A large body of research has found that crime is much more likely to occur at certain places relative to others. Attempting to capitalize on this finding to maximize crime prevention, many police administrators have sought to narrow their department’s operational focus and allocate resources and attention to the most problematic locations. However, in the face of a growing number of technological advances in crime forecasting that have facilitated this trend, it is still unclear how to best identify the most appropriate set of places to which resources and attention should be directed. Our goal was to examine this issue by exploring the ways in which spatial vulnerabilities and exposures could be used to identify the best target areas for policing. Using the Theory of Risky Places as a guide, we employed kernel density estimation (KDE) to measure crime exposures and risk terrain modeling (RTM) to identify crime vulnerabilities with the expectation that crime would be predicted more accurately by integrating the outputs from these two methods. To test this hypothesis, our analysis utilized 1 year of data on street robbery in Brooklyn, New York. A common metric, the prediction accuracy index (PAI), was computed for KDE, RTM, and the integrated approach, over 1 month and 3 month intervals. We found that the integrated approach, on average and most frequently, produces the most accurate predictions. These results demonstrate that place-based policing and related policies can be improved via actionable intelligence produced from multiple crime analysis tools that are designed to measure unique aspects of the spatial dynamics of crime.
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Although crime ‘forecasting’ and ‘prediction’ are technically different concepts, they are often used synonymously in practice (see RAND, 2013). Therefore, we use the two terms interchangeably throughout this paper.
See a reference map of Brooklyn, NY on Google Maps: https://goo.gl/maps/aE7no4KH3SR2
This definition is consistent with New York State Penal Law, see CJI2d [NY] Penal Law §160.00. The NYPD classifies crime incidents using these definitions (see http://www.nyc.gov/html/nypd/html/crime_prevention/crime_statistics.shtml).
Robbery and risk factor data provided to the research team by the NYPD were likely geocoded using a composite method that matched first to streets then to parcels (which would explain the points that were offset as much as 160 ft). Since XY coordinates were provided to us, we do not know more about the exact geocoding method used by NYPD. For the point risk factors we manually geocoded, we used a 15-ft offset. Polygon risk factors, conversely, pertained to parcel and building footprints in New York city and were not georeferenced according to street centerline.
The robbery data used for this study was offset from street centerlines by as much as 160 ft. Most cells in the fishnet of Brooklyn were within this distance from streets. However, some other study settings may not be similar. For future research in other settings, we recommend that the fishnet used for testing predictive validity of KDE and/or RTM be limited to only those cells that intersect streets if the crime incident data used for the analysis are geocoded to street centerlines. Excluding non-intersecting cells before testing predictive validity would account for where crimes could actually occur within the study setting; It would exclude the cells that could never have a crime occur due to the technicalities of geocoding addresses to streets. This would likely enhance the results of future research.
We use ArcGIS to perform each KDE, which by default employs interpolation based on the quartic method.
We use the Nearest Neighbor Threshold (NNT) to select operationalization parameters. The NNT can be calculated using the following formula: 2 * (Block Length * Number of Analysis Increments). This formula produced a NNT of 4344. If the features were not significantly clustered or if the observed mean distance (reported by the NN analysis) were greater than the NNT, the ‘proximity’ to features was tested. If the features were significantly clustered and the observed mean distance was less than or equal to the NNT, ‘both’ proximity to and density of features was tested.
The two means of the average PAI values for RTM and KDE across all months were compared in t-tests with sample sizes of 11 months. The small size of sample (n = 11) could be a limitation to the statistical conclusion.
Risk terrain models produced for this study identified the most problematic environmental features for each month and for each quarter over the course of 1 year. It is interesting to note that while some of the 27 environmental features tested in the models were rarely or never identified as risk factors, others were consistently found to increase the risk of robbery. In particular, grocery stores were risk factors for robbery every month and food pantries and soup kitchens and subway entrances were risk factors in most months. Conversely, banks, billiard halls, chemical dependency facilities, cinemas, clubs, colleges and universities, homeless facilities, hospitals, hotels and motels, mental health facilities, developments, parking facilities, parks, postal facilities, and recreation centers were never risk factors for robbery.
Adepeju, M., Rosser, G., & Cheng, T. (2016). Novel evaluation metrics for sparse spatio-temporal point process hotspot predictions: a crime case study. International Journal of Geographical Information Science, 30(11), 2133–2154.
Andresen, M. (2014). Environmental criminology: evolution, theory, and practice. London: Routledge.
Andresen, M. A., Curman, A. S., & Linning, S. J. (2016). The trajectories of crime at places: understanding the patterns of disaggregated crime types. Journal of Quantitative Criminology, 33, 427–449. https://doi.org/10.1007/s10940-016-9301-1.
