Predictive Policing Research

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Predictive Policing Demonstration and Evaluation Program

For the first phase of a two-phase effort, NIJ issued a competitive solicitation for Fiscal Year 2009 seeking proposals from law enforcement agencies to plan predictive policing models in their jurisdictions. Seven agencies won awards to develop plans to implement a predictive policing demonstration and evaluation program. For the second phase, NIJ issued a solicitation for Fiscal Year 2011 that invited Phase 1 awardees to submit proposals to implement the plans they developed. Two agencies — the Chicago Police Department (CPD) and the Shreveport (La.) Police Department — won grants to continue their work by putting their plans in place.

RAND Corp. won an award to provide technical assistance to the agencies in Phase 1 and to serve as an independent evaluator in Phase 2. In its role as evaluator, RAND will evaluate the efficacy of the concept of predictive policing as seen in Chicago and Shreveport’s predictive policing models.

The Chicago Program
CPD received an award to implement a pilot that in part will evaluate the ability of modified pattern-matching software currently used for medical diagnostic purposes to predict crime patterns. It will also evaluate the efficacy of a software tool that quantifies and maps gang activity to predict emerging areas of gang conflict. RAND will evaluate the pilot in terms of accuracy of prediction, process and impact, using randomized, retrospective and quasi-experimental studies. CPD’s research partner is the Illinois Institute of Technology (IIT).

The Shreveport Program
The Shreveport Police Department received an award to implement a pilot that will use a randomized experimental design using experimental and control groups involving six of the highest-crime policing districts in Shreveport. The pilot will evaluate the “broken windows” theory of policing in an operational setting and employ a predictive model using leading indicators related to that theory such as juvenile complaints, loud music, disorderly persons, suspicious activity, loitering, disputes and prowlers. RAND will measure the efficacy of the pilot in terms of its ability to reduce tactical crimes such as shootings, robbery, burglary, auto break-ins, outside residential thefts, outside business thefts and auto thefts.

Geospatial Police Strategies

NIJ has awarded grants to several agencies to study geospatial strategies in policing.

Geospatial Predictive Policing Research Projects
Title and Grant Details​Description​
Linking Theory to Practice: Testing Geospatial Predictive Policing in a Medium-Sized Police Agency
Justice & Security Strategies, Inc.
NIJ grant 2013-IJ-CX-0054 (see grant details)
​Predictive policing is based on the premise that crime can be accurately forecasted or predicted for specific areas. Justice & Security Strategies, Inc., will examine the process of predictive policing in Columbia, S.C. The project will address the following questions: How does a police agency implement predictive policing? What are the underlying theories that guide the strategy? What are the data that are needed? What types of software or hardware are necessary? How does predictive policing "work" in the field? What is the practical utility of it? How do we know that predictive policing is effective?

Learn more about the study in Columbia, S.C.
Research on Offender Decision-Making Utilizing Geo-Narratives
Kent State University
NIJ grant 2013-R2-CX-0004 (see grant details)
Theory and research suggest that both opportunity and rational choice play key roles in shaping the spatial distribution and specific targets of crime. Although place matters in shaping crime patterns, research indicates that offenders are active agents within these larger contexts, making decisions that are inexorably linked to their environment. Researchers from Kent State University are investigating how place and space shape criminal events. They will draw on geo-narrative data, or “crime insight maps,” created with ex-offenders, law enforcement and other residents to understand better how environmental cues lead to certain locations becoming crime hot spots.

Learn more about the study by Kent State researchers.
Policing by Place: A Proposed Multi-Level Analysis of the Effectiveness of Risk Terrain Modeling for Allocating Police Resources
New York City Police Department
NIJ grant 2013-IJ-CX-0053 (see grant details)​
​The New York City Police Department spearheaded the development of hot spot policing with the CompStat process. New York City has seen remarkable drops in crime, but persistent hot spots remain. The Department, in partnership with Rutgers University and John Jay College of Criminal Justice, seeks to further reduce hot spots through risk terrain modeling (RTM) research. This four-phase project will test the applicability of RTM across New York City; develop a conjunctive analysis to highlight key risk factors; and historically and prospectively test the interaction of risk, crime and enforcement strategies. Together, these phases will produce the ability to model enforcement strategies before committing resources. 

Learn more about the study on risk-terrain modeling in New York City.
Risk Terrain Modeling Experiment: A Multi-Jurisdictional Place-Based Test of an Environmental Risk-Based Patrol Deployment Strategy
Rutgers University
NIJ grant 2012-IJ-CX-0038
See grant details
Does sending police patrols to areas at high-risk for crime affect the number and location of new crimes? Rutgers University is conducting an experiment to measure the extent of the effect. Researchers are using Risk Terrain Modeling to define high-risk areas.[1] The project has two primary goals: 1) to replicate and validate Risk Terrain Modeling in multiple jurisdictions and across many crime types; and 2) to evaluate theoretically- and empirically-grounded risk-based interventions targeted at high-risk micro-level environments.[2] High-risk areas are matched with equivalent control areas through a propensity score matching technique. Participating police agencies are Arlington, Texas; Chicago, Ill.; Colorado Springs, Col.; Glendale, Ariz.; Kansas City, Mo.; and Newark, N.J. 

Learn more about the study on risk terrain modeling.
Translating “Near Repeat” Theory into a Geospatial Police Strategy: A Randomized Experiment Testing a Theoretically-Informed Strategy for Preventing Residential Burglary
The Police Foundation
NIJ grant 2012-IJ-CX-0039 (See grant details)
Research has shown that once a burglary occurs on a street, the homes on that street and on nearby streets are at a much higher risk of burglary over the next one to two weeks. This research finding has not yet been translated into actionable crime prevention strategies for police agencies. To address this gap, the Police Foundation will use a randomized controlled trial to test whether quickly notifying community residents that they are at an increased risk for a burglary and providing them with burglary prevention tips reduces incidents of further burglary in the high-risk time period. Participating police agencies are Baltimore, Md., and Redlands, Calif. 

Learn more about the study burglary notifications.


[note 1] Risk Terrain Modeling is an approach to spatial risk analysis that uses a geographic information system to attribute qualities of the real world to places on a digitized map. It operationalizes the spatial influence of risk factors to common geographic units, then combines separate layers to produce “risk terrain” maps showing the presence, absence or intensity of all risk factors at every location throughout the landscape. Theoretically- and empirically-grounded risk terrain maps show where conditions are conducive for crimes or other hazardous events to occur in the future. Risk Terrain Modeling offers a statistically valid way to articulate and communicate criminogenic and vulnerable areas at the micro level.

[note 2] The term micro-level environments has no set definition and can refer to a variety of units smaller than a city, such as a neighborhood, block group, block, street face or even an individual address.

Date Created: January 13, 2014