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Vulnerability and Exposure to Crime: Applying Risk Terrain Modeling to the Study of Assault in Chicago

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Abstract

Prior research has applied risk assessment and spatial analysis techniques to the study of violence. This paper builds on those results, tying the practical outcomes of spatial risk analysis methods to broader spatial issues on the articulation of risky places for aggravated assault. We begin by conceptualizing key relationships, addressing the effects of environmental factors on creating distinct, identifiable areas that are conducive to crime. Propositions of the theory of risky places are posed and then empirically tested using a GIS based program, RTMDx, on aggravated assault data in an urban area. Given the current thinking about crime vulnerability based on concentration and spatial influence of features and events, this paper offers an analytical strategy to model risky places that combines the conceptual insights of crime emergence and persistence, advances in geo-spatial analytical techniques, and micro-level data.

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Notes

  1. The layers were not mutually exclusive. However, spatial influence of the convenience store gas stations may be different from the overall dataset, so it made sense to test them on their own.

  2. 14 potential risk factors were tested for spatial influences of both distance and/or density, up to three blocks, whole increments. Nine potential risk factors were tested for spatial influences only as a function of distance. [See page 28 of Caplan et al. (2013b) for a detailed explanation as to why these parameters were chosen.]

  3. (14 factors * 2 operationalizations * 3 blocks) + (9 factors * 1 operationalization * 3 blocks) = 111 variables.

  4. Created by rescaling the grid cell values between the minimum and maximum values.

  5. Results from a Poisson goodness-of-fit test confirmed that the 2012 aggravated assault incident locations (the dependent variable) follow a negative binomial distribution (Pearson gof = 53187.49, p < 0.001).

  6. Moran’s I was 0.24 and was significant at p < 0.001. The Moran’s I analysis was conducted within the Geoda spatial analysis software. Geoda was also used to generate the spatial lag variable (first order Queen Continuity).

  7. The IRR compares to a baseline of zero, in RTM, risk scores have a baseline of 1. So, we subtracted 1 from the mean prior to calculating the interpretation statistics.

  8. For example: Given the expected occurrence of near repeat incidents in Chicago within a certain distance (i.e., 426 ft) and period of time (i.e. one week) from new crime incidents, an analytical strategy for prioritizing new crime incidents could be to evaluate each new crime incident for its propensity to become an instigator for near repeats – based upon the proportion of high-risk places within the expected near repeat bandwidth. Imagine, for instance, a crime analyst plotting a new crime incident on a risk terrain map. She then draws a buffer around it equal to the expected near repeat bandwidth to isolate all the micro-level places within the buffer. Then she identifies all the places within the buffer that are exceptionally high risk, based on the risk terrain model. Now she can advise commanders about the proportion of high-risk places within the buffer (e.g., 87 out of 237 = 37 %). This can inform decisions about whether to allocate resources to that crime’s buffer area given the vulnerable places within it for near repeat crimes over the next 7-day period. This resource allocation decision could be made in consideration of all other recent crime incidents that have occurred in the jurisdiction so that priority can be given to those areas with the greatest propensity for new crimes to emerge in the near future. Although a risk terrain may be time-stable once it is produced (unless actions are taken to affect spatial vulnerabilities), places within the terrain can be very dynamic as each new crime incident creates exposures that subsequently alter the spatial dynamics of crime.

References

  • Arlot, S., & Celisse, A. (2010). A survey of cross-validation procedures for model selection. Statistics Surveys, 4, 40–79.

    Article  Google Scholar 

  • Barnum, J. D., Caplan, J. M., Kennedy, L. W., & Piza, E. L. (2013). Testing the cross jurisdictional spatial influence of attractors and generators of crime in the urban environment. Presentation at the American Society of Criminology 2013 Annual Meeting, Atlanta, Georgia.

  • Bernasco, W., & Block, R. (2011). Robberies in Chicago: a block-level analysis of the influence of crime generators, crime attractors, and offender anchor points. Journal of Research in Crime and Delinquency, 48(1), 33–57.

    Article  Google Scholar 

  • Block, R., & Block, C. (1995). Space, place, and crime: Hotspot areas and hot places of liquor related crime. In J. Eck & D. Weisburd (Eds.), Crime and place (Crime prevention studies, Vol. 4, pp. 145–184). Monsey: Criminal Justice Press.

    Google Scholar 

  • Bowers, K. J., & Johnson, S. D. (2005). Domestic burglary repeats and space-time clusters: the dimensions of risk. European Journal of Criminology, 2(1), 67–92.

