Law enforcement officials (as well as their analysts) have long sought to explain why crime occurs at certain places and times. Multiple approaches to studying crime and victimization, including hot spot policing, Place Network Investigations (PNI), or Risk Terrain Modeling (RTM), among others, have helped paint a more complete picture of the underlying processes that contribute to crime occurrence.
But no matter which strategy is chosen, it is important to keep in mind what drives virtually all crime reduction and prevention strategies: data and, more importantly, analysis of that data.
Data analytics, specifically in the context of public safety and law enforcement, have accelerated over the last two decades. Whereas data itself was once the missing ingredient, data is readily available nowadays for agencies of all sizes and types. If anything, the rise of big data has further supplied law enforcement agencies and related departments with more data than many can effectively ingest and utilize to their advantage.
The problem, then, is not necessarily data or its availability. The problem is modern law enforcement and public safety organizations need people and experts that have knowledge and skills to harness the latent energy found within the mountains of data. This is why agency leaders must remember “to keep the analyst in crime analysis.”
Consider the rapid advent of AI and other modeling techniques in policing. An agency’s analytical model of choice may produce an “accurate” prediction about a crime happening where expected. Although encouraging on one hand (i.e., the model worked, depending how you look at it), a more practical assessment reveals a failed opportunity to intervene and possibly prevent the crime from occurring in the first place. Law enforcement leaders should therefore expect more from algorithms or even more traditional analysis products that claim to predict where, and possibly when, crimes are most likely to occur. That is only half the equation.
The other, more powerful half of the equation rests in analysts. No matter how much data is available or how powerful the analytic models for crime prevention, all require human judgement within the decision-making process. The task of the analyst is not to simply explain data, charts, or other analytical products, but to provide actionable, evidence-based prescriptions for how to use insights gained from the same data or products to combat crime and its other ill effects. Put another way, the analyst’s task is to facilitate and moderate human judgements into decision-making. Command staff ultimately make the decision and allocate resources, to be sure, but analysts must be a key component of that process.
The talent of a skilled analyst is to first listen to empirical evidence and subjective human judgement, then blend the two. A person’s understanding of what is seen or heard changes within the context each is acquired. The analyst’s mind needs to be in a constant state of defense against all the junk that’s trying to mislead it, allowing the analyst to communicate meaning out of the signals and noise. The result is the capacity to soundly advise leadership about how to monitor connections to crime, how to assess spatial or temporal vulnerabilities, and how to act in order to reduce the worst effects of their predictions.
With this balance of powerful analytics, capable analysts, and open-minded leadership in place, positive outcomes will follow.
Post inspired by Risk Terrain Modeling: Crime Prediction and Risk Reduction (2016; UCPress), by Caplan & Kennedy and by “The Undoing Project” by Michael Lewis. Endnote from pg. 31 of “The Undoing Project” by Michael Lewis.