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Randomized Controlled Field Trials of Predictive Policing

Author

Listed:
  • G. O. Mohler
  • M. B. Short
  • Sean Malinowski
  • Mark Johnson
  • G. E. Tita
  • Andrea L. Bertozzi
  • P. J. Brantingham

Abstract

The concentration of police resources in stable crime hotspots has proven effective in reducing crime, but the extent to which police can disrupt dynamically changing crime hotspots is unknown. Police must be able to anticipate the future location of dynamic hotspots to disrupt them. Here we report results of two randomized controlled trials of near real-time epidemic-type aftershock sequence (ETAS) crime forecasting, one trial within three divisions of the Los Angeles Police Department and the other trial within two divisions of the Kent Police Department (United Kingdom). We investigate the extent to which (i) ETAS models of short-term crime risk outperform existing best practice of hotspot maps produced by dedicated crime analysts, (ii) police officers in the field can dynamically patrol predicted hotspots given limited resources, and (iii) crime can be reduced by predictive policing algorithms under realistic law enforcement resource constraints. While previous hotspot policing experiments fix treatment and control hotspots throughout the experimental period, we use a novel experimental design to allow treatment and control hotspots to change dynamically over the course of the experiment. Our results show that ETAS models predict 1.4--2.2 times as much crime compared to a dedicated crime analyst using existing criminal intelligence and hotspot mapping practice. Police patrols using ETAS forecasts led to an average 7.4% reduction in crime volume as a function of patrol time, whereas patrols based upon analyst predictions showed no significant effect. Dynamic police patrol in response to ETAS crime forecasts can disrupt opportunities for crime and lead to real crime reductions.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:jnlasa:v:110:y:2015:i:512:p:1399-1411
    DOI: 10.1080/01621459.2015.1077710
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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. Sukanya Samanta & Goutam Sen & Soumya Kanti Ghosh, 2022. "A literature review on police patrolling problems," Annals of Operations Research, Springer, vol. 316(2), pages 1063-1106, September.
    3. 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.
    4. 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.
    5. Wheeler, Andrew Palmer & Reuter, Sydney, 2020. "Redrawing hot spots of crime in Dallas, Texas," SocArXiv nmq8r, Center for Open Science.
    6. 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.
    7. Haberman, Cory P. & Hatten, David & Carter, Jeremy G. & Piza, Eric L., 2021. "The sensitivity of repeat and near repeat analysis to geocoding algorithms," Journal of Criminal Justice, Elsevier, vol. 73(C).
    8. Guido de Blasio & Alessio D'Ignazio & Marco Letta, 2020. "Predicting Corruption Crimes with Machine Learning. A Study for the Italian Municipalities," Working Papers 16/20, Sapienza University of Rome, DISS.
    9. Vitezslav Titl & Deni Mazrekaj & Fritz Schiltz, 2024. "Identifying Politically Connected Firms: A Machine Learning Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(1), pages 137-155, February.
    10. Carter, Jeremy G. & Mohler, George & Raje, Rajeev & Chowdhury, Nahida & Pandey, Saurabh, 2021. "The Indianapolis harmspot policing experiment," Journal of Criminal Justice, Elsevier, vol. 74(C).
    11. Steve J. Bickley & Alison Macintyre & Benno Torgler, 2021. "Safety in Smart, Livable Cities: Acknowledging the Human Factor," CREMA Working Paper Series 2021-17, Center for Research in Economics, Management and the Arts (CREMA).
    12. Santitissadeekorn, N. & Short, M.B. & Lloyd, D.J.B., 2018. "Sequential data assimilation for 1D self-exciting processes with application to urban crime data," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 163-183.
    13. 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.
    14. Mohammed A. A. Al-qaness & Ahmed A. Ewees & Hong Fan & Mohamed Abd Elaziz, 2020. "Optimized Forecasting Method for Weekly Influenza Confirmed Cases," IJERPH, MDPI, vol. 17(10), pages 1-12, May.
    15. Liberatore, Federico & Camacho-Collados, Miguel & Quijano-Sánchez, Lara, 2023. "Towards social fairness in smart policing: Leveraging territorial, racial, and workload fairness in the police districting problem," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    16. 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.
    17. Laura Jaitman, 2019. "Frontiers in the economics of crime: lessons for Latin America and the Caribbean," Latin American Economic Review, Springer;Centro de Investigaciòn y Docencia Económica (CIDE), vol. 28(1), pages 1-36, December.
    18. Santitissadeekorn, Naratip & Lloyd, David J.B. & Short, Martin B. & Delahaies, Sylvain, 2020. "Approximate filtering of conditional intensity process for Poisson count data: Application to urban crime," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    19. 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.
    20. Brandt, Tobias & Wagner, Sebastian & Neumann, Dirk, 2021. "Prescriptive analytics in public-sector decision-making: A framework and insights from charging infrastructure planning," European Journal of Operational Research, Elsevier, vol. 291(1), pages 379-393.
    21. Maha Shaikh & Emmanuelle Vaast, 2023. "Algorithmic Interactions in Open Source Work," Information Systems Research, INFORMS, vol. 34(2), pages 744-765, June.

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