    Article  Google Scholar 

  • Braga, A., & Clarke, R. V. (2014). Explaining high-risk concentrations of crime in the city: social disorganization, crime opportunities, and important next steps. Journal of Research in Crime and Delinquency (Jan), online first.

  • Braga, A. A., Pierce, G. L., McDevitt, J., Bond, B. J., & Cronin, S. (2008). The strategic prevention of gun violence among gang-involved offenders. Justice Quarterly, 25(1), 132–162.

  • Braga, A., Papachristos, A., & Hureau, D. (2014). The effects of hot spots policing on crime: an updated systematic review and meta analysis. Justice Quarterly, 31(4), 633-663.

  • Brantingham, P., & Brantingham, P. (1981). Environmental criminology. Beverly Hills: Sage Publications.

    Google Scholar 

  • Brantingham, P., & Brantingham, P. (1995). Criminality of place: crime generators and crime attractors. European Journal on Criminal Policy and Research, 3, 1–26.

    Article  Google Scholar 

  • Bursik, R., & Grasmick, H. G. (1993). Neighborhoods and crime. San Francisco: Jossey Bass.

    Google Scholar 

  • Caplan, J. M. (2011). Mapping the spatial influence of crime correlates: a comparison of operationalization schemes and implications for crime analysis and CJ practice. Cityscape, 13(3), 57–83.

    Google Scholar 

  • Caplan, J. M., & Kennedy, L. W. (2010). Risk terrain modeling manual: Theoretical framework and technical steps of spatial risk assessment. Newark, NJ: Rutgers Center on Public Security. [available at www.riskterrainmodeling.com].

  • Caplan, J. M., Kennedy, L. W., & Miller, J. (2011). Risk terrain modeling: brokering criminological theory and GIS methods for crime forecasting. Justice Quarterly, 28(2), 360–381.

    Article  Google Scholar 

  • Caplan, J. M., Kennedy, L. W., & Baughman, J. (2012). Kansas City’s violent crime initiative. Crime Mapping, 4(2), 9–37.

    Google Scholar 

  • Caplan, J., Kennedy, L. W., & Piza, E. (2013a). Joint utility of event-dependent and environmental crime analysis techniques for violent crime forecasting. Crime and Delinquency, 59(2), 243–270.

    Article  Google Scholar 

  • Caplan, J. M., Kennedy, L. W., & Piza, E. (2013b). Risk terrain modeling diagnostic user manual (Version 1.0). Newark: Rutgers Center on Public Security.

    Google Scholar 

  • Chicago Police Department. Crime type categories: Definition & description. Retrieved on December 19, 2014 from http://gis.chicagopolice.org/clearmap_crime_sums/crime_types.html#N04A.

  • City of Chicago (2014). The city of Chicago’s official site. Retrieved on September 17, 2014 from http://www.cityofchicago.org/city/en/narr/foia/CityData.html.

  • Drawve, G. (2014). A metric comparison of predictive hot spot techniques and RTM. Justice Quarterly, (ahead-of-print), 1–29.

  • Dugato, M. (2013). Assessing the validity of risk terrain modeling in a European city: preventing robberies in the city of Milan. Crime Mapping: A Journal of Research and Practice, 5(1), 63–89.

    Google Scholar 

  • Eck, J. E., Chainey, S., Cameron, J. G., Leitner, L., & Wilson, R. E. (2005). Mapping crime: Understanding hotspots. Washington: National Institute of Justice.

    Google Scholar 

  • Eck, J., Clarke, R., & Guerette, R. (2007). Ricky facilities: crime concentration in homogeneous sets of establishments and facilities. Crime Prevention Studies, 21, 225–264.

    Google Scholar 

  • Freundschuh, S., & Egenhofer, M. (1997). Human conceptions of spaces: implications for geographic information systems. Transactions in GIS, 2(4), 361–375.

    Article  Google Scholar 

  • Golledge, R. G., & Stimson, R. J. (1997). Spatial behavior: A geographic perspective. N.Y.: Guildford Press.

    Google Scholar 

  • Groff, E. R., & La Vigne, N. G. (2002). Forecasting the future of predictive crime mapping. Crime Prevention Studies, 13, 29–58.

    Google Scholar 

  • Guerette, R. T., & Bowers, K. J. (2009). Assessing the extent of crime displacement and diffusion of benefits: a review of situational crime prevention evaluations. Criminology, 47, 1331–1368.

