6ys9Pd1s2rTJHD582PzjH/8ws2bNMv7+/mbjxo3GGGNWr15tbrnlFrN48WJz4MAB81//9V8mICDAbN682bGM8ePHm9jYWPP555+bXbt2mR//+MdVXlJ15513mm3btplt27aZbt26VXlJ1U9+8hOza9cu8/nnn5vY2FinS6rOnTtn2rVrZ4YPH252795tVq9ebUJDQ732kqra1NY2xnx3mVnLli1NdnZ2lcugbdyjtra5//77zR133GG++OILU1xcbJYuXWqCgoLMokWLHMugbRpfbe3yhz/8wXzxxRfm0KFD5tNPPzXx8fHmySefdFoG7dI0brxqxpjvLt8NCwszq1evNrt37zbDhw+v8vJd2sZ1Hh+IjB071sTHx5vAwEDTtm1b85Of/MTpH50xxuTk5JiOHTuaoKAg0717d6d7IRhjzMWLF82kSZNMeHi4CQ4ONo888ogpKSlxKnP69GkzcuRI07p1a9O6dWszcuRIc/bsWacy//rXv8zDDz9sgoODTXh4uJk0aZLT5VPGGPOPf/zD3HfffcZms5n27dubV1991Ssvp6qLurTNu+++a4KDg825c+eqXAZt4x61tU1paal59tlnTXR0tAkKCjKdO3c2b7/9ttP+oG0aX23t8pvf/MbExsaaW265xdx2221mzpw5xm63Oy2Ddmka3w9EKisrzS9/+UvTvn17Y7PZzI9+9COze/dupzq0Tf34GeOtt2IDAADezivHiAAAAN9AIAIAACxDIAIAACxDIAIAACxDIAIAACxDIAIAACxDIAIAACxDIAIAACxDIAIAACxDIAIAACxDIAIAACxDIAIAACzz/wPj9QB5rmEVdwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the suitible locations\n",
    "accessibility_grid.plot(column=\"suitible_area\", linewidth=0)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}