Program Profile: Risk-Based Policing Initiative (Kansas City, Missouri)

This program was a place-based crime policing initiative implemented for 1 year to reduce violent crime. Risk-based policing initiatives promote data-informed decisions based on a process of defining the problem, gathering information, and analyzing data. The program is rated Promising. The program resulted in a statistically significant reduction of 22.6 percent in violent crimes in the intervention areas, compared with the comparison areas.

A Promising rating implies that implementing the program may result in the intended outcome(s).

Program Goals/Target Sites

The Risk-Based Policing Initiative (Kansas City, Missouri) was a place-based crime policing initiative implemented for 1 year to reduce violent crime. In 2017 a newly appointed Chief of Police made reducing violent crime his primary issue to be addressed by the risk-based policing initiative. His goal was to counter violent crime in a way that would be efficient and sustainable in terms of police officer buy-in and police–community relations. Implementation of the initiative began in 2019.

Risk-based policing is an operational approach to prevent crime by reducing and managing “place-based crime risks” (Kennedy, Caplan, and Piza 2018). Risk-based policing initiatives promote data-informed decisions based on a process of defining the problem, gathering information, and conducting data analysis. This type of policing initiative provides police officers with actionable information about where to go and what environmental features to focus on when they arrive at priority places. Police officers employ strategies both to deter illegal behaviors at high-risk areas and to mitigate risky settings for long-term prevention, rather than employing reactive strategies that rely on calls for service or law enforcement actions.

Program Theory

Risk-based policing is based on the theoretical framework of situational crime prevention, which implies that certain characteristics of a location or setting can attract illegal behavior and policing methods should be focused on proactively addressing the settings that encourage crime (as cited in Caplan et al. 2021). Police target the spatial crime patterns of an area over time and consider the locations’ crime attractors (characteristics that draw individuals to the location to commit crimes) and crime generators (increased opportunities in those locations to commit crimes) [Brantingham and Brantingham 1995].

Program Activities/Components

The Risk-Based Policing Initiative in Kansas City consisted of police departmentwide training, data management technology and procedures, data-informed decisionmaking, policing at target areas, and multistakeholder accountability meetings.

Training. Two half-day workshops concentrated on the principles and best practices of risk-based policing. One workshop was for Kansas City Police Department command staff to convey the police chief’s goals for the initiative. The other workshop was for civilian crime analysts. Training and continuing education sessions for all involved stakeholders continued every month for the first 5 months of implementation. These sessions provided extra information about risk-based policing to stakeholders, which included patrol officers, community interaction officers, and social workers.

Data Management. The Kansas City Police Department’s computer-aided dispatch (CAD) system was used to track policing activity in the target areas through an “RTM” (risk terrain modeling) CAD disposition code. Officers used this code at the conclusion of any incident that aligned with the scope of the risk-based policing initiative. Officers were instructed to add notes in RTM CAD call records describing what was done to address a risk factor or what interagency follow-up might be needed. A custom CAD report extracted all incidents that used the RTM disposition code along with officers’ notes. This report was run for each patrol division on a weekly basis and allowed division commanders to easily review, track, and follow up on activities related to the risk-based policing initiative.

Data Informed Decision-making. The Kansas City Police Department collected data to inform how the department designated targeted areas, and data to inform policing activities at the targeted areas. This information is provided to evaluators as needed.

• Data for Designating Target Areas. Crime analysts shared risk terrain maps and other analytic outputs with patrol division commanders. Division commanders were given maps displaying the highest-risk places and related risk factors, along with chronic violent crime hot spots. Highest-risk places were defined as places greater than two standard deviations from the mean relative risk score; hot spots were defined as places with kernel density estimation (KDE) values greater than two standard deviations from the mean KDE value (Hart and Zandbergen 2014).These maps were layered to form a single image that enabled commanders to easily see the most problematic places in their patrol divisions as a function of current vulnerabilities (that is, high-risk places) and recent past exposures (i.e., hot spots) to crime. Command staff used these maps to prioritize and select target areas based on what they felt they could address with available resources.
• Data for Policing Activities. Activities for this initiative in the target areas included 1) directed patrols, 2) business checks, 3) coordination and deployments of nonpolice resources, and 4) positive police–community engagements. One-page intelligence reports were created for patrol commanders and officers for each target area. This intelligence report provided a map, a list of ranked environmental risk factors to address, and peak days/times of target crimes in the area. The purpose of the intelligence report was to give patrol officers what they needed to know on a single page: where to go, what to look for and concentrate on when they got there, and when they needed to show up. Spatial intelligence helped patrol officers make decisions about what to do at the target areas beyond traditional law enforcement actions. These activities were intended to be easily manageable, forged buy-in among patrol officers working the target areas, and kept command staff apprised of these activities within their divisions. This approach to policing involved multiple stakeholders sharing the burden of crime prevention by deploying existing resources more efficiently.

