Hourly Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1c4a9778-d49e-4db9-aade-2d272c10ef5b",
   "metadata": {},
   "source": [
    "# Fine Granularity Analysis - Fine Granularity Human Sentiments of Heat Exposure"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "468e384c-519f-4f45-9c9c-c5ec4b494bbd",
   "metadata": {},
   "source": [
    "Author: Fangzheng Lyu\n",
    "\n",
    "This notebook is related to the paper [Mapping dynamic human sentiments of heat exposure with location-based social media data](https://www.tandfonline.com/doi/full/10.1080/13658816.2024.2343063)\n",
    "\n",
    "Evaulting Twitter-based heat exposure dynamics can be analyzed at a finer temporal resolution. In this study, we further looked at how the heat exposure changes on September 26th, 2021. On September 26rh, there is a significant temperate rises in Chicago where the highest temperature increased from 70 degrees Fahrenheit to 84 degrees Fahrenheit from September 25th, 2021, to September 26th, 2021. We investigated the dynamics of heat exposure at a fine temporal resolution to gain insight into the changes in people’s exposure to dramatic temperature rises within a day."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a85cca6-2329-4a0c-ab6f-e128f0706a48",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Notebook Outline\n",
    "- [Read Twitter Data and Data Processing](#read)\n",
    "- [Understand the Human Sentiments of Heat Exposure of Twitter Data](#understand)\n",
    "- [Distribute All the Social Media Data into the 3-hour Temporal Bins](#Distribute)\n",
    "- [Visualization](#Visualization)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18e296ca-e514-440e-80d2-40a4a2be8716",
   "metadata": {},
   "source": [
    "Import necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dbc66dc7-886e-4fe0-8685-4e397ef67aea",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1973/4013879678.py:4: DeprecationWarning: Shapely 2.0 is installed, but because PyGEOS is also installed, GeoPandas still uses PyGEOS by default. However, starting with version 0.14, the default will switch to Shapely. To force to use Shapely 2.0 now, you can either uninstall PyGEOS or set the environment variable USE_PYGEOS=0. You can do this before starting the Python process, or in your code before importing geopandas:\n",
      "\n",
      "import os\n",
      "os.environ['USE_PYGEOS'] = '0'\n",
      "import geopandas\n",
      "\n",
      "In the next release, GeoPandas will switch to using Shapely by default, even if PyGEOS is installed. If you only have PyGEOS installed to get speed-ups, this switch should be smooth. However, if you are using PyGEOS directly (calling PyGEOS functions on geometries from GeoPandas), this will then stop working and you are encouraged to migrate from PyGEOS to Shapely 2.0 (https://shapely.readthedocs.io/en/latest/migration_pygeos.html).\n",
      "  import geopandas as gpd\n"
     ]
    }
   ],
   "source": [
    "import pytz\n",
    "from datetime import datetime, timedelta\n",
    "import os\n",
    "import geopandas as gpd\n",
    "import json\n",
    "from shapely.geometry import Polygon, Point, MultiPolygon\n",
    "import shapefile\n",
    "import re\n",
    "import shapefile as shp  # Requires the pyshp package\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import matplotlib.colors as mcolors\n",
    "import numpy as np\n",
    "import random\n",
    "random.seed(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8c96c81-f0a6-4b4b-80bf-1e26eccd8c99",
   "metadata": {},
   "source": [
    "<a id='read'></a>\n",
    "\n",
    "## 1. Read Twitter Data and data processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abc9d54d-5fe0-470a-9e02-4f24db0f6154",
   "metadata": {},
   "source": [
    "Read the shapefile of Chicago."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "92008218-5d97-4e7b-ac68-b286cd0c8232",
   "metadata": {},
   "outputs": [],
   "source": [
    "shapefile = gpd.read_file(\"./geo/geo_export_5bb8636f-65b7-450a-8fd9-7f01027fd84b.shp\")\n",
    "chicago_shape = shapefile[\"geometry\"][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f4a05ad-ccff-41e8-96df-ee287e2c3430",
   "metadata": {},
   "source": [
    "Retrieve the information of all the Twitter file available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8d555ca6-448f-4a2b-b80e-40782a137d91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['250000-tweets-2021-09-26_22-13-26.json',\n",
       " '250000-tweets-2021-09-26_15-10-52.json',\n",
       " '250000-tweets-2021-09-25_20-12-57.json',\n",
       " '250000-tweets-2021-09-26_04-56-08.json',\n",
       " '250000-tweets-2021-09-25_15-34-24.json',\n",
       " '250000-tweets-2021-09-26_01-46-49.json',\n",
       " '250000-tweets-2021-09-26_17-16-06.json',\n",
       " '250000-tweets-2021-09-25_04-59-49.json',\n",
       " '250000-tweets-2021-09-25_07-33-30.json',\n",
       " '250000-tweets-2021-09-26_07-36-30.json',\n",
