FinalProject_group096.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - Final Project \n",
    "## Surveillance Cameras: An Effective Deterrent or A Waste of Public Resources?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our project sought to discern any quantifiable effects on the rate of change in violent crime from the installation of surveillance cameras in various neighborhoods of Chicago. This task was accomplished by comparing violent crime rates within neighborhoods with high crime rates before and after their installation. Overall, we found no evidence to suggest that surveillance cameras are effective way to curtail violent crimes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- Chaitanya Patel\n",
    "- Kristine Marie Baluyot\n",
    "- Linh Le\n",
    "- Namit Mishra\n",
    "- Tiffany Zhang\n",
    "- Robert Eaton"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Was there a significant change to the year-on-year crime rate for violent crimes in neighborhoods of Chicago in a three year period before and after activation of the surveillance cameras in 2003?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Violent crime has become a politically and socially divisive issue in recent years in America. The recent attack on a mosque in New Zealand is also a reminder that the situation is not very different in some other countries as well. Over the past 10 years, many measures have been suggested and implemented for prevention of these types of crimes in various cities throughout America and all over the world. Such measures include surveillance cameras, increased number of police patrols, and new legislation. Over the past quarter century, we have seen that violent crime actually decreasing yearly in the US.[1] However, America still has more violent crimes compared to other democratic nations.[2] Further, with regards to gun crime, we have seen a higher number of deaths by guns in 2018 than in any of the last 50 years.[3] While looking at data on violent crime we discovered that despite a decrease in crime, Chicago still holds the record of having the highest rate of violent crime in the country.[4] We also discovered that 5% of all violent crimes in America, occur in the city of Chicago, which is also the highest in the country.[5] Chicago has a network of over 30,000 cameras and sensors to monitor the city streets. The first set of cameras was installed in the summer of 2003, with full activation later that year.[6] Some have been skeptical about the effectiveness of these cameras while others have praised their usefulness in conducting ensuing investigations.[7] Further, these cameras come with a risk of intrusion and abuse, adding to the already apparent skepticism. \n",
    "\n",
    "No significant amount of data-driven analysis is currently available on whether or not these surveillance tools have been effective deterrents to violent crimes. Due to Chicago having a high amount of violent crimes, coupled with the availability of location data for the cameras as well as an extensive database of crimes, we decided to focus our efforts on the city of Chicago to analyze the effectiveness of such cameras in reducing violent crime rate over the years. We will look at the crime rates before and after their installation by calculating crime rates in the periods 2001-03 and 2004-06. We plan to look at the neighborhoods with higher amounts of both crime and cameras, to filter out outlier data. If we can find a noticeable change in the year-on-year crime rate between the two time periods, we will be inclined to say that the cameras may have had some effect.\n",
    "\n",
    "References (include links):\n",
    "\n",
    "- 1) [Source:https://www.pewresearch.org/fact-tank/2019/01/03/5-facts-about-crime-in-the-u-s/]\n",
    "- 2) [Source:https://prospect.org/article/violent-crime-increasing]\n",
    "- 3) [Source:https://www.nytimes.com/2018/12/18/us/gun-deaths.html]\n",
    "- 4) [Source:https://www.nbcnews.com/news/us-news/chicago-sees-decline-murders-other-violent-crimes-second-straight-year-n95345]\n",
    "- 5) [Source:https://www.kaggle.com/jameslko/gun-violence-data]\n",
    "- 6) [Source:https://home.chicagopolice.org/inside-the-cpd/pod-program/]\n",
    "- 7) [Source:https://www.nytimes.com/2018/05/26/us/chicago-police-surveillance.html]\n",
    "\n",
    "Prior work:\n",
    "\n",
    "- 1) A previous class project analysing the relation between crime and quality of businesses in the vicinity. https://github.com/COGS108/FinalProjects-Sp17/blob/master/006-FinalProject.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Research so far has not definitively linked lower crime rates with the installation of cameras. Other studies have shown them to simply push crime to other, less visible areas.[1] Thus, we do not expect to see any noticeable change in overall trends for violent crimes.\n",
    "\n",
    "References (include links):\n",
    "\n",
    "- 1) [Source:https://www.aclu.org/blog/privacy-technology/surveillance-technologies/police-chief-surveillance-cameras-dont-help-fight]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First dataset\n",
    "- Dataset Name: Crimes - 2001 to present\n",
    "- Link to the dataset: https://data.cityofchicago.org/Public-Safety/Crimes-2001-to-present/ijzp-q8t2\n",
    "- Number of observations: 6.8 million\n",
    "- Actual number of observations used: 170,000\n",
    "\n",
    "\n",
    "This dataset from the City of Chicago contains all crime data from 2001 to the present. \n",
    "It contains a wealth of information, and many columns not very necessary to our analysis. \n",
    "We focused on the following columns:\n",
    "- Date - to sort it into the 2001-03 block or the 2004-06 block, and ignored the rest.\n",
    "- IUCR - crime code, keeping only violent crimes as defined in Illinois \n",
    "- Primary Type - to sort crimes more specifically\n",
    "- Location description - stating what time of place the crime occurred (such as inside or on the street)\n",
    "- Latitude, Longitude, Location - pinpoints the location of the crime. The ‘Location’ column is simply the Latitude and Longitude pair\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Second dataset\n",
    "- Dataset Name: Blue Light Cameras in the city of Chicago\n",
    "- Link to the dataset: https://redshiftzero.github.io/policesurveillance/\n",
    "- Number of observations: 715 camera locations\n",
    "\n",
    "This dataset contains over 700 camera locations which were installed by the City of Chicago under their POD camera program. The data was obtained by the Lucy Parsons Labs organization through various Freedom of Information Act requests. The camera locations were indicated on a map. Therefore, the data we needed, that is the latitude and logitude of camera locations was embedded in HTML code with Javascript. We used Beautiful Soup along with python regular expressions to extract the data from source code of the link above. The script for extracting the data is as below. The latitude and logitude were saved in a csv file named 'lat_long.csv'. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import requests\n",
    "import bs4\n",
    "from bs4 import BeautifulSoup\n",
    "import re\n",
    "import csv\n",
    "import matplotlib \n",
    "\n",
    "cameras = 'https://redshiftzero.github.io/policesurveillance/pod.html'\n",
    "data = requests.get(cameras)\n",
    "soup = BeautifulSoup(data.content, 'html.parser')\n",
    "\n",
    "right_table = soup.find_all('script')\n",
    "what_we_need = right_table[6]\n",
    "\n",
    "# Regular expression to find patterns of data we need\n",
    "pattern = re.compile(\".*var marker_.* = L.marker\\(\\[.*\\n.*\\n\")\n",
    "all_patterns = pattern.findall(what_we_need.string)\n",
    "\n",
    "# Using string functions to convert data to needed form\n",
    "with open('lat_long.csv', mode='w') as camera_file:\n",
    "    camera_file = csv.writer(camera_file, delimiter=',', \\\n",
    "                  lineterminator = '\\n', quotechar='\"',\\\n",
    "                  quoting=csv.QUOTE_MINIMAL)\n",
    "    camera_file.writerow([\"Latitude\", \"Longitude\"])\n",
    "    for each in all_patterns:\n",
    "        each = each.strip()\n",
    "        each = ' '.join(each.split())\n",
    "        each = each[each.find('[') + 1:each.find(']')]\n",
    "        (lat, long) = each.split(',')\n",
    "        lat = lat.strip()\n",
    "        long = long.strip()\n",
    "        camera_file.writerow([lat, long])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import pylab\n",
    "import pandas as pd\n",
    "\n",
    "from bs4 import BeautifulSoup\n",
    "import requests\n",
    "import gps_to_neighborhood\n",
    "\n",
    "import statsmodels.api as sm\n",
    "import patsy\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import ttest_ind, chisquare, normaltest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing both the datasets into pandas dataframes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Crimes_-_2001_to_present.csv')\n",
    "df_camera = pd.read_csv('lat_long.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the camera dataset was extracted by us, we already cleaned it to the required standards. \n",
    "#### Below are the dataset to clean the Chicago crime dataset. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let us keep only the necessary columns: ID, Date, Block, IUCR, Description, Location Description, Arrest, Latitude, Longitude, and Location. Let us also drop any datapoint will NaN value for the location as the analysis is dependent on knowing the neighborhood where the crime occured."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>ID</th>\n",
       "      <th>Date</th>\n",
       "      <th>IUCR</th>\n",
       "      <th>Primary Type</th>\n",
       "      <th>Description</th>\n",
       "      <th>Location Description</th>\n",
       "      <th>Arrest</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Community Areas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>24560</td>\n",
       "      <td>05/26/2019 06:48:00 AM</td>\n",
       "      <td>0110</td>\n",
       "      <td>HOMICIDE</td>\n",
       "      <td>FIRST DEGREE MURDER</td>\n",
       "      <td>CHA PARKING LOT</td>\n",
       "      <td>False</td>\n",
       "      <td>41.864278</td>\n",
       "      <td>-87.659660</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>24559</td>\n",
       "      <td>05/26/2019 02:11:00 AM</td>\n",
       "      <td>0110</td>\n",
       "      <td>HOMICIDE</td>\n",
       "      <td>FIRST DEGREE MURDER</td>\n",
       "      <td>STREET</td>\n",
       "      <td>False</td>\n",
       "      <td>41.864278</td>\n",
       "      <td>-87.659682</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>794</th>\n",
       "      <td>24558</td>\n",
       "      <td>05/25/2019 11:43:00 PM</td>\n",
       "      <td>0110</td>\n",
       "      <td>HOMICIDE</td>\n",
       "      <td>FIRST DEGREE MURDER</td>\n",
       "      <td>STREET</td>\n",
       "      <td>False</td>\n",
       "      <td>41.754424</td>\n",
       "      <td>-87.634786</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>24557</td>\n",
       "      <td>05/25/2019 07:01:00 PM</td>\n",
       "      <td>0110</td>\n",
       "      <td>HOMICIDE</td>\n",
       "      <td>FIRST DEGREE MURDER</td>\n",
       "      <td>GANGWAY</td>\n",
       "      <td>False</td>\n",
       "      <td>41.695280</td>\n",
       "      <td>-87.626419</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2312</th>\n",
       "      <td>24556</td>\n",
       "      <td>05/24/2019 02:12:00 AM</td>\n",
       "      <td>0110</td>\n",
       "      <td>HOMICIDE</td>\n",
       "      <td>FIRST DEGREE MURDER</td>\n",
       "      <td>GARAGE</td>\n",
       "      <td>False</td>\n",
       "      <td>41.783199</td>\n",
       "      <td>-87.657851</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID                    Date  IUCR Primary Type          Description  \\\n",
       "561   24560  05/26/2019 06:48:00 AM  0110     HOMICIDE  FIRST DEGREE MURDER   \n",
       "680   24559  05/26/2019 02:11:00 AM  0110     HOMICIDE  FIRST DEGREE MURDER   \n",
       "794   24558  05/25/2019 11:43:00 PM  0110     HOMICIDE  FIRST DEGREE MURDER   \n",
       "1000  24557  05/25/2019 07:01:00 PM  0110     HOMICIDE  FIRST DEGREE MURDER   \n",
       "2312  24556  05/24/2019 02:12:00 AM  0110     HOMICIDE  FIRST DEGREE MURDER   \n",
       "\n",
       "     Location Description  Arrest   Latitude  Longitude  Community Areas  \n",
       "561       CHA PARKING LOT   False  41.864278 -87.659660             29.0  \n",
       "680                STREET   False  41.864278 -87.659682             29.0  \n",
       "794                STREET   False  41.754424 -87.634786             67.0  \n",
