A version of this appears as my written statement submitted with testimony to the California State Legislative Committee on July 12, 2024. See Exhibit G, here: ​clrc.ca.gov/CRPC/Pub/Memos/CRPC24-04s1.pdf

Police are under pressure to prevent crime without focusing on people. But they don’t have the tools to focus effectively on places instead. The solution is crime analysis that tells you which settings attract criminal behavior in order to disrupt opportunities for crime by focusing on the environmental conditions that attract criminals and opportunities for victimization. Crime analytics that “drive actions” – not merely admire the problems, make connections between crime incident location patterns and features of landscapes in ways that empower multiple local stakeholders to change environments to impact behavior. This delivers better policing and enhanced public safety through place management, and is achieved using Risk Terrain Modeling (RTM).
 
Policing & Public Safety:
Police actions have an important role to play in affecting crime risks. They can deter offenders, embolden victims, and assist in the hardening of targets. These products can have the overall impact of reducing crime occurrence. But we must separate what we would see as policing and law enforcement from crime prevention and public safety.
 
Policing and law enforcement affect public safety, but public safety is more than both of those things. Policing encompasses a wide range of activities carried out by police officers, especially with respect to maintenance of order and law, and other matters affecting public welfare. Law enforcement is a key function of policing but refers specifically to enforcing the written rules governing society by deterring, discovering, stopping, and/or seizing people who violate the law. Public safety refers more broadly to the general welfare and protection of the public from various dangers affecting persons, property, and collective well-being.
 
Public safety programming yields crime prevention and risk reduction benefits when multiple local stakeholders identify and address key parts of the environmental conditions in which crime is likely to appear. Risk Terrain Modeling (RTM) diagnoses crime location patterns to support such multifaceted programs whereby stakeholders maintain their independence, but they operate in a coordinated fashion by being equally data-informed. The combined actions at places throughout the jurisdiction contribute to a comprehensive crime prevention strategy overall. This place-based public safety programming informed by RTM addresses various elements of vulnerable settings and disrupts the situational opportunities that lead to new crime incidents. It’s an upstream approach to crime problems that focuses on places, not people.
 
Places & People:
We should recognize that while person-focused interventions are necessary, we can be more prescriptive with those efforts by focusing on the places where those people operate. We can coordinate multiple existing local resources for place management and service delivery intended for the purpose of crime prevention and public safety. By using RTM analytics to inform our decisions about what to do to make vulnerable settings safer spaces that are less attractive to criminal behavior, we can also be more transparent about why we are doing what we’re doing where we’re doing it.
 
Evidence of Crime Prevention with Risk Terrain Modeling:
RTM represents a culmination of over 40 years of rigorous research, systematic investigation, fieldwork, and professional experience. It’s evidence-based and proven to work via practical user experiences and over 75 peer-reviewed journal articles from the U.S. and around the world (see the bibliography). In January 2023, the U.S. Department of Justice (DOJ) National Institute of Justice (NIJ) labeled RTM as a “science-based method” for “identifying and measuring crime risk posed by the features of a specific physical location” (see nij.ojp.gov/term-month#17-0). Crime prevention with RTM focuses on places and doesn’t target people. This has been independently tested and demonstrated in multiple jurisdictions:

• Newark, NJ reduced gun violence by 35% and motor vehicle theft by 40% as part of an NIJ study. Use of RTM in Newark is now an exemplary model for cities and towns across the nation to deliver public safety with DICE through a ‘public safety collaborative’ model (see bja.ojp.gov/funding/awards/15pbja-22-gk-04502-jagp)
• Atlantic City, NJ reduced robberies by 63% (see PDF research brief)
• Fayetteville, NC reduced motor vehicle break-ins by 35% and violent crimes by 11% citywide, and wrote about it in Police Chief Magazine
• Dallas, TX reduced murders by 16%, and codified RTM into city ordinance
• Kansas City, MO reduced gun violence by 22%, and saw an estimated cost savings of $4.9M to local criminal justice and emergency healthcare systems (see PDF research brief). The city subsequently codified the use of RTM into local ordinance, creating a multiagency taskforce that uses RTM to identify the group or groups best suited to address key elements of crime problems at particular places.
• Essex Police (UK) reduced violent crime by 47%, and realized they saved approximately $10 for every $1 spent on RTM according to their cost-benefit analysis (see PDF research brief).

