Yun-Final-Assignment-2-Choropleth-Map.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import the data\n",
    "sf_raw=pd.read_csv('Police_Department_Incidents_-_Previous_Year__2016_.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# count crime numbers for each district\n",
    "sf_s=sf_raw.groupby(['PdDistrict'], sort=0).apply(lambda g: pd.Series({\\\n",
    "                                                                 'Count': g.IncidntNum.count(),\\\n",
    "                                                                 }))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# reset index\n",
    "sf_s.reset_index(level=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PdDistrict</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>SOUTHERN</td>\n",
       "      <td>28445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>BAYVIEW</td>\n",
       "      <td>14303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>TENDERLOIN</td>\n",
       "      <td>9942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>MISSION</td>\n",
       "      <td>19503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>NORTHERN</td>\n",
       "      <td>20100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>TARAVAL</td>\n",
       "      <td>11325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>INGLESIDE</td>\n",
       "      <td>11594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>CENTRAL</td>\n",
       "      <td>17666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>RICHMOND</td>\n",
       "      <td>8922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>PARK</td>\n",
       "      <td>8699</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PdDistrict  Count\n",
       "0    SOUTHERN  28445\n",
       "1     BAYVIEW  14303\n",
       "2  TENDERLOIN   9942\n",
       "3     MISSION  19503\n",
       "4    NORTHERN  20100\n",
       "5     TARAVAL  11325\n",
       "6   INGLESIDE  11594\n",
       "7     CENTRAL  17666\n",
       "8    RICHMOND   8922\n",
       "9        PARK   8699"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# rename columns\n",
    "sf=sf_s.rename(columns={'PdDistrict':'Neighborhood'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    }\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>Neighborhood</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>SOUTHERN</td>\n",
       "      <td>28445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>BAYVIEW</td>\n",
       "      <td>14303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>TENDERLOIN</td>\n",
       "      <td>9942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>MISSION</td>\n",
       "      <td>19503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>NORTHERN</td>\n",
       "      <td>20100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>TARAVAL</td>\n",
       "      <td>11325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>INGLESIDE</td>\n",
       "      <td>11594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>CENTRAL</td>\n",
       "      <td>17666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>RICHMOND</td>\n",
       "      <td>8922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>PARK</td>\n",
       "      <td>8699</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Neighborhood  Count\n",
       "0     SOUTHERN  28445\n",
       "1      BAYVIEW  14303\n",
       "2   TENDERLOIN   9942\n",
       "3      MISSION  19503\n",
       "4     NORTHERN  20100\n",
       "5      TARAVAL  11325\n",
       "6    INGLESIDE  11594\n",
       "7      CENTRAL  17666\n",
       "8     RICHMOND   8922\n",
       "9         PARK   8699"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you should be ready to proceed with creating the Choropleth map.\n",
    "\n",
    "As you learned in the Choropleth maps lab, you will need a GeoJSON file that marks the boundaries of the different neighborhoods in San Francisco. In order to save you the hassle of looking for the right file, I already downloaded it for you and I am making it available via this link: https://cocl.us/sanfran_geojson."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the map, make sure that:\n",
    "\n",
    "it is centred around San Francisco,\n",
    "\n",
    "you use a zoom level of 12,\n",
    "\n",
    "you use fill_color = 'YlOrRd',\n",
    "\n",
    "you define fill_opacity = 0.7,\n",
    "\n",
    "you define line_opacity=0.2, and,\n",
    "\n",
    "you define a legend and use the default threshold scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solving environment: done\n",
      "\n",
      "\n",
      "==> WARNING: A newer version of conda exists. <==\n",
      "  current version: 4.5.11\n",
      "  latest version: 4.7.12\n",
      "\n",
      "Please update conda by running\n",
      "\n",
      "    $ conda update -n base -c defaults conda\n",
      "\n",
      "\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /home/jupyterlab/conda/envs/python\n",
      "\n",
      "  added / updated specs: \n",
      "    - folium=0.5.0\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    certifi-2019.9.11          |           py36_0         147 KB  conda-forge\n",
      "\n",
      "The following packages will be UPDATED:\n",
      "\n",
      "    certifi: 2019.9.11-py36_0 anaconda --> 2019.9.11-py36_0  conda-forge\n",
      "\n",
      "The following packages will be DOWNGRADED:\n",
      "\n",
      "    openssl: 1.1.1-h7b6447c_0 anaconda --> 1.1.1c-h516909a_0 conda-forge\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "certifi-2019.9.11    | 147 KB    | ##################################### | 100% \n",
      "Preparing transaction: done\n",
      "Verifying transaction: done\n",
      "Executing transaction: done\n",
      "Folium installed and imported!\n"
     ]
    }
   ],
   "source": [
    "!conda install -c conda-forge folium=0.5.0 --yes\n",
    "import folium\n",
    "\n",
    "print('Folium installed and imported!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Read the json file\n",
    "sf_geo = r'san-francisco.geojson' # geojson file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<folium.folium.Map at 0x7fd8b38293c8>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define the world map\n",
    "world_map = folium.Map()\n",
    "\n",
    "# display world map\n",
    "world_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# San Francisco latitude and longitude values\n",
    "latitude = 37.77\n",
    "longitude = -122.42\n",
    "\n",
    "# create map and display it\n",
    "sanfran_map = folium.Map(location=[latitude, longitude], zoom_start=12)\n",
    "\n",
    "# display the map of San Francisco\n",
    "sanfran_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create a plain world map\n",
    "sf_map = folium.Map(location=[37.77, -122.42], zoom_start=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate choropleth map using the total crime numbers of each neighborhood of san francisco from 1980 to 2013\n",
    "sf_map.choropleth(\n",
    "    geo_data=sf_geo,\n",
    "    data=sf,\n",
    "    columns=['Neighborhood', 'Count'],\n",
    "    key_on='feature.properties.DISTRICT',\n",
    "    fill_color='YlOrRd', \n",
    "    fill_opacity=0.7, \n",
    "    line_opacity=0.2,\n",
    "    legend_name='Crime Numbers of Each Neighborhood in San Francisco'\n",
    ")\n",
    "\n",
    "# display map\n",
    "sf_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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