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lnksz/analyze_building_n_edge.ipynb

Last active September 3, 2017 14:57
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Explore visually the data preparation for rivus -- JOIN(building, edge)
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analyze_building_n_edge.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Visualize: buildings + edges = edge_plus"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import geopandas\n",
"import os\n",
"from rivus.utils import pandashp as pdshp\n",
"from datetime import datetime\n",
"ospno = os.path.normpath"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"base_directory = os.path.join(ospno('./data'), 'haag15')\n",
"building_shapefile = os.path.join(base_directory, 'building.shp')\n",
"edge_shapefile = os.path.join(base_directory, 'edge.shp')\n",
"to_edge_shapefile = os.path.join(base_directory, 'to_edge.shp')\n",
"vertex_shapefile = os.path.join(base_directory, 'vertex.shp')\n",
"data_spreadsheet = os.path.join(base_directory, 'data.xlsx')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# This is the function we want to explore\n",
"def prepare_edge(edge_shapefile, building_shapefile):\n",
" \"\"\"Create edge graph with grouped building demands.\n",
" \"\"\"\n",
" # load buildings and sum by type and nearest edge ID\n",
" # 1. read shapefile to DataFrame (with special geometry column)\n",
" # 2. group DataFrame by columns 'nearest' (ID of nearest edge) and 'type'\n",
" # (residential, commercial, industrial, other)\n",
" # 3. sum by group and unstack, i.e. convert secondary index 'type' to\n",
" # columns\n",
" buildings = geopandas.read_file(building_shapefile)\n",
" buildings = buildings.convert_objects(convert_numeric=True)\n",
" building_type_mapping = {\n",
" 'basin': 'other', 'chapel': 'other', 'church': 'other',\n",
" 'farm_auxiliary': 'other', 'greenhouse': 'other',\n",
" 'school': 'public',\n",
" 'office': 'commercial', 'restaurant': 'commercial',\n",
" 'yes': 'residential', 'house': 'residential'}\n",
" buildings.replace(to_replace={'type': building_type_mapping}, inplace=True)\n",
" buildings = buildings.to_crs(epsg=32632)\n",
" buildings['AREA'] = buildings.area\n",
" buildings_grouped = buildings.groupby(['nearest', 'type'])\n",
" total_area = buildings_grouped.sum()['AREA'].unstack()\n",
"\n",
" # load edges (streets) and join with summed areas\n",
" # 1. read shapefile to DataFrame (with geometry column)\n",
" # 2. join DataFrame total_area on index (=ID)\n",
" # 3. fill missing values with 0\n",
" edge = geopandas.read_file(edge_shapefile)\n",
" edge = edge.set_index('Edge')\n",
" edge = edge.join(total_area)\n",
" edge = edge.fillna(0)\n",
" return edge"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Have a look inside prepare_edge()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
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" <td>None</td>\n",
" <td>Euronics XXL</td>\n",
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" <td>27482425</td>\n",
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" <td>27482427</td>\n",
" <td>None</td>\n",
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" <td>None</td>\n",
" <td>supermarket</td>\n",
" <td>retail</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1295.101702</td>\n",
" <td>yes</td>\n",
" <td>Einzelhandel</td>\n",
" <td>POLYGON ((12.16846829998401 48.16375659995224,...</td>\n",
" <td>None</td>\n",
" <td>Penny</td>\n",
" <td>40</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
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],
"text/plain": [
" admin_leve amenity area building cluster \\\n",
"0 None None 2826.504627 yes Einzelhandel \n",
"1 None None 2353.301977 yes Einzelhandel \n",
"2 None None 1197.697244 yes Einzelhandel \n",
"3 None None 1603.551916 yes Einzelhandel \n",
"4 None None 1295.101702 yes Einzelhandel \n",
"\n",
" geometry man_made name \\\n",
"0 POLYGON ((12.17066699998406 48.16282049995247,... None None \n",
"1 POLYGON ((12.16622309998395 48.16498569995202,... None Edeka Neukauf \n",
"2 POLYGON ((12.16717579998398 48.16471269995211,... None Euronics XXL \n",
"3 POLYGON ((12.16660029998396 48.16411089995206,... None Lidl \n",
"4 POLYGON ((12.16846829998401 48.16375659995224,... None Penny \n",
"\n",
" nearest office osm_id osm_way_id other_tags perimeter place shop \\\n",
"0 59 None None 27482213 None 213.714235 None mall \n",
"1 34 None None 27482423 None 216.232143 None supermarket \n",
"2 34 None None 27482425 None 139.223865 None electronics \n",
"3 28 None None 27482427 None 183.319654 None supermarket \n",
"4 40 None None 27482428 None 160.751446 None supermarket \n",
"\n",
" type \n",
"0 retail \n",
"1 retail \n",
"2 retail \n",
"3 retail \n",
"4 retail "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"\"\"\"Create edge graph with grouped building demands.\n",
"\"\"\"\n",
"# load buildings and sum by type and nearest edge ID\n",
"# 1. read shapefile to DataFrame (with special geometry column)\n",
"# 2. group DataFrame by columns 'nearest' (ID of nearest edge) and 'type'\n",
"# (residential, commercial, industrial, other)\n",
"# 3. sum by group and unstack, i.e. convert secondary index 'type' to\n",
"# columns\n",
"buildings = geopandas.read_file(building_shapefile)\n",
"buildings.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Kristof\\Anaconda2\\envs\\fion\\lib\\site-packages\\ipykernel\\__main__.py:1: FutureWarning: convert_objects is deprecated. Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n",
" if __name__ == '__main__':\n"
]
},
{
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" <td>Einzelhandel</td>\n",
" <td>POLYGON ((12.16717579998398 48.16471269995211,...</td>\n",
" <td>None</td>\n",
" <td>Euronics XXL</td>\n",
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" <td>None</td>\n",
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" <td>None</td>\n",
" <td>supermarket</td>\n",
" <td>retail</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1295.101702</td>\n",
" <td>yes</td>\n",
" <td>Einzelhandel</td>\n",
" <td>POLYGON ((12.16846829998401 48.16375659995224,...</td>\n",
" <td>None</td>\n",
" <td>Penny</td>\n",
" <td>40</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>27482428</td>\n",
" <td>None</td>\n",
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],
"text/plain": [
" admin_leve amenity area building cluster \\\n",
"0 None None 2826.504627 yes Einzelhandel \n",
"1 None None 2353.301977 yes Einzelhandel \n",
"2 None None 1197.697244 yes Einzelhandel \n",
"3 None None 1603.551916 yes Einzelhandel \n",
"4 None None 1295.101702 yes Einzelhandel \n",
"\n",
" geometry man_made name \\\n",
"0 POLYGON ((12.17066699998406 48.16282049995247,... None None \n",
"1 POLYGON ((12.16622309998395 48.16498569995202,... None Edeka Neukauf \n",
"2 POLYGON ((12.16717579998398 48.16471269995211,... None Euronics XXL \n",
"3 POLYGON ((12.16660029998396 48.16411089995206,... None Lidl \n",
"4 POLYGON ((12.16846829998401 48.16375659995224,... None Penny \n",
"\n",
" nearest office osm_id osm_way_id other_tags perimeter place \\\n",
"0 59 None None 27482213 None 213.714235 None \n",
"1 34 None None 27482423 None 216.232143 None \n",
"2 34 None None 27482425 None 139.223865 None \n",
"3 28 None None 27482427 None 183.319654 None \n",
"4 40 None None 27482428 None 160.751446 None \n",
"\n",
" shop type \n",
"0 mall retail \n",
"1 supermarket retail \n",
"2 electronics retail \n",
"3 supermarket retail \n",
