herndon_2020_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "\n",
    "\n",
    "module_path = os.path.abspath(os.path.join('..', \"openpolicedata\"))\n",
    "if module_path not in sys.path:\n",
    "    sys.path.append(module_path)\n",
    "\n",
    "import openpolicedata as opd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "src = opd.Source(\"Virginia Community Policing Act\")\n",
    "data = src.load_from_url(year=2020, jurisdiction_filter=\"Herndon Police Department\")\n",
    "df = data.table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['incident_date', 'agency_name', 'jurisdiction', 'reason_for_stop',\n",
       "       'race', 'ethnicity', 'age', 'gender', 'action_taken', 'violation_type',\n",
       "       'specific_violation', 'person_searched', 'vehicle_searched',\n",
       "       'additional_arrest'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"month\"] = df[\"incident_date\"].dt.to_period(\"M\")\n",
    "df[\"month\"].unique()\n",
    "\n",
    "df[\"race_eth\"] = df[\"race\"]\n",
    "df.loc[df[\"ethnicity\"] == \"HISPANIC\", \"race_eth\"] = \"HISPANIC\"\n",
    "df.loc[df[\"ethnicity\"] == \"UNKNOWN\", \"race_eth\"] = \"UNKNOWN\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TRAFFIC VIOLATION      1536\n",
       "EQUIPMENT VIOLATION     119\n",
       "OTHER                    27\n",
       "CALLS FOR SERVICE         2\n",
       "TERRY STOP                1\n",
       "Name: reason_for_stop, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Totals for reason for stop\n",
    "df[\"reason_for_stop\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='month'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = df[df[\"reason_for_stop\"] == \"TRAFFIC VIOLATION\"]\n",
    "a = a.groupby([\"month\",\"race_eth\"]).size().unstack(\"race_eth\")\n",
    "a.plot.line()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>race_eth</th>\n",
       "      <th>AMERICAN INDIAN</th>\n",
       "      <th>ASIAN/PACIFIC ISLANDER</th>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <th>HISPANIC</th>\n",
       "      <th>UNKNOWN</th>\n",
       "      <th>WHITE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>action_taken</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ARREST</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CITATION/SUMMONS</th>\n",
       "      <td>72.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO ENFORCEMENT ACTION</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WARNING</th>\n",
       "      <td>28.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race_eth               AMERICAN INDIAN  ASIAN/PACIFIC ISLANDER  \\\n",
       "action_taken                                                     \n",
       "ARREST                             NaN                     NaN   \n",
       "CITATION/SUMMONS                  72.0                    54.0   \n",
       "NO ENFORCEMENT ACTION              NaN                     NaN   \n",
       "WARNING                           28.0                    46.0   \n",
       "\n",
       "race_eth               BLACK OR AFRICAN AMERICAN  HISPANIC  UNKNOWN  WHITE  \n",
       "action_taken                                                                \n",
       "ARREST                                       NaN       3.0      4.0    1.0  \n",
       "CITATION/SUMMONS                            57.0      50.0     31.0   67.0  \n",
       "NO ENFORCEMENT ACTION                        1.0       0.0      NaN    0.0  \n",
       "WARNING                                     42.0      46.0     66.0   32.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "action_by_race = df.groupby([\"action_taken\",\"race_eth\"]).size().unstack(\"race_eth\")\n",
    "perc_by_race = np.round(action_by_race / action_by_race.sum() *100)\n",
    "perc_by_race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NO     1683\n",
       "YES       2\n",
       "Name: person_searched, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Totals for people searched\n",
    "df[\"person_searched\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NO     1677\n",
       "YES       8\n",
       "Name: vehicle_searched, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Totals for vehicle search\n",
    "df[\"vehicle_searched\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NO    1685\n",
       "Name: additional_arrest, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"additional_arrest\"].value_counts()"
   ]
  },
  {
   "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>race_eth</th>\n",
       "      <th>AMERICAN INDIAN</th>\n",
       "      <th>ASIAN/PACIFIC ISLANDER</th>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <th>HISPANIC</th>\n",
       "      <th>UNKNOWN</th>\n",
       "      <th>WHITE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reason_for_stop</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CALLS FOR SERVICE</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EQUIPMENT VIOLATION</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTHER</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TERRY STOP</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRAFFIC VIOLATION</th>\n",
       "      <td>26.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>309.0</td>\n",
       "      <td>407.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>637.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race_eth             AMERICAN INDIAN  ASIAN/PACIFIC ISLANDER  \\\n",
       "reason_for_stop                                                \n",
       "CALLS FOR SERVICE                NaN                     NaN   \n",
       "EQUIPMENT VIOLATION              6.0                     7.0   \n",
       "OTHER                            NaN                     1.0   \n",
       "TERRY STOP                       NaN                     NaN   \n",
       "TRAFFIC VIOLATION               26.0                    81.0   \n",
       "\n",
       "race_eth             BLACK OR AFRICAN AMERICAN  HISPANIC  UNKNOWN  WHITE  \n",
       "reason_for_stop                                                           \n",
       "CALLS FOR SERVICE                          NaN       1.0      NaN    1.0  \n",
       "EQUIPMENT VIOLATION                       26.0      45.0      9.0   26.0  \n",
       "OTHER                                      2.0       4.0      NaN   20.0  \n",
       "TERRY STOP                                 1.0       NaN      NaN    NaN  \n",
       "TRAFFIC VIOLATION                        309.0     407.0     76.0  637.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  }
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