A new computer model uses publicly available data to predict crime accurately in eight cities in the U.S., while revealing increased police response in wealthy neighborhoods at the expense of less advantaged areas.
Advances in artificial intelligence and machine learning have sparked interest from governments that would like to use these tools for predictive policing to deter crime. However, early efforts at crime prediction have been controversial, because they do not account for systemic biases in police enforcement and its complex relationship with crime and society.
University of Chicago data and social scientists have developed a new algorithm that forecasts crime by learning patterns in time and geographic locations from public data on violent and property crimes. It has demonstrated success at predicting future crimes one week in advance with approximately 90% accuracy.
In a separate model, the team of researchers 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.
“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,” said Ishanu Chattopadhyay, PhD, Assistant Professor of Medicine at UChicago and senior author of the new study, which was published on June 30, 2022, in the journal Nature Human Behaviour.
The new 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.
“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.”
— Ishanu Chattopadhyay, PhD
Previous efforts at crime prediction often use an epidemic or seismic approach, where crime is depicted as emerging in “hotspots” that spread to surrounding areas. These tools miss out on the complex social environment of cities, however, and don’t 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, PhD, Max Palevsky Professor at UChicago 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 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.
Chattopadhyay is careful to note that the tool’s accuracy does not mean that it should be used to direct law enforcement, with police departments using it to swarm neighborhoods proactively to prevent crime. Instead, it should be added to a toolbox of urban policies and policing strategies to address crime.
“We created a digital twin of urban environments. If you feed it data from happened in the past, it will tell you what’s going to happen in future. It’s not magical, there are limitations, but we validated it and it works really well,” Chattopadhyay said. “Now you can use this as a simulation tool to see what happens if crime goes up in one area of the city, or there is increased enforcement in another area. If you apply all these different variables, you can see how the systems evolves in response.”
Reference: “Event-level Prediction of Urban Crime Reveals Signature of Enforcement Bias in U.S. Cities” by Victor Rotaru, Yi Huang, Timmy Li, James Evans and Ishanu Chattopadhyay, 30 June 2022, Nature Human Behaviour.
The study was supported by the Defense Advanced Research Projects Agency and the Neubauer Collegium for Culture and Society. Additional authors include Victor Rotaru, Yi Huang, and Timmy Li from the University of Chicago.
Looks like we don’t have free will after all!!
Does this model account for citizen cooperation with police? Should be easy to measure with amount of sworn witness statements and a 5 point quality score of those witness statements pertaining to establishing identity of suspect and the elements of the offense. It might explain more accurately why more arrests occurr in wealthier neighborhoods as opposed to disadvantaged ones. I estimate that more arrests occurr due to better quality of cooperation with police in wealthier neighborhoods. Also, a simple look at staffing would also help with that conclusion. In my city, for instance, there are 3 times the amount of officers and resources poured into the economically disadvantaged neighborhoods than are committed to wealthier neighborhooods. An analysis of call logs shows that they are sending officers to economically disadvantaged neighborhoods because more crime is reported by citizens in those neighborhoods. Whether any further cooperation goes beyond reporting crime remains to be seen, however. If the lack of cooperation is statistically proven then perhaps instead of a predictor of crime we need to study how to gain citizen cooperation from those whom reside in those disadvantaged neighborhoods, then worry about predicting crime afterwards.