IoB_08_Singa_enrichtable.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b389ec0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "390752b6",
   "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>osmid</th>\n",
       "      <th>amenity</th>\n",
       "      <th>name</th>\n",
       "      <th>building</th>\n",
       "      <th>addr_house</th>\n",
       "      <th>addr_stree</th>\n",
       "      <th>addr_hou_1</th>\n",
       "      <th>addr_postc</th>\n",
       "      <th>building_l</th>\n",
       "      <th>start_date</th>\n",
       "      <th>...</th>\n",
       "      <th>height</th>\n",
       "      <th>roof_colou</th>\n",
       "      <th>roof_mater</th>\n",
       "      <th>roof_shape</th>\n",
       "      <th>residentia</th>\n",
       "      <th>roof_level</th>\n",
       "      <th>addr_neigh</th>\n",
       "      <th>build_prog</th>\n",
       "      <th>build_ager</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30527021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sim Lim Square</td>\n",
       "      <td>retail</td>\n",
       "      <td>1</td>\n",
       "      <td>Rochor Canal Road</td>\n",
       "      <td>NaN</td>\n",
       "      <td>188504</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>deco_box</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((103.85308 1.30254, 103.85302 1.30259...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32342169</td>\n",
       "      <td>NaN</td>\n",
       "      <td>National Museum of Singapore</td>\n",
       "      <td>public</td>\n",
       "      <td>93</td>\n",
       "      <td>Stamford Road</td>\n",
       "      <td>NaN</td>\n",
       "      <td>178897</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1887.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>civic</td>\n",
       "      <td>1875-1899</td>\n",
       "      <td>POLYGON ((103.84797 1.29710, 103.84808 1.29718...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32507125</td>\n",
       "      <td>police</td>\n",
       "      <td>Police Cantonment Complex</td>\n",
       "      <td>yes</td>\n",
       "      <td>391</td>\n",
       "      <td>New Bridge Road</td>\n",
       "      <td>NaN</td>\n",
       "      <td>088762</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000-2024</td>\n",
       "      <td>POLYGON ((103.83885 1.27855, 103.83912 1.27869...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32507157</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>office</td>\n",
       "      <td>11</td>\n",
       "      <td>Outram Road</td>\n",
       "      <td>NaN</td>\n",
       "      <td>169078</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>glass_box</td>\n",
       "      <td>1975-1999</td>\n",
       "      <td>POLYGON ((103.83794 1.28024, 103.83794 1.28044...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32507166</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>public</td>\n",
       "      <td>3</td>\n",
       "      <td>Second Hospital Avenue</td>\n",
       "      <td>Health Promotion Board Building</td>\n",
       "      <td>168937</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>civic</td>\n",
       "      <td>1975-1999</td>\n",
       "      <td>POLYGON ((103.83840 1.27965, 103.83823 1.27965...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      osmid amenity                          name building addr_house   \n",
       "0  30527021     NaN                Sim Lim Square   retail          1  \\\n",
       "1  32342169     NaN  National Museum of Singapore   public         93   \n",
       "2  32507125  police     Police Cantonment Complex      yes        391   \n",
       "3  32507157     NaN                           NaN   office         11   \n",
       "4  32507166     NaN                           NaN   public          3   \n",
       "\n",
       "               addr_stree                       addr_hou_1 addr_postc   \n",
       "0       Rochor Canal Road                              NaN     188504  \\\n",
       "1           Stamford Road                              NaN     178897   \n",
       "2         New Bridge Road                              NaN     088762   \n",
       "3             Outram Road                              NaN     169078   \n",
       "4  Second Hospital Avenue  Health Promotion Board Building     168937   \n",
       "\n",
       "  building_l  start_date  ... height roof_colou roof_mater roof_shape   \n",
       "0          6         NaN  ...    NaN        NaN        NaN        NaN  \\\n",
       "1        NaN      1887.0  ...    NaN        NaN        NaN        NaN   \n",
       "2        NaN      2001.0  ...      0        NaN        NaN        NaN   \n",
       "3        NaN      1981.0  ...      0        NaN        NaN        NaN   \n",
       "4        NaN      1981.0  ...      0        NaN        NaN        NaN   \n",
       "\n",
       "  residentia roof_level addr_neigh build_prog build_ager   \n",
       "0        NaN        NaN        NaN   deco_box        NaN  \\\n",
       "1        NaN        NaN        NaN      civic  1875-1899   \n",
       "2        NaN        NaN        NaN        NaN  2000-2024   \n",