"4 supermarket retail "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"buildings = buildings.convert_objects(convert_numeric=True)\n",
"buildings.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
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" <td>160.751446</td>\n",
" <td>None</td>\n",
" <td>supermarket</td>\n",
" <td>retail</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" admin_leve amenity area building cluster \\\n",
"0 None None 2826.504627 yes Einzelhandel \n",
"1 None None 2353.301977 yes Einzelhandel \n",
"2 None None 1197.697244 yes Einzelhandel \n",
"3 None None 1603.551916 yes Einzelhandel \n",
"4 None None 1295.101702 yes Einzelhandel \n",
"\n",
" geometry man_made name \\\n",
"0 POLYGON ((735759.1584048691 5339259.793506399,... None None \n",
"1 POLYGON ((735418.8462222115 5339486.786609476,... None Edeka Neukauf \n",
"2 POLYGON ((735490.9253872562 5339459.367663603,... None Euronics XXL \n",
"3 POLYGON ((735450.8963961252 5339390.725584941,... None Lidl \n",
"4 POLYGON ((735591.3989202955 5339357.078318694,... None Penny \n",
"\n",
" nearest office osm_id osm_way_id other_tags perimeter place \\\n",
"0 59 None None 27482213 None 213.714235 None \n",
"1 34 None None 27482423 None 216.232143 None \n",
"2 34 None None 27482425 None 139.223865 None \n",
"3 28 None None 27482427 None 183.319654 None \n",
"4 40 None None 27482428 None 160.751446 None \n",
"\n",
" shop type \n",
"0 mall retail \n",
"1 supermarket retail \n",
"2 electronics retail \n",
"3 supermarket retail \n",
"4 supermarket retail "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"building_type_mapping = {\n",
" 'basin': 'other', 'chapel': 'other', 'church': 'other',\n",
" 'farm_auxiliary': 'other', 'greenhouse': 'other',\n",
" 'school': 'public',\n",
" 'office': 'commercial', 'restaurant': 'commercial',\n",
" 'yes': 'residential', 'house': 'residential'}\n",
"buildings.replace(to_replace={'type': building_type_mapping}, inplace=True)\n",
"buildings = buildings.to_crs(epsg=32632)\n",
"buildings.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" text-align: right;\n",
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" text-align: left;\n",
" }\n",
"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>admin_leve</th>\n",
" <th>amenity</th>\n",
" <th>area</th>\n",
" <th>building</th>\n",
" <th>cluster</th>\n",
" <th>geometry</th>\n",
" <th>man_made</th>\n",
" <th>name</th>\n",
" <th>nearest</th>\n",
" <th>office</th>\n",
" <th>osm_id</th>\n",
" <th>osm_way_id</th>\n",
" <th>other_tags</th>\n",
" <th>perimeter</th>\n",
" <th>place</th>\n",
" <th>shop</th>\n",
" <th>type</th>\n",
" <th>AREA</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2826.504627</td>\n",
" <td>yes</td>\n",
" <td>Einzelhandel</td>\n",
" <td>POLYGON ((735759.1584048691 5339259.793506399,...</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>59</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>27482213</td>\n",
" <td>None</td>\n",
" <td>213.714235</td>\n",
" <td>None</td>\n",
" <td>mall</td>\n",
" <td>retail</td>\n",
" <td>2832.189206</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2353.301977</td>\n",
" <td>yes</td>\n",
" <td>Einzelhandel</td>\n",
" <td>POLYGON ((735418.8462222115 5339486.786609476,...</td>\n",
" <td>None</td>\n",
" <td>Edeka Neukauf</td>\n",
" <td>34</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>27482423</td>\n",
" <td>None</td>\n",
" <td>216.232143</td>\n",
" <td>None</td>\n",
" <td>supermarket</td>\n",
" <td>retail</td>\n",
" <td>2358.133449</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1197.697244</td>\n",
" <td>yes</td>\n",
" <td>Einzelhandel</td>\n",
" <td>POLYGON ((735490.9253872562 5339459.367663603,...</td>\n",
" <td>None</td>\n",
" <td>Euronics XXL</td>\n",
" <td>34</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>27482425</td>\n",
" <td>None</td>\n",
" <td>139.223865</td>\n",
" <td>None</td>\n",
" <td>electronics</td>\n",
" <td>retail</td>\n",
" <td>1200.071553</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1603.551916</td>\n",
" <td>yes</td>\n",
" <td>Einzelhandel</td>\n",
" <td>POLYGON ((735450.8963961252 5339390.725584941,...</td>\n",
" <td>None</td>\n",
" <td>Lidl</td>\n",
" <td>28</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>27482427</td>\n",
" <td>None</td>\n",
" <td>183.319654</td>\n",
" <td>None</td>\n",
" <td>supermarket</td>\n",
" <td>retail</td>\n",
" <td>1606.707165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1295.101702</td>\n",
" <td>yes</td>\n",
" <td>Einzelhandel</td>\n",
" <td>POLYGON ((735591.3989202955 5339357.078318694,...</td>\n",
" <td>None</td>\n",
" <td>Penny</td>\n",
" <td>40</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>27482428</td>\n",
" <td>None</td>\n",
" <td>160.751446</td>\n",
" <td>None</td>\n",
" <td>supermarket</td>\n",
" <td>retail</td>\n",
" <td>1297.783949</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" admin_leve amenity area building cluster \\\n",
"0 None None 2826.504627 yes Einzelhandel \n",
"1 None None 2353.301977 yes Einzelhandel \n",
"2 None None 1197.697244 yes Einzelhandel \n",
"3 None None 1603.551916 yes Einzelhandel \n",
"4 None None 1295.101702 yes Einzelhandel \n",
"\n",
" geometry man_made name \\\n",
"0 POLYGON ((735759.1584048691 5339259.793506399,... None None \n",
"1 POLYGON ((735418.8462222115 5339486.786609476,... None Edeka Neukauf \n",
"2 POLYGON ((735490.9253872562 5339459.367663603,... None Euronics XXL \n",
"3 POLYGON ((735450.8963961252 5339390.725584941,... None Lidl \n",
"4 POLYGON ((735591.3989202955 5339357.078318694,... None Penny \n",
"\n",
" nearest office osm_id osm_way_id other_tags perimeter place \\\n",
"0 59 None None 27482213 None 213.714235 None \n",
"1 34 None None 27482423 None 216.232143 None \n",
"2 34 None None 27482425 None 139.223865 None \n",
"3 28 None None 27482427 None 183.319654 None \n",
"4 40 None None 27482428 None 160.751446 None \n",
"\n",
" shop type AREA \n",
"0 mall retail 2832.189206 \n",
"1 supermarket retail 2358.133449 \n",
"2 electronics retail 1200.071553 \n",
"3 supermarket retail 1606.707165 \n",
"4 supermarket retail 1297.783949 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"buildings['AREA'] = buildings.area\n",
"buildings.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Why `AREA != area` ??__ we just assigned it from one an other..."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" <thead>\n",
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" <th></th>\n",
" <th></th>\n",
" <th colspan=\"8\" halign=\"left\">AREA</th>\n",
" <th colspan=\"2\" halign=\"left\">area</th>\n",
" <th>...</th>\n",
" <th colspan=\"2\" halign=\"left\">osm_way_id</th>\n",
" <th colspan=\"8\" halign=\"left\">perimeter</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
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" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
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" <th>25%</th>\n",
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" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" <tr>\n",
" <th>nearest</th>\n",
" <th>type</th>\n",
" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">0</th>\n",
" <th>farm</th>\n",
" <td>1.0</td>\n",
" <td>613.107966</td>\n",
" <td>NaN</td>\n",
" <td>613.107966</td>\n",
" <td>613.107966</td>\n",
" <td>613.107966</td>\n",
" <td>613.107966</td>\n",
" <td>613.107966</td>\n",
" <td>1.0</td>\n",
" <td>6.118382e+02</td>\n",
" <td>...</td>\n",
" <td>2.816692e+08</td>\n",
" <td>281669205.0</td>\n",
