Tracking Crime in Real Time

Almost everything we do leaves a digital trace, whether we send an email to a friend or make a purchase online. That includes law-abiding citizens -- and criminals. And with digital information multiplying by the second, there are seemingly endless amounts of information for criminal investigators to gather and process.

Now Prof. Irad Ben-Gal, Dr. Eugene Kagan and Ph.D. student Aviv Gruber of the Department of Industrial Engineering in Tel Aviv University's Ibi and Aladar Fleischman Faculty of Engineering are using these digital traces to catch criminals and beef up homeland security against the threat of terrorism, developing high-powered context-based search algorithms to analyze digital data on-the-fly. With the ability to process new pieces of information instantly, these algorithms move at a high speed to support ongoing investigations.

This research was recently presented at the Convention of the Institute for Electrical and Electronic Engineers (IEEE) in Israel, and details of the algorithms will soon be published in the journal Quality Technology & Quantitative Management.

Sherlock Holmes goes digital

Like digital files, people are always on the move, Prof. Ben-Gal says. It's not enough to process the information you have and assume the output will remain relevant. "If the object is moving, modelling and eventually catching it is mathematically complex," he says. Prof. Ben-Gal and his fellow researchers work with leading companies in homeland security on how to establish patterns of terrorist or criminal activity using mostly communication files. New pieces of information are automatically plugged in to existing data, and the algorithm's analysis of the criminal's movement or pattern is reformatted.

The algorithm works like a computerized sleuth, taking pieces of information such as phone calls, emails, or credit card interactions and reducing them to a set of random variables for further analysis. All of these communications are actually pieces of one long message waiting to be decoded, explains Prof. Ben-Gal. In a single telephone call, for example, there are several variables to consider -- the recipient of the call, its length, the location of the caller himself. Once all this is known, the algorithm not only assesses patterns of crime to predict future movements, but also creates a probability map displaying the possible locations of the person or group of interest.

Like a topographical map, the probability map is divided into zones where the subject (a criminal, a terrorist organization or a drug dealing ring) is likely operating. Each zone is assigned a statistical level of probability that the subject is there. Although refining the programming of the original algorithm could take some hours, each new piece of information afterwards can be processed in a matter of milliseconds, and the analysis can be used instantly.

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