New York City Police Officer

The New York City Department is using a new pattern-spotting software to help connect crimes across precincts and solve crimes more quickly.

The software, Patternizr, uses machine learning to sort through hundreds of thousands of crime files logged in the NYPD’s database, helping analysts save time and money and find their suspects quicker.

Patternizr was co-created by Evan Levine, NYPD Assistant Commissioner of Data Analytics and NYU CUSP Alum Alex Chohlas-Wood, former NYPD Director of Analytics.

As reported by the New York Post,

“They said the idea came from a New York University study on trend-spotting that didn’t turn out a workable program.

“We had some weakness in finding patterns that span those large geographic areas,” Chohlas-Wood said…noting the prior issue of connecting crimes details in one precinct to another.

Levine and Chohlas-Wood spent two years developing the software before deploying it in December 2016. However, the department only disclosed the use of the software this month in an article in the INFORMS Journal on Applied Analytics.

As reported by the Associated Press,

The two 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.

Like human crime analysts, the software compares factors such as method of entry, type of goods taken and the distance between crimes. Levin and Chohlas-Wood sought out the uniformed officers who had decades of experience identifying patterns using traditional methods.

“The real advantage of the tool 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,” said Chohlas-Wood, now the deputy director of the Stanford Computational Policy Lab at Stanford University.

Learn more about Patternizr: