Researchers from the University of Surrey and Georgia Tech have joined forces to create a crime-fighting algorithm of sorts.

The algorithm has been designed for the purpose of lending law enforcement — who often experience strained resources — a hand by quickly processing data in real-time to make predictions about the reoccurrence of illegal activity.

The new method for predicting where crimes might occur relies on a popular predictive policing software called epidemic type aftershock sequence (ETAS) — which operates like a grid-map of urban crime data — in combination with technology used in forecasting weather and monitoring the Apollo space missions.

The combination of approaches resulted in what the research team calls the Ensemble Poisson Kalman Filter (EnPKF). The EnPKF relies on real-time crime data as well as the ETAS model to make predictions about where crimes might be repeated and will also make suggestions about where short-lived hotbeds of criminal activity might crop up.

The algorithm was tested on a database containing information on over 1,000 violent gang crimes that took place in Los Angeles during the late 1990s and early 2000s. Researchers believe that the algorithm might also one day be used to monitor train delays and aftershocks from earthquakes.

Dr. David Lloyd from the University of Surrey's Department of Mathematics said: "We are cautiously excited about the Ensemble Poisson Kalman Filter, an approach that has given us an insight into when crime can be predicted, and has shown us the importance of using real-time data to make the overall system stronger. We are already well on our way to strengthening the algorithm and have even tested it against data from Chicago.

"It is important to remember that EnPKF, and algorithms similar to this, are tools used to help our law enforcement who work hard to keep our communities safe. Their use will ultimately be determined by the needs of individual departments."

The study appears in the journal Computational Statistics & Data Analysis.

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