Randomized Controlled Field Trials of Predictive Policing
Open accessAbstract
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.
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It was estimated that using predictive hot spot maps for 31 minutes per day for each hot spot would save the LAPD $17,258,801 per year compared to traditional hot spot mapping, which would save half as much.
“Based on these calculations, it was estimated that using predictive hot spot maps for 31 minutes per day for each hot spot would save the LAPD $17,258,801 per year vis-à-vis applying traditional hot spot mapping techniques by crime analysts, which would only save half as much (Mohler et al., 2015).”
From ‘Led by Intelligence': A Scoping Review on the Experimental Evaluation of Intelligence-Led Policing (2024) · cites this paper as Mohler et al., 2015
The Discussion projects $17,258,801 in annual savings for LAPD using ETAS with 31 minutes of patrol per hotspot per day, and $8,223,519 under analyst-selected conditions—less than half as much. But the $17,258,801 figure is calculated against no patrol, not against traditional analyst-selected hot spots, as the citing sentence implies. (LLM, conf=0.90)
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Cites
Neighborhoods and Violent Crime: A Multilevel Study of Collective Efficacy
Sampson, R. J., Raudenbush, S. W., & Earls, F. (1997) · Science · doi:10.1126/science.277.5328.918 · Cited by 11,999 · see citing works
Harrison, G. W., & List, J. A. (2004) · Journal of Economic Literature · doi:10.1257/0022051043004577 · Cited by 2,282 · see citing works
HOT SPOTS OF PREDATORY CRIME: ROUTINE ACTIVITIES AND THE CRIMINOLOGY OF PLACE*
SHERMAN, L. W., GARTIN, P. R., & BUERGER, M. E. (1989) · Criminology · doi:10.1111/j.1745-9125.1989.tb00862.x · Cited by 2,104 · see citing works
Keizer, K., Lindenberg, S. M., & Steg, L. (2008) · Science · doi:10.1126/science.1161405 · Cited by 1,151 · see citing works
General deterrent effects of police patrol in crime “hot spots”: A randomized, controlled trial
Sherman, L. W., & Weisburd, D. (1995) · Justice Quarterly · doi:10.1080/07418829500096221 · Cited by 1,029 · see citing works
From RankNet to LambdaRank to LambdaMART: An Overview
Burges, C. J. C. (2010) · Cited by 1,005 · see citing works
Self-Exciting Point Process Modeling of Crime
Mohler, G., Short, M., Brantingham, P., Schoenberg, F., & Tita, G. (2011) · Journal of the American Statistical Association · doi:10.1198/jasa.2011.ap09546 · Cited by 998 · see citing works
Self-citationBurn, O. (1982) · Journal of Criminal Justice · doi:10.1016/0047-2352(82)90028-9 · Cited by 975 · see citing works
What Can Police Do to Reduce Crime, Disorder, and Fear?
Weisburd, D., & Eck, J. E. (2004) · The ANNALS of the American Academy of Political and Social Science · doi:10.1177/0002716203262548 · Cited by 790 · see citing works
Goffman, A. (2014) · University of Chicago Press eBooks · doi:10.7208/chicago/9780226136851.001.0001 · Cited by 724 · see citing works
Ratcliffe, J. H. (2016) · doi:10.4324/9781315717579 · Cited by 646 · see citing works
The cost of crime to society: New crime-specific estimates for policy and program evaluation
McCollister, K. E., French, M. T., & Fang, H. (2010) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2009.12.002 · Cited by 584 · see citing works
WEISBURD, D., WYCKOFF, L. A., READY, J., ECK, J. E., HINKLE, J. C., & GAJEWSKI, F. (2006) · Criminology · doi:10.1111/j.1745-9125.2006.00057.x · Cited by 533 · see citing works
What Can Police Do to Reduce Crime, Disorder, and Fear?
