IoB_09_join_gm+conserved.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e0b58750",
   "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": 41,
   "id": "84bf83f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>mp_prog</th>\n",
       "      <th>far</th>\n",
       "      <th>osmlanduse</th>\n",
       "      <th>mp_plotare</th>\n",
       "      <th>heritage_n</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>453364545</td>\n",
       "      <td>RESERVE SITE</td>\n",
       "      <td>EVA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>366</td>\n",
       "      <td>Former Royal Air Force (RAF) Seletar</td>\n",
       "      <td>POLYGON ((103.87237 1.41113, 103.87233 1.41144...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>161718587</td>\n",
       "      <td>RESERVE SITE</td>\n",
       "      <td>EVA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1381</td>\n",
       "      <td>Former Royal Air Force (RAF) Seletar</td>\n",
       "      <td>POLYGON ((103.87381 1.41291, 103.87352 1.41315...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>161718589</td>\n",
       "      <td>RESERVE SITE</td>\n",
       "      <td>EVA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131</td>\n",
       "      <td>Former Royal Air Force (RAF) Seletar</td>\n",
       "      <td>POLYGON ((103.87364 1.41326, 103.87362 1.41328...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>161718592</td>\n",
       "      <td>RESERVE SITE</td>\n",
       "      <td>EVA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>124</td>\n",
       "      <td>Former Royal Air Force (RAF) Seletar</td>\n",
       "      <td>POLYGON ((103.87352 1.41315, 103.87351 1.41313...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>531916524</td>\n",
       "      <td>PLACE OF WORSHIP</td>\n",
       "      <td>EVA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>950</td>\n",
       "      <td>Malabar Mosque</td>\n",
       "      <td>POLYGON ((103.85984 1.30533, 103.85997 1.30514...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       osmid           mp_prog  far osmlanduse  mp_plotare   \n",
       "0  453364545      RESERVE SITE  EVA        NaN         366  \\\n",
       "1  161718587      RESERVE SITE  EVA        NaN        1381   \n",
       "2  161718589      RESERVE SITE  EVA        NaN         131   \n",
       "3  161718592      RESERVE SITE  EVA        NaN         124   \n",
       "4  531916524  PLACE OF WORSHIP  EVA        NaN         950   \n",
       "\n",
       "                             heritage_n   \n",
       "0  Former Royal Air Force (RAF) Seletar  \\\n",
       "1  Former Royal Air Force (RAF) Seletar   \n",
       "2  Former Royal Air Force (RAF) Seletar   \n",
       "3  Former Royal Air Force (RAF) Seletar   \n",
       "4                        Malabar Mosque   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((103.87237 1.41113, 103.87233 1.41144...  \n",
       "1  POLYGON ((103.87381 1.41291, 103.87352 1.41315...  \n",
       "2  POLYGON ((103.87364 1.41326, 103.87362 1.41328...  \n",
       "3  POLYGON ((103.87352 1.41315, 103.87351 1.41313...  \n",
       "4  POLYGON ((103.85984 1.30533, 103.85997 1.30514...  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdfa = gpd.read_file(r'C:\\WORK_IAAC\\submissions\\Internet of Buildings\\IoB_WIP\\IoB_Singa\\masterplan landuse+osm landuse+area+conserv/masterplan landuse+osm landuse+area+conserv.shp', encoding='latin-1')\n",
    "gdfa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "24c8fce0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "111889"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdfa['osmid'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8dce4cc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(113784, 7)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdfa.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3deb920c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(111889, 6)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove rows with duplicate geometries\n",
    "gdfx = gdfa.dissolve(by='osmid', aggfunc='first')\n",
    "gdfx.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "31a192d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "      <th>mp_prog</th>\n",
       "      <th>far</th>\n",
       "      <th>osmlanduse</th>\n",
       "      <th>mp_plotare</th>\n",
       "      <th>heritage_n</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>osmid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22875450</th>\n",
       "      <td>POLYGON ((103.749 1.349, 103.749 1.349, 103.74...</td>\n",
       "      <td>RESIDENTIAL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>commercial</td>\n",
       "      <td>3063</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22886442</th>\n",
       "      <td>POLYGON ((103.705 1.338, 103.705 1.338, 103.70...</td>\n",
       "      <td>MASS RAPID TRANSIT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>5117</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24680746</th>\n",
       "      <td>POLYGON ((103.888 1.371, 103.888 1.371, 103.88...</td>\n",
       "      <td>SPORTS &amp; RECREATION</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>4326</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26561741</th>\n",
       "      <td>POLYGON ((103.994 1.365, 103.994 1.365, 103.99...</td>\n",
       "      <td>PORT / AIRPORT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>120476</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30527021</th>\n",
       "      <td>POLYGON ((103.853 1.303, 103.853 1.303, 103.85...</td>\n",
       "      <td>COMMERCIAL</td>\n",
       "      <td>4.2</td>\n",
       "      <td>None</td>\n",
       "      <td>5964</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   geometry   \n",
       "osmid                                                         \n",
       "22875450  POLYGON ((103.749 1.349, 103.749 1.349, 103.74...  \\\n",
       "22886442  POLYGON ((103.705 1.338, 103.705 1.338, 103.70...   \n",
       "24680746  POLYGON ((103.888 1.371, 103.888 1.371, 103.88...   \n",
       "26561741  POLYGON ((103.994 1.365, 103.994 1.365, 103.99...   \n",
       "30527021  POLYGON ((103.853 1.303, 103.853 1.303, 103.85...   \n",
       "\n",
       "                      mp_prog  far  osmlanduse  mp_plotare heritage_n  \n",
       "osmid                                                                  \n",
       "22875450          RESIDENTIAL  3.5  commercial        3063       None  \n",
       "22886442   MASS RAPID TRANSIT  NaN        None        5117       None  \n",
       "24680746  SPORTS & RECREATION  NaN        None        4326       None  \n",
       "26561741       PORT / AIRPORT  NaN        None      120476       None  \n",
       "30527021           COMMERCIAL  4.2        None        5964       None  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdfx.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "df80c12c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "111885"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdfx['geometry'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "8033a3dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#yes and no to nulls in building column\n",
    "gdfx['far'] = gdfx['far'].replace({'EVA': None, 'NA': None, 'SDP': None, 'LND': None})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ea14bc0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([None, 'Subordinate Courts', 'Dairy Farm', 'Clarke Quay',\n",
       "       'Emerald Hill', 'Orchard Road No. 14 to 38',\n",
       "       'Bras Basah Road Conservation Area', 'Singapore Cricket Club',\n",
       "       'Capitol Theatre, Capitol Building and Stamford House',\n",
       "       'Fullerton Building', 'Joo Chiat Conservation Area',\n",
       "       'Singapore Botanic Gardens',\n",
       "       'University of Singapore, Former (Bukit Timah Campus)',\n",
       "       'Chinatown (Kreta Ayer)', 'Chinatown (Bukit Pasoh)',\n",
       "       'China Square', 'Asia Insurance Building, Former',\n",
       "       'Robinson Road No. 35 (TAS Building, Former)', 'Sentosa',\n",
       "       'Sri Krishnan Temple', 'Margaret  Drive No 53', 'Southern Ridges',\n",
       "       'Pulau Ubin No. 1', 'Chinatown (Tanjong Pagar)',\n",
       "       'Jurong Town Hall, Former',\n",
       "       'Convent of the Holy Infant Jesus, Former',\n",
       "       'CLIFFORD PIER, FORMER CUSTOMS HARBOUR BRANCH BUILDING AND CHANGE ALLEY PLAZA AT COLLYER QUAY',\n",
       "       'Alexandra Hospital',\n",
       "       'Traffic Police Headquarters Building, Former and Former Customs House',\n",
       "       'Pepys Road No. 31K',\n",
       "       'MacTaggart Road No. 2 (Khong Guan Biscuit Factory)', 'Boat Quay',\n",
       "       'Singapore River Bridges', 'Upper Circular',\n",
       "       'Armenian Street No.45 to 53',\n",
       "       '40 to 44 Armenian Street and 71 Stamford Road', 'Loke Yew Street',\n",
       "       'Magazine Road Conservation Area', 'Mount  Sophia',\n",
       "       'Joo Chiat Conservation Area-Chapel Road/Marine Parade',\n",
       "       'St. Anthony058e+004', 'Kampong Glam', 'Little India',\n",
       "       'Sri Veeramakaliamman Temple', 'Beach Road',\n",
       "       'MacRitchie Reservoir', 'Former Royal Air Force (RAF) Seletar',\n",
       "       'Gallop Road/Tyersall Avenue', 'Rochester Park',\n",
       "       'Draycott Drive No.4', 'Chatsworth Park', 'Chinatown (Telok Ayer)',\n",
       "       'Maxwell Road No. 38', 'Red Cross House', 'Kampong Silat',\n",
       "       'Tiong Bahru', 'Outram Road No 226', 'Cantonment Road',\n",
       "       'Fort Canning and Coleman Street',\n",
       "       'Ban Siew San Temple and Koon Seng Ting Temple',\n",
       "       'Balestier Road Conservation Area', 'Cheang Jim Chwan Place',\n",
       "       'Former Beach Road Central Police Station',\n",
       "       'Waterloo Street No. 155 (Former Stamford Girlsap.html?areaid=SRBT01',\n",
       "       'Short Street', 'River Valley', 'Mohamed Sultan Road Nos 72-13',\n",
       "       'Blair Plain', 'Waterboat House', 'River Valley Road No. 338E',\n",
       "       'Church of St. Bernadette',\n",
       "       'Alexandra Road No. 332 (Princess House)', 'Bukit Chermin',\n",
       "       'Tudor Court', 'Grange Road No. 53 (Spring Grove)',\n",
       "       'Jellicoe Road Conservation Area',\n",
       "       'Ellison Building and Rex Cinema',\n",
       "       'Mount Sophia Conservation Area- 7 Mount Sophia',\n",
       "       'Mount Sophia Conservation Area- 2 Adis Road',\n",
       "       'Joo Chiat Coservation Area-25 Still Road South',\n",
       "       \"St. Patrick's Road No. 124 and 126\", 'Bukit Timah Road No. 501',\n",
       "       'Joo Chiat Conservation Area-25 Chapel Road',\n",
       "       'Joo Chiat Conservation Area -37 Marine Parade',\n",
       "       'Joo Chiat Conservation Area (Ph2) -26 Still Road South',\n",
       "       'Joo Chiat Conservation Area (Ph2)-272 to 292 East Coast Road',\n",
       "       'Toa Payoh Town Park', \"Pearl's Hill\",\n",
       "       'Bukit Timah Railway Station', 'Alexandra Road (Lot 1997pt MK1)',\n",
       "       'Queen of Peace Church',\n",
       "       'Chatsworth Park (11) Lot 55-4 TS 24 (3 Chatsworth Ave)',\n",
       "       'Cairnhill Road No.42', 'Cairnhill Conservation Area',\n",
       "       'Lot 884 TS 27 at No. 42 Cairnhill Road', 'Robertson Quay',\n",
       "       'Moulmein Road / Mandalay Road', 'Upper Seletar Reservoir Park',\n",
       "       'Holland Park and Ridout Road', 'St. Patrick948e+002',\n",
       "       'Fairy Point Hill', 'Gilstead Road No. 10',\n",
       "       'White House Park/Nassim Rd(5) Lot 55 TS 25 (24 Nassim Rd)',\n",
       "       'Tanjong Katong (includes 64, 65, 110 & 112 Tanjong Katong Road)',\n",
       "       'Joo Chiat Conservtion Area- 17 Pennefather Road',\n",
       "       'Sian Keng Tong Temple', 'Mountbatten Road -Amber Road No.23',\n",
       "       'Nepal Park', 'Mount Sophia Conservation Area- 11A  Mount Sophia',\n",
       "       'Mount Sophia Conservation Area- 12 Mount Sophia',\n",
       "       'White House Park/Nassim Rd(6) Lot 111 TS 25 (28 Nassim Rd)',\n",
       "       'Jalan Besar Conservation Area',\n",
       "       'White House Park/Nassim Rd(1) Lot 468 TS 25 (1 Dalvey Estate',\n",
       "       'White House Park/Nassim Rd(12) Lot 77-6 TS 25 (6 & 10 Lermit',\n",
       "       'White House Park/Nassim Rd(8) Lot 61-13 TS 25 (11 Ladyhill R',\n",
       "       'White House Park/Nassim Rd(4) Lot 99-24 TS 25 (12 Dalvey Est',\n",
       "       'White House Park/Nassim Rd(13) Lot 539 TS 25 (75 Whitehouse',\n",
       "       'White House Park/Nassim Rd(2) Lot 464 TS 25 (2 Dalvey Estate',\n",
       "       'White House Park/Nassim Rd(9) Lot 470 TS 25 (32 Nassim Rd)',\n",
       "       'White House Park/Nassim Rd(10) Lot 48-2 TS 25 (40 Nassim Rd)',\n",
       "       'White House Park/Nassim Rd(3) Lot 96-2 TS 25 (5 Dalvey Estat',\n",
       "       'White House Park/Nassim Rd(11) Lot 44-8 TS 25 (56 Dalvey Rd)',\n",
       "       'Mahatma Gandhi Memorial Hall',\n",
       "       'Mountbatten Road- Amber Road No. 43',\n",
