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The New York City Police Department’s Domain Awareness System

Author

Listed:
  • E. S. Levine

    (New York City Police Department, New York, New York 10038)

  • Jessica Tisch

    (New York City Police Department, New York, New York 10038)

  • Anthony Tasso

    (New York City Police Department, New York, New York 10038)

  • Michael Joy

    (New York City Police Department, New York, New York 10038)

Abstract

The New York City Police Department (NYPD), the largest state or local police force in the United States, is charged with securing New York City from crime and terrorism. The NYPD’s Domain Awareness System (DAS) is a citywide network of sensors, databases, devices, software, and infrastructure that informs decision making by delivering analytics and tailored information to officers’ smartphones and precinct desktops. DAS development began in earnest in 2008; since then, the NYPD has used the system to employ a unique combination of analytics and information technology, including pattern recognition, machine learning, and data visualization. DAS is used throughout the NYPD, and the DAS software has been sold to other agencies, bringing in revenue for New York City. Through improving the efficiency of the NYPD’s staff, DAS has generated estimated savings of $50 million per year. Most importantly, the NYPD has used it to combat terrorism and improve its crime-fighting effectiveness. Since DAS was deployed department wide in 2013, the overall crime index in the city has fallen by six percent.

Suggested Citation

  • E. S. Levine & Jessica Tisch & Anthony Tasso & Michael Joy, 2017. "The New York City Police Department’s Domain Awareness System," Interfaces, INFORMS, vol. 47(1), pages 70-84, February.
    • Handle: RePEc:inm:orinte:v:47:y:2017:i:1:p:70-84
    DOI: 10.1287/inte.2016.0860
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    Citations

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    Cited by:

    1. Alex Chohlas-Wood & E. S. Levine, 2019. "A Recommendation Engine to Aid in Identifying Crime Patterns," Interfaces, INFORMS, vol. 49(2), pages 154-166, March.
    2. Brian Jordan Jefferson, 2018. "Computerizing carceral space: Coded geographies of criminalization and capture in New York City," Environment and Planning A, , vol. 50(5), pages 969-988, August.
    3. Michelle Sydes & Lorelei Hine & Angela Higginson & James McEwan & Laura Dugan & Lorraine Mazerolle, 2023. "Criminal justice interventions for preventing radicalisation, violent extremism and terrorism: An evidence and gap map," Campbell Systematic Reviews, John Wiley & Sons, vol. 19(4), December.
    4. Shixiang Zhu & He Wang & Yao Xie, 2022. "Data-Driven Optimization for Atlanta Police-Zone Design," Interfaces, INFORMS, vol. 52(5), pages 412-432, September.
    5. Jiyong Park & Min-Seok Pang & Junetae Kim & Byungtae Lee, 2021. "The Deterrent Effect of Ride-Sharing on Sexual Assault and Investigation of Situational Contingencies," Information Systems Research, INFORMS, vol. 32(2), pages 497-516, June.

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