Accountability. The risk-based policing initiative was incorporated into existing weekly accountability meetings for Kansas City Police Department commanders from the patrol and investigations units. The accountability meetings consisted of discussions on the ongoing crime problems and the unit’s plans to address those problems; updates and follow-ups from previous meetings; and formalizing requests for support from elements outside of patrol. One meeting every 4 to 6 weeks was dedicated to incorporating elements of the risk-based policing initiative. The risk-based policing meetings consisted of discussions on the status of their target areas in terms of crime metrics, risk-reduction actions performed by officers, or challenges that require more than police resources. The police chief emphasized during these meetings that, to prevent crimes, policing strategies designed to mitigate environmental risk should be prioritized over enforcement-based policing tactics against individuals.

Study 1

Caplan and colleagues (2021) used a time-series design to examine the effectiveness of the yearlong implementation of the Risk-Based Policing Initiative in Kansas City, Missouri, on reducing violent crime in the intervention areas, compared with the comparison areas. Implementation of the initiative began on March 15, 2019. The pre-intervention period was March 15, 2018, through March 14, 2019. The post-intervention period was March 15, 2019, through March 14, 2020. The estimated population of Kansas City, Missouri, is 495,000 individuals residing across four counties (Cass, Clay, Jackson, and Platte) under the jurisdiction of the Kansas City Police Department (KCPD).

The intervention areas received the Risk-Based Policing Initiative, while the comparison areas received policing as usual.

Intervention areas were selected from maps displaying the highest-risk places and related risk factors diagnosed by risk terrain modeling using RTMDx software, and chronic violent crime hot spots produced by kernel density estimation (KDE) in ArcGIS. Highest-risk places were defined as places greater than two standard deviations from the mean relative risk score; hot spots were defined as places with KDE values greater than two standard deviations from the mean KDE value (Hart and Zandbergen 2014). Thirteen intervention areas were selected across four (of six) patrol divisions in Kansas City: 1) Central, 2) Metro, 3) East, and 4) South. These patrol divisions cover half of the city’s total land area and accounted for more than 90 percent of violent crimes in the 12 months before the risk-based policing initiative. Between one and four noncontiguous intervention areas were located in each patrol division. The average size of intervention areas within these patrol divisions was 0.38 square miles, with a total coverage area across all divisions of 4.9 square miles, which accounted for 1.5 percent of the city’s land area. The count for violent crimes during the pre-intervention period in the target areas was 1) Central = 166 violent crimes, 2) East = 158 violent crimes, 3) Metro = 210 violent crimes, and 4) South = 178 violent crimes.

Comparison areas were identified at the same time as the intervention areas, based on the same initial set of maps and analytic outputs given to patrol division commanders. Comparison areas were selected if they had qualities similar to the intervention areas in terms of zoning and geographic size. One comparison area was selected in each patrol division. The average size of the comparison areas was 0.85 square miles, with a total coverage of 3.4 square miles, which accounted for 1.1 percent of the city’s land area. The count for violent crimes during the pre-intervention period in the comparison areas was 1) Central = 46 violent crimes, 2) East = 36 violent crimes, 3) Metro = 59 violent crimes, and 4) South = 39 violent crimes.

The outcome of interest was violent crime, which was defined as homicide, aggravated assault, and robbery incidents that involved a weapon. This data were obtained from the KCPD records management system and manually verified by KCPD staff for reliability and validity. The KCPD provided the evaluators with data and details about how the department designated target and comparison areas and how data were used to inform policing activities at the target areas, along with examples pulled from computer-aided dispatch calls for service notes. Target buffer areas were included to assess displacement or diffusion of benefits through a weighted displacement difference test. The ABC spreadsheet calculator version 1.4 (Ratcliffe 2019) was used to assess statistically significant net effects of crime changes in the target areas, compared with the comparison areas. No subgroup analyses were conducted.

Caplan and colleagues (2021) used a time-series design to examine the effectiveness of the yearlong implementation of the Risk-Based Policing Initiative in Kansas City, Missouri, on reducing violent crime in the intervention areas, compared with the comparison areas. The evaluators included target buffer areas to assess displacement or diffusion benefits using a weighted displacement difference test (Wheeler and Ratcliffe 2018). The average street block length in Kansas City is 466 feet. Buffer areas extended 500 feet around target areas. This distance approximates the length of one street block to also include both sides of a street. The evaluators found that post-intervention the target buffer areas performed better than the comparison areas, but not as well as the target areas, meaning a diffusion of benefits amplified the target area effects.

These sources were used in the development of the program profile:

These sources were used in the development of the program profile: