This essay is reprinted with permission from the Violence Reduction Project
America needs a data-informed approach to crime prevention that maximizes many existing local services and resources. Police, but not just the police, have an important role to play in deterring offenders, hardening targets and affecting crime rates. Other stakeholders must also share the burden of crime prevention. The data-informed community engagement (DICE) framework for public safety involves police, but it does not prioritize law enforcement or use it as a singular intervention strategy.
DICE coordinates responses by multiple stakeholders to develop and deploy place-based strategies that disrupt situational contexts and opportunities for crime.
DICE efforts in Atlantic City, Newark, Dallas and Kansas City have shifted the focus of crime prevention away from strictly person-based law enforcement by analyzing data to learn where and why people tend to commit crimes in certain settings. Multifaceted responses disrupt these contexts for crime. These four cities exemplify current “best practices” for DICE, and offer insights for how to adopt it in other municipalities of all sizes across the country.
Atlantic City police connect shootings to drug sales
Police in Atlantic City, New Jersey used data analytics to connect shootings to drug sales and related turf conflicts whereby certain “convenience stores” are the places where drug buyers are solicited. Nearby “laundromats” are locations where drug transactions are made. “Vacant buildings” located nearby are used by drug dealers as stash houses for drugs and weapons and by drug buyers to use drugs after purchase. These data-driven insights were shared with community leaders to form a consensus that certain areas will probably experience shooting incidents in the future.
Multiple local perspectives were solicited to interpret the data analysis and plan a strategy to mitigate this crime problem at key areas. Police focused patrols around laundromats and conducted business checks with convenience store managers every shift. The city’s Planning and Development Department prioritized remediation of vacant properties and the installations of new LED streetlights around these businesses. The local electric utility authority and mercantile association also collaborated in the process. Data analytics and ground-level input helped police in Atlantic City solve crime problems in diverse ways, beyond the status quo.
Bright data remedies
Andrew Ferguson, a former public defender, introduced the term “bright data” in his book, “The Rise of Big Data Policing.” He explains that a data-driven ability to identify crime risks does not always require a law enforcement remedy.
Ferguson recommends remedies that are “smart (precise and focused),” “illuminating (revealing hidden problems and patterns)” and that decouple crime problems from policing solutions. Such “bright data remedies” offer a contrast to big data policing because “in some cases, police may be necessary to address the identified risk, but in other cases, a nonpolice response might suggest a better, more long-lasting solution.”
Newark Public Safety Collaborative
Contemporary data analysis methods can identify risks and situational contexts for crime with a focus on places, not people. [1] Techniques like risk terrain modeling [2] diagnose environmental conditions that connect with crime problems. Checked and balanced by human analysts and local knowledge, these analytic resources can empower change agents with “bright data insights” to best use existing local resources to address problems in coordinated and comprehensive ways. Newark, New Jersey offers a compelling and sustainable example of this.
The Newark Public Safety Collaborative [3] launched in 2018 based on evidence from a prior National Institute of Justice study that reduced gun crimes in high-risk areas of Newark by 35% compared to control areas.
Housed in the School of Criminal Justice at Rutgers University, the Newark Public Safety Collaborative coordinates the various strengths of dozens of community stakeholders working to address problems that connect with violent crime throughout the city. It democratizes data and analytics that were once available only to the police department and makes them accessible and understandable to everyone – including other city agencies, non-profit organizations, businesses, and neighborhood groups.
This informs a variety of local efforts by local stakeholders, such as:
- Ensuring safe passages for children to walk to school;
- Adding better lighting in priority areas;
- Remediating vacant lots at high-risk places;
- Improving access to housing for people at risk of victimization.
One recent initiative involves a poster contest and flyer distribution campaign to reduce auto theft. Preliminary evidence suggests it’s working quite well, plus community members are invested in the intervention program and share credit for its success.
a Place-based, data-informed and community-engaged approach to crime prevention
Community leaders and change agents who participate in DICE programs like the Newark Public Safety Collaborative gain locally contextualized data and insights that supplement their own expertise and resources to efficiently focus on places that need them most.
