eWEEK content and product recommendations are editorially independent. We may make money when you click on links to our partners. Learn More.
What if IT could help law enforcement outmaneuver the criminal before the criminal got to the crime scene? If this scenario sounds like something right out of the science fiction film “Minority Report,” its not.
Using data mining, predictive analysis and business intelligence tools, state and local police in Richmond, Va., are ushering in a new era in crime analysis that hopes to accomplish exactly that.
Combining technology expertise from vendors SPSS and Information Builders, police at the RPD (Richmond Police Department) are applying information-based policy to—they hope—prevent future crime from occurring.
What this means to the general public is, at a minimum, the RPD can respond more rapidly to crime by knowing where to deploy police power and, ultimately, can use information to deter crime by having an increased police presence in the right places at the right times.
“This is a huge step forward for law enforcement,” said Rodney Monroe, the RPDs chief of police. “We can capture data, analyze it and put it to use to better serve the community.”
For technology partners SPSS, in Chicago, and Information Builders, in New York, the law enforcement application presents a rebranding opportunity that the vendors plan to market to other police jurisdictions.
The application combines predictive analytics from SPSS with Information Builders enterprise BI capabilities, along with an innovative analytic framework developed by RTI International, in Research Triangle Park, N.C.
For 10 years, Colleen McCue, senior research scientist at RTI International, has worked both for and with the RPD, in what she calls “a unique opportunity to apply research in an operational law enforcement setting.”
While serving as the supervisor of the crime analysis unit at the RPD from 2000 to 2004, McCue said she used standard records management systems to analyze crime. Then she began using more advanced statistics and modeling techniques to get a better handle on violent crime.
“We started to do some interesting things, like linking crimes and motives using advanced statistics and modeling for investigative support,” McCue said.
But in the aftermath of Sept. 11, 2001, McCue said she knew the time had come to take her research beyond academic output to making it operationally actionable. Or, in other words, to make police analysis work more like science than fiction.
“By better characterizing crime trends and patterns to predict where crime was likely to occur, the police department could anticipate criminal activity and proactively place their resources,” McCue said.
The good news, according to McCue, was that, like most human behavior, criminal activity follows predictable patterns: for example, time of day, week, month and so on; geographic location; event; and weather conditions. In addition, she said, police investigators are natural data miners, sorting through tons of data to make decisions on how to solve crimes.
“Ninety percent of what police departments do is gather, analyze and disseminate data,” Monroe said.
Next Page: SPSS enterprise data mining workbench, Clementine.
SPSS Enterprise Data Mining
Workbench, Clementine”>
Unfortunately, the lack of sophisticated analytical tools was a stumbling block to doing more advanced crime prevention work, according to McCue, who described the technology in use at the time she arrived at the RPD as low-tech, descriptive statistics.
Not for long. The spring of 2002 became a major turning point for the RPD. McCue obtained an evaluation copy of Clementine, an enterprise data mining workbench from SPSS, which enabled her to make rapid progress in her work.
The software, according to the company, lets users develop predictive models using business expertise and deploy them into business operations to improve decision making.
McCue played around with Clementine, tweaking it to better accommodate the law-enforcement-type results she was seeking. In some cases, she worked independently; at other times, she consulted with SPSS experts. At this point, a more robust integrator-enabled solution was yet to come.
The Clementine workbench gave McCue the advanced IT tools she said she needed to explore her ideas. First, she looked at threat assessment in investigative support work, linking crime based on motive, and then she looked at firearms-related aggravated assaults.
In both cases, she used Clementine for data mining predictable patterns of behavior to gain insight into where it made the most sense to deploy police resources.
Digging for data
The data mining tools allowed the crime analyst to look at the interaction among data, both present and past, such as arrest records, motive and crime at a particular location based on calendar day, time, and weather. So, for example, if the police were better able to understand under what conditions a person was most likely to be victimized, the police could deploy resources to deter the crime from happening.
“Using Clementine was like using a power tool after using a hammer and chisel,” McCue said. The RPD uses the Clementine Server and Solution Publisher components of Clementine 10.
It wasnt until after the RPD tested the value of the law enforcement work that McCue was doing that SPSS and Information Builders joined forces to develop a robust law enforcement application.
