MyPerfectHousingDistrict_NUSLibrary.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# important library\n",
    "import psycopg2\n",
    "import psycopg2.extras\n",
    "import sys\n",
    "import pprint\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import json\n",
    "import re\n",
    "from PIL import Image\n",
    "import PIL.Image\n",
    "import collections\n",
    "import io\n",
    "from IPython.display import Image\n",
    "from shapely.geometry.multipolygon import MultiPolygon\n",
    "from shapely.geometry.polygon import Polygon\n",
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connecting to database\n",
      "\t->host='localhost' dbname='WTP_NUS_library'\n",
      "Connected!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# connect to database. Postgres must be opened!\n",
    "conn_string = \"host='localhost' dbname='WTP_NUS_library'\"\n",
    "print (\"Connecting to database\\n\t->%s\" % (conn_string))\n",
    "conn = psycopg2.connect(conn_string)\n",
    "print (\"Connected!\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all users\n",
    "db_users = pd.read_sql_query (\"SELECT * FROM \\\"user\\\"\", conn)\n",
    "# number of submission for each users\n",
    "db_count_submissions = pd.read_sql_query(\"\"\"SELECT author_id,COUNT(scenario.id) as n FROM SCENARIO GROUP BY author_id ORDER BY author_id\"\"\",conn)\n",
    "# latest submission\n",
    "db_scenarios_late = pd.read_sql_query(\"SELECT id, author_id, last_update, description, image, encode(geometry,'escape') as geo FROM scenario as a WHERE id = (SELECT max(b.id) FROM scenario as b where a.author_id = b.author_id) ORDER BY author_id\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all user actions\n",
    "db_user_scenario_load =pd.read_sql_query( \"SELECT * FROM user_scenario_load\",conn)\n",
    "db_user_scenario_update =pd.read_sql_query( \"SELECT * FROM user_scenario_update\",conn)\n",
    "db_user_scenario_action =pd.read_sql_query( \"SELECT * FROM user_scenario_action\",conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all cross feedback\n",
    "db_criterion = pd.read_sql_query( \"SELECT * FROM criterion\",conn)\n",
    "db_vote = pd.read_sql_query( \"SELECT * FROM vote\",conn)\n",
    "db_peer_review = pd.read_sql_query( \"SELECT * FROM review\",conn)\n",
    "db_expert_review = pd.read_sql_query(\"SELECT * FROM  expert_review\",conn)\n",
    "db_voter_score = pd.read_sql_query(\"SELECT * FROM vote_rating\", conn)\n",
    "db_voter_scenario = pd.read_sql_query(\"SELECT * FROM scenario\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# final grade and ratings\n",
    "db_grade = pd.read_sql_query(\"SELECT * FROM current_scenario\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Short cuts for important tables\n",
    "ld = db_user_scenario_load\n",
    "ud = db_user_scenario_update\n",
    "ac = db_user_scenario_action\n",
    "lt = db_scenarios_late"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>author_id</th>\n",
       "      <th>last_update</th>\n",
       "      <th>description</th>\n",
       "      <th>image</th>\n",
       "      <th>geo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>2018-01-21 09:23:24.601339+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>2018-01-20 08:48:59.247419+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>2018-01-20 08:57:10.925012+00:00</td>\n",
       "      <td>Evertafkdkj sdfsf hello!</td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>2018-01-21 06:58:45.763373+00:00</td>\n",
       "      <td>jhkjhjlk</td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>2018-01-21 06:59:28.594111+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>2018-01-21 07:00:16.297330+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"properties\":{},\"geometry\":{\"type\":\"FeatureCo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>2018-01-21 07:26:30.269080+00:00</td>\n",
       "      <td>Test 1</td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "      <td>2018-01-21 08:34:52.451539+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>2018-01-21 08:44:27.082751+00:00</td>\n",
       "      <td>this looks great</td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>2018-01-21 09:20:29.692103+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>13</td>\n",
       "      <td>26</td>\n",
       "      <td>2018-01-21 09:31:14.509166+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14</td>\n",
       "      <td>27</td>\n",
       "      <td>2018-01-21 09:51:38.219200+00:00</td>\n",
       "      <td>dddddd</td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15</td>\n",
       "      <td>28</td>\n",
       "      <td>2018-01-21 09:56:35.023665+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>17</td>\n",
       "      <td>29</td>\n",
       "      <td>2018-01-21 10:08:50.449267+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>18</td>\n",
       "      <td>30</td>\n",
       "      <td>2018-01-21 10:48:24.347190+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>19</td>\n",
       "      <td>31</td>\n",
       "      <td>2018-01-21 10:53:00.607236+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1c\",\"properties\":{\"hiddenPro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>20</td>\n",
       "      <td>34</td>\n",
       "      <td>2018-01-21 11:09:06.610125+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1c\",\"properties\":{\"hiddenPro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>21</td>\n",
       "      <td>50</td>\n",
       "      <td>2018-01-22 01:56:58.503538+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>47</td>\n",
       "      <td>53</td>\n",
       "      <td>2018-01-22 04:07:36.863589+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>26</td>\n",
       "      <td>54</td>\n",
       "      <td>2018-01-22 02:32:42.635017+00:00</td>\n",
       "      <td>Green connection from Mt Faber through to the ...</td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>22</td>\n",
       "      <td>55</td>\n",
       "      <td>2018-01-22 02:22:57.958041+00:00</td>\n",
