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Risk-based
policing
(RBP)

Program Title:

 

Risk-based policing (RBP)

 

Program Blurb:

 

After using Risk Terrain Modeling (RTM) to identify which areas of a jurisdiction are at increased risk for a particular crime based on the presence and/or clustering of landscape features (e.g., corner stores, fast-food restaurants, gas stations, etc.), Risk-Based Policing (RBP) is a strategy that applies RTM analyses to crime prevention efforts. An example of such efforts may include increased patrols to high-risk areas identified by RTM, working with managers of risky places, among other approaches to prevent crime in these areas.

See our Square One assessment on RTM, here:

www.CAN-SEBP.net/riskterrainmodeling       

Assessment:

Is the program based on research?

Y

RTM itself is built upon nearly a centuries-worth of academic literature that has been generated in the area of space and crime. RTM allows us to take this prior research (i.e., which landscape features are correlated with certain types of crime) to predict areas of increased risk of a particular crime. Prior research on RTM has shown that it is an effective, evidence-based method of crime prediction, and RBP allows us to apply these RTM analyses to crime prevention efforts.

Has the program been independently evaluated?

N

With RTM only being developed within the last decade, RBP is an equally new strategy to policing. To-date, RBP has only been tested in a single, multi-city study that was generated by scholars who also contributed to the development of RTM.

Was the program rigorously tested?

Y

Kennedy, Caplan, and Piza (2018) used a quasi-experimental design across four American cities (Colorado Springs, Newark, Kansas City, and Glendale). In each city, RTM analyses were generated for a crime type that was an issue in each respective city. High-risk areas identified by RTM in each city were split into treatment and control areas, with treatment areas being targeted by RBP strategies for a 90-day period. All four cities experienced crime reductions for the crime type they initially analyzed through RTM. 

Has the program evaluation been replicated?

N

To-date, no other RBP evaluations have been generated.

Was the program tested in Canada?

N

To-date, RBP has not been tested in Canada.

Comments/cautions:

Despite there being limited research generated on RBP to-date, the practice appears to be an extremely promising method of crime prevention. Future evaluations of RBP are likely to solidify this practice as being evidence-based. This is supported by: (1) the deep roots that this practice has in prior research and theory on crime and place; (2) the numerous studies that have been generated on RTM showing that it is an effective, evidence-based method of crime prediction; and (3) the findings of Kennedy et al. (2018) who showed that RTM-informed policing strategies (i.e., RBP) can successfully prevent crime.

Assessor:

Jacek Koziarski, University of Western Ontario

 

Jacek Koziarski is a PhD student in the Sociology department at the University of Western Ontario, and a Research Associate for the Canadian Society of Evidence-Based Policing (CAN-SEBP). Jacek has a broad interest in policing research and developing evidence-based approaches to policing, but his most recent work has specifically focused on specialized police responses to persons in crisis, missing persons, the spatial analysis of crime, and hot spots policing.

Reviewer:

Dr. Andrew Wheeler, University of Texas at Dallas

Dr. Wheeler's research and expertise focuses on the spatial analysis of crime, practical problems faced by crime analysts, the evaluation of crime reduction programs, data visualization and statistical analysis.

https://andrewpwheeler.wordpress.com/about

Suggested readings:

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. (brief of RBP evaluation available, here: http://www.rutgerscps.org/uploads/2/7/3/7/27370595/nij6city_results_inbrief_final.pdf)

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