Data Type.ipynb

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   "source": [
    "# GIS Data Type - Vector Data & Raster Data"
   ]
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    "Author: Fangzheng Lyu\n",
    "\n",
    "In Geographic Information Science (GIS), data types refer to the different ways in which geographic data can be represented and stored. The two main data types in GIS are:\n",
    "\n",
    "<b>Vector Data</b> represents geographic features as points, lines, and polygons.\n",
    "- <b>Point Data</b> are used to represent discrete locations, such as cities, landmarks, or specific coordinates.\n",
    "- <b>Line Data</b> are used to represent linear features, such as roads, rivers.\n",
    "- <b>Polygons</b> are used to represent areas or regions, such as countries, states, or land parcels.\n",
    "\n",
    "\n",
    "<b>Raster Data</b> represents geographic information as a grid of cells or pixels, where each cell contains a value.\n",
    "- <b>Continuous Raster Data</b> are used to represent continuous surfaces or phenomena, such as elevation, temperature, or satellite imagery.\n",
    "- <b>Discrete Raster Data</b> are used to represent discrete rasters that have a specific theme or class, and each pixel is assigned to a specific class such as land cover/land use map.\n"
   ]
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   "source": [
    "## Notebook Outline\n",
    "- [Vector Data](#vector)\n",
    "    - [Point Data](#point)\n",
    "    - [Line Data](#line)\n",
    "    - [Polygon Data](#polygon)\n",
    "- [Raster Data](#raster)\n",
    "    - [Continuous Raster Data](#continuous)\n",
    "    - [Discrete Raster Data](#discrete)"
   ]
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     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: geopandas==0.13.2 in /home/jovyan/.local/iguide/lib/python3.8/site-packages (0.13.2)\n",
      "Requirement already satisfied: fiona>=1.8.19 in /cvmfs/iguide.purdue.edu/software/conda/iguide/lib/python3.8/site-packages (from geopandas==0.13.2) (1.9.4)\n",
      "Requirement already satisfied: packaging in /cvmfs/iguide.purdue.edu/software/conda/iguide/lib/python3.8/site-packages (from geopandas==0.13.2) (23.1)\n",
      "Requirement already satisfied: pandas>=1.1.0 in /cvmfs/iguide.purdue.edu/software/conda/iguide/lib/python3.8/site-packages (from geopandas==0.13.2) (2.0.2)\n",
      "Requirement already satisfied: pyproj>=3.0.1 in /cvmfs/iguide.purdue.edu/software/conda/iguide/lib/python3.8/site-packages (from geopandas==0.13.2) (3.5.0)\n",
      "Requirement already satisfied: shapely>=1.7.1 in /cvmfs/iguide.purdue.edu/software/conda/iguide/lib/python3.8/site-packages (from geopandas==0.13.2) (2.0.1)\n",
      "Requirement already satisfied: attrs>=19.2.0 in /cvmfs/iguide.purdue.edu/software/conda/iguide/lib/python3.8/site-packages (from fiona>=1.8.19->geopandas==0.13.2) (23.1.0)\n",
      "Requirement already satisfied: certifi in /cvmfs/iguide.purdue.edu/software/conda/iguide/lib/python3.8/site-packages (from fiona>=1.8.19->geopandas==0.13.2) (2023.5.7)\n",
      "Requirement already satisfied: click~=8.0 in /cvmfs/iguide.purdue.edu/software/conda/iguide/lib/python3.8/site-packages (from fiona>=1.8.19->geopandas==0.13.2) (8.1.3)\n",
      "Requirement already satisfied: click-plugins>=1.0 in /cvmfs/iguide.purdue.edu/software/conda/iguide/lib/python3.8/site-packages (from fiona>=1.8.19->geopandas==0.13.2) (1.1.1)\n",
      "Requirement already satisfied: cligj>=0.5 in /cvmfs/iguide.purdue.edu/software/conda/iguide/lib/python3.8/site-packages (from fiona>=1.8.19->geopandas==0.13.2) (0.7.2)\n",
      "Requirement already satisfied: six in /cvmfs/iguide.purdue.edu/software/conda/iguide/lib/python3.8/site-packages (from fiona>=1.8.19->geopandas==0.13.2) (1.16.0)\n",
      "Requirement already satisfied: importlib-metadata in /cvmfs/iguide.purdue.edu/software/conda/iguide/lib/python3.8/site-packages (from fiona>=1.8.19->geopandas==0.13.2) (6.6.0)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /cvmfs/iguide.purdue.edu/software/conda/iguide/lib/python3.8/site-packages (from pandas>=1.1.0->geopandas==0.13.2) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /cvmfs/iguide.purdue.edu/software/conda/iguide/lib/python3.8/site-packages (from pandas>=1.1.0->geopandas==0.13.2) (2023.3)\n",
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      "Requirement already satisfied: numpy>=1.20.3 in /cvmfs/iguide.purdue.edu/software/conda/iguide/lib/python3.8/site-packages (from pandas>=1.1.0->geopandas==0.13.2) (1.24.3)\n",
      "Requirement already satisfied: zipp>=0.5 in /cvmfs/iguide.purdue.edu/software/conda/iguide/lib/python3.8/site-packages (from importlib-metadata->fiona>=1.8.19->geopandas==0.13.2) (3.15.0)\n"
     ]
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   ],
   "source": [
    "!pip install geopandas==0.13.2"
   ]
  },
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     "text": [
      "/tmp/ipykernel_1323/3328501683.py:5: 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 Library\n",
    "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 geopandas as gpd\n",
    "import matplotlib.colors as mcolors\n",
    "import numpy as np\n",
    "import random\n",
    "import csv\n",
    "from urllib.request import urlopen"
   ]
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   "id": "53e8f960-d788-416f-9f7d-ff92523c3700",
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   "source": [
    "<a id='vector'></a>\n",
    "\n",
    "## 1. Vector Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe022d9e-a7ed-48d6-af19-fc4d8d6cd0e0",
   "metadata": {},
   "source": [
    "Vector data in GIS (Geographic Information Systems) refers to the digital representation of geographic features using geometrical primitives such as points, lines, and polygons. Unlike raster data, which represents spatial data as a grid of cells or pixels, vector data stores the precise coordinates of the vertices that define the shape and location of geographic features."