 
In these recent examples, crime prevention was achieved without an abundance of law enforcement actions against people located at the focus areas. Police-initiated stops, arrests and citations significantly decreased. RTM has a proven track record with successful outcomes reflecting local priorities. It meets community expectations for crime prevention and the operational needs of police at all levels of government (municipal, county, state). The book Risk Terrain Modeling: Crime Prediction and Risk Reduction (Caplan & Kennedy, 2016) offers transparency for the analytic methods and original best-practices for public safety programming.
 
Within this framework, the following points should be considered regarding any criminal justice reforms, programming, or revisions to laws other policies:
 
1. Focus on places, not only people, to prevent crime.

• Rather than continuously labeling the same places as “hot spots”, address the built environments that enable and perpetuate the undesired behaviors in those locations, and other vulnerable areas that you have influence over. Fix the “hot spots” and create safer spaces throughout the jurisdiction through place-based risk governance.

 
2. Use a place-based analytic like Risk Terrain Modeling (RTM) for crime analysis. RTM breaks down large crime trends into smaller, location-oriented problems that are easier to understand for intervention purposes. Multifaceted and multi-stakeholder prevention strategies can then focus on individual elements of the crime problem at key places.

• State, County, and Municipal departments with specific domains, purposes, and resources are brought together to address different aspects of larger problems without relying on police to perform actions out of their core duties and training. Similarly, community-based organizations with funding for their own mission-oriented services are invited to bring additional expertise and resources. Each individual stakeholder involved stays within their agency’s mission while providing a necessary piece to a wholistic intervention strategy. Contextual associations from RTM analysis highlights which departments or organizations align with priority problems at spaces they could directly impact, maintain, or influence. This application of RTM is referred to as data-informed community engagement (DICE): washingtonpost.com/crime-law/2021/12/14/crime-prevention-location-analysis.
• DICE is a best-practice for place-based public safety programming without being too prescriptive, enabling local control and empowering local vested interests to be part of a process that drives data-informed actions for crime prevention and other public safety outcomes. See, for example, the St. Louis Public Safety Collaborative (psc-stl.org).

 
3. Adopt data-informed community engagement (DICE)

• Use DICE to maximize existing local resources while meeting local needs and expectations. Allow multiple stakeholders to coproduce public safety through coordinated actions intended to disrupt situational contexts and opportunities for crime (e.g., see disrupting risk narratives: riskterrainmodeling.com/situational-context.html). Enable the stakeholders to communicate and source ideas and plans for crime prevention activities that are informed by data and analytics. Coordinate existing resources at small priority focus areas in ways that can make a big impact on the areas. Bring more perspectives to the table, allowing for clearer expectations and comprehensive problem solving.

A place-based, data-informed, and community-engaged approach to crime prevention takes the focus off of personal characteristics and, instead, considers why certain interactions among people occur at particular places.

Offenders select the locations where they’ll commit crime, and these settings become “hot spots” because they are the most suitable places for illegal behavior over-and-over again. Hot spots persist when the contexts for crime located there are not addressed. They’re symptoms of issues that demand further inquiry.
 
Police and other community stakeholders must try to understand what makes problem places attractive settings for illegal behaviors. Stopping the inquiry at only where the problem persists is like documenting repeated playful behaviors at a particular place without acknowledging the presence of swings, slides, open fields, and other features that make the area attractive, and suitable, for the expected outcome of playful activity.

To prevent crime, we need to analyze why human interactions at particular places result in repeated crime outcomes. Focusing on places to prevent crime is doable with Risk Terrain Modeling and the risk narratives that form from that spatial diagnostic technique.

Psychologists have revealed that physical landscapes consciously and unconsciously influence human behaviors. So a good way to change undesired outcomes is to alter the environment [1, 2]. A meta-analysis performed on crime intervention programs [3] found that decreases in crime were related to modified environments where offenders operated.

Altering the physical design of landscapes is a main goal of 'Crime Prevention Through Environmental Design' (CPTED). CPTED is a multidisciplinary approach to deterring criminal behavior that focuses on interactions of people at places and how these places look and feel.

