Originally published in StateTechMagazine, May, 2019
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The NYPD is leading the way on using machine learning applications like pattern recognition to more easily identify similar types of crime.
The world is awash in data, and police departments are no exception. Now, they are beginning to make better use of that data.
Police departments are starting to use machine learning and layering on artificial intelligence to make smarter, more informed policing decisions. Although the technology is still in its infancy, public safety agencies like the New York Police Department see significant potential in pattern recognition algorithms, AI and the ability to turn unstructured data into structured data that supports and informs police work.
“AI is the next logical evolution in policing,” Jonathan Lewin, chief of the Chicago Police Department’s Bureau of Technical Services, tells Government Technology. “We have all this data, a lot of sensors, and incoming information from other open sources, including crime tips from citizens. So, plugging all of this into some kind of engine to gain insights and make connections that wouldn’t be obvious to a human is the next logical step.”
Public safety organizations face a complex landscape that includes global threats, terrorism, nation-state cyberattacks and identity theft, notes Richard Zak, Microsoft‘s director of justice and public safety solutions. However, he noted, they are inundated with data and often have “a single-source, ask-one question-and-get-one-answer relationship” with that data.
“Being smart on crime addresses how law enforcement, communities and the government (local and federal) can use data and artificial intelligence to proactively identify correlating data points and transform them into actionable insights,” Zak says, adding that “by doing so, it enables people and resources to provide safer responses and reduced risk.”
Predictive policing can be defined as “the application of analytical techniques — particularly quantitative techniques — to identify likely targets for police intervention and prevent crime or solve past crimes by making statistical predictions,” according to a 2013 RAND Corporation report.
Police investigators are often tasked with hunting for and identifying groups of related crimes — crime patterns. “Investigators have historically built patterns with a process that is manual, time-consuming, memory based, and liable to inefficiency,” Evan Levine, the NYPD’s assistant commissioner of data analytics, and Alex Chohlas-Wood, former director of analytics for the NYPD, say in a February article in INFORMS Journal on Applied Analytics. “To improve this process, we developed a set of three supervised machine-learning models, which we called Patternizr.”
About the Author:
Phil Goldstein is the web editor for FedTech and StateTech. Besides keeping up with the latest in technology trends, he is also an avid lover of the New York Yankees, poetry, photography, traveling and escaping humidity.