       "      <td>Waterfront living - for HDBs. Low rise buildin...</td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>23</td>\n",
       "      <td>56</td>\n",
       "      <td>2018-01-22 02:24:31.160823+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>24</td>\n",
       "      <td>59</td>\n",
       "      <td>2018-01-22 02:25:32.359048+00:00</td>\n",
       "      <td>views towards the sea should be meant for resi...</td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>31</td>\n",
       "      <td>61</td>\n",
       "      <td>2018-01-22 03:12:55.568573+00:00</td>\n",
       "      <td>water front green space</td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>28</td>\n",
       "      <td>62</td>\n",
       "      <td>2018-01-22 02:41:17.896090+00:00</td>\n",
       "      <td>an open area with green field for activities /...</td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1c\",\"properties\":{\"hiddenPro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>63</td>\n",
       "      <td>2018-01-22 02:29:14.388905+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "      <td>2018-01-22 02:55:53.887520+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1c\",\"properties\":{\"hiddenPro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>30</td>\n",
       "      <td>68</td>\n",
       "      <td>2018-01-22 03:07:55.346388+00:00</td>\n",
       "      <td>I like the aesthetics</td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>32</td>\n",
       "      <td>78</td>\n",
       "      <td>2018-01-22 03:13:59.688621+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1c\",\"properties\":{\"hiddenPro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>38</td>\n",
       "      <td>79</td>\n",
       "      <td>2018-01-22 03:31:31.955601+00:00</td>\n",
       "      <td>Ideal neighbourhood with plenty of green publi...</td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</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>42</th>\n",
       "      <td>48</td>\n",
       "      <td>100</td>\n",
       "      <td>2018-01-22 04:18:23.737089+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1c\",\"properties\":{\"hiddenPro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>54</td>\n",
       "      <td>101</td>\n",
       "      <td>2018-01-22 04:28:43.629157+00:00</td>\n",
       "      <td>Random</td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>55</td>\n",
       "      <td>105</td>\n",
       "      <td>2018-01-22 04:31:52.726701+00:00</td>\n",
       "      <td>A lively and comfortable waterfront living emb...</td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>51</td>\n",
       "      <td>106</td>\n",
       "      <td>2018-01-22 04:25:44.221631+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>52</td>\n",
       "      <td>107</td>\n",
       "      <td>2018-01-22 04:26:40.454642+00:00</td>\n",
       "      <td>Must have park near the water with BBQ pits !</td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>50</td>\n",
       "      <td>108</td>\n",
       "      <td>2018-01-22 04:24:29.119740+00:00</td>\n",
       "      <td>more greenery</td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>53</td>\n",
       "      <td>109</td>\n",
       "      <td>2018-01-22 04:27:50.397733+00:00</td>\n",
       "      <td>less high rise residential buildings, trying t...</td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>57</td>\n",
       "      <td>110</td>\n",
       "      <td>2018-01-22 04:36:40.595281+00:00</td>\n",
       "      <td>I think it'd be nice to keep the buildings nea...</td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>56</td>\n",
       "      <td>111</td>\n",
       "      <td>2018-01-22 04:33:32.068008+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>58</td>\n",
       "      <td>112</td>\n",
       "      <td>2018-01-22 04:46:39.059098+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>60</td>\n",
       "      <td>114</td>\n",
       "      <td>2018-01-22 04:52:03.958181+00:00</td>\n",
       "      <td>I believe that public places such as this shou...</td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1c\",\"properties\":{\"hiddenPro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>59</td>\n",
       "      <td>116</td>\n",
       "      <td>2018-01-22 04:50:33.361022+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>62</td>\n",
       "      <td>117</td>\n",
       "      <td>2018-01-22 04:56:09.096594+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>65</td>\n",
       "      <td>119</td>\n",
       "      <td>2018-01-22 05:16:15.997385+00:00</td>\n",
       "      <td>Lots of green open space including a large par...</td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>68</td>\n",
       "      <td>120</td>\n",
       "      <td>2018-01-22 05:33:29.044802+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>67</td>\n",
       "      <td>121</td>\n",
       "      <td>2018-01-22 05:32:09.833629+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1c\",\"properties\":{\"hiddenPro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>63</td>\n",
       "      <td>122</td>\n",
       "      <td>2018-01-22 05:11:33.803541+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>66</td>\n",
       "      <td>123</td>\n",
       "      <td>2018-01-22 05:25:40.121018+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>64</td>\n",
       "      <td>124</td>\n",
       "      <td>2018-01-22 05:15:10.891201+00:00</td>\n",
       "      <td>More green spaces to reduce sea breeze</td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>70</td>\n",
       "      <td>128</td>\n",
       "      <td>2018-01-22 05:46:56.970155+00:00</td>\n",
       "      <td>The current tanjong pagar container terminal i...</td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1c\",\"properties\":{\"hiddenPro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>69</td>\n",
       "      <td>129</td>\n",
       "      <td>2018-01-22 05:41:01.180887+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>72</td>\n",
       "      <td>130</td>\n",
