Police know that most crimes are committed by repeat offenders, so a staple of police work is identifying patterns that link crimes together and deduce the results to specific individuals. This becomes difficult when analysts look at thousands of cases each year.
Though the human brain excels at identifying patterns, it can succumb to information overload.
Chicago Police Department crime analysts sometimes look at 100 cases a week, sifting through hundreds of data elements trying to unravel patterns that might lead to a break. It requires a lot of legwork and some luck. However, police brass hope a new neural network system will take some chance out of the equation, and add more of a scientific formula to solving crimes.
"The average cases kind of blend together after a while," said Steve Maris, assistant director for Information Services for the Chicago Police Department. "This would be able to segment those for us."
The Classification System for Serial Criminal Patterns (CSSCP) is the brainchild of Dr. Tom Muscarello, an assistant professor at DePaul University.
It's different from other crime-analysis systems being used by law enforcement in that the CSSCP thinks 24 hours a day, seven days a week -- not just when prompted by an analyst. It can, however, be prompted to search for a particular data set, analyze data from multiple crimes and find patterns that link crimes without human intervention.
Running 24/7, the system combs through police department IT systems, searching for patterns or clusters of data elements that might tie together a string of crimes and give police the data they need to find the perpetrators. The system assigns numerical values to different data elements in each crime, including crime type, suspect description and profile, getaway vehicle and so forth.
The system uses pattern-recognition software that is "trained" to find those clusters of data.
Neural networks are considered artificial intelligence -- the networks attempt to imitate the human brain in the way the brain programs data structures and recognizes patterns. Neural networks function by creating connections between processing elements, which are the equivalent of neurons to the computer system.
These networks become adept at predicting events when they have a large database of examples from which to draw, and are typically "trained" by being fed large amounts of data and "taught" rules about interpreting relationships between that data.
"It cuts down on manual intervention," Maris said. "[A detective] reads 100 cases this week, he reads 100 next week. Can he go back and remember which case belongs where?
"Right now, we have a lack of computer tools to assist the crime analyst," he continued. "Crime analysts are doing a lot of legwork -- reading lots of cases using text searches to find cases. Nothing is grouping the case by offender patterns, MO [Modus Operandi], things like that."
Also, since cases are often assigned arbitrarily, communication between detectives may not be what it should, and links between cases may go uncovered without such a system.
"Some sergeant is passing [case assignments] out to people," Muscarello said. "If [the sergeant] doesn't know right off the top of his head that it sounds like a case that's related to others, it's kind of a round-robin thing; 'Well, Joe you got two yesterday so I'm going to give these to Frank.' A lot of times people don't communicate with each other as often as you'd think people in an office environment would."
Modeling the Brain
The CSSCP has been in the works for a decade, and after considerable tinkering, should be ready for the Chicago Police Department this year. Muscarello said changes in leadership at the police department and adjustments to the system have delayed its advance.