Ariel, B., & Partridge, H. (2016). Predictable policing: measuring the crime control benefits of hotspots policing at bus stops. Journal of Quantitative Criminology, 33, 809–833. https://doi.org/10.1007/s10940-016-9312-y.
Bernasco, W., & Block, R. (2009). Where offenders choose to attack: a discrete choice model of robberies in Chicago. Criminology, 47, 93–130.
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, 33–57.
Block, R., & Block, C. R. (2004). Spatial and temporal analysis of crime (STAC). In N. Levine (Ed.), CrimeStat III: A spatial statistics program for the analysis of crime incident locations (pp. 7.1–7.18). Houston: Ned Levine & Associates. Washington, DC: The National Institute of Justice.
Braga, A. 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, 51(4), 480–498.
Braga, A. A., & Weisburd, D. (2010). Policing problem places: Crime hot spots and effective prevention. New York: Oxford University Press.
Braga, A. A., Papachristos, A. V., & Hureau, D. M. (2010). The concentration and stability of gun violence at micro places in Boston, 1980–2008. Journal of Quantitative Criminology, 26(1), 33–53.
Braga, A. A., Hureau, D. M., & Papachristos, A. V. (2011). The relevance of micro places to citywide robbery trends: a longitudinal analysis of robbery incidents at street corners and block faces in Boston. Journal of Research in Crime and Delinquency, 48(1), 7–32.
Braga, A. A., Papachristos, A. V., & Hureau, D. M. (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. (1995). Criminality of place. European Journal on Criminal Policy and Research, 3, 5–26.
Brantingham, P., & Brantingham, P. (2008). Crime pattern theory. In R. Wortley & L. Mazerolle (Eds.), Environmental criminology and crime analysis (pp. 78–93). New York: Routledge.
Caplan, J. M. (2011). Mapping the spatial influence of crime correlates: a comparison of operationalization schemes and implications for crime analysis and criminal justice practice. Cityscape, 13, 57–83.
Caplan, J. M., & Kennedy, L. W. (2013). Risk terrain modeling diagnostics utility (version 1.0). Newark: Rutgers Center on Public Security.
Caplan, J. M., & Kennedy, L. W. (2016). Risk terrain modeling: Crime prediction and risk reduction. Oakland: University of California Press.
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.
Caplan, J. M., Kennedy, L. W., & Piza, E. L. (2013a). Joint utility of event-dependent and environmental crime analysis techniques for violent crime forecasting. Crime & Delinquency, 59(2), 243–270.
Caplan, J. M., Kennedy, L. W., & Piza, E. L. (2013b). Risk terrain modeling diagnostics utility user manual (version 1.0). Newark: Rutgers Center on Public Security.
Caplan, J. M., Kennedy, L. W., Barnum, J. D., & Piza, E. L. (2015). Risk terrain modeling for spatial risk assessment. Cityscape: A Journal of Policy Development and Research, 17, 7–16.
Chainey, S., Tompson, L., & Uhlig, S. (2008). The utility of hotspot mapping for predicting spatial patterns of crime. Security Journal, 21, 4–28.
Clarke, R. V. (1980). Situational crime prevention: theory and practice. British Journal of Criminology, 20, 136–147.
Drawve, G. (2016). A metric comparison of predictive hot spot techniques and RTM. Justice Quarterly, 33(3), 369–397.
Drawve, G., Moak, S. C., & Berthelot, E. R. (2016). Predictability of gun crimes: a comparison of hot spot and risk terrain modelling techniques. Policing and Society, 26(3), 312–331.
Drawve, G., Grubb, J., Steinman, H., & Belongie, M. (2019). Enhancing data-driven law enforcement efforts: exploring how risk terrain modeling and conjunctive analysis fit in a crime and traffic safety framework. American Journal of Criminal Justice, 44(1), 106–124.
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, 63–89.
Eck, J. E. & Weisburd, D. (1995). Crime and place. Monsey, N.Y.: Criminal Justice Press.
Eck, J. E., Chainey, S., Cameron, J. G., Leitner, M., & Wilson, R. E. (2005). Mapping crime: Understanding hot spots. Washington, DC: National Institute of Justice.
Federal Bureau of Investigation. (2014). Crime in the United States. Washington, DC: United States Department of Justice.
Garnier, S., Caplan, J. M., & Kennedy, L. W. (2018). Predicting dynamical crime distribution from environmental and social influences. Frontiers in Applied Mathematics and Statistics, 4. https://doi.org/10.3389/fams.2018.00013.
Goldstein, H. (1990). Problem-oriented policing. Philadelphia: Temple University Press.
Groff, E. R., & Lockwood, B. (2014). Criminogenic facilities and crime across street segments in Philadelphia: uncovering evidence about the spatial extent of facility influence. Journal of Research in Crime and Delinquency, 51, 277–314.