Eck, J. E., & Weisburd, D. L. (2007) · CRC Press eBooks · doi:10.1201/9781420053883.ch14 · Cited by 525 · see citing works
POLICING CRIME AND DISORDER HOT SPOTS: A RANDOMIZED CONTROLLED TRIAL*
BRAGA, A. A., & BOND, B. J. (2008) · Criminology · doi:10.1111/j.1745-9125.2008.00124.x · Cited by 497 · see citing works
GUERETTE, R. T., & BOWERS, K. J. (2009) · Criminology · doi:10.1111/j.1745-9125.2009.00177.x · Cited by 492 · see citing works
Deterring Delinquents: A Rational Choice Model of Theft and Violence
Matsueda, R. L., Kreager, D. A., & Huizinga, D. (2006) · American Sociological Review · doi:10.1177/000312240607100105 · Cited by 467 · cited 3× · see citing works
“[33] Ross L Matsueda, Derek A Kreager, and David Huizinga.”
RATCLIFFE, J. H., TANIGUCHI, T., GROFF, E. R., & WOOD, J. D. (2011) · Criminology · doi:10.1111/j.1745-9125.2011.00240.x · Cited by 436 · see citing works
The Utility of Hotspot Mapping for Predicting Spatial Patterns of Crime
Chainey, S., Tompson, L., & Uhlig, S. (2008) · Security Journal · doi:10.1057/palgrave.sj.8350066 · Cited by 424 · see citing works
Valparaiso University Law Review
Wright, R. G. (2011) · Cited by 422 · see citing works
Extending Earthquakes' Reach Through Cascading
Marsan, D., & Lengliné, O. (2008) · Science · doi:10.1126/science.1148783 · Cited by 398 · see citing works
Koper, C. S. (1995) · Justice Quarterly · doi:10.1080/07418829500096231 · Cited by 388 · see citing works
Infectious Burglaries. A Test of the Near Repeat Hypothesis
Townsley, M. (2003) · The British Journal of Criminology · doi:10.1093/bjc/43.3.615 · Cited by 350 · see citing works
Space–Time Patterns of Risk: A Cross National Assessment of Residential Burglary Victimization
Johnson, S. D., Bernasco, W., BOWERS, K. J., Elffers, H., Ratcliffe, J. H., Rengert, G. F., & Townsley, M. (2007) · Journal of Quantitative Criminology · doi:10.1007/s10940-007-9025-3 · Cited by 334 · see citing works
GIS and Crime Mapping: Chainey/GIS and Crime Mapping
Chainey, S., & Ratcliffe, J. (2005) · Wiley eBooks · doi:10.1002/9781118685181 · Cited by 332 · see citing works
Prospective Hot-Spotting: The Future of Crime Mapping?
BOWERS, K. J. (2004) · The British Journal of Criminology · doi:10.1093/bjc/azh036 · Cited by 316 · see citing works
Randomized experiments in criminology: what have we learned in the last two decades?
Farrington, D. P., & Welsh, B. C. (2005) · Journal of Experimental Criminology · doi:10.1007/s11292-004-6460-0 · Cited by 297 · see citing works
Estimation of Space–Time Branching Process Models in Seismology Using an EM–Type Algorithm
Veen, A., & Schoenberg, F. P. (2008) · Journal of the American Statistical Association · doi:10.1198/016214508000000148 · Cited by 294 · see citing works
Kennedy, L., Caplan, J. M., & Piza, E. L. (2011) · Journal of Quantitative Criminology · doi:10.1007/s10940-010-9126-2 · Cited by 283 · see citing works
On the Run: Fugitive Life in an American City
Platt, T. (2016) · Journal of American History · doi:10.1093/jahist/jav722 · Cited by 278 · see citing works
Local Police Departments, 2003
Hickman, M. J. (2012) · Cited by 276 · see citing works
Heckman, J. J. (2008) · International Statistical Review · doi:10.1111/j.1751-5823.2007.00024.x · Cited by 260 · see citing works
Hot spots policing and crime prevention: A systematic review of randomized controlled trials
Braga, A. A. (2005) · Journal of Experimental Criminology · doi:10.1007/s11292-005-8133-z · Cited by 249 · see citing works
Pulling Levers: Chronic Offenders, High-Crime Settings, and a Theory of Prevention
Kennedy, D. M. (2011) · ValpoScholar (Valparaiso University) · Cited by 237 · see citing works
