Police Helps Lower Crime Rates with Crime Prediction Software December 23, 2008Posted by Bahadir Sahin in Calismalar (Studies), English, Haber (News).
Tags: crime and GIS, Crime Prediction Software, Geographical Information System (GIS)
The Department of Pre-Crime is an intriguing concept used in the 2002 science fiction film Minority Report, and it’s inching toward reality for the Richmond (Va.) Police Department (RPD). Instead of the film’s fictitious “precogs” who float in a pool of water while foreseeing crimes, the police department uses crime analysis and prediction.
The concept is based on the idea that criminal behavior often follows identifiable patterns that can be used to predict criminal acts. By collecting yesterday’s crime statistics and external factors – weather, time, day, moon phase, etc. – officers can estimate when and where tomorrow’s crimes will occur using business intelligence (BI) capabilities.
Beginning in 2006, a new system was launched in a phased implementation that provides predictive crime analysis, data mining, reporting and GIS capabilities to the RPD. Officers receive the most current information available, including predictions of crime hot spots they can access before a shift. Data from the records-management system is integrated and analyzed continuously.
The RPD’s innovative enterprise platform produced dramatic results. By moving from a “reactive crisis management structure” to a “proactive problem deference model,” the department lowered the city’s ranking from fifth most dangerous U.S. city in 2004 to 15th most dangerous city in 2005, and a 21 percent reduction in major crimes from 2005 to 2006. The department also won Gartner’s 2007 BI Excellence Award.
“We’re replicating the intuitive nature of the seasoned veteran cops – the guys who have been on the force for 25 years and know certain sections of the city really well and operate almost out of complete intuition – who know more than a crime map might show them,” said Stephen Hollifield, information services manager of the RPD. “Our application attempts to do the same thing, but provides that type of intuitive picture about areas and gives them to our ‘green officers’ who have been on the force for only two years and haven’t developed that sense yet. This kind of speeds up the process and gives pictures based on all the crimes in the past, weather, times of day, day of week and moon phases.”
BI Approach Works
The RPD began down the road of crime prediction in 2005, when it named Rodney Monroe the police chief of its 700 officers. The immediate objective was lowering the Richmond’s crime rate for its 220,000 residents, so Monroe met with BI software vendor Information Builders and analytical software vendor SPSS Inc. to see how technology could help.
At the time, the police department was data-rich and information-poor: A wealth of historical data was gleaned from its mature 911 system, the computer-aided dispatch system and the records management system, which were all used to track crime and ensure quality of service. But like many organizations, the police lacked a BI solution that could use the data.
Data has become a valuable resource for many government organizations, but its effective utilization – through tools like BI – has lagged behind. That’s why BI has consistently ranked as a top priority for CIOs the last few years, according to IT research firm Gartner.
BI offers organizations invaluable system analysis by collecting, analyzing and integrating data, while providing historical, current and predictive views of business operations. Integrated reporting and analysis lets managers determine better management practices, improve services, identify effective strategies, enhance security and increase efficiency, among other things.
The RPD’s first task was to identify data that would be used to create predictive crime reports – factors that wouldn’t change drastically over short periods of time. This created a model that automatically improves itself and avoids the manual refreshing of variables. The chosen data included: time, day, holidays, weather, moon phases, city events, paydays and crime records. All the analyzed data was at least five years old to ensure the integrity of the predictions.