       " '250000-tweets-2021-09-25_09-29-48.json',\n",
       " '250000-tweets-2021-09-26_09-39-36.json',\n",
       " '250000-tweets-2021-09-25_22-39-35.json',\n",
       " '250000-tweets-2021-09-26_13-18-44.json',\n",
       " '250000-tweets-2021-09-25_01-48-23.json',\n",
       " '250000-tweets-2021-09-25_17-48-51.json',\n",
       " '250000-tweets-2021-09-26_19-43-23.json',\n",
       " '250000-tweets-2021-09-25_13-24-18.json',\n",
       " '250000-tweets-2021-09-26_11-29-11.json',\n",
       " '250000-tweets-2021-09-25_11-25-23.json']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir('./data/')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aef5e49-625f-410a-be67-a70f291a3c94",
   "metadata": {},
   "source": [
    "Find all the geo-tagged Twitter data in the city of Chicago."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fecf0793-ab83-4752-8da3-082dfcd40e3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['250000-tweets-2021-09-26_22-13-26.json',\n",
       " '250000-tweets-2021-09-26_15-10-52.json',\n",
       " '250000-tweets-2021-09-25_20-12-57.json',\n",
       " '250000-tweets-2021-09-26_04-56-08.json',\n",
       " '250000-tweets-2021-09-25_15-34-24.json',\n",
       " '250000-tweets-2021-09-26_01-46-49.json',\n",
       " '250000-tweets-2021-09-26_17-16-06.json',\n",
       " '250000-tweets-2021-09-25_04-59-49.json',\n",
       " '250000-tweets-2021-09-25_07-33-30.json',\n",
       " '250000-tweets-2021-09-26_07-36-30.json',\n",
       " '250000-tweets-2021-09-25_09-29-48.json',\n",
       " '250000-tweets-2021-09-26_09-39-36.json',\n",
       " '250000-tweets-2021-09-25_22-39-35.json',\n",
       " '250000-tweets-2021-09-26_13-18-44.json',\n",
       " '250000-tweets-2021-09-25_01-48-23.json',\n",
       " '250000-tweets-2021-09-25_17-48-51.json',\n",
       " '250000-tweets-2021-09-26_19-43-23.json',\n",
       " '250000-tweets-2021-09-25_13-24-18.json',\n",
       " '250000-tweets-2021-09-26_11-29-11.json',\n",
       " '250000-tweets-2021-09-25_11-25-23.json']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## get the twitter in chicago\n",
    "## City scale analysis\n",
    "## This block of code will takes a long time\n",
    "## We iterate through all the twitter collected for find twitter in chicago\n",
    "## Get the filename\n",
    "filelist = os.listdir('./data/')\n",
    "filelist\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "791bac86-437a-47b5-945b-4629f694b5fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "## select all files on 09-25\n",
    "filelist = ['250000-tweets-2021-09-25_01-48-23.json', \n",
    "            '250000-tweets-2021-09-25_04-59-49.json', \n",
    "            '250000-tweets-2021-09-25_07-33-30.json',\n",
    "            '250000-tweets-2021-09-25_09-29-48.json',\n",
    "            '250000-tweets-2021-09-25_11-25-23.json',\n",
    "            '250000-tweets-2021-09-25_13-24-18.json',\n",
    "            '250000-tweets-2021-09-25_15-34-24.json',\n",
    "            '250000-tweets-2021-09-25_17-48-51.json',\n",
    "            '250000-tweets-2021-09-25_20-12-57.json',\n",
    "            '250000-tweets-2021-09-25_22-39-35.json'\n",
    "           ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c6c72968-e103-4ebf-9c0d-1530ffd81459",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data/250000-tweets-2021-09-25_01-48-23.json\n",
      "./data/250000-tweets-2021-09-25_04-59-49.json\n",
      "./data/250000-tweets-2021-09-25_07-33-30.json\n",
      "./data/250000-tweets-2021-09-25_09-29-48.json\n",
      "./data/250000-tweets-2021-09-25_11-25-23.json\n",
      "./data/250000-tweets-2021-09-25_13-24-18.json\n",
      "./data/250000-tweets-2021-09-25_15-34-24.json\n",
      "./data/250000-tweets-2021-09-25_17-48-51.json\n",
      "./data/250000-tweets-2021-09-25_20-12-57.json\n",
      "./data/250000-tweets-2021-09-25_22-39-35.json\n"
     ]
    }
   ],
   "source": [
    "twitter_in_chicago = []\n",
    "\n",
    "# Opening JSON file\n",
    "for filename in filelist:\n",
    "    filepath = \"./data/\"+filename\n",
    "    print(filepath)\n",
    "    f = open(filepath)\n",
    "    data = json.load(f)\n",
    "    \n",
    "    ## Read the data if the centroid of the twitter point polygon lies within the boundary of the city of Chicago\n",
    "    for i in range(0, len(data)):\n",
    "        try:\n",
    "            ##Need to deal with case when the shapefile is too big\n",
    "            text = data[i][\"text\"]\n",
    "            t = data[i]['created_at']\n",
    "            ## Case 1\n",
    "            ## Twitter with exact geospatial location\n",
    "            if (data[i]['geo']!=None):\n",
    "                lat = data[i]['geo']['coordinates'][0]\n",
    "                lon = data[i]['geo']['coordinates'][1]\n",
    "                exact_loc = Point(lon, lat)\n",
    "                if chicago_shape.contains(exact_loc):\n",