Guerry, A. M. (1833). Essai sur la statistique morale de la France [Essay on the moral statistics of France]. Paris: Crochard.
Haberman, C. P., Groff, E. R., & Taylor, R. B. (2013). The variable impacts of public housing community proximity on nearby street robberies. Journal of Research in Crime and Delinquency, 50, 163–188.
Hart, T. C., & Miethe, T. D. (2014). Street robbery and public bus stops: a case study of activity nodes and situational risk. Security Journal, 27, 180–193.
Hart, T. C., & Zandbergen, P. A. (2012). Effects of data quality on predictive hotspot mapping. Washington, DC: National Institute of Justice.
Hart, T. C., & Zandbergen, P. A. (2014). Kernel density estimation and hotspot mapping: Examining the influence of interpolation method, grid cell size, and bandwidth on crime forecasting. Policing: An International Journal of Police Strategies & Management, 37(2), 305–323.
Irvin-Erickson, Y. (2014). Identifying risky places for crime: An analysis of the criminogenic spatiotemporal influences of landscape features on street robberies (Doctoral dissertation). Rutgers University-Graduate School, Newark, NJ.
Johnson, L., & Ratcliffe, J. H. (2013). When does a drug market become a drug market? Finding the boundaries of illicit event concentrations. In M. Leitner (Ed.), Crime modeling and mapping using geospatial technologies (pp. 25–48). New York: Springer.
Kennedy, L. W., & Van Brunschot, E. G. (2009). The risk in crime. Lanham: Rowman & Littlefield Publishers, Inc.
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, 27, 339–362.
Kennedy, L. W., Caplan, J. M., & Piza, E. L. (2012). A primer on the spatial dynamics of crime emergence and persistence. Newark: Rutgers Center on Public Security.
Kennedy, L. W., Caplan, J. M., & Piza, E. L. (2015a). A multi-jurisdictional test of risk terrain modeling and place-based evaluation of environmental risk-based patrol deployment strategies. Newark: Rutgers Center on Public Security.
Kennedy, L. W., Caplan, J. M., Piza, E. L., & Buccine-Schraeder. (2015b). Vulnerability and exposure to crime: applying risk terrain modeling to the study of assault in Chicago. Applied Spatial Analysis, 9, 529–548. https://doi.org/10.1007/s12061-015-9165-z.
Kennedy, L. W., Caplan, J. M., & Piza, E. L. (2018). Risk-based policing: evidence-based crime prevention with big data and spatial analytics. Oakland: University of California Press.
Koper, C. S. (1995). Just enough police presence: reducing crime and disorderly behavior by optimizing patrol time in crime hot spots. Justice Quarterly, 12(4), 649–672.
Koper, C. S., Taylor, B. G., & Woods, D. J. (2013). A randomized test of initial and residual deterrence from directed patrols and use of license plate readers at crime hot spots. Journal of Experimental Criminology, 9(2), 213–244.
LaVigne, N. G. (1996). Safe transport: Security by design on the Washington Metro. In R. V. Clarke (Ed.), Preventing mass transit crime. Monsey: Criminal Justice Press.
Levine, N. (2004). CrimeStat III: A spatial statistics program for the analysis of crime incident locations (Version 3.0). Houston, TX: Ned Levine & Associates. Washington, DC: The National Institute of Justice.
Levine, N. (2008). The “hottest” part of a hotspot: comments on “the utility of hotspot mapping for predicting spatial patterns of crime”. Security Journal, 21, 295–302.
Maple, J., & Mitchell, C. (1999). The crime fighter. How you can make your community crime-free. New York: Broadway Books.
McGarrell, E. F., Chermak, S., & Weiss, A. (1999). Reducing firearms violence through directed police patrol: Final report on the evaluation of the Indianapolis Police Department’s directed patrol project. Washington, DC: The National Institute of Justice.
Moreto, W. D., Piza, E. L., & Caplan, J. M. (2014). “A plague on both your houses?”: risks, repeats and reconsiderations of urban residential burglary. Justice Quarterly, 31(6), 1102–1126.
Ohyama, T., & Amemiya, M. (2018). Applying crime prediction techniques to Japan: a comparison between risk terrain modeling and other methods. European Journal on Criminal Policy and Research., 24, 469–487.
Perry, L. P., McInnis, B., Price, C. C., Smith, S. C., & Hollywood, J. S. (2013). Predictive policing: The role of crime forecasting in law enforcement operations. Santa Monica, CA: RAND Corporation. Washington, DC: The National Institute of Justice.
Piza, E. L., & O’Hara, B. A. (2014). Saturation foot-patrol in a high-violence area: a quasi-experimental evaluation. Justice Quarterly, 31(4), 693–718.