Dissipation and displacement of hotspots in reaction-diffusion models of crime
Short, M. B., Brantingham, P. J., Bertozzi, A. L., & Tita, G. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0910921107 · Cited by 236 · see citing works
Self-citationRisky Facilities: Crime Concentration in Homogeneous Sets of Establishments and Facilities
Eck, J. E., Clarke, R. V., & Guerette, R. T. (2007) · Cited by 226 · see citing works
Mosher, C. J., Miethe, T. D., & Hart, T. C. (2011) · Sage eBooks · doi:10.4135/9781483349497 · Cited by 209 · see citing works
Once bitten, twice bitten: repeat victimisation and its implications for crime prevention
Farrell, G., & Pease, K. (1993) · Loughborough University Institutional Repository (Loughborough University) · Cited by 192 · see citing works
Telep, C. W., Mitchell, R. J., & Weisburd, D. L. (2012) · Justice Quarterly · doi:10.1080/07418825.2012.710645 · Cited by 189 · see citing works
Marked point process hotspot maps for homicide and gun crime prediction in Chicago
Mohler, G. (2014) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2014.01.004 · Cited by 182 · see citing works
Self-citationRepeat burglary victimisation: a tale of two theories
Johnson, S. D. (2008) · Journal of Experimental Criminology · doi:10.1007/s11292-008-9055-3 · Cited by 160 · see citing works
BOWERS, K. J. (2013) · Journal of Quantitative Criminology · doi:10.1007/s10940-013-9208-z · Cited by 147 · see citing works
Compstat: an analysis of an emerging police managerial paradigm
Walsh, W. F. (2001) · Policing An International Journal · doi:10.1108/13639510110401717 · Cited by 144 · see citing works
Kansas City Preventive Patrol Experiment
Gaines, D. C. (2013) · Encyclopedia of Community Policing and Problem Solving · doi:10.4135/9781452276113.n79 · Cited by 133 · see citing works
Criminal incident prediction using a point-pattern-based density model
Liu, H., & Brown, D. E. (2003) · International Journal of Forecasting · doi:10.1016/s0169-2070(03)00094-3 · Cited by 110 · see citing works
Predictive Mapping of Crime by ProMap: Accuracy, Units of Analysis, and the Environmental Backcloth
Johnson, S. D., BOWERS, K. J., Birks, D., & Pease, K. (2008) · Springer eBooks · doi:10.1007/978-0-387-09688-9_8 · Cited by 91 · see citing works
The spatio-temporal modeling for criminal incidents
Wang, X., & Brown, D. E. (2012) · Security Informatics · doi:10.1186/2190-8532-1-2 · Cited by 74 · see citing works
Ratcliffe, J. H. (2012) · Willan eBooks · doi:10.4324/9780203118245 · Cited by 74 · see citing works
Fielding, M., & Jones, V. (2012) · International Journal of Police Science & Management · doi:10.1350/ijps.2012.14.1.260 · Cited by 58 · see citing works
RESEARCH ARTICLE A Nonparametric EM algorithm for Multiscale Hawkes Processes
Lewis, E., & Mohler, G. (2011) · Cited by 40 · see citing works
Self-citationTompson, L., & Townsley, M. (2009) · International Journal of Police Science & Management · doi:10.1350/ijps.2010.12.1.148 · Cited by 39 · see citing works
Naish, T., & Zwartz, D. (2012) · Nature Climate Change · doi:10.1038/nclimate1504 · Cited by 24 · see citing works
Kansas City Preventive Patrol Experiment
Davis, M. S. (2002) · The Concise Dictionary of Crime and Justice · doi:10.4135/9781452229300.n1067 · Cited by 18 · see citing works
Andresen, M. A. (2023) · Routledge eBooks · doi:10.4324/9781003377955 · Cited by 10 · see citing works
Infectious Burglaries. A Test of the Near Repeat Hypothesis
Townsley, M. (2003) · British Journal of Criminology · doi:10.1093/bjc/azg615
Flagged first, then by citations
Research works printed but not linked: 1
- Roger E Kirk. Experimental design. Wiley Online Library.