" <td>1.0</td>\n",
" <td>126.744403</td>\n",
" <td>NaN</td>\n",
" <td>126.744403</td>\n",
" <td>126.744403</td>\n",
" <td>126.744403</td>\n",
" <td>126.744403</td>\n",
" <td>126.744403</td>\n",
" </tr>\n",
" <tr>\n",
" <th>other</th>\n",
" <td>1.0</td>\n",
" <td>519.074015</td>\n",
" <td>NaN</td>\n",
" <td>519.074015</td>\n",
" <td>519.074015</td>\n",
" <td>519.074015</td>\n",
" <td>519.074015</td>\n",
" <td>519.074015</td>\n",
" <td>1.0</td>\n",
" <td>5.179353e+02</td>\n",
" <td>...</td>\n",
" <td>2.815083e+08</td>\n",
" <td>281508305.0</td>\n",
" <td>1.0</td>\n",
" <td>107.276628</td>\n",
" <td>NaN</td>\n",
" <td>107.276628</td>\n",
" <td>107.276628</td>\n",
" <td>107.276628</td>\n",
" <td>107.276628</td>\n",
" <td>107.276628</td>\n",
" </tr>\n",
" <tr>\n",
" <th>residential</th>\n",
" <td>3.0</td>\n",
" <td>239.346601</td>\n",
" <td>30.872299</td>\n",
" <td>205.464289</td>\n",
" <td>226.077832</td>\n",
" <td>246.691375</td>\n",
" <td>256.287757</td>\n",
" <td>265.884139</td>\n",
" <td>3.0</td>\n",
" <td>2.389415e+02</td>\n",
" <td>...</td>\n",
" <td>2.816692e+08</td>\n",
" <td>281669209.0</td>\n",
" <td>3.0</td>\n",
" <td>62.853104</td>\n",
" <td>4.143033</td>\n",
" <td>58.163383</td>\n",
" <td>61.271527</td>\n",
" <td>64.379670</td>\n",
" <td>65.197965</td>\n",
" <td>66.016260</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">1</th>\n",
" <th>farm</th>\n",
" <td>1.0</td>\n",
" <td>2115.137359</td>\n",
" <td>NaN</td>\n",
" <td>2115.137359</td>\n",
" <td>2115.137359</td>\n",
" <td>2115.137359</td>\n",
" <td>2115.137359</td>\n",
" <td>2115.137359</td>\n",
" <td>1.0</td>\n",
" <td>2.110956e+03</td>\n",
" <td>...</td>\n",
" <td>2.815083e+08</td>\n",
" <td>281508306.0</td>\n",
" <td>1.0</td>\n",
" <td>224.696828</td>\n",
" <td>NaN</td>\n",
" <td>224.696828</td>\n",
" <td>224.696828</td>\n",
" <td>224.696828</td>\n",
" <td>224.696828</td>\n",
" <td>224.696828</td>\n",
" </tr>\n",
" <tr>\n",
" <th>residential</th>\n",
" <td>4.0</td>\n",
" <td>209.959588</td>\n",
" <td>75.186861</td>\n",
" <td>142.504014</td>\n",
" <td>152.647210</td>\n",
" <td>197.012138</td>\n",
" <td>254.324516</td>\n",
" <td>303.310062</td>\n",
" <td>4.0</td>\n",
" <td>2.095329e+02</td>\n",
" <td>...</td>\n",
" <td>2.815083e+08</td>\n",
" <td>281508313.0</td>\n",
" <td>4.0</td>\n",
" <td>58.663606</td>\n",
" <td>11.891804</td>\n",
" <td>47.904944</td>\n",
" <td>49.864683</td>\n",
" <td>56.500447</td>\n",
" <td>65.299370</td>\n",
" <td>73.748587</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">2</th>\n",
" <th>farm</th>\n",
" <td>2.0</td>\n",
" <td>694.419395</td>\n",
" <td>116.201162</td>\n",
" <td>612.252765</td>\n",
" <td>653.336080</td>\n",
" <td>694.419395</td>\n",
" <td>735.502709</td>\n",
" <td>776.586024</td>\n",
" <td>2.0</td>\n",
" <td>6.932345e+02</td>\n",
" <td>...</td>\n",
" <td>2.816894e+08</td>\n",
" <td>281689428.0</td>\n",
" <td>2.0</td>\n",
" <td>124.666924</td>\n",
" <td>15.093892</td>\n",
" <td>113.993930</td>\n",
" <td>119.330427</td>\n",
" <td>124.666924</td>\n",
" <td>130.003420</td>\n",
" <td>135.339917</td>\n",
" </tr>\n",
" <tr>\n",
" <th>residential</th>\n",
" <td>8.0</td>\n",
" <td>205.324196</td>\n",
" <td>120.489247</td>\n",
" <td>98.398435</td>\n",
" <td>106.558282</td>\n",
" <td>164.345127</td>\n",
" <td>265.589578</td>\n",
" <td>397.912785</td>\n",
" <td>8.0</td>\n",
" <td>2.048858e+02</td>\n",
" <td>...</td>\n",
" <td>2.816894e+08</td>\n",
" <td>302586968.0</td>\n",
" <td>8.0</td>\n",
" <td>60.975753</td>\n",
" <td>22.800008</td>\n",
" <td>41.034070</td>\n",
" <td>44.039173</td>\n",
" <td>53.460730</td>\n",
" <td>68.926916</td>\n",
" <td>98.905103</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">3</th>\n",
" <th>farm</th>\n",
" <td>2.0</td>\n",
" <td>606.588576</td>\n",
" <td>290.706853</td>\n",
" <td>401.027789</td>\n",
" <td>503.808182</td>\n",
" <td>606.588576</td>\n",
" <td>709.368969</td>\n",
" <td>812.149362</td>\n",
" <td>2.0</td>\n",
" <td>6.052987e+02</td>\n",
" <td>...</td>\n",
" <td>2.816894e+08</td>\n",
" <td>281689439.0</td>\n",
" <td>2.0</td>\n",
" <td>115.942788</td>\n",
" <td>32.690592</td>\n",
" <td>92.827048</td>\n",
" <td>104.384918</td>\n",
" <td>115.942788</td>\n",
" <td>127.500657</td>\n",
" <td>139.058527</td>\n",
" </tr>\n",
" <tr>\n",
" <th>other</th>\n",
" <td>1.0</td>\n",
" <td>288.162026</td>\n",
" <td>NaN</td>\n",
" <td>288.162026</td>\n",
" <td>288.162026</td>\n",
" <td>288.162026</td>\n",
" <td>288.162026</td>\n",
" <td>288.162026</td>\n",
" <td>1.0</td>\n",
" <td>2.875666e+02</td>\n",
" <td>...</td>\n",
" <td>2.816894e+08</td>\n",
" <td>281689438.0</td>\n",
" <td>1.0</td>\n",
" <td>69.650115</td>\n",
" <td>NaN</td>\n",
" <td>69.650115</td>\n",
" <td>69.650115</td>\n",
" <td>69.650115</td>\n",
" <td>69.650115</td>\n",
" <td>69.650115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>residential</th>\n",
" <td>23.0</td>\n",
" <td>225.786187</td>\n",
" <td>118.602536</td>\n",
" <td>34.980934</td>\n",
" <td>162.674701</td>\n",
" <td>229.366828</td>\n",
" <td>293.652306</td>\n",
" <td>468.258848</td>\n",
" <td>23.0</td>\n",
" <td>2.254115e+02</td>\n",
" <td>...</td>\n",
" <td>2.816894e+08</td>\n",
" <td>303596049.0</td>\n",
" <td>23.0</td>\n",
" <td>60.980293</td>\n",
" <td>19.623507</td>\n",
" <td>24.948542</td>\n",
" <td>51.522762</td>\n",
" <td>61.193566</td>\n",
" <td>76.850867</td>\n",
" <td>96.669758</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">4</th>\n",
" <th>farm</th>\n",
" <td>1.0</td>\n",
" <td>897.389164</td>\n",
" <td>NaN</td>\n",
" <td>897.389164</td>\n",
" <td>897.389164</td>\n",
" <td>897.389164</td>\n",
" <td>897.389164</td>\n",
" <td>897.389164</td>\n",
" <td>1.0</td>\n",
" <td>1.396132e+07</td>\n",
" <td>...</td>\n",
" <td>2.817009e+08</td>\n",
" <td>281700854.0</td>\n",
" <td>1.0</td>\n",
" <td>139.912865</td>\n",
" <td>NaN</td>\n",
" <td>139.912865</td>\n",
" <td>139.912865</td>\n",
" <td>139.912865</td>\n",
" <td>139.912865</td>\n",
" <td>139.912865</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hotel</th>\n",
" <td>1.0</td>\n",
" <td>1772.569197</td>\n",
" <td>NaN</td>\n",
" <td>1772.569197</td>\n",
" <td>1772.569197</td>\n",
" <td>1772.569197</td>\n",
" <td>1772.569197</td>\n",
" <td>1772.569197</td>\n",
" <td>1.0</td>\n",
" <td>1.768936e+03</td>\n",
" <td>...</td>\n",
" <td>2.816692e+08</td>\n",
" <td>281669211.0</td>\n",
" <td>1.0</td>\n",
" <td>273.758075</td>\n",
" <td>NaN</td>\n",
" <td>273.758075</td>\n",
" <td>273.758075</td>\n",
" <td>273.758075</td>\n",
" <td>273.758075</td>\n",
" <td>273.758075</td>\n",
" </tr>\n",
" <tr>\n",
" <th>other</th>\n",
" <td>1.0</td>\n",
" <td>875.885988</td>\n",
" <td>NaN</td>\n",
" <td>875.885988</td>\n",
" <td>875.885988</td>\n",
" <td>875.885988</td>\n",
" <td>875.885988</td>\n",
" <td>875.885988</td>\n",
" <td>1.0</td>\n",
" <td>8.741183e+02</td>\n",
" <td>...</td>\n",
" <td>2.816894e+08</td>\n",
" <td>281689434.0</td>\n",
" <td>1.0</td>\n",
" <td>125.234574</td>\n",
" <td>NaN</td>\n",
" <td>125.234574</td>\n",
" <td>125.234574</td>\n",
" <td>125.234574</td>\n",
" <td>125.234574</td>\n",
" <td>125.234574</td>\n",
" </tr>\n",
" <tr>\n",
" <th>residential</th>\n",
" <td>15.0</td>\n",
" <td>259.377472</td>\n",
" <td>202.675034</td>\n",
" <td>42.900938</td>\n",
" <td>94.067225</td>\n",
" <td>243.549398</td>\n",
" <td>364.255829</td>\n",
" <td>748.319821</td>\n",
" <td>15.0</td>\n",
" <td>3.608283e+05</td>\n",
" <td>...</td>\n",
" <td>2.817009e+08</td>\n",
" <td>302586970.0</td>\n",
" <td>15.0</td>\n",
" <td>67.784448</td>\n",
" <td>32.779619</td>\n",
" <td>26.226804</td>\n",
