NYPD deploys pattern-recognition software to nab thieves

Patternizr, pattern-recognition software developed by the New York Police Department that allows crime analysts to quickly compare robberies, larcenies and thefts to hundreds of thousands of crimes logged in the NYPD’s database, was the subject of a recent AP story that received nationwide attention just days after researchers published a detailed paper on Patternizr in the INFORMS Journal on Applied Analytics (formerly Interfaces).

According to the AP story, Evan Levine, the NYPD’s assistant commissioner of data analytics, and Alex Chohlas-Wood, the department’s former director of analytics, spent two years developing the software before rolling it out in December 2016. Since its deployment, Patternizr has been used to solve several crimes in New York City.

“The goal of Patternizr is, of course, to improve public safety,” Levine, an astrophysicist by academic training, told the AP. “The more easily that we can identify patterns in those crimes, the more quickly we can identify and apprehend perpetrators.” The AP reported that Levine and Chohlas-Wood “trained the program on 10 years of patterns that the department had manually identified. In testing, it accurately re-created old crime patterns one-third of the time and returned parts of patterns 80 percent of the time. The NYPD says the cost was minimal because the two developers were already on staff.”

Chohlas-Wood, who holds a master’s degree from New York University’s Center for Urban Science and Progress, is now the deputy director of the Stanford Computational Policy Lab. “The real advantage of the tool,” he told the AP, “is that we minimize the amount of leg work and busy work that analysts or detectives have to do and really allow them to leverage their expertise and their experience in going through a much smaller list of results.”

Levine, a member of INFORMS, and Chohlas-Wood were interviewed in a recent INFORMS podcast - listen here. 

To read the AP story, click here.