       "      <td>2018-01-22 05:58:39.787456+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>78</td>\n",
       "      <td>132</td>\n",
       "      <td>2018-01-22 06:06:49.075425+00:00</td>\n",
       "      <td>Computer hanged</td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1c\",\"properties\":{\"hiddenPro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>73</td>\n",
       "      <td>133</td>\n",
       "      <td>2018-01-22 05:59:24.383347+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>75</td>\n",
       "      <td>134</td>\n",
       "      <td>2018-01-22 06:03:51.114043+00:00</td>\n",
       "      <td>district based within the new town. preferably...</td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>74</td>\n",
       "      <td>135</td>\n",
       "      <td>2018-01-22 05:59:55.249048+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>76</td>\n",
       "      <td>137</td>\n",
       "      <td>2018-01-22 06:04:57.321417+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>77</td>\n",
       "      <td>138</td>\n",
       "      <td>2018-01-22 06:06:25.728599+00:00</td>\n",
       "      <td>space centered around city park with park conn...</td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>80</td>\n",
       "      <td>139</td>\n",
       "      <td>2018-01-22 06:11:31.293447+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>79</td>\n",
       "      <td>140</td>\n",
       "      <td>2018-01-22 06:09:46.832625+00:00</td>\n",
       "      <td></td>\n",
       "      <td>[b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...</td>\n",
       "      <td>{\"name\":\"Exercise_1c\",\"properties\":{\"hiddenPro...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    id  author_id                      last_update  \\\n",
       "0   12          3 2018-01-21 09:23:24.601339+00:00   \n",
       "1    1         11 2018-01-20 08:48:59.247419+00:00   \n",
       "2    2         12 2018-01-20 08:57:10.925012+00:00   \n",
       "3    3         15 2018-01-21 06:58:45.763373+00:00   \n",
       "4    4         16 2018-01-21 06:59:28.594111+00:00   \n",
       "5    5         17 2018-01-21 07:00:16.297330+00:00   \n",
       "6    7         18 2018-01-21 07:26:30.269080+00:00   \n",
       "7    9         20 2018-01-21 08:34:52.451539+00:00   \n",
       "8   10         21 2018-01-21 08:44:27.082751+00:00   \n",
       "9   11         25 2018-01-21 09:20:29.692103+00:00   \n",
       "10  13         26 2018-01-21 09:31:14.509166+00:00   \n",
       "11  14         27 2018-01-21 09:51:38.219200+00:00   \n",
       "12  15         28 2018-01-21 09:56:35.023665+00:00   \n",
       "13  17         29 2018-01-21 10:08:50.449267+00:00   \n",
       "14  18         30 2018-01-21 10:48:24.347190+00:00   \n",
       "15  19         31 2018-01-21 10:53:00.607236+00:00   \n",
       "16  20         34 2018-01-21 11:09:06.610125+00:00   \n",
       "17  21         50 2018-01-22 01:56:58.503538+00:00   \n",
       "18  47         53 2018-01-22 04:07:36.863589+00:00   \n",
       "19  26         54 2018-01-22 02:32:42.635017+00:00   \n",
       "20  22         55 2018-01-22 02:22:57.958041+00:00   \n",
       "21  23         56 2018-01-22 02:24:31.160823+00:00   \n",
       "22  24         59 2018-01-22 02:25:32.359048+00:00   \n",
       "23  31         61 2018-01-22 03:12:55.568573+00:00   \n",
       "24  28         62 2018-01-22 02:41:17.896090+00:00   \n",
       "25  25         63 2018-01-22 02:29:14.388905+00:00   \n",
       "26  29         66 2018-01-22 02:55:53.887520+00:00   \n",
       "27  30         68 2018-01-22 03:07:55.346388+00:00   \n",
       "28  32         78 2018-01-22 03:13:59.688621+00:00   \n",
       "29  38         79 2018-01-22 03:31:31.955601+00:00   \n",
       "..  ..        ...                              ...   \n",
       "42  48        100 2018-01-22 04:18:23.737089+00:00   \n",
       "43  54        101 2018-01-22 04:28:43.629157+00:00   \n",
       "44  55        105 2018-01-22 04:31:52.726701+00:00   \n",
       "45  51        106 2018-01-22 04:25:44.221631+00:00   \n",
       "46  52        107 2018-01-22 04:26:40.454642+00:00   \n",
       "47  50        108 2018-01-22 04:24:29.119740+00:00   \n",
       "48  53        109 2018-01-22 04:27:50.397733+00:00   \n",
       "49  57        110 2018-01-22 04:36:40.595281+00:00   \n",
       "50  56        111 2018-01-22 04:33:32.068008+00:00   \n",
       "51  58        112 2018-01-22 04:46:39.059098+00:00   \n",
       "52  60        114 2018-01-22 04:52:03.958181+00:00   \n",
       "53  59        116 2018-01-22 04:50:33.361022+00:00   \n",
       "54  62        117 2018-01-22 04:56:09.096594+00:00   \n",
       "55  65        119 2018-01-22 05:16:15.997385+00:00   \n",
       "56  68        120 2018-01-22 05:33:29.044802+00:00   \n",
       "57  67        121 2018-01-22 05:32:09.833629+00:00   \n",
       "58  63        122 2018-01-22 05:11:33.803541+00:00   \n",
       "59  66        123 2018-01-22 05:25:40.121018+00:00   \n",
       "60  64        124 2018-01-22 05:15:10.891201+00:00   \n",
       "61  70        128 2018-01-22 05:46:56.970155+00:00   \n",
       "62  69        129 2018-01-22 05:41:01.180887+00:00   \n",
       "63  72        130 2018-01-22 05:58:39.787456+00:00   \n",
       "64  78        132 2018-01-22 06:06:49.075425+00:00   \n",
       "65  73        133 2018-01-22 05:59:24.383347+00:00   \n",
       "66  75        134 2018-01-22 06:03:51.114043+00:00   \n",
       "67  74        135 2018-01-22 05:59:55.249048+00:00   \n",
       "68  76        137 2018-01-22 06:04:57.321417+00:00   \n",
       "69  77        138 2018-01-22 06:06:25.728599+00:00   \n",
       "70  80        139 2018-01-22 06:11:31.293447+00:00   \n",
       "71  79        140 2018-01-22 06:09:46.832625+00:00   \n",
       "\n",
       "                                          description  \\\n",
       "0                                                       \n",
       "1                                                       \n",
       "2                            Evertafkdkj sdfsf hello!   \n",
       "3                                            jhkjhjlk   \n",