" <td>38.770922</td>\n",
" <td>66.563069</td>\n",
" <td>91.892214</td>\n",
" <td>133.012831</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">5</th>\n",
" <th>farm</th>\n",
" <td>1.0</td>\n",
" <td>542.460212</td>\n",
" <td>NaN</td>\n",
" <td>542.460212</td>\n",
" <td>542.460212</td>\n",
" <td>542.460212</td>\n",
" <td>542.460212</td>\n",
" <td>542.460212</td>\n",
" <td>1.0</td>\n",
" <td>5.414658e+02</td>\n",
" <td>...</td>\n",
" <td>2.817008e+08</td>\n",
" <td>281700849.0</td>\n",
" <td>1.0</td>\n",
" <td>99.826185</td>\n",
" <td>NaN</td>\n",
" <td>99.826185</td>\n",
" <td>99.826185</td>\n",
" <td>99.826185</td>\n",
" <td>99.826185</td>\n",
" <td>99.826185</td>\n",
" </tr>\n",
" <tr>\n",
" <th>other</th>\n",
" <td>2.0</td>\n",
" <td>565.674526</td>\n",
" <td>276.900248</td>\n",
" <td>369.876483</td>\n",
" <td>467.775504</td>\n",
" <td>565.674526</td>\n",
" <td>663.573547</td>\n",
" <td>761.472569</td>\n",
" <td>2.0</td>\n",
" <td>5.644801e+02</td>\n",
" <td>...</td>\n",
" <td>2.817009e+08</td>\n",
" <td>281700851.0</td>\n",
" <td>2.0</td>\n",
" <td>102.352744</td>\n",
" <td>32.953477</td>\n",
" <td>79.051117</td>\n",
" <td>90.701931</td>\n",
" <td>102.352744</td>\n",
" <td>114.003557</td>\n",
" <td>125.654371</td>\n",
" </tr>\n",
" <tr>\n",
" <th>residential</th>\n",
" <td>10.0</td>\n",
" <td>159.083292</td>\n",
" <td>97.777833</td>\n",
" <td>56.673996</td>\n",
" <td>82.480964</td>\n",
" <td>130.246117</td>\n",
" <td>231.299164</td>\n",
" <td>317.240462</td>\n",
" <td>10.0</td>\n",
" <td>1.587249e+02</td>\n",
" <td>...</td>\n",
" <td>2.817009e+08</td>\n",
" <td>281700862.0</td>\n",
" <td>10.0</td>\n",
" <td>50.668861</td>\n",
" <td>17.602476</td>\n",
" <td>31.701653</td>\n",
" <td>37.101670</td>\n",
" <td>47.017746</td>\n",
" <td>60.850418</td>\n",
" <td>83.874135</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">7</th>\n",
" <th>residential</th>\n",
" <td>2.0</td>\n",
" <td>320.631344</td>\n",
" <td>23.164990</td>\n",
" <td>304.251223</td>\n",
" <td>312.441283</td>\n",
" <td>320.631344</td>\n",
" <td>328.821405</td>\n",
" <td>337.011466</td>\n",
" <td>2.0</td>\n",
" <td>3.200414e+02</td>\n",
" <td>...</td>\n",
" <td>2.945240e+08</td>\n",
" <td>302729800.0</td>\n",
" <td>2.0</td>\n",
" <td>72.972374</td>\n",
" <td>1.961609</td>\n",
" <td>71.585307</td>\n",
" <td>72.278841</td>\n",
" <td>72.972374</td>\n",
" <td>73.665908</td>\n",
" <td>74.359441</td>\n",
" </tr>\n",
" <tr>\n",
" <th>warehouse</th>\n",
" <td>2.0</td>\n",
" <td>110.165182</td>\n",
" <td>1.281146</td>\n",
" <td>109.259275</td>\n",
" <td>109.712229</td>\n",
" <td>110.165182</td>\n",
" <td>110.618136</td>\n",
" <td>111.071089</td>\n",
" <td>2.0</td>\n",
" <td>1.099617e+02</td>\n",
" <td>...</td>\n",
" <td>2.699065e+08</td>\n",
" <td>269906533.0</td>\n",
" <td>2.0</td>\n",
" <td>43.057298</td>\n",
" <td>1.378396</td>\n",
" <td>42.082625</td>\n",
" <td>42.569961</td>\n",
" <td>43.057298</td>\n",
" <td>43.544635</td>\n",
" <td>44.031971</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">8</th>\n",
" <th>farm</th>\n",
" <td>1.0</td>\n",
" <td>1336.139222</td>\n",
" <td>NaN</td>\n",
" <td>1336.139222</td>\n",
" <td>1336.139222</td>\n",
" <td>1336.139222</td>\n",
" <td>1336.139222</td>\n",
" <td>1336.139222</td>\n",
" <td>1.0</td>\n",
" <td>1.333456e+03</td>\n",
" <td>...</td>\n",
" <td>3.027298e+08</td>\n",
" <td>302729801.0</td>\n",
" <td>1.0</td>\n",
" <td>187.509876</td>\n",
" <td>NaN</td>\n",
" <td>187.509876</td>\n",
" <td>187.509876</td>\n",
" <td>187.509876</td>\n",
" <td>187.509876</td>\n",
" <td>187.509876</td>\n",
" </tr>\n",
" <tr>\n",
" <th>other</th>\n",
" <td>1.0</td>\n",
" <td>322.006706</td>\n",
" <td>NaN</td>\n",
" <td>322.006706</td>\n",
" <td>322.006706</td>\n",
" <td>322.006706</td>\n",
" <td>322.006706</td>\n",
" <td>322.006706</td>\n",
" <td>1.0</td>\n",
" <td>3.213029e+02</td>\n",
" <td>...</td>\n",
" <td>3.027298e+08</td>\n",
" <td>302729804.0</td>\n",
" <td>1.0</td>\n",
" <td>79.702736</td>\n",
" <td>NaN</td>\n",
" <td>79.702736</td>\n",
" <td>79.702736</td>\n",
" <td>79.702736</td>\n",
" <td>79.702736</td>\n",
" <td>79.702736</td>\n",
" </tr>\n",
" <tr>\n",
" <th>residential</th>\n",
" <td>2.0</td>\n",
" <td>275.931534</td>\n",
" <td>286.854827</td>\n",
" <td>73.094540</td>\n",
" <td>174.513037</td>\n",
" <td>275.931534</td>\n",
" <td>377.350030</td>\n",
" <td>478.768527</td>\n",
" <td>2.0</td>\n",
" <td>2.753217e+02</td>\n",
" <td>...</td>\n",
" <td>3.027298e+08</td>\n",
" <td>302729806.0</td>\n",
" <td>2.0</td>\n",
" <td>68.533906</td>\n",
" <td>48.110341</td>\n",
" <td>34.514757</td>\n",
" <td>51.524331</td>\n",
" <td>68.533906</td>\n",
" <td>85.543480</td>\n",
" <td>102.553054</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <th>warehouse</th>\n",
" <td>1.0</td>\n",
" <td>404.260374</td>\n",
" <td>NaN</td>\n",
" <td>404.260374</td>\n",
" <td>404.260374</td>\n",
" <td>404.260374</td>\n",
" <td>404.260374</td>\n",
" <td>404.260374</td>\n",
" <td>1.0</td>\n",
" <td>4.034089e+02</td>\n",
" <td>...</td>\n",
" <td>3.027298e+08</td>\n",
" <td>302729802.0</td>\n",
" <td>1.0</td>\n",
" <td>96.428673</td>\n",
" <td>NaN</td>\n",
" <td>96.428673</td>\n",
" <td>96.428673</td>\n",
" <td>96.428673</td>\n",
" <td>96.428673</td>\n",
" <td>96.428673</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">11</th>\n",
" <th>other</th>\n",
" <td>1.0</td>\n",
" <td>270.482948</td>\n",
" <td>NaN</td>\n",
" <td>270.482948</td>\n",
" <td>270.482948</td>\n",
" <td>270.482948</td>\n",
" <td>270.482948</td>\n",
" <td>270.482948</td>\n",
" <td>1.0</td>\n",
" <td>2.699605e+02</td>\n",
" <td>...</td>\n",
" <td>3.027413e+08</td>\n",
" <td>302741259.0</td>\n",
" <td>1.0</td>\n",
" <td>68.918471</td>\n",
" <td>NaN</td>\n",
" <td>68.918471</td>\n",
" <td>68.918471</td>\n",
" <td>68.918471</td>\n",
" <td>68.918471</td>\n",
" <td>68.918471</td>\n",
" </tr>\n",
" <tr>\n",
" <th>residential</th>\n",
" <td>1.0</td>\n",
" <td>250.160405</td>\n",
" <td>NaN</td>\n",
" <td>250.160405</td>\n",
" <td>250.160405</td>\n",
" <td>250.160405</td>\n",
" <td>250.160405</td>\n",
" <td>250.160405</td>\n",
" <td>1.0</td>\n",
" <td>2.496481e+02</td>\n",
" <td>...</td>\n",
" <td>3.027298e+08</td>\n",
" <td>302729805.0</td>\n",
" <td>1.0</td>\n",
" <td>73.133029</td>\n",
" <td>NaN</td>\n",
" <td>73.133029</td>\n",
" <td>73.133029</td>\n",
" <td>73.133029</td>\n",
" <td>73.133029</td>\n",
" <td>73.133029</td>\n",
" </tr>\n",
" <tr>\n",
" <th>warehouse</th>\n",
" <td>2.0</td>\n",
" <td>93.384707</td>\n",
" <td>42.742733</td>\n",
" <td>63.161030</td>\n",
" <td>78.272868</td>\n",
" <td>93.384707</td>\n",
" <td>108.496545</td>\n",
" <td>123.608383</td>\n",
" <td>2.0</td>\n",
" <td>9.320389e+01</td>\n",
" <td>...</td>\n",
" <td>3.027411e+08</td>\n",
" <td>302741258.0</td>\n",
" <td>2.0</td>\n",
" <td>38.693779</td>\n",
" <td>8.989685</td>\n",
" <td>32.337112</td>\n",
" <td>35.515445</td>\n",
" <td>38.693779</td>\n",
" <td>41.872113</td>\n",
" <td>45.050446</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">12</th>\n",
" <th>industrial</th>\n",
" <td>1.0</td>\n",
" <td>1443.309281</td>\n",
" <td>NaN</td>\n",
" <td>1443.309281</td>\n",
" <td>1443.309281</td>\n",
" <td>1443.309281</td>\n",
" <td>1443.309281</td>\n",
" <td>1443.309281</td>\n",
" <td>1.0</td>\n",
" <td>1.440611e+03</td>\n",
" <td>...</td>\n",
" <td>2.697017e+08</td>\n",
" <td>269701680.0</td>\n",
" <td>1.0</td>\n",
" <td>164.042627</td>\n",
" <td>NaN</td>\n",
" <td>164.042627</td>\n",
" <td>164.042627</td>\n",
" <td>164.042627</td>\n",
" <td>164.042627</td>\n",
