Isochrones with Python and APIs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Isochrones with Python and APIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"BuildingName\": \"Cook County Building, Board Room\",\n",
      "    \"AddressNumber\": \"118\",\n",
      "    \"StreetNamePreDirectional\": \"North\",\n",
      "    \"StreetName\": \"Clark\",\n",
      "    \"StreetNamePostType\": \"Street\",\n",
      "    \"PlaceName\": \"Chicago\",\n",
      "    \"StateName\": \"Illinois\"\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "# We'll use this to output some of our intermediate data structures in a somewhat standard format\n",
    "import json\n",
    "\n",
    "# We'll use this to break our address into street, city, state pieces for us\n",
    "import usaddress\n",
    "\n",
    "# Lots of meetings are at the Cook County Building.  This is one example from the data.\n",
    "location_name = \"Cook County Building, Board Room, 118 North Clark Street, Chicago, Illinois\"\n",
    "\n",
    "# Many geocoders, especially free ones need us to break the address into pieces.\n",
    "# Let's use the awesome usaddress package to do this for us\n",
    "parsed_location_name, detected_type = usaddress.tag(location_name)\n",
    "\n",
    "print(json.dumps(parsed_location_name, indent=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"city\": \"Chicago\",\n",
      "    \"state\": \"Illinois\",\n",
      "    \"street_address\": \"118 North Clark Street\"\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "# Let's merge these very atomic fields into ones that are closer to those\n",
    "# accepted by many geocoding APIs\n",
    "street_address = ' '.join([\n",
    "    parsed_location_name['AddressNumber'],\n",
    "    parsed_location_name['StreetNamePreDirectional'],\n",
    "    parsed_location_name['StreetName'],\n",
    "    parsed_location_name['StreetNamePostType']\n",
    "])\n",
    "address = {\n",
    "    'street_address': street_address,\n",
    "    'city': parsed_location_name['PlaceName'],\n",
    "    'state': parsed_location_name['StateName']\n",
    "}\n",
    "\n",
    "print(json.dumps(address, indent=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"FeatureMatchingGeographyType\": \"StreetSegment\",\n",
      "  \"\": \"\",\n",
      "  \"Version\": \"4.1\",\n",
      "  \"TransactionId\": \"be5319e6-1f88-4f8a-97b0-3285b42d138a\",\n",
      "  \"Latitude\": \"41.8834456872408\",\n",
      "  \"TimeTaken\": \"0.0430043\",\n",
      "  \"NAACCRGISCoordinateQualityCode\": \"03\",\n",
      "  \"RegionSize\": \"3174.85431915501\",\n",
      "  \"MatchScore\": \"96.0059171597633\",\n",
      "  \"Longitude\": \"-87.631003848859\",\n",
      "  \"RegionSizeUnits\": \"Meters\",\n",
      "  \"MatchType\": \"Relaxed\",\n",
      "  \"MatchedLocationType\": \"LOCATION_TYPE_STREET_ADDRESS\",\n",
      "  \"QueryStatusCodeValue\": \"200\",\n",
      "  \"FeatureMatchingResultCount\": \"1\",\n",
      "  \"NAACCRGISCoordinateQualityName\": \"StreetSegmentInterpolation\",\n",
      "  \"FeatureMatchingResultType\": \"Success\"\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "import io\n",
    "\n",
    "# We'll use this to simplify making our HTTP requests to the geocoding APIs\n",
    "import requests\n",
    "\n",
    "# Let's merge our parsed address into fields that are\n",
    "\n",
    "# We'll use the Texas A&M Geoservices API because it's free (for few requests)\n",
    "# and has pretty liberal terms of service.  It does however require an API\n",
    "# key.\n",
    "TAMU_GEOSERVICES_API_KEY = os.environ.get('TAMU_GEOSERVICES_API_KEY')\n",
    "\n",
    "# API parameters, passed via the URL\n",
    "# That is:\n",
    "# https://geoservices.tamu.edu/Services/Geocode/WebService/GeocoderWebServiceHttpNonParsed_V04_01.aspx?version=4.01&streetAddres=118+North+Clark+Street...\n",
    "api_params = {\n",
    "    'apiKey': TAMU_GEOSERVICES_API_KEY,\n",
    "    'version': '4.01',\n",
    "    'streetAddress': address['street_address'],\n",
    "    'city': address['city'],\n",
    "    'state': address['state'],\n",
    "    'includeHeader': 'true',\n",
    "}\n",
    "\n",