       "3        NaN        NaN        NaN  glass_box  1975-1999   \n",
       "4        NaN        NaN        NaN      civic  1975-1999   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((103.85308 1.30254, 103.85302 1.30259...  \n",
       "1  POLYGON ((103.84797 1.29710, 103.84808 1.29718...  \n",
       "2  POLYGON ((103.83885 1.27855, 103.83912 1.27869...  \n",
       "3  POLYGON ((103.83794 1.28024, 103.83794 1.28044...  \n",
       "4  POLYGON ((103.83840 1.27965, 103.83823 1.27965...  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdfx = gpd.read_file(r'C:/WORK_IAAC/submissions/Internet of Buildings/IoB_WIP/IoB_QGIS/buildings/buildings_FIN.shp', encoding='latin-1')\n",
    "gdfx.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b4f2dfbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the count of non-NaN values for each column\n",
    "non_nan_count = gdfx.count()\n",
    "\n",
    "# create a bar plot of non-NaN counts with red color\n",
    "ax = non_nan_count.plot(kind='bar', color='red')\n",
    "\n",
    "# add the count percentage above each bar\n",
    "for i, v in enumerate(non_nan_count):\n",
    "    ax.text(i, v+5, str(round((v/gdfx.shape[0])*100, 2)) + '%', ha='center', rotation=90, size=8)\n",
    "    \n",
    "# set the x-label and y-label\n",
    "ax.set_xlabel('Columns')\n",
    "ax.set_ylabel('Number of non-NaN values')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "9da87d82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['SG', nan], dtype=object)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdfx['addr_count'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "eaa237ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "733"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdfx['residentia'].value_counts().get('hdb', 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f6ed54fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17670"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#replace building programs in column build_prog\n",
    "word1 = 'residential'\n",
    "word2 = 'resident_high'\n",
    "# mask = gdfx['name'].str.contains('Cafe', case=False)\n",
    "mask = gdfx['residentia'].notna()\n",
    "# gdfx[mask]\n",
    "gdfx.loc[mask & ((gdfx['building'] == 'yes') | (gdfx['building'] == 'no')), 'building'] = word1\n",
    "gdfx.loc[mask & ((gdfx['build_prog'].isna())), 'build_prog'] = word2\n",
    "\n",
    "gdfx['build_prog'].value_counts().get(word2, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fef252d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'police', 'food_court', 'hospital', 'library',\n",
       "       'place_of_worship', 'bank', 'social_facility', 'conference_centre',\n",
       "       'college', 'restaurant', 'parking', 'shelter', 'clinic', 'fuel',\n",
       "       'theatre', 'toilets', 'fast_food', 'cafe', 'nursing_home',\n",
       "       'childcare', 'music_school', 'bar', 'public_building',\n",
       "       'vehicle_inspection', 'fire_station', 'kindergarten',\n",
       "       'arts_centre', 'marketplace', 'pub', 'ferry_terminal',\n",
       "       'community_centre', 'dressing_room', 'car_wash', 'doctors',\n",
       "       'cinema', 'monastery', 'bus_station', 'school', 'studio',\n",
       "       'prep_school', 'car_rental', 'ice_cream', 'language_school',\n",
       "       'events_venue', 'shower', 'clubhouse', 'waste_transfer_station',\n",
       "       'stage', 'waste_disposal', 'pharmacy', 'atm', 'driving_school',\n",
       "       'commercial', 'crematorium', 'post_office', 'veterinary',\n",
       "       'dentist', 'social_centre', 'university', 'taxi', 'Clubhouse',\n",
       "       'townhall', 'bicycle_rental', 'bicycle_parking'], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdfx['amenity'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "b6d4e2ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "waste_disposal = 23, waste_transfer_station = 35\n"
     ]
    }
   ],
   "source": [
    "#check amount of waste facilites in amenities column\n",
    "w1=gdfx['amenity'].value_counts().get(amelist[49],0)\n",
    "w2=gdfx['amenity'].value_counts().get(amelist[47],0)\n",
    "print(f\"{amelist[49]} = {w2}, {amelist[47]} = {w1}\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9f3e023e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['deco_box', 'civic', 'glass_box', nan, 'industrial', 'medic',\n",
       "       'resident_high', 'resident_low', 'transport', 'religious',\n",
       "       'education', 'small_box', 'sport', 'ruins', 'construction_site'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdfx['build_prog'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "422a9ba7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'waste_disposal'"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amelist[49]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "9d758db3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "438"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#replace building programs in column build_prog\n",
    "amelist = gdfx['amenity'].unique()\n",