   ]
  },
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   "cell_type": "markdown",
   "id": "9f62f328-18ae-42ba-8abf-ec747ef46b71",
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   "source": [
    "<a id='point'></a>\n",
    "\n",
    "### 1.1. Point Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8020ba79-f89e-43dc-8cbc-25e62251c2ac",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Twitter Data as Point Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fa52c88b-b834-43dc-82b7-071df4d51ba7",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Use an example, could add more if you want to involves more social media data\n",
    "filelist = ['250000-tweets-2021-09-25_01-48-23.json']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bced7ff6-8027-476e-b9ed-a0f319ac63d6",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data/250000-tweets-2021-09-25_01-48-23.json\n"
     ]
    }
   ],
   "source": [
    "## Use the file on \n",
    "\n",
    "## Create a dictionary for storing all Twitter with index = GEOID\n",
    "\n",
    "point_data = []\n",
    "polygon_data = []\n",
    "\n",
    "# Opening JSON file\n",
    "for filename in filelist:\n",
    "    filepath = \"./data/\"+filename\n",
    "    print(filepath)\n",
    "    #print(len(heat_twitter))\n",
    "    f = open(filepath)\n",
    "    data = json.load(f)\n",
    "    \n",
    "    \n",
    "    for i in range(0, len(data)):\n",
    "        #if (i%50000 == 0):\n",
    "        #    print(i)\n",
    "        try:\n",
    "            \n",
    "            ##Need to deal with case when the shapefile is too big\n",
    "            text = data[i][\"text\"]\n",
    "            exact_loc = -1\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",
    "                point_data.append(exact_loc)\n",
    "            elif (data[i]['place']!=None):\n",
    "                ## Twitter with a polygon bounding box\n",
    "                ## We check which county the centroid of the point lies in\n",
    "                poly = MultiPolygondata[i]['place']['bounding_box'][\"coordinates\"][0]\n",
    "                polygon_data.append(poly)\n",
    "        except:\n",
    "            ## no geographical location\n",
    "            pass\n",
    "    # Closing file\n",
    "    f.close()"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "19081"
      ]
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   "source": [
    "len(point_data)"
   ]
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   "cell_type": "code",
   "execution_count": 6,
   "id": "23ccb6c9-62e2-4235-8f88-8e464bcefee2",
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    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"75.1167 30.6833 2.0 2.0000000000000036\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,63.366600000000005)\"><circle cx=\"76.1167\" cy=\"31.6833\" r=\"0.06000000000000011\" stroke=\"#555555\" stroke-width=\"0.020000000000000035\" fill=\"#66cc99\" opacity=\"0.6\" /></g></svg>"
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       "<POINT (76.117 31.683)>"
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   "source": [
    "point_data[0]"
   ]
  },
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   "id": "1b1a6895-a35b-4658-9acb-d223bcaa4961",
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       "(76.1167, 31.6833)"
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    "point_data[0].x, point_data[0].y"
   ]
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   "source": [
    "<a id='line'></a>\n",
    "\n",
    "### 1.2. Line Data"
   ]
  },
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   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "### US Primary Road Network"
   ]
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "url_road = r'https://www2.census.gov/geo/tiger/TIGER2019/PRIMARYROADS/tl_2019_us_primaryroads.zip'\n",
    "road = gpd.read_file(url_road)\n",
    "road.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "293dd7d2-3ddc-4fcf-822b-fdc7b9bfa671",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LINEARID</th>\n",
       "      <th>FULLNAME</th>\n",
       "      <th>RTTYP</th>\n",
       "      <th>MTFCC</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1104430641961</td>\n",
       "      <td>State Hwy 42</td>\n",
       "      <td>S</td>\n",
       "      <td>S1100</td>\n",
       "      <td>LINESTRING (-89.34466 31.35903, -89.34374 31.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1104430639695</td>\n",