       "       'Beach Road NCO Club at Beach Rd Lot 200-1pt TS 11',\n",
       "       'Block 1 of Former Beach Road Camp',\n",
       "       'Block 9 of Former Beach Road Camp',\n",
       "       'Block 14 of Former Beach Road Camp', 'Geylang Conservation Area',\n",
       "       'Sri Manmatha Karuneshvarar Temple', 'Former Kallang Airport',\n",
       "       'Angullia Mosque Gatehouse', 'Matilda House', 'Wak Tanjong Mosque',\n",
       "       'Geylang Fire Station, Former', 'Former Victoria School',\n",
       "       \"Former St. Matthew's Church\", 'Pasir Panjang Road No. 222 to 240',\n",
       "       'Beaulieu House', 'Mountbatten Road', 'Meyer Place No. 2',\n",
       "       'Former Chee Kong Tong (Thekchen Choling) Entrance Gate',\n",
       "       'Townerville', 'Seventh-Day Adventist Church',\n",
       "       'Kwong Wai Shui Hosptial', 'Commonwealth Avenu No 38',\n",
       "       'Jalan Lim Tai See No. 27 (Hoon San Temple)',\n",
       "       'Jalan Jurong Kechil', 'Kiew Lee Tong Temple',\n",
       "       'Joo Chiat Conservation Area (Ph2) - 445 to 459 East Coast Ro',\n",
       "       'Joo Chiat Conservation Area (Ph2) - 417 to 431 East Coast Ro',\n",
       "       'Ramakrishna Mission', 'Sembawang Fire Station, Former',\n",
       "       'Buckley Road No.9', 'Leong San See Temple', 'Grange Road No. 25',\n",
       "       'Geylang Conservation Area-Lorong19/Geylang',\n",
       "       'Sims Avenue No. 514 to 530', \"St. Andrew's School\",\n",
       "       'Alexandra Road No 394', 'Singapore River Warehouses',\n",
       "       'Church of the Blessed Sacrament',\n",
       "       'Singapore Hakka Methodist Church',\n",
       "       'Jalan Besar Conservation area',\n",
       "       'Sri Vadapathira Kaliamman Temple', \"St. Gregory's Place\",\n",
       "       'Malabar Mosque', 'Tang Gah Beo Temple',\n",
       "       'Tanglin Officers Mess, Former (MFA Building)',\n",
       "       'Merbau Road No 20',\n",
       "       \"Former Fairfield Methodist Girls' School (Neil Road Conservation Area)\"],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdfx['heritage_n'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7788275e",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdfx['far'] = gdfx['far'].astype(float, errors='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "f3ce237a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
      "Index: 111889 entries, 22875450 to 1158969194\n",
      "Data columns (total 6 columns):\n",
      " #   Column      Non-Null Count   Dtype   \n",
      "---  ------      --------------   -----   \n",
      " 0   geometry    111889 non-null  geometry\n",
      " 1   mp_prog     111675 non-null  object  \n",
      " 2   far         44964 non-null   float64 \n",
      " 3   osmlanduse  85478 non-null   object  \n",
      " 4   mp_plotare  111889 non-null  int64   \n",
      " 5   heritage_n  7773 non-null    object  \n",
      "dtypes: float64(1), geometry(1), int64(1), object(3)\n",
      "memory usage: 10.0+ MB\n"
     ]
    }
   ],
   "source": [
    "gdfx.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1cbb4005",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>osmid</th>\n",
       "      <th>mp_prog</th>\n",
       "      <th>far</th>\n",
       "      <th>osmlanduse</th>\n",
       "      <th>mp_plotarea</th>\n",
       "      <th>heritage_name</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>453364545</td>\n",
       "      <td>RESERVE SITE</td>\n",
       "      <td>EVA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>366</td>\n",
       "      <td>Former Royal Air Force (RAF) Seletar</td>\n",
       "      <td>POLYGON ((103.87237 1.41113, 103.87233 1.41144...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>161718587</td>\n",
       "      <td>RESERVE SITE</td>\n",
       "      <td>EVA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1381</td>\n",
       "      <td>Former Royal Air Force (RAF) Seletar</td>\n",
       "      <td>POLYGON ((103.87381 1.41291, 103.87352 1.41315...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>161718589</td>\n",
       "      <td>RESERVE SITE</td>\n",
       "      <td>EVA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131</td>\n",
       "      <td>Former Royal Air Force (RAF) Seletar</td>\n",
       "      <td>POLYGON ((103.87364 1.41326, 103.87362 1.41328...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>161718592</td>\n",
       "      <td>RESERVE SITE</td>\n",
       "      <td>EVA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>124</td>\n",
       "      <td>Former Royal Air Force (RAF) Seletar</td>\n",
       "      <td>POLYGON ((103.87352 1.41315, 103.87351 1.41313...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>531916524</td>\n",
       "      <td>PLACE OF WORSHIP</td>\n",
       "      <td>EVA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>950</td>\n",
       "      <td>Malabar Mosque</td>\n",
       "      <td>POLYGON ((103.85984 1.30533, 103.85997 1.30514...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       osmid           mp_prog  far osmlanduse  mp_plotarea   \n",
       "0  453364545      RESERVE SITE  EVA        NaN          366  \\\n",
       "1  161718587      RESERVE SITE  EVA        NaN         1381   \n",
       "2  161718589      RESERVE SITE  EVA        NaN          131   \n",
       "3  161718592      RESERVE SITE  EVA        NaN          124   \n",
       "4  531916524  PLACE OF WORSHIP  EVA        NaN          950   \n",
       "\n",
       "                          heritage_name   \n",
       "0  Former Royal Air Force (RAF) Seletar  \\\n",
       "1  Former Royal Air Force (RAF) Seletar   \n",
       "2  Former Royal Air Force (RAF) Seletar   \n",
       "3  Former Royal Air Force (RAF) Seletar   \n",
       "4                        Malabar Mosque   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((103.87237 1.41113, 103.87233 1.41144...  \n",
       "1  POLYGON ((103.87381 1.41291, 103.87352 1.41315...  \n",
       "2  POLYGON ((103.87364 1.41326, 103.87362 1.41328...  \n",
       "3  POLYGON ((103.87352 1.41315, 103.87351 1.41313...  \n",
       "4  POLYGON ((103.85984 1.30533, 103.85997 1.30514...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#do it only once with the initial file\n",
    "gdfy.rename(columns={'LU_DESCLU_': 'mp_prog', 'LU_DESCGPR': 'far', 'fclass': 'osmlanduse', 'area': 'mp_plotarea', 'GROUP_NAME': 'heritage_name'}, inplace=True)\n",
    "gdfy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4480e88f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>POLYGON ((103.962 1.337, 103.961 1.337, 103.96...</td>\n",
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       "      <td>garage</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>POLYGON ((103.849 1.351, 103.849 1.351, 103.84...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>POLYGON ((104.031 1.407, 104.030 1.407, 104.03...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1447583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>POLYGON ((103.958 1.350, 103.958 1.349, 103.95...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1447589</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>POLYGON ((103.958 1.349, 103.958 1.349, 103.95...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113425</th>\n",
       "      <td>1165422507</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>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>POLYGON ((103.775 1.427, 103.775 1.427, 103.77...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113426</th>\n",
       "      <td>1165422508</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>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>POLYGON ((103.775 1.427, 103.775 1.427, 103.77...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113427</th>\n",
       "      <td>1165450372</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1925-1949</td>\n",
       "      <td>4.0</td>\n",
       "      <td>POLYGON ((103.859 1.302, 103.859 1.302, 103.85...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113428</th>\n",
       "      <td>1165450373</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1925-1949</td>\n",
       "      <td>4.0</td>\n",
       "      <td>POLYGON ((103.859 1.302, 103.859 1.302, 103.85...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113429</th>\n",
       "      <td>1165450374</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1925-1949</td>\n",
       "      <td>4.0</td>\n",
       "      <td>POLYGON ((103.859 1.302, 103.859 1.302, 103.85...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>113430 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             osmid  amenity name building levels  age  height build_prog   \n",
       "0          1337396      NaN  NaN      NaN    NaN  NaN     NaN  small_box  \\\n",
       "1          1362701  parking  NaN   garage      3  NaN     NaN        NaN   \n",
       "2          1407265      NaN  NaN      NaN    NaN  NaN     NaN        NaN   \n",
       "3          1447583      NaN   20      NaN     12  NaN    70.0        NaN   \n",
       "4          1447589      NaN   22      NaN     12  NaN    70.0        NaN   \n",
       "...            ...      ...  ...      ...    ...  ...     ...        ...   \n",
       "113425  1165422507      NaN  NaN      NaN    NaN  NaN     NaN        NaN   \n",
       "113426  1165422508      NaN  NaN      NaN    NaN  NaN     NaN        NaN   \n",
       "113427  1165450372      NaN  NaN      NaN     29  NaN    29.0        NaN   \n",
       "113428  1165450373      NaN  NaN      NaN     29  NaN    29.0        NaN   \n",
       "113429  1165450374      NaN  NaN      NaN     29  NaN    29.0        NaN   \n",
       "\n",
       "       build_ager  floor                                           geometry  \n",
       "0             NaN    4.0  POLYGON ((103.962 1.337, 103.961 1.337, 103.96...  \n",
       "1             NaN    4.0  POLYGON ((103.849 1.351, 103.849 1.351, 103.84...  \n",
       "2             NaN    4.0  POLYGON ((104.031 1.407, 104.030 1.407, 104.03...  \n",
       "3             NaN    4.0  POLYGON ((103.958 1.350, 103.958 1.349, 103.95...  \n",
       "4             NaN    4.0  POLYGON ((103.958 1.349, 103.958 1.349, 103.95...  \n",
       "...           ...    ...                                                ...  \n",
       "113425        NaN    4.0  POLYGON ((103.775 1.427, 103.775 1.427, 103.77...  \n",
       "113426        NaN    4.0  POLYGON ((103.775 1.427, 103.775 1.427, 103.77...  \n",
       "113427  1925-1949    4.0  POLYGON ((103.859 1.302, 103.859 1.302, 103.85...  \n",
       "113428  1925-1949    4.0  POLYGON ((103.859 1.302, 103.859 1.302, 103.85...  \n",
       "113429  1925-1949    4.0  POLYGON ((103.859 1.302, 103.859 1.302, 103.85...  \n",
       "\n",
       "[113430 rows x 11 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdfb = gpd.read_file(r'C:/WORK_IAAC/submissions/Internet of Buildings/IoB_WIP/IoB_QGIS/buildings/buildings_01.shp', encoding='latin-1')\n",
    "gdfb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "8449a823",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Geographic 2D CRS: EPSG:4326>\n",
       "Name: WGS 84\n",
       "Axis Info [ellipsoidal]:\n",
       "- Lat[north]: Geodetic latitude (degree)\n",
       "- Lon[east]: Geodetic longitude (degree)\n",
       "Area of Use:\n",
       "- name: World.\n",
       "- bounds: (-180.0, -90.0, 180.0, 90.0)\n",
       "Datum: World Geodetic System 1984 ensemble\n",
       "- Ellipsoid: WGS 84\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdfx.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "1340e2c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdfx.crs = 3414"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e6042e3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdfb = gdfb.drop(columns=['floor'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "be914f57",
   "metadata": {},
   "outputs": [
    {
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       "      <td>POLYGON ((103.962 1.337, 103.961 1.337, 103.96...</td>\n",
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       "      <th>1</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>POLYGON ((103.849 1.351, 103.849 1.351, 103.84...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1407265</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1447583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((103.958 1.350, 103.958 1.349, 103.95...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1447589</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((103.958 1.349, 103.958 1.349, 103.95...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     osmid  amenity name building levels  age  height build_prog build_ager   \n",
       "0  1337396      NaN  NaN      NaN    NaN  NaN     NaN  small_box        NaN  \\\n",
       "1  1362701  parking  NaN   garage      3  NaN     NaN        NaN        NaN   \n",
       "2  1407265      NaN  NaN      NaN    NaN  NaN     NaN        NaN        NaN   \n",
       "3  1447583      NaN   20      NaN     12  NaN    70.0        NaN        NaN   \n",