    "api_url = 'https://geoservices.tamu.edu/Services/Geocode/WebService/GeocoderWebServiceHttpNonParsed_V04_01.aspx'\n",
    "r = requests.get(api_url, params=api_params)\n",
    "\n",
    "\n",
    "# The TAMU geocoder returns the data as CSV.  We need to parse this out\n",
    "\n",
    "geocoded = None\n",
    "inf = io.StringIO(r.text)\n",
    "reader = csv.DictReader(inf)\n",
    "\n",
    "for row in reader:\n",
    "    geocoded = row\n",
    "    # Only read one row\n",
    "    break\n",
    "    \n",
    "print(json.dumps(geocoded, indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-87.631003848859, 41.8834456872408]\n"
     ]
    }
   ],
   "source": [
    "# Our longitude and latitude are available as keys of the parsed dictionary\n",
    "print(\"[{0}, {1}]\".format(geocoded['Longitude'], geocoded['Latitude']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-03-08T17:00:00-06:00\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "from pytz import timezone\n",
    "\n",
    "start_time = \"March 8, 2017 5:00pm\"\n",
    "\n",
    "parsed_start_time = datetime.strptime(start_time, \"%B %d, %Y %I:%M%p\")\n",
    "\n",
    "# Convert to Chicago's time zone\n",
    "central = timezone('US/Central')\n",
    "parsed_start_time = central.localize(parsed_start_time)\n",
    "\n",
    "print(parsed_start_time.isoformat())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Isochrones from the MapZen Isochrone service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# http://colorbrewer2.org/?type=sequential&scheme=BuGn&n=5\n",
    "COLORS_BLUE_GREEN = [\n",
    "    '#edf8fb',\n",
    "    '#b2e2e2',\n",
    "    '#66c2a4',\n",
    "    '#2ca25f',\n",
    "    '#006d2c',\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import requests\n",
    "\n",
    "# Read our API key from an environment variable.\n",
    "# This is a good way to avoid hard-coding credentials like API keys in our code.\n",
    "MAPZEN_API_KEY = os.environ.get('MAPZEN_API_KEY')\n",
    "\n",
    "# See https://mapzen.com/documentation/mobility/isochrone/api-reference/ for the API reference\n",
    "# These are API parameters we'll use for all of the transportation types we're trying\n",
    "base_api_params = {\n",
    "    'api_key': MAPZEN_API_KEY,\n",
    "    # Location and costing (transportation type)\n",
    "    'json': {\n",
    "        'locations': [\n",
    "            {\n",
    "                'lat': geocoded['Latitude'],\n",
    "                'lon': geocoded['Longitude'],\n",
    "            },\n",
    "        ], \n",
    "        # Contours\n",
    "        # You can have a maximum of 4\n",
    "        'contours': [\n",
    "            {\n",
    "                'time': 30,\n",
    "                'color': COLORS_BLUE_GREEN[0].lstrip('#'),\n",
    "            },\n",
    "            {\n",
    "                'time': 60,\n",
    "                'color': COLORS_BLUE_GREEN[2].lstrip('#'),\n",
    "            },\n",
    "            {\n",
    "                'time': 90,\n",
    "                'color': COLORS_BLUE_GREEN[4].lstrip('#'),\n",
    "            },\n",
    "        ],\n",
    "        # Return GeoJSON polygons instead of linestrings.\n",
    "        # Linestrings are smaller and faster to render, but we use polygons for colored shading.\n",
    "        'polygons': True,\n",
    "        # Distance in meters used as an input to simplify the polygons.\n",
    "        # A higher number will result in more simplifications.\n",
    "        # I'm doing this here, because without some simplification, the GeoJSON is too long\n",
    "        # to pass as a URL parameter to geojson.io, which we're using to display our maps.\n",
    "        'generalize': 300,\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multimodal (transit + walking)\n",
    "\n",
    "[Example](http://bl.ocks.org/d/be742d6ba8cfbb1f19683efd3557ee29)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "from copy import deepcopy\n",
    "import datetime\n",
    "import json\n",
    "\n",
    "api_params = deepcopy(base_api_params)\n",
    "\n",
    "# Calculate a start time 1 hour before the meeting time.  This is hacky, but the API doesn't support an arrival time\n",
    "# yet.\n",