    "                    ## print(\"inside\")\n",
    "                    twitter_in_chicago.append((exact_loc, t, text))\n",
    "            else:\n",
    "                ## Twitter with a polygon bounding box\n",
    "                poly = data[i]['place']['bounding_box'][\"coordinates\"][0]\n",
    "                lon = -1000\n",
    "                lat = -1000\n",
    "\n",
    "                lon = [p[0] for p in poly]\n",
    "                lat = [p[1] for p in poly]\n",
    "                centroid = (sum(lon) / len(poly), sum(lat) / len(poly))\n",
    "                point = Point(centroid)\n",
    "                ## check if a centroid is in the bounding box of chicago\n",
    "                if chicago_shape.contains(point):\n",
    "                    ## print(\"inside\")\n",
    "                    twitter_in_chicago.append((poly, t, text))\n",
    "        except:\n",
    "            ## no geographical location\n",
    "            pass\n",
    "    # Closing file\n",
    "    f.close()\n",
    "    data = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "008af25d-84ba-4195-8b03-93b8a29b4624",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are in total 10897 geo-tagged Twitter Collected in Chicago\n"
     ]
    }
   ],
   "source": [
    "print (\"There are in total \"+str(len(twitter_in_chicago))+\" geo-tagged Twitter Collected in Chicago\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39c7a96b-ac52-4e6a-867e-551c0019f434",
   "metadata": {},
   "source": [
    "<a id='understand'></a>\n",
    "\n",
    "## 2. Understand the human sentiments of heat exposure of Twitter data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1058f2b3-5191-4e53-8f5c-48b5677bc029",
   "metadata": {},
   "source": [
    "Read the heat dictionary of social media data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d4047ef7-9082-4255-a166-089eee05ebac",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Read the word heat dictionary\n",
    "f = open('./geo/data20000.txt','r')\n",
    "content = f.read()\n",
    "f.close()\n",
    "dict_word = {}\n",
    "content_list = content.split(\",\")\n",
    "for i in range(0,len(content_list)):\n",
    "    try:\n",
    "        word = content_list[i].split(\":\")[0].split(\"'\")[1]\n",
    "        #print(content_list[i].split(\":\"))\n",
    "        val = float(content_list[i].split(\":\")[1])\n",
    "        dict_word[word] = val\n",
    "    except:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "951a82a4-4103-4355-8907-82893a35e9ae",
   "metadata": {},
   "source": [
    "Find all the weather-realted Twitter data in the city of Chicago from all the Twitter data in Chicago using the heat dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "daefad10-4cc0-4509-9a14-9a17ca018c25",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Iterate through all twitter data in chicago\n",
    "\n",
    "d_twitter = []\n",
    "for i in range(0, len(twitter_in_chicago)):\n",
    "    loc = twitter_in_chicago[i][0]\n",
    "    t = twitter_in_chicago[i][1]\n",
    "    text = twitter_in_chicago[i][2]\n",
    "    res = re.findall(r'\\w+', text.lower())\n",
    "    val = 0\n",
    "    for word in res:\n",
    "        if word in dict_word.keys():\n",
    "            val = val + dict_word[word]\n",
    "    ## remove weather-irrelevant twitter\n",
    "    ## if none of the word in the heat dictionary show up \n",
    "    if (val!=0):\n",
    "        d_twitter.append((loc, t, val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "279cb4c4-2f81-4aa6-9bb6-4e172fde1c90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 410 weather-related Twitter in Chicago\n"
     ]
    }
   ],
   "source": [
    "print(\"There are \"+str(len(d_twitter))+\" weather-related Twitter in Chicago\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c43cab67-279f-4aef-ac05-49806e2b4bc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "m_dic = {}\n",
    "m_dic['Jan'] = 1\n",
    "m_dic['Feb'] = 2\n",
    "m_dic['Mar'] = 3\n",
    "m_dic['Apr'] = 4\n",
    "m_dic['May'] = 5\n",
    "m_dic['Jun'] = 6\n",
    "m_dic['Jul'] = 7\n",
    "m_dic['Aug'] = 8\n",
    "m_dic['Sep'] = 9\n",
    "m_dic['Oct'] = 10\n",
    "m_dic['Nov'] = 11\n",
    "m_dic['Dec'] = 12"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e1771d0-fb3b-427c-8990-982f1190af33",
   "metadata": {},
   "source": [
    "Set up the current datetime, the Central Time in the city of Chicago in the Summer is GMT-5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "09c66417-68f1-470a-9109-04f91a62c005",
   "metadata": {},
   "outputs": [],
   "source": [
    "curr = datetime(2021,9, 25, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12250d32-81c3-466d-83ca-c83e867c7a08",
   "metadata": {},
   "source": [
