The NYPD recently announced that it is using an internally developed artificial intelligence tool for crime fighting analytics.

Patternizr is an algorithmic machine-learning software that sifts through police data to find patterns and connect similar crimes. It has been in use by NYPD since December 2016, but its existence was first disclosed by the department this month.

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“The goal of all of this is to identify patterns of crime," says Alex Chohlis-Wood, the former director of analytics for NYPD and one of the researchers who worked on Patternizr. He is currently the deputy director of Stanford University’s Computational Policy Lab. "When we identify patterns more quickly, it helps us make arrests more quickly,” he told

New York City has the largest police force in the country, with 77 precincts spread across five boroughs. “It’s difficult to identify patterns that happen across precinct boundaries or across boroughs,” says Evan Levine, NYPD's assistant commissioner of data analytics.

Patternizr automates much of that process. The algorithm scours all reports within NYPD's database, looking at certain aspects -- such as method of entry, weapons used and the distance between incidents -- and then ranks them with a similarity score. A human data analyst then determines which complaints should be grouped together and presents those to detectives to help winnow their investigations.