       "1000              GANGWAY   False  41.695280 -87.626419             45.0  \n",
       "2312               GARAGE   False  41.783199 -87.657851             65.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['Block','Case Number','Community Area','Domestic',\n",
    "         'Beat','District','Ward','FBI Code','X Coordinate',\n",
    "         'Y Coordinate','Year','Updated On',\n",
    "         'Historical Wards 2003-2015', 'Zip Codes',\n",
    "         'Census Tracts','Boundaries - ZIP Codes', 'Location',\n",
    "         'Wards','Police Districts','Police Beats'], axis=1, \n",
    "        inplace=True)\n",
    "df.dropna(subset=['Latitude', 'Longitude'], axis='rows', inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following are the total type of crimes in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['HOMICIDE', 'OTHER OFFENSE', 'ROBBERY', 'THEFT', 'NARCOTICS',\n",
       "       'BATTERY', 'ASSAULT', 'CRIMINAL DAMAGE', 'CRIMINAL TRESPASS',\n",
       "       'PUBLIC PEACE VIOLATION', 'MOTOR VEHICLE THEFT',\n",
       "       'DECEPTIVE PRACTICE', 'WEAPONS VIOLATION',\n",
       "       'INTERFERENCE WITH PUBLIC OFFICER', 'BURGLARY',\n",
       "       'CRIM SEXUAL ASSAULT', 'OFFENSE INVOLVING CHILDREN',\n",
       "       'PUBLIC INDECENCY', 'SEX OFFENSE', 'KIDNAPPING', 'PROSTITUTION',\n",
       "       'INTIMIDATION', 'ARSON', 'LIQUOR LAW VIOLATION',\n",
       "       'CONCEALED CARRY LICENSE VIOLATION', 'GAMBLING',\n",
       "       'OTHER NARCOTIC VIOLATION', 'STALKING', 'OBSCENITY',\n",
       "       'HUMAN TRAFFICKING', 'NON-CRIMINAL',\n",
       "       'NON-CRIMINAL (SUBJECT SPECIFIED)', 'NON - CRIMINAL', 'RITUALISM',\n",
       "       'DOMESTIC VIOLENCE'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Primary Type\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As evidenced, there are multiple types of crimes. However, only certain ones are deemed 'violent' under Illinois and federal laws. The next step is to remove rows with non-violent crimes. The source for the definition of violent crimes is http://gis.chicagopolice.org/CLEARMap_crime_sums/crime_types.html."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "crimes = df[(df[\"Primary Type\"] == 'BATTERY') & (df[\"IUCR\"] != \"0440\") & \n",
    "            (df[\"IUCR\"] != \"0486\") & (df[\"IUCR\"] != \"0460\") & \n",
    "            (df[\"IUCR\"] != '0484') & (df['IUCR'] != '0454') & \n",
    "            (df['IUCR'] != '0487') & (df['IUCR'] != '0475')|\n",
    "            (df[\"Primary Type\"] == 'ROBBERY') | \n",
    "            (df[\"Primary Type\"] == 'ASSAULT') & (df[\"IUCR\"] != '0560') & \n",
    "            (df['IUCR'] != '0554') & (df['IUCR'] != '0545')|\n",
    "            (df[\"Primary Type\"] == 'CRIM SEXUAL ASSAULT') | \n",
    "            (df[\"Primary Type\"] == 'HOMICIDE') | \n",
    "            (df[\"IUCR\"] ==\"1753\") | (df[\"IUCR\"] == \"1754\") | \n",
    "            (df['IUCR']==\"0510\") | \n",
    "            (df[\"Primary Type\"] == \"RITUALISM\") & \n",
    "            (df[\"IUCR\"] != \"0494\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, since such cameras are only in public locations, it would make sense to remove crimes which occur inside. Obviously, there is some discretion with what place categories are selected. Since the CPD cameras in the dataset are only on streets (not inside housing, restaurants, or in transit stations), crimes which did not explicitly occur in a public location were removed. Looking at the different types of locations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(394473, 10)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.mode.chained_assignment = None\n",
    "crimes.dropna(subset=['Location Description'], inplace=True)\n",
    "crimes = crimes[(crimes[\"Location Description\"] == \"PARKING LOT/GARAGE(NON.RESID.)\") | \n",
    "                (crimes[\"Location Description\"] == \"STREET\" )| \n",
    "                (crimes[\"Location Description\"] == \"ALLEY\") |\n",
    "                (crimes[\"Location Description\"] == \"SIDEWALK\")|\n",
    "                (crimes[\"Location Description\"] == \"RESIDENCE PORCH/HALLWAY\")|\n",
    "                (crimes[\"Location Description\"] == \"CHA PARKING LOT/GROUNDS\")|\n",
    "                (crimes[\"Location Description\"] == \"GAS STATION\")|\n",
    "                (crimes[\"Location Description\"] == \"POLICE FACILITY/VEH PARKING LOT\")|\n",
    "                (crimes[\"Location Description\"] == \"VACANT LOT/LAND\")|\n",
    "                (crimes[\"Location Description\"] == \"PARK PROPERTY\")|\n",
    "                (crimes[\"Location Description\"] == \"CTA GARAGE / OTHER PROPERTY\")|\n",
    "                (crimes[\"Location Description\"] == \"DRIVEWAY - RESIDENTIAL\") |\n",
    "                (crimes[\"Location Description\"] == \"PARKING LOT\") | \n",
    "                (crimes[\"Location Description\"] == \"PORCH\")|\n",
    "                (crimes[\"Location Description\"] == \"YARD\")| \n",
    "                (crimes[\"Location Description\"] == \"RESIDENTIAL YARD (FRONT/BACK)\") |\n",
    "                (crimes[\"Location Description\"] == \"HIGHWAY/EXPRESSWAY\")|\n",
    "                (crimes[\"Location Description\"] == \"CHA PARKING LOT\") |\n",
    "                (crimes[\"Location Description\"] == \"BRIDGE\")|\n",
    "                (crimes[\"Location Description\"] == \"YARD\")|\n",
    "                (crimes[\"Location Description\"] == \"LAKEFRONT/WATERFRONT/RIVERBANK\")|\n",
    "                (crimes[\"Location Description\"] == \"DRIVEWAY\")]\n",
    "crimes.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let us look at the Date column. Out analysis requires a comparison between 2001-2003 crime and 2004-2006 crime. Hence, we separate the crime data of the two time periods. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "crimesBefore = crimes[(crimes[\"Date\"].str[6:10] == '2001') | \n",
    "                      (crimes[\"Date\"].str[6:10] == '2002') | \n",
    "                      (crimes[\"Date\"].str[6:10] == '2003')]\n",
    "crimesAfter = crimes[(crimes[\"Date\"].str[6:10] == '2004') | \n",
    "                     (crimes[\"Date\"].str[6:10] == '2005') | \n",