Cited by
Sorted by citations
Big Data Surveillance: The Case of Policing
Brayne, S. (2017) · American Sociological Review · doi:10.1177/0003122417725865 · Cited by 884 · see citing works
Lum, K., & Isaac, W. (2016) · Significance · doi:10.1111/j.1740-9713.2016.00960.x · Cited by 611 · see citing works
Algorithmic Prediction in Policing: Assumptions, Evaluation, and Accountability
Moses, L. B., & Chan, J. B. L. (2016) · Policing & Society · doi:10.1080/10439463.2016.1253695 · Cited by 416 · see citing works
Predictive policing: Review of benefits and drawbacks
Meijer, A., & Wessels, M. (2019) · International Journal of Public Administration · doi:10.1080/01900692.2019.1575664 · Cited by 383 · see citing works
Predict and Surveil: Data, Discretion, and the Future of Policing
Brayne, S. (2020) · doi:10.1093/oso/9780190684099.001.0001 · Cited by 334 · see citing works
Technologies of Crime Prediction: The Reception of Algorithms in Policing and Criminal Courts
Brayne, S., & Christin, A. (2021) · Social Problems · doi:10.1093/socpro/spaa004 · Cited by 279 · see citing works
Hot spots policing of small geographic areas effects on crime
Braga, A. A., Turchan, B., Papachristos, A. V., & Hureau, D. M. (2019) · Campbell Systematic Reviews: Better Evidence for a Better World · doi:10.1002/cl2.1046 · Cited by 226 · see citing works
Artificial Intelligence, Predictive Policing, and Risk Assessment for Law Enforcement
Berk, R. A. (2021) · Annual Review of Criminology · doi:10.1146/annurev-criminol-051520-012342 · Cited by 213 · see citing works
Saunders, J., Hunt, P., & Hollywood, J. S. (2016) · Journal of Experimental Criminology · doi:10.1007/s11292-016-9272-0 · Cited by 193 · see citing works
Crisis analytics: big data-driven crisis response
Qadir, J., Ali, A., Rasool, R. U., Zwitter, A., Sathiaseelan, A., & Crowcroft, J. (2016) · Journal of International Humanitarian Action · doi:10.1186/s41018-016-0013-9 · Cited by 177 · see citing works
A review of predictive policing from the perspective of fairness
Alikhademi, K., Drobina, E., Prioleau, D., Richardson, B., Purves, D., & Gilbert, J. E. (2022) · Artificial Intelligence and Law · doi:10.1007/s10506-021-09286-4 · Cited by 167 · see citing works
Does Predictive Policing Lead to Biased Arrests? Results From a Randomized Controlled Trial
Brantingham, P. J., Valasik, M. A., & Mohler, G. (2018) · Statistics and Public Policy · doi:10.1080/2330443x.2018.1438940 · Cited by 165 · see citing works
Self-citationEvaluating saliency map explanations for convolutional neural networks: a user study
Alqaraawi, A., Schuessler, M., Weiß, P., Costanza, E., & Berthouze, N. (2020) · Proceedings of the 25th International Conference on Intelligent User Interfaces · doi:10.1145/3377325.3377519 · Cited by 163 · see citing works
Predictive policing: not yet, but soon preemptive?