    Article  Google Scholar 

  • Harries, K. (1999). Mapping crime: Principle and practice. Washington: U.S. Department of Justice, Office of Justice Programs.

    Google Scholar 

  • Heffner, J. (2013). Statistics of the RTMDx utility. In J. Caplan, L. W. Kennedy, & E. Piza (Eds.), Risk Terrain Modeling Diagnostics (RTMDx) utility user manual (Version 1) (pp. 26–34). Newark: Rutgers Center on Public Security.

    Google Scholar 

  • Hunter, R., & Jeffery, C. (1997). Preventing convenience store robbery through environmental design. In R. Clarke (Ed.), Situational crime prevention, successful case studies, second edition. Monsey: Criminal Justice Press.

    Google Scholar 

  • Irvin-Erickson, Y. (2015). Identifying risky places for crime: An analysis of the criminogenic spatiotemporal influences of landscape features on street robberies. Washington: Department of Justice.

    Google Scholar 

  • Kennedy, L. W., & Caplan, J. M. (2013). A theory of risky places. Research brief. Newark: Rutgers Center on Public Security.

    Google Scholar 

  • Kennedy, L. W., & Van Brunschot, E. (2009). The risk in crime. Lantham: Rowman and Littlefield.

    Google Scholar 

  • Kennedy, L. W., Caplan, J. M., & Piza, E. L. (2011). Risk clusters, hotspots, and spatial intelligence: risk terrain modeling as an algorithm for police resource allocation strategies. Journal of Quantitative Criminology, 28(2), 360–381.

    Google Scholar 

  • Koper, C. (2014). Assessing the practice of hot spots policing: survey results from a National Convenience Sample of Local Police Agencies Journal of Contemporary Criminal Justice1043986214525079, first published on March 12, 2014.

  • Lawson, A., Biggeri, A., Böhning, D., Lesaffre, E., Viel, J. F., & Bertollini, R. (1999). Disease mapping and risk assessment for public health. Wiley.

  • Lim, S. S., Vos, T., Flaxman, A. D., Danaei, G., Shibuya, K., Adair-Rohani, H., & Davis, A. (2013). A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. The Lancet, 380(9859), 2224–2260.

    Article  Google Scholar 

  • Lum, C., & Koper, C. S. (2013). Evidence-based policing in smaller agencies: challenges, prospects, and opportunities. The Police Chief 80 (April): 42–47.

  • Madensen, T. D., & Eck, J. E. (2008). Spectator violence in stadiums. Washington: Office of Community Oriented Policing Services, U.S. Department of Justice.

    Google Scholar 

  • Maguire, K. (Ed.). (2007). Sourcebook of criminal justice statistics. Retrieved from http://www.albany.edu/sourcebook/pdf/t5452004.pdf.

  • Martinez, R., Jr., Rosenfeld, R., & Mares, D. (2008). Social disorganization, drug market activity, and neighborhood violent crime. Urban Affairs Review, 43(6), 846–874.

    Article  Google Scholar 

  • Mastrofski, S. D., Weisburd, D., & Braga, A. A. (2010). Rethinking policing: The policy implications of hotspots of crime. In N. A. Frost, J. D. Freilich, & T. R. Clear (Eds.), Contemporary issues in criminal justice policy: Policy proposals from the American Society of Criminology Conference (pp. 251–264). Belmont: Wadsworth.

    Google Scholar 

  • Mayhew, H. (1861). London labour and the London poor. NY: Dover (republished in 1968).

  • Moreto, W. D., Piza, E. L., & Caplan, J. M. (2013). ‘A plague on both your houses?’: risks, repeats and reconsiderations of urban residential burglary. Justice Quarterly. doi:10.1080/07418825.2012.754921.

    Google Scholar 

  • Office of National Drug Control Policy. (2000). Drug-related crime fact sheet. Washington: Office of National Drug Control Policy.

    Google Scholar 

  • Park, R. E., McKenzie, R. D., & Burgess, E. (1925). The city: Suggestions for the study of human nature in the urban environment. Chicago: University of Chicago Press.

    Google Scholar 

  • Pridemore, W. A., & Grubesic, T. (2013). Alcohol outlets and community levels of interpersonal violence: spatial density, outlet type, and seriousness of assault. Journal of Research in Crime and Delinquency, 50(1), 132–159.

    Article  Google Scholar 

  • Rand, M. R. (2008). Criminal victimization, 2007. Washington: U.S. Department of Justice, Bureau of Justice Statistics.

    Book  Google Scholar 

  • Ratcliffe, J. (2009). Near Repeat Calculator (version 1.3). Temple University, Philadelphia, PA and the National Institute of Justice, Washington, DC.

  • Ratcliffe, J. H., & Rengert, G. (2008). Near repeat patterns in Philadelphia shootings. Security Journal, 21(1–2), 58–76.

    Article  Google Scholar 

  • Rengert, G., Ratcliffe, J., & Chakravorty, S. (2005). Policing illegal drug markets: Geographic approaches to crime reduction. Monsey: Criminal Justice Press.

    Google Scholar 

  • Roncek, D. W., & Faggiani, D. (1985). High schools and crime. The Sociological Quarterly, 26, 491–505.

    Article  Google Scholar 

  • Scott, M. S., & Dedel, K. (2006). Assault in and around bars (2nd ed.). Washington: Office of Community Oriented Policing Services, U.S. Department of Justice.

    Google Scholar 

  • Shaw, C., & McKay, H. (1969). Juvenile delinquency and urban areas. Chicago: University of Chicago Press.

    Google Scholar 

  • Sherman, L., & Rogan, D. (1995). Deterrent effects of police raids on crack houses: a randomized controlled experiment. Justice Quarterly, 12, 755–782.

    Article  Google Scholar 

  • Sherman, L. W., Gartin, P. R., & Buerger, M. E. (1989). Hotspots of predatory crime: routine activities and the criminology of place. Criminology, 27, 27–55.

    Article  Google Scholar 

  • Silver, N. (2012). The signal and the noise: Why so many predictions fail but some don’t. N.Y.: Penguin.

    Google Scholar 

  • Snowden, A. J., & Pridemore, W. A. (2013). Alcohol and violence in a nonmetropolitan college town: alcohol outlet density, outlet type and assault. Journal of Drug Issues, 43(3), 357–373.

    Article  Google Scholar 

  • Stucky, T. D., & Ottensmann, J. R. (2009). Land use and violent crime. Criminology, 47(4), 1223–1264.

    Article  Google Scholar 

  • Taylor, R. B. (1988). Human territorial functioning. New York: University of Cambridge Press.

    Book  Google Scholar 

  • Taylor, R. B., & Harrell, A. V. (1996). Physical environment and crime. Washington: National Institute of Justice.

    Google Scholar 

  • Tobler, W. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46(2), 234–240.

    Article  Google Scholar 

  • Tomlin, C. D. (1994). Map algebra: one perspective. Landscape and Urban Planning, 30, 3–12.

    Article  Google Scholar 

  • U.S. Department of Justice, National Gang Intelligence Center. (2009). National gang threat assessment. Washington: U.S. Government Printing Office.

    Google Scholar 

  • Weisburd, D., Wyckoff, L., Ready, J., Eck, J., Hinkle, J., & Gajewski, F. (2006). Does crime just move around the corner? A controlled study of spatial displacement and diffusion of crime control benefits. Criminology, 44, 549–592.

    Article  Google Scholar 

  • Weisburd, D., Morris, N., & Groff, E. (2009). Hot spots of juvenile crime: a longitudinal study of arrest incidents at street segments in Seattle, Washington. Journal of Quantitative Criminology, 25, 443–467.

    Article  Google Scholar 

  • Weisburd, D., Groff, E., & Yang, S. (2012). The criminology of place: Street segments and our understanding of the crime problem. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Yerxa, M. (2013). Evaluating the temporal parameters of risk terrain modeling with residential burglary. Crime Mapping, 5(1), 7–38.

    Google Scholar 

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Authors and Affiliations

  1. Rutgers Center on Public Security, Rutgers University School of Criminal Justice, Newark, NJ, USA

    Leslie W. Kennedy, Joel M. Caplan & Henri Buccine-Schraeder

  2. Department of Law and Police Science, John Jay College of Criminal Justice, New York, NY, USA

    Eric L. Piza

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  1. Leslie W. Kennedy
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  2. Joel M. Caplan
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  4. Henri Buccine-Schraeder
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Correspondence to Leslie W. Kennedy.

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Kennedy, L.W., Caplan, J.M., Piza, E.L. et al. Vulnerability and Exposure to Crime: Applying Risk Terrain Modeling to the Study of Assault in Chicago. Appl. Spatial Analysis 9, 529–548 (2016). https://doi.org/10.1007/s12061-015-9165-z

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