" <td>164.042627</td>\n",
" </tr>\n",
" <tr>\n",
" <th>residential</th>\n",
" <td>1.0</td>\n",
" <td>97.773269</td>\n",
" <td>NaN</td>\n",
" <td>97.773269</td>\n",
" <td>97.773269</td>\n",
" <td>97.773269</td>\n",
" <td>97.773269</td>\n",
" <td>97.773269</td>\n",
" <td>1.0</td>\n",
" <td>9.761862e+01</td>\n",
" <td>...</td>\n",
" <td>2.703062e+08</td>\n",
" <td>270306179.0</td>\n",
" <td>1.0</td>\n",
" <td>39.543040</td>\n",
" <td>NaN</td>\n",
" <td>39.543040</td>\n",
" <td>39.543040</td>\n",
" <td>39.543040</td>\n",
" <td>39.543040</td>\n",
" <td>39.543040</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">13</th>\n",
" <th>other</th>\n",
" <td>1.0</td>\n",
" <td>606.305920</td>\n",
" <td>NaN</td>\n",
" <td>606.305920</td>\n",
" <td>606.305920</td>\n",
" <td>606.305920</td>\n",
" <td>606.305920</td>\n",
" <td>606.305920</td>\n",
" <td>1.0</td>\n",
" <td>6.050569e+02</td>\n",
" <td>...</td>\n",
" <td>3.027413e+08</td>\n",
" <td>302741260.0</td>\n",
" <td>1.0</td>\n",
" <td>108.349557</td>\n",
" <td>NaN</td>\n",
" <td>108.349557</td>\n",
" <td>108.349557</td>\n",
" <td>108.349557</td>\n",
" <td>108.349557</td>\n",
" <td>108.349557</td>\n",
" </tr>\n",
" <tr>\n",
" <th>residential</th>\n",
" <td>3.0</td>\n",
" <td>198.829456</td>\n",
" <td>92.272053</td>\n",
" <td>106.804693</td>\n",
" <td>152.570771</td>\n",
" <td>198.336849</td>\n",
" <td>244.841838</td>\n",
" <td>291.346827</td>\n",
" <td>3.0</td>\n",
" <td>1.984457e+02</td>\n",
" <td>...</td>\n",
" <td>3.027444e+08</td>\n",
" <td>302744717.0</td>\n",
" <td>3.0</td>\n",
" <td>59.760548</td>\n",
" <td>15.218449</td>\n",
" <td>43.366174</td>\n",
" <td>52.922449</td>\n",
" <td>62.478725</td>\n",
" <td>67.957735</td>\n",
" <td>73.436746</td>\n",
" </tr>\n",
" <tr>\n",
" <th>warehouse</th>\n",
" <td>2.0</td>\n",
" <td>236.589180</td>\n",
" <td>19.061928</td>\n",
" <td>223.110362</td>\n",
" <td>229.849771</td>\n",
" <td>236.589180</td>\n",
" <td>243.328589</td>\n",
" <td>250.067999</td>\n",
" <td>2.0</td>\n",
" <td>2.361424e+02</td>\n",
" <td>...</td>\n",
" <td>3.027445e+08</td>\n",
" <td>302744718.0</td>\n",
" <td>2.0</td>\n",
" <td>63.935222</td>\n",
" <td>5.635799</td>\n",
" <td>59.950110</td>\n",
" <td>61.942666</td>\n",
" <td>63.935222</td>\n",
" <td>65.927778</td>\n",
" <td>67.920334</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <th>industrial</th>\n",
" <td>2.0</td>\n",
" <td>28630.388591</td>\n",
" <td>40142.208794</td>\n",
" <td>245.560541</td>\n",
" <td>14437.974566</td>\n",
" <td>28630.388591</td>\n",
" <td>42822.802616</td>\n",
" <td>57015.216641</td>\n",
" <td>2.0</td>\n",
" <td>2.857195e+04</td>\n",
" <td>...</td>\n",
" <td>2.108386e+08</td>\n",
" <td>270306167.0</td>\n",
" <td>2.0</td>\n",
" <td>888.977046</td>\n",
" <td>1159.835068</td>\n",
" <td>68.849804</td>\n",
" <td>478.913425</td>\n",
" <td>888.977046</td>\n",
" <td>1299.040666</td>\n",
" <td>1709.104287</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">16</th>\n",
" <th>farm</th>\n",
" <td>1.0</td>\n",
" <td>696.000014</td>\n",
" <td>NaN</td>\n",
" <td>696.000014</td>\n",
" <td>696.000014</td>\n",
" <td>696.000014</td>\n",
" <td>696.000014</td>\n",
" <td>696.000014</td>\n",
" <td>1.0</td>\n",
" <td>6.945302e+02</td>\n",
" <td>...</td>\n",
" <td>3.027447e+08</td>\n",
" <td>302744715.0</td>\n",
" <td>1.0</td>\n",
" <td>131.033367</td>\n",
" <td>NaN</td>\n",
" <td>131.033367</td>\n",
" <td>131.033367</td>\n",
" <td>131.033367</td>\n",
" <td>131.033367</td>\n",
" <td>131.033367</td>\n",
" </tr>\n",
" <tr>\n",
" <th>residential</th>\n",
" <td>2.0</td>\n",
" <td>117.289386</td>\n",
" <td>85.934256</td>\n",
" <td>56.524691</td>\n",
" <td>86.907039</td>\n",
" <td>117.289386</td>\n",
" <td>147.671734</td>\n",
" <td>178.054081</td>\n",
" <td>2.0</td>\n",
" <td>1.170470e+02</td>\n",
" <td>...</td>\n",
" <td>3.027447e+08</td>\n",
" <td>302744716.0</td>\n",
" <td>2.0</td>\n",
" <td>43.691846</td>\n",
" <td>15.588828</td>\n",
" <td>32.668880</td>\n",
" <td>38.180363</td>\n",
" <td>43.691846</td>\n",
" <td>49.203329</td>\n",
" <td>54.714812</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <th>residential</th>\n",
" <td>2.0</td>\n",
" <td>242.578550</td>\n",
" <td>107.193653</td>\n",
" <td>166.781191</td>\n",
" <td>204.679871</td>\n",
" <td>242.578550</td>\n",
" <td>280.477230</td>\n",
" <td>318.375909</td>\n",
" <td>2.0</td>\n",
" <td>2.416488e+02</td>\n",
" <td>...</td>\n",
" <td>2.703062e+08</td>\n",
" <td>270306182.0</td>\n",
" <td>2.0</td>\n",
" <td>66.187342</td>\n",
" <td>19.827973</td>\n",
" <td>52.166848</td>\n",
" <td>59.177095</td>\n",
" <td>66.187342</td>\n",
" <td>73.197589</td>\n",
" <td>80.207836</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">19</th>\n",
" <th>residential</th>\n",
" <td>4.0</td>\n",
" <td>311.255995</td>\n",
" <td>270.367952</td>\n",
" <td>31.230764</td>\n",
" <td>121.162559</td>\n",
" <td>293.297178</td>\n",
" <td>483.390614</td>\n",
" <td>627.198860</td>\n",
" <td>4.0</td>\n",
" <td>3.106302e+02</td>\n",
" <td>...</td>\n",
" <td>3.028779e+08</td>\n",
" <td>302877881.0</td>\n",
" <td>4.0</td>\n",
" <td>76.571112</td>\n",
" <td>48.644461</td>\n",
" <td>22.499745</td>\n",
" <td>43.855169</td>\n",
" <td>77.109826</td>\n",
" <td>109.825770</td>\n",
" <td>129.565051</td>\n",
" </tr>\n",
" <tr>\n",
" <th>warehouse</th>\n",
" <td>1.0</td>\n",
" <td>51.625470</td>\n",
" <td>NaN</td>\n",
" <td>51.625470</td>\n",
" <td>51.625470</td>\n",
" <td>51.625470</td>\n",
" <td>51.625470</td>\n",
" <td>51.625470</td>\n",
" <td>1.0</td>\n",
" <td>5.129327e+01</td>\n",
" <td>...</td>\n",
" <td>3.028779e+08</td>\n",
" <td>302877882.0</td>\n",
" <td>1.0</td>\n",
" <td>30.585017</td>\n",
" <td>NaN</td>\n",
" <td>30.585017</td>\n",
" <td>30.585017</td>\n",
" <td>30.585017</td>\n",
" <td>30.585017</td>\n",
" <td>30.585017</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">28</th>\n",
" <th>commercial</th>\n",
" <td>1.0</td>\n",
" <td>626.065086</td>\n",
" <td>NaN</td>\n",
" <td>626.065086</td>\n",
" <td>626.065086</td>\n",
" <td>626.065086</td>\n",
" <td>626.065086</td>\n",
" <td>626.065086</td>\n",
" <td>1.0</td>\n",
" <td>6.248344e+02</td>\n",
" <td>...</td>\n",
" <td>3.213150e+07</td>\n",
" <td>32131503.0</td>\n",
" <td>1.0</td>\n",
" <td>150.386199</td>\n",
" <td>NaN</td>\n",
" <td>150.386199</td>\n",
" <td>150.386199</td>\n",
" <td>150.386199</td>\n",
" <td>150.386199</td>\n",
" <td>150.386199</td>\n",
" </tr>\n",
" <tr>\n",
" <th>residential</th>\n",
" <td>2.0</td>\n",
" <td>136.662383</td>\n",
" <td>63.588313</td>\n",
" <td>91.698656</td>\n",
" <td>114.180519</td>\n",
" <td>136.662383</td>\n",
" <td>159.144247</td>\n",
" <td>181.626110</td>\n",
" <td>2.0</td>\n",
" <td>1.364283e+02</td>\n",
" <td>...</td>\n",
" <td>3.213151e+07</td>\n",
" <td>32131513.0</td>\n",
" <td>2.0</td>\n",
" <td>66.749869</td>\n",
" <td>15.978725</td>\n",
" <td>55.451204</td>\n",
" <td>61.100537</td>\n",
" <td>66.749869</td>\n",
" <td>72.399202</td>\n",
" <td>78.048534</td>\n",
" </tr>\n",
" <tr>\n",
" <th>retail</th>\n",
" <td>1.0</td>\n",
" <td>1606.707165</td>\n",
" <td>NaN</td>\n",
" <td>1606.707165</td>\n",
" <td>1606.707165</td>\n",
" <td>1606.707165</td>\n",
" <td>1606.707165</td>\n",
" <td>1606.707165</td>\n",
" <td>1.0</td>\n",
" <td>1.603552e+03</td>\n",
" <td>...</td>\n",
" <td>2.748243e+07</td>\n",
" <td>27482427.0</td>\n",
" <td>1.0</td>\n",
" <td>183.319654</td>\n",
" <td>NaN</td>\n",
" <td>183.319654</td>\n",
" <td>183.319654</td>\n",
" <td>183.319654</td>\n",
" <td>183.319654</td>\n",
" <td>183.319654</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>40 rows × 32 columns</p>\n",
