notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Street racing analysis\n",
    "\n",
    "By [Ben Welsh](http://palewi.re/who-is-ben-welsh/)\n",
    "\n",
    "This analysis of street racing fatalities in Los Angeles County was conducted for the March 16, 2018, _Los Angeles Times_ story [\"Out of Control: The deadly toll of Los Angeles street racing.\"](http://www.latimes.com/projects/la-me-street-racing/)\n",
    "\n",
    "From 2000 to 2017, at least 179 people in Los Angeles County have died in crashes where street racing was suspected, according to a database of coroner’s records, police reports and media accounts compiled by Los Angeles Times reporters James Queally and Nicole Santa Cruz. \n",
    "\n",
    "The deadliest year on record during that span was 2007, with 18 fatal crashes. The dead are overwhelmingly male and young: More than half are 21 or younger, including a 4-year-old girl killed with her mother and 8-year-old brother. Slightly less than half of the victims— 47% — were pushing the accelerator. The rest were either passengers in the speeding vehicles, spectators or people simply walking on a sidewalk or driving home.\n",
    "\n",
    "Deaths have occurred in all areas of Los Angeles County, but some places — Long Beach, Santa Clarita, industrial zones Southeast of downtown and the wide boulevards that stretch across South L.A. and the San Fernando Valley — have become hotbeds of fatal activity.\n",
    "\n",
    "In the past three years, seven people have died on the streets of Commerce, a city of about 13,000 traversed by the 5 and 710 freeways. Three of those deaths — including two teenagers on their way home from Disneyland — occurred in a fiery 2016 crash set off when a suspected street racer driving more than 100 mph lost control on the 5.\n",
    "\n",
    "The data and computer code that generated those conclusions is below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import third-party libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import custom libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src import maps\n",
    "from src import charts\n",
    "from src import geocoder\n",
    "from src import transform\n",
    "from src import calculate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Configure the notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import source data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "fatalities = gpd.pd.read_csv(\"./input/street-racing-fatalities.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Put our events on the map using a geocoder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "fatalities = fatalities.apply(geocoder.geocode, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analyze how many have been mapped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x180 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "charts.bar(fatalities.is_mapped.value_counts(), figwidth=3, figheight=2.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trim and clean the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "fatalities = fatalities.rename(columns=transform.columns)[list(transform.columns.values())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert it into a GeoDataFrame for mapping."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "fatalities = transform.df_to_gdf(fatalities)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the total number of fatalities?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(fatalities)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What has the trend been over time?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "fatalities['death_year'] = fatalities.death_datetime.apply(lambda x: gpd.pd.to_datetime(x).year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "charts.bar(fatalities.death_year.value_counts(), figwidth=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Who are the victims?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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></th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>179.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>25.871508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.732345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>27.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>81.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age\n",
       "count  179.000000\n",
       "mean    25.871508\n",
       "std     12.732345\n",
       "min      4.000000\n",
       "25%     19.000000\n",
       "50%     21.000000\n",
       "75%     27.500000\n",
       "max     81.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fatalities.age.describe().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "charts.bar(fatalities.gender.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "charts.tree(fatalities.victim_role.value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Where do they happen?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "points = transform.groupby_point(fatalities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "maps.circles(points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What neighborhoods have the most?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merge the fatalities to the Los Angeles Times neighborhood definitions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "hoods = gpd.read_file(\"./input/hoods.shp\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group and count by neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "fatalities_with_hoods = gpd.sjoin(hoods, fatalities, how=\"inner\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "hood_counts = fatalities_with_hoods.groupby(\"name\").size().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "hood_counts.columns = [\"name\", \"total\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at the gross totals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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></th>\n",
       "      <th>name</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Commerce</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Long Beach</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Pomona</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Palmdale</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Santa Clarita</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Florence-Firestone</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Norwalk</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>North Hills</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Santa Fe Springs</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Eagle Rock</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  name  total\n",
       "13            Commerce     11\n",
       "38          Long Beach     11\n",
       "54              Pomona      6\n",
       "50            Palmdale      6\n",
       "59       Santa Clarita      5\n",
       "24  Florence-Firestone      5\n",
       "47             Norwalk      4\n",
       "44         North Hills      4\n",
       "60    Santa Fe Springs      4\n",
       "20          Eagle Rock      4"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hood_counts[['name', 'total']].sort_values(\"total\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the per-capita rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "hood_counts_with_pop = gpd.pd.merge(hood_counts, hoods, on=\"name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "hood_counts_with_pop['per_capita'] = hood_counts_with_pop.apply(\n",
    "    lambda x: calculate.per_capita(x.total, x.population),\n",
    "    axis=1\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exclude the very small neighborhoods and see the result."