But what specifically about environments should be altered to prevent different types of crime? High-crime areas should receive priority for CPTED interventions. But what makes these areas problematic? Do some co-located features of the landscape interact to aggravate crime risks? Why are some places so attractive and suitable for crime?

Spatial analysis tools such as risk terrain modeling (RTM) diagnose environmental conditions that lead to crime. RTM identifies attractors and generators that create behavior settings for the emergence and persistence of crime.  It helps allocate resources and guide CPTED activities at crime hot spots.

CPTED without RTM is like playing darts blindfolded. Know where to aim! RTM before CPTED helps target interventions on the most important environmental factors. It gets the right resources to the places that need them most. This results in the most impactful crime prevention programming.

CPTED works well. RTM does too. They're great together!

Click here to learn more. Or get started right away with RTM.

Teaching police recruits the practical value of data and crime analysis serves a public good. It will be time and money well spent. Police officers are both the generators of original data and the end users of crime analyses. Yet, they are rarely, if ever, formally trained to preserve the integrity of data measures, to see value in datasets, or to fully harness analytical products. They should be.

Billions of public dollars are spent on real estate, buildings, records management systems, and technologies to collect, manage, analyze and communicate the many, many, petabytes of data that police agencies generate. These capital assets and other related resources cost millions of dollars to build and setup, plus more to maintain and staff. It’s a multi-billion dollar industry in the United States, alone.
 
Police leaders and elected officials clearly value data because they invest heavily in its infrastructure. And most of them use the data to measure various aspects of success or failure. But there’s an obvious void: investment in the human elements that make data reliable and actionable.
 
Police of all ranks have a symbiotic relationship with data and analytic products. They are the front line brokers of crime analysis results to operational practice. Every day data informs strategies, tactics and resource deployments. It aids criminal investigations and is discoverable in courts of law. Data analysis informs command decisions and patrol activities that can directly affect officer safety, public safety, and police-community relations. Skilled analysts in police departments throughout the country turn ‘big data’ into ‘smart data’ and, when used wisely, these products offer insights to prevent crime, reduce risks, and minimize bias.
 
Despite the vital role of data in policing, lessons on it are largely missing from police academies. Skills to collect and analyze data, or interpret crime analysis products, are often unnurtured.
 
Basic law enforcement training programs in the United States last an average of 840 hours, or 21 weeks, according to the Bureau of Justice Statistics’ (BJS) survey of state and local academies. Major training areas include operations (an average of 213 hours per recruit); firearms, self-defense, and use of force (168 hours); self-improvement (89 hours); and legal education (86 hours). Data or analysis is not mentioned.

Adding an hour long module to basic training would account for less than 1% of training time, but could yield a huge return on investment for individual officers, their departments, and the communities they serve. A data-informed police force can reduce injuries on-the-job, better allocate resources, and prevent crime.
 
Policy-makers responsible for police academy curricula should add learning objectives to teach recruits why data is important, how it relates to their job, how it can be reliably collected, how it should inform their decision-making, how it can be used to develop crime prevention strategies, and how it can justly identify places for resource deployments. Topics should also include digital record-keeping, open public data portals, reading maps and tables, and measuring outcomes. Recruits should graduate with clear expectations of how they’ll produce and use data on the job, and how it will play a role in the orders they’ll be given. Police officers should be shown how the reliability and validity of data analyses can be affected by the discretionary decisions they make and the actions they take every shift; and that directly or indirectly, this feedback loop affects their future work duties and related liabilities.
 
Data becomes actionable when people interpret them in meaningful ways. This takes training and practice, and it must start with an honest introduction early in a new police recruit’s career. It requires a similar level of dedicated training that is already given to shooting accurately, driving patrol cars safely, or handcuffing quickly.
 
Cities and towns will realize huge long-term benefits when police see value in data, and how to harness it. Communities will witness a more effective, responsive and transparent police department when police officers are trained to balance empirical evidence with professional experience.

The demands of 21st century policing require that new generations of recruits learn the practical value of data and analysis. Police academies are the best places to start nurturing this trend.