       "      <td>State Hwy 42</td>\n",
       "      <td>S</td>\n",
       "      <td>S1100</td>\n",
       "      <td>LINESTRING (-89.32506 31.37999, -89.32826 31.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1108296451323</td>\n",
       "      <td>State Hwy 67</td>\n",
       "      <td>S</td>\n",
       "      <td>S1100</td>\n",
       "      <td>LINESTRING (-88.49978 43.14901, -88.49788 43.1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1108296451337</td>\n",
       "      <td>State Hwy 67</td>\n",
       "      <td>S</td>\n",
       "      <td>S1100</td>\n",
       "      <td>LINESTRING (-88.49978 43.14878, -88.49820 43.1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1109097223373</td>\n",
       "      <td>State Hwy 114</td>\n",
       "      <td>S</td>\n",
       "      <td>S1100</td>\n",
       "      <td>LINESTRING (-97.18355 32.98982, -97.18214 32.9...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17490</th>\n",
       "      <td>11013473368094</td>\n",
       "      <td>I- 25 (Express Lanes)</td>\n",
       "      <td>I</td>\n",
       "      <td>S1100</td>\n",
       "      <td>LINESTRING (-105.00571 39.76043, -105.00484 39...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17491</th>\n",
       "      <td>1104271365671</td>\n",
       "      <td>I- 25 (Express Lanes)</td>\n",
       "      <td>I</td>\n",
       "      <td>S1100</td>\n",
       "      <td>LINESTRING (-104.98859 39.79109, -104.98831 39...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17492</th>\n",
       "      <td>1109094380320</td>\n",
       "      <td>Homestead Extension of Florida's Tpke</td>\n",
       "      <td>M</td>\n",
       "      <td>S1100</td>\n",
       "      <td>LINESTRING (-80.34125 25.95717, -80.34196 25.9...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17493</th>\n",
       "      <td>1108296486865</td>\n",
       "      <td>Homestead Extension of Florida's Tpke</td>\n",
       "      <td>M</td>\n",
       "      <td>S1100</td>\n",
       "      <td>LINESTRING (-80.34088 25.95715, -80.33988 25.9...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17494</th>\n",
       "      <td>1108296486869</td>\n",
       "      <td>Homestead Extension of Florida's Tpke</td>\n",
       "      <td>M</td>\n",
       "      <td>S1100</td>\n",
       "      <td>LINESTRING (-80.24078 25.97268, -80.24657 25.9...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17495 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             LINEARID                               FULLNAME RTTYP  MTFCC  \\\n",
       "0       1104430641961                           State Hwy 42     S  S1100   \n",
       "1       1104430639695                           State Hwy 42     S  S1100   \n",
       "2       1108296451323                           State Hwy 67     S  S1100   \n",
       "3       1108296451337                           State Hwy 67     S  S1100   \n",
       "4       1109097223373                          State Hwy 114     S  S1100   \n",
       "...               ...                                    ...   ...    ...   \n",
       "17490  11013473368094                  I- 25 (Express Lanes)     I  S1100   \n",
       "17491   1104271365671                  I- 25 (Express Lanes)     I  S1100   \n",
       "17492   1109094380320  Homestead Extension of Florida's Tpke     M  S1100   \n",
       "17493   1108296486865  Homestead Extension of Florida's Tpke     M  S1100   \n",
       "17494   1108296486869  Homestead Extension of Florida's Tpke     M  S1100   \n",
       "\n",
       "                                                geometry  \n",
       "0      LINESTRING (-89.34466 31.35903, -89.34374 31.3...  \n",
       "1      LINESTRING (-89.32506 31.37999, -89.32826 31.3...  \n",
       "2      LINESTRING (-88.49978 43.14901, -88.49788 43.1...  \n",
       "3      LINESTRING (-88.49978 43.14878, -88.49820 43.1...  \n",
       "4      LINESTRING (-97.18355 32.98982, -97.18214 32.9...  \n",
       "...                                                  ...  \n",
       "17490  LINESTRING (-105.00571 39.76043, -105.00484 39...  \n",
       "17491  LINESTRING (-104.98859 39.79109, -104.98831 39...  \n",
       "17492  LINESTRING (-80.34125 25.95717, -80.34196 25.9...  \n",
       "17493  LINESTRING (-80.34088 25.95715, -80.33988 25.9...  \n",
       "17494  LINESTRING (-80.24078 25.97268, -80.24657 25.9...  \n",
       "\n",
