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A Spatial Analysis of Predictors of Different Types of Crime in Chicago Community Areas

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.personal.psu.edu/zul112/.

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A Spatial Analysis of Predictors of Different Types of Crime in Chicago Community Areas

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  1. ASpatial 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.personal.psu.edu/zul112/ Source: http://www.kgarner.com/blog/archives/2011/08/26/photo-238-chicago-skyline/

  2. Outline • Background • Goals and Objectives • Proposed Methodology • Work Completed • Hypothesis • Timeline

  3. 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/

  4. Literature Review • Spatial crime studies increasingly popular • Origins date back to 1820s(France) • Data and methods have evolved

  5. Chicago Studies • Focused on 5 studies from 1990-2009 • All regression or modeling techniques • Numerous standard outcome and predictor variables

  6. 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

  7. 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

  8. Variables Predictor Variables • Outcome Variables *rate per 100,000 people Source: http://www.ucrdatatool.gov/

  9. Time Frame and Unit of • Analysis • 2007-2011 • 77 Chicago Community Areas

  10. Methodology • Step 1: Collect the data • Step 2: Manipulate data • Step 3: Analyze manipulated data

  11. 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-american-community-survey/

  12. 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

  13. Preliminary Analysis • Map each outcome and predictor variable by Community Area • Visually identify patterns and irregularities • Descriptive analysis-mean, standard deviation, min, and max

  14. Preliminary Analysis Outcome Variable Maps

  15. Preliminary Analysis Outcome Variable Maps

  16. Preliminary Analysis Predictor Variable Maps

  17. Preliminary Analysis Predictor Variable Maps

  18. 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 .

  19. Hypotheses • Affect of surrounding neighborhoods will be strong • Percent of vacant housing will have the most influence on total crime rate

  20. Limitations • Small number of observations for the unit of analysis (77) • ACS is an estimate

  21. Timeline • Winter 2014-Perform more advanced analysis on data and finish paper • Spring-2014-Present at ILGISA Regional Conference

  22. Acknowledgements • Advisor-Stephen A. Matthews • Geography 586 Instructor-David O’Sullivan • Capstone Workshop-Pat Kennelly • Overall Guidance-Doug Miller and Beth King

  23. 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 Studies13:3, 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 Sciences105:3, 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.

  24. Questions Brett Beardsley brett.a.beardsley@gmail.com

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