Egbert, S., & Krasmann, S. (2019) · Policing & Society · doi:10.1080/10439463.2019.1611821 · Cited by 150 · see citing works
Rummens, A., Hardyns, W., & Pauwels, L. J. R. (2017) · Applied Geography · doi:10.1016/j.apgeog.2017.06.011 · Cited by 147 · see citing works
Private Accountability in an Age of Artificial Intelligence
Katyal, S. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108680844.004 · Cited by 141 · see citing works
CAN HOT SPOTS POLICING REDUCE CRIME IN URBAN AREAS? AN AGENT‐BASED SIMULATION*
WEISBURD, D., BRAGA, A. A., GROFF, E. R., & WOODITCH, A. (2017) · Criminology · doi:10.1111/1745-9125.12131 · Cited by 140 · see citing works
To predict and to manage. Predictive policing in the United States
Benbouzid, B. (2019) · Big Data & Society · doi:10.1177/2053951719861703 · Cited by 134 · see citing works
Mapping the Risk Terrain for Crime Using Machine Learning
Wheeler, A. P., & Steenbeek, W. (2020) · Journal of Quantitative Criminology · doi:10.1007/s10940-020-09457-7 · Cited by 134 · see citing works
A systematic review on spatial crime forecasting
Kounadi, O., Ristea, A., de Araujo, A., & Leitner, M. (2020) · Crime Science · doi:10.1186/s40163-020-00116-7 · Cited by 128 · see citing works
Egbert, S., & Leese, M. (2020) · Routledge eBooks · doi:10.4324/9780429328732 · Cited by 124 · see citing works
Predictive Policing as a New Tool for Law Enforcement? Recent Developments and Challenges
Hardyns, W., & Rummens, A. (2017) · European Journal on Criminal Policy and Research · doi:10.1007/s10610-017-9361-2 · Cited by 102 · see citing works
Lessons for artificial intelligence from the study of natural stupidity
Rich, A. S., & Gureckis, T. M. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0038-z · Cited by 94 · see citing works
Predictive Policing for Reform? Indeterminacy and Intervention in Big Data Policing
Shapiro, A. (2019) · Surveillance & Society · doi:10.24908/ss.v17i3/4.10410 · Cited by 82 · see citing works
Predictive policing: The risks associated with risk assessment
Strikwerda, L. (2020) · The Police Journal Theory Practice and Principles · doi:10.1177/0032258x20947749 · Cited by 82 · see citing works
Kadar, C., Maculan, R., & Feuerriegel, S. (2019) · Decision Support Systems · doi:10.1016/j.dss.2019.03.001 · Cited by 79 · see citing works
Learning on the job: Studying expertise in residential burglars using virtual environments
Nee, C., van Gelder, J., Otte, M., Vernham, Z., & Meenaghan, A. (2019) · Criminology · doi:10.1111/1745-9125.12210 · Cited by 78 · see citing works
Algorithms in the public sector. Why context matters
Wenzelburger, G., König, P. D., Felfeli, J., & Achtziger, A. (2022) · Public Administration · doi:10.1111/padm.12901 · Cited by 77 · see citing works
Crime is Terribly Revealing: Information Technology and Police Productivity
Mastrobuoni, G. (2017) · The Review of Economic Studies · doi:10.1093/restud/rdaa009 · Cited by 76 · see citing works
Hälterlein, J. (2021) · Big Data & Society · doi:10.1177/20539517211003118 · Cited by 76 · see citing works
Self-Exciting Point Processes with Spatial Covariates: Modelling the Dynamics of Crime
Reinhart, A., & Greenhouse, J. (2018) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/rssc.12277 · Cited by 76 · see citing works
Haberman, C. P. (2017) · Criminology & Public Policy · doi:10.1111/1745-9133.12303 · Cited by 71 · see citing works
Policing in the Era of Big Data
Ridgeway, G. (2018) · Annual Review of Criminology · doi:10.1146/annurev-criminol-062217-114209 · Cited by 55 · see citing works
What is wrong about Robocops as consultants? A technology-centric critique of predictive policing
Degeling, M., & Berendt, B. (2017) · AI & Society · doi:10.1007/s00146-017-0730-7 · Cited by 61 · see citing works
Flaxman, S. R., Chirico, M., Pereira, P., & Loeffler, C. E. (2019) · The Annals of Applied Statistics · doi:10.1214/19-aoas1284 · Cited by 60 · see citing works
“Part Man, Part Machine, All Cop”: Automation in Policing
Adensamer, A., & Klausner, L. D. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.655486 · Cited by 60 · see citing works
Human Rights and Technology: New Challenges for Justice and Accountability
Land, M. K., & Aronson, J. D. (2020) · Annual Review of Law and Social Science · doi:10.1146/annurev-lawsocsci-060220-081955 · Cited by 56 · see citing works
Stop and Risk: Policing, Data, and the Digital Age of Discrimination
Browning, M., & Arrigo, B. A. (2020) · American Journal of Criminal Justice · doi:10.1007/s12103-020-09557-x · Cited by 56 · see citing works
Is Gang Violent Crime More Contagious than Non-Gang Violent Crime?