       "[17495 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "road"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ea2dc982-2a18-4a53-a108-42022d40535e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LINEARID</th>\n",
       "      <th>FULLNAME</th>\n",
       "      <th>RTTYP</th>\n",
       "      <th>MTFCC</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1104430641961</td>\n",
       "      <td>State Hwy 42</td>\n",
       "      <td>S</td>\n",
       "      <td>S1100</td>\n",
       "      <td>LINESTRING (-89.34466 31.35903, -89.34374 31.3...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        LINEARID      FULLNAME RTTYP  MTFCC  \\\n",
       "0  1104430641961  State Hwy 42     S  S1100   \n",
       "\n",
       "                                            geometry  \n",
       "0  LINESTRING (-89.34466 31.35903, -89.34374 31.3...  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hwy42 = road[road[\"LINEARID\"]==\"1104430641961\"]\n",
    "hwy42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "92b56285-dffe-4cd4-b9c0-ed8ebb952019",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-89.34551147999998 31.35817952 0.021277960000006146 0.023043959999988317\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,62.73940299999999)\"><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.00046087919999976634\" points=\"-89.34465799999998,31.359033 -89.34374299999999,31.35998 -89.34303299999999,31.360754 -89.34261999999998,31.361196999999997 -89.342286,31.361564999999988 -89.34208199999999,31.361791999999998 -89.34180599999999,31.362078999999998 -89.34114799999999,31.362763999999988 -89.34049899999998,31.363471999999998 -89.33897199999998,31.365164999999998 -89.33882199999998,31.365331999999988 -89.33819099999998,31.36600299999999 -89.33790799999998,31.36631099999999 -89.33656599999999,31.367777 -89.336534,31.367811999999997 -89.336455,31.367898999999998 -89.33639999999998,31.36796 -89.33634399999998,31.368020999999988 -89.335633,31.368783999999998 -89.33548799999998,31.36894099999999 -89.33485999999999,31.369601999999997 -89.33396899999998,31.37060299999999 -89.33318899999999,31.371455999999988 -89.332363,31.372335999999997 -89.332104,31.37261099999999 -89.32846099999999,31.376715999999988 -89.32508699999998,31.38036999999999\" opacity=\"0.8\" /></g></svg>"
      ],
      "text/plain": [
       "<LINESTRING (-89.345 31.359, -89.344 31.36, -89.343 31.361, -89.343 31.361, ...>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hwy42[\"geometry\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f72e5b20-16c7-4b0a-a71e-5a2990ea8203",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LINESTRING (-89.34465799999998 31.359033, -89.34374299999999 31.35998, -89.34303299999999 31.360754, -89.34261999999998 31.361196999999997, -89.342286 31.361564999999988, -89.34208199999999 31.361791999999998, -89.34180599999999 31.362078999999998, -89.34114799999999 31.362763999999988, -89.34049899999998 31.363471999999998, -89.33897199999998 31.365164999999998, -89.33882199999998 31.365331999999988, -89.33819099999998 31.36600299999999, -89.33790799999998 31.36631099999999, -89.33656599999999 31.367777, -89.336534 31.367811999999997, -89.336455 31.367898999999998, -89.33639999999998 31.36796, -89.33634399999998 31.368020999999988, -89.335633 31.368783999999998, -89.33548799999998 31.36894099999999, -89.33485999999999 31.369601999999997, -89.33396899999998 31.37060299999999, -89.33318899999999 31.371455999999988, -89.332363 31.372335999999997, -89.332104 31.37261099999999, -89.32846099999999 31.376715999999988, -89.32508699999998 31.38036999999999)\n"
     ]
    }
   ],
   "source": [
    "print(hwy42[\"geometry\"][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94e283ab-5cf8-444c-811f-b211aea465c7",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a id='polygon'></a>\n",
    "\n",
    "### 1.3. Polygon Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ddbab55-6391-41ca-bed1-7fedaac0a98c",
   "metadata": {},
   "source": [