       "4                                                       \n",
       "5                                                       \n",
       "6                                              Test 1   \n",
       "7                                                       \n",
       "8                                    this looks great   \n",
       "9                                                       \n",
       "10                                                      \n",
       "11                                             dddddd   \n",
       "12                                                      \n",
       "13                                                      \n",
       "14                                                      \n",
       "15                                                      \n",
       "16                                                      \n",
       "17                                                      \n",
       "18                                                      \n",
       "19  Green connection from Mt Faber through to the ...   \n",
       "20  Waterfront living - for HDBs. Low rise buildin...   \n",
       "21                                                      \n",
       "22  views towards the sea should be meant for resi...   \n",
       "23                            water front green space   \n",
       "24  an open area with green field for activities /...   \n",
       "25                                                      \n",
       "26                                                      \n",
       "27                              I like the aesthetics   \n",
       "28                                                      \n",
       "29  Ideal neighbourhood with plenty of green publi...   \n",
       "..                                                ...   \n",
       "42                                                      \n",
       "43                                             Random   \n",
       "44  A lively and comfortable waterfront living emb...   \n",
       "45                                                      \n",
       "46      Must have park near the water with BBQ pits !   \n",
       "47                                      more greenery   \n",
       "48  less high rise residential buildings, trying t...   \n",
       "49  I think it'd be nice to keep the buildings nea...   \n",
       "50                                                      \n",
       "51                                                      \n",
       "52  I believe that public places such as this shou...   \n",
       "53                                                      \n",
       "54                                                      \n",
       "55  Lots of green open space including a large par...   \n",
       "56                                                      \n",
       "57                                                      \n",
       "58                                                      \n",
       "59                                                      \n",
       "60             More green spaces to reduce sea breeze   \n",
       "61  The current tanjong pagar container terminal i...   \n",
       "62                                                      \n",
       "63                                                      \n",
       "64                                    Computer hanged   \n",
       "65                                                      \n",
       "66  district based within the new town. preferably...   \n",
       "67                                                      \n",
       "68                                                      \n",
       "69  space centered around city park with park conn...   \n",
       "70                                                      \n",
       "71                                                      \n",
       "\n",
       "                                                image  \\\n",
       "0   [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "1   [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "2   [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "3   [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "4   [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "5   [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "6   [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "7   [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "8   [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "9   [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "10  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "11  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "12  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "13  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "14  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "15  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "16  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "17  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "18  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "19  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "20  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "21  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "22  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "23  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "24  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "25  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "26  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "27  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "28  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "29  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "..                                                ...   \n",
