IDEAS home Printed from https://ideas.repec.org/a/inm/orinte/v49y2019i2p154-166.html

A Recommendation Engine to Aid in Identifying Crime Patterns

Author

Listed:
  • Alex Chohlas-Wood

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

  • E. S. Levine

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

Abstract

Police investigators are routinely asked to search for and identify groups of related crimes, known as patterns. Investigators have historically built patterns with a process that is manual, time-consuming, memory based, and liable to inefficiency. To improve this process, we developed a set of three supervised machine-learning models, which we called Patternizr , to help identify related burglaries, robberies, and grand larcenies. Patternizr was trained on 10 years of manually identified patterns. Problematic administrative boundaries and sensitive suspect attributes were hidden from the models. In tests on historical examples from New York City, the models perfectly rebuild approximately one-third of test patterns and at least partially rebuild approximately four-fifths of these test patterns. The models have been deployed to every uniformed member of the New York City Police Department through a custom software application, allowing investigators to prioritize crimes for review when building a pattern. They are used by a team of civilian crime analysts to discover new crime patterns and aid in making arrests.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:orinte:v:49:y:2019:i:2:p:154-166
    DOI: 10.1287/inte.2019.0985
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/inte.2019.0985
    Download Restriction: no
    File URL: https://libkey.io/10.1287/inte.2019.0985?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Linda V. Green & Peter J. Kolesar, 2004. "ANNIVERSARY ARTICLE: Improving Emergency Responsiveness with Management Science," Management Science, INFORMS, vol. 50(8), pages 1001-1014, August.
    2. G. O. Mohler & M. B. Short & Sean Malinowski & Mark Johnson & G. E. Tita & Andrea L. Bertozzi & P. J. Brantingham, 2015. "Randomized Controlled Field Trials of Predictive Policing," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1399-1411, December.
    3. Michael D. Porter, 2016. "A Statistical Approach to Crime Linkage," The American Statistician, Taylor & Francis Journals, vol. 70(2), pages 152-165, May.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jose, Esther & Mukherjee, Sayanti & Swaminathan, Jose, 2025. "Evaluating socioeconomic factors for crime against women in developing countries: A data-centric statistical learning approach," Socio-Economic Planning Sciences, Elsevier, vol. 101(C).
    2. Andrew P. Wheeler & Wouter Steenbeek, 2021. "Mapping the Risk Terrain for Crime Using Machine Learning," Journal of Quantitative Criminology, Springer, vol. 37(2), pages 445-480, June.
    3. Tschernutter, Daniel & Feuerriegel, Stefan, 2025. "Data-driven dynamic police patrolling: An efficient Monte Carlo tree search," European Journal of Operational Research, Elsevier, vol. 321(1), pages 177-191.
    4. Justin J. Boutilier & Timothy C. Y. Chan, 2023. "Introducing and Integrating Machine Learning in an Operations Research Curriculum: An Application-Driven Course," INFORMS Transactions on Education, INFORMS, vol. 23(2), pages 64-83, January.
    5. Wheeler, Andrew Palmer & Steenbeek, Wouter, 2020. "Mapping the risk terrain for crime using machine learning," SocArXiv xc538, Center for Open Science.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shixiang Zhu & He Wang & Yao Xie, 2022. "Data-Driven Optimization for Atlanta Police-Zone Design," Interfaces, INFORMS, vol. 52(5), pages 412-432, September.
    2. Nabil Channouf & Pierre L’Ecuyer & Armann Ingolfsson & Athanassios Avramidis, 2007. "The application of forecasting techniques to modeling emergency medical system calls in Calgary, Alberta," Health Care Management Science, Springer, vol. 10(1), pages 25-45, February.
