SoylentNews

hubie [soylentnews.org] writes:

A new computer model uses publicly available data to predict crime accurately in eight U.S. cities [uchicago.edu]:

Advances in machine learning and artificial intelligence have sparked interest from governments that would like to use these tools for predictive policing to deter crime. Early efforts at crime prediction have been controversial, however, because they do not account for systemic biases in police enforcement and its complex relationship with crime and society.

Data and social 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.

In a separate model, the 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.

[...] 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.

[...] 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.

This strikes me as something more useful for determining where to improve crime prevention strategies (better lighting, etc.) than it is for catching perps in action.

Journal Reference:
Rotaru, V., Huang, Y., Li, T. et al. Event-level prediction of urban crime reveals a signature of enforcement bias in US cities. Nat Hum Behav (2022). DOI: 10.1038/s41562-022-01372-0 [doi.org]

Original Submission