    "                     (crimes[\"Date\"].str[6:10] == '2006')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the limits of neighborhoods decided by the Chicago city change every few years, they were not consistent throughout the dataset. So for consistency, we used a script available on GitHub to ascertain the neighborhoods of the crime. Chicago city used the term community areas for different neighborhoods, so from this point on neighborhoods and community area will be used synonimously in this report. Source of the script to find neighborhoods is: https://github.com/jkgiesler/parse-chicago-neighborhoods "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let us update the dataframes to add the neighborhoods of each crime. Starting with the camera dataset, let's add another column indicating community area of each camera. We will do the same for crimesBefore and crimesAfter. There are chunks of code commented out below because this code takes a long time for large datasets, approximately 20 minutes, we saved the resulting dataframes to csv files so we do not have to repeat the process multiple times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all_neighborhoods = gps_to_neighborhood.get_all_neighborhoods()\n",
    "# list_name = []\n",
    "# list_area = []\n",
    "# for index, row in df_camera.iterrows():\n",
    "#     neighborhood = gps_to_neighborhood.find_neighborhood(row['Longitude'],\\\n",
    "#                     row['Latitude'],all_neighborhoods)\n",
    "#     if neighborhood is not None:\n",
    "#         list_name.append(neighborhood[0])\n",
    "#         list_area.append(neighborhood[1])\n",
    "#     else:\n",
    "#         list_name.append('None')\n",
    "#         list_area.append('None')\n",
    "# df_camera['Community Areas'] = list_name\n",
    "# df_camera['Area(unit)'] = list_area\n",
    "\n",
    "# df_camera = df_camera[df_camera['Community Areas'] != 'None']\n",
    "# df_camera.to_csv('lat_long.csv', index=False, header=True)\n",
    " \n",
    "df_camera = pd.read_csv('lat_long.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us do the same for the crimes dataframe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Date</th>\n",
       "      <th>IUCR</th>\n",
       "      <th>Primary Type</th>\n",
       "      <th>Description</th>\n",
       "      <th>Location Description</th>\n",
       "      <th>Arrest</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Community Areas</th>\n",
       "      <th>Area(unit)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3118562</td>\n",
       "      <td>12/31/2003 11:55:00 PM</td>\n",
       "      <td>0313</td>\n",
       "      <td>ROBBERY</td>\n",
       "      <td>ARMED: OTHER DANGEROUS WEAPON</td>\n",
       "      <td>PARKING LOT/GARAGE(NON.RESID.)</td>\n",
       "      <td>False</td>\n",
       "      <td>41.757747</td>\n",
       "      <td>-87.624748</td>\n",
       "      <td>Grand Crossing</td>\n",
       "      <td>9.885317e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3115633</td>\n",
       "      <td>12/31/2003 11:45:00 PM</td>\n",
       "      <td>031A</td>\n",
       "      <td>ROBBERY</td>\n",
       "      <td>ARMED: HANDGUN</td>\n",
       "      <td>ALLEY</td>\n",
       "      <td>False</td>\n",
       "      <td>41.926927</td>\n",
       "      <td>-87.752242</td>\n",
       "      <td>Belmont Cragin</td>\n",
       "      <td>1.090994e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3115075</td>\n",
       "      <td>12/31/2003 11:45:00 PM</td>\n",
       "      <td>031A</td>\n",
       "      <td>ROBBERY</td>\n",
       "      <td>ARMED: HANDGUN</td>\n",
       "      <td>STREET</td>\n",
       "      <td>False</td>\n",
       "      <td>41.765693</td>\n",
       "      <td>-87.595842</td>\n",
       "      <td>Grand Crossing</td>\n",
       "      <td>9.885317e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3118958</td>\n",
       "      <td>12/31/2003 10:55:00 PM</td>\n",
       "      <td>033A</td>\n",
       "      <td>ROBBERY</td>\n",
       "      <td>ATTEMPT: ARMED-HANDGUN</td>\n",
       "      <td>STREET</td>\n",
       "      <td>True</td>\n",
       "      <td>41.894642</td>\n",
       "      <td>-87.715067</td>\n",
       "      <td>Humboldt Park</td>\n",
       "      <td>1.250104e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3113368</td>\n",
       "      <td>12/31/2003 10:40:00 PM</td>\n",
       "      <td>051A</td>\n",
       "      <td>ASSAULT</td>\n",
       "      <td>AGGRAVATED: HANDGUN</td>\n",
       "      <td>STREET</td>\n",
       "      <td>False</td>\n",
       "      <td>41.774849</td>\n",
       "      <td>-87.682168</td>\n",
       "      <td>Chicago Lawn</td>\n",
       "      <td>9.827947e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID                    Date  IUCR Primary Type  \\\n",
       "0  3118562  12/31/2003 11:55:00 PM  0313      ROBBERY   \n",
       "1  3115633  12/31/2003 11:45:00 PM  031A      ROBBERY   \n",
       "2  3115075  12/31/2003 11:45:00 PM  031A      ROBBERY   \n",
       "3  3118958  12/31/2003 10:55:00 PM  033A      ROBBERY   \n",
       "4  3113368  12/31/2003 10:40:00 PM  051A      ASSAULT   \n",
       "\n",
       "                     Description            Location Description  Arrest  \\\n",
       "0  ARMED: OTHER DANGEROUS WEAPON  PARKING LOT/GARAGE(NON.RESID.)   False   \n",
       "1                 ARMED: HANDGUN                           ALLEY   False   \n",
       "2                 ARMED: HANDGUN                          STREET   False   \n",
       "3         ATTEMPT: ARMED-HANDGUN                          STREET    True   \n",
       "4            AGGRAVATED: HANDGUN                          STREET   False   \n",
       "\n",
       "    Latitude  Longitude Community Areas    Area(unit)  \n",
       "0  41.757747 -87.624748  Grand Crossing  9.885317e+07  \n",
       "1  41.926927 -87.752242  Belmont Cragin  1.090994e+08  \n",
       "2  41.765693 -87.595842  Grand Crossing  9.885317e+07  \n",
       "3  41.894642 -87.715067   Humboldt Park  1.250104e+08  \n",
       "4  41.774849 -87.682168    Chicago Lawn  9.827947e+07  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list_name = []\n",
    "# list_area = []\n",
    "# for index, row in crimesBefore.iterrows():\n",
    "#     neighborhood = gps_to_neighborhood.find_neighborhood(row['Longitude'],\\\n",
    "#                     row['Latitude'],all_neighborhoods)\n",
    "#     if neighborhood is not None:\n",
    "#         list_name.append(neighborhood[0])\n",
    "#         list_area.append(neighborhood[1])\n",
    "#     else:\n",
    "#         list_name.append('None')\n",
    "#         list_area.append('None')\n",
    "# crimesBefore['Community Areas'] = list_name\n",
    "# crimesBefore['Area(unit)'] = list_area\n",
    "# crimesBefore = crimesBefore[crimesBefore['Community Areas'] != 'None']\n",
    "\n",
    "# crimesBefore.to_csv('CrimesBefore.csv', index=False, header=True)\n",
    "crimesBefore = pd.read_csv('CrimesBefore.csv')\n",
    "crimesBefore.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Doing the same for crimes that occured in the years 2004-06. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Date</th>\n",
       "      <th>IUCR</th>\n",
       "      <th>Primary Type</th>\n",
       "      <th>Description</th>\n",
       "      <th>Location Description</th>\n",
       "      <th>Arrest</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Community Areas</th>\n",
       "      <th>Area(unit)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5221564</td>\n",
       "      <td>12/31/2006 11:00:00 PM</td>\n",
       "      <td>041A</td>\n",
       "      <td>BATTERY</td>\n",
       "      <td>AGGRAVATED: HANDGUN</td>\n",
       "      <td>SIDEWALK</td>\n",
       "      <td>False</td>\n",
       "      <td>41.903306</td>\n",
       "      <td>-87.763866</td>\n",
       "      <td>Austin</td>\n",
       "      <td>1.700378e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5218084</td>\n",
       "      <td>12/31/2006 10:55:00 PM</td>\n",
       "      <td>0453</td>\n",
       "      <td>BATTERY</td>\n",
       "      <td>AGGRAVATED PO: OTHER DANG WEAP</td>\n",
       "      <td>SIDEWALK</td>\n",
       "      <td>False</td>\n",
       "      <td>41.892682</td>\n",
       "      <td>-87.708912</td>\n",
       "      <td>Humboldt Park</td>\n",
       "      <td>1.250104e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5218442</td>\n",
       "      <td>12/31/2006 10:50:00 PM</td>\n",
       "      <td>0320</td>\n",
       "      <td>ROBBERY</td>\n",
       "      <td>STRONGARM - NO WEAPON</td>\n",
       "      <td>STREET</td>\n",
       "      <td>False</td>\n",
       "      <td>41.875194</td>\n",
       "      <td>-87.731223</td>\n",
       "      <td>Garfield Park</td>\n",
       "      <td>8.997607e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5217016</td>\n",
       "      <td>12/31/2006 09:17:20 PM</td>\n",
       "      <td>0281</td>\n",
       "      <td>CRIM SEXUAL ASSAULT</td>\n",
       "      <td>NON-AGGRAVATED</td>\n",
       "      <td>ALLEY</td>\n",
       "      <td>False</td>\n",
       "      <td>41.699589</td>\n",
       "      <td>-87.611116</td>\n",
       "      <td>Roseland</td>\n",
       "      <td>1.343137e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5227622</td>\n",
       "      <td>12/31/2006 08:59:00 PM</td>\n",
       "      <td>0496</td>\n",
       "      <td>BATTERY</td>\n",
       "      <td>AGGRAVATED DOMESTIC BATTERY: KNIFE/CUTTING INST</td>\n",
       "      <td>SIDEWALK</td>\n",
       "      <td>True</td>\n",
       "      <td>41.750707</td>\n",
       "      <td>-87.602741</td>\n",
       "      <td>Chatham</td>\n",
       "      <td>8.232067e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID                    Date  IUCR         Primary Type  \\\n",
       "0  5221564  12/31/2006 11:00:00 PM  041A              BATTERY   \n",
       "1  5218084  12/31/2006 10:55:00 PM  0453              BATTERY   \n",
       "2  5218442  12/31/2006 10:50:00 PM  0320              ROBBERY   \n",
       "3  5217016  12/31/2006 09:17:20 PM  0281  CRIM SEXUAL ASSAULT   \n",
       "4  5227622  12/31/2006 08:59:00 PM  0496              BATTERY   \n",
       "\n",
       "                                       Description Location Description  \\\n",
       "0                              AGGRAVATED: HANDGUN             SIDEWALK   \n",
       "1                   AGGRAVATED PO: OTHER DANG WEAP             SIDEWALK   \n",
       "2                            STRONGARM - NO WEAPON               STREET   \n",
       "3                                   NON-AGGRAVATED                ALLEY   \n",
       "4  AGGRAVATED DOMESTIC BATTERY: KNIFE/CUTTING INST             SIDEWALK   \n",
       "\n",
       "   Arrest   Latitude  Longitude Community Areas    Area(unit)  \n",
       "0   False  41.903306 -87.763866          Austin  1.700378e+08  \n",
       "1   False  41.892682 -87.708912   Humboldt Park  1.250104e+08  \n",
       "2   False  41.875194 -87.731223   Garfield Park  8.997607e+07  \n",
       "3   False  41.699589 -87.611116        Roseland  1.343137e+08  \n",
       "4    True  41.750707 -87.602741         Chatham  8.232067e+07  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list_name = []\n",
    "# list_area = []\n",
    "# for index, row in crimesAfter.iterrows():\n",
    "#     neighborhood = gps_to_neighborhood.find_neighborhood(row['Longitude'],/\n",
    "#                    row['Latitude'],all_neighborhoods)\n",
    "#     if neighborhood is not None:\n",
    "#         list_name.append(neighborhood[0])\n",
    "#         list_area.append(neighborhood[1])\n",
    "#     else:\n",
    "#         list_name.append('None')\n",
    "#         list_area.append('None')\n",
    "# crimesAfter['Community Areas'] = list_name\n",
    "# crimesAfter['Area(unit)'] = list_area\n",
    "# crimesAfter = crimesAfter[crimesAfter['Community Areas'] != 'None']\n",
    "\n",
    "# crimesAfter.to_csv('crimesAfter.csv', index=False, header=True)\n",
    "crimesAfter = pd.read_csv('CrimesAfter.csv')\n",
    "crimesAfter.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let us group the crime data by neighborhoods. This way we will be able to analyse the effectiveness of cameras by neighborhoods. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>Community Areas</th>\n",
       "      <th>2001-03</th>\n",
       "      <th>2004-06</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Englewood</td>\n",
       "      <td>6528</td>\n",
       "      <td>5644</td>\n",
       "      <td>-884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austin</td>\n",
       "      <td>5379</td>\n",
       "      <td>4891</td>\n",
       "      <td>-488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Humboldt Park</td>\n",
       "      <td>3955</td>\n",
       "      <td>3044</td>\n",
       "      <td>-911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Garfield Park</td>\n",
