☕️+🍺+🏠.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "import matplotlib as plt\n",
    "\n",
    "import time\n",
    "import requests\n",
    "\n",
    "from shapely.geometry import Point\n",
    "from shapely.geometry import Polygon\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "MAPZEN_KEY = 'mapzen-kLUbkiJ'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "listings = gpd.read_file('Data/NYClisting.geojson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40586"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(listings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'init': 'epsg:4326'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "listings.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Get 🍺\n",
    "beer = gpd.read_file('Data/yelp_beer.geojson')\n",
    "beer.crs = {'init' :'epsg:4326'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(beer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Get ☕️\n",
    "coffee = gpd.read_file('Data/coffee.geojson')\n",
    "coffee.crs = {'init' :'epsg:4326'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "375"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(coffee)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "poly = r.json()['features'][0]['geometry']['coordinates']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def generate_walksheds(geometries):   \n",
    "    a = []\n",
    "    \n",
    "    for location in geometries:\n",
    "        lon = location.x\n",
    "        lat = location.y\n",
    "\n",
    "        url='http://matrix.mapzen.com/isochrone?json={\"locations\":[{\"lat\":'+str(lat)+',\"lon\":'+str(lon)+'}],\"costing\":\"pedestrian\",\"contours\":[{\"time\":10}]}&api_key='+(MAPZEN_KEY)\n",
    "        r = requests.get(url)\n",
    "        \n",
    "        a.append(Polygon(r.json()['features'][0]['geometry']['coordinates']))\n",
    "        time.sleep(0.5)\n",
    "        \n",
    "    return a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "beer_walksheds = generate_walksheds(beer.geometry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "coffee_walksheds = generate_walksheds(coffee.geometry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "beerWalk = gpd.GeoDataFrame()\n",
    "beerWalk['geometry'] = beer_walksheds\n",
    "beerWalk.crs = {'init' :'epsg:4326'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "coffeeWalk = gpd.GeoDataFrame()\n",
    "coffeeWalk['geometry'] = coffee_walksheds\n",
    "coffeeWalk.crs = {'init' :'epsg:4326'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coffeeWalk.geometry.intersects(listing.geometry).value_counts()[True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "coffee_count = []\n",
    "\n",
    "for geo in listings.geometry:\n",
    "    counts = coffeeWalk.geometry.intersects(geo).value_counts()\n",
    "    \n",
    "    if True in counts.keys():\n",
    "        coffee_count.append(counts[True])\n",
    "    else:\n",
    "        coffee_count.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "listings['coffee_count'] = coffee_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "beer_count = []\n",
    "\n",
    "for geo in listings.geometry:\n",
    "    counts = beerWalk.geometry.intersects(geo).value_counts()\n",
    "    \n",
    "    if True in counts.keys():\n",
    "        beer_count.append(counts[True])\n",
    "    else:\n",
    "        beer_count.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "listings['beer_count'] = beer_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "listings.drop('coffeeshops', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('NYClisting.geojson', 'w') as f:\n",
    "   f.write(listings.to_json())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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