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IT World Canada Features PredPol

IT World Canada Features PredPol

Imagine thwarting a crime even before it occurs by predicting when and where it will happen. Shades of the 2002 Tom Cruise futuristic action thriller Minority Report? Well, in a way yes. No ‘precogs” are being tethered to a computer yet, but some police agencies south of the border are experimenting with analytics technologies and what is being called predictive policing.

For example, for the last seven years the PredPol algorithm developed by a team of mathematicians and social scientists at the University of Los Angeles (UCLA), University of Santa Clara and UC Irvine, has been analyzing crime incident information to some members of the Los Angeles Police predict where certain types of property-related crimes are most likely to happen during certain police patrol shifts.

Similar experiments in police precincts in New York and Boston using other technologies have also been carried out.

As the PredPol Web site states, its mission is simple: “Place officers at the right time and location to them the best chance of preventing crime.

The technology is designed to predict where crimes are likely to occur in place-based “prediction boxes” as small as 500’ x 500’.

PredPol is a secure, cloud-based, software-as-a-service (SaaS) platform. Crime data, specific to crime type and specific to police shifts is mapped into it instantaneously as the information is reported to the system.

Its developers say PredPol is built around the same technology used to predict earthquakes. Crimes, they said, occur along fault line and happen in predictable patterns much like earthquakes.

The PredPol report is shared is distributed to police during the roll call and call also be delivered to officers in the form of a paper report on transmitted to their smart phones or tablet devices.

While traditional crime analysis tools such as ComStat provide a “rear view mirror policing” by mapping past crime to come up with “some hints at the future,” PredPol tells the police where crime is likely to happen in the near future. For example, the model can actually predict the risk of a crime in one location for the next 10 to 12 hours even if the latest crime reported occurred in a different location, according to users.

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