notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Los Angeles Weedmaps analysis\n",
    "\n",
    "By [Ben Welsh](https://palewi.re/who-is-ben-welsh/)\n",
    "\n",
    "This analysis was conducted for the May 29, 2019, Los Angeles Times story [\" Black market cannabis shops thrive in L.A. even as city cracks down\"](https://www.latimes.com/local/lanow/la-me-weed-pot-dispensaries-illegal-marijuana-weedmaps-black-market-los-angeles-20190529-story.html). It found more than 220 dispensaries inside Los Angeles advertised on Weedmaps.com that are not on the city's list of authorized retailers.\n",
    "\n",
    "Due to the volatilty of the marijuana business and the high frequency of law enforcement actions against illegal operators, the number of listings can vary from day to day. So if you rerun this notebook you are likely to get slightly different results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How we did it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import Python tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import time\n",
    "import requests\n",
    "import pandas as pd\n",
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scrape the WeedMaps API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scrape():\n",
    "    \"\"\"\n",
    "    Provided a page number, returns all LA-area listings from the WeedMaps API\n",
    "    \"\"\"\n",
    "    base_url = \"https://api-g.weedmaps.com/discovery/v1/listings\"\n",
    "    \n",
    "    # Configure our request payload\n",
    "    params = {\n",
    "        # The type of business we want to be returned\n",
    "        'filter[any_retailer_services][]': ['doctor', 'storefront', 'delivery'],\n",
    "        # A bounding box around the city of Los Angeles, with some area to spare\n",
    "        'filter[bounding_box]': ['33.0,-119.0,36.0,-117.0'],\n",
    "        # The number items we want in the return the page. I believe this is the maximum allowed.\n",
    "        'page_size': [\"150\"],\n",
    "        'size': ['100'],\n",
    "        # The page number we are requesting\n",
    "        'page': [1]\n",
    "    }\n",
    "\n",
    "    # Make an initial probe to determine how many pages we need to scrape\n",
    "    r = requests.get(base_url, params=params)\n",
    "    total_listings = r.json()['meta']['total_listings']\n",
    "    \n",
    "    # Do the math\n",
    "    print(f\"{total_listings} listings found\")\n",
    "    pages = int(math.ceil(total_listings / 100))\n",
    "    print(f\"Downloading {pages} pages\")\n",
    "    \n",
    "\n",
    "    # Loop through and download each page\n",
    "    dict_list = []    \n",
    "    for page in range(1, pages+1):\n",
    "        print(f\"Downloading page {page}\")\n",
    "        \n",
    "        # Reconfigure the payload\n",
    "        params['page'] = [str(page)]\n",
    "\n",
    "        # Make the request\n",
    "        r = requests.get(base_url, params=params)\n",
    "\n",
    "        # Parse each item\n",
    "        for d in r.json()['data']['listings']:\n",
    "            dict_list.append(dict(\n",
    "                name=d['name'],\n",
    "                slug=d['slug'],\n",
    "                type=d['type'],\n",
    "                address=d['address'],\n",
    "                city=d['city'],\n",
    "                state=d['state'],\n",
    "                zipcode=d['zip_code'],\n",
    "                x=d['longitude'],\n",
    "                y=d['latitude'],\n",
    "                url=d['web_url']\n",
    "            ))\n",
    "            \n",
    "        # Give the API a break\n",
    "        time.sleep(1)\n",
    "    \n",
    "    # Return the result\n",
    "    return dict_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_list = scrape()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the results into a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(dict_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in the Los Angeles city boundaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "la_city = gpd.read_file(\"http://boundaries.latimes.com/1.0/boundary/los-angeles-census-place-2012/?format=geojson\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert our DataFrame to a GeoDataFrame with a matching CRS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf = gpd.GeoDataFrame(\n",
    "    df,\n",
    "    crs=la_city.crs,\n",
    "    geometry=geometry=gpd.points_from_xy(df.x, df.y)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pull out the LA city geometry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_limits = la_city.iloc[0].geometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "<shapely.geometry.multipolygon.MultiPolygon at 0x7fa6c4855550>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_limits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter the scrape to the city limits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf_la = gdf[gdf.geometry.within(city_limits)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clean up the file a little."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf_trimmed = gdf_la[[\n",
    "    'name',\n",
    "    'slug',\n",
    "    'type',\n",
    "    'address',\n",
    "    'city',\n",
    "    'zipcode',\n",
    "    'url',\n",
    "    'x',\n",
    "    'y',\n",
    "    'geometry'\n",
    "]].sort_values(\"name\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write the result to a file we can vet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf_trimmed.to_csv(\n",
    "    \"./data/scraped-la-listings.csv\",\n",
    "    index=False,\n",
    "    encoding=\"utf-8\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Offline this scraped file was compared to the [official list of retailers authorized by the city](https://cannabis.lacity.org/resources/authorized-retail-businesses). Licensed storefronts were marked manually. That refined list will be read back in for analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "analysis_df = pd.read_csv(\"./data/vetted-la-dispensaries.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many total dispensaries were listed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "365"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(analysis_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many are registered versus unregistered?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "analysis_df.registered.fillna(False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    221\n",
