Local police departments now have the capacity to do what's routinely done in the corporate sector, whereby private businesses seek to ensure that the risk management mission is instituted enterprise-wide and provides communication channels to various decision-makers and partners for asset protection and security threats. For public safety professionals, in addition to responding to crime incidents as they happen, ActionHub enables police management practices that optimize prevention, response, and mitigation to crimes and crime risks -- both at current hot spots and other vulnerable areas.
Guided by Simsi Analytics, the ActionHub compiles data-informed judgments to reduce uncertainty in the mission of crime risk governance for the goal of sustainable public safety. It extends the strong tradition of geographic analysis in the policing profession and takes advantage of contemporary analytical and technical tools like Risk Terrain Modeling (RTM) to improve and extend this history. What ActionHub demonstrates is that police leaders can tell patrol officers where to go to confront crime but also, based on their understanding of spatial vulnerabilities and recent past exposures, what to do when they get there, and how they can get other stakeholders engaged in the process. Police leaders are increasingly realizing that the burden of public safety needs to be shared in order to manage crime threats before problems emerge or cluster. ActionHub is the platform that empowers police to prevent crime and assign shared responsibility for public safety. Check out this eBook to see how ActionHub fits with DICE™ and Risk-Based Policing. For nearly two decades RTM has aided in the task of determining threats in an environment and marshaling resources to moderate the worst effects. But to efficiently and effectively act on risk assessments, police agencies must also enlist the support of a variety of other stakeholders. ActionHub enables this and empowers police to coordinate local resources and partners in convincing ways. The result is sustainable crime risk governance, measurable crime reductions, and enhanced public safety everywhere.
Modern policing has reconsidered the concept of place, shifting the focus to micro-units of analysis—such as block faces or street segments—that better reflect the scale at which public safety issues arise. This shift has brought renewed interest in environmental criminology theories, including routine activities, rational choice, crime pattern theory, and the theory of risky places. These perspectives help explain why crime concentrates in certain locations and how environmental factors influence criminal behavior.
The central role of place in policing is reinforced by the proven successes of place-based prevention strategies. Geographically focused strategies have had some of the strongest records of effectiveness, a conclusion supported by systematic reviews and meta-analyses. By integrating risk terrain modeling and other analytics that go "beyond hot spots", policing agencies can better understand the structural factors that contribute to crime at specific locations. This allows for more effective crime prevention strategies that go beyond traditional enforcement measures, aligning policing efforts with broader public safety goals.
In Cohen and Felson's original article on routine activities back in 1979, they wrote "the risk of criminal victimization varies dramatically among the circumstances and locations in which people place themselves and their property". It follows that motivated offenders commit crimes against suitable targets at certain places according to the environmental characteristics of those places, making it easier to complete crimes successfully and evade capture. Therefore, the context of high-crime places should be incorporated into crime prevention programming. Until the advent of Risk Terrain Modeling (RTM), this had yet to occur on a widespread basis, partly due to the then current analytical products commonly used in place-based interventions. Hot spot maps, for instance, show the concentration of crime but offer little insight into the physical structure of these places. This is akin to what Reboussian et al. (1995) refer to as a "mapless map"—a mere description of crime distribution without an analysis of why crime clusters in specific locations. Mapless maps have facilitated hot spot policing activities that are largely one-dimensional, focusing primarily on concentrating resources in high-crime places. However, little problem-oriented policing (POP) or S.A.R.A. effort has been given to modifying the features of places that attract illegal behaviors or give rise to crime. As Anthony Braga explained in 2015, "Too many police departments seem to rely on over-simplistic tactics, such as ‘putting cops on dots’ or launching indiscriminate zero-tolerance initiatives rather than engaging a coherent crime prevention strategy." Malcolm Sparrow reinforced this critique in 2016: "For anyone familiar with crime analysis, this is not new. And it is particularly not new when the default intervention strategy involves putting cops on dots." Jeffrey Brantingham, founder of a former predictive policing software company, explained that in response to place-based predictions, officers are instructed to use their "knowledge, skills, experience, and training in the most appropriate way to stop crime" (Huet, 2015). Ambiguity about what to do at crime hot spots is not surprising, and follows a recurring theme in policing whereby technological advancements often reinforce established analytic and tactical approaches rather than foster new and innovative ones. Over a six-year study, Peter Manning found that crime mapping and information technology were never used to challenge existing strategies but rather adapted to support current practices. How can the scope of place-based policing practices be expanded to incorporate the structure of criminogenic places? The answer may lie in moving beyond hot spots. Sparrow contends that "the only way to break out of this circularity trap—where operational methods determine what analyses are commissioned, and the analyses conducted determine the types of problems that are detected—is to throw wide open the analytic operation and demand much greater versatility… By deliberately increasing the versatility of the analytic operation, the organization increases the range of problems it can detect. Discovering new types of problems, in turn, then challenges the organization to develop relevant and novel operational responses." To be clear, current analytical products serve police well in many respects, given the demonstrated crime prevention utility of place-based approaches. But an honest reflection of the status quo highlights the importance of evaluating police responses not just by crime reduction but also in terms of efficiency, risk governance, and cost-effectiveness. Simsi Analytics, with risk terrain modeling, provides a more in-depth understanding of how structural factors and the interactions of people at places facilitate crime emergence and persistence. Simsi helps agencies expand the scope of place-based policing, enabling chiefs and mayors to enhance risk governance. This is how many cities are serving their communities with data-informed, justifiable actions for crime prevention and service delivery. Paraphrased from "Risk-Based Policing" by Kennedy, Caplan & Piza (2018). See this book for complete references cited here.