   ]
  },
  {
   "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></th>\n",
       "      <th>name</th>\n",
       "      <th>total</th>\n",
       "      <th>population</th>\n",
       "      <th>per_capita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Commerce</td>\n",
       "      <td>11</td>\n",
       "      <td>13017.000000</td>\n",
       "      <td>8.450488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Northwest Palmdale</td>\n",
       "      <td>1</td>\n",
       "      <td>3634.035612</td>\n",
       "      <td>2.751762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Santa Fe Springs</td>\n",
       "      <td>4</td>\n",
       "      <td>17162.000000</td>\n",
       "      <td>2.330731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Shadow Hills</td>\n",
       "      <td>3</td>\n",
       "      <td>13639.821376</td>\n",
       "      <td>2.199442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Pico-Robertson</td>\n",
       "      <td>3</td>\n",
       "      <td>19955.525533</td>\n",
       "      <td>1.503343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Willowbrook</td>\n",
       "      <td>3</td>\n",
       "      <td>20685.000000</td>\n",
       "      <td>1.450326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Athens</td>\n",
       "      <td>1</td>\n",
       "      <td>8320.000000</td>\n",
       "      <td>1.201923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Eagle Rock</td>\n",
       "      <td>4</td>\n",
       "      <td>33550.245583</td>\n",
       "      <td>1.192242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Cypress Park</td>\n",
       "      <td>1</td>\n",
       "      <td>9383.436395</td>\n",
       "      <td>1.065708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Hancock Park</td>\n",
       "      <td>1</td>\n",
       "      <td>10993.024559</td>\n",
       "      <td>0.909668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  name  total    population  per_capita\n",
       "13            Commerce     11  13017.000000    8.450488\n",
       "46  Northwest Palmdale      1   3634.035612    2.751762\n",
       "60    Santa Fe Springs      4  17162.000000    2.330731\n",
       "63        Shadow Hills      3  13639.821376    2.199442\n",
       "53      Pico-Robertson      3  19955.525533    1.503343\n",
       "83         Willowbrook      3  20685.000000    1.450326\n",
       "4               Athens      1   8320.000000    1.201923\n",
       "20          Eagle Rock      4  33550.245583    1.192242\n",
       "16        Cypress Park      1   9383.436395    1.065708\n",
       "28        Hancock Park      1  10993.024559    0.909668"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hood_counts_with_pop[hood_counts_with_pop.population > 1000][[\n",
    "    'name',\n",
    "    'total',\n",
    "    'population',\n",
    "    'per_capita'\n",
    "]].sort_values(\"per_capita\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many fatalities occured in the Los Angeles city limits?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(fatalities_with_hoods[\n",
    "    (fatalities_with_hoods.city == 'los-angeles') |\n",
    "    # We're adding in this case because it happened on the other\n",
    "    # side of the street from the city limits and it was investigated\n",
    "    # by the Los Angeles Police Department.\n",
    "    (fatalities_with_hoods.case_number == '2007-09388')\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Output\n",
    "\n",
    "Export files for graphics and further exploration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "fatalities_with_hoods.to_csv(\"./output/fatalities.csv\", encoding=\"utf-8\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "points.to_file(\"./output/points.shp\")"
   ]
  }
 ],
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