       "42  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "43  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "44  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "45  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "46  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "47  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "48  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "49  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "50  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "51  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "52  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "53  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "54  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "55  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "56  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "57  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "58  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "59  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "60  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "61  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "62  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "63  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "64  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "65  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "66  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "67  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "68  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "69  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "70  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "71  [b'\\x89', b'P', b'N', b'G', b'\\r', b'\\n', b'\\x...   \n",
       "\n",
       "                                                  geo  \n",
       "0   {\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...  \n",
       "1   {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "2   {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "3   {\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...  \n",
       "4   {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "5   {\"properties\":{},\"geometry\":{\"type\":\"FeatureCo...  \n",
       "6   {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "7   {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "8   {\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...  \n",
       "9   {\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...  \n",
       "10  {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "11  {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "12  {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "13  {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "14  {\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...  \n",
       "15  {\"name\":\"Exercise_1c\",\"properties\":{\"hiddenPro...  \n",
       "16  {\"name\":\"Exercise_1c\",\"properties\":{\"hiddenPro...  \n",
       "17  {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "18  {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "19  {\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...  \n",
       "20  {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "21  {\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...  \n",
       "22  {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "23  {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "24  {\"name\":\"Exercise_1c\",\"properties\":{\"hiddenPro...  \n",
       "25  {\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...  \n",
       "26  {\"name\":\"Exercise_1c\",\"properties\":{\"hiddenPro...  \n",
       "27  {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "28  {\"name\":\"Exercise_1c\",\"properties\":{\"hiddenPro...  \n",
       "29  {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "..                                                ...  \n",
       "42  {\"name\":\"Exercise_1c\",\"properties\":{\"hiddenPro...  \n",
       "43  {\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...  \n",
       "44  {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "45  {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "46  {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "47  {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "48  {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "49  {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "50  {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "51  {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "52  {\"name\":\"Exercise_1c\",\"properties\":{\"hiddenPro...  \n",
       "53  {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "54  {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "55  {\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...  \n",
       "56  {\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...  \n",
       "57  {\"name\":\"Exercise_1c\",\"properties\":{\"hiddenPro...  \n",
       "58  {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "59  {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "60  {\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...  \n",
       "61  {\"name\":\"Exercise_1c\",\"properties\":{\"hiddenPro...  \n",
       "62  {\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...  \n",
       "63  {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "64  {\"name\":\"Exercise_1c\",\"properties\":{\"hiddenPro...  \n",
       "65  {\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...  \n",
       "66  {\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...  \n",
       "67  {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "68  {\"name\":\"Exercise_1a\",\"lon\":103.84056835,\"lat\"...  \n",
       "69  {\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...  \n",
       "70  {\"name\":\"Exercise_1b\",\"lon\":103.830289094693,\"...  \n",
       "71  {\"name\":\"Exercise_1c\",\"properties\":{\"hiddenPro...  \n",
       "\n",
       "[72 rows x 6 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading data and data cleansing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Extract the geojson data from all designs\n",
    "\n",
    "regex = re.compile(r'\\\\(?![/u\"])')\n",
    "for i, geo_val in enumerate(db_scenarios_late.geo):\n",
    "  #Select if Exercise 1a, 1b or 1c should be chosen. It is the 19th letter in each db_scenarios_late.geo file\n",
    "  if lt.geo[i][19]=='a':\n",