    "Find the time difference between the current time and the time when the Twitter was posted to conduct fine graunlarity analysis of human sentiments of heat exposure. And find all the weather-related Twitter related data that is collected within the 24 hour timeframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "817f52e0-eeda-4147-91b0-da584c4ad3d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Find the time difference between the current time and the Twitter post time\n",
    "weather_related_twitter = []\n",
    "for twitter in d_twitter:\n",
    "    loc = twitter[0]\n",
    "    t = twitter[1].split()\n",
    "    val = twitter[2]\n",
    "    month = m_dic[t[1]]\n",
    "    day = int(t[2])\n",
    "    year = int(t[5])\n",
    "    hour = int(t[3].split(\":\")[0])\n",
    "    minute = int(t[3].split(\":\")[1])\n",
    "    twitter_t = datetime(year, month, day, hour, minute)\n",
    "    diff_hour = (twitter_t - curr).total_seconds() / (60.0*60)\n",
    "    if (diff_hour<0 or diff_hour>24):\n",
    "        #print(diff_hour)\n",
    "        pass\n",
    "    else:\n",
    "        weather_related_twitter.append((loc, diff_hour, val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a3afca0e-83f8-445e-9bf1-c6c76c93c778",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "384"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(weather_related_twitter)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a418d474-33cc-4221-93f5-81b1ff091adb",
   "metadata": {},
   "source": [
    "Find the maximum, minimun when the Twitter data is collected and also the number of Twitter getting collected within the 24-hour timeframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "260d732d-59ad-4610-93b1-c89408e4bf26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 22.533333333333335, 384)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = []\n",
    "for ele in weather_related_twitter:\n",
    "    t.append(ele[1])\n",
    "min(t),max(t),len(t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d9cb837-4256-4ac1-a0b3-3c759d95f276",
   "metadata": {},
   "source": [
    "<a id='Distribute'></a>\n",
    "\n",
    "## 3. Distribute All the Social Media Data into the 3-hour Temporal Bins"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c45649bf-8989-48af-9012-70cac8aba4a8",
   "metadata": {},
   "source": [
    "Put all the Twitter collected in the 24-hour timeframe into 3-hour temporal bins. There are 8 different seperate timeslice and each one is of 3 hours. The 8 bins correspond to the timeframe at 0-3, 3-6, 6-9. 9-12. 12-15, 15-18, 18-21, 21-24."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "41acd7aa-82e4-4780-b3d9-0c658f13babb",
   "metadata": {},
   "outputs": [],
   "source": [
    "s1 = []\n",
    "s2 = []\n",
    "s3 = []\n",
    "s4 = []\n",
    "s5 = []\n",
    "s6 = []\n",
    "s7 = []\n",
    "s8 = []\n",
    "for ele in weather_related_twitter:\n",
    "    t = ele[1]\n",
    "    if (t>0 and t<=3):\n",
    "        s1.append(ele)\n",
    "    elif (t>3 and t<=6):\n",
    "        s2.append(ele)\n",
    "    elif (t>6 and t<=9):\n",
    "        s3.append(ele)\n",
    "    elif (t>9 and t<=12):\n",
    "        s4.append(ele)\n",
    "    elif (t>12 and t<=15):\n",
    "        s5.append(ele)\n",
    "    elif (t>15 and t<=18):\n",
    "        s6.append(ele)\n",
    "    elif (t>18 and t<=21):\n",
    "        s7.append(ele)\n",
    "    elif (t>21 and t<=24):\n",
    "        s8.append(ele)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b51f9aa6-71e5-418f-85a1-e564cd7a9eec",
   "metadata": {},
   "source": [
    "Get how many weather-related Twitter posts are falling into each time bins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7a79c545-1dd5-4c0a-adb5-5f06c4fdf52d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24, 9, 48, 71, 59, 61, 78, 33)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s1),len(s2),len(s3),len(s4),len(s5),len(s6), len(s7),len(s8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21550686-abc1-46a5-bc75-213951b26eba",
   "metadata": {},
   "source": [
    "<a id='Visualization'></a>\n",
    "\n",
    "## 4. Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4601320-5b23-4044-b295-84663d244eed",
   "metadata": {},
   "source": [
    "Visualize the fine-temporal-granularity maps for human sentiments of heat exposure.\n",
    "\n",
    "The analysis in condected at 1km * 1km spatial resoluiton."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1395b26d-d81b-45ca-8c7a-b9502d71b3e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Generate raster based about 1 km spatial resoltuion \n",
    "## one Degree latitude = 111 km\n",
    "## In chicago, where latitude = 41.881832, one Degree longitude = 82 km\n",
    "## We use this estimation for the following ananlsis\n",
    "## This work as the city of Chicago is small\n",
    "lat_start = 41.05\n",
    "lon_start = -87.96\n",
    "\n",
    "incre_lat = 1/111\n",
    "incre_lon = 1/82\n",
    "\n",
    "lat_end = 42.05\n",
    "lon_end = -87.5\n",
    "\n",
