(UPI) -- Thefts are down eight percent in a California town thanks to a computer model that can predict where burglaries are going to happen.
The model, created in a collaboration between University of California, Riverside sociologist Robert Nash Parker and the Indio Police Department, examines census block groups in order to predict areas of criminal activity.
By looking at these tiny sections and then cross-referencing them with crime data and truancy records, the model is able to find patterns of crime over time and space
“This is the wave of the future,” said Indio Police Chief Richard Twiss. “It is my hope this relationship with Dr. Parker will continue throughout my tenure with this department, not only on this project, but with others as well.”
Parker and Twiss presented the project at the International Association of Chiefs of Police Conference in October.
“This is still cutting-edge and experimental,” Parker said.
“Big data gives you statistical power to make these kinds of predictions. It makes it possible for us to anticipate crime patterns, especially hot spots of crime, which allows law enforcement agencies to engage in targeted prevention activities that could disrupt the cause of crime before the crime happens.”
Parker and the IPD discovered that there was a definite link between truancy arrests and burglaries. “We assumed there was a correlation between daytime burglaries and truancy,” Twiss said. “When you actually have the data that shows it, then you can evaluate the processes, and the breakdowns in the processes.”
Armed with the findings, police launched a burglary and truancy prevention task force, a media campaign and conducted community outreach.
“We are deploying people differently and doing more community outreach,” Twiss said.
“We discuss in briefing those areas that are being impacted. We had our crime analyst put maps together a few months ago based on trends we were seeing and we did pro-active patrols in those areas. Instead of having to respond to past crimes our arrests went up and instances of theft were reduced. We want to produce real-time, weekly hotspot maps that will predict patterns and trends. That’s the direction we’re heading.”