    "### US County Shapefile as Polygon Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c5140564-7289-4646-8951-d00f89fdf707",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yeHzAVIsBx2s7e3KS4pS42qTHv914JaZwmX0ioohj+Bjlvq6xo76l5/AhBOAOGi7O7+9DsFDIZEhLUAct3+r0ot3tRYJaAaM++EcyJchaH133LLhU1/t0+brGjsW//wy+INW0tbuRGKdEVWMbWp0eLJuSiY23TYRKEfsVAImIRiqGj1GurIe1PSLJ6xM4FyLkxCllSNepMSY5DsVVzfAGSQmODg++lZcC50UhKE7dPXxolXLcMzcXANDc6sLRWhvePlAZNHgAgFohIev8TJqkOCVmXZbC4EFENMgYPka5mZclY3pOElweHwQ6L31IkJCoVaK53X2+lIAECUZD5ywStUJ2vnznN7pOM7CPUZvbhzZ352WcSdkGfFnV/bJJQ6sLDa0u/POiSy9zr0jFlOxEAAI+0Tk7JSclDmqlHPe8cRiflp6Dz+dDfmYiJmVfmGkjQYJKIcPBskaMSY6HACCXJKjVCvyvHSeQkxqPGbnJA2oTERGFxvAxyqUmqHGmqd0/BqLL9JwkfFHVHPQ1GXp1wDiRfLMeVxk7p1N3jduQcGHEhxCdl0iuNung8wmcCDJOo0uoPofc1Dicqgt8XWVjGyoa2s7XNxlfljfiWI0dH5V03ktIpZDB40PQdhi0CszISYbH54NMkpCuV+P2aVnw+tDPdUyIiKivGD5GuarGtm7BA+gMDKFIl6wGdvGUW3OiBjXN3Y/X1WPRWy9JVWM7puckwes7368iAB8EVHIZyuoD1xYJrGP3CqfEq1AbpG0AoNcqcbzWhsI5OZh7RRquyUmGnDNaiIiiguFjlMtOjsO/zhyDPx6o7PuLBvIdLQQsKXG9HuJIZXPA8+k54c86GZMch6wk7fnFyzrDjNcnIJMkKOUSrLYO/GBODtJ1vD8MEVE0MXwQJmcndgsfPfUC9Bgcepn4IgT8l0pCqW5qR05KHMovKne4oglTxiSi+KJQ4vRcWAhNpZBhTLIWlY3tUMolTMg04MD5FVuT45W4PC0BLq/AOUcH6hxOTMwy4Hd3TmbwICKKAQ7rJ9w2NQtLJpkDth0oa8SM3OSga28MNo9PIF4dmIuFAFwXzXTJTY1HQ4sLWqUcM3OT8Y+TDahuaodBq8ScvBR/z0laghppCRocLG/CF1XNyNBr4PYKpMSpsHhiYJuJiCg6GD4IMpmEtQuv7HaDuYNljZhgNiAuxHoawUi9jJvo+3qn3Useq7Fj5vlZKOZEDbKStJDL4O/h8InOdTvO2joQp5RBp5bDaNAELNl+pLIZGXo1bp2a1eeaEBFRZPGyCwEAzIlx+PG8PDz+/tGA7V+dscGSrEWHx+ef4SIE/CHg0oiglsuQdf7GbsH2ubznp/Se3x8qqrQ43UG3n6xrwZRsg/8meJfe9C07qbOucrkMTo8PJUFWO41XKXBTvjHEOxMR0WBj+CC/Wyab8cLfT8JqD5whUtHYjuQ4FfLS4nHqXCvMiRp/b8OlspK0qG5qD7pvYqYBtnaXf7ZMml6Ng0FuGgcAsy9LQU5yXECA0WsViFcpcKa5DTq1Ao1tbnRFHJkEqOQyWG3tyElNgCU5DtXN7TBolP5LR0J0Djttc3nR4vRAp1H2/eQQEVHEMHyQn16jxB8Kr8HyV/bD4fQE7Gtsc6HN5UF+pr7XAaOhSFJnkOmSYQg92NMrBMobA99HKZeQplMHncrrE4A5UQuDVoHiIIuUXerv/6zDLZMzw6g9ERFFCsMHBcjPNOD7sy14cfepbvs6PD4cr7Hj8gwdGlpdg1qPYJdjJmcnwu0VSNepYdCq0NzmgiRJMGiVsLe7AEgwxCmREq+Cy+NDXno8lHIZTp1rReP5+mbo1bjn25dhYb5pUOtPREShMXxQN8tnZCNdp0ZxZTN2naiDveNCL4hXAInayFyu+LKqGZOyDFApZKho6Lyxm1cIxKnk3W4+lxSnxKlzLWhs7RwLMiXbgC+rO3s4Lk9PwMnzq59KAK4y6XC81oEvzveAmBM1yEzUYOmUTDy84Mpu40SIiCi6GD6om+zkeCyfOQZLJ2fitupMPPe30oBFv0KtGtqbS1dG7fD4/AECABQyQCZJaGx1o8PlDSibmxofUIcW54X9Zy4aYyIAVDW0ITtZi6rzl3hkkoT//8C3kaBWMHgQEQ0BDB8UlFohh1ohR16GDutuHo/H3j+KtAQ1EuOU2H86+GBTABA9rMve2/d+5zIena8vOWNHZqIGgARJ6ryZ3cVs7Rdmw7S7vQGLkrW4vEhB5yDUvLQE/OfyKUiMU/X85kREFDUMH9SjzEQtMhO1+GDVt/3bam3t+PDLGvy/L2pw7KL7unQxaJVQyiXkpMQHBI6e7hdzKYHOgNHVw5GuUwfsr29xBjxPuGRRsorGdtw9YwweW3w14lT8mBMRDSX8rUxhMxm0+PG8PPx4Xh5O1rVge0kt7Od7ItRKGf51pgXmRC1Kzzrww9cPhZx625txJgMOlnf2spx1OHss6/F1TzY/+nYugwcR0RDE38w0IJenJ+DB668Iuu+KDB3e/ekc3PPG4aCLffXm4l6Ts7Z2KGUSJmQZcKSyGXFKObKStahoaIPHJyCXdS58JgTg9vqg0ypxeXpCf5tFRESDiOGDBlWGXoP7CvKw8u0jAICpYxLR5vLin1ZHL68EFHIJGqUMHW6ffzxIeX0rfvStHNwyORNZSVrotUq8sOskXvu8DHdek4o391XA5e28B0xTqwtJ8RzrQUQ01DB80KBbmG/Er5bm45NjVrzxwxkQAH75Xgm2Hq4KWt6o1+B/3zUZ03OS0OLy4niNHe8eqcbBskZkJmkx94o0TMpO9Jf/2fVX4L6CPGiUcmQlabHuw68BAB98WYPCOTmD30AiIgqLJHqanhADdrsdBoMBNpsNer1+cN6jww09l9aOqaZWF771zN/RdsmU2u/NGoPVN4xFaoI66Os8Xh8U8tD3QxRC4NvP7MKZ5nakJqhx6NHrOb2WiCgKwvn+Duuuths2bMD06dOh0+mQnp6OpUuX4sSJEwFlVqxYAUmSAh6zZs0KvxWD6OLgcepcSwxrMnolxauw8baJuPgmuJv+ZRKeWjohZPAA0GPwADqXWT93fnBqfYsTlY39WwqeiIgGT1jhY8+ePVi5ciX279+PHTt2wOPxYMGCBWhtbQ0od9NNN6G2ttb/2L59e0QrHUl5aRyUGCs3TzJjYlYiACAlXoVbpwz8XitymYRlUy8cZ1vxmQEfk4iIIiusMR9/+ctfAp5v3rwZ6enpKCoqwrx58/zb1Wo1jMbhdctyIQS752Ng9Q1X4N/f/Qr1La6I3Wn2JwV52Hm8DvUtTv8qp0RENHSE1fNxKZutc/pkcnJywPbdu3cjPT0dY8eOxb333ou6urqBvE1UODunU5z/txdur6+H0hQp116Zjk9/cR223T8Hyl4uqfRVbmo8Hl98NSQJSNOFvoRDRESx0e8Bp0II3HLLLWhqasJnn33m375161YkJCTAYrGgrKwMjz/+ODweD4qKiqBWd/8icDqdcDovLCBlt9uRnZ09qANO+8Pt9UXsy5EGT9fHub7FhZV/PIInbh6H8WZDjGtFRDTyhTPgtN/hY+XKlfjoo4/w+eefIysrK2S52tpaWCwWbNmyBcuWLeu2f926dXjyySe7bR9q4YOGB7fXh3aXFwfLGjErL6XbsutERDQ4Bm22S5cHHngAH3zwAXbt2tVj8AAAk8kEi8WC0tLSoPvXrl0Lm83mf1RVBV/7Yaios3fe0ZWXZYYmpVwGvVaJG8ZlMHgQEQ1RYf12FkLggQcewLZt27B7927k5ub2+pqGhgZUVVXBZDIF3a9Wq4Nejhmq0vUaAAi4BOPy+KBS8JIMERFRX4T1jbly5Uq89dZbePvtt6HT6WC1WmG1WtHe3jmjoKWlBQ8//DD27duH8vJy7N69G0uWLEFqaipuvfXWQWnAUNAVPNxeHz4uqUWbyxPjGhEREQ1dYYWPl156CTabDddeey1MJpP/sXXrVgCAXC5HSUkJbrnlFowdOxaFhYUYO3Ys9u3bB51ONygNGEpanB7MyUuBVinH2wcqcdbeAW+Qu60SERGNZqNyefVoEELgeK0N7xbV4N55l8Fo0MS6SkRENAi8PgGfEKN+RuSgDzil3kmShHHmRPxi4ZX44MszONPMxa6IiEYimQQ43d7eC5Ifw8cgUyvk+O+iavw5xB1ciYhoeOu6jxn1HeciRsF/ff8a5KbGx7oa1A9NrU5Y7U6k69TQaRRQKeSxrhIRDUFymYQOtxcaJX9H9AXDRxQweAxP9g43Dlc04dVPy3ClUYfrr0rD3LHpkMv4Fw4RBVLJZWhudzN89BHDB1EIeo0S88cZMX9c500SW50eMHcQUTAymQSvj4tP9hXDB1EfxXPFVCIKwenxjvrZLuHgb1MiIqIBUspk8CmG1MoVQxpjGhER0QC1uDxoanPHuhrDBsMHERHRAAkhYOJikn3G8EFERDQAbS4PWp1ervURBoYPIiKiAaizO5GhZ69HODjglIiIaAByuJZT2NjzQURERFHF8EFERERRxfBBREREUcXwQURERFHF8EFERERRxfBBREREUcXwQURERFHF8EFERERRxfBBREREUcXwQURERFHF8EFERERRxfBBREREUcXwQURERFE1aOHjxRdfRG5uLjQaDaZNm4bPPvtssN6KiIiIhpFBCR9bt27F6tWr8eijj6K4uBhz587FwoULUVlZORhvR0RERMOIJIQQkT7ozJkzMXXqVLz00kv+bVdffTWWLl2KDRs29Phau90Og8EAm80GvV4f6aoRERHRIAjn+zviPR8ulwtFRUVYsGBBwPYFCxZg79693co7nU7Y7faABxEREY1cEQ8f9fX18Hq9yMjICNiekZEBq9XarfyGDRtgMBj8j+zs7EhXiYiIiIaQQRtwKklSwHMhRLdtALB27VrYbDb/o6qqarCqREREREOAItIHTE1NhVwu79bLUVdX1603BADUajXUanWkq0FERERDVMR7PlQqFaZNm4YdO3YEbN+xYwfmzJkT6bcjIiKiYSbiPR8A8NBDD+H73/8+rrnmGsyePRuvvPIKKisrcd999w3G2xEREdEwMijh484770RDQwPWr1+P2tpa5OfnY/v27bBYLIPxdkRERDSMDMo6HwPBdT6IiIiGn5iu80FERETUE4YPIiIiiiqGDyIiIooqhg8iIiKKKoYPIiIiiiqGDyIiIooqhg8iIiKKKoYPIiIiiiqGjxCG2NprREREIwbDRwhuL8MHERHRYGD4CEGl4KkhIiIaDPyGJSIioqhi+CAiIqKoYvgIwWrrgM8n0NTq4uBTIiKiCGL4CMFo0EAmk5AUr4LHx/BBREQUKQwffaCU8zQRERFFCr9ViYiIKKoYPoiIiCiqGD6IiIgoqhSxrsClumaW2O32GNeEiIiI+qrre7svM0SHXPhwOBwAgOzs7BjXhIiIiMLlcDhgMBh6LCOJIbaIhc/nQ01NDXQ6HSRJinV1Bp3dbkd2djaqqqqg1+tjXZ2oGq1tH63tBth2tn10tX20tVsIAYfDAbPZDJms51EdQ67nQyaTISsrK9bViDq9Xj8qPpzBjNa2j9Z2A2w72z66jKZ299bj0YUDTomIiCiqGD6IiIgoqhg+YkytVuOJJ56AWq2OdVWibrS2fbS2G2Db2fbR1fbR2u6+GHIDTomIiGhkY88HERERRRXDBxEREUUVwwcRERFFFcMHERERRRXDR5Q8/fTTmDNnDuLi4pCYmBi0jCRJ3R4vv/xyQJmSkhIUFBRAq9UiMzMT69ev79M6+rHUl7ZXVlZiyZIliI+PR2pqKh588EG4XK6AMsOx7ZfKycnp9jN+5JFHAsr05VwMVy+++CJyc3Oh0Wgwbdo0fPbZZ7GuUkStW7eu28/XaDT69wshsG7dOpjNZmi1Wlx77bU4duxYDGvcf59++imWLFkCs9kMSZLw/vvvB+zvS1udTiceeOABpKamIj4+HjfffDOqq6uj2Ir+6a3tK1as6PY5mDVrVkCZ4dr2SGH4iBKXy4U77rgDP/3pT3sst3nzZtTW1vofhYWF/n12ux3z58+H2WzGoUOH8Pvf/x7PPvssNm3aNNjVH5De2u71erFo0SK0trbi888/x5YtW/Duu+9izZo1/jLDte3BrF+/PuBn/Nhjj/n39eVcDFdbt27F6tWr8eijj6K4uBhz587FwoULUVlZGeuqRdT48eMDfr4lJSX+fb/5zW+wadMmPP/88zh06BCMRiPmz5/vv6fVcNLa2opJkybh+eefD7q/L21dvXo1tm3bhi1btuDzzz9HS0sLFi9eDK/XG61m9EtvbQeAm266KeBzsH379oD9w7XtESMoqjZv3iwMBkPQfQDEtm3bQr72xRdfFAaDQXR0dPi3bdiwQZjNZuHz+SJc08gL1fbt27cLmUwmzpw549/2pz/9SajVamGz2YQQw7/tXSwWi/jd734Xcn9fzsVwNWPGDHHfffcFbLvqqqvEI488EqMaRd4TTzwhJk2aFHSfz+cTRqNRbNy40b+to6NDGAwG8fLLL0ephoPj0t9dfWlrc3OzUCqVYsuWLf4yZ86cETKZTPzlL3+JWt0HKtjv7cLCQnHLLbeEfM1IaftAsOdjiFm1ahVSU1Mxffp0vPzyy/D5fP59+/btQ0FBQcCCNTfeeCNqampQXl4eg9pGxr59+5Cfnw+z2ezfduONN8LpdKKoqMhfZqS0/ZlnnkFKSgomT56Mp59+OuCSSl/OxXDkcrlQVFSEBQsWBGxfsGAB9u7dG6NaDY7S0lKYzWbk5ubirrvuwunTpwEAZWVlsFqtAedArVajoKBgxJ2DvrS1qKgIbrc7oIzZbEZ+fv6IOB+7d+9Geno6xo4di3vvvRd1dXX+fSO97X0x5G4sN5r96le/wvXXXw+tVou//e1vWLNmDerr6/3d8larFTk5OQGvycjI8O/Lzc2NdpUjwmq1+tvRJSkpCSqVClar1V9mJLT9Zz/7GaZOnYqkpCQcPHgQa9euRVlZGf7whz8A6Nu5GI7q6+vh9Xq7tS0jI2NYt+tSM2fOxJtvvomxY8fi7NmzeOqppzBnzhwcO3bM385g56CioiIW1R00fWmr1WqFSqVCUlJStzLD/TOxcOFC3HHHHbBYLCgrK8Pjjz+O73znOygqKoJarR7Rbe8r9nwMQLDBZZc+Dh8+3OfjPfbYY5g9ezYmT56MNWvWYP369fjtb38bUEaSpIDn4vyAy0u3D7ZItz1Y/YUQAduHStsvFc65+PnPf46CggJMnDgR99xzD15++WW89tpraGho8B+vL+diuAr2MxwJ7eqycOFC3HbbbZgwYQJuuOEGfPTRRwCAN954w19mpJ+Di/WnrSPhfNx5551YtGgR8vPzsWTJEnz88cf45ptv/J+HUEZC2/uKPR8DsGrVKtx11109lrn0r/VwzJo1C3a7HWfPnkVGRgaMRmO3VNzVlXfpXxiDLZJtNxqNOHDgQMC2pqYmuN1uf7uGUtsvNZBz0TUC/uTJk0hJSenTuRiOUlNTIZfLg/4Mh3O7ehMfH48JEyagtLQUS5cuBdD5F7/JZPKXGYnnoGuGT09tNRqNcLlcaGpqCugBqKurw5w5c6Jb4UFmMplgsVhQWloKYHS1PRT2fAxAamoqrrrqqh4fGo2m38cvLi6GRqPxT0+dPXs2Pv3004AxAp988gnMZvOAQk5/RLLts2fPxtGjR1FbW+vf9sknn0CtVmPatGn+MkOl7ZcayLkoLi4GAP8v6L6ci+FIpVJh2rRp2LFjR8D2HTt2jOhftk6nE8ePH4fJZEJubi6MRmPAOXC5XNizZ8+IOwd9aeu0adOgVCoDytTW1uLo0aMj7nw0NDSgqqrK//98NLU9pJgNdR1lKioqRHFxsXjyySdFQkKCKC4uFsXFxcLhcAghhPjggw/EK6+8IkpKSsTJkyfFq6++KvR6vXjwwQf9x2hubhYZGRli+fLloqSkRLz33ntCr9eLZ599NlbN6pPe2u7xeER+fr64/vrrxZEjR8TOnTtFVlaWWLVqlf8Yw7XtF9u7d6/YtGmTKC4uFqdPnxZbt24VZrNZ3Hzzzf4yfTkXw9WWLVuEUqkUr732mvj666/F6tWrRXx8vCgvL4911SJmzZo1Yvfu3eL06dNi//79YvHixUKn0/nbuHHjRmEwGMR7770nSkpKxPLly4XJZBJ2uz3GNQ+fw+Hw/18G4P9sV1RUCCH61tb77rtPZGVliZ07d4ojR46I73znO2LSpEnC4/HEqll90lPbHQ6HWLNmjdi7d68oKysTu3btErNnzxaZmZkjou2RwvARJYWFhQJAt8euXbuEEEJ8/PHHYvLkySIhIUHExcWJ/Px88dxzzwm32x1wnK+++krMnTtXqNVqYTQaxbp164b8VNPe2i5EZ0BZtGiR0Gq1Ijk5WaxatSpgWq0Qw7PtFysqKhIzZ84UBoNBaDQaceWVV4onnnhCtLa2BpTry7kYrl544QVhsViESqUSU6dOFXv27Il1lSLqzjvvFCaTSSiVSmE2m8WyZcvEsWPH/Pt9Pp944oknhNFoFGq1WsybN0+UlJTEsMb9t2vXrqD/rwsLC4UQfWtre3u7WLVqlUhOThZarVYsXrxYVFZWxqA14emp7W1tbWLBggUiLS1NKJVKMWbMGFFYWNitXcO17ZEiCTHMlogkIiKiYY1jPoiIiCiqGD6IiIgoqhg+iIiIKKoYPoiIiCiqGD6IiIgoqhg+iIiIKKoYPoiIiCiqGD6IiIgoqhg+iIiIKKoYPoiIiCiqGD6IiIgoqhg+iIiIKKr+B1quTurkyhfqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "url_states = r'https://www2.census.gov/geo/tiger/TIGER2019/STATE/tl_2019_us_state.zip'\n",
    "states = gpd.read_file(url_states)\n",
    "states.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "058ed0ca-423e-4271-a97a-9825bed74201",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## look into the shape for states Illinois\n",
    "states[states[\"GEOID\"] == \"17\"].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bcc8e0e-c213-4b57-aacd-0e0e798dcb7f",
