Because RTM (1) is place-based, not person focused; (2) is sustainable, with better measures of success and officer productivity; (3) More actionable and predictive than hot spot mapping; (4) is proven and evidence-based; (5) respects constitutional protections.
(1) Place-based, not person focused:
Police responses to crime have traditionally been offender-focused and, thus, disconnected with the spatial analysis that informed the response. One benefit of risk terrain modeling is an emphasis on intervention activities that focus on places, not just people located at certain places. In an hour-long interview[i], New York City Police Commissioner Bill Bratton explained how the latest manifestation of frayed relations between the police and Black communities was a byproduct of the implementation of CompStat in the 1990s. CompStat was developed as a means to communicate timely information about crime events and threats, and to identify emerging patterns and trends, based on administrative crime records. The treatment prescribed for hot spot areas in New York City was a Broken Windows style of policing, explained Bratton, whereby lower-level offenses were given higher priority and police officers were mandated to measure productivity and demonstrate success on the job by stopping, frisking, citing and arresting individuals located in spatially-defined problem areas. Ultimately, minority communities bore the brunt of this focused attention and treatment by police. Notably, such practices ordered by police commanders were not enjoyed by line-level officers, as they saw first-hand (and early on) the rift that stop-question-and-frisk (SQF) policies created between them and the public they served. Over the years, police officers bore the brunt of ill will from this crime problem “remedy” that was forced on them by multiple political administrations, Bratton explained.
One lesson from this is that crime statistics, alone, should not dictate police action. Simmering frustrations and frayed relations between police and the public they serve can be exacerbated when crime analyses and intelligence products fail to elucidate root attractors of illegal behavior; especially, when responses to spatial intelligence fail to acutely address the qualities of places and fail to look beyond merely the people located there. There can be many disconnects among crime intelligence products, policies and policing practices. One of which, as articulated by Bratton in New York City, occurs when a statistical assessment process focuses on crime counts and locations, and then when the response plan focuses almost exclusively on people at certain places (i.e., and ignores the spatial attractors of illegal behavior located there). Secondly, disconnect often happens when measures of police productivity are reliant on the persistence of the illegal activities that are sought to be prevented (i.e., such as when productivity measures depend on people to be stopped, ticketed or arrested), rather than on more sustainable and benign measures, such as efforts/actions associated with reducing the influences of risky features that are identified in a risk terrain model.
(2) Sustainable, w/ improved measures of success and officer productivity:
Sparrow (2015) wrote a provocative article about measuring performance in a modern police organization. He argues that reported crime rates will always be important indicators for police departments. However, substantial and recurrent reductions in crime figures are only possible when crime problems have first grown out of control. A sole reliance on the metric of crime reduction, Sparrow explained (p. 5), would “utterly fail” to reflect the very best performance in crime control practices when police actions are successful at keeping crime rates low and nipping emerging crime problems as they bud. Beyond looking at crime rate changes (which is what CompStat primarily does), a risk reduction approach to solving crime problems (i.e., RTM and ACTION) can suggest success in interventions when factors other than crime counts improve. So, risk reduction strategies have dual objectives: One to reduce crime counts and the other to reduce the spatial influences of known risk factors at certain places.
RTM is a sustainable technique because past crime data are not always needed to continue to make valid forecasts. Police throughout the world use RTM to be problem-oriented and proactive, to prevent new crimes without concern that a high success rate (and no new crime data) will hamper their ability to make new forecasts. Police are able to measure their effects on mitigating the spatial influences of risky features, and to judge their productivity without the burden of relying on crimes to occur and be reported. With RTM, police define their own intended foci and intents of policing actions, record productivity data accordingly, and then measure success by re-evaluating one or more risk factors’ weights in post-intervention risk terrain models. Ideally, their actions suppress a risk factor’s attractive qualities completely, rendering it empirically absent from the post model altogether. Reported crime rate reductions will always be an important performance measure for police agencies. But, RTM removes the need for a sole dependency on them.
See also this article by Malcom Sparrow from Harvard, titled “Measuring Performance in a Modern Police Organization”.
(3) More actionable and predictive than hot spot mapping:
RTM is a crime diagnostic and forecasting tool developed through empirical research at Rutgers University. RTM offers a statistically valid way to diagnose environmental attractors of crime and to predict new locations where crimes are likely to emerge and cluster. Multiple research studies found that RTM was more accurate and precise in predicting new crime locations compared to hot spot mapping.
Another takeaway from New York City’s history with CompStat is that interventions by police or other stakeholders at hot spot areas should be robust, sustainable, and flexible. Crime events do not occur out of spatial context. Policing strategies based on RTM and ACTION deliberately focus on the behavior settings for crime by mitigating the spatial influences of environmental features that attract, generate or enable illegal behavior. This leads places to experience less crime in the short-term and be less problematic in the long-term. It may seem reasonable that targeting individual offenders at risky places would remove the chance of reoccurrence of crime and reduce its rate. But vulnerable environments need to be reformed to effectively control crime, or else new offenders will take the place of old. When law enforcement relies exclusively on deterrence and incapacitation at high-crime areas (e.g., via a CompStat model), it is rarely effective or equitable, particularly regarding minority neighborhoods.
Also, RTM enables police to think differently about crime analysis and intervention planning so they can stop playing Whack-a-Mole. I.e., police will often focus on problem “hot spot” areas, only to have crime emerge elsewhere and then return to the original location once police leave. Targeting crime problems requires a concerted effort to not only focus on the individual offenders present at hot spots at any given moment in time, but to think about the mechanisms that enable hot spots to emerge, persist and desist over time. In this way, RTM makes crime analysis more precise and actionable.
Here are 7 tips for successful crime forecasting. RTM achieves all of these points.
(4) Proven and evidence-based:
In the realm of policing, the intel produced from RTM helps deploy resources and develop risk reduction strategies, with the goal of sustainable crime control. We recently completed a research project (at Rutgers University) to test Risk Terrain Modeling in several US cities. Results from the 6-city study are excellent. For Newark, NJ for instance, which focused on Gun Violence, a risk terrain model found high risk places to have 58 times greater likelihood of crime than some other locations. Further analysis found that these highest risk behavior settings for Gun Violence cover about 5% of the study area and account for nearly 30% of all crime incidents. Based on this, an intervention strategy was implemented over 3 months (that focused on mitigating risk at places.... the focus was on places, NOT people). The intervention strategy achieved a 35% reduction in the target area, compared to control areas, during the 3-month post-intervention period. Significant crime reductions were also achieved for various crime types (e.g., robbery, shootings, motor vehicle theft, etc.) in other cities. Reductions were as high as 42% in just 3 months, compared to control areas.
Even NYPD recognizes the value of RTM and, with a grant from the National Institute of Justice, is testing it for its own policing operations.
(5) Respects constitutional protections:
Legal scholars have written articles explaining how RTM offers an “exceptional opportunity” to quantify high crime areas in ways that respect constitutional protections for citizens. Here are direct links to the law reviews:
- Leveraging predictive policing algorithms to restore Fourth Amendment protections in high-crime areas in a post-Wardlow world. Chicago-Kent Law Review. By Kelly K. Koss. Link
- Predictive policing and reasonable suspicion. Emory Law Review. By Andrew G. Ferguson. Link
[i] On Charlie Rose, aired January 12, 2015 [Retrieved January 19, 2015 from http://www.hulu.com/watch/737448]