    "raster = []\n",
    "\n",
    "lat = lat_start\n",
    "\n",
    "while(lat<lat_end):\n",
    "    lon = lon_start\n",
    "    while(lon<lon_end):\n",
    "        curr_point = Point(lon, lat)\n",
    "        if (curr_point.within(chicago_shape)):\n",
    "            raster.append([lon, lat])\n",
    "        lon = lon+incre_lon\n",
    "    lat = lat+incre_lat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eb4882b-6c0a-49d5-b25d-6e65971507b4",
   "metadata": {},
   "source": [
    "Generate reuslt for the human sentiments of heat exposure at different timeframe at fine temporal granularity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1dac2ec1-ec38-4b13-b5f3-51850021cad2",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Generate a random point from a polygon\n",
    "import random\n",
    "\n",
    "def generate_random(number, polygon):\n",
    "    minx, miny, maxx, maxy = polygon.bounds\n",
    "    pnt = Point(random.uniform(minx, maxx), random.uniform(miny, maxy))\n",
    "    return pnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a09248ea-9dea-4b17-b59b-e0a3305c5259",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Function to generate random location twitter\n",
    "## For monte caro experiment\n",
    "## Enable exact and poly if you want to see how many twitter has exact location and how many comes with a polygon\n",
    "#exact = 0\n",
    "#poly = 0\n",
    "def generate_random_loc(weather_related_twitter):\n",
    "    random_loc_twitter = []\n",
    "    for ele in weather_related_twitter:\n",
    "        loc = ele[0]\n",
    "        point = 0\n",
    "        #print(loc)\n",
    "        if (type(loc)==Point):\n",
    "            ## exact location extracted\n",
    "            point = loc\n",
    "            #exact = exact+1\n",
    "        else:\n",
    "            ## Select a random point from a multi-polygon\n",
    "            point = generate_random(1, Polygon(loc))\n",
    "            #poly = poly+1\n",
    "        random_loc_twitter.append([point, ele[2]])\n",
    "    return random_loc_twitter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "541324f5-12d1-46f9-8d0c-10280dc8616e",
   "metadata": {},
   "source": [
    "Calcualte the human sentiments of heat exposure. Using Inverse Distance Weighting (IDW) for those spatial unit that doesn't have a points. And using Monte-Carlo simulation to take care of those multi-polygon locations in the social media posts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5894c6e9-7c93-45fd-8d70-095b1d1247e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "## for 8 bins\n",
    "def get_heat_exposure(weather_related_twitter):\n",
    "    d_final = {}\n",
    "\n",
    "    for i in range(0, 10):\n",
    "        ## try 10 random time\n",
    "        #print(\"current \"+str(i))\n",
    "\n",
    "        ## Conduct kernel density estimation\n",
    "        random_loc_twitter = generate_random_loc(weather_related_twitter)\n",
    "\n",
    "        ### fill with inverse distance weighting\n",
    "\n",
    "\n",
    "        for ele in raster:\n",
    "            lon = ele[0]\n",
    "            lat = ele[1]\n",
    "            ## iterate through all the values in the existing twitter\n",
    "            up = 0\n",
    "            down = 0\n",
    "            IDW = 0\n",
    "            for twitter in random_loc_twitter:\n",
    "                pt = twitter[0]\n",
    "                curr_x = pt.x\n",
    "                curr_y = pt.y\n",
    "                curr_val = twitter[1]\n",
    "\n",
    "                distx = (curr_x-lon)*82\n",
    "                disty = (curr_y-lat)*111\n",
    "\n",
    "                w = 1/np.sqrt(distx*distx+disty*disty)\n",
    "\n",
    "                down = down+w\n",
    "                up = up+w*curr_val\n",
    "            rt = up/down\n",
    "\n",
    "            key = (ele[0],ele[1])\n",
    "            if (key not in d_final.keys()):\n",
    "                d_final[key]=[rt]\n",
    "            else:\n",
    "                d_final[key].append(rt)\n",
    "    return d_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d474e8b2-9b1c-4e11-8d33-67a1400597e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "s1_rt = get_heat_exposure(s1)\n",
    "s2_rt = get_heat_exposure(s2)\n",
    "s3_rt = get_heat_exposure(s3)\n",
    "s4_rt = get_heat_exposure(s4)\n",
    "s5_rt = get_heat_exposure(s5)\n",
    "s6_rt = get_heat_exposure(s6)\n",
    "s7_rt = get_heat_exposure(s7)\n",
    "s8_rt = get_heat_exposure(s8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b7da863-ab7e-4693-849b-415c3dcb0bf1",
   "metadata": {},
   "source": [
    "Get the heat exposure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1d764464-e838-4385-ad8e-e32c2fcd89f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "heat_exposure_map_1 = {}\n",
    "for key in s1_rt.keys():\n",
    "    ## Get the average hot exposure\n",
