Predicting Crime with a High Degree of Accuracy

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Predicting Crime with a High Degree of Accuracy

Jim Windell


            So, you thought the CBS-TV crime-fighting show “Person of Interest” was just a bit of police science fiction when it debuted in 2011, right?

            In the show, two men, one a former CIA agent and the other a genius software developer, combine to fight crime – vigilante style – by using cameras mounted around the city to predict crime and stop it before it happened. Pure TV fantasy? Wishful thinking? Won’t happen in our lifetime?

            If that’s what you were thinking during the show’s run from 2011 to 2016, a new report that comes from the University of Chicago, may surprise you.

           The report, published recently in Nature Human Behavior, indicates that scientists from the University of Chicago have developed a new algorithm that forecasts crime by learning patterns in time and geographic locations from public data on violent and property crimes. The model can predict future crimes one week in advance with about 90% accuracy.

           While predictive policing to deter crime has been around for a number of years – at least as a theory – some of the initial efforts at crime prediction have been controversial. That controversy has arisen because previous efforts do not account for systemic biases in police enforcement and its complex relationship with crime and society.

           In a separate model, the University of Chicago research team also studied the police response to crime by analyzing the number of arrests following incidents and comparing those rates among neighborhoods with different socioeconomic status. They saw that crime in wealthier areas resulted in more arrests, while arrests in disadvantaged neighborhoods dropped. Crime in poor neighborhoods didn’t lead to more arrests, however, suggesting bias in police response and enforcement.

           According to Ishanu Chattopadhyay, Ph.D., Assistant Professor of Medicine at the University of Chicago and senior author of the study, “What we’re seeing is that when you stress the system, it requires more resources to arrest more people in response to crime in a wealthy area and draws police resources away from lower socioeconomic status areas.”

           The algorithm tool was tested and validated using historical data from the City of Chicago around two broad categories of reported events: violent crimes (homicides, assaults, and batteries) and property crimes (burglaries, thefts, and motor vehicle thefts). These data were used because they were most likely to be reported to police in urban areas where there is historical distrust and lack of cooperation with law enforcement. Such crimes are also less prone to enforcement bias, as is the case with drug crimes, traffic stops, and other misdemeanor infractions.

           Although previous efforts at crime prediction often have used an epidemic or seismic approach, where crime is depicted as emerging in “hotspots” that spread to surrounding areas, the new algorithm avoids some pitfalls of the older approach. That is, the previously used approaches missed out on the complex social environment of cities and failed to consider the relationship between crime and the effects of police enforcement.

           “Spatial models ignore the natural topology of the city,” said sociologist and co-author James Evans, Ph.D., Max Palevsky Professor at the University of Chicago and the Santa Fe Institute. “Transportation networks respect streets, walkways, train and bus lines. Communication networks respect areas of similar socio-economic background. Our model enables discovery of these connections.”

           The new model isolates crime by looking at the time and spatial coordinates of discrete events and detecting patterns to predict future events. It divides the city into spatial tiles roughly 1,000 feet across and predicts crime within these areas instead of relying on traditional neighborhood or political boundaries, which are also subject to bias. The model was not found to be unique to Chicago. The model performed just as well with data from seven other U.S. cities: Atlanta, Austin, Detroit, Los Angeles, Philadelphia, Portland, and San Francisco.

           “We demonstrate the importance of discovering city-specific patterns for the prediction of reported crime, which generates a fresh view on neighborhoods in the city, allows us to ask novel questions, and lets us evaluate police action in new ways,” Evans said.

           Emily M. Bender, professor of linguistics at the University of Washington, in commenting on the study said that the focus should be on targeting underlying inequities rather than on predictive policing. She also noted that the research appears to ignore securities fraud or environmental crimes.

           Also, other crime prediction models previously used by law enforcers have been found to erroneously target certain people based on a narrower set of factors. In 2012, the Chicago Police Department – along with academic researchers –  implemented the “Crime and Victimization Risk Model” that produced a list of so-called strategic subjects, or potential victims and perpetrators of shooting incidents, which were determined by factors such as age and arrest history. This model assigned a score that determined how urgently people on the list needed to be monitored, and a higher score meant they were more likely to be perceived as either a potential victim or perpetrator of a gun crime.

           However, after a lengthy legal battle, a Chicago Sun-Times investigation revealed in 2017 that nearly half of the people identified by the model as potential perpetrators had never been charged with illegal gun possession, while 13% had never been charged with a serious offense. In contrast, the tool designed by Chattopadhyay and his colleagues uses hundreds of thousands of sociological patterns to figure out the risk of crime at a particular time and space.

           To read the original study, find it with this reference:

Rotaru, V., Huang, Y., Li, T., Evans, J., & Chattopadhyay, I. (2022). Event-level prediction of urban crime reveals a signature of enforcement bias in US cities. Nature Human Behaviour, 6(8), 1056–1068.


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