    "program_list = gdfx['build_prog'].unique()\n",
    "word = program_list[10]\n",
    "mask = gdfx['amenity'].str.contains(amelist[59], case=False)\n",
    "mask = mask.fillna(False)\n",
    "\n",
    "gdfx.loc[mask & ((gdfx['building'] == 'yes') | (gdfx['building'] == 'no')), 'building'] = word\n",
    "gdfx.loc[mask & ((gdfx['build_prog'].isna())), 'build_prog'] = word\n",
    "\n",
    "gdfx['build_prog'].value_counts().get(word, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "56280999",
   "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>osmid</th>\n",
       "      <th>amenity</th>\n",
       "      <th>name</th>\n",
       "      <th>building</th>\n",
       "      <th>addr_house</th>\n",
       "      <th>addr_stree</th>\n",
       "      <th>addr_hou_1</th>\n",
       "      <th>addr_postc</th>\n",
       "      <th>building_l</th>\n",
       "      <th>start_date</th>\n",
       "      <th>...</th>\n",
       "      <th>roof_colou</th>\n",
       "      <th>roof_mater</th>\n",
       "      <th>roof_shape</th>\n",
       "      <th>residentia</th>\n",
       "      <th>roof_level</th>\n",
       "      <th>addr_neigh</th>\n",
       "      <th>build_prog</th>\n",
       "      <th>build_ager</th>\n",
       "      <th>geometry</th>\n",
       "      <th>build_footprint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>104960240</td>\n",
       "      <td>nursing_home</td>\n",
       "      <td>Orange Valley Nursing Home</td>\n",
       "      <td>medic</td>\n",
       "      <td>221</td>\n",
       "      <td>Clementi Avenue 4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>129881</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>medic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((103.769 1.319, 103.769 1.319, 103.76...</td>\n",
       "      <td>6.935187e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15610</th>\n",
       "      <td>372138391</td>\n",
       "      <td>nursing_home</td>\n",
       "      <td>Saint Bernadette Lifestyle Village</td>\n",
       "      <td>residential</td>\n",
       "      <td>255B</td>\n",
       "      <td>Bukit Timah Road</td>\n",
       "      <td>NaN</td>\n",
       "      <td>259693</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>resident_high</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((103.837 1.315, 103.837 1.315, 103.83...</td>\n",
       "      <td>2.104512e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75384</th>\n",
       "      <td>464928945</td>\n",
       "      <td>nursing_home</td>\n",
       "      <td>NTUC Health Nursing Home</td>\n",
       "      <td>medic</td>\n",
       "      <td>50</td>\n",
       "      <td>Jurong West Street 93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>648967</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>resident_high</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((103.692 1.341, 103.692 1.341, 103.69...</td>\n",
       "      <td>1.328948e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93917</th>\n",
       "      <td>293480960</td>\n",
       "      <td>nursing_home</td>\n",
       "      <td>Orange Valley Nursing Home</td>\n",
       "      <td>medic</td>\n",
       "      <td>52</td>\n",
       "      <td>Biggin Hill Road</td>\n",
       "      <td>NaN</td>\n",
       "      <td>509945</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>medic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((103.982 1.389, 103.982 1.389, 103.98...</td>\n",
       "      <td>5.842240e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99833</th>\n",
       "      <td>464928937</td>\n",
       "      <td>nursing_home</td>\n",
       "      <td>NTUC Health Nursing Home</td>\n",
       "      <td>medic</td>\n",
       "      <td>35</td>\n",
       "      <td>Chai Chee Street</td>\n",
       "      <td>NaN</td>\n",
       "      <td>468984</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>resident_high</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((103.922 1.328, 103.922 1.328, 103.92...</td>\n",
       "      <td>1.095009e-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           osmid       amenity                                name   \n",
       "612    104960240  nursing_home          Orange Valley Nursing Home  \\\n",
       "15610  372138391  nursing_home  Saint Bernadette Lifestyle Village   \n",
       "75384  464928945  nursing_home            NTUC Health Nursing Home   \n",
       "93917  293480960  nursing_home          Orange Valley Nursing Home   \n",
       "99833  464928937  nursing_home            NTUC Health Nursing Home   \n",
       "\n",
       "          building addr_house             addr_stree addr_hou_1 addr_postc   \n",
       "612          medic        221      Clementi Avenue 4        NaN     129881  \\\n",
       "15610  residential       255B       Bukit Timah Road        NaN     259693   \n",
       "75384        medic         50  Jurong West Street 93        NaN     648967   \n",
       "93917        medic         52       Biggin Hill Road        NaN     509945   \n",
       "99833        medic         35       Chai Chee Street        NaN     468984   \n",