       "4  1447589      NaN   22      NaN     12  NaN    70.0        NaN        NaN   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((103.962 1.337, 103.961 1.337, 103.96...  \n",
       "1  POLYGON ((103.849 1.351, 103.849 1.351, 103.84...  \n",
       "2  POLYGON ((104.031 1.407, 104.030 1.407, 104.03...  \n",
       "3  POLYGON ((103.958 1.350, 103.958 1.349, 103.95...  \n",
       "4  POLYGON ((103.958 1.349, 103.958 1.349, 103.95...  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdfb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "221aebd6",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>osmid</th>\n",
       "      <th>geometry_x</th>\n",
       "      <th>mp_prog</th>\n",
       "      <th>far</th>\n",
       "      <th>osmlanduse</th>\n",
       "      <th>mp_plotare</th>\n",
       "      <th>heritage_n</th>\n",
       "      <th>amenity</th>\n",
       "      <th>name</th>\n",
       "      <th>building</th>\n",
       "      <th>levels</th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "      <th>build_prog</th>\n",
       "      <th>build_ager</th>\n",
       "      <th>geometry_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22875450</td>\n",
       "      <td>POLYGON ((103.749 1.349, 103.749 1.349, 103.74...</td>\n",
       "      <td>RESIDENTIAL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>commercial</td>\n",
       "      <td>3063.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>train_station</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>transport</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((103.749 1.349, 103.749 1.349, 103.74...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22886442</td>\n",
       "      <td>POLYGON ((103.705 1.338, 103.705 1.338, 103.70...</td>\n",
       "      <td>MASS RAPID TRANSIT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>5117.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>train_station</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>transport</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((103.705 1.338, 103.705 1.338, 103.70...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24680746</td>\n",
       "      <td>POLYGON ((103.888 1.371, 103.888 1.371, 103.88...</td>\n",
       "      <td>SPORTS &amp; RECREATION</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>4326.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>civic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>civic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((103.888 1.371, 103.888 1.371, 103.88...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26561741</td>\n",
       "      <td>POLYGON ((103.994 1.365, 103.994 1.365, 103.99...</td>\n",
       "      <td>PORT / AIRPORT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>120476.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Changi Airport Terminal 1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>transport</td>\n",
       "      <td>1975-1999</td>\n",
       "      <td>POLYGON ((103.994 1.365, 103.995 1.365, 103.99...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30527021</td>\n",
       "      <td>POLYGON ((103.853 1.303, 103.853 1.303, 103.85...</td>\n",
       "      <td>COMMERCIAL</td>\n",
       "      <td>4.2</td>\n",
       "      <td>None</td>\n",
       "      <td>5964.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sim Lim Square</td>\n",
       "      <td>retail</td>\n",
       "      <td>6</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      osmid                                         geometry_x   \n",
       "0  22875450  POLYGON ((103.749 1.349, 103.749 1.349, 103.74...  \\\n",
       "1  22886442  POLYGON ((103.705 1.338, 103.705 1.338, 103.70...   \n",
       "2  24680746  POLYGON ((103.888 1.371, 103.888 1.371, 103.88...   \n",
       "3  26561741  POLYGON ((103.994 1.365, 103.994 1.365, 103.99...   \n",
       "4  30527021  POLYGON ((103.853 1.303, 103.853 1.303, 103.85...   \n",
       "\n",
       "               mp_prog  far  osmlanduse  mp_plotare heritage_n amenity   \n",
       "0          RESIDENTIAL  3.5  commercial      3063.0       None     NaN  \\\n",
       "1   MASS RAPID TRANSIT  NaN        None      5117.0       None     NaN   \n",
       "2  SPORTS & RECREATION  NaN        None      4326.0       None     NaN   \n",
       "3       PORT / AIRPORT  NaN        None    120476.0       None     NaN   \n",
       "4           COMMERCIAL  4.2        None      5964.0       None     NaN   \n",
       "\n",
       "                        name       building levels     age  height build_prog   \n",
       "0                        NaN  train_station    NaN     NaN     NaN  transport  \\\n",
       "1                        NaN  train_station    NaN     NaN     NaN  transport   \n",
       "2                        NaN          civic    NaN     NaN     NaN      civic   \n",
       "3  Changi Airport Terminal 1            NaN      3  1981.0    30.0  transport   \n",
       "4             Sim Lim Square         retail      6     NaN     NaN   deco_box   \n",
       "\n",
       "  build_ager                                         geometry_y  \n",
       "0        NaN  POLYGON ((103.749 1.349, 103.749 1.349, 103.74...  \n",
       "1        NaN  POLYGON ((103.705 1.338, 103.705 1.338, 103.70...  \n",
       "2        NaN  POLYGON ((103.888 1.371, 103.888 1.371, 103.88...  \n",
       "3  1975-1999  POLYGON ((103.994 1.365, 103.995 1.365, 103.99...  \n",
       "4        NaN  POLYGON ((103.853 1.303, 103.853 1.303, 103.85...  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = gdfx.merge(gdfb, on='osmid', how='outer', indicator=False, validate='one_to_one')\n",
    "merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "748974e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "113430"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged['osmid'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "bd0e73c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged = merged.drop(columns=['geometry_y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "7f48065c",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged = merged.rename(columns={'geometry_x':'geometry'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "776e452e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert pandas to geopandas\n",
    "gff = gpd.GeoDataFrame(merged, crs='EPSG:3414')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "b3c1b4cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(113430, 16)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "6dfc0204",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "geopandas.geodataframe.GeoDataFrame"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(gdfx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "ffbfe668",
   "metadata": {},
   "outputs": [],
   "source": [
    "gff.to_file('C:/WORK_IAAC/submissions/Internet of Buildings/IoB_WIP/IoB_QGIS/buildings/buildings_02.shp', driver='ESRI Shapefile')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "6fa11121",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['RESIDENTIAL', 'MASS RAPID TRANSIT', 'SPORTS & RECREATION',\n",
       "       'PORT / AIRPORT', 'COMMERCIAL', 'ROAD',\n",
       "       'CIVIC & COMMUNITY INSTITUTION', 'COMMERCIAL & RESIDENTIAL',\n",
       "       'HEALTH & MEDICAL CARE', 'EDUCATIONAL INSTITUTION',\n",
       "       'PLACE OF WORSHIP', 'TRANSPORT FACILITIES', 'UTILITY',\n",
       "       'BUSINESS 1', 'BUSINESS PARK', 'BUSINESS 1 - WHITE',\n",
       "       'RESIDENTIAL WITH COMMERCIAL AT 1ST STOREY', 'PARK', 'OPEN SPACE',\n",
       "       'RESERVE SITE', 'HOTEL', 'WHITE', 'SPECIAL USE', 'BUSINESS 2',\n",
       "       None, 'BUSINESS 2 - WHITE', 'BUSINESS PARK - WHITE', 'BEACH AREA',\n",
       "       'WATERBODY', 'AGRICULTURE', 'COMMERCIAL / INSTITUTION',\n",
       "       'LIGHT RAPID TRANSIT', 'RESIDENTIAL / INSTITUTION', 'CEMETERY',\n",
       "       nan], dtype=object)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged['mp_prog'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "4d193307",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['transport', 'civic', 'deco_box', nan, 'glass_box', 'small_box',\n",
       "       'education', 'industrial', 'medic', 'resident_high',\n",
       "       'resident_low', 'religious', 'sport', 'construction_site', 'ruins'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged['build_prog'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "72df58e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#replace building programs in column build_prog by masterplan program\n",
    "build_joined = merged.copy()\n",
    "ml = build_joined['mp_prog'].unique()\n",
    "pl = build_joined['build_prog'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "3c220fca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('resident_high',\n",
       " 'resident_low',\n",
       " 'RESIDENTIAL',\n",
       " 'COMMERCIAL & RESIDENTIAL',\n",
       " 'RESIDENTIAL WITH COMMERCIAL AT 1ST STOREY',\n",
       " 'HOTEL')"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl[9],pl[10],ml[0],ml[7],ml[16],ml[20],"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "fee6c21d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('transport',\n",
       " 'MASS RAPID TRANSIT',\n",
       " 'TRANSPORT FACILITIES',\n",
       " 'LIGHT RAPID TRANSIT')"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl[0],ml[1],ml[11],ml[-4],"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "aa62cf8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('deco_box',\n",
       " 'SPORTS & RECREATION',\n",
       " 'PORT / AIRPORT',\n",
       " 'ROAD',\n",
       " 'PARK',\n",
       " 'OPEN SPACE')"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl[2],ml[2],ml[3],ml[5],ml[17],ml[18],"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "d076f46a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('glass_box',\n",
       " 'COMMERCIAL',\n",
       " 'BUSINESS PARK',\n",
       " 'BUSINESS 1 - WHITE',\n",
       " 'WHITE',\n",
       " 'BUSINESS 2 - WHITE',\n",
       " 'BUSINESS PARK - WHITE')"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl[4],ml[4],ml[14],ml[15],ml[21],ml[25],ml[26],"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "62a01d07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('industrial',\n",
       " 'BUSINESS 1',\n",
       " 'RESERVE SITE',\n",
       " 'SPECIAL USE',\n",
       " 'BUSINESS 2',\n",
       " 'AGRICULTURE')"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl[7],ml[13],ml[19],ml[22],ml[23],ml[-6],"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "f6d16ce2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('civic',\n",
       " 'CIVIC & COMMUNITY INSTITUTION',\n",
       " 'WATERBODY',\n",
       " 'BEACH AREA',\n",
       " 'CEMETERY')"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl[1],ml[6],ml[-7],ml[-8],ml[-2],"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "c986c4fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('education',\n",
       " 'EDUCATIONAL INSTITUTION',\n",
       " 'RESIDENTIAL / INSTITUTION',\n",
       " 'COMMERCIAL / INSTITUTION')"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl[6],ml[9],ml[-3],ml[-5],"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "f2571c84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('medic', 'HEALTH & MEDICAL CARE')"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl[8],ml[8],"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "ac6b2d03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('religious', 'PLACE OF WORSHIP')"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl[11],ml[10],"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "934790af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('sport', 'SPORTS & RECREATION')"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl[12],ml[2],"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "0c9f6039",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'transport': ('MASS RAPID TRANSIT',\n",
       "  'TRANSPORT FACILITIES',\n",
       "  'LIGHT RAPID TRANSIT'),\n",
       " 'civic': ('CIVIC & COMMUNITY INSTITUTION',\n",
       "  'WATERBODY',\n",
       "  'BEACH AREA',\n",
       "  'CEMETERY'),\n",
       " 'deco_box': ('SPORTS & RECREATION',\n",
       "  'PORT / AIRPORT',\n",
       "  'ROAD',\n",
       "  'PARK',\n",
       "  'OPEN SPACE'),\n",
       " 'glass_box': ('COMMERCIAL',\n",
       "  'BUSINESS PARK',\n",
       "  'BUSINESS 1 - WHITE',\n",
       "  'WHITE',\n",
       "  'BUSINESS 2 - WHITE',\n",
       "  'BUSINESS PARK - WHITE'),\n",
       " 'education': ('EDUCATIONAL INSTITUTION',\n",
       "  'RESIDENTIAL / INSTITUTION',\n",
       "  'COMMERCIAL / INSTITUTION'),\n",
       " 'industrial': ('BUSINESS 1',\n",
       "  'RESERVE SITE',\n",
       "  'SPECIAL USE',\n",
       "  'BUSINESS 2',\n",
       "  'AGRICULTURE'),\n",
       " 'medic': 'HEALTH & MEDICAL CARE',\n",
       " 'resident_high': ('RESIDENTIAL',\n",
       "  'COMMERCIAL & RESIDENTIAL',\n",
       "  'RESIDENTIAL WITH COMMERCIAL AT 1ST STOREY',\n",
       "  'HOTEL'),\n",
       " 'religious': 'PLACE OF WORSHIP',\n",
       " 'sport': 'SPORTS & RECREATION'}"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#categorize masterplan programs corresponding to building programs\n",