       "      <td>3754</td>\n",
       "      <td>3385</td>\n",
       "      <td>-369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Shore</td>\n",
       "      <td>2866</td>\n",
       "      <td>2691</td>\n",
       "      <td>-175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Community Areas  2001-03  2004-06  Difference\n",
       "0       Englewood     6528     5644        -884\n",
       "1          Austin     5379     4891        -488\n",
       "2   Humboldt Park     3955     3044        -911\n",
       "3   Garfield Park     3754     3385        -369\n",
       "4     South Shore     2866     2691        -175"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grouping by community areas before and after the cameras were installed.\n",
    "sumCrimesBefore = crimesBefore['Community Areas'].value_counts().\\\n",
    "rename_axis('Community Areas').reset_index(name='2001-03')\n",
    "sumCrimesAfter = crimesAfter['Community Areas'].value_counts().\\\n",
    "rename_axis('Community Areas').reset_index(name='2004-06')\n",
    "\n",
    "# We will use left merge to create a singular dataframe with columns \n",
    "# community areas, crimes in 2001-03, crimes in 2004-06 and the difference between the two. \n",
    "totalCrimes = sumCrimesBefore.merge(sumCrimesAfter, how = 'left')\n",
    "totalCrimes['Difference'] = totalCrimes['2004-06'] - totalCrimes['2001-03']\n",
    "\n",
    "totalCrimes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, let's do the same wrangling for the camera dataset. We will avoid analysing neighborhoods with less than 10 cameras. Reason being, less than 10 datapoints will not give us a clear idea. The number 10 was decided by manually trying different numbers are observing the bias."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Community Areas</th>\n",
       "      <th>Counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Austin</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Humboldt Park</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Englewood</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Garfield Park</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lower West Side</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Community Areas  Counts\n",
       "0           Austin      66\n",
       "1    Humboldt Park      45\n",
       "2        Englewood      39\n",
       "3    Garfield Park      35\n",
       "4  Lower West Side      29"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "above_10 = df_camera.groupby('Community Areas').filter(lambda x : len(x)>9)\n",
    "df_camera_above_10 = above_10['Community Areas'].value_counts().\\\n",
    "rename_axis('Community Areas').reset_index(name='Counts')\n",
    "df_camera_above_10.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To analyse the effects of the surveillance cameras, we first need to analyse how they were installed. Let us plot the crimes before the cameras were installed along with the location of the cameras. This will give a rich visualization and understanding of how the cameras were installed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Crimes in 2001-03 mapped along with the location of cameras')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#location = list(zip(crimesBefore['Latitude'], crimesBefore['Longitude']))\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.scatter(crimesBefore['Longitude'], crimesBefore['Latitude'], \\\n",
    "            s=0.02, color = 'k', label='Crimes')\n",
    "plt.plot(df_camera['Longitude'], df_camera['Latitude'], '.',\\\n",
    "         color='C1', label='Cameras')\n",
    "pylab.legend(loc='upper right')\n",
    "axes = plt.gca()\n",
    "axes.set_xlabel('Longitude')\n",
    "axes.set_ylabel('Latitude')\n",
    "pylab.title('Crimes in 2001-03 mapped along with the location of cameras')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From a cursory look, cameras were already installed in community areas where the crimes were maximum. We still need to prove this mathematically. Now let us create a dataset to see the relation between crimes in years 2001-03 in each community area, and cameras per square area installed in 2003. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of datapoints')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "buffer = crimesBefore.groupby('Community Areas').filter(lambda x : len(x)>0)\n",
    "crimesBefore_by_community = buffer['Community Areas'].value_counts().\\\n",
    "                            rename_axis('Community Areas').\\\n",
    "                            reset_index(name='CrimesCount')\n",
    "cameraCounts = []\n",
    "areas = []\n",
    "to_drop = []\n",
    "for index, row in crimesBefore_by_community.iterrows():\n",
    "    camCount = df_camera_above_10.loc[df_camera_above_10['Community Areas']\\\n",
    "                                      == row['Community Areas']]['Counts'].values\n",
    "    if len(camCount) == 1:    \n",
    "        area = df_camera.loc[df_camera['Community Areas'] == \\\n",
    "                             row['Community Areas']]['Area(unit)'].values\n",
    "        cameraCounts.append(camCount[0])\n",
    "        areas.append(area[0])\n",
    "    else:\n",
    "        to_drop.append(index)\n",
    "    \n",
    "crimesBefore_by_community.drop(crimesBefore_by_community.index[to_drop], inplace=True)\n",
    "assert(crimesBefore_by_community.shape[0] == len(cameraCounts))\n",
    "assert(crimesBefore_by_community.shape[0] == len(areas))\n",
    "crimesBefore_by_community['Area(unit)'] = areas\n",
    "crimesBefore_by_community['Cameras'] = cameraCounts\n",
    "crimesBefore_by_community['CamerasPerArea'] = (crimesBefore_by_community['Cameras'] \\\n",
    "                                               /crimesBefore_by_community['Area(unit)']) * 100000000\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(crimesBefore_by_community['CamerasPerArea'], crimesBefore_by_community['CrimesCount'])\n",
    "plt.xlabel('Cameras per sq. area')\n",
    "plt.ylabel('Number of Crimes')\n",
    "pylab.title('Distribution of datapoints')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we can run the linear regression analysis, we will need to take perform logarithmic transformation of the data as it is not linear. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:         CamerasPerArea   R-squared:                       0.010\n",