    "    heat_exposure_map_1[key] = np.mean(s1_rt[key])\n",
    "    \n",
    "heat_exposure_map_2 = {}\n",
    "for key in s2_rt.keys():\n",
    "    ## Get the average hot exposure\n",
    "    heat_exposure_map_2[key] = np.mean(s2_rt[key])\n",
    "    \n",
    "heat_exposure_map_3 = {}\n",
    "for key in s3_rt.keys():\n",
    "    ## Get the average hot exposure\n",
    "    heat_exposure_map_3[key] = np.mean(s3_rt[key])\n",
    "    \n",
    "heat_exposure_map_4 = {}\n",
    "for key in s4_rt.keys():\n",
    "    ## Get the average hot exposure\n",
    "    heat_exposure_map_4[key] = np.mean(s4_rt[key])\n",
    "    \n",
    "heat_exposure_map_5 = {}\n",
    "for key in s5_rt.keys():\n",
    "    ## Get the average hot exposure\n",
    "    heat_exposure_map_5[key] = np.mean(s5_rt[key])\n",
    "    \n",
    "heat_exposure_map_6 = {}\n",
    "for key in s6_rt.keys():\n",
    "    ## Get the average hot exposure\n",
    "    heat_exposure_map_6[key] = np.mean(s6_rt[key])\n",
    "    \n",
    "heat_exposure_map_7 = {}\n",
    "for key in s7_rt.keys():\n",
    "    ## Get the average hot exposure\n",
    "    heat_exposure_map_7[key] = np.mean(s7_rt[key])\n",
    "    \n",
    "heat_exposure_map_8 = {}\n",
    "for key in s8_rt.keys():\n",
    "    ## Get the average hot exposure\n",
    "    heat_exposure_map_8[key] = np.mean(s8_rt[key])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90219f87-5588-4f83-bf9c-e954cc62537c",
   "metadata": {},
   "source": [
    "Get the maximum and minimum to normalize the human sentiments of heat exposure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a2caeaa2-48e3-4852-a67f-6d3afeea719e",
   "metadata": {},
   "outputs": [],
   "source": [
    "mn = min(min(heat_exposure_map_1.values()),min(heat_exposure_map_2.values()),min(heat_exposure_map_3.values()),min(heat_exposure_map_4.values()),min(heat_exposure_map_5.values()),min(heat_exposure_map_6.values()), min(heat_exposure_map_7.values()),min(heat_exposure_map_8.values()))\n",
    "mx = max(max(heat_exposure_map_1.values()),max(heat_exposure_map_2.values()),max(heat_exposure_map_3.values()),max(heat_exposure_map_4.values()),max(heat_exposure_map_5.values()),max(heat_exposure_map_6.values()), max(heat_exposure_map_7.values()),max(heat_exposure_map_8.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "01d1f7f7-3322-4bc3-a83c-69cda0b66438",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.34526938819666136, 0.1485242181797817)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mn, mx"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7468d6ce-7ac0-45bb-96f7-a916ca5a1a46",
   "metadata": {},
   "source": [
    "Calcualte the normalized human sentiments of heat exposure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "08b5ec2e-9748-422f-8881-b27734bb8ffe",
   "metadata": {},
   "outputs": [],
   "source": [
    "for key in heat_exposure_map_1.keys():\n",
    "    norm = (heat_exposure_map_1[key]-mn)/(mx-mn)\n",
    "    heat_exposure_map_1[key] = norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9833713e-0db7-44ad-8bb9-44c9c1a9419f",
   "metadata": {},
   "outputs": [],
   "source": [
    "for key in heat_exposure_map_2.keys():\n",
    "    norm = (heat_exposure_map_2[key]-mn)/(mx-mn)\n",
    "    heat_exposure_map_2[key] = norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "16bf3ad5-7d30-4311-a2fd-4360f9a79fd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "for key in heat_exposure_map_3.keys():\n",
    "    norm = (heat_exposure_map_3[key]-mn)/(mx-mn)\n",
    "    heat_exposure_map_3[key] = norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0623a14d-2d4e-4845-a997-e76e24217752",
   "metadata": {},
   "outputs": [],
   "source": [
    "for key in heat_exposure_map_4.keys():\n",
    "    norm = (heat_exposure_map_4[key]-mn)/(mx-mn)\n",
    "    heat_exposure_map_4[key] = norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "062b7c68-32a9-433e-920d-c181122949d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "for key in heat_exposure_map_5.keys():\n",
    "    norm = (heat_exposure_map_5[key]-mn)/(mx-mn)\n",
    "    heat_exposure_map_5[key] = norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "46d65cb7-db5c-4d30-969b-1bffa0368b2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "for key in heat_exposure_map_6.keys():\n",
    "    norm = (heat_exposure_map_6[key]-mn)/(mx-mn)\n",
    "    heat_exposure_map_6[key] = norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "062c30f5-81c6-4188-83e2-54e856ce94b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "for key in heat_exposure_map_7.keys():\n",
    "    norm = (heat_exposure_map_7[key]-mn)/(mx-mn)\n",
    "    heat_exposure_map_7[key] = norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cf32de6a-07bc-4ee8-9430-59d1d9b57d2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "for key in heat_exposure_map_8.keys():\n",
    "    norm = (heat_exposure_map_8[key]-mn)/(mx-mn)\n",
