NYPD's Machine-Learning Software Spots Crime Patterns

The models that comprise NYPD's Patternizr are supervised machine-learning classifiers; that is, they are statistical models that learn from historical examples where classifications are known and are then used to predict the classification for samples. (souce: GCN)

The models that comprise NYPD's Patternizr are supervised machine-learning classifiers; that is, they are statistical models that learn from historical examples where classifications are known and are then used to predict the classification for samples. (souce: GCN)

April 24, 2019 | Source: GCN, gcn.com, Stephanie Kanowitz, 11 April 2019

The NYPD developed Paternizer software uses a machine learning (ML) based approach to recognize patterns in large databases of activity reports. The techniques developed and lessons learned could prove useful for similar counter-terrorism and military activities.


Pattern-recognizing algorithms are helping the New York City Police Department sort through crime data to find relationships among three crime types with greater efficiency and less bias.

The department has been using the software, known as Patternizr, since December 2016, but discussed it last month in a report in the INFORMS Journal on Applied Analytics. NYPD is the first law enforcement agency to use this type of tool, according to the report.

Based on machine learning, Patternizr was trained using manually identified patterns for burglaries, robberies and grand larcenies in the city to find relationships among them. The final models were incorporated into NYPD’s Domain Awareness System -- a citywide network of sensors, databases, devices, software and infrastructure.  All historical pairs of complaints were then processed in the cloud against 10 years of records of burglaries and robberies, and three years of grand larcenies data. To keep the software up-to-date, similarity scores were calculated and updated for new and revised complaints three times a day, and each was scored against the existing crime data before being incorporated into DAS.

Patternizr has separate models for the three crime types, each of which has many manually identified patterns -- about 10,000 apiece between 2006 and 2015. Each type also has about 30,000 complaint records in which the same person was arrested multiple times for the same offense within two days. Those complaints include unstructured text about the crime and structured data such as date, time, location and suspect information. That data lays the foundation for calculating the five types of crime similarities that Patternizr spots: location, date and time, categorical, suspect and unstructured text.

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