Using Clementine for the preliminary law enforcement application required that new rules be developed: that is, what would be acceptable error, and so on, which ultimately became the final analytical framework in the application that the RPD uses today. McCue spent about a year using Clementine to look at aggravated assaults.
On New Years Eve, 2004, and the Fourth of July that year, the RPD put the analytic application to the test by deploying police resources aimed at more aggressive enforcement of firearm safety—based on the applications statistics on assaults and random gun violence.
Looking at historical data for the specific events on particular days, the application predicted with a high or low degree of probability where criminal activity was likely to occur, in the present, under similar circumstances.
Based on the analytical information, police resources were strategically deployed.
More specifically, the application looked at where it made sense to deploy police based on knowing the likelihood of a crime being committed at a certain time, place and so on. For example, if there is a high probability that an assault will occur outside a particular stadium after a football game, after 9 oclock on a moonless night, the RPD can put police in the area earlier in the evening to deter crime.
According to Monroe, there was a substantial reduction in the number of calls made to the police reporting sounds of gunshots, for example. “Calls were reduced by 70 percent,” he said.
On average, the RPD gets 500,000 calls for service annually, 30,000 of which actually involve criminal behavior. “The other 470,000 calls are [from] citizens saying they want police in the area because they perceive a problem,” said Monroe.
Without doing the math, Monroe said he knows that if the law enforcement application can target areas where crime might occur, the police department can deploy officers proactively, raise the level of safety in the defined areas and reduce the number of calls for assistance. At the same time, this would allow the RPD to use police officers time more effectively. Calls for service result in the departments sending two officers out in a patrol car for 20 to 30 minutes per call. Officers are the departments most expensive resource.
The successful New Years Eve test of the law enforcement application and a second test on the Fourth of July prompted the vendor to take the application to the next level.
“We always thought [McCues] work would be more useful with a map interface,” said Bill Haffey, technical director for the public sector at SPSS. So did McCue, who said that, until Information Builders was brought into the picture, “We had to use brute-force technology to create visual representations of the SPSS analytical output.” SPSS picked up the ball to make the application more visual.
It took no time for Haffey to select a technology partner to add the visual capability that the law enforcement application lacked at that time. “We had worked in the past with Information Builders and recognized that they had the technology that was needed,” he said.
Next Page: Information Builders maps the data.
Information Builders Maps the
Data”>
Information Builders used components of its Web-based flagship product WebFocus, a BI tool, which it tied to mapping software from ESRI to create an easy-to-understand, intuitive visual presentation of the law enforcement application.
This was the vendors first time developing this capability for a law enforcement application.
Information Builders and ESRI met with the RPD for a requirements analysis to determine the type of interface most appropriate for the data, such as a table or a map or a combination of the two. The map views are also critical to the application.
The maps provide a photo of the city from space. Users have the ability to zoom in on a neighborhood, block or street and see houses, cars and the location of a field. More precise zooming allows users to see doors, windows or the configuration of a yard.
Information Builders also created symbols, such as a gun and a needle, and a legend for the maps. The software also has the ability to display hot zones and the probability of criminal activity occurring in a particular area based on color coding, such as green for low probability, yellow for medium probability and red for high probability.
“We explored particular needs and critical functionality for the police department,” said Ivan Blas, director of business development for channels at Information Builders. In the past, crime analysis tools churned out long hard-copy reports on a daily basis that were statistical in nature and difficult and tedious to read through, according to McCue.
“The new application is portable, and the data and information is easy to see, analyze, modify and move around,” Blas said. Portability is key to the applications successful deployment, according to Monroe. Richmond, with a population of approximately 210,000, has 750 police officers in four precincts. Within each precinct are 12 sectors, each assigned 18 to 30 officers who are responsible for crime, calls and problems within their sector.
This winter, the law enforcement application is slated to be rolled out at the RPD and training provided to all officers.
“I thought the biggest challenge would be getting the police officers to accept the data mining capabilities and crime forecasting capabilities of the application,” Haffey said. “But Colleen McCue, while on staff at the RPD, convinced them it was a worthy product.”
Not only is the RPD excited about the new application, but Monroe said he wants the police officers to be more analytical. “When the officers get in their cars and start up their engines, they need to know where to go to reduce crime,” he said, “rather than being reactive to crime.”
Lynn Haber is a freelance writer based in Norwell, Mass. She can be reached at [email protected].
Check out eWEEK.coms for the latest database news, reviews and analysis.