Predictive Policing®

The Predictive Policing Company ®

PredPol’s cloud-based software enables law enforcement agencies to better prevent crime in their communities by generating predictions on the places and times that future crimes are most likely to occur.

See PredPol in Action:

“I’m not going to get more money. I’m not going to get more cops. I have to be better at using what I have, and that’s what predictive policing is about… If this old street cop can change the way that he thinks about this stuff, then I know that my [officers] can do the same.”

-Los Angeles Police Chief Charlie Beck

Mission:

place officers at the right time and location to give them the best chance of preventing crime. Communities across the US and overseas are experiencing dramatic reductions in crime thanks in large part to PredPol technology — helping law enforcement prevent crime before it occurs.

  • LAPD 's Foothill division saw a 20% drop in predicted crimes year over year from January 2013 –2014
  • Modesto, CA PD just reported the lowest crime rates in 3 years since deployment in January 2014
  • Alhambra, CA PD reported reported a 32% drop in burglaries and a 20% drop in vehicle theft since deploying in January 2013. The city reported its lowest month of crime in history in May 2014
Jurisdictions large and small have seen dramatic reductions in crime since deploying PredPol.

The Challenge

Police departments nationwide are facing budget freezes and deep cuts, requiring them to manage their resources more effectively while still responding to public demand for crime prevention and reduction.

The Solution

Based on models for predicting aftershocks from earthquakes, PredPol’s patent-pending technology forecasts highest risk times and places for future crimes. The program complements officers’ intuition by targeting place-based prediction “boxes” as small as 500' by 500'.

The Difference

In contrast to technology that simply maps past crime data, PredPol applies advanced mathematics and adaptive computer learning. It has resulted in predictions twice as accurate as those made through existing best practices by building on the knowledge and experience that already exists.

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