       "\n",
       "      building_l  start_date  ... roof_colou roof_mater roof_shape residentia   \n",
       "612            6         NaN  ...        NaN        NaN        NaN        NaN  \\\n",
       "15610        NaN         NaN  ...        NaN        NaN        NaN        NaN   \n",
       "75384        NaN         NaN  ...        NaN        NaN        NaN        NaN   \n",
       "93917        NaN         NaN  ...        NaN        NaN        NaN        NaN   \n",
       "99833         10         NaN  ...        NaN        NaN        NaN        NaN   \n",
       "\n",
       "      roof_level addr_neigh     build_prog build_ager   \n",
       "612          NaN        NaN          medic        NaN  \\\n",
       "15610        NaN        NaN  resident_high        NaN   \n",
       "75384        NaN        NaN  resident_high        NaN   \n",
       "93917        NaN        NaN          medic        NaN   \n",
       "99833        NaN        NaN  resident_high        NaN   \n",
       "\n",
       "                                                geometry build_footprint  \n",
       "612    POLYGON ((103.769 1.319, 103.769 1.319, 103.76...    6.935187e-08  \n",
       "15610  POLYGON ((103.837 1.315, 103.837 1.315, 103.83...    2.104512e-08  \n",
       "75384  POLYGON ((103.692 1.341, 103.692 1.341, 103.69...    1.328948e-07  \n",
       "93917  POLYGON ((103.982 1.389, 103.982 1.389, 103.98...    5.842240e-08  \n",
       "99833  POLYGON ((103.922 1.328, 103.922 1.328, 103.92...    1.095009e-07  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdfx[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "94bd5ad7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#add column with age ranges\n",
    "def generate_ranges(start_year, end_year, step):\n",
    "    ranges = []\n",
    "    for i in range(start_year, end_year+1, step):\n",
    "        end = min(i+step-1, end_year)\n",
    "        ranges.append(f\"{i}-{end}\")\n",
    "    return ranges\n",
    "\n",
    "gdfx['build_agerange'] = gdfx['start_date'].apply(lambda x: generate_ranges(1800, 2025, 25)[int(x-1800)//25] if not np.isnan(x) else np.NaN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed49fc17",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdfx = gdfx.drop(columns=[''])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bc26772d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Derived Projected CRS: EPSG:3414>\n",
       "Name: SVY21 / Singapore TM\n",
       "Axis Info [cartesian]:\n",
       "- N[north]: Northing (metre)\n",
       "- E[east]: Easting (metre)\n",
       "Area of Use:\n",
       "- name: Singapore - onshore and offshore.\n",
       "- bounds: (103.59, 1.13, 104.07, 1.47)\n",
       "Coordinate Operation:\n",
       "- name: Singapore Transverse Mercator\n",
       "- method: Transverse Mercator\n",
       "Datum: SVY21\n",
       "- Ellipsoid: WGS 84\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdfx.crs = 3414\n",
    "gdfx.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "95e6a896",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(111889, 23)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdfx.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7ad3f013",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "104332"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the number of changes made\n",
    "gdfx['residentia'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ae2a0ca2",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdfx['build_footprint'] = gdfx['geometry'].area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "635c1747",
   "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>osmid</th>\n",
       "      <th>amenity</th>\n",
       "      <th>name</th>\n",
       "      <th>building</th>\n",
       "      <th>addr_house</th>\n",
       "      <th>addr_stree</th>\n",
       "      <th>addr_hou_1</th>\n",
       "      <th>addr_postc</th>\n",
       "      <th>building_l</th>\n",
       "      <th>start_date</th>\n",
       "      <th>...</th>\n",
       "      <th>roof_colou</th>\n",
       "      <th>roof_mater</th>\n",
       "      <th>roof_shape</th>\n",
       "      <th>residentia</th>\n",
       "      <th>roof_level</th>\n",
       "      <th>addr_neigh</th>\n",
       "      <th>build_prog</th>\n",
       "      <th>build_ager</th>\n",
       "      <th>geometry</th>\n",
       "      <th>build_footprint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30527021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sim Lim Square</td>\n",
       "      <td>retail</td>\n",
       "      <td>1</td>\n",
       "      <td>Rochor Canal Road</td>\n",
       "      <td>NaN</td>\n",
       "      <td>188504</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>deco_box</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((103.853 1.303, 103.853 1.303, 103.85...</td>\n",
       "      <td>4.846470e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32342169</td>\n",
       "      <td>NaN</td>\n",
       "      <td>National Museum of Singapore</td>\n",
       "      <td>public</td>\n",
       "      <td>93</td>\n",