    "departure_time = parsed_start_time - datetime.timedelta(hours=1)\n",
    "\n",
    "# Specify transit/walking\n",
    "api_params['json']['costing'] = 'multimodal'\n",
    "api_params['json']['date_time'] = {\n",
    "    # `1` means specified departure time.  Ideally we would use `2`, the arrival time, but the API docs say this\n",
    "    # isn't supported yet.\n",
    "    'type': 1,\n",
    "    'value': departure_time.replace(tzinfo=None).strftime(\"%Y-%m-%dT%H:%M\"),\n",
    "}\n",
    "\n",
    "# Convert `json` parameter to JSON\n",
    "api_params['json'] = json.dumps(api_params['json'])\n",
    "\n",
    "r = requests.get('https://matrix.mapzen.com/isochrone', params=api_params)\n",
    "\n",
    "contour_geojson = json.dumps(r.json())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import geojsonio\n",
    "\n",
    "# Embed a map in this notebook\n",
    "# This should work, but doesn't.  Maybe it's just a problem on Linux\n",
    "#geojsonio.embed(contour_geojson)\n",
    "\n",
    "geojsonio.display(contour_geojson)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bicycling\n",
    "\n",
    "[Example](http://bl.ocks.org/d/6539fcf5a3ed64d052a01a17ac035a68)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "from copy import deepcopy\n",
    "import json\n",
    "\n",
    "api_params = deepcopy(base_api_params)\n",
    "\n",
    "# Specify bicycling costing methods\n",
    "# There are a lot more options, just use the defaults for now\n",
    "# See https://mapzen.com/documentation/mobility/turn-by-turn/api-reference/#bicycle-costing-options\n",
    "api_params['json']['costing'] = 'bicycle'\n",
    "\n",
    "# Convert `json` parameter to JSON\n",
    "api_params['json'] = json.dumps(api_params['json'])\n",
    "\n",
    "r = requests.get('https://matrix.mapzen.com/isochrone', params=api_params)\n",
    "\n",
    "contour_geojson = json.dumps(r.json())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       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      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geojsonio.display(contour_geojson)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Isocrones from the HERE Routing API\n",
    "\n",
    "[Example](http://bl.ocks.org/d/6838e193d9f72a7f14e08ab42fefa7ad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import requests\n",
    "\n",
    "# Let's get an isochrone using the HERE Routing API Isoline endpoint\n",
    "\n",
    "HERE_APP_ID = os.environ.get('HERE_APP_ID')\n",
    "HERE_APP_CODE = os.environ.get('HERE_APP_CODE')\n",
    "\n",
    "api_params = {\n",
    "    'app_id': HERE_APP_ID,\n",
    "    'app_code': HERE_APP_CODE,\n",
    "    'mode': \"fastest;car;traffic:enabled\",\n",
    "    'destination': \"geo!{lat},{lng}\".format(lat=geocoded['Latitude'], lng=geocoded['Longitude']),\n",
    "    'arrival': parsed_start_time.isoformat(),\n",
    "    # 1 hour\n",
    "    'range': 1 * 60 * 60,\n",
    "    'rangetype': 'time'\n",
    "}\n",
    "\n",
    "r = requests.get(\"https://isoline.route.cit.api.here.com/routing/7.2/calculateisoline.json\", params=api_params)\n",
    "\n",
    "isoline_response = r.json()['response']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the isoline API response to GeoJSON\n",
    "import json\n",
    "\n",
    "isoline_geojson = {\n",
    "    'type': 'FeatureCollection',\n",
    "    'features': [],\n",
    "}\n",
    "\n",
    "isoline_geojson['features'].append({\n",
    "    'type': 'Feature',\n",
    "    'geometry': {\n",
    "        'type': 'Point',\n",
    "        'coordinates': [\n",
    "            isoline_response['center']['longitude'],\n",
    "            isoline_response['center']['latitude']\n",
    "        ],\n",
    "    },\n",
    "    'properties': {},\n",
    "})\n",
    "\n",
    "isoline_geojson['features'].append({\n",
    "    'type': 'Feature',\n",
    "    'geometry': {\n",
    "        'type': 'Polygon',\n",
    "        'coordinates': [\n",
    "            [\n",
    "                [float(c) for c in x.split(',')[::-1]]\n",
    "                for x\n",
    "                in isoline_response['isoline'][0]['component'][0]['shape']\n",
    "            ],\n",
    "        ],\n",
    "    },\n",