Crime analysis has been invaluable to the development of contemporary police strategies. However, a review of the literature suggests that there is room for improvement in how police analyze crime problems.
CompStat, when initially developed by the NYPD in the early 1990s, adhered to four main principles: 1) accurate and timely intelligence; 2) rapid deployment; 3) effective tactics; and 4) relentless follow-up and assessment. While NYPD’s original intent with CompStat was to enhance problem-solving capacities, later efforts in New York City and elsewhere disproportionately emphasized the "relentless follow-up and assessment" principle. This shift in focus transformed CompStat into a process where "analysis" rarely strayed beyond tallying crime counts and comparing numbers from current and prior periods. This limited approach diminished the role of problem-solving in favor of reinforcing standard police responses and bureaucratic models of organization. As Malcolm Sparrow articulated in 2016, police departments around the U.S. largely implemented CompStat programs as "de facto substitutes for any broader problem-solving approach, thereby restricting or narrowing both the types of problems police can address and the range of solutions they are able to consider." The end result is police commanders making assumptions, failing to control for uncertainties, and taking disproportionate operational responses based on minor, often insignificant, differences in crime counts. Research has consistently shown that crimes cluster at specific locations, with such clustering persisting over extensive time periods in certain cases. Given that crime patterns are spatially concentrated, many scholars including Anthony Braga, Andrew Papachristos and David Hureau have argued that crime prevention resources "should be similarly concentrated rather than diffused across urban areas" to achieve maximum impact. Risk terrain maps help to isolate and zoom-in on priority places for optimal allocations of resources and effective crime prevention programming. Here's an award-winning example in Kansas City, Missouri. Paraphrased from "Risk-Based Policing" by Kennedy, Caplan & Piza (2018). See this book for complete references cited here.
Public safety leaders operate in a world of uncertainty. Their job is not simply to be right all the time but to make informed decisions that maximize the odds of success.
Risk assessment involves evaluating the probabilities of particular outcomes. Since risk is probabilistic, the extent to which police can minimize uncertainty directly influences how well they judge crime threats and their consequences. Human judgement is required to act wherever there's uncertainty and the opportunity for human fallibility exists wherever there's judgement. Using data analytics to reduce uncertainty while also acknowledging what we know we don't know helps determine the confidence level in what we do know.
At some point, even the most data-driven, artificially intelligent predictions must invite human judgment into the decision-making process. Crime risk governance is the moment when analytics is considered alongside other pieces of information to make informed judgments about managing crime risks with the greatest odds of short- and long-term success. Here, success includes solving crime problems while also ensuring solutions align with local needs and community expectations -- which adds to the legitimacy of actions and the sustainability of impacts. So, to deploy resources, implement intervention programs, and otherwise act with confidence, we must acknowledge the inherent limitations of even the most experienced human intuitions due to uncertainties in decision-making. In such a context, a fundamental goal of crime analysis is to reduce as much uncertainty as possible. This is achieved by synthesizing insights from human judgment, data, and empirical analyses through structured processes that inform decision-making and guide crime prevention programming and risk reduction efforts. This process is realized with Data-Informed Community Engagement, a systematic approach that integrates Risk Terrain Modeling (RTM) analytics with real-world contexts to enhance public safety outcomes for everyone. |
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The official website of Risk Terrain Modeling (RTM) based out of Rutgers University and in partnership with Simsi
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