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.
When crime or violence problems emerge or persist, focusing on the actual places, and the situational contexts for crime that they offer, yields lasting outcomes for prevention and safety.
CPTED without RTM is like playing darts blindfolded.
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  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.
1. Louiselli, J. K., & Cameron, M. J. (Eds.). (1998). Antecedent control: Innovative approaches to behavioral support. Baltimore, MD: Brookes Publishing.
2. Kennedy, L. W., & Forde, D. R. (1998). When push comes to shove: A routine conflict approach to violence. Albany, NY: State University of New York Press.
3. Braga, A. A., & Weisburd, D. (2010). Policing problem places: Crime hot spots and effective prevention. Oxford: Oxford University Press.
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.