    "    'properties': {},\n",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       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      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And display it using geojson.io\n",
    "\n",
    "import geojsonio\n",
    "geojsonio.display(json.dumps(isoline_geojson))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Factor this example into functions so we can get multiple areas\n",
    "\n",
    "import requests\n",
    "\n",
    "def get_isoline(lat, lng, mode, rng, arrival, app_id, app_code):\n",
    "    api_params = {\n",
    "        'app_id': app_id,\n",
    "        'app_code': app_code,\n",
    "        'mode': mode,\n",
    "        'destination': \"geo!{lat},{lng}\".format(lat=lat, lng=lng),\n",
    "        'arrival': arrival.isoformat(),\n",
    "        'range': rng,\n",
    "        'rangetype': 'time'\n",
    "    }\n",
    "\n",
    "    r = requests.get(\"https://isoline.route.cit.api.here.com/routing/7.2/calculateisoline.json\", params=api_params)\n",
    "\n",
    "    return r.json()['response']\n",
    "\n",
    "def isoline_geojson_feature(isoline):\n",
    "    return {\n",
    "        'type': 'Feature',\n",
    "        'geometry': {\n",
    "            'type': 'Polygon',\n",
    "            'coordinates': [\n",
    "                [\n",
    "                    [float(c) for c in x.split(',')[::-1]]\n",
    "                    for x\n",
    "                    in isoline['isoline'][0]['component'][0]['shape']\n",
    "                ],\n",
    "            ],\n",
    "        },\n",
    "        'properties': {},\n",
    "    }\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "ranges = [\n",
    "    {\n",
    "        'range': 1 * 60 * 60,\n",
    "        'properties': {\n",
    "            'fill': COLORS_BLUE_GREEN[0],\n",
    "        },\n",
    "    },\n",
    "    {\n",
    "        'range': 1 * 60 * 30,\n",
    "        'properties': {\n",
    "            'fill': COLORS_BLUE_GREEN[1],\n",
    "        },\n",
    "    },\n",
    "]\n",
    "\n",
    "isolines = {\n",
    "    'type': 'FeatureCollection',\n",
    "    'features': [],\n",
    "}\n",
    "\n",
    "isolines['features'].append({\n",
    "    'type': 'Feature',\n",
    "    'geometry': {\n",
    "        'type': 'Point',\n",
    "        'coordinates': [\n",
    "            float(geocoded['Longitude']),\n",
    "            float(geocoded['Latitude']), \n",
    "        ],\n",
    "    },\n",
    "    'properties': {},\n",
    "})\n",
    "\n",
    "for rng in ranges:\n",
    "    isoline = get_isoline(\n",
    "        geocoded['Latitude'], \n",
    "        geocoded['Longitude'], \n",
    "        'fastest;car;traffic:enabled',\n",
    "        rng['range'],\n",
    "        parsed_start_time,\n",
    "        HERE_APP_ID,\n",
    "        HERE_APP_CODE\n",
    "    )\n",
    "\n",
    "    isoline_feature = isoline_geojson_feature(isoline)\n",
    "    isoline_feature['properties'].update(**rng['properties'])\n",
    "    isoline_feature['properties']['range'] = rng['range']\n",
    "    isolines['features'].append(isoline_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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C%2041.8531036%5D%2C%20%5B-87.83638%2C%2041.8572235%5D%2C%20%5B-87.8391266%2C%2041.8599701%5D%2C%20%5B-87.8411865%2C%2041.8606567%5D%2C%20%5B-87.8439331%2C%2041.8606567%5D%2C%20%5B-87.845993%2C%2041.8613434%5D%2C%20%5B-87.8528595%2C%2041.8682098%5D%2C%20%5B-87.8549194%2C%2041.8688965%5D%2C%20%5B-87.857666%2C%2041.8688965%5D%2C%20%5B-87.859726%2C%2041.8695831%5D%2C%20%5B-87.8604126%2C%2041.8716431%5D%2C%20%5B-87.8600693%2C%2041.873703%5D%2C%20%5B-87.8590393%2C%2041.8743896%5D%2C%20%5B-87.8573227%2C%2041.8743896%5D%5D%5D%2C%20%22type%22%3A%20%22Polygon%22%7D%7D%5D%7D'"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And display it using geojson.io\n",
    "\n",
    "import geojsonio\n",
    "\n",
    "# TODO: Simplify GeoJSON because it's too long to pass to geojson.io via the URL\n",
    "geojsonio.display(json.dumps(isolines))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "print(json.dumps(isolines))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}