    "    fixed = regex.sub(r\"\\\\\\\\\", geo_val) #remove the \"/\" in some fields to for json conversion\n",
    "    b = json.loads(fixed)\n",
    "    # this would remove an empty MultiLineString object so that it can be displayed in the current qua-viewer. However the shape is still distorted because of the limitation of allowed vertex\n",
    "    with open(\"output/geo_a_\" + str(db_scenarios_late.loc[i,'author_id']) + \".json\",\"w\") as f:\n",
    "        json.dump(b,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(db_scenarios_late.geo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read the original geojson file for the exercise\n",
    "geo = gpd.read_file('Exercise_1a_reflex_for_upload.geojson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create dictionary with all geojson files for exercise 1a\n",
    "geo_a={}\n",
    "for i,j in enumerate(lt['author_id']):\n",
    "    if lt.geo[i][19]=='a':\n",
    "        geo_a[\"geo_author_{0}\".format(j)]= gpd.read_file('output/geo_a_'+str(j)+'.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create list of all authors that worked on Exercise 1a\n",
    "author_id_a = []\n",
    "for i,j in enumerate(lt['author_id']):\n",
    "    if lt.geo[i][19]=='a':\n",
    "        author_id_a.append(j)\n",
    "# Delete buggy submissions (manually detected)\n",
    "y=[16]\n",
    "author_id_a = sorted(list(set(author_id_a).difference(set(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data correction: For 'Chinatown, Central Area', the precinctID is missing in the original data set\n",
    "for j in list(author_id_a):\n",
    "    for i in range(0,len(geo_a['geo_author_'+str(j)])):\n",
    "        if geo_a['geo_author_'+str(j)]['precinctName'][i] == \"Chinatown, Central Area\":\n",
    "            geo_a['geo_author_'+str(j)].loc[i,'precinctID'] = 102\n",
    "        if geo_a['geo_author_'+str(j)]['name'][i] == \"greenery small\":\n",
    "            geo_a['geo_author_'+str(j)].loc[i,'precinctID'] = 401\n",
    "        if geo_a['geo_author_'+str(j)]['name'][i] == \"greenery medium\":\n",
    "            geo_a['geo_author_'+str(j)].loc[i,'precinctID'] = 402\n",
    "        if geo_a['geo_author_'+str(j)]['name'][i] == \"greenery large\":\n",
    "            geo_a['geo_author_'+str(j)].loc[i,'precinctID'] = 403\n",
    "#Overwrite existing .json files with updated precinctID\n",
    "    with open('output/geo_a_'+str(j)+'.json', 'w') as f:\n",
    "        f.write(geo_a['geo_author_'+str(j)].to_json())\n",
    "#Change original geojson file with added precinctID for the greenery\n",
    "for i in range(1,len(geo)):\n",
    "    if geo.loc[i,'name'] == 'greenery small':\n",
    "        geo.loc[i,'precinctID'] = 401\n",
    "    if geo.loc[i,'name'] == 'greenery medium':\n",
    "        geo.loc[i,'precinctID'] = 402\n",
    "    if geo.loc[i,'name'] == 'greenery large':\n",
    "        geo.loc[i,'precinctID'] = 403\n",
    "with open('Exercise_1a_reflex_for_upload_updated.json', 'w') as f:\n",
    "        f.write(geo.to_json())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating the frequency of objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write function for the the number of elements placed\n",
    "\n",
    "def counter_old(file,obj):\n",
    "    #Check if file is a string\n",
    "    if isinstance(file, str) == False:\n",
    "        print('First variable must be a string variable')\n",
    "    \n",
    "    #Extract the integers/numbers out of a string\n",
    "    import re\n",
    "    author_id = re.findall(r'\\d+', file)\n",
    "\n",
    "    import geopandas as gpd\n",
    "    file = gpd.read_file('output/'+file+'.json')\n",
    "    #Here it is important to take the updated version of the original source file as 'greenery' needs values for 'precinctID'.\n",
    "    geo = gpd.read_file('Exercise_1a_reflex_for_upload_updated.json')\n",
    "    \n",
    "    \n",
    "    if obj == 'ALL':\n",
    "        #choose all buildings typologies, incl. greenery\n",
    "        colnames_num = [101,102,201,202,203,204,205,206,207,301,302,303,401,402,403]\n",
    "    if obj == 'buildings':\n",
    "        #choose all building typologies\n",
    "        colnames_num = [101,102,201,202,203,204,205,206,207,301,302,303]\n",
    "    if obj == 'low-rise':\n",
    "        #choose all low-rise\n",
    "        colnames_num = [101,102]\n",
    "    if obj == 'mid-rise':\n",
    "        #choose all mid-rise\n",
    "        colnames_num = [201,202,301]\n",
    "    if obj == 'high-rise':\n",
    "        #choose all high-rise\n",
    "        colnames_num = [203,204,205,206,207,302,303]\n",
    "    if obj == 'HDB':\n",
    "        #choose all HDB\n",
    "        colnames_num = [201,202,203,204,205,206,207]\n",
    "    if obj == 'Condo':\n",
    "        #choose all Condo\n",
    "        colnames_num = [301,302,303]\n",
    "    if obj == 'mixed-use':\n",
    "        #choose all building typologies that have a mixed-use\n",
    "        colnames_num = [102,202,203,204]\n",
    "    if obj == 'green_elements':\n",
    "        #choose all building typologies that have a green elements\n",
    "        colnames_num = [401,402,403]\n",
    "    if obj in [101,102,201,202,203,204,205,206,207,301,302,303,401,402,403]:\n",
    "        #choose the specific typology\n",
    "        colnames_num = [obj]\n",
    "        \n",
    "    #Create colnames for dataframe\n",
    "    colnames=[]\n",
    "    for i in colnames_num:\n",
    "        colnames.append('count_'+str(i))\n",
    "    \n",
    "    import pandas as pd\n",
    "    freq = pd.DataFrame(columns=colnames)  # dataframe\n",
    "    \n",
    "    import collections\n",
    "    for k in colnames_num:\n",
    "        freq.loc[0,'count_'+str(k)] = collections.Counter(file.precinctID)[k]-collections.Counter(geo.precinctID)[k]\n",
    "\n",
    "    #Adjustment of the frequencies\n",
    "    \n",
    "    # Adjusting count by number of group elements for each precinct typology\n",
    "\n",
    "    if 'count_101' in freq.columns:\n",
    "        freq['count_101']= freq['count_101']/2\n",
    "    if 'count_102' in freq.columns:\n",
