Predictive Policing Blog

Predictive Analytics: Beyond Policing

The concept of predictive policing has been embraced by a growing number of law enforcement agencies as an effective tool to reduce crime rates and victimization. Predictive policing is the practice of applying analytical techniques to large crime datasets to enable law enforcement to identify where and when specific crimes are most likely to occur. This allows officers to take a more proactive approach to combating crimes, rather than responding to the scene after a crime is reported.

A variety of software platforms, including PredPol, have been developed to help police identify locations, crime types, and the time of day when these crimes are most likely to occur. PredPol uses only three data points to make its predictions: crime type, crime location and the date and time of the crime. PredPol does not use any personal or demographic data, such as information about the offender or the neighborhood because this information actually makes predictions less effective. An additional benefit of this approach is that it eliminates any chance of inadvertent bias or profiling.

Although local law enforcement agencies have been the pioneers in applying predictive policing technologies, they’re not the only organizations that can benefit.

Applying Predictive Analytics to Retail

The National Association for Shoplifting Prevention reports there are about 27 million shoplifters nationally—that’s 2 in 11 people! The costs can add up, especially for big store retailers that can see anywhere from millions to billions of dollars in loss prevention expenses and costs annually.

Applying PredPol’s existing algorithms and using the three data points mentioned above, a platform such as PredPol could analyze data for shoplifting events by store location and time of day. Identifying shoplifting patterns would allow loss-prevention officers to proactively patrol these locations or take other measures to reduce theft. The National Retail Federation  shared a success story of a Co-Op in the United Kingdom that significantly reduced crime, theft and operating costs with the help of data analytics.

Protecting the Masses

Large corporate campuses often use private security services or their own staff to respond to non-critical security events before law enforcement gets involved. With sufficient event data, their staff can also proactively patrol targeted areas to reduce rates of minor crimes such as auto burglaries materials thefts.

Large private/public event spaces such as malls and stadiums can also use predictive analytics to identify event hotspots and deploy security personnel to reduce the rate of crimes common to such spaces, such as pickpocketing and robberies.

Could the use of PredPol in the private sector be in the near future? There’s certainly a need for it.

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