Starbucks Dists.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "id": "2787bcb7",
   "metadata": {},
   "source": [
    "LA County Zip Codes can be downloaded here:\n",
    "https://geohub.lacity.org/datasets/71b2fed2c1f24fc8850f5b7f8d2a320a/explore?location=33.799343%2C-118.302795%2C9.25\n",
    "\n",
    "LA Country GeoJSON can be downloaded here:\n",
    "https://github.com/ritvikmath/StarbucksStoreScraping/blob/master/laMap.geojson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "580719e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import re\n",
    "import folium\n",
    "import json\n",
    "from folium import plugins\n",
    "import geopy.distance\n",
    "import seaborn as sns\n",
    "from shapely.geometry import Point\n",
    "from shapely.geometry.polygon import Polygon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "238fdf24",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_store_lat_lngs(zcode):\n",
    "    request = f'https://www.starbucks.com/store-locator?place={zcode}'\n",
    "    response = requests.get(request)\n",
    "    lat_lngs = re.findall(r'\"coordinates\":\\{\"latitude\":(.*?)\\,\"longitude\":(.*?)\\}', response.text)\n",
    "    lat_lngs = [(float(item[0]), float(item[1])) for item in lat_lngs]\n",
    "    return lat_lngs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a3ed910",
   "metadata": {},
   "source": [
    "# Get all Starbucks Locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "274cd529",
   "metadata": {},
   "outputs": [],
   "source": [
    "la_zips = pd.read_csv('Zip_Codes_(LA_County).csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "49d2743f",
   "metadata": {},
   "outputs": [],
   "source": [
    "starbucks_locs = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "79b341e4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "for i,zcode in enumerate(la_zips.ZIPCODE.values):\n",
    "    print(f'{i} / {len(la_zips)}')\n",
    "    starbucks_locs += get_store_lat_lngs(zcode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "db49399d",
   "metadata": {},
   "outputs": [],
   "source": [
    "starbucks_locs = set(starbucks_locs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8b0958ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "starbucks_locs = list(starbucks_locs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8398a21f",
   "metadata": {},
   "source": [
    "# Distance to Closest Starbucks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bc7e26a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "dist_matrix = np.empty((len(starbucks_locs), len(starbucks_locs)))\n",
    "for i1,s1 in enumerate(starbucks_locs):\n",
    "    for i2,s2 in enumerate(starbucks_locs):\n",
    "        if i1 <= i2:\n",
    "            d = geopy.distance.geodesic(s1, s2).mi\n",
    "            dist_matrix[i1,i2] = d\n",
    "            dist_matrix[i2,i1] = d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "69a1472d",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.fill_diagonal(dist_matrix, np.inf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c21be714",
   "metadata": {},
   "outputs": [],
   "source": [
    "closest_locs = np.argmin(dist_matrix, axis=0)\n",
    "closest_dists = dist_matrix[np.arange(len(starbucks_locs)), closest_locs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "49bcb22f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#map of LA county\n",
    "with open('laMap.geojson') as f:\n",
    "    laArea = json.load(f)\n",
    "    \n",
    "la_lng_lats = laArea['features'][0]['geometry']['coordinates'][0][0]\n",
    "polygon = Polygon(la_lng_lats)\n",
    "\n",
    "#initialize the map around LA County\n",
    "laMap = folium.Map(location=[34.0522,-118.2437], tiles='Stamen Toner', zoom_start=9)\n",
    "\n",
    "#add the shape of LA County to the map\n",
    "folium.GeoJson(laArea).add_to(laMap)\n",
    "\n",
    "#for each row in the Starbucks dataset, plot the corresponding latitude and longitude on the map\n",
    "for lat,lng in starbucks_locs:\n",
    "    if polygon.contains(Point(lng,lat)):\n",
    "        folium.CircleMarker((lat, lng), radius=4, weight=2, color='red', fill_color='red', fill_opacity=.5).add_to(laMap)\n",
    "   \n",
    "closest_dists_lim = []\n",
    "closest_locs = np.argmin(dist_matrix, axis=0)\n",
    "for i1,s1 in enumerate(starbucks_locs):\n",
    "    if polygon.contains(Point(s1[1],s1[0])):\n",
    "        s2 = starbucks_locs[closest_locs[i1]]\n",
    "        d = round(closest_dists[i1], 2)\n",
    "        folium.PolyLine([s1,s2], color='black', popup=f'{d} miles', fontsize=20).add_to(laMap)\n",
    "        closest_dists_lim.append(d)\n",
    "closest_dists_lim = np.array(closest_dists_lim)\n",
    "    \n",
    "#save the map as an html    \n",
    "laMap.save('laSbuxMap.html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "232ba8df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 1600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,5))\n",
    "sns.histplot(closest_dists_lim, kde=True, line_kws={'linewidth': 3}, stat='density')\n",
    "plt.xlabel('miles to closest Starbucks', fontsize=20)\n",
    "plt.xticks(np.arange(0, 3.1), fontsize=16)\n",
    "plt.ylabel('Density', fontsize=20)\n",
    "plt.axvline(np.mean(closest_dists), color='k', linestyle='--', linewidth=2.5, alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9832c610",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}