    "        freq['count_102'] = freq['count_102']/3\n",
    "    if 'count_201' in freq.columns:\n",
    "        freq['count_201'] = freq['count_201']/2\n",
    "    if 'count_202' in freq.columns:\n",
    "        freq['count_202'] = freq['count_202']/3\n",
    "    if 'count_203' in freq.columns:\n",
    "        freq['count_203'] = freq['count_203']/4\n",
    "    if 'count_204' in freq.columns:\n",
    "        freq['count_204'] = freq['count_204']/3\n",
    "    if 'count_205' in freq.columns:\n",
    "        freq['count_205'] = freq['count_205']/3\n",
    "    if 'count_206' in freq.columns:\n",
    "        freq['count_206'] = freq['count_206']/2\n",
    "    if 'count_207' in freq.columns:\n",
    "        freq['count_207'] = freq['count_207']/3\n",
    "    if 'count_301' in freq.columns:\n",
    "        freq['count_301'] = freq['count_301']/2\n",
    "    if 'count_302' in freq.columns:\n",
    "        freq['count_302'] = freq['count_302']/3\n",
    "    if 'count_303' in freq.columns:\n",
    "        freq['count_303'] = freq['count_303']/2\n",
    "        \n",
    "    freq.insert(len(colnames),'total', freq.values.sum())\n",
    "    \n",
    "    freq = freq.astype(int)\n",
    "    #Add author_id to the dataframe as first (0th) row\n",
    "    freq.insert(0, 'author_id', author_id, allow_duplicates=False)\n",
    "    return freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating averages for specific objects and object groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculating the average for specific types, here just one example\n",
    "\n",
    "# Defining which authors contributed useful submissions\n",
    "# Delete non-useful submissions (manually decided, quite strict), author_id 16 was already deleted before\n",
    "x = [11,12,18,20,26,27,28,50,53,55,94,106,108,109,116,117,122,120,135,137]\n",
    "author_id_a = sorted(list(set(author_id_a).difference(set(x))))\n",
    "#author_id_a = pd.DataFrame(author_id_a, columns = ['author_id_a'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "def counter_old_mean(authors,obj):\n",
    "   \n",
    "    #Input for authors: list of author_id\n",
    "    s = pd.DataFrame()\n",
    "    for i in authors:\n",
    "        s = s.append(counter_old('geo_a_'+str(i),obj))\n",
    "    #s = float(s / len(authors))\n",
    "    col = list(s.columns.values)\n",
    "\n",
    "    t = s[col[1:len(col)]].mean(axis=0)\n",
    "    t = list(t)\n",
    "    return col[1:len(col)], t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [],
   "source": [
    "col = counter_old_mean(author_id_a,'ALL')[0]\n",
    "\n",
    "colnames = []\n",
    "\n",
    "for k in col:\n",
    "    colnames.append(dic[k])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define function for plotting barchar from the average number of objects\n",
    "\n",
    "def counter_old_bar(authors, obj= 'ALL',total = False):\n",
    "    \n",
    "    pf = pd.read_csv('Precincts_final.csv')\n",
    "\n",
    "    colnames_num = [101,102,201,202,203,204,205,206,207,301,302,303,401,402,403]\n",
    "\n",
    "    dic = {}\n",
    "    for k,l in enumerate(colnames_num):\n",
    "        dic['count_'+str(l)]=pf.name[k]\n",
    "    dic['total']='total'\n",
    "\n",
    "    col, mean_count = counter_old_mean(authors,obj)\n",
    "    \n",
    "    if total == False:\n",
    "        col.remove('total')\n",
    "        del mean_count[-1]\n",
    "    \n",
    "    colnames = []\n",
    "    for k in col:\n",
    "        colnames.append(dic[k])\n",
    "    \n",
    "    plt.bar(np.arange(len(mean_count)),mean_count,align='center', color=['#7A9E7E'])\n",
    "    plt.xticks(np.arange(len(mean_count)),colnames,rotation='vertical')\n",
    "    plt.title(\"Average number of objects\")\n",
    "    #plt.savefig('figures/fig_bar_'+str(i)+'.png')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": false
   },
   "source": [
    "Calculating 'Number Of Units'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "metadata": {},
   "outputs": [],
   "source": [
    "def units_old(file):\n",
    "    \n",
    "    pf = pd.read_csv('Precincts_final.csv')\n",
    "    \n",
    "    col = counter_old(file,'buildings')\n",
    "    col = col.drop('total',axis=1)\n",
    "    col = col.drop('author_id',axis=1)\n",
    "    \n",
    "    colnames_num = [101,102,201,202,203,204,205,206,207,301,302,303]\n",
    "\n",
    "    dic = {}\n",
    "    for k,l in enumerate(colnames_num):\n",
    "        dic['count_'+str(l)]=pf.numberOfUnits[k]\n",
    "    \n",
    "    s = 0\n",
    "    for i in range(0,len(col.columns)):\n",
    "        s += dic[col.columns.values[i]] * col[col.columns.values[i]][0]\n",
    "    s = int(s)   \n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "metadata": {},
   "outputs": [],
   "source": [
    "def units_old_mean(authors):\n",
    "    \n",
    "    pf = pd.read_csv('Precincts_final.csv')\n",
    "    \n",
    "    col, val = counter_old_mean(authors,'buildings')\n",
    "    del col[-1]\n",
    "    \n",
    "    colnames_num = [101,102,201,202,203,204,205,206,207,301,302,303]\n",
    "\n",
    "    dic = {}\n",
    "    for k,l in enumerate(colnames_num):\n",
    "        dic['count_'+str(l)]=pf.numberOfUnits[k]\n",
    "    \n",
    "    s = 0\n",
    "    for i in range(0,len(col)):\n",
    "        s += (dic[col[i]] * val[i])\n",
    "        \n",
    "    return s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating the total tile area, total plot area and GPR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 564,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tile_area_old(file):\n",
    "    \n",
    "    pf = pd.read_csv('Precincts_final.csv')\n",
    "    \n",
    "    col = counter_old(file,'buildings')\n",
    "    col = col.drop('total',axis=1)\n",
    "    col = col.drop('author_id',axis=1)\n",
    "    \n",
    "    colnames_num = [101,102,201,202,203,204,205,206,207,301,302,303]\n",
    "\n",
    "    dic = {}\n",
    "    for k,l in enumerate(colnames_num):\n",
    "        dic['count_'+str(l)]=pf.tileArea[k]\n",
    "    \n",
    "    a = 0\n",
