Mapping data from police reports can be an effective way to analyze where crimes are taking place. The resulting visualization can be combined with other geographic data (such as the location of schools, parks, and industrial complexes) and used to analyze and investigate crime patterns and inform responses.
In particular, the past decade has seen advances in analytical capabilities within the criminal justice community, making it possible to add more geographic and social dimensions to statistical analyzes to predict where crimes are likely to occur.
The NIJ is a longtime investor in mapping and analytical research. Over the years, the Institute has funded projects that research, evaluate, and create analytical techniques and technology to support law enforcement agencies using place-based policing practices and strategies to help answer the question: "How can we reduce better crime and improve public safety? security? security?"
This article traces the evolution of the field—from crime mapping to crime prediction (and, in some cases, crime prediction)—and discusses NIJ research investments and their future directions.
a little story
In 1829, Adriano Balbi and André Michel Guerry created maps showing the relationships between the level of education and violent and property crime in France. This is often cited as the first example of crime mapping.[1]Following this work, Joseph Fletcher in 1849 and Henry Mayhew in 1861 produced maps showing the incarceration of men and crime rates in the county, respectively.[2]
In the early 20th century, Clifford Shaw and Henry McKay mapped thousands of incidents of juvenile delinquency and analyzed the relationships between crime and various social conditions.[3]
In the 1950s, Jane Jacobs examined the built (urban) environment and the needs of city dwellers.[4]In his work, he introduced concepts that are still used today in location-based research, such as "eyes on the street" and "social capital." Although Jacobs did not attempt to predict crime, his work led to subsequent research that held that crime has spatial patterns and therefore should be predictable.[5]
In the 1970s, criminologists began to emphasize the importance of the place. Lawrence Cohen and Marcus Felson's Routine Activity Theory (RAT) described how routine activities affect crime.[6]According to the RAT, for a crime to occur, three things must coincide in the same place and time: a person motivated to commit a crime, a suitable objective and the lack of competent guardianship. Because of the consistency of our routines, Cohen and Felson argued, we should be able to predict crime: "The spatial and temporal structure of customary legal activities[7]should play an important role in determining the location, type, and amount of illegal acts that occur in a given community or society.”[8]
Similarly, Paul and Patricia Brantingham proposed the theory of environmental criminology, arguing that crime is a complex event in which four things intersect at the same time: a law, a person motivated to commit a crime, a target, and a target. place.[9]They defined this fourth dimension –place– as a distinct place where the other three dimensions intersect and provided seven propositions that describe how, where, and why people decide to commit crimes.[10]These propositions provide a framework for arguing that crimes can cluster spatially because an offender has already spent time and energy policing a neighborhood (a form of “capital”) or because learned behavior can lead to a walk cycle. The suggestions lead to the idea that the place –and not the people– is the key element of the crime. Therefore, the Brantinghams believe that "it should be possible to predict the spatial distribution of crime and to explain some of the variations in the volume of crime between urban areas and between cities."[11]
In 1979, Herman Goldstein proposed a problem-oriented policing approach.[12]This approach advocated that law enforcement officials follow a scan, analyze, response, and assess process (now known as the SARA approach) to identify, analyze, and resolve problems.[13]In the 1990s,[14]Compstat emerged as an alternative police practice to reduce crime.[15]Made famous by then-Chief Bill Bratton while he was working with the NYPD, Compstat is a truly data-driven approach to driving accountability in the police department. While these practices and strategies were not necessarily based on criminological theory, they used statistical analysis to solve crime-related problems, indicating that they were based on spatial or temporal patterns.
As these local policing theories and approaches continued to take shape, researchers began to test them. For example, Lawrence Sherman, Patrick Gartin, and Michael Buerger—with support from the NIJ—examined 323,979 calls to the Minneapolis Police Department between December 15, 1985, and December 15, 1986, to test the spatial hypothesis behind the RAT. .[sixteen]Using real addresses and intersections, the research team found that 50% of all calls came from only 3% of all possible locations. Sherman also found a higher concentration of crime around microlocations than around individuals,[17]which led to the question, "Why don't we think more of Wheredunit instead of Whodunit?"[18]These results marked the beginning of police surveillance in hotspots.[19]
From 1989 to 2007, researchers examined specific responses to crime, the effects of foot patrols, and crime trajectories. The researchers also looked at problem-oriented policing in Madison, Wisconsin. Baltimore, Maryland; and Newport News, Virginia in the 1980s[20]and began experimenting with Compstat and community policing in the 1990s and early 2000s. Today, we still have problem-oriented policing, Compstat, community policing, and hotspot policing, along with problem-oriented policing. intelligence-based problems. police surveillance and many other variations and combinations.
The critical role of the NIJ
During the 1980s, the NIJ funded evaluations of location-based policing strategies, including research by Sherman and colleagues, as well as similar research in Chicago. The NIJ also began funding the development of technologies that were later incorporated into crime mapping software.[21]
In 1997, the NIJ created the Center for Crime Mapping Research, which surveyed law enforcement departments to determine how they used analytical mapping. The center began developing training programs to improve departments' ability to use maps and spatial data sets. From 1997 to 2014, the NIJ funded the development of CrimeStat software to help practitioners and academics perform spatial analyses.[22]
In the early 2000s, the NIJ began to expand from evaluating local policing practices and strategies (for example, hotspot policing) to investigating statistical techniques used to predict and predict crime and how it affects effectiveness. and efficiency of local police practices and strategies. In 2008, Bratton, then chief of the Los Angeles Police Department, began working with deputy directors of the NIJ and the Bureau of Justice Assistance on a new approach called "predictive policing."[23]As a result, in 2009, the NIJ funded seven agencies to build predictive policing models in their jurisdictions. In 2011, the NIJ invited these agencies to propose implementation plans for the models, which would then be evaluated. The NIJ funded models developed by the Chicago Police Department and the Shreveport (Louisiana) Police Department and also funded the RAND Corporation to provide technical assistance and evaluation of the two models.