      "Model:                            OLS   Adj. R-squared:                 -0.032\n",
      "Method:                 Least Squares   F-statistic:                    0.2360\n",
      "Date:                Wed, 12 Jun 2019   Prob (F-statistic):              0.632\n",
      "Time:                        21:18:31   Log-Likelihood:                -97.145\n",
      "No. Observations:                  26   AIC:                             198.3\n",
      "Df Residuals:                      24   BIC:                             200.8\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "Intercept      14.6064     18.126      0.806      0.428     -22.804      52.017\n",
      "CrimesCount     2.7694      5.701      0.486      0.632      -8.996      14.535\n",
      "==============================================================================\n",
      "Omnibus:                        5.888   Durbin-Watson:                   1.519\n",
      "Prob(Omnibus):                  0.053   Jarque-Bera (JB):                2.338\n",
      "Skew:                           0.394   Prob(JB):                        0.311\n",
      "Kurtosis:                       1.761   Cond. No.                         30.5\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "crimesBefore_by_community['CrimesCount'] = \\\n",
    "np.log10(crimesBefore_by_community['CrimesCount'])\n",
    "\n",
    "outcome, predictors = patsy.dmatrices('CamerasPerArea ~\\\n",
    "CrimesCount', crimesBefore_by_community)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above regression analysis states that for a increase of 100 crimes in a neighborhood, approximately 3 cameras are added per square area. This proves that the cameras were infact installed in neighborhoods with high rates of violent crime. This analysis will come in handy while trying to explain the results of further analysis on direct relation between cameras per square area and decrease in crime. Please refer to end of this section for further interpretation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let us analyse the direct effects of the camera. We have information for each neighborhood about the numbers of crimes that occured, the number of cameras, the square area, and the difference in violent crimes between 2004-06 and 2001-03.\n",
    "We use this to check if any relation exists between the number of cameras in square area of a neighborhood and the change in crime. We try to find a linear relation between the two variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Community</th>\n",
       "      <th>CamerasPerArea</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Austin</td>\n",
       "      <td>3.881491</td>\n",
       "      <td>-488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Humboldt Park</td>\n",
       "      <td>3.599700</td>\n",
       "      <td>-911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Englewood</td>\n",
       "      <td>2.246544</td>\n",
       "      <td>-884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Garfield Park</td>\n",
       "      <td>3.889923</td>\n",
       "      <td>-369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lower West Side</td>\n",
       "      <td>3.556069</td>\n",
       "      <td>-295</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Community  CamerasPerArea Difference\n",
       "0           Austin        3.881491       -488\n",
       "1    Humboldt Park        3.599700       -911\n",
       "2        Englewood        2.246544       -884\n",
       "3    Garfield Park        3.889923       -369\n",
       "4  Lower West Side        3.556069       -295"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_analysis = pd.DataFrame(columns=['Community', 'CamerasPerArea', 'Difference'])\n",
    "\n",
    "for index, row in df_camera_above_10.iterrows():\n",
    "    community = row['Community Areas']\n",
    "    camera_count = row['Counts']\n",
    "    # Getting area and difference in crimes for each neighborhood with above 10 cameras\n",
    "    area = df_camera[df_camera['Community Areas'] == \\\n",
    "                     community].iloc[0]['Area(unit)']\n",
    "    diff = totalCrimes[totalCrimes['Community Areas'] == \\\n",
    "                       community].iloc[0]['Difference']\n",
    "    df_analysis = df_analysis.append({'Community' : community , \\\n",
    "                                      'CamerasPerArea' : camera_count/(area/10000000), \n",
    "                                      'Difference' : diff } , ignore_index=True)\n",
    "    \n",
    "df_analysis.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x217a148eba8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "slope, intercept, r_value, p_value, std_err = \\\n",
    "stats.linregress(df_analysis['CamerasPerArea'].tolist(),\\\n",
    "                 df_analysis['Difference'].tolist())\n",
    "\n",
    "line = slope*(df_analysis['CamerasPerArea'].values) + intercept\n",
    "\n",
    "plt.figure(figsize=(12, 7))\n",
    "ax = plt.axes()\n",
    "plt.xlabel('CamerasPerArea')\n",
    "plt.ylabel('Difference between 2004-06 and 2001-03')\n",
    "plt.plot(df_analysis['CamerasPerArea'].tolist(),\\\n",
    "         df_analysis['Difference'].tolist(),'o',\\\n",
    "         label = 'Original data')\n",
    "plt.plot(df_analysis['CamerasPerArea'].tolist(), \\\n",
    "         line, label = 'Linear fit')\n",
    "pylab.title('Relation between Cameras/Area and difference in crime between 2004-06 and 2001-03')\n",
    "pylab.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The overall trend indicates a decrease in crime when the cameras per square area increases, but it is neither substanial nor conclusive. Because there are a number of outliers in the data. And as we will see below, the decrease in violent crime is no more than the general trend before the cameras were installed. Below is a plot of decrease in crime each year between 2001-10. The orange line is based on the slope of figures prior to the installation of the cameras.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Violent crimes each year from 2001-10')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "crimesYearwise = []\n",
    "for i in range(2001,2011):\n",
    "    crimesYearwise.append(crimes[(crimes[\"Date\"].str[6:10] == str(i))].shape[0])\n",
    "    \n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(range(2001,2004), crimesYearwise[:3])\n",