    "    heat_exposure_map_8[key] = norm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b2ea741-e93d-4802-b617-4a1b938cf515",
   "metadata": {},
   "source": [
    "Create dataframe for visualzation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "68afb522-eb6a-4760-b52b-a610f2999b81",
   "metadata": {},
   "outputs": [],
   "source": [
    "lonl = []\n",
    "latl = []\n",
    "var1 = []\n",
    "var2 = []\n",
    "var3 = []\n",
    "var4 = []\n",
    "var5 = []\n",
    "var6 = []\n",
    "var7 = []\n",
    "var8 = []\n",
    "\n",
    "final_heat_exposure_map = {}\n",
    "for key in heat_exposure_map_1.keys():\n",
    "    \n",
    "    lonl.append(key[0])\n",
    "    latl.append(key[1])\n",
    "    var1.append(heat_exposure_map_1[key])\n",
    "    var2.append(heat_exposure_map_2[key])\n",
    "    var3.append(heat_exposure_map_3[key])\n",
    "    var4.append(heat_exposure_map_4[key])\n",
    "    var5.append(heat_exposure_map_5[key])\n",
    "    var6.append(heat_exposure_map_6[key])\n",
    "    var7.append(heat_exposure_map_7[key])\n",
    "    var8.append(heat_exposure_map_8[key])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "dbe5f7f4-572a-4bfe-8b6e-b2942bf8380a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.column_stack([lonl, latl, var1, var2, var3, var4, var5, var6, var7, var8]), \n",
    "                  columns=['lon', 'lat', '0-3', '3-6', '6-9', '9-12', '12-15', '15-18', '18-21', '21-24'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "911b5b07-0072-4581-a0d4-b74d470d7d92",
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>0-3</th>\n",
       "      <th>3-6</th>\n",
       "      <th>6-9</th>\n",
       "      <th>9-12</th>\n",
       "      <th>12-15</th>\n",
       "      <th>15-18</th>\n",
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       "      <th>0</th>\n",
       "      <td>-87.545366</td>\n",
       "      <td>41.644595</td>\n",
       "      <td>0.550791</td>\n",
       "      <td>0.743605</td>\n",
       "      <td>0.538795</td>\n",
       "      <td>0.669702</td>\n",
       "      <td>0.477598</td>\n",
       "      <td>0.598336</td>\n",
       "      <td>0.498338</td>\n",
       "      <td>0.402866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-87.533171</td>\n",
       "      <td>41.644595</td>\n",
       "      <td>0.550114</td>\n",
       "      <td>0.742129</td>\n",
       "      <td>0.553362</td>\n",
       "      <td>0.671330</td>\n",
       "      <td>0.467874</td>\n",
       "      <td>0.600275</td>\n",
       "      <td>0.501506</td>\n",
       "      <td>0.401017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-87.618537</td>\n",
       "      <td>41.653604</td>\n",
       "      <td>0.567151</td>\n",
       "      <td>0.752737</td>\n",
       "      <td>0.569812</td>\n",
       "      <td>0.721843</td>\n",
       "      <td>0.550830</td>\n",
       "      <td>0.598024</td>\n",
       "      <td>0.491121</td>\n",
       "      <td>0.458432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-87.606341</td>\n",
       "      <td>41.653604</td>\n",
       "      <td>0.565761</td>\n",
       "      <td>0.750637</td>\n",
       "      <td>0.560150</td>\n",
       "      <td>0.700079</td>\n",
       "      <td>0.546759</td>\n",
       "      <td>0.599459</td>\n",
       "      <td>0.498040</td>\n",
       "      <td>0.443509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-87.594146</td>\n",
       "      <td>41.653604</td>\n",
       "      <td>0.564294</td>\n",
       "      <td>0.747339</td>\n",
       "      <td>0.548355</td>\n",
       "      <td>0.697790</td>\n",
       "      <td>0.552224</td>\n",
       "      <td>0.595521</td>\n",
       "      <td>0.495291</td>\n",
       "      <td>0.418354</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>-87.777073</td>\n",
       "      <td>42.013964</td>\n",
       "      <td>0.585476</td>\n",
       "      <td>0.622538</td>\n",
       "      <td>0.602144</td>\n",
       "      <td>0.681413</td>\n",
       "      <td>0.480125</td>\n",
       "      <td>0.557446</td>\n",
       "      <td>0.518444</td>\n",
       "      <td>0.440844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>-87.703902</td>\n",
       "      <td>42.013964</td>\n",
       "      <td>0.594570</td>\n",
       "      <td>0.624376</td>\n",
       "      <td>0.558886</td>\n",
       "      <td>0.684891</td>\n",
       "      <td>0.463361</td>\n",
       "      <td>0.550309</td>\n",
       "      <td>0.532106</td>\n",
       "      <td>0.459377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>-87.691707</td>\n",
       "      <td>42.013964</td>\n",
       "      <td>0.582082</td>\n",
       "      <td>0.626352</td>\n",
       "      <td>0.554122</td>\n",
       "      <td>0.676982</td>\n",
       "      <td>0.466290</td>\n",
       "      <td>0.550889</td>\n",
       "      <td>0.525906</td>\n",
