Like human crime analysts, the software compares factors such as method of entry, type of goods taken and the distance between crimes.
NYPD analyst Rebecca Shutt says she used Patternizr, the department's new pattern-recognition software, to help her solve an assault case near Yankee Stadium.
It’s much faster than the old method, which involved analysts sifting through reports, racking their brains for key details about various crimes and deciding whether they fit into a pattern. It’s more comprehensive, too, with analysts able to spot patterns across the city instead of just in their precinct.
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. Levine and Chohlas-Wood were inspired by the work of a New York University team that studied a similar approach to pattern recognition but never produced a workable version.
“The real advantage of the tool is that we minimize the amount of legwork and busywork 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.
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