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Marked point process hotspot maps for homicide and gun crime prediction in Chicago

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  • Mohler, George

Abstract

Crime hotspot maps are a widely used and successful method of displaying spatial crime patterns and allocating police resources. However, hotspot maps are often created over a single timescale using only one crime type. In the case of short-term hotspot maps that utilize several weeks of crime data, risk estimates suffer from a high variance, especially for low frequency crimes such as homicide. Long-term hotspot maps that utilize several years of data fail to take into account near-repeat effects and emerging hotspot trends. In this paper we show how point process models of crime can be extended to include leading indicator crime types, while capturing both short-term and long-term patterns of risk, through a marked point process approach. Several years of data and many different crime types are systematically combined to yield accurate hotspot maps that can be used for the purpose of predictive policing of gun-related crime. We apply the methodology to a large, open source data set which has been made available to the general public online by the Chicago Police Department.

Suggested Citation

  • Mohler, George, 2014. "Marked point process hotspot maps for homicide and gun crime prediction in Chicago," International Journal of Forecasting, Elsevier, vol. 30(3), pages 491-497.
    • Handle: RePEc:eee:intfor:v:30:y:2014:i:3:p:491-497
    DOI: 10.1016/j.ijforecast.2014.01.004
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    References listed on IDEAS

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    Cited by:

    1. Kajita, Mami & Kajita, Seiji, 2020. "Crime prediction by data-driven Green’s function method," International Journal of Forecasting, Elsevier, vol. 36(2), pages 480-488.
    2. Baichuan Yuan & Frederic P. Schoenberg & Andrea L. Bertozzi, 2021. "Fast estimation of multivariate spatiotemporal Hawkes processes and network reconstruction," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(6), pages 1127-1152, December.
    3. Joseph G. Eisenhauer, 2025. "Gun Taxes and Homicide Rates," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 51(2), pages 225-245, April.
    4. Haoting Zhang & Donglin Zhan & James Anderson & Rhonda Righter & Zeyu Zheng, 2025. "Clustering Then Estimation of Spatio-Temporal Self-Exciting Processes," INFORMS Journal on Computing, INFORMS, vol. 37(4), pages 874-893, July.
    5. Rummens, Anneleen & Hardyns, Wim, 2021. "The effect of spatiotemporal resolution on predictive policing model performance," International Journal of Forecasting, Elsevier, vol. 37(1), pages 125-133.
    6. Álvaro Briz-Redón & Jorge Mateu, 2026. "A self-exciting spatio-temporal model with a smooth space-time-varying productivity parameter," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 110(1), pages 65-89, March.
    7. Mota, Caroline Maria de Miranda & Figueiredo, Ciro José Jardim de & Pereira, Débora Viana e Sousa, 2021. "Identifying areas vulnerable to homicide using multiple criteria analysis and spatial analysis," Omega, Elsevier, vol. 100(C).
    8. Eric L. Piza & George O. Mohler & Nathan T. Connealy & Rachael Arietti & Jeremy G. Carter, 2025. "Space-Time Association between Gunshot Detection Alerts, Calls for Service, and Police Enforcement in Chicago: Differences Across Citizen Race and Incident Type," Journal of Quantitative Criminology, Springer, vol. 41(1), pages 53-74, March.
    9. Tomlinson, Matthew F. & Greenwood, David & Mucha-Kruczyński, Marcin, 2024. "2T-POT Hawkes model for left- and right-tail conditional quantile forecasts of financial log returns: Out-of-sample comparison of conditional EVT models," International Journal of Forecasting, Elsevier, vol. 40(1), pages 324-347.
    10. Alex Reinhart & Joel Greenhouse, 2018. "Self‐exciting point processes with spatial covariates: modelling the dynamics of crime," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1305-1329, November.
    11. Seppo Virtanen & Mark Girolami, 2021. "Spatio‐temporal mixed membership models for criminal activity," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1220-1244, October.
    12. Racek, Daniel & Thurner, Paul & Kauermann, Goeran, 2024. "Integrating Spatio-temporal Diffusion into Statistical Forecasting Models of Armed Conflict via Non-parametric Smoothing," OSF Preprints q59dr, Center for Open Science.
    13. Amir Hassan Zadeh & Hamed M. Zolbanin & Ramesh Sharda & Dursun Delen, 2019. "Social Media for Nowcasting Flu Activity: Spatio-Temporal Big Data Analysis," Information Systems Frontiers, Springer, vol. 21(4), pages 743-760, August.
    14. John Leverso & Youness Diouane & George Mohler, 2025. "Measuring Online–Offline Spillover of Gang Violence Using Bivariate Hawkes Processes," Journal of Quantitative Criminology, Springer, vol. 41(1), pages 103-131, March.
    15. Alsenafi, Abdulaziz & Barbaro, Alethea B.T., 2018. "A convection–diffusion model for gang territoriality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 765-786.
    16. 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.
    17. Noriyoshi Sukegawa & Shohei Suzuki & Yoshiko Ikebe & Yoshito Hirata, 2024. "On Computing Medians of Marked Point Process Data Under Edit Distance," Journal of Optimization Theory and Applications, Springer, vol. 200(1), pages 178-193, January.
    18. Kieran Kalair & Colm Connaughton & Pierfrancesco Alaimo Di Loro, 2021. "A non‐parametric Hawkes process model of primary and secondary accidents on a UK smart motorway," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 80-97, January.
    19. de Blasio, Guido & D'Ignazio, Alessio & Letta, Marco, 2022. "Gotham city. Predicting ‘corrupted’ municipalities with machine learning," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    20. Charles Loeffler & Seth Flaxman, 2018. "Is Gun Violence Contagious? A Spatiotemporal Test," Journal of Quantitative Criminology, Springer, vol. 34(4), pages 999-1017, December.
    21. Mohler, George & Carter, Jeremy & Raje, Rajeev, 2018. "Improving social harm indices with a modulated Hawkes process," International Journal of Forecasting, Elsevier, vol. 34(3), pages 431-439.
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    23. 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.

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