Crimes by beat.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we need to know how far back to go. We want equal number of days before and after November 18, 2014."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "426 days, 0:00:00\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime, timedelta\n",
    "pivot = datetime.strptime('11/18/2014', '%m/%d/%Y')\n",
    "today = datetime.strptime('1/18/2016', '%m/%d/%Y')\n",
    "print today - pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2013-09-18 00:00:00\n"
     ]
    }
   ],
   "source": [
    "period = timedelta(days=426)\n",
    "print pivot - period"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This was run on 1/26/2016, 434 days after November 18, 2014. But the Data Portal only has data up to 1/18/2016, so we want to go to 426 days before the pivot, which works out to 9/18/2013."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's download the crime data for processing. We've already filtered it on the Data Portal side to make sure it only contains a) the date range we want, b) the UCRs we're interested in, and c) the beats we're interested in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         ID Case Number                Date                 Block  IUCR  \\\n",
      "0  10381833    HZ117903 2016-01-16 13:40:00      018XX S STATE ST  0486   \n",
      "1  10381906    HZ116891 2016-01-15 16:15:00  024XX S MICHIGAN AVE  0486   \n",
      "\n",
      "  Primary Type              Description Location Description Arrest Domestic  \\\n",
      "0      BATTERY  DOMESTIC BATTERY SIMPLE            RESIDENCE   True     True   \n",
      "1      BATTERY  DOMESTIC BATTERY SIMPLE                OTHER  False     True   \n",
      "\n",
      "               ...                Ward  Community Area  FBI Code  \\\n",
      "0              ...                   3              33       08B   \n",
      "1              ...                   2              33       08B   \n",
      "\n",
      "   X Coordinate Y Coordinate  Year              Updated On   Latitude  \\\n",
      "0       1176618      1891424  2016  01/23/2016 03:54:34 PM  41.857417   \n",
      "1       1177594      1888281  2016  01/22/2016 04:00:44 PM  41.848771   \n",
      "\n",
      "   Longitude                       Location  \n",
      "0 -87.627187  (41.857417366, -87.627187001)  \n",
      "1 -87.623700  (41.848770678, -87.623699896)  \n",
      "\n",
      "[2 rows x 22 columns]\n",
      "280 crimes found\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "url = 'https://data.cityofchicago.org/api/views/qa42-2iy9/rows.csv?accessType=DOWNLOAD'\n",
    "frame = pd.read_csv(url, parse_dates=['Date'])\n",
    "print frame.head(2)\n",
    "print '%d crimes found' % len(frame)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we do anything, we should add a column for just date so we can do statistics at the level of individual dates, which is kinda what we're after."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "frame['Date Only'] = pd.to_datetime(frame['Date'].apply(lambda x: x.date()))\n",
    "pivot = pivot.date()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our first question: were there more crimes of all types committed before or after the pivot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "150 crimes on or after 2014-11-18\n"
     ]
    }
   ],
   "source": [
    "print '%d crimes on or after %s' % (frame[frame['Date Only'] >= pivot].Date.count(), pivot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "130 crimes before 2014-11-18\n"
     ]
    }
   ],
   "source": [
    "print '%d crimes before %s' % (frame[frame['Date Only'] < pivot].Date.count(), pivot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's graph it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x10f6390d0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YXwfws24l77ruS1zXfWHXtvNjH6fyXLdCsYhimceEWkpyUBFC0Kp8UoXBiFv0YeCrjVVm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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c74ff10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's get nicer-looking plots. Can't use ggplot because my version of matplotlib is too old (I think).\n",
    "pd.set_option('display.mpl_style', 'default') \n",
    "pd.set_option('display.width', 10000) \n",
    "pd.set_option('display.max_columns', 60)\n",
    "# We need to specifically ask matplotlib to display plots inline\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "frame.groupby('Date Only').count().plot(legend=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Last question (for now): Did any particular type of crime spike before or after the pivot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0486\n",
      "108 crimes on or after 2014-11-18\n",
      "111 crimes before 2014-11-18\n",
      "---\n",
      "0530\n",
      "4 crimes on or after 2014-11-18\n",
      "1 crimes before 2014-11-18\n",
      "---\n",
      "0520\n",
      "7 crimes on or after 2014-11-18\n",
      "5 crimes before 2014-11-18\n",
      "---\n",
      "0484\n",
      "13 crimes on or after 2014-11-18\n",
      "2 crimes before 2014-11-18\n",
      "---\n",
      "051A\n",
      "8 crimes on or after 2014-11-18\n",
      "2 crimes before 2014-11-18\n",
      "---\n",
      "0545\n",
      "2 crimes on or after 2014-11-18\n",
      "3 crimes before 2014-11-18\n",
      "---\n",
      "0479\n",
      "1 crimes on or after 2014-11-18\n",
      "2 crimes before 2014-11-18\n",
      "---\n",
      "0483\n",
      "2 crimes on or after 2014-11-18\n",
      "0 crimes before 2014-11-18\n",
      "---\n",
      "0554\n",
      "4 crimes on or after 2014-11-18\n",
      "3 crimes before 2014-11-18\n",
      "---\n",
      "0550\n",
      "1 crimes on or after 2014-11-18\n",
      "0 crimes before 2014-11-18\n",
      "---\n",
      "0555\n",
      "0 crimes on or after 2014-11-18\n",
      "1 crimes before 2014-11-18\n",
      "---\n"
     ]
    }
   ],
   "source": [
    "for ucr in frame['IUCR'].unique():\n",
    "    print ucr\n",
    "    ucr_frame = frame[frame['IUCR'] == ucr]\n",
    "    print '%d crimes on or after %s' % (ucr_frame[ucr_frame['Date Only'] >= pivot].Date.count(), pivot)\n",
    "    print '%d crimes before %s' % (ucr_frame[ucr_frame['Date Only'] < pivot].Date.count(), pivot)\n",
    "    print '---'"
   ]
  }
 ],
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