RAND's evaluation of Shreveport's predictive policing model showed three main successes.[24]First, the model improved community relations, which increased the community's willingness to interact with the police and generated better leads. Second, the Shreveport Police Department concluded that the predictions were feasible, although not truly predictive. Lastly, the model has improved actionable intelligence, leading to better competencies among analysts, leading to better pattern recognition and more relevant and timely data.[25]
As these awards drew to a close, the NIJ began issuing research calls to test geospatial surveillance strategies and explore their relationship to criminology theories. For example, the NIJ funded a risky terrain modeling assessment in six cities.[26]The evaluation concluded that conjoint analysis (an improved version of hazardous terrain modelling) could predict areas that were most at risk of committing a variety of future crimes in five cities. The models also identified environmental factors that played a role in these areas, thus allowing law enforcement to develop strategies to address them.
NIJ-sponsored assessments of near-repetitive residential burglaries concluded that bureaus are likely overestimating the number of burglaries in NR and therefore should temper their expectations. The evaluations also revealed that providing alerts to people in potential areas of the RN results in little or no reduction in thefts in the NR. However, communities within jurisdictions still prefer to share.[27]
The NIJ also funded an evaluation of the operational realism of the predictive policing model. This review was the first NIJ randomized clinical trial to investigate the effect of different police patrol strategies on violent and property crime rates. Examining the strategies of marked patrols, unmarked patrols, and an awareness patrol (with knowledge of high-crime areas but no dedicated patrols), the researchers found that a marked unit may have a modest effect on property crime, but they found no other effects. for crimes against property or violent crimes.[28]
See "Predictive Policing: The Role of Crime Prediction in Law Enforcement Operations"
In 2013, the NIJ sponsored research that compared the effectiveness of different crime prediction software. The most effective software was then used to conduct a randomized clinical trial in Denver, Colorado, testing the effects of a hotspot policing approach in targeted areas.[29]The investigation is ongoing.
In 2015, the NIJ turned its attention to investigating the value of data for law enforcement. That year, the NIJ funded research to create a flexible tool for departments to better understand the value of the data they collect. An important preliminary finding from this ongoing research is that the perceived value of data can vary widely within an office, even more so than variations within and between entire police departments.
In 2016, the NIJ launched the Real-Time Crime Prediction Challenge, which asked contestants to predict where crime is likely to be concentrated in the future within the jurisdiction of the Portland (Oregon) Police Department. Competitors submitted forecasts for all service calls, thefts, highway crime, and car thefts for the next week, two weeks, one month, two months, and three months. Initial analysis of the results seems to indicate that even the naive model can compete when there is enough crime to predict. However, when crime is rare, even the most sophisticated models have not been able to effectively or efficiently predict crime. A more detailed analysis of the results will follow.[30]
future directions
So what has changed in location-based policing over the years? The short answer is everything and nothing.
Technology has played a critical role in advancing this field and has become so accessible that most, if not all, law enforcement departments can now afford electronic records and some version of mapping software. The technology also provided the computational power needed to perform data analysis and improve analyst training. All of this allowed external departments and investigators to conduct further investigation.
But we are still trying to answer the original question: What is the best way to reduce crime? We learned that crime is concentrated in hotspots. We learned that there is stability in these hot spots over long periods of time, but much less stability when looking at short periods. We also know that the public is reluctant, and that we know very little about how these strategies affect individuals, their neighborhoods, and the community at large.
To help fill investigative gaps, the NIJ has recently shifted its focus to funding location-based (and, to some extent, people) police investigations. In 2017, the NIJ asked survey candidates to look beyond administrative data (for example, crime rates, calls for service, and arrests) and instead develop and use metrics that take into account the potential impact of policing practices and strategies in individuals, neighborhoods and communities. and law enforcement organizations (including individual officers) to determine their success or failure.
In 2018, grant applicants were invited to propose research to investigate and assess the impact of police practices and strategies on officer safety, investigative outcomes, and prosecution outcomes, while also measuring the impacts in crime rates. In addition, the NIJ wanted applicants to consider the effects of focused deterrence, hotspot conservation, and intermediate variables (eg, neighborhood and police department characteristics). The aim is to provide a more holistic understanding of the impacts of local police practices and strategies.
Smart, effective and proactive policing is clearly preferable to simply reacting to criminal acts. While there are many methods that help law enforcement respond to crime and conduct investigations more effectively, predicting where and when a crime is likely to occur (and who is likely to be responsible for past crimes) has gained significant importance. . Law enforcement agencies across the United States use a variety of predictive policing approaches.
With support from the NIJ, the RAND Corporation has developed a reference guide for law enforcement agencies interested in predictive policing. Predictive policing: the role of crime prediction in police operationsprovides a focused look at predictive techniques currently in use, identifies techniques that hold promise when used in conjunction with other policing methods, and shares findings and recommendations to inform future research and clarify the policy implications of predictive policing.
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About this article
This article was published inNIJ Journal Edition no. 281, July 2019.