An ounce of prevention

      It's 3 a.m., and you and a few other officers are on surveillance. The city has been plagued by a rash of hot-prowl burglaries over the past few weeks. The suspect has not been seen, usually because the victims were asleep when the burglary...


   One public example was developed by the Richmond (Va.) Police Department in 2003. Richmond had issues with random gunfire every New Year's Eve holiday. The police department took several steps to solve this problem using predictive analytics. Historical data was analyzed and prioritized with considerations made for locations that had escalations in gunfire complaints since the first of the year. The information provided by the crime analysis unit included a plan to strategically place personnel where analysts expected history to repeat itself during the 2003 to 2004 New Year's holiday. The outcomes included a 49-percent reduction in gunfire complaints on New Year's Eve, and a 26-percent reduction in gunfire complaints on the following days. Forty-five weapons were also seized. An unanticipated additional benefit to this operation was a $15,000 reduction in overtime expenses, as personnel were placed where they were needed most and 50 people were able to take the holiday off, according to McCue.

   But the success of those agencies using predictive analysis is not always known, she cautions. Those agencies pursuing advanced analytics in the law enforcement arena are not always making their information public. Until that happens, she says it's difficult to know with any certainty who is making progress and what outcomes are actually occurring.

   Another real challenge is that uninformed consumers can wreak havoc on the very tools that are necessary to the predictive process. Sen. Russ Feingold of Wisconsin, for instance, nearly killed advanced analytics for police agencies when he introduced the Data Mining Moratorium Act of 2003. The bill died in committee, but it's a reminder that law enforcement must be careful as it proceeds with data gathering in the name of solving problems.

A regional approach

   The trend for local agencies to combine their resources to form regional approaches to predictive crime analysis is also helpful for many reasons. First and foremost, the costs are spread among several agencies, allowing them to maximize contributed dollars. The open sharing of data also increases the potential for agencies to place less emphasis on their political boundaries, since criminals do not tend to observe those same boundaries when they commit crimes. An added benefit to combining data is that rare and unusual events are more likely to be captured and considered when predicting crimes for an area.

   As police problem solving strives to move from a reactive (counting mode) to a proactive (anticipation and response mode), advanced predictive analytics offers the opportunity to change outcomes. While this is a simple statement, the implications have far-reaching consequences and positive potential for any law enforcement agency. A police agency willing to commit to the concept and pull resources together to put predictive analytics into practice is an agency that will be far ahead of those agencies still counting their crimes but not doing anything helpful with the numbers.

   Problem solving has always been an expectation of law enforcement agencies, but today the industry is at a point where the power of prevention has significant and perhaps immeasurably positive consequences. It appears with predictive crime analysis that an ounce of prevention is definitely worth a pound of cure.

   Eric Mills is the commander of the Strategic Services Division in the Pasadena (Calif.) Police Department.

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