    3. Marcos Singer & Patricio Donoso & Natalia Jadue, 2004. "Evaluacion De Las Oportunidades De Mejoramiento De La Logistica Directa De Emergencia," Abante, Escuela de Administracion. Pontificia Universidad Católica de Chile., vol. 7(2), pages 179-209.
    4. Nagarajan, Magesh & Shaw, Duncan & Albores, Pavel, 2012. "Disseminating a warning message to evacuate: A simulation study of the behaviour of neighbours," European Journal of Operational Research, Elsevier, vol. 220(3), pages 810-819.
    5. Jens Ludwig & Sendhil Mullainathan, 2021. "Fragile Algorithms and Fallible Decision-Makers: Lessons from the Justice System," Journal of Economic Perspectives, American Economic Association, vol. 35(4), pages 71-96, Fall.
    6. Victoria A. Sytsma, 2024. "Vulnerable Populations, Crime, and a Social Justice and Social Impact Agenda: Lessons for Crime Prevention," The ANNALS of the American Academy of Political and Social Science, , vol. 714(1), pages 40-55, July.
    7. Sperling, Martina & Schryen, Guido, 2022. "Decision support for disaster relief: Coordinating spontaneous volunteers," European Journal of Operational Research, Elsevier, vol. 299(2), pages 690-705.
    8. Baixun Li & Meng Li & Chao Liang, 2023. "Cry‐wolf syndrome in recommendation," Production and Operations Management, Production and Operations Management Society, vol. 32(2), pages 347-358, February.
    9. P. Daniel Wright & Matthew J. Liberatore & Robert L. Nydick, 2006. "A Survey of Operations Research Models and Applications in Homeland Security," Interfaces, INFORMS, vol. 36(6), pages 514-529, December.
    10. Cors, Tobias & Fliedner, Malte & Haase, Knut & Vlćek, Tobias, 2025. "Simulative assessment of patrol car allocation and response time," European Journal of Operational Research, Elsevier, vol. 324(1), pages 290-307.
    11. Andrew P. Wheeler & Wouter Steenbeek, 2021. "Mapping the Risk Terrain for Crime Using Machine Learning," Journal of Quantitative Criminology, Springer, vol. 37(2), pages 445-480, June.
    12. Wheeler, Andrew Palmer & Riddell, Jordan R. & Haberman, Cory P., 2019. "Breaking the chain: How arrests reduce the probability of near repeat crimes," SocArXiv 7tazd, Center for Open Science.
    13. George Mohler & P. Jeffrey Brantingham & Jeremy Carter & Martin B. Short, 2019. "Reducing Bias in Estimates for the Law of Crime Concentration," Journal of Quantitative Criminology, Springer, vol. 35(4), pages 747-765, December.
    14. Marcos Singer & Patricio Donoso & Alan Scheller-Wolf, 2008. "Una Introducción A La Teoría De Colas Aplicada A La Gestión De Servicios," Abante, Escuela de Administracion. Pontificia Universidad Católica de Chile., vol. 11(2), pages 93-120.
    15. P. Jeffrey Brantingham & Baichuan Yuan & Denise Herz, 2021. "Is Gang Violent Crime More Contagious than Non-Gang Violent Crime?," Journal of Quantitative Criminology, Springer, vol. 37(4), pages 953-977, December.
    16. Rauchecker, Gerhard & Schryen, Guido, 2019. "An exact branch-and-price algorithm for scheduling rescue units during disaster response," European Journal of Operational Research, Elsevier, vol. 272(1), pages 352-363.
    17. Valasik, Matthew, 2018. "Gang violence predictability: Using risk terrain modeling to study gang homicides and gang assaults in East Los Angeles," Journal of Criminal Justice, Elsevier, vol. 58(C), pages 10-21.
    18. Wheeler, Andrew Palmer & Reuter, Sydney, 2020. "Redrawing hot spots of crime in Dallas, Texas," SocArXiv nmq8r, Center for Open Science.
    19. Jifan Zhang & Salih Tutun & Samira Fazel Anvaryazdi & Mohammadhossein Amini & Durai Sundaramoorthi & Hema Sundaramoorthi, 2024. "Management of resource sharing in emergency response using data-driven analytics," Annals of Operations Research, Springer, vol. 339(1), pages 663-692, August.
    20. Sumi Kim & Seongmoon Kim, 2015. "Differentiated waiting time management according to patient class in an emergency care center using an open Jackson network integrated with pooling and prioritizing," Annals of Operations Research, Springer, vol. 230(1), pages 35-55, July.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orinte:v:49:y:2019:i:2:p:154-166. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.