    "recode = {    \n",
    "    pl[0]:(ml[1],ml[11],ml[-4]),\n",
    "    pl[1]:(ml[6],ml[-7],ml[-8],ml[-2]),\n",
    "    pl[2]:(ml[2],ml[3],ml[5],ml[17],ml[18]),\n",
    "    pl[4]:(ml[4],ml[14],ml[15],ml[21],ml[25],ml[26]),\n",
    "    pl[6]:(ml[9],ml[-3],ml[-5]),\n",
    "    pl[7]:(ml[13],ml[19],ml[22],ml[23],ml[-6]),\n",
    "    pl[8]:(ml[8]),\n",
    "    pl[9]:(ml[0],ml[7],ml[16],ml[20]),\n",
    "    pl[11]:(ml[10]),\n",
    "    pl[12]:(ml[2]),\n",
    "}\n",
    "recode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "03e06bc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'BEACH AREA'"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert dictionary to list and check selection procedure\n",
    "rl = list(recode.items())\n",
    "rl[1][1][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "b7c38b5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('MASS RAPID TRANSIT', 'TRANSPORT FACILITIES', 'LIGHT RAPID TRANSIT')"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rl[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "6cd3bdde",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "civic = CIVIC & COMMUNITY INSTITUTION\n",
      "civic = WATERBODY\n",
      "civic = BEACH AREA\n",
      "civic = CEMETERY\n"
     ]
    }
   ],
   "source": [
    "#check correlation of recoding, change rl index from 0 to 9\n",
    "build_program_index = rl[1][0]\n",
    "master_correlation = rl[1][1]\n",
    "for x in master_correlation:\n",
    "    print(build_program_index+\" = \"+x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "7844d7e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#if masterplan program is:\n",
    "for mp_x in master_correlation:\n",
    "#replace program value from NaN to:\n",
    "    build_x = build_program_index\n",
    "#     mask = build_joined['mp_prog'].str.contains(mp_x, case=False)\n",
    "    mask = build_joined['mp_prog'].isin(master_correlation)\n",
    "    build_joined.loc[mask & ((build_joined['build_prog'].isna())), 'build_prog'] = build_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "4635b88c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#no of items before change\n",
    "merged['build_prog'].value_counts().get(build_program_index, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "b207f8fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#no of items before change\n",
    "build_joined['build_prog'].value_counts().get(build_program_index, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "id": "032144c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert pandas to geopandas\n",
    "build_prog = gpd.GeoDataFrame(build_joined, crs='EPSG:3414')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "id": "b32257ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "build_prog.to_file('C:/WORK_IAAC/submissions/Internet of Buildings/IoB_WIP/IoB_QGIS/buildings/buildings_03.shp', crs='EPSG:4326')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "ee80f38d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\zipfile.py:1514: UserWarning: Duplicate name: 'buildings_03.shp'\n",
      "  return self._open_to_write(zinfo, force_zip64=force_zip64)\n"
     ]
    }
   ],
   "source": [
    "#zip archive shapefile\n",
    "import zipfile\n",
    "import os\n",
    "# Define output file names\n",
    "shp_path = 'C:/WORK_IAAC/submissions/Internet of Buildings/IoB_WIP/IoB_QGIS/buildings/buildings_03.shp'\n",
    "zip_path = 'C:/WORK_IAAC/submissions/Internet of Buildings/IoB_WIP/IoB_QGIS/buildings/buildings_03.zip'\n",
    "\n",
    "# Create zip file and add shapefile\n",
    "with zipfile.ZipFile(zip_path, 'w', compression=zipfile.ZIP_DEFLATED) as zipf:\n",
    "    for ext in ['shp', 'shx', 'dbf', 'prj']:\n",
    "        filename = shp_path.replace('.shp', '.{}'.format(ext))\n",
    "        zipf.write(filename, arcname=os.path.basename(filename))\n",
    "    zipf.write(shp_path, arcname=os.path.basename(shp_path))\n",
    "\n",
    "# Remove shapefile from disk\n",
    "os.remove(shp_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "8a4a6808",
   "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",
    "gdfy = build_joined\n",
    "counts = gdfy.count()\n",
    "\n",
    "# create a bar plot of non-NaN counts with red color\n",
    "ax = counts.plot(kind='bar', color='red')\n",
    "\n",
    "# add the count percentage above each bar\n",
    "for i, v in enumerate(counts):\n",
    "    ax.text(i, v+5, str(round((v/gdfy.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": 357,
   "id": "b8cef351",
   "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>mp_prog</th>\n",
       "      <th>far</th>\n",
       "      <th>osmlanduse</th>\n",
       "      <th>mp_plotare</th>\n",
       "      <th>heritage_n</th>\n",
       "      <th>amenity</th>\n",
       "      <th>name</th>\n",
       "      <th>building</th>\n",
       "      <th>levels</th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "      <th>build_prog</th>\n",
       "      <th>build_ager</th>\n",
       "      <th>build_foot</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22875450</td>\n",
       "      <td>RESIDENTIAL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>commercial</td>\n",
       "      <td>3063.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>train_station</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>transport</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3063.0</td>\n",
       "      <td>POLYGON ((103.74901 1.34852, 103.74912 1.34866...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22886442</td>\n",
       "      <td>MASS RAPID TRANSIT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5117.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>train_station</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>transport</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5117.0</td>\n",
       "      <td>POLYGON ((103.70508 1.33839, 103.70518 1.33842...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24680746</td>\n",
       "      <td>SPORTS &amp; RECREATION</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4326.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>civic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>civic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4326.0</td>\n",
       "      <td>POLYGON ((103.88808 1.37098, 103.88812 1.37102...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26561741</td>\n",
       "      <td>PORT / AIRPORT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120476.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Changi Airport Terminal 1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>transport</td>\n",
       "      <td>1975-1999</td>\n",
       "      <td>120476.0</td>\n",
       "      <td>POLYGON ((103.99431 1.36489, 103.99437 1.36494...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30527021</td>\n",
       "      <td>COMMERCIAL</td>\n",
       "      <td>4.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5964.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sim Lim Square</td>\n",
       "      <td>retail</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>deco_box</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5964.0</td>\n",
       "      <td>POLYGON ((103.85308 1.30254, 103.85302 1.30259...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      osmid              mp_prog  far  osmlanduse  mp_plotare heritage_n   \n",
       "0  22875450          RESIDENTIAL  3.5  commercial      3063.0        NaN  \\\n",
       "1  22886442   MASS RAPID TRANSIT  NaN         NaN      5117.0        NaN   \n",
       "2  24680746  SPORTS & RECREATION  NaN         NaN      4326.0        NaN   \n",
       "3  26561741       PORT / AIRPORT  NaN         NaN    120476.0        NaN   \n",
       "4  30527021           COMMERCIAL  4.2         NaN      5964.0        NaN   \n",
       "\n",
       "  amenity                       name       building levels     age  height   \n",
       "0     NaN                        NaN  train_station    NaN     NaN     NaN  \\\n",
       "1     NaN                        NaN  train_station    NaN     NaN     NaN   \n",
       "2     NaN                        NaN          civic    NaN     NaN     NaN   \n",
       "3     NaN  Changi Airport Terminal 1            NaN      3  1981.0    30.0   \n",
       "4     NaN             Sim Lim Square         retail      6     NaN     NaN   \n",
       "\n",
       "  build_prog build_ager  build_foot   \n",
       "0  transport        NaN      3063.0  \\\n",
       "1  transport        NaN      5117.0   \n",
       "2      civic        NaN      4326.0   \n",
       "3  transport  1975-1999    120476.0   \n",
       "4   deco_box        NaN      5964.0   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((103.74901 1.34852, 103.74912 1.34866...  \n",
       "1  POLYGON ((103.70508 1.33839, 103.70518 1.33842...  \n",
       "2  POLYGON ((103.88808 1.37098, 103.88812 1.37102...  \n",
       "3  POLYGON ((103.99431 1.36489, 103.99437 1.36494...  \n",
       "4  POLYGON ((103.85308 1.30254, 103.85302 1.30259...  "
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "build_prog = gpd.read_file(r'C:/WORK_IAAC/submissions/Internet of Buildings/IoB_WIP/IoB_QGIS/buildings/buildings_04.shp', encoding='latin-1')\n",
    "build_prog.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "646160ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "build_prog = gpd.GeoDataFrame(build_joined, crs='EPSG:3414')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "684a8196",
   "metadata": {},
   "outputs": [],
   "source": [
    "build_prog['build_cent'] = build_prog['geometry'].centroid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "id": "db847cf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "build_prog['levels'] = pd.to_numeric(build_prog['levels'], errors='coerce').fillna(0).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "id": "521d4584",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
      "RangeIndex: 113430 entries, 0 to 113429\n",
      "Data columns (total 17 columns):\n",
      " #   Column      Non-Null Count   Dtype   \n",
      "---  ------      --------------   -----   \n",
      " 0   osmid       113430 non-null  int64   \n",
      " 1   mp_prog     111675 non-null  object  \n",
      " 2   far         44964 non-null   float64 \n",
      " 3   osmlanduse  85478 non-null   object  \n",
      " 4   mp_plotare  111889 non-null  float64 \n",
      " 5   heritage_n  7773 non-null    object  \n",
      " 6   amenity     3131 non-null    object  \n",
      " 7   name        11262 non-null   object  \n",
      " 8   building    40688 non-null   object  \n",
      " 9   levels      113430 non-null  int32   \n",
      " 10  age         3617 non-null    float64 \n",
      " 11  height      4214 non-null    float64 \n",
      " 12  build_prog  111147 non-null  object  \n",
      " 13  build_ager  3725 non-null    object  \n",
      " 14  build_foot  111889 non-null  float64 \n",
      " 15  geometry    111889 non-null  geometry\n",
      " 16  gfa         111888 non-null  float64 \n",
      "dtypes: float64(6), geometry(1), int32(1), int64(1), object(8)\n",
      "memory usage: 14.3+ MB\n"
     ]
    }
   ],
   "source": [
    "build_prog.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "id": "e66bac38",
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate gfa from footprint * levels\n",
    "build_prog['gfa'] = build_prog.apply(lambda row: row['build_foot'] * row['levels'] if row['build_foot'] is not None \n",
    "                      and row['build_foot'] != 0 and row['levels'] is not None and row['levels'] != 0 else None, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "7f9e6a8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate gfa from plot area * gross plot ratio\n",
    "build_prog.loc[build_prog['gfa'].isna(), 'gfa'] = build_prog.loc[build_prog['gfa'].isna(), 'far'] * build_prog.loc[build_prog['gfa'].isna(), 'mp_plotare']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "id": "fb404791",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50.0"
      ]
     },
     "execution_count": 406,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "build_prog['gfa'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "id": "8819d3aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "build_prog.loc[build_prog['gfa'] < 50, 'gfa'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "id": "9287f240",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4070070.0"
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "build_prog['gfa'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "id": "0d0c4631",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "111728"
      ]
     },
     "execution_count": 393,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(build_prog[build_prog['build_foot'] < 20000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "id": "83c0de36",
   "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>mp_prog</th>\n",
       "      <th>far</th>\n",
       "      <th>osmlanduse</th>\n",
       "      <th>mp_plotare</th>\n",
       "      <th>heritage_n</th>\n",
       "      <th>amenity</th>\n",
