Learning to Detect Patterns of Crime

• Activity Number: 214
• Type: Topic Contributed
• Date/Time: Monday, August 4, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistical Learning and Data Mining
• Author(s): Cynthia D. Rudin*+ and Tong Wang and Dan Wagner and Rich Sevieri
• Companies: MIT and MIT and Cambridge Police Department and Cambridge Police Department
• Keywords: criminology ; machine learning ; pattern detection ; community detection in networks ; optimization

Abstract

Our goal is to automatically detect patterns of crime. Among a large set of crimes that happen every year in a major city, it is challenging, time-consuming, and labor-intensive for crime analysts to determine which ones may have been committed by the same individual(s). If automated, data-driven tools for crime pattern detection are made available to assist analysts, these tools could help police to better understand patterns of crime, leading to more precise attribution of past crimes, and the apprehension of suspects.

We will describe work from the Prediction Analysis Lab at MIT and the Crime Analysis Unit of the Cambridge Police Department. We have been developing algorithms that find patterns of crimes from within a database. These methods have had promising results on identifying patterns within Cambridge's historical crime data.