    "    for i in range(0,len(col.columns)):\n",
    "        a += dic[col.columns.values[i]] * col[col.columns.values[i]][0]  \n",
    "        \n",
    "    part = round((a / 1.7) * 100,2)\n",
    "    \n",
    "    return a, part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 565,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tile_area_old_mean(authors):\n",
    "    \n",
    "    pf = pd.read_csv('Precincts_final.csv')\n",
    "    \n",
    "    col, val = counter_old_mean(authors,'buildings')\n",
    "    del col[-1]\n",
    "    \n",
    "    colnames_num = [101,102,201,202,203,204,205,206,207,301,302,303]\n",
    "\n",
    "    dic = {}\n",
    "    for k,l in enumerate(colnames_num):\n",
    "        dic['count_'+str(l)]=pf.tileArea[k]\n",
    "    \n",
    "    a = 0\n",
    "    for i in range(0,len(col)):\n",
    "        a += (dic[col[i]] * val[i])\n",
    "        \n",
    "    part = round((a / 1.7) * 100,2)\n",
    "    \n",
    "    return a, part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 566,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_area_old(file):\n",
    "    \n",
    "    pf = pd.read_csv('Precincts_final.csv')\n",
    "    \n",
    "    col = counter_old(file,'buildings')\n",
    "    col = col.drop('total',axis=1)\n",
    "    col = col.drop('author_id',axis=1)\n",
    "    \n",
    "    colnames_num = [101,102,201,202,203,204,205,206,207,301,302,303]\n",
    "\n",
    "    dic = {}\n",
    "    for k,l in enumerate(colnames_num):\n",
    "        dic['count_'+str(l)]=pf.plotArea[k]\n",
    "    \n",
    "    a = 0\n",
    "    for i in range(0,len(col.columns)):\n",
    "        a += dic[col.columns.values[i]] * col[col.columns.values[i]][0]  \n",
    "        \n",
    "    part = round((a / 1.7) * 100,2)\n",
    "    \n",
    "    return a, part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 567,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_area_old_mean(authors):\n",
    "    \n",
    "    pf = pd.read_csv('Precincts_final.csv')\n",
    "    \n",
    "    col, val = counter_old_mean(authors,'buildings')\n",
    "    del col[-1]\n",
    "    \n",
    "    colnames_num = [101,102,201,202,203,204,205,206,207,301,302,303]\n",
    "\n",
    "    dic = {}\n",
    "    for k,l in enumerate(colnames_num):\n",
    "        dic['count_'+str(l)]=pf.plotArea[k]\n",
    "    \n",
    "    a = 0\n",
    "    for i in range(0,len(col)):\n",
    "        a += (dic[col[i]] * val[i])\n",
    "        \n",
    "    part = round((a / 1.7) * 100,2)\n",
    "    \n",
    "    return a, part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 574,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gpr_old(file):\n",
    "    \n",
    "    pf = pd.read_csv('Precincts_final.csv')\n",
    "    \n",
    "    col = counter_old(file,'buildings')\n",
    "    col = col.drop('total',axis=1)\n",
    "    col = col.drop('author_id',axis=1)\n",
    "    \n",
    "    colnames_num = [101,102,201,202,203,204,205,206,207,301,302,303]\n",
    "\n",
    "    dic = {}\n",
    "    for k,l in enumerate(colnames_num):\n",
    "        dic['count_'+str(l)]=pf.floorArea[k]\n",
    "    \n",
    "    a = 0\n",
    "    for i in range(0,len(col.columns)):\n",
    "        a += dic[col.columns.values[i]] * col[col.columns.values[i]][0]  \n",
    "        \n",
    "    gpr = round(a / 1.7,2)\n",
    "    \n",
    "    return gpr, a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 572,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gpr_old_mean(authors):\n",
    "    \n",
    "    pf = pd.read_csv('Precincts_final.csv')\n",
    "    \n",
    "    col, val = counter_old_mean(authors,'buildings')\n",
    "    del col[-1]\n",
    "    \n",
    "    colnames_num = [101,102,201,202,203,204,205,206,207,301,302,303]\n",
    "\n",
    "    dic = {}\n",
    "    for k,l in enumerate(colnames_num):\n",
    "        dic['count_'+str(l)]=pf.floorArea[k]\n",
    "    \n",
    "    a = 0\n",
    "    for i in range(0,len(col)):\n",
    "        a += (dic[col[i]] * val[i])\n",
    "        \n",
    "    gpr = round(a / 1.7,2)\n",
    "    \n",
    "    return gpr, a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 590,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[29, 59, 61, 68, 79, 83, 87, 89, 90, 92, 93, 105, 107, 110, 111, 112, 123, 130]"
      ]
     },
     "execution_count": 590,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tile_area_old"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 625,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = []\n",
    "y = []\n",
    "for i in author_id_a:\n",
    "    x.append(units_old('geo_a_'+str(i)))\n",
    "    y.append(tile_area_old('geo_a_'+str(i))[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 626,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cbbaa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120bee208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#x.append(70000)\n",
    "#y.append(4.5)\n",
    "#If 70000 units should be built on this ground (4.5 as far as I know), 26444 must be in average built on that site (size: 1.7).\n",
    "plt.scatter(x,y)\n",
    "plt.show()\n",
    "plt.boxplot(y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating Gross Flot Ratio:\n",
    "# https://en.wikipedia.org/wiki/Floor_area_(building)#In_Singapore\n",
    "# The NUS campus and ONLINE study worked with two different study areas:\n",
    "# Study area NUS Campus [in square kilometer]\n",
    "area_total = 1.7\n",
    "#Study areas for online study\n",
    "area_1 = 0.546715\n",
    "area_2 = 0.322409\n",
    "#Please note that some participants just built on area_1 (where the spawn point of objects was)\n",
    "# The areas for each object can be found in prec_desc\n",
    "# Explanation: plotArea: area of the building plot (without the surrounding green), floorArea: levels*plotArea, \n",
    "# tileArea: area of the platform (greenery around building + plotArea), floorAreaRatio: floorArea/tileArea\n",
    "# Greeneries do not need to be considered as they do not count for the GFA\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}