       "      <th>name</th>\n",
       "      <th>building</th>\n",
       "      <th>levels</th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "      <th>build_prog</th>\n",
       "      <th>build_ager</th>\n",
       "      <th>build_foot</th>\n",
       "      <th>geometry</th>\n",
       "      <th>gfa</th>\n",
       "      <th>levels_proxy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22875450</td>\n",
       "      <td>RESIDENTIAL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>commercial</td>\n",
       "      <td>3063.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>train_station</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>transport</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3063.0</td>\n",
       "      <td>POLYGON ((103.74901 1.34852, 103.74912 1.34866...</td>\n",
       "      <td>10720.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22886442</td>\n",
       "      <td>MASS RAPID TRANSIT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5117.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>train_station</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>transport</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5117.0</td>\n",
       "      <td>POLYGON ((103.70508 1.33839, 103.70518 1.33842...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24680746</td>\n",
       "      <td>SPORTS &amp; RECREATION</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4326.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>civic</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>civic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4326.0</td>\n",
       "      <td>POLYGON ((103.88808 1.37098, 103.88812 1.37102...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26561741</td>\n",
       "      <td>PORT / AIRPORT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120476.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Changi Airport Terminal 1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>transport</td>\n",
       "      <td>1975-1999</td>\n",
       "      <td>120476.0</td>\n",
       "      <td>POLYGON ((103.99431 1.36489, 103.99437 1.36494...</td>\n",
       "      <td>361428.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30527021</td>\n",
       "      <td>COMMERCIAL</td>\n",
       "      <td>4.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5964.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sim Lim Square</td>\n",
       "      <td>retail</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>deco_box</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5964.0</td>\n",
       "      <td>POLYGON ((103.85308 1.30254, 103.85302 1.30259...</td>\n",
       "      <td>35784.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113425</th>\n",
       "      <td>1165422507</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>NaN</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>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113426</th>\n",
       "      <td>1165422508</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>NaN</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>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113427</th>\n",
       "      <td>1165450372</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>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1925-1949</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113428</th>\n",
       "      <td>1165450373</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>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1925-1949</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113429</th>\n",
       "      <td>1165450374</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>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1925-1949</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>113430 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             osmid              mp_prog  far  osmlanduse  mp_plotare   \n",
       "0         22875450          RESIDENTIAL  3.5  commercial      3063.0  \\\n",
       "1         22886442   MASS RAPID TRANSIT  NaN         NaN      5117.0   \n",
       "2         24680746  SPORTS & RECREATION  NaN         NaN      4326.0   \n",
       "3         26561741       PORT / AIRPORT  NaN         NaN    120476.0   \n",
       "4         30527021           COMMERCIAL  4.2         NaN      5964.0   \n",
       "...            ...                  ...  ...         ...         ...   \n",
       "113425  1165422507                  NaN  NaN         NaN         NaN   \n",
       "113426  1165422508                  NaN  NaN         NaN         NaN   \n",
       "113427  1165450372                  NaN  NaN         NaN         NaN   \n",
       "113428  1165450373                  NaN  NaN         NaN         NaN   \n",
       "113429  1165450374                  NaN  NaN         NaN         NaN   \n",
       "\n",
       "       heritage_n amenity                       name       building  levels   \n",
       "0             NaN     NaN                        NaN  train_station       0  \\\n",
       "1             NaN     NaN                        NaN  train_station       0   \n",
       "2             NaN     NaN                        NaN          civic       0   \n",
       "3             NaN     NaN  Changi Airport Terminal 1            NaN       3   \n",
       "4             NaN     NaN             Sim Lim Square         retail       6   \n",
       "...           ...     ...                        ...            ...     ...   \n",
       "113425        NaN     NaN                        NaN            NaN       0   \n",
       "113426        NaN     NaN                        NaN            NaN       0   \n",
       "113427        NaN     NaN                        NaN            NaN      29   \n",
       "113428        NaN     NaN                        NaN            NaN      29   \n",
       "113429        NaN     NaN                        NaN            NaN      29   \n",
       "\n",
       "           age  height build_prog build_ager  build_foot   \n",
       "0          NaN     NaN  transport        NaN      3063.0  \\\n",
       "1          NaN     NaN  transport        NaN      5117.0   \n",
       "2          NaN     NaN      civic        NaN      4326.0   \n",
       "3       1981.0    30.0  transport  1975-1999    120476.0   \n",
       "4          NaN     NaN   deco_box        NaN      5964.0   \n",
       "...        ...     ...        ...        ...         ...   \n",
       "113425     NaN     NaN        NaN        NaN         NaN   \n",
       "113426     NaN     NaN        NaN        NaN         NaN   \n",
       "113427     NaN    29.0        NaN  1925-1949         NaN   \n",
       "113428     NaN    29.0        NaN  1925-1949         NaN   \n",
       "113429     NaN    29.0        NaN  1925-1949         NaN   \n",
       "\n",
       "                                                 geometry       gfa   \n",
       "0       POLYGON ((103.74901 1.34852, 103.74912 1.34866...   10720.5  \\\n",
       "1       POLYGON ((103.70508 1.33839, 103.70518 1.33842...       NaN   \n",
       "2       POLYGON ((103.88808 1.37098, 103.88812 1.37102...       NaN   \n",
       "3       POLYGON ((103.99431 1.36489, 103.99437 1.36494...  361428.0   \n",
       "4       POLYGON ((103.85308 1.30254, 103.85302 1.30259...   35784.0   \n",
       "...                                                   ...       ...   \n",
       "113425                                               None       NaN   \n",
       "113426                                               None       NaN   \n",
       "113427                                               None       NaN   \n",
       "113428                                               None       NaN   \n",
       "113429                                               None       NaN   \n",
       "\n",
       "        levels_proxy  \n",
       "0                  0  \n",
       "1                  0  \n",
       "2                  0  \n",
       "3                  3  \n",
       "4                  6  \n",
       "...              ...  \n",
       "113425             0  \n",
       "113426             0  \n",
       "113427            29  \n",
       "113428            29  \n",
       "113429            29  \n",
       "\n",
       "[113430 rows x 18 columns]"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "build_prog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "id": "946d0c9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "build_prog = build_prog.drop(columns=['levels_proxy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "id": "b3e6a799",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Geographic 2D CRS: EPSG:4326>\n",
       "Name: WGS 84\n",
       "Axis Info [ellipsoidal]:\n",
       "- Lat[north]: Geodetic latitude (degree)\n",
       "- Lon[east]: Geodetic longitude (degree)\n",
       "Area of Use:\n",
       "- name: World.\n",
       "- bounds: (-180.0, -90.0, 180.0, 90.0)\n",
       "Datum: World Geodetic System 1984 ensemble\n",
       "- Ellipsoid: WGS 84\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 385,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "build_prog.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "id": "f571eecb",
   "metadata": {},
   "outputs": [],
   "source": [
    "build_prog.crs = 3857"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "id": "a9acfdf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "build_prog.crs = 3414"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "id": "31bf96db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "build_prog.plot(color='blue', edgecolor='black')\n",
    "\n",
    "# show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "id": "b2926b36",
   "metadata": {},
   "outputs": [],
   "source": [
    "# build_prog['build_foot'] = build_prog.geometry.area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "id": "7b3a5e42",
   "metadata": {},
   "outputs": [],
   "source": [
    "build_prog['levels'] = build_prog['levels'].replace({0: None})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "id": "0c3a5a8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#copy levels to levels_proxy\n",
    "build_prog['levels_proxy'] = build_prog['levels']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "id": "ad50aa6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate levels from division of GFA by building footprint not exceedind 65 (max floors built)\n",
    "mask = (build_prog['gfa'] >= 50) & (build_prog['build_foot'] >= 50)\n",
    "build_prog.loc[mask, 'levels_proxy'] = build_prog['gfa'] // build_prog['build_foot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "id": "b70650fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 430,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count = (build_prog['levels_proxy'] >= 70).sum()\n",
    "count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "id": "da1f465c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 423,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "build_prog['levels_proxy'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "id": "6ae4f410",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70"
      ]
     },
     "execution_count": 429,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "build_prog['levels_proxy'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "id": "ca7e1b02",
   "metadata": {},
   "outputs": [],
   "source": [
    "#equalize skyscrapers\n",
    "import random\n",
    "\n",
    "# create a boolean mask to select values larger than 70\n",
    "mask = build_prog['levels_proxy'] >= 70\n",
    "\n",
    "# count the number of values larger than 70\n",
    "count = mask.sum()\n",
    "\n",
    "# randomly select values between 50 and 69\n",
    "random_values = random.choices(range(50, 70), k=count)\n",
    "\n",
    "# set values larger than 70 to 70 with a proportion of 3:1\n",
    "build_prog.loc[mask, 'levels_proxy'] = [70 if i % 4 == 0 else random_values.pop() for i in range(count)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "id": "a5d16925",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4717"
      ]
     },
     "execution_count": 440,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "mask1 = (build_prog['levels_proxy'] <= 5) & (~build_prog['heritage_n'].isnull())\n",
    "\n",
    "len(build_prog[mask1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "id": "c2e40d81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2465"
      ]
     },
     "execution_count": 442,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask2 = (build_prog['levels_proxy'].isnull()) & (~build_prog['heritage_n'].isnull())\n",
    "\n",
    "len(build_prog[mask2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "id": "ad630cd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#add heritage house levels_proxy = 5\n",
    "random_numbers = np.random.randint(low=1, high=6, size=sum(mask2))\n",
    "\n",
    "# Use the mask to update the 'levels_proxy' column with the random numbers\n",
    "build_prog.loc[mask2, 'levels_proxy'] = random_numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "id": "12a34d45",
   "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",