   "metadata": {},
   "source": [
    "<a id='raster'></a>\n",
    "\n",
    "## 2. Raster Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3c69168-38d4-4c3a-9c59-5184a883b9f3",
   "metadata": {},
   "source": [
    "Raster data in GIS (Geographic Information Systems) refers to the digital representation of spatial data as a grid of cells or pixels, with each cell containing a value that represents a specific geographic information or attribute. Raster data is typically used to represent continuous geographic phenomena, such as elevation, land cover, temperature, or remotely sensed imagery. The key characteristics of raster data include:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66a6a84d-7b19-4ff7-b040-b72a71d284a8",
   "metadata": {},
   "source": [
    "<a id='continuous'></a>\n",
    "\n",
    "### 2.1. Continuous Raster Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbafb88b-9c64-4ea5-8b41-2649701df3d1",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Continuous Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bff0ada7-2fb9-4efd-a0a0-2bf99db95b76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>val</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-87.545366</td>\n",
       "      <td>41.644595</td>\n",
       "      <td>0.269728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-87.533171</td>\n",
       "      <td>41.644595</td>\n",
       "      <td>0.279292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-87.618537</td>\n",
       "      <td>41.653604</td>\n",
       "      <td>0.340616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-87.606341</td>\n",
       "      <td>41.653604</td>\n",
       "      <td>0.291826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-87.594146</td>\n",
       "      <td>41.653604</td>\n",
       "      <td>0.302488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>588</td>\n",
       "      <td>-87.777073</td>\n",
       "      <td>42.013964</td>\n",
       "      <td>0.366731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>589</td>\n",
       "      <td>-87.703902</td>\n",
       "      <td>42.013964</td>\n",
       "      <td>0.252858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>590</td>\n",
       "      <td>-87.691707</td>\n",
       "      <td>42.013964</td>\n",
       "      <td>0.287384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>591</td>\n",
       "      <td>-87.679512</td>\n",
       "      <td>42.013964</td>\n",
       "      <td>0.264091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>592</td>\n",
       "      <td>-87.667317</td>\n",
       "      <td>42.013964</td>\n",
       "      <td>0.241318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>593 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0        lon        lat       val\n",
       "0             0 -87.545366  41.644595  0.269728\n",
       "1             1 -87.533171  41.644595  0.279292\n",
       "2             2 -87.618537  41.653604  0.340616\n",
       "3             3 -87.606341  41.653604  0.291826\n",
       "4             4 -87.594146  41.653604  0.302488\n",
       "..          ...        ...        ...       ...\n",
       "588         588 -87.777073  42.013964  0.366731\n",
       "589         589 -87.703902  42.013964  0.252858\n",
       "590         590 -87.691707  42.013964  0.287384\n",
       "591         591 -87.679512  42.013964  0.264091\n",
       "592         592 -87.667317  42.013964  0.241318\n",
       "\n",
       "[593 rows x 4 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./human_sentiments_of_heat/1km_chicago_heat_exposure.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7de4fc6f-abd8-42ba-a741-02c8b5b9134e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<Axes: xlabel='lon', ylabel='lat'>], dtype=object)"
      ]
     },
     "execution_count": 16,
     "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='Continuous Data - Human Sentiments of Heat Expososure', x='lon', y='lat', c='val', figsize = [10,10], subplots=True, marker=\"s\", s = 155, colormap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24c10175-e3ed-4a46-9751-5437e317d215",
   "metadata": {},
   "source": [
    "<a id='discrete'></a>\n",
    "\n",
    "### 2.2. Discrete Raster Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0af92805-a992-45fb-915b-747faec75b2e",
   "metadata": {},
   "source": [
    "### Discrete Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f8e85a1-bc80-4acd-90e5-b81b2c7acc9e",