       "      <td>0.453156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>-87.679512</td>\n",
       "      <td>42.013964</td>\n",
       "      <td>0.573613</td>\n",
       "      <td>0.626790</td>\n",
       "      <td>0.549433</td>\n",
       "      <td>0.673707</td>\n",
       "      <td>0.456134</td>\n",
       "      <td>0.563584</td>\n",
       "      <td>0.522293</td>\n",
       "      <td>0.434294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>-87.667317</td>\n",
       "      <td>42.013964</td>\n",
       "      <td>0.562963</td>\n",
       "      <td>0.626429</td>\n",
       "      <td>0.549354</td>\n",
       "      <td>0.676047</td>\n",
       "      <td>0.457368</td>\n",
       "      <td>0.575169</td>\n",
       "      <td>0.518447</td>\n",
       "      <td>0.437898</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>593 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           lon        lat       0-3       3-6       6-9      9-12     12-15  \\\n",
       "0   -87.545366  41.644595  0.550791  0.743605  0.538795  0.669702  0.477598   \n",
       "1   -87.533171  41.644595  0.550114  0.742129  0.553362  0.671330  0.467874   \n",
       "2   -87.618537  41.653604  0.567151  0.752737  0.569812  0.721843  0.550830   \n",
       "3   -87.606341  41.653604  0.565761  0.750637  0.560150  0.700079  0.546759   \n",
       "4   -87.594146  41.653604  0.564294  0.747339  0.548355  0.697790  0.552224   \n",
       "..         ...        ...       ...       ...       ...       ...       ...   \n",
       "588 -87.777073  42.013964  0.585476  0.622538  0.602144  0.681413  0.480125   \n",
       "589 -87.703902  42.013964  0.594570  0.624376  0.558886  0.684891  0.463361   \n",
       "590 -87.691707  42.013964  0.582082  0.626352  0.554122  0.676982  0.466290   \n",
       "591 -87.679512  42.013964  0.573613  0.626790  0.549433  0.673707  0.456134   \n",
       "592 -87.667317  42.013964  0.562963  0.626429  0.549354  0.676047  0.457368   \n",
       "\n",
       "        15-18     18-21     21-24  \n",
       "0    0.598336  0.498338  0.402866  \n",
       "1    0.600275  0.501506  0.401017  \n",
       "2    0.598024  0.491121  0.458432  \n",
       "3    0.599459  0.498040  0.443509  \n",
       "4    0.595521  0.495291  0.418354  \n",
       "..        ...       ...       ...  \n",
       "588  0.557446  0.518444  0.440844  \n",
       "589  0.550309  0.532106  0.459377  \n",
       "590  0.550889  0.525906  0.453156  \n",
       "591  0.563584  0.522293  0.434294  \n",
       "592  0.575169  0.518447  0.437898  \n",
       "\n",
       "[593 rows x 10 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9eb0266-0a74-4a83-b7a7-4d8c9a9d3b9d",
   "metadata": {},
   "source": [
    "Visualize result for 6-9."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8c26d466-9b57-4866-9ef9-261a168cb578",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<Axes: xlabel='lon', ylabel='lat'>], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(title='Human Sentiments of Heat Expososure from 6 to 9', x='lon', y='lat', c='6-9', figsize = [10,10], subplots=True, marker=\"s\", s = 155, colormap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0dc1d7e-b8a7-4877-8a5e-dfc2b3db06b3",
   "metadata": {},
   "source": [
    "Visualize result for 9-12."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d82e5df6-dfdf-4e88-995f-9b2b2a08c9a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<Axes: xlabel='lon', ylabel='lat'>], dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(title='Human Sentiments of Heat Expososure from 9 to 12', x='lon', y='lat', c='9-12', figsize = [10,10], subplots=True, marker=\"s\", s = 155, colormap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "841eb8c3-bfe9-40ec-b787-7734da964d36",
   "metadata": {},
   "source": [
    "Visualize result for 12-15."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "445ae672-b67e-4704-be88-95b50e1616ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<Axes: xlabel='lon', ylabel='lat'>], dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(title='Human Sentiments of Heat Expososure from 12 to 15', x='lon', y='lat', c='12-15', figsize = [10,10], subplots=True, marker=\"s\", s = 155, colormap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a45b111d-8290-4e8b-91b5-aebfaf75a744",
   "metadata": {},
   "source": [
    "Visualize result for 15-18."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "651c486d-4416-4c1d-90fe-b978080733b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<Axes: xlabel='lon', ylabel='lat'>], dtype=object)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(title='Human Sentiments of Heat Expososure from 15 to 18', x='lon', y='lat', c='15-18', figsize = [10,10], subplots=True, marker=\"s\", s = 155, colormap='viridis')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "iguide",
   "language": "python",
   "name": "iguide"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}