    "line = slope*(range(2001,2011)) + intercept\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(range(2001,2011),crimesYearwise,  label = 'Original data')\n",
    "plt.plot(range(2001,2011), line, label = 'Line based on 2001-03 trend')\n",
    "pylab.legend(loc='upper right')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Total violent crime')\n",
    "pylab.title('Violent crimes each year from 2001-10')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is minor decrease in the rate of change between 2002-04. But in subsequent years, the rate of change decreased to a mere minimal, and the rate of decrease between the years 2001-03 did not sustain. In 2010, the figure was much higher than what it should have been if the rate of change prior to installation of camera had sustained. From our earlier analysis on location of the cameras, we can conclude that like installing surveillance cameras, there were other targetted efforts to reduce violent crime that resulted in an overall reduction in violent crime in years 2001-10, but there is no conclusive evidence to prove that the cameras were effective. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The datasets that we are using for this project are available for public use, and there are no restrictions in using them. Our data usage will be split in two. The first part of the analysis is parsing both the locations of the crimes and the cameras using longitude and latitude values. The second part will be cross-referencing the longitudinal and latitudinal coordinates to an existing community area and analyzing the number of violent crimes in that area as well as how many cameras exist in that area. We will focus only on statistically relevant information; this includes the type of crimes that are deemed ‘violent’ according to Illinois law. One consideration is that the parsing of the long/lat lines to determine a community area may reveal specific addresses of its residents. However, as this information is only as detailed as a block within the neighborhood, we have concluded that this will not infringe upon any individual’s privacy. Further, we deemed ZIP codes unnecessary to our analysis, so we dropped them from our dataset. Otherwise, the original dataset otherwise did not contain any personally identifying data.\n",
    "\n",
    "Besides privacy concerns, we will also take into account biased data collection and reporting. The data collection is highly unlikely to be biased as it was collected by the Chicago Police Department’s CLEAR system which adds data to its database based on police reports. (This itself is based on the assumption that crimes are reported with equal frequency across all areas, which we think is a safe assumption to make in our analysis.) The reporting of our findings may be skewed as we have prior knowledge that the cameras were installed in areas with a higher rate of crime; however, since these cameras were billed as a deterrent to crime, analyzing their effectiveness in the worst areas is still very informative.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our project, we decided to examine whether installing surveillance cameras had an effect on the rate of change of violent crime in certain neighborhoods in the city of Chicago. We collected two different datasets: crime and camera. The crime data contained all the crime reports in Chicago from 2001 to the present. In this dataset, we focused mainly on the Date, IUCR, Primary Type, Location Description, Latitude, Longitude, Location, and Community Area columns. We selected these columns because we were only interested in the type, location and the date of the crime. The camera dataset contained the various cameras that were installed in Chicago by the Chicago Police Department’s camera program, Police Observation Devices (PODs). For this dataset, we were interested in the location of the cameras. To examine our data, we conducted two types of analysis. We first compared the number of cameras in a community area to the rate of violent crime in that area. We compared this to the unit area of the community area because each community area had a different number of cameras installed. The second analysis that we did was to compare the rates of violent crime before and after the cameras were installed in a specific area. With this analysis, we discovered whether the rate of violent crime increased or decreased as a result of the camera installation.\n",
    "\n",
    "Through our analysis, **we found that there was no substantial relationship between the rate of violent crimes and the installation of surveillance cameras**. When plotting the data, we found that there were many outliers. Because of this, we could not conclude a definite relationship between the two. One limitation to our project is that we only analyzed the relationship for six years. We also compared the general trend from years 2001-03 with the real data of 2001-10. We found that violent crimes in 2010 was way higher than anticipation based on trend prior to installation of the cameras. \n",
    "\n",
    "Further analysis could look at whether there was any noticeable change on the rate for smaller, non-violent crimes which were dropped from our analysis, for completeness. If little change occurred within the small crimes dataset as well, then we could more definitively make a statement as to the usefulness of cameras for reducing crimes as a whole. Another intriguing possibility would be to run an analysis on whether the proliferation of these cameras assisted in increasing the amount of evidence brought in court for these crimes. If having extra cameras meant more crimes were visibly documented and could be more successfully prosecuted, then they may still be useful. Lastly, another stream of thought can be to analyse if installation of cameras pushed more crime into areas with less camera coverage. \n",
    "\n",
    "In the meantime, this project could be useful in informing efforts for funding public safety. Evidence from analysis like the one conducted here could inform willing public servants to divert money towards developing more useful methods for violent crime reduction. Further, this analysis would allow neighborhoods with great numbers of surveillance cameras to petition for the removal of this intrusion into their public lives."
   ]
  }
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