    "gdfy = build_prog\n",
    "counts = gdfy.count()\n",
    "\n",
    "# create a bar plot of non-NaN counts with red color\n",
    "ax = counts.plot(kind='bar', color='red')\n",
    "\n",
    "# add the count percentage above each bar\n",
    "for i, v in enumerate(counts):\n",
    "    ax.text(i, v+5, str(round((v/gdfy.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": 446,
   "id": "6525c9ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rromu\\AppData\\Local\\Temp\\ipykernel_9396\\822801641.py:1: UserWarning: Column names longer than 10 characters will be truncated when saved to ESRI Shapefile.\n",
      "  build_prog.to_file('C:/WORK_IAAC/submissions/Internet of Buildings/IoB_WIP/IoB_QGIS/buildings/buildings_05.shp', crs='EPSG:4326')\n"
     ]
    }
   ],
   "source": [
    "build_prog.to_file('C:/WORK_IAAC/submissions/Internet of Buildings/IoB_WIP/IoB_QGIS/buildings/buildings_05.shp', crs='EPSG:4326')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "id": "fc27cfef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>descriptio</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>begin</th>\n",
       "      <th>end</th>\n",
       "      <th>altitudeMo</th>\n",
       "      <th>tessellate</th>\n",
       "      <th>extrude</th>\n",
       "      <th>visibility</th>\n",
       "      <th>drawOrder</th>\n",
       "      <th>...</th>\n",
       "      <th>AWARD</th>\n",
       "      <th>HYPERLINK</th>\n",
       "      <th>LANDXADDRE</th>\n",
       "      <th>LANDYADDRE</th>\n",
       "      <th>PHOTOURL</th>\n",
       "      <th>INC_CRC</th>\n",
       "      <th>FMEL_UPD_D</th>\n",
       "      <th>ADDRESSUNI</th>\n",
       "      <th>snippet</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>yo:HA Commercial @ Tampines</td>\n",
       "      <td>FY18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Gold</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F10AF38C524E6E40</td>\n",
       "      <td>20190214162336</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT Z (103.94177 1.33928 0.00000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Volari</td>\n",
       "      <td>FY09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>https://www.bca.gov.sg/green_mark/Sustain/Buil...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61808A22148C5DB7</td>\n",
       "      <td>20190214162336</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT Z (103.83293 1.31489 0.00000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9-11 Buckley</td>\n",
       "      <td>FY10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Gold Plus</td>\n",
       "      <td>https://www.bca.gov.sg/green_mark/Sustain/Buil...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82A734FB534CD168</td>\n",
       "      <td>20190214162336</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT Z (103.84033 1.31796 0.00000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Regent Singapore, A Four Seasons Hotel</td>\n",
       "      <td>FY07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Gold Plus</td>\n",
       "      <td>https://www.bca.gov.sg/green_mark/Sustain/Buil...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FE4C128E9D8412DF</td>\n",
       "      <td>20190214162336</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT Z (103.82490 1.30467 0.00000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Livia</td>\n",
       "      <td>FY08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Gold Plus</td>\n",
       "      <td>https://www.bca.gov.sg/green_mark/Sustain/Buil...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>09BD8DB302533B8C</td>\n",
       "      <td>20190214162336</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT Z (103.94382 1.37234 0.00000)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Name descriptio  timestamp  begin   \n",
       "0                 yo:HA Commercial @ Tampines       FY18        NaN    NaN  \\\n",
       "1                                      Volari       FY09        NaN    NaN   \n",
       "2                                9-11 Buckley       FY10        NaN    NaN   \n",
       "3  The Regent Singapore, A Four Seasons Hotel       FY07        NaN    NaN   \n",
       "4                                       Livia       FY08        NaN    NaN   \n",
       "\n",
       "   end  altitudeMo  tessellate  extrude  visibility  drawOrder  ...   \n",
       "0  NaN         NaN          -1        0          -1        NaN  ...  \\\n",
       "1  NaN         NaN          -1        0          -1        NaN  ...   \n",
       "2  NaN         NaN          -1        0          -1        NaN  ...   \n",
       "3  NaN         NaN          -1        0          -1        NaN  ...   \n",
       "4  NaN         NaN          -1        0          -1        NaN  ...   \n",
       "\n",
       "       AWARD                                          HYPERLINK  LANDXADDRE   \n",
       "0       Gold                                                NaN           0  \\\n",
       "1   Platinum  https://www.bca.gov.sg/green_mark/Sustain/Buil...           0   \n",
       "2  Gold Plus  https://www.bca.gov.sg/green_mark/Sustain/Buil...           0   \n",
       "3  Gold Plus  https://www.bca.gov.sg/green_mark/Sustain/Buil...           0   \n",
       "4  Gold Plus  https://www.bca.gov.sg/green_mark/Sustain/Buil...           0   \n",
       "\n",
       "   LANDYADDRE PHOTOURL           INC_CRC      FMEL_UPD_D ADDRESSUNI snippet   \n",
       "0           0      NaN  F10AF38C524E6E40  20190214162336        NaN     NaN  \\\n",
       "1           0      NaN  61808A22148C5DB7  20190214162336        NaN     NaN   \n",
       "2           0      NaN  82A734FB534CD168  20190214162336        NaN     NaN   \n",
       "3           0      NaN  FE4C128E9D8412DF  20190214162336        NaN     NaN   \n",
       "4           0      NaN  09BD8DB302533B8C  20190214162336        NaN     NaN   \n",
       "\n",
       "                              geometry  \n",
       "0  POINT Z (103.94177 1.33928 0.00000)  \n",
       "1  POINT Z (103.83293 1.31489 0.00000)  \n",
       "2  POINT Z (103.84033 1.31796 0.00000)  \n",
       "3  POINT Z (103.82490 1.30467 0.00000)  \n",
       "4  POINT Z (103.94382 1.37234 0.00000)  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 449,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bgm = gpd.read_file(r'C:\\WORK_IAAC\\submissions\\Internet of Buildings\\IoB_WIP\\IoB_QGIS\\buildings\\buildings_greenmark.shp', encoding='latin-1')\n",
    "bgm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "id": "153ba63a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
      "RangeIndex: 2105 entries, 0 to 2104\n",
      "Data columns (total 27 columns):\n",
      " #   Column      Non-Null Count  Dtype   \n",
      "---  ------      --------------  -----   \n",
      " 0   Name        2105 non-null   object  \n",
      " 1   descriptio  2105 non-null   object  \n",
      " 2   timestamp   0 non-null      float64 \n",
      " 3   begin       0 non-null      float64 \n",
      " 4   end         0 non-null      float64 \n",
      " 5   altitudeMo  0 non-null      float64 \n",
      " 6   tessellate  2105 non-null   int64   \n",
      " 7   extrude     2105 non-null   int64   \n",
      " 8   visibility  2105 non-null   int64   \n",
      " 9   drawOrder   0 non-null      float64 \n",
      " 10  icon        0 non-null      float64 \n",
      " 11  ADDRESSBLO  0 non-null      float64 \n",
      " 12  ADDRESSBUI  0 non-null      float64 \n",
      " 13  ADDRESSFLO  0 non-null      float64 \n",
      " 14  ADDRESSPOS  2105 non-null   object  \n",
      " 15  ADDRESSSTR  0 non-null      float64 \n",
      " 16  ADDRESSTYP  0 non-null      float64 \n",
      " 17  AWARD       2105 non-null   object  \n",
      " 18  HYPERLINK   1500 non-null   object  \n",
      " 19  LANDXADDRE  2105 non-null   object  \n",
      " 20  LANDYADDRE  2105 non-null   object  \n",
      " 21  PHOTOURL    0 non-null      float64 \n",
      " 22  INC_CRC     2105 non-null   object  \n",
      " 23  FMEL_UPD_D  2105 non-null   object  \n",
      " 24  ADDRESSUNI  0 non-null      float64 \n",
      " 25  snippet     0 non-null      float64 \n",
      " 26  geometry    2105 non-null   geometry\n",
      "dtypes: float64(14), geometry(1), int64(3), object(9)\n",
      "memory usage: 444.1+ KB\n"
     ]
    }
   ],
   "source": [
    "bgm.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "id": "96f66d1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>descriptio</th>\n",
       "      <th>AWARD</th>\n",
       "      <th>INC_CRC</th>\n",
       "      <th>FMEL_UPD_D</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>yo:HA Commercial @ Tampines</td>\n",
       "      <td>FY18</td>\n",
       "      <td>Gold</td>\n",
       "      <td>F10AF38C524E6E40</td>\n",
       "      <td>20190214162336</td>\n",
       "      <td>POINT Z (103.94177 1.33928 0.00000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Volari</td>\n",
       "      <td>FY09</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>61808A22148C5DB7</td>\n",
       "      <td>20190214162336</td>\n",
       "      <td>POINT Z (103.83293 1.31489 0.00000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9-11 Buckley</td>\n",
       "      <td>FY10</td>\n",
       "      <td>Gold Plus</td>\n",
       "      <td>82A734FB534CD168</td>\n",
       "      <td>20190214162336</td>\n",
       "      <td>POINT Z (103.84033 1.31796 0.00000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Regent Singapore, A Four Seasons Hotel</td>\n",
       "      <td>FY07</td>\n",
       "      <td>Gold Plus</td>\n",
       "      <td>FE4C128E9D8412DF</td>\n",
       "      <td>20190214162336</td>\n",
       "      <td>POINT Z (103.82490 1.30467 0.00000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Livia</td>\n",
       "      <td>FY08</td>\n",
       "      <td>Gold Plus</td>\n",
       "      <td>09BD8DB302533B8C</td>\n",
       "      <td>20190214162336</td>\n",
       "      <td>POINT Z (103.94382 1.37234 0.00000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100</th>\n",
       "      <td>N1.1</td>\n",
       "      <td>FY15</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>1D5749FF6C021D5D</td>\n",
       "      <td>20190214162337</td>\n",
       "      <td>POINT Z (103.67882 1.34676 0.00000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2101</th>\n",
       "      <td>Administration Building</td>\n",
       "      <td>FY15</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>1D5749FFFDAD444B</td>\n",
       "      <td>20190214162337</td>\n",
       "      <td>POINT Z (103.67882 1.34676 0.00000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2102</th>\n",
       "      <td>School of Materials Science and Engineering (B...</td>\n",
       "      <td>FY15</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>1D5749FFA6625D1B</td>\n",
       "      <td>20190214162337</td>\n",
       "      <td>POINT Z (103.67882 1.34676 0.00000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2103</th>\n",
       "      <td>School of Mechanical &amp; Aerospace Engineering (...</td>\n",
       "      <td>FY15</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>1D5749FF98D70383</td>\n",
       "      <td>20190214162337</td>\n",
       "      <td>POINT Z (103.67882 1.34676 0.00000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2104</th>\n",
       "      <td>School of Mechanical &amp; Aerospace Engineering (...</td>\n",
       "      <td>FY15</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>F27AD03B0A92270B</td>\n",
       "      <td>20190214162337</td>\n",
       "      <td>POINT Z (103.68233 1.34757 0.00000)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2105 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   Name descriptio      AWARD   \n",
       "0                           yo:HA Commercial @ Tampines       FY18       Gold  \\\n",
       "1                                                Volari       FY09   Platinum   \n",
       "2                                          9-11 Buckley       FY10  Gold Plus   \n",
       "3            The Regent Singapore, A Four Seasons Hotel       FY07  Gold Plus   \n",
       "4                                                 Livia       FY08  Gold Plus   \n",
       "...                                                 ...        ...        ...   \n",
       "2100                                               N1.1       FY15   Platinum   \n",
       "2101                            Administration Building       FY15   Platinum   \n",
       "2102  School of Materials Science and Engineering (B...       FY15   Platinum   \n",
       "2103  School of Mechanical & Aerospace Engineering (...       FY15   Platinum   \n",
       "2104  School of Mechanical & Aerospace Engineering (...       FY15   Platinum   \n",
       "\n",
       "               INC_CRC      FMEL_UPD_D                             geometry  \n",
       "0     F10AF38C524E6E40  20190214162336  POINT Z (103.94177 1.33928 0.00000)  \n",
       "1     61808A22148C5DB7  20190214162336  POINT Z (103.83293 1.31489 0.00000)  \n",
       "2     82A734FB534CD168  20190214162336  POINT Z (103.84033 1.31796 0.00000)  \n",
