A Spatial Analysis of Predictors of Different Types of Crime in Chicago Community Areas Brett Beardsley Pennsylvania State University MGIS Candidate Geog 596A 12/19/13 Stephen A. Matthews Faculty Advisor Source: http://www.kgarner.com/blog/archives/2011/08/26/photo-238-chicago-skyline/ Source: http://www.personal.psu.edu/zul112/ Outline • Background • Goals and Objectives • Proposed Methodology • Work Completed • Hypothesis • Timeline Background • Chicago is the 3rd Largest City in the United States with 2.7 million people Source: http://marshallmashup.usc.edu/taking-part-in-uscs-most-respected-rivalry-the-notredame-game/ • Much higher rates of crime than New York City and Los Angeles Source: http://www.chicagoclout.com/weblog/archives/2008/04/ Literature Review • Spatial crime studies increasingly popular • Origins date back to 1820s(France) • Data and methods have evolved Chicago Studies • Focused on 5 studies from 1990-2009 • All regression or modeling techniques • Numerous standard outcome and predictor variables Previous Studies’ Conclusions • Surrounding areas have an effect on one another (i.e., Spatial dependence matters) • Traditional indicators of crime ring true (e.g. unemployment, poverty, population density) • Not every variation can be explained Goals and Objectives • Analyze homicide, aggravated assault, criminal sexual assault, robbery, burglary, motor vehicle theft, larceny/theft, and arson within the 77 Community Areas in Chicago from 2007 to 2011 • Identify most influential factors of crime in Chicago Community Areas • Identify common themes in high crime areas • Identify how strong of an affect surrounding community areas have on one another Variables • Outcome Variables *rate per 100,000 people Source: http://www.ucrdatatool.gov/ • Predictor Variables Time Frame and Unit of Analysis • 2007-2011 • 77 Chicago Community Areas Methodology • Step 1: Collect the data • Step 2: Manipulate data • Step 3: Analyze manipulated data Data Collection • Crime data came from the Chicago Police Department • Retrieved some ready to use predictor variable data from the Chicago Data Portal • Most of the predictor variables came from the 5 year (2007-2011) American Community Survey (ACS) Source: https://portal.chicagopolice.org/portal/page/portal/ClearPath Source: https://data.cityofchicago.org/ Source: http://njplanning.org/position-statements/take-action-now-support-the-americancommunity-survey/ Data Manipulation • Combined all crime data over the 5 year span • Determined which attributes I needed from ACS data • Create centroids for each Community Area • Assigned 805 modified ACS tracts a Community Area name and number based on location to centroids • Dissolve ACS tracts by Community Area name and number and compiled statistics for each • Spatially joined 805 modified ACS tracts to 77 Community Area Centroids based on Community Area name and number • Finally did field calculations for percentages and means Preliminary Analysis • Map each outcome and predictor variable by Community Area • Visually identify patterns and irregularities • Descriptive analysis-mean, standard deviation, min, and max Preliminary Analysis Outcome Variable Maps Preliminary Analysis Outcome Variable Maps Preliminary Analysis Predictor Variable Maps Preliminary Analysis Predictor Variable Maps Further Analysis • As shown there is likely spatial autocorrelation within both the outcome and predictor variables and correlation between them. Calculate Moran's I (global) and LISA (local) spatial autocorrelation/dependence measures • Create spatial weights matrices in GeoDa • Run Ordinary Least Squares (OLS) regression models using spatial weights matrices on all crimes, violent crimes, property crimes, and finally each individual crime. • Check model assumptions and regression diagnostics • As necessary run spatial lag or spatial error models . Hypotheses • Affect of surrounding neighborhoods will be strong • Percent of vacant housing will have the most influence on total crime rate Limitations • Small number of observations for the unit of analysis (77) • ACS is an estimate Timeline • Winter 2014-Perform more advanced analysis on data and finish paper • Spring-2014-Present at ILGISA Regional Conference Acknowledgements • Advisor-Stephen A. Matthews • Geography 586 Instructor-David O’Sullivan • Capstone Workshop-Pat Kennelly • Overall Guidance-Doug Miller and Beth King References • Arnio, A. N. & Baumer, E. P. (2012). 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Questions Brett Beardsley brett.a.beardsley@gmail.com