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Risk Clusters, Hotspots, and Spatial Intelligence: Risk Terrain Modeling as an Algorithm for Police Resource Allocation Strategies

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Abstract

The study reported here follows the suggestion by Caplan et al. (Justice Q, 2010) that risk terrain modeling (RTM) be developed by doing more work to elaborate, operationalize, and test variables that would provide added value to its application in police operations. Building on the ideas presented by Caplan et al., we address three important issues related to RTM that sets it apart from current approaches to spatial crime analysis. First, we address the selection criteria used in determining which risk layers to include in risk terrain models. Second, we compare the “best model” risk terrain derived from our analysis to the traditional hotspot density mapping technique by considering both the statistical power and overall usefulness of each approach. Third, we test for “risk clusters” in risk terrain maps to determine how they can be used to target police resources in a way that improves upon the current practice of using density maps of past crime in determining future locations of crime occurrence. This paper concludes with an in depth exploration of how one might develop strategies for incorporating risk terrains into police decision-making. RTM can be developed to the point where it may be more readily adopted by police crime analysts and enable police to be more effectively proactive and identify areas with the greatest probability of becoming locations for crime in the future. The targeting of police interventions that emerges would be based on a sound understanding of geographic attributes and qualities of space that connect to crime outcomes and would not be the result of identifying individuals from specific groups or characteristics of people as likely candidates for crime, a tactic that has led police agencies to be accused of profiling. In addition, place-based interventions may offer a more efficient method of impacting crime than efforts focused on individuals.

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Notes

  1. The specific parameters for density calculations used in this study were a bandwidth of 1,000 feet and a cell size of 145 feet. A 1,000 foot bandwidth was selected because it seemed a reasonable sphere of influence for shooters—the median blockface is approximately 290 feet (Felson 1995; Taylor 1997). 145 × 145 foot cells were the smallest area that our computers could process reasonably fast and, for this study, if a Risk Terrain Model could predict locations of shootings at the smallest (but reasonable) geographic units, it would best exemplify the utility of RTM for operational policing compared to larger, less specific, units of analysis.

  2. Rows in the attribute tables of each map were sorted in descending order in SPSS by their risk values after random numbers were assigned to each cell. Random numbers were necessary to randomize the sorting of cells with the same risk values. For example, if 11 out of 100 cells had a risk value of eight, and they were sorted in descending order, the top 10% of cells to be designated as “high risk” would all have values of eight. But, the 11% cell would be excluded due to a rather arbitrary sorting algorithm. The random number ensured that every cell had an equal chance of being sorted above or below each cut point.

  3. As is evident from Fig. 1, RTM’s superiority over the retrospective maps declined dramatically in Period 4. This may be the result of numerous place-based operations of the Newark Police Department. Starting in early 2008, the Newark Police began systematic, intensive street level enforcement in various “hot zones” around the city. When an area is designated as a “hot zone,” a vast amount of resources is dedicated to the area. City-wide and precinct-level squads (such as the central narcotics unit and precinct level “conditions” unit) are tasked with conducting operations in these areas on a daily basis. Police patrols are also significantly heighted. Commonly, police units and foot patrols are stationed at these places 24-h a day. These operations are ongoing; resources are not pulled from an area when crime levels drop. Instead, the heightened level of enforcement remains steady even as additional areas are designated as “hot zones.” As of the date of this writing, each of Newark’s four police precincts designates five areas as “hot zones,” according to department documents. In addition, The Newark Police has formed a partnership with various federal and state agencies active within the city. This “Violent Enterprise Strategy Team (VEST),” as it is referred to, targets criminal gangs seen to significantly contribute to violence. VEST operations are conducted throughout numerous areas of the city. Target areas of these aforementioned efforts happen to be “high-risk” areas as denoted by our Risk Terrain Model. Being that the number of such specialized zones has increased over time, more “high risk” areas received higher levels of police attention and enforcement in Period 4 than during the previous study periods. Thus, in Period 4, the true “risk” of violence is most likely lower than the Risk Terrain suggests, which obviously impacts the model’s predictive capability. In light of this observation, future research on the Risk Terrain approach should incorporate police activity in the model as a mitigating factor.

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Authors and Affiliations

  1. Rutgers School of Criminal Justice, Newark, NJ, 07105, USA

    Leslie W. Kennedy & Joel M. Caplan

  2. Rutgers School of Criminal Justice, Newark PD, Newark, NJ, USA

    Eric Piza

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  1. Leslie W. Kennedy
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  2. Joel M. Caplan
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Correspondence to Leslie W. Kennedy.

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Kennedy, L.W., Caplan, J.M. & Piza, E. Risk Clusters, Hotspots, and Spatial Intelligence: Risk Terrain Modeling as an Algorithm for Police Resource Allocation Strategies. J Quant Criminol 27, 339–362 (2011). https://doi.org/10.1007/s10940-010-9126-2

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