Police Foundation. (1981). The Newark foot patrol experiment. Washington, DC: Police Foundation.
Quetelet, M. A. (1842). A treatise on man. Edinburgh: William & Robert Chambers.
Ratcliffe, J. H., Taniguchi, T., Groff, E. R., & Wood, J. D. (2011). The Philadelphia foot patrol experiment: a randomized controlled trial of police patrol effectiveness in violent crime hotspots. Criminology, 49(3), 795–831.
Rengert, G. F. (1997). Auto theft in Central Philadelphia. In R. Homel (Ed.), Policing for prevention: Reducing crime, public intoxication and injury (pp. 199–219). Mosney: Criminal Justice Press.
Roncek, D. W., & Faggiani, D. (1985). High schools and crime: A replication. The Sociological Quarterly, 26, 491–505.
Roncek, D. W., & Maier, P. A. (1991). Bars, blocks, and crimes revisited: linking the theory of routine activities to the empiricism of “hot spots”. Criminology, 29, 725–753.
Roncek, D. W., Bell, R., & Francik, J. M. (1981). Housing projects and crime: testing a proximity hypothesis. Social Problems, 29, 151–166.
Rosenfeld, R., Deckard, M. J., & Blackburn, E. (2014). The effects of directed patrol and self-initiated enforcement on firearm violence: a randomized controlled study of hot spot policing. Criminology, 52(3), 428–449.
Santos, R. B. (2012). Crime analysis with crime mapping. Thousand Oaks: Sage Publications, Inc..
Shaw, C. R., & McKay, H. D. (1942). Juvenile delinquency in urban areas. Chicago: University of Chicago Press.
Sherman, L. W. (1995). Hot spots of crime and criminal careers of places. In J. E. Eck & D. Weisburd (Eds.), Crime and place (pp. 13–52). Monsey: Criminal Justice Press.
Sherman, L. W., & Weisburd, D. (1995). General deterrent effects of police patrol in crime “hot spots”: a randomized, controlled trial. Justice Quarterly, 12(4), 625–648.
Sherman, L. W., Gartin, P. R., & Buerger, M. E. (1989). Hot spots of predatory crime: routine activities and the criminology of place. Criminology, 27, 27–56.
Skogan, W., & Frydl, K. (2004). Fairness and effectiveness in policing: The evidence. Committee to review research on police policy and practices. Committee on law and justice, division of behavioral and social sciences and education. Washington, D.C.: The National Academies Press.
Smith, W. R., Frazee, S. G., & Davison, E. L. (2000). Furthering the integration of routine activity and social disorganization theories: small units of analysis and the study of street robbery as a diffusion process. Criminology, 38, 489–524.
Sousa, W., & Kelling, G. (2006). Of “broken windows,” criminology, and criminal justice. In D. Weisburd & A. Braga (Eds.), Police innovation: Contrasting perspectives (pp. 77–97). New York: Cambridge University Press.
St. Jean, P. K. B. (2007). Pockets of crime: Broken windows, collective efficacy, and the criminal point of view. Chicago: University of Chicago Press.
Taylor, B., Koper, C. S., & Woods, D. J. (2011). A randomized controlled trial of different policing strategies at hot spots of violent crime. Journal of Experimental Criminology, 7(2), 149–181.
Van Patten, I. T., McKeldin-Coner, J., & Cox, D. (2009). A microspatial analysis of robbery: Prospective hot spotting in a small city. Crime Mapping, 1, 7–32.
Weisburd, D. (2008). Place-based policing. In Ideas in American policing. Washington, DC: Police Foundation.
Weisburd, D. L., Groff, E. R., & Yang, S. M. (2012). The criminology of place: Street segments and our understanding of the crime problem. New York: Oxford University Press.
Wortley, R., & Mazerolle, L. (2008). Environmental criminology and crime analysis. New York: Routledge.
Wright, R. T., & Decker, S. H. (1997). Armed robbers in action: Stickups and street culture. Lebanon: Northeastern University Press.
Yerxa, M. (2013). Evaluating the temporal parameters of risk terrain modeling with residential burglary. Crime Mapping, 5(1), 7–38.
This research was supported in part by a grant from the National Institute of Justice (Award #2013-IJ-CX-0053). The views presented are those of the authors and do not necessarily represent the position of the National Institute of Justice.
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Caplan, J.M., Kennedy, L.W., Piza, E.L. et al. Using Vulnerability and Exposure to Improve Robbery Prediction and Target Area Selection. Appl. Spatial Analysis 13, 113–136 (2020). https://doi.org/10.1007/s12061-019-09294-7
- Risk terrain modeling
- Kernel density estimation
- Prediction accuracy index