       "3     FE4C128E9D8412DF  20190214162336  POINT Z (103.82490 1.30467 0.00000)  \n",
       "4     09BD8DB302533B8C  20190214162336  POINT Z (103.94382 1.37234 0.00000)  \n",
       "...                ...             ...                                  ...  \n",
       "2100  1D5749FF6C021D5D  20190214162337  POINT Z (103.67882 1.34676 0.00000)  \n",
       "2101  1D5749FFFDAD444B  20190214162337  POINT Z (103.67882 1.34676 0.00000)  \n",
       "2102  1D5749FFA6625D1B  20190214162337  POINT Z (103.67882 1.34676 0.00000)  \n",
       "2103  1D5749FF98D70383  20190214162337  POINT Z (103.67882 1.34676 0.00000)  \n",
       "2104  F27AD03B0A92270B  20190214162337  POINT Z (103.68233 1.34757 0.00000)  \n",
       "\n",
       "[2105 rows x 6 columns]"
      ]
     },
     "execution_count": 452,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reduce columns\n",
    "bgm.iloc[:, [0, 1, 17, 22, 23, 26]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "id": "74e02a02",
   "metadata": {},
   "outputs": [],
   "source": [
    "bgm2.crs = 3414\n",
    "build_prog.crs = 3414"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "id": "803bad61",
   "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": 457,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bgm.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "id": "9b86fafa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>osmid_left</th>\n",
       "      <th>Name_2</th>\n",
       "      <th>descriptio</th>\n",
       "      <th>ADDRESSPOS</th>\n",
       "      <th>AWARD</th>\n",
       "      <th>HYPERLINK</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>124529184</td>\n",
       "      <td>yo:HA Commercial @ Tampines</td>\n",
       "      <td>FY18</td>\n",
       "      <td>528839</td>\n",
       "      <td>Gold</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((103.94075 1.33870, 103.94079 1.33908...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>727217814</td>\n",
       "      <td>9-11 Buckley</td>\n",
       "      <td>FY10</td>\n",
       "      <td>309767</td>\n",
       "      <td>Gold Plus</td>\n",
       "      <td>https://www.bca.gov.sg/green_mark/Sustain/Buil...</td>\n",
       "      <td>POLYGON ((103.84042 1.31835, 103.84059 1.31829...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>320467194</td>\n",
       "      <td>The Regent Singapore, A Four Seasons Hotel</td>\n",
       "      <td>FY07</td>\n",
       "      <td>249715</td>\n",
       "      <td>Gold Plus</td>\n",
       "      <td>https://www.bca.gov.sg/green_mark/Sustain/Buil...</td>\n",
       "      <td>POLYGON ((103.82428 1.30455, 103.82504 1.30501...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>453795749</td>\n",
       "      <td>Livia</td>\n",
       "      <td>FY08</td>\n",
       "      <td>518215</td>\n",
       "      <td>Gold Plus</td>\n",
       "      <td>https://www.bca.gov.sg/green_mark/Sustain/Buil...</td>\n",
       "      <td>POLYGON ((103.94381 1.37256, 103.94408 1.37245...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>543793947</td>\n",
       "      <td>Lower Seletar Waterworks (Membrane Filtration ...</td>\n",
       "      <td>FY16</td>\n",
       "      <td>797490</td>\n",
       "      <td>Gold</td>\n",
       "      <td>https://www.bca.gov.sg/green_mark/Sustain/Buil...</td>\n",
       "      <td>POLYGON ((103.85538 1.40045, 103.85540 1.40029...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   osmid_left                                             Name_2 descriptio   \n",
       "0   124529184                        yo:HA Commercial @ Tampines       FY18  \\\n",
       "1   727217814                                       9-11 Buckley       FY10   \n",
       "2   320467194         The Regent Singapore, A Four Seasons Hotel       FY07   \n",
       "3   453795749                                              Livia       FY08   \n",
       "4   543793947  Lower Seletar Waterworks (Membrane Filtration ...       FY16   \n",
       "\n",
       "  ADDRESSPOS      AWARD                                          HYPERLINK   \n",
       "0     528839       Gold                                                NaN  \\\n",
       "1     309767  Gold Plus  https://www.bca.gov.sg/green_mark/Sustain/Buil...   \n",
       "2     249715  Gold Plus  https://www.bca.gov.sg/green_mark/Sustain/Buil...   \n",
       "3     518215  Gold Plus  https://www.bca.gov.sg/green_mark/Sustain/Buil...   \n",
       "4     797490       Gold  https://www.bca.gov.sg/green_mark/Sustain/Buil...   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((103.94075 1.33870, 103.94079 1.33908...  \n",
       "1  POLYGON ((103.84042 1.31835, 103.84059 1.31829...  \n",
       "2  POLYGON ((103.82428 1.30455, 103.82504 1.30501...  \n",
       "3  POLYGON ((103.94381 1.37256, 103.94408 1.37245...  \n",
       "4  POLYGON ((103.85538 1.40045, 103.85540 1.40029...  "
      ]
     },
     "execution_count": 460,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bgm1 = gpd.read_file(r'C:\\WORK_IAAC\\submissions\\Internet of Buildings\\IoB_WIP\\IoB_Singa\\separate\\only_greenmark\\only_greenmark.shp', encoding='latin-1')\n",
    "bgm1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "id": "39d78b62",
   "metadata": {},
   "outputs": [],
   "source": [
    "bgm2 = bgm1.dropna(subset=['AWARD'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "id": "58b18f52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
      "Index: 1683 entries, 0 to 1682\n",
      "Data columns (total 7 columns):\n",
      " #   Column      Non-Null Count  Dtype   \n",
      "---  ------      --------------  -----   \n",
      " 0   osmid_left  1683 non-null   int64   \n",
      " 1   Name_2      1683 non-null   object  \n",
      " 2   descriptio  1683 non-null   object  \n",
      " 3   ADDRESSPOS  1683 non-null   object  \n",
      " 4   AWARD       1683 non-null   object  \n",
      " 5   HYPERLINK   1176 non-null   object  \n",
      " 6   geometry    1683 non-null   geometry\n",
      "dtypes: geometry(1), int64(1), object(5)\n",
      "memory usage: 105.2+ KB\n"
     ]
    }
   ],
   "source": [
    "bgm2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "id": "df52f169",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
      "Index: 1683 entries, 0 to 1682\n",
      "Data columns (total 7 columns):\n",
      " #   Column    Non-Null Count  Dtype   \n",
      "---  ------    --------------  -----   \n",
      " 0   osmid     1683 non-null   int64   \n",
      " 1   gm_name   1683 non-null   object  \n",
      " 2   gm_phase  1683 non-null   object  \n",
      " 3   postcode  1683 non-null   object  \n",
      " 4   gm_award  1683 non-null   object  \n",
      " 5   gm_link   1176 non-null   object  \n",
      " 6   geometry  1683 non-null   geometry\n",
      "dtypes: geometry(1), int64(1), object(5)\n",
      "memory usage: 105.2+ KB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rromu\\AppData\\Local\\Temp\\ipykernel_9396\\2081528711.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  bgm2.rename(columns={'osmid_left': 'osmid', 'Name_2': 'gm_name', 'descriptio': 'gm_phase', 'ADDRESSPOS': 'postcode', 'AWARD': 'gm_award', 'HYPERLINK': 'gm_link'}, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "#rename columns\n",
    "bgm2.rename(columns={'osmid_left': 'osmid', 'Name_2': 'gm_name', 'descriptio': 'gm_phase', 'ADDRESSPOS': 'postcode', 'AWARD': 'gm_award', 'HYPERLINK': 'gm_link'}, inplace=True)\n",
    "bgm2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "id": "443db8d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "bgm3 = pd.DataFrame(bgm2)\n",
    "bgm4 = bgm3.drop(columns=bgm3.columns[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "id": "9c13fbbe",
   "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>mp_prog</th>\n",
       "      <th>far</th>\n",
       "      <th>osmlanduse</th>\n",
       "      <th>mp_plotare</th>\n",
       "      <th>heritage_n</th>\n",
       "      <th>amenity</th>\n",
       "      <th>name</th>\n",
       "      <th>building</th>\n",
       "      <th>levels</th>\n",
       "      <th>...</th>\n",
       "      <th>build_ager</th>\n",
       "      <th>build_foot</th>\n",
       "      <th>geometry</th>\n",
       "      <th>gfa</th>\n",
       "      <th>levels_proxy</th>\n",
       "      <th>gm_name</th>\n",
       "      <th>gm_phase</th>\n",
       "      <th>postcode</th>\n",
       "      <th>gm_award</th>\n",
       "      <th>gm_link</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22875450</td>\n",
       "      <td>RESIDENTIAL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>commercial</td>\n",
       "      <td>3063.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>train_station</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3063.0</td>\n",
       "      <td>POLYGON ((103.749 1.349, 103.749 1.349, 103.74...</td>\n",
       "      <td>10720.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22886442</td>\n",
       "      <td>MASS RAPID TRANSIT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5117.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>train_station</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5117.0</td>\n",
       "      <td>POLYGON ((103.705 1.338, 103.705 1.338, 103.70...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24680746</td>\n",
       "      <td>SPORTS &amp; RECREATION</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4326.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>civic</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4326.0</td>\n",
       "      <td>POLYGON ((103.888 1.371, 103.888 1.371, 103.88...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26561741</td>\n",
       "      <td>PORT / AIRPORT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120476.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Changi Airport Terminal 1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1975-1999</td>\n",
       "      <td>120476.0</td>\n",
       "      <td>POLYGON ((103.994 1.365, 103.994 1.365, 103.99...</td>\n",
       "      <td>361428.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Changi Airport Terminal 1</td>\n",
       "      <td>FY13</td>\n",
       "      <td>819642</td>\n",
       "      <td>Gold</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26561741</td>\n",
       "      <td>PORT / AIRPORT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120476.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Changi Airport Terminal 1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1975-1999</td>\n",
       "      <td>120476.0</td>\n",
       "      <td>POLYGON ((103.994 1.365, 103.994 1.365, 103.99...</td>\n",
       "      <td>361428.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Changi Airport Terminal 1</td>\n",
       "      <td>FY13</td>\n",
       "      <td>819642</td>\n",
       "      <td>Gold</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>114027</th>\n",
       "      <td>1165422507</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>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114028</th>\n",
       "      <td>1165422508</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>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114029</th>\n",
       "      <td>1165450372</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>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>1925-1949</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114030</th>\n",
       "      <td>1165450373</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>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>1925-1949</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114031</th>\n",
       "      <td>1165450374</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>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>1925-1949</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>114032 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             osmid              mp_prog  far  osmlanduse  mp_plotare   \n",
       "0         22875450          RESIDENTIAL  3.5  commercial      3063.0  \\\n",
       "1         22886442   MASS RAPID TRANSIT  NaN         NaN      5117.0   \n",
       "2         24680746  SPORTS & RECREATION  NaN         NaN      4326.0   \n",
       "3         26561741       PORT / AIRPORT  NaN         NaN    120476.0   \n",
       "4         26561741       PORT / AIRPORT  NaN         NaN    120476.0   \n",
       "...            ...                  ...  ...         ...         ...   \n",
       "114027  1165422507                  NaN  NaN         NaN         NaN   \n",
       "114028  1165422508                  NaN  NaN         NaN         NaN   \n",
       "114029  1165450372                  NaN  NaN         NaN         NaN   \n",
       "114030  1165450373                  NaN  NaN         NaN         NaN   \n",
       "114031  1165450374                  NaN  NaN         NaN         NaN   \n",
       "\n",
       "       heritage_n amenity                       name       building levels   \n",
       "0             NaN     NaN                        NaN  train_station   None  \\\n",
       "1             NaN     NaN                        NaN  train_station   None   \n",
