covid.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gmaps\n",
    "import gmaps.datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "gmaps.configure(api_key=\"AIzaSyAyQ2dT8u1fCaOuPJpWx5NGkKMiQX3Tugc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = gmaps.Map()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import googlemaps\n",
    "import json\n",
    "data = pd.read_csv(\"input_file_v1_dashboard.csv\",encoding=\"ISO-8859-1\",low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "latlon = json.load(open('BMC_Wards.geo.json'))\n",
    "\n",
    "feat = latlon['features']\n",
    "l = []\n",
    "for i in range(len(feat)):\n",
    "    l.append(feat[i]['geometry']['coordinates'][0][0])\n",
    "# len(l[0])\n",
    "r = []\n",
    "o = []\n",
    "b = []\n",
    "g = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = pd.read_csv(\"Facilities_in_Mumbai_COVID_19_Cases.csv\",encoding=\"ISO-8859-1\",low_memory=False)\n",
    "data1 = data1.fillna(0)\n",
    "n1 = data1['Number of Cases- Very Congested Area']\n",
    "n2 = data1['Number of Cases- Medium Congested']\n",
    "n3 = data1['Number of Cases- Standalone Structure']\n",
    "p1 = data['Population']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(n1)-1):\n",
    "    if ((n1[i+1]*1.5 + n2[i+1]*2.6 + n3[i+1]*3.6)/p1[i])>=0.0002:\n",
    "        r.append(l[i])\n",
    "    elif 0.0001<=((n1[i+1]*1.5 + n2[i+1]*2.6 + n3[i+1]*3.6)/p1[i])<0.0002:\n",
    "        o.append(l[i])\n",
    "    elif 0<((n1[i+1]*1.5 + n2[i+1]*2.6 + n3[i+1]*3.6))<0.0001:\n",
    "        b.append(l[i])\n",
    "    else:\n",
    "        g.append(l[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# len(r[0][0])\n",
    "lat_lon_red = []\n",
    "for i in range(len(r)):\n",
    "    for j in range(len(r[i])):\n",
    "        tup = (r[i][j][1],r[i][j][0])\n",
    "        lat_lon_red.append(tup)\n",
    "# lat_lon_red[1]\n",
    "wt_red = [0]*len(lat_lon_red)\n",
    "\n",
    "# len(o[0][0])\n",
    "lat_lon_orange = []\n",
    "for i in range(len(o)):\n",
    "    for j in range(len(o[i])):\n",
    "        tup = (o[i][j][1],o[i][j][0])\n",
    "        lat_lon_orange.append(tup)\n",
    "# lat_lon_orange[1]\n",
    "wt_orange = [1]*len(lat_lon_orange)\n",
    "\n",
    "# len(b[0][0])\n",
    "lat_lon_blue = []\n",
    "for i in range(len(b)):\n",
    "    for j in range(len(b[i])):\n",
    "        tup = (b[i][j][1],b[i][j][0])\n",
    "        lat_lon_blue.append(tup)\n",
    "# lat_lon_blue[1]\n",
    "wt_blue = [2]*len(lat_lon_blue)\n",
    "\n",
    "# len(g[0][0])\n",
    "lat_lon_green = []\n",
    "for i in range(len(g)):\n",
    "    for j in range(len(g[i])):\n",
    "        tup = (g[i][j][1],g[i][j][0])\n",
    "        lat_lon_green.append(tup)\n",
    "# lat_lon_red[1]\n",
    "wt_green = [3]*len(lat_lon_green)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = gmaps.Map()\n",
    "if(len(lat_lon_red)!=0):\n",
    "    # red_layer = gmaps.symbol_layer(lat_lon_red,fill_color='red', stroke_color='red')\n",
    "    red_layer = gmaps.heatmap_layer(lat_lon_red,weights=wt_red,max_intensity=30, point_radius=5.0)\n",
    "    m.add_layer(red_layer)\n",
    "\n",
    "if(len(lat_lon_orange)!=0):\n",
    "    # orange_layer = gmaps.symbol_layer(lat_lon_orange,fill_color='orange', stroke_color='orange')\n",
    "    orange_layer = gmaps.heatmap_layer(lat_lon_orange,weights=wt_orange,max_intensity=30, point_radius=5.0)\n",
    "    m.add_layer(orange_layer)\n",
    "\n",
    "if(len(lat_lon_blue)!=0):\n",
    "    # blue_layer = gmaps.symbol_layer(lat_lon_blue,fill_color='blue', stroke_color='blue')\n",
    "    blue_layer = gmaps.heatmap_layer(lat_lon_blue,weights=wt_blue,max_intensity=30, point_radius=5.0)\n",
    "    m.add_layer(blue_layer)\n",
    "\n",
    "if(len(lat_lon_green)!=0):\n",
    "    # green_layer = gmaps.symbol_layer(lat_lon_green,fill_color='green', stroke_color='green')\n",
    "    green_layer = gmaps.heatmap_layer(lat_lon_green,weights=wt_green,max_intensity=30, point_radius=5.0)\n",
    "    m.add_layer(green_layer)\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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