"</div>"
],
"text/plain": [
" AREA \\\n",
" count mean std min \n",
"nearest type \n",
"0 farm 1.0 613.107966 NaN 613.107966 \n",
" other 1.0 519.074015 NaN 519.074015 \n",
" residential 3.0 239.346601 30.872299 205.464289 \n",
"1 farm 1.0 2115.137359 NaN 2115.137359 \n",
" residential 4.0 209.959588 75.186861 142.504014 \n",
"2 farm 2.0 694.419395 116.201162 612.252765 \n",
" residential 8.0 205.324196 120.489247 98.398435 \n",
"3 farm 2.0 606.588576 290.706853 401.027789 \n",
" other 1.0 288.162026 NaN 288.162026 \n",
" residential 23.0 225.786187 118.602536 34.980934 \n",
"4 farm 1.0 897.389164 NaN 897.389164 \n",
" hotel 1.0 1772.569197 NaN 1772.569197 \n",
" other 1.0 875.885988 NaN 875.885988 \n",
" residential 15.0 259.377472 202.675034 42.900938 \n",
"5 farm 1.0 542.460212 NaN 542.460212 \n",
" other 2.0 565.674526 276.900248 369.876483 \n",
" residential 10.0 159.083292 97.777833 56.673996 \n",
"7 residential 2.0 320.631344 23.164990 304.251223 \n",
" warehouse 2.0 110.165182 1.281146 109.259275 \n",
"8 farm 1.0 1336.139222 NaN 1336.139222 \n",
" other 1.0 322.006706 NaN 322.006706 \n",
" residential 2.0 275.931534 286.854827 73.094540 \n",
"9 warehouse 1.0 404.260374 NaN 404.260374 \n",
"11 other 1.0 270.482948 NaN 270.482948 \n",
" residential 1.0 250.160405 NaN 250.160405 \n",
" warehouse 2.0 93.384707 42.742733 63.161030 \n",
"12 industrial 1.0 1443.309281 NaN 1443.309281 \n",
" residential 1.0 97.773269 NaN 97.773269 \n",
"13 other 1.0 606.305920 NaN 606.305920 \n",
" residential 3.0 198.829456 92.272053 106.804693 \n",
" warehouse 2.0 236.589180 19.061928 223.110362 \n",
"14 industrial 2.0 28630.388591 40142.208794 245.560541 \n",
"16 farm 1.0 696.000014 NaN 696.000014 \n",
" residential 2.0 117.289386 85.934256 56.524691 \n",
"18 residential 2.0 242.578550 107.193653 166.781191 \n",
"19 residential 4.0 311.255995 270.367952 31.230764 \n",
" warehouse 1.0 51.625470 NaN 51.625470 \n",
"28 commercial 1.0 626.065086 NaN 626.065086 \n",
" residential 2.0 136.662383 63.588313 91.698656 \n",
" retail 1.0 1606.707165 NaN 1606.707165 \n",
"\n",
" \\\n",
" 25% 50% 75% max \n",
"nearest type \n",
"0 farm 613.107966 613.107966 613.107966 613.107966 \n",
" other 519.074015 519.074015 519.074015 519.074015 \n",
" residential 226.077832 246.691375 256.287757 265.884139 \n",
"1 farm 2115.137359 2115.137359 2115.137359 2115.137359 \n",
" residential 152.647210 197.012138 254.324516 303.310062 \n",
"2 farm 653.336080 694.419395 735.502709 776.586024 \n",
" residential 106.558282 164.345127 265.589578 397.912785 \n",
"3 farm 503.808182 606.588576 709.368969 812.149362 \n",
" other 288.162026 288.162026 288.162026 288.162026 \n",
" residential 162.674701 229.366828 293.652306 468.258848 \n",
"4 farm 897.389164 897.389164 897.389164 897.389164 \n",
" hotel 1772.569197 1772.569197 1772.569197 1772.569197 \n",
" other 875.885988 875.885988 875.885988 875.885988 \n",
" residential 94.067225 243.549398 364.255829 748.319821 \n",
"5 farm 542.460212 542.460212 542.460212 542.460212 \n",
" other 467.775504 565.674526 663.573547 761.472569 \n",
" residential 82.480964 130.246117 231.299164 317.240462 \n",
"7 residential 312.441283 320.631344 328.821405 337.011466 \n",
" warehouse 109.712229 110.165182 110.618136 111.071089 \n",
"8 farm 1336.139222 1336.139222 1336.139222 1336.139222 \n",
" other 322.006706 322.006706 322.006706 322.006706 \n",
" residential 174.513037 275.931534 377.350030 478.768527 \n",
"9 warehouse 404.260374 404.260374 404.260374 404.260374 \n",
"11 other 270.482948 270.482948 270.482948 270.482948 \n",
" residential 250.160405 250.160405 250.160405 250.160405 \n",
" warehouse 78.272868 93.384707 108.496545 123.608383 \n",
"12 industrial 1443.309281 1443.309281 1443.309281 1443.309281 \n",
" residential 97.773269 97.773269 97.773269 97.773269 \n",
"13 other 606.305920 606.305920 606.305920 606.305920 \n",
" residential 152.570771 198.336849 244.841838 291.346827 \n",
" warehouse 229.849771 236.589180 243.328589 250.067999 \n",
"14 industrial 14437.974566 28630.388591 42822.802616 57015.216641 \n",
"16 farm 696.000014 696.000014 696.000014 696.000014 \n",
" residential 86.907039 117.289386 147.671734 178.054081 \n",
"18 residential 204.679871 242.578550 280.477230 318.375909 \n",
"19 residential 121.162559 293.297178 483.390614 627.198860 \n",
" warehouse 51.625470 51.625470 51.625470 51.625470 \n",
"28 commercial 626.065086 626.065086 626.065086 626.065086 \n",
" residential 114.180519 136.662383 159.144247 181.626110 \n",
" retail 1606.707165 1606.707165 1606.707165 1606.707165 \n",
"\n",
" area ... osm_way_id \\\n",
" count mean ... 75% \n",
"nearest type ... \n",
"0 farm 1.0 6.118382e+02 ... 2.816692e+08 \n",
" other 1.0 5.179353e+02 ... 2.815083e+08 \n",
" residential 3.0 2.389415e+02 ... 2.816692e+08 \n",
"1 farm 1.0 2.110956e+03 ... 2.815083e+08 \n",
" residential 4.0 2.095329e+02 ... 2.815083e+08 \n",
"2 farm 2.0 6.932345e+02 ... 2.816894e+08 \n",
" residential 8.0 2.048858e+02 ... 2.816894e+08 \n",
"3 farm 2.0 6.052987e+02 ... 2.816894e+08 \n",
" other 1.0 2.875666e+02 ... 2.816894e+08 \n",
" residential 23.0 2.254115e+02 ... 2.816894e+08 \n",
"4 farm 1.0 1.396132e+07 ... 2.817009e+08 \n",
" hotel 1.0 1.768936e+03 ... 2.816692e+08 \n",
" other 1.0 8.741183e+02 ... 2.816894e+08 \n",
" residential 15.0 3.608283e+05 ... 2.817009e+08 \n",
"5 farm 1.0 5.414658e+02 ... 2.817008e+08 \n",
" other 2.0 5.644801e+02 ... 2.817009e+08 \n",
" residential 10.0 1.587249e+02 ... 2.817009e+08 \n",
"7 residential 2.0 3.200414e+02 ... 2.945240e+08 \n",
" warehouse 2.0 1.099617e+02 ... 2.699065e+08 \n",
"8 farm 1.0 1.333456e+03 ... 3.027298e+08 \n",
" other 1.0 3.213029e+02 ... 3.027298e+08 \n",
" residential 2.0 2.753217e+02 ... 3.027298e+08 \n",
"9 warehouse 1.0 4.034089e+02 ... 3.027298e+08 \n",
"11 other 1.0 2.699605e+02 ... 3.027413e+08 \n",
" residential 1.0 2.496481e+02 ... 3.027298e+08 \n",
" warehouse 2.0 9.320389e+01 ... 3.027411e+08 \n",
"12 industrial 1.0 1.440611e+03 ... 2.697017e+08 \n",
" residential 1.0 9.761862e+01 ... 2.703062e+08 \n",
"13 other 1.0 6.050569e+02 ... 3.027413e+08 \n",
" residential 3.0 1.984457e+02 ... 3.027444e+08 \n",
" warehouse 2.0 2.361424e+02 ... 3.027445e+08 \n",
"14 industrial 2.0 2.857195e+04 ... 2.108386e+08 \n",
"16 farm 1.0 6.945302e+02 ... 3.027447e+08 \n",
" residential 2.0 1.170470e+02 ... 3.027447e+08 \n",
"18 residential 2.0 2.416488e+02 ... 2.703062e+08 \n",
"19 residential 4.0 3.106302e+02 ... 3.028779e+08 \n",
" warehouse 1.0 5.129327e+01 ... 3.028779e+08 \n",
"28 commercial 1.0 6.248344e+02 ... 3.213150e+07 \n",
" residential 2.0 1.364283e+02 ... 3.213151e+07 \n",
" retail 1.0 1.603552e+03 ... 2.748243e+07 \n",
"\n",
" perimeter \\\n",
" max count mean std \n",
"nearest type \n",
"0 farm 281669205.0 1.0 126.744403 NaN \n",
" other 281508305.0 1.0 107.276628 NaN \n",
" residential 281669209.0 3.0 62.853104 4.143033 \n",
"1 farm 281508306.0 1.0 224.696828 NaN \n",
" residential 281508313.0 4.0 58.663606 11.891804 \n",
"2 farm 281689428.0 2.0 124.666924 15.093892 \n",
" residential 302586968.0 8.0 60.975753 22.800008 \n",
"3 farm 281689439.0 2.0 115.942788 32.690592 \n",
" other 281689438.0 1.0 69.650115 NaN \n",
" residential 303596049.0 23.0 60.980293 19.623507 \n",
"4 farm 281700854.0 1.0 139.912865 NaN \n",
" hotel 281669211.0 1.0 273.758075 NaN \n",
" other 281689434.0 1.0 125.234574 NaN \n",
" residential 302586970.0 15.0 67.784448 32.779619 \n",
"5 farm 281700849.0 1.0 99.826185 NaN \n",