       "2             NaN     NaN                        NaN          civic   None   \n",
       "3             NaN     NaN  Changi Airport Terminal 1            NaN      3   \n",
       "4             NaN     NaN  Changi Airport Terminal 1            NaN      3   \n",
       "...           ...     ...                        ...            ...    ...   \n",
       "114027        NaN     NaN                        NaN            NaN   None   \n",
       "114028        NaN     NaN                        NaN            NaN   None   \n",
       "114029        NaN     NaN                        NaN            NaN     29   \n",
       "114030        NaN     NaN                        NaN            NaN     29   \n",
       "114031        NaN     NaN                        NaN            NaN     29   \n",
       "\n",
       "        ...  build_ager  build_foot   \n",
       "0       ...         NaN      3063.0  \\\n",
       "1       ...         NaN      5117.0   \n",
       "2       ...         NaN      4326.0   \n",
       "3       ...   1975-1999    120476.0   \n",
       "4       ...   1975-1999    120476.0   \n",
       "...     ...         ...         ...   \n",
       "114027  ...         NaN         NaN   \n",
       "114028  ...         NaN         NaN   \n",
       "114029  ...   1925-1949         NaN   \n",
       "114030  ...   1925-1949         NaN   \n",
       "114031  ...   1925-1949         NaN   \n",
       "\n",
       "                                                 geometry       gfa   \n",
       "0       POLYGON ((103.749 1.349, 103.749 1.349, 103.74...   10720.5  \\\n",
       "1       POLYGON ((103.705 1.338, 103.705 1.338, 103.70...       NaN   \n",
       "2       POLYGON ((103.888 1.371, 103.888 1.371, 103.88...       NaN   \n",
       "3       POLYGON ((103.994 1.365, 103.994 1.365, 103.99...  361428.0   \n",
       "4       POLYGON ((103.994 1.365, 103.994 1.365, 103.99...  361428.0   \n",
       "...                                                   ...       ...   \n",
       "114027                                               None       NaN   \n",
       "114028                                               None       NaN   \n",
       "114029                                               None       NaN   \n",
       "114030                                               None       NaN   \n",
       "114031                                               None       NaN   \n",
       "\n",
       "        levels_proxy                    gm_name  gm_phase postcode gm_award   \n",
       "0                3.0                        NaN       NaN      NaN      NaN  \\\n",
       "1               None                        NaN       NaN      NaN      NaN   \n",
       "2               None                        NaN       NaN      NaN      NaN   \n",
       "3                3.0  Changi Airport Terminal 1      FY13   819642     Gold   \n",
       "4                3.0  Changi Airport Terminal 1      FY13   819642     Gold   \n",
       "...              ...                        ...       ...      ...      ...   \n",
       "114027          None                        NaN       NaN      NaN      NaN   \n",
       "114028          None                        NaN       NaN      NaN      NaN   \n",
       "114029            29                        NaN       NaN      NaN      NaN   \n",
       "114030            29                        NaN       NaN      NaN      NaN   \n",
       "114031            29                        NaN       NaN      NaN      NaN   \n",
       "\n",
       "       gm_link  \n",
       "0          NaN  \n",
       "1          NaN  \n",
       "2          NaN  \n",
       "3          NaN  \n",
       "4          NaN  \n",
       "...        ...  \n",
       "114027     NaN  \n",
       "114028     NaN  \n",
       "114029     NaN  \n",
       "114030     NaN  \n",
       "114031     NaN  \n",
       "\n",
       "[114032 rows x 23 columns]"
      ]
     },
     "execution_count": 501,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joint = build_prog.merge(bgm4, on='osmid', how='left')\n",
    "joint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "id": "3bb9e39c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(114032, 23)"
      ]
     },
     "execution_count": 502,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joint.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "id": "ad589f12",
   "metadata": {},
   "outputs": [],
   "source": [
    "# joint.rename(columns={'geometry_x': 'geometry'}, inplace=True)\n",
    "#convert pandas to geopandas\n",
    "joint = gpd.GeoDataFrame(joint, crs='EPSG:3414')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "id": "2f9ac74e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(113430, 22)"
      ]
     },
     "execution_count": 504,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dissolve green mark duplicating data in dataset\n",
    "jointx = joint.dissolve(by='osmid', aggfunc='first')\n",
    "jointx.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "id": "c607c940",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\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>geometry</th>\n",
       "      <th>mp_prog</th>\n",
       "      <th>far</th>\n",
       "      <th>osmlanduse</th>\n",
       "      <th>mp_plotare</th>\n",
       "      <th>heritage_n</th>\n",
       "      <th>amenity</th>\n",
       "      <th>name</th>\n",
       "      <th>building</th>\n",
       "      <th>levels</th>\n",
       "      <th>...</th>\n",
       "      <th>build_prog</th>\n",
       "      <th>build_ager</th>\n",
       "      <th>build_foot</th>\n",
       "      <th>gfa</th>\n",
       "      <th>levels_proxy</th>\n",
       "      <th>gm_name</th>\n",
       "      <th>gm_phase</th>\n",
       "      <th>postcode</th>\n",
       "      <th>gm_award</th>\n",
       "      <th>gm_link</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>osmid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>small_box</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>None</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <td>None</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>1925-1949</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "    <tr>\n",
       "      <th>1165450374</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>29</td>\n",
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       "      <td>None</td>\n",
       "      <td>1925-1949</td>\n",
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       "      <td>29</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>113430 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           geometry mp_prog  far osmlanduse  mp_plotare heritage_n  amenity   \n",
       "osmid                                                                         \n",
       "1337396        None    None  NaN       None         NaN       None     None  \\\n",
       "1362701        None    None  NaN       None         NaN       None  parking   \n",
       "1407265        None    None  NaN       None         NaN       None     None   \n",
       "1447583        None    None  NaN       None         NaN       None     None   \n",
       "1447589        None    None  NaN       None         NaN       None     None   \n",
       "...             ...     ...  ...        ...         ...        ...      ...   \n",
       "1165422507     None    None  NaN       None         NaN       None     None   \n",
       "1165422508     None    None  NaN       None         NaN       None     None   \n",
       "1165450372     None    None  NaN       None         NaN       None     None   \n",
       "1165450373     None    None  NaN       None         NaN       None     None   \n",
       "1165450374     None    None  NaN       None         NaN       None     None   \n",
       "\n",
       "            name building levels  ...  build_prog  build_ager build_foot gfa   \n",
       "osmid                             ...                                          \n",
       "1337396     None     None   None  ...   small_box        None        NaN NaN  \\\n",
       "1362701     None   garage      3  ...        None        None        NaN NaN   \n",
       "1407265     None     None   None  ...        None        None        NaN NaN   \n",
       "1447583       20     None     12  ...        None        None        NaN NaN   \n",
       "1447589       22     None     12  ...        None        None        NaN NaN   \n",
       "...          ...      ...    ...  ...         ...         ...        ...  ..   \n",
       "1165422507  None     None   None  ...        None        None        NaN NaN   \n",
       "1165422508  None     None   None  ...        None        None        NaN NaN   \n",
       "1165450372  None     None     29  ...        None   1925-1949        NaN NaN   \n",
       "1165450373  None     None     29  ...        None   1925-1949        NaN NaN   \n",
       "1165450374  None     None     29  ...        None   1925-1949        NaN NaN   \n",
       "\n",
       "            levels_proxy  gm_name gm_phase postcode gm_award gm_link  \n",
       "osmid                                                                 \n",
       "1337396             None     None     None     None     None    None  \n",
       "1362701                3     None     None     None     None    None  \n",
       "1407265             None     None     None     None     None    None  \n",
       "1447583               12     None     None     None     None    None  \n",
       "1447589               12     None     None     None     None    None  \n",
       "...                  ...      ...      ...      ...      ...     ...  \n",
       "1165422507          None     None     None     None     None    None  \n",
       "1165422508          None     None     None     None     None    None  \n",
       "1165450372            29     None     None     None     None    None  \n",
       "1165450373            29     None     None     None     None    None  \n",
       "1165450374            29     None     None     None     None    None  \n",
       "\n",
       "[113430 rows x 22 columns]"
      ]
     },
     "execution_count": 493,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jointx.drop(columns=jointx.columns[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "id": "8f3756c9",
   "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",
    "gdfy = jointx\n",
    "counts = gdfy.count()\n",
    "\n",
    "# create a bar plot of non-NaN counts with red color\n",
    "ax = counts.plot(kind='bar', color='red')\n",
    "\n",
    "# add the count percentage above each bar\n",
    "for i, v in enumerate(counts):\n",
    "    ax.text(i, v+5, str(round((v/gdfy.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": 506,
   "id": "907dc082",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
      "Index: 113430 entries, 1337396 to 1165450374\n",
      "Data columns (total 22 columns):\n",
      " #   Column        Non-Null Count   Dtype   \n",
      "---  ------        --------------   -----   \n",
      " 0   geometry      111889 non-null  geometry\n",
      " 1   mp_prog       111675 non-null  object  \n",
      " 2   far           44964 non-null   float64 \n",
      " 3   osmlanduse    85478 non-null   object  \n",
      " 4   mp_plotare    111889 non-null  float64 \n",
      " 5   heritage_n    7773 non-null    object  \n",
      " 6   amenity       3131 non-null    object  \n",
      " 7   name          11262 non-null   object  \n",
      " 8   building      40688 non-null   object  \n",
      " 9   levels        24044 non-null   object  \n",
      " 10  age           3617 non-null    float64 \n",
      " 11  height        4214 non-null    float64 \n",
      " 12  build_prog    111147 non-null  object  \n",
      " 13  build_ager    3725 non-null    object  \n",
      " 14  build_foot    111889 non-null  float64 \n",
      " 15  gfa           53294 non-null   float64 \n",
      " 16  levels_proxy  56162 non-null   object  \n",
      " 17  gm_name       1081 non-null    object  \n",
      " 18  gm_phase      1081 non-null    object  \n",
      " 19  postcode      1081 non-null    object  \n",
      " 20  gm_award      1081 non-null    object  \n",
      " 21  gm_link       781 non-null     object  \n",
      "dtypes: float64(6), geometry(1), object(15)\n",
      "memory usage: 23.9+ MB\n"
     ]
    }
   ],
   "source": [
    "jointx.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "id": "e042b5db",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rromu\\AppData\\Local\\Temp\\ipykernel_9396\\182196821.py:1: UserWarning: Column names longer than 10 characters will be truncated when saved to ESRI Shapefile.\n",
      "  jointx.to_file('C:/WORK_IAAC/submissions/Internet of Buildings/IoB_WIP/IoB_QGIS/buildings/buildings_06.shp', crs='EPSG:4326')\n"
     ]
    }
   ],
   "source": [
    "jointx.to_file('C:/WORK_IAAC/submissions/Internet of Buildings/IoB_WIP/IoB_QGIS/buildings/buildings_06.shp', crs='EPSG:4326')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef89cf30",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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