While each of their initiatives may appear separate from one another, they combine to produce a deliberate and impactful response to crime problems throughout the city as a whole because everyone is acting on the same information to guide their plans and actions. The data, analyses, interpretations of results and intervention plans are accessible and transparent. The result is a comprehensive, sustainable, and measured crime prevention strategy for the City of Newark. Policing is just one small part of the larger effort.
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 environments 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 are symptoms of other phenomena that demand further inquiry.
Police and other community leaders must seek to understand what makes problem places attractive settings for illegal behaviors. Ending 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 must endeavor to analyze why interactions of people occur at particular places resulting in repeated crime outcomes. Such an assessment invites new ways of addressing chronic problems at these settings that go beyond a law enforcement response. Retired Police Chief Henry White went so far as to explain [4] how DICE “improves community relations and does not define people as the problem…. [it can] “reduce crime without contributing to mass incarceration.”
Dallas, Kansas City initiatives
Dallas, Texas is another city poised to be a leader of data-informed community engagement through its newly created Office of Integrated Public Safety Solutions housed within city hall. This office was heavily influenced by Operation Safe Surroundings, a short downloadable eBook from the Rutgers Center on Public Security that serves as a DICE implementation guide.
Another initiative in Kansas City, Missouri is spearheaded by the Kansas City Police Department but invites participation by multiple city agencies. An article in the peer-reviewed journal Police Quarterly [5] reports that this initiative significantly reduced violent crimes by over 22% during the first year.
Sharing the burden of public safety with multiple stakeholders
These and other cases demonstrate the potential for DICE to support innovative problem-solving and fairer distributions of public resources. For example, the Kansas City Police Department officers shared risk terrain modeling analyses with management-level members of municipal departments such as Regulated Industries, Public Works and the Health Department. A business with numerous code violations was identified in the risky area and dealt with by the fire marshal. At the same time, the transit authority removed a problematic bus stop nearby. These facilities were connected to the situational contexts of crimes in the problem area. So, the responsive actions were designed to mitigate these spatial risks and encourage new routine activities in the area.
Debates over defunding police have led to a general desire to reimagine crime prevention and public safety through a process that transfers some police responsibilities to other entities. Success in this regard will depend largely on access to data and analytics to inform strategic plans and allocate related resources in ways that meet community expectations.
Coordinated responses to crime problems require a routine and synchronized sharing of data analytics among all partners involved. To date, these tools remain largely in the hands of police departments, who also control the messages informed by them. This monopoly tends to yield outcomes that prioritize law enforcement activities or do not necessarily align with community priorities. This may be partly due to unrealistic expectations police leaders believe are set for their departments, as they’re often tasked with single-handedly solving complex crime problems. But preventing crime does not have to be so one-sided and isolating.
Reimagining crime prevention requires embracing new expectations for policing that share the burden of public safety with multiple stakeholders. Data-informed community engagement is crime prevention and policing reimagined.
References
1. Caplan JM, Kennedy LW. Risk Terrain Modeling: Crime Prediction and Risk Reduction. Berkeley, CA: University of California Press, 2016.
2. Caplan JM, Kennedy LW, Barnum JD, Piza EL. (2015). Risk Terrain Modeling for Spatial Risk Assessment. Cityscape, 2015; 17(1), 11-20.
3. Giménez Santana A, Caplan JM, Kennedy LW. (forthcoming). Data-Informed Community Engagement: The Newark Public Safety Collaborative. In E. Piza & B. Welsh (Eds). The Globalization of Evidence-Based Policing: Innovations in Bridging the Research-Practice Divide. London, UK: Routledge Press.
4. Melamed S. Can Atlantic City’s Bold Experiment take Racial Bias out of Predictive Policing? The Philadelphia Inquirer, August 10, 2017.
5. Caplan JM, Kennedy LW, Drawve G, Baughman J. (In Press). Data-Informed and Place-Based Violent Crime Prevention: The Kansas City, Missouri Risk-Based Policing Initiative. Police Quarterly.