       "      <td>Stamford Road</td>\n",
       "      <td>NaN</td>\n",
       "      <td>178897</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1887.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>civic</td>\n",
       "      <td>1875-1899</td>\n",
       "      <td>POLYGON ((103.848 1.297, 103.848 1.297, 103.84...</td>\n",
       "      <td>5.711683e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32507125</td>\n",
       "      <td>police</td>\n",
       "      <td>Police Cantonment Complex</td>\n",
       "      <td>yes</td>\n",
       "      <td>391</td>\n",
       "      <td>New Bridge Road</td>\n",
       "      <td>NaN</td>\n",
       "      <td>088762</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000-2024</td>\n",
       "      <td>POLYGON ((103.839 1.279, 103.839 1.279, 103.83...</td>\n",
       "      <td>6.811147e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32507157</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>office</td>\n",
       "      <td>11</td>\n",
       "      <td>Outram Road</td>\n",
       "      <td>NaN</td>\n",
       "      <td>169078</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>glass_box</td>\n",
       "      <td>1975-1999</td>\n",
       "      <td>POLYGON ((103.838 1.280, 103.838 1.280, 103.83...</td>\n",
       "      <td>4.196015e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32507166</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>public</td>\n",
       "      <td>3</td>\n",
       "      <td>Second Hospital Avenue</td>\n",
       "      <td>Health Promotion Board Building</td>\n",
       "      <td>168937</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>civic</td>\n",
       "      <td>1975-1999</td>\n",
       "      <td>POLYGON ((103.838 1.280, 103.838 1.280, 103.83...</td>\n",
       "      <td>4.833084e-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      osmid amenity                          name building addr_house   \n",
       "0  30527021     NaN                Sim Lim Square   retail          1  \\\n",
       "1  32342169     NaN  National Museum of Singapore   public         93   \n",
       "2  32507125  police     Police Cantonment Complex      yes        391   \n",
       "3  32507157     NaN                           NaN   office         11   \n",
       "4  32507166     NaN                           NaN   public          3   \n",
       "\n",
       "               addr_stree                       addr_hou_1 addr_postc   \n",
       "0       Rochor Canal Road                              NaN     188504  \\\n",
       "1           Stamford Road                              NaN     178897   \n",
       "2         New Bridge Road                              NaN     088762   \n",
       "3             Outram Road                              NaN     169078   \n",
       "4  Second Hospital Avenue  Health Promotion Board Building     168937   \n",
       "\n",
       "  building_l  start_date  ... roof_colou roof_mater roof_shape residentia   \n",
       "0          6         NaN  ...        NaN        NaN        NaN        NaN  \\\n",
       "1        NaN      1887.0  ...        NaN        NaN        NaN        NaN   \n",
       "2        NaN      2001.0  ...        NaN        NaN        NaN        NaN   \n",
       "3        NaN      1981.0  ...        NaN        NaN        NaN        NaN   \n",
       "4        NaN      1981.0  ...        NaN        NaN        NaN        NaN   \n",
       "\n",
       "  roof_level addr_neigh build_prog build_ager   \n",
       "0        NaN        NaN   deco_box        NaN  \\\n",
       "1        NaN        NaN      civic  1875-1899   \n",
       "2        NaN        NaN        NaN  2000-2024   \n",
       "3        NaN        NaN  glass_box  1975-1999   \n",
       "4        NaN        NaN      civic  1975-1999   \n",
       "\n",
       "                                            geometry build_footprint  \n",
       "0  POLYGON ((103.853 1.303, 103.853 1.303, 103.85...    4.846470e-07  \n",
       "1  POLYGON ((103.848 1.297, 103.848 1.297, 103.84...    5.711683e-07  \n",
       "2  POLYGON ((103.839 1.279, 103.839 1.279, 103.83...    6.811147e-07  \n",
       "3  POLYGON ((103.838 1.280, 103.838 1.280, 103.83...    4.196015e-07  \n",
       "4  POLYGON ((103.838 1.280, 103.838 1.280, 103.83...    4.833084e-07  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdfx.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "5bb3bfbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rromu\\AppData\\Local\\Temp\\ipykernel_2740\\1213618896.py:2: UserWarning: Column names longer than 10 characters will be truncated when saved to ESRI Shapefile.\n",
      "  gdfx.to_file('C:/WORK_IAAC/submissions/Internet of Buildings/IoB_WIP/IoB_QGIS/buildings/buildings_FIN.shp')\n"
     ]
    }
   ],
   "source": [
    "# Save the modified shapefile\n",
    "gdfx.to_file('C:/WORK_IAAC/submissions/Internet of Buildings/IoB_WIP/IoB_QGIS/buildings/buildings_FIN.shp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d051c42",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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