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/
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Outline
• Background
• Goals and Objectives
• Proposed Methodology
• Work Completed
• Hypothesis
• Timeline
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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/
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Literature Review
• Spatial crime studies increasingly popular
• Origins date back to 1820s(France)
• Data and methods have evolved
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Chicago Studies
• Focused on 5 studies from 1990-2009
• All regression or modeling techniques
• Numerous standard outcome and predictor variables
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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
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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
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Variables• Outcome Variables • Predictor Variables
*rate per 100,000 people
Source: http://www.ucrdatatool.gov/
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Time Frame and Unit ofAnalysis
• 2007-2011
• 77 Chicago Community Areas
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Methodology
• Step 1: Collect the data
• Step 2: Manipulate data
• Step 3: Analyze manipulated data
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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
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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
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Preliminary Analysis
• Map each outcome and predictor variable by Community Area
• Visually identify patterns and irregularities
• Descriptive analysis-mean, standard deviation, min, and max
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Preliminary Analysis Outcome Variable Maps
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Preliminary Analysis Outcome Variable Maps
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Preliminary Analysis Predictor Variable Maps
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Preliminary Analysis Predictor Variable Maps
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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 .
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Hypotheses
• Affect of surrounding neighborhoods will be strong
• Percent of vacant housing will have the most influence on total crime rate
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Limitations
• Small number of observations for the unit of analysis (77)
• ACS is an estimate
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Timeline
• Winter 2014-Perform more advanced analysis on data and finish paper
• Spring-2014-Present at ILGISA Regional Conference
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Acknowledgements• Advisor-Stephen A. Matthews
• Geography 586 Instructor-David O’Sullivan
• Capstone Workshop-Pat Kennelly
• Overall Guidance-Doug Miller and Beth King
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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.
• Boggs, S. (1965). Urban Crime Patterns. American Sociological Review 30:6. 899-908.
• Bowers, K. & Hirschfield, A. (1999). Exploring links between crime and disadvantage in north-west England: an analysis using geographical information systems. International Journal of Geographical Information Science 13:2. 159-184.
• Ceccato, V. (2005). Homicide in Sao Paulo, Brazil: Assessing spatial-temporal and weather variations. Journal of Environmental Psychology 25:3, 307-321.
• Earls, F., Morenoff, J.D, & Sampson, R.J. (1999). Beyond Social Capital: Spatial Dynamics of Collective Efficacy for Children. American Sociological Review 64:5, 633-660.
• Graif, C. & Sampson, R. J. (2009). Spatial Heterogeneity in the Effects of Immigration and Diversity on Neighborhood Homicide Rates. Homicide Studies 13:3, 242-260.
• Matthews, S.A., Yang T-C., Hayslett, K.L., & Ruback, R.B. (2010). Built environment and property crime in Seattle, 1998-2000: a Bayesian analysis. Environment and Planning 42:6, 1403-1420.
• Morenoff, J.D. (2003). Neighborhood Mechanisms and the Spatial Dynamics of Birth Weight. American Journal of Sociology 108:5, 976-1017.
• Raudenbush, S.W., Sampson, R.J., & Sharkey, P. (2008). Durable effects of concentrated disadvantage on verbal ability among African-American children. Proceedings of the National Academy of Sciences 105:3, 845-852.
• Shaw, C.R. (1929). Delinquency Areas. Chicago: University of Chicago Press.
• White, R.C. (1932). The Relation to Felonies to Environmental Factors in Indianapolis. Social Forces 10:4, 498-509.