" other 281700851.0 2.0 102.352744 32.953477 \n",
" residential 281700862.0 10.0 50.668861 17.602476 \n",
"7 residential 302729800.0 2.0 72.972374 1.961609 \n",
" warehouse 269906533.0 2.0 43.057298 1.378396 \n",
"8 farm 302729801.0 1.0 187.509876 NaN \n",
" other 302729804.0 1.0 79.702736 NaN \n",
" residential 302729806.0 2.0 68.533906 48.110341 \n",
"9 warehouse 302729802.0 1.0 96.428673 NaN \n",
"11 other 302741259.0 1.0 68.918471 NaN \n",
" residential 302729805.0 1.0 73.133029 NaN \n",
" warehouse 302741258.0 2.0 38.693779 8.989685 \n",
"12 industrial 269701680.0 1.0 164.042627 NaN \n",
" residential 270306179.0 1.0 39.543040 NaN \n",
"13 other 302741260.0 1.0 108.349557 NaN \n",
" residential 302744717.0 3.0 59.760548 15.218449 \n",
" warehouse 302744718.0 2.0 63.935222 5.635799 \n",
"14 industrial 270306167.0 2.0 888.977046 1159.835068 \n",
"16 farm 302744715.0 1.0 131.033367 NaN \n",
" residential 302744716.0 2.0 43.691846 15.588828 \n",
"18 residential 270306182.0 2.0 66.187342 19.827973 \n",
"19 residential 302877881.0 4.0 76.571112 48.644461 \n",
" warehouse 302877882.0 1.0 30.585017 NaN \n",
"28 commercial 32131503.0 1.0 150.386199 NaN \n",
" residential 32131513.0 2.0 66.749869 15.978725 \n",
" retail 27482427.0 1.0 183.319654 NaN \n",
"\n",
" \\\n",
" min 25% 50% 75% \n",
"nearest type \n",
"0 farm 126.744403 126.744403 126.744403 126.744403 \n",
" other 107.276628 107.276628 107.276628 107.276628 \n",
" residential 58.163383 61.271527 64.379670 65.197965 \n",
"1 farm 224.696828 224.696828 224.696828 224.696828 \n",
" residential 47.904944 49.864683 56.500447 65.299370 \n",
"2 farm 113.993930 119.330427 124.666924 130.003420 \n",
" residential 41.034070 44.039173 53.460730 68.926916 \n",
"3 farm 92.827048 104.384918 115.942788 127.500657 \n",
" other 69.650115 69.650115 69.650115 69.650115 \n",
" residential 24.948542 51.522762 61.193566 76.850867 \n",
"4 farm 139.912865 139.912865 139.912865 139.912865 \n",
" hotel 273.758075 273.758075 273.758075 273.758075 \n",
" other 125.234574 125.234574 125.234574 125.234574 \n",
" residential 26.226804 38.770922 66.563069 91.892214 \n",
"5 farm 99.826185 99.826185 99.826185 99.826185 \n",
" other 79.051117 90.701931 102.352744 114.003557 \n",
" residential 31.701653 37.101670 47.017746 60.850418 \n",
"7 residential 71.585307 72.278841 72.972374 73.665908 \n",
" warehouse 42.082625 42.569961 43.057298 43.544635 \n",
"8 farm 187.509876 187.509876 187.509876 187.509876 \n",
" other 79.702736 79.702736 79.702736 79.702736 \n",
" residential 34.514757 51.524331 68.533906 85.543480 \n",
"9 warehouse 96.428673 96.428673 96.428673 96.428673 \n",
"11 other 68.918471 68.918471 68.918471 68.918471 \n",
" residential 73.133029 73.133029 73.133029 73.133029 \n",
" warehouse 32.337112 35.515445 38.693779 41.872113 \n",
"12 industrial 164.042627 164.042627 164.042627 164.042627 \n",
" residential 39.543040 39.543040 39.543040 39.543040 \n",
"13 other 108.349557 108.349557 108.349557 108.349557 \n",
" residential 43.366174 52.922449 62.478725 67.957735 \n",
" warehouse 59.950110 61.942666 63.935222 65.927778 \n",
"14 industrial 68.849804 478.913425 888.977046 1299.040666 \n",
"16 farm 131.033367 131.033367 131.033367 131.033367 \n",
" residential 32.668880 38.180363 43.691846 49.203329 \n",
"18 residential 52.166848 59.177095 66.187342 73.197589 \n",
"19 residential 22.499745 43.855169 77.109826 109.825770 \n",
" warehouse 30.585017 30.585017 30.585017 30.585017 \n",
"28 commercial 150.386199 150.386199 150.386199 150.386199 \n",
" residential 55.451204 61.100537 66.749869 72.399202 \n",
" retail 183.319654 183.319654 183.319654 183.319654 \n",
"\n",
" \n",
" max \n",
"nearest type \n",
"0 farm 126.744403 \n",
" other 107.276628 \n",
" residential 66.016260 \n",
"1 farm 224.696828 \n",
" residential 73.748587 \n",
"2 farm 135.339917 \n",
" residential 98.905103 \n",
"3 farm 139.058527 \n",
" other 69.650115 \n",
" residential 96.669758 \n",
"4 farm 139.912865 \n",
" hotel 273.758075 \n",
" other 125.234574 \n",
" residential 133.012831 \n",
"5 farm 99.826185 \n",
" other 125.654371 \n",
" residential 83.874135 \n",
"7 residential 74.359441 \n",
" warehouse 44.031971 \n",
"8 farm 187.509876 \n",
" other 79.702736 \n",
" residential 102.553054 \n",
"9 warehouse 96.428673 \n",
"11 other 68.918471 \n",
" residential 73.133029 \n",
" warehouse 45.050446 \n",
"12 industrial 164.042627 \n",
" residential 39.543040 \n",
"13 other 108.349557 \n",
" residential 73.436746 \n",
" warehouse 67.920334 \n",
"14 industrial 1709.104287 \n",
"16 farm 131.033367 \n",
" residential 54.714812 \n",
"18 residential 80.207836 \n",
"19 residential 129.565051 \n",
" warehouse 30.585017 \n",
"28 commercial 150.386199 \n",
" residential 78.048534 \n",
" retail 183.319654 \n",
"\n",
"[40 rows x 32 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"buildings_grouped = buildings.groupby(['nearest', 'type'])\n",
"buildings_grouped.describe().head(40)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>area</th>\n",
" <th>osm_way_id</th>\n",
" <th>perimeter</th>\n",
" <th>AREA</th>\n",
" </tr>\n",
" <tr>\n",
" <th>nearest</th>\n",
" <th>type</th>\n",
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" </tr>\n",
" </thead>\n",
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" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">0</th>\n",
" <th>farm</th>\n",
" <td>611.838159</td>\n",
" <td>281669205</td>\n",
" <td>126.744403</td>\n",
" <td>613.107966</td>\n",
" </tr>\n",
" <tr>\n",
" <th>other</th>\n",
" <td>517.935265</td>\n",
" <td>281508305</td>\n",
" <td>107.276628</td>\n",
" <td>519.074015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>residential</th>\n",
" <td>716.824605</td>\n",
" <td>844846723</td>\n",
" <td>188.559313</td>\n",
" <td>718.039803</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">1</th>\n",
" <th>farm</th>\n",
" <td>2110.955784</td>\n",
" <td>281508306</td>\n",
" <td>224.696828</td>\n",
" <td>2115.137359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>residential</th>\n",
" <td>838.131703</td>\n",
" <td>1126033245</td>\n",
" <td>234.654424</td>\n",
" <td>839.838351</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">2</th>\n",
" <th>farm</th>\n",
" <td>1386.469064</td>\n",
" <td>563378849</td>\n",
" <td>249.333847</td>\n",
" <td>1388.838789</td>\n",
" </tr>\n",
" <tr>\n",
" <th>residential</th>\n",
" <td>1639.086737</td>\n",
" <td>2274372479</td>\n",
" <td>487.806022</td>\n",
" <td>1642.593569</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <th>farm</th>\n",
" <td>1210.597372</td>\n",
" <td>563378872</td>\n",
" <td>231.885575</td>\n",
" <td>1213.177151</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" area osm_way_id perimeter AREA\n",
"nearest type \n",
"0 farm 611.838159 281669205 126.744403 613.107966\n",
" other 517.935265 281508305 107.276628 519.074015\n",
" residential 716.824605 844846723 188.559313 718.039803\n",
"1 farm 2110.955784 281508306 224.696828 2115.137359\n",
" residential 838.131703 1126033245 234.654424 839.838351\n",
"2 farm 1386.469064 563378849 249.333847 1388.838789\n",
" residential 1639.086737 2274372479 487.806022 1642.593569\n",
"3 farm 1210.597372 563378872 231.885575 1213.177151"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# split up total_area = buildings_grouped.sum()['AREA'].unstack()\n",
"bgsum = buildings_grouped.sum()\n",
"bgsum.head(8)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"nearest type \n",
"0 farm 613.107966\n",
" other 519.074015\n",