       "True     144\n",
       "Name: registered, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis_df.registered.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What's the percentage of each?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    0.605479\n",
       "True     0.394521\n",
       "Name: registered, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis_df.registered.value_counts() / len(analysis_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many are on Florence Avenue?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "florence = analysis_df[\n",
    "    (analysis_df.address.str.upper().str.contains(\"FLORENCE\")) &\n",
    "    (analysis_df.registered == False)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(florence)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the totals in our different regions of LA city?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "analysis_gdf = gpd.GeoDataFrame(\n",
    "    analysis_df,\n",
    "    crs={'init': 'epsg:4326'},\n",
    "    geometry=gpd.points_from_xy(analysis_df.x, analysis_df.y)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def spatial_pivot(gdf, shapes):\n",
    "    sjoin = gpd.sjoin(shapes, gdf, op=\"intersects\")\n",
    "    spivot = sjoin.groupby([\"name_left\", \"registered\"]).size().rename(\"dispensaries\").reset_index().pivot(\n",
    "        index=\"name_left\",\n",
    "        columns=\"registered\",\n",
    "        values=\"dispensaries\"\n",
    "    )\n",
    "    spivot['total'] = spivot.sum(axis=1)\n",
    "    spivot['percent_unlicensed'] = spivot[False] / spivot['total']\n",
    "    return spivot.sort_values(\"percent_unlicensed\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "regions = gpd.read_file(\"http://boundaries.latimes.com/1.0/boundary-set/la-county-municipal-regions-v6/?format=geojson\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>registered</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>total</th>\n",
       "      <th>percent_unlicensed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name_left</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>South</th>\n",
       "      <td>70</td>\n",
       "      <td>8</td>\n",
       "      <td>78</td>\n",
       "      <td>0.897436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Harbor</th>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>0.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Eastside</th>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>0.695652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Central</th>\n",
       "      <td>51</td>\n",
       "      <td>30</td>\n",
       "      <td>81</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Valley</th>\n",
       "      <td>50</td>\n",
       "      <td>69</td>\n",
       "      <td>119</td>\n",
       "      <td>0.420168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Westside</th>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>30</td>\n",
       "      <td>0.366667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northeast</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "registered    False  True  total  percent_unlicensed\n",
       "name_left                                           \n",
       "South            70     8     78            0.897436\n",
       "Harbor           22     8     30            0.733333\n",
       "The Eastside     16     7     23            0.695652\n",
       "Central          51    30     81            0.629630\n",
       "The Valley       50    69    119            0.420168\n",
       "The Westside     11    19     30            0.366667\n",
       "Northeast         1     3      4            0.250000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spatial_pivot(analysis_gdf, regions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the totals in different neighborhoods of LA city?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "hoods = gpd.read_file(\"http://boundaries.latimes.com/1.0/boundary-set/la-county-neighborhoods-v6/?format=geojson\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>registered</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>total</th>\n",
       "      <th>percent_unlicensed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name_left</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Downtown</th>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florence</th>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.933333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Hollywood</th>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.684211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wilmington</th>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boyle Heights</th>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.714286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "registered       False  True  total  percent_unlicensed\n",
       "name_left                                              \n",
       "Downtown          21.0   7.0   28.0            0.750000\n",
       "Florence          14.0   1.0   15.0            0.933333\n",
       "North Hollywood   13.0   6.0   19.0            0.684211\n",
       "Wilmington        10.0   1.0   11.0            0.909091\n",
       "Boyle Heights     10.0   4.0   14.0            0.714286"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spatial_pivot(analysis_gdf, hoods).sort_values(False, ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write out file for mapping."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "analysis_gdf.to_file(\"./data/map.geojson\", driver=\"GeoJSON\")"
   ]
  }
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