a spatial analysis of predictors of different types of crime in chicago community areas brett...
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A Spatial Analysis of Predictors of Different Types of Crime in Chicago Community
Areas
Brett BeardsleyPennsylvania State University MGIS Candidate
Geog 596A12/19/13
Stephen A. MatthewsFaculty Advisor
Source: http://www.personal.psu.edu/zul112/Source: http://www.kgarner.com/blog/archives/2011/08/26/photo-238-chicago-skyline/
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
• Much higher rates of crime than New York City and Los Angeles
Source: http://marshallmashup.usc.edu/taking-part-in-uscs-most-respected-rivalry-the-notre-dame-game/
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 • Predictor Variables
*rate per 100,000 people
Source: http://www.ucrdatatool.gov/
Time Frame and Unit ofAnalysis
• 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:
http://njplanning.org/position-statements/take-action-now-support-the-american-community-survey/
Source: https://data.cityofchicago.org/
Source: https://portal.chicagopolice.org/portal/page/portal/ClearPath
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). Demography, foreclosure, and crime: Assessing spatial heterogeneity in contemporary models of neighborhood crime rates.
Demographic Research 26:18, 449-488.
• Berg, M.T., Brunson, R.K., Stewart, E.A., & Simons, R.L (2011). Neighborhood Cultural Heterogeneity and Adolescent Violence. Journal of Quantitative Criminology 28, 411-435.
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• Morenoff, J.D. (2003). Neighborhood Mechanisms and the Spatial Dynamics of Birth Weight. American Journal of Sociology 108:5, 976-1017.
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