" residential 718.039803\n",
"1 farm 2115.137359\n",
" residential 839.838351\n",
"2 farm 1388.838789\n",
" residential 1642.593569\n",
"3 farm 1213.177151\n",
" other 288.162026\n",
" residential 5193.082310\n",
"4 farm 897.389164\n",
" hotel 1772.569197\n",
" other 875.885988\n",
" residential 3890.662073\n",
"5 farm 542.460212\n",
" other 1131.349052\n",
" residential 1590.832921\n",
"7 residential 641.262688\n",
" warehouse 220.330364\n",
"8 farm 1336.139222\n",
"Name: AREA, dtype: float64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bgsum['AREA'].head(20)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" <th>type</th>\n",
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" <td>NaN</td>\n",
" <td>1642.593569</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>NaN</td>\n",
" <td>1213.177151</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>288.162026</td>\n",
" <td>NaN</td>\n",
" <td>5193.082310</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1772.569197</td>\n",
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" <td>NaN</td>\n",
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],
"text/plain": [
"type commercial farm garage hospital hotel industrial \\\n",
"nearest \n",
"0 NaN 613.107966 NaN NaN NaN NaN \n",
"1 NaN 2115.137359 NaN NaN NaN NaN \n",
"2 NaN 1388.838789 NaN NaN NaN NaN \n",
"3 NaN 1213.177151 NaN NaN NaN NaN \n",
"4 NaN 897.389164 NaN NaN 1772.569197 NaN \n",
"\n",
"type other public residential retail warehouse \n",
"nearest \n",
"0 519.074015 NaN 718.039803 NaN NaN \n",
"1 NaN NaN 839.838351 NaN NaN \n",
"2 NaN NaN 1642.593569 NaN NaN \n",
"3 288.162026 NaN 5193.082310 NaN NaN \n",
"4 875.885988 NaN 3890.662073 NaN NaN "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"total_area = bgsum['AREA'].unstack()\n",
"total_area.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"scrolled": true
},
"outputs": [
{
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" <th>4</th>\n",
" <td>Moosham</td>\n",
" <td>4</td>\n",
" <td>45</td>\n",
" <td>109</td>\n",
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],
"text/plain": [
" Cluster Edge Vertex1 Vertex2 \\\n",
"0 Moosham 0 31 123 \n",
"1 Moosham 1 4 31 \n",
"2 Moosham 2 23 31 \n",
"3 Moosham 3 23 109 \n",
"4 Moosham 4 45 109 \n",
"\n",
" geometry length x0 \\\n",
"0 LINESTRING (12.13846526289267 48.1869855970568... 160.220094 733255.29 \n",
"1 LINESTRING (12.14060007803448 48.1867898718811... 142.647898 733414.83 \n",
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"4 LINESTRING (12.14558850186704 48.1862331650699... 173.175415 733788.07 \n",
"\n",
" x1 y0 y1 \n",
"0 733414.83 5341846.97 5341831.71 \n",
"1 733353.52 5341831.71 5341702.86 \n",
"2 733499.78 5341831.71 5341795.57 \n",
"3 733499.78 5341785.01 5341795.57 \n",
"4 733639.65 5341785.01 5341874.33 "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# load edges (streets) and join with summed areas\n",
"# 1. read shapefile to DataFrame (with geometry column)\n",
"# 2. join DataFrame total_area on index (=ID)\n",
"# 3. fill missing values with 0\n",
"edge = geopandas.read_file(edge_shapefile)\n",
"edge.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" <th>3</th>\n",
" <td>Moosham</td>\n",
" <td>23</td>\n",
" <td>109</td>\n",
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" <td>5341795.57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Moosham</td>\n",
" <td>45</td>\n",
" <td>109</td>\n",
" <td>LINESTRING (12.14558850186704 48.1862331650699...</td>\n",
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"</table>\n",
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],
"text/plain": [
" Cluster Vertex1 Vertex2 \\\n",
"Edge \n",
"0 Moosham 31 123 \n",
"1 Moosham 4 31 \n",
"2 Moosham 23 31 \n",
"3 Moosham 23 109 \n",
"4 Moosham 45 109 \n",
"\n",
" geometry length \\\n",
"Edge \n",
"0 LINESTRING (12.13846526289267 48.1869855970568... 160.220094 \n",
"1 LINESTRING (12.14060007803448 48.1867898718811... 142.647898 \n",
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"4 LINESTRING (12.14558850186704 48.1862331650699... 173.175415 \n",
"\n",
" x0 x1 y0 y1 \n",
"Edge \n",
"0 733255.29 733414.83 5341846.97 5341831.71 \n",
"1 733414.83 733353.52 5341831.71 5341702.86 \n",
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]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"edge = edge.set_index('Edge')\n",
"edge.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
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" Cluster Vertex1 Vertex2 \\\n",
"Edge \n",
"0 Moosham 31 123 \n",
"1 Moosham 4 31 \n",
"2 Moosham 23 31 \n",
"3 Moosham 23 109 \n",
"4 Moosham 45 109 \n",
"\n",
" geometry length \\\n",
"Edge \n",
"0 LINESTRING (12.13846526289267 48.1869855970568... 160.220094 \n",
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"4 LINESTRING (12.14558850186704 48.1862331650699... 173.175415 \n",
"\n",
" x0 x1 y0 y1 commercial farm \\\n",
"Edge \n",
"0 733255.29 733414.83 5341846.97 5341831.71 NaN 613.107966 \n",
"1 733414.83 733353.52 5341831.71 5341702.86 NaN 2115.137359 \n",
"2 733414.83 733499.78 5341831.71 5341795.57 NaN 1388.838789 \n",
"3 733788.07 733499.78 5341785.01 5341795.57 NaN 1213.177151 \n",
"4 733788.07 733639.65 5341785.01 5341874.33 NaN 897.389164 \n",
"\n",
" garage hospital hotel industrial other public \\\n",
"Edge \n",
"0 NaN NaN NaN NaN 519.074015 NaN \n",
"1 NaN NaN NaN NaN NaN NaN \n",
"2 NaN NaN NaN NaN NaN NaN \n",
"3 NaN NaN NaN NaN 288.162026 NaN \n",
"4 NaN NaN 1772.569197 NaN 875.885988 NaN \n",
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" residential retail warehouse \n",
"Edge \n",
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"2 1642.593569 NaN NaN \n",
"3 5193.082310 NaN NaN \n",
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]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"edge = edge.join(total_area)\n",
"edge.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
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" Cluster Vertex1 Vertex2 \\\n",
"Edge \n",
"0 Moosham 31 123 \n",
"1 Moosham 4 31 \n",
"2 Moosham 23 31 \n",
"3 Moosham 23 109 \n",
"4 Moosham 45 109 \n",
"\n",
" geometry length \\\n",
"Edge \n",
"0 LINESTRING (12.13846526289267 48.1869855970568... 160.220094 \n",
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"4 LINESTRING (12.14558850186704 48.1862331650699... 173.175415 \n",
"\n",
" x0 x1 y0 y1 commercial farm \\\n",
"Edge \n",
"0 733255.29 733414.83 5341846.97 5341831.71 0.0 613.107966 \n",
"1 733414.83 733353.52 5341831.71 5341702.86 0.0 2115.137359 \n",
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"4 733788.07 733639.65 5341785.01 5341874.33 0.0 897.389164 \n",
"\n",
" garage hospital hotel industrial other public \\\n",
"Edge \n",
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"3 0.0 0.0 0.000000 0.0 288.162026 0.0 \n",
"4 0.0 0.0 1772.569197 0.0 875.885988 0.0 \n",
"\n",
" residential retail warehouse \n",
"Edge \n",
"0 718.039803 0.0 0.0 \n",
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]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"edge = edge.fillna(0)\n",
"edge.head(5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
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@lnksz
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Author

lnksz commented Sep 3, 2017

If you get an error from fiona, during geopandas.read_file(buildings) check your buildings.cpg file, It should contain UTF-8 and nothing else. (Esp. not System)

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