   "metadata": {},
   "source": [
    "Define 4 types of data by the value of normalized heat exposure (NHE)\n",
    "- Type I -> NHE<=0.25\n",
    "- Type II -> 0.25<NHE<=0.5\n",
    "- Type III -> 0.5<NHE<=0.75\n",
    "- Type IV -> 0.75<NHE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c905dd10-ce8a-4a8e-8a53-c8f058a15248",
   "metadata": {},
   "outputs": [],
   "source": [
    "tp = []\n",
    "val = list(df['val'])\n",
    "for ele in val:\n",
    "    if (ele<=0.25):\n",
    "        tp.append(\"Type I\")\n",
    "    elif (ele<=0.5):\n",
    "        tp.append(\"Type II\")\n",
    "    elif (ele<=0.75):\n",
    "        tp.append(\"Type III\")\n",
    "    else:\n",
    "        tp.append(\"Type IV\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fb6df11b-5ad4-46af-b83e-0eadfc1030ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"type\"] = tp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2da7845c-1236-4666-a2e8-af3dbc141e0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>val</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-87.545366</td>\n",
       "      <td>41.644595</td>\n",
       "      <td>0.269728</td>\n",
       "      <td>Type II</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-87.533171</td>\n",
       "      <td>41.644595</td>\n",
       "      <td>0.279292</td>\n",
       "      <td>Type II</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-87.618537</td>\n",
       "      <td>41.653604</td>\n",
       "      <td>0.340616</td>\n",
       "      <td>Type II</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-87.606341</td>\n",
       "      <td>41.653604</td>\n",
       "      <td>0.291826</td>\n",
       "      <td>Type II</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-87.594146</td>\n",
       "      <td>41.653604</td>\n",
       "      <td>0.302488</td>\n",
       "      <td>Type II</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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>588</td>\n",
       "      <td>-87.777073</td>\n",
       "      <td>42.013964</td>\n",
       "      <td>0.366731</td>\n",
       "      <td>Type II</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>589</td>\n",
       "      <td>-87.703902</td>\n",
       "      <td>42.013964</td>\n",
       "      <td>0.252858</td>\n",
       "      <td>Type II</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>590</td>\n",
       "      <td>-87.691707</td>\n",
       "      <td>42.013964</td>\n",
       "      <td>0.287384</td>\n",
       "      <td>Type II</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>591</td>\n",
       "      <td>-87.679512</td>\n",
       "      <td>42.013964</td>\n",
       "      <td>0.264091</td>\n",
       "      <td>Type II</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>592</td>\n",
       "      <td>-87.667317</td>\n",
       "      <td>42.013964</td>\n",
       "      <td>0.241318</td>\n",
       "      <td>Type I</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>593 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0        lon        lat       val     type\n",
       "0             0 -87.545366  41.644595  0.269728  Type II\n",
       "1             1 -87.533171  41.644595  0.279292  Type II\n",
       "2             2 -87.618537  41.653604  0.340616  Type II\n",
       "3             3 -87.606341  41.653604  0.291826  Type II\n",
       "4             4 -87.594146  41.653604  0.302488  Type II\n",
       "..          ...        ...        ...       ...      ...\n",
       "588         588 -87.777073  42.013964  0.366731  Type II\n",
       "589         589 -87.703902  42.013964  0.252858  Type II\n",
       "590         590 -87.691707  42.013964  0.287384  Type II\n",
       "591         591 -87.679512  42.013964  0.264091  Type II\n",
       "592         592 -87.667317  42.013964  0.241318   Type I\n",
       "\n",
       "[593 rows x 5 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "555b2ff0-9115-4eae-8720-fed1e9310062",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<Axes: xlabel='lon', ylabel='lat'>], dtype=object)"
      ]
     },
     "execution_count": 20,
     "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": [
    "import plotly.express as px\n",
    "df[\"type\"] = df[\"type\"].astype(\"category\")\n",
    "\n",
    "df.plot.scatter(title='Discrete Data - Human Sentiments of Heat Expososure', x='lon', y='lat', c='type', 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
}