Brantingham, P. J., Yuan, B., & Herz, D. C. (2020) · Journal of Quantitative Criminology · doi:10.1007/s10940-020-09479-1 · Cited by 55 · see citing works
Self-citationUgwudike, P. (2021) · AI and Ethics · doi:10.1007/s43681-021-00117-5 · Cited by 55 · see citing works
König, P. D., Felfeli, J., Achtziger, A., & Wenzelburger, G. (2022) · Public Management Review · doi:10.1080/14719037.2022.2144938 · Cited by 55 · see citing works
The machine that ate bad people: The ontopolitics of the precrime assemblage
Mantello, P. A. (2016) · Big Data & Society · doi:10.1177/2053951716682538 · Cited by 52 · see citing works
Event-level prediction of urban crime reveals a signature of enforcement bias in US cities
Rotaru, V., Huang, Y., Li, T., Evans, J. A., & Chattopadhyay, I. (2022) · Nature Human Behaviour · doi:10.1038/s41562-022-01372-0 · Cited by 52 · see citing works
The Effectiveness of Big Data-Driven Predictive Policing: Systematic Review
Lee, Y., Bradford, B., & Pósch, K. (2024) · Justice Evaluation Journal · doi:10.1080/24751979.2024.2371781 · Cited by 52 · see citing works
Zhuang, J., & Mateu, J. (2018) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/rssa.12429 · Cited by 52 · see citing works
Brandt, T., Wagner, S., & Neumann, D. (2020) · European Journal of Operational Research · doi:10.1016/j.ejor.2020.09.034 · Cited by 52 · see citing works
Data in Policing: An Integrative Review
Afzal, M., & Panagiotopoulos, P. (2024) · International Journal of Public Administration · doi:10.1080/01900692.2024.2360586 · Cited by 51 · see citing works
Lavorgna, A., & Ugwudike, P. (2021) · Big Data & Society · doi:10.1177/20539517211049670 · Cited by 50 · see citing works
Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction
Yuan, B., Li, H., Bertozzi, A. L., Brantingham, P. J., & Porter, M. A. (2019) · SIAM Journal on Mathematics of Data Science · doi:10.1137/18m1226993 · Cited by 50 · see citing works
Self-citationThemes in data mining, big data, and crime analytics
Oatley, G. (2021) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1432 · Cited by 50 · see citing works
Valasik, M. A., Brault, E., & Martinez, S. M. (2019) · Social Science Research · doi:10.1016/j.ssresearch.2018.12.023 · Cited by 37 · see citing works
The Criminal Law and Law Enforcement Implications of Big Data
Brayne, S. (2018) · Annual Review of Law and Social Science · doi:10.1146/annurev-lawsocsci-101317-030839 · Cited by 48 · see citing works
Macbeth, E., & Ariel, B. (2017) · Justice Quarterly · doi:10.1080/07418825.2017.1360379 · Cited by 46 · see citing works
Valasik, M. A. (2018) · Journal of Criminal Justice · doi:10.1016/j.jcrimjus.2018.06.001 · Cited by 46 · see citing works
A literature review on police patrolling problems
Samanta, S., Sen, G., & Ghosh, S. K. (2021) · Annals of Operations Research · doi:10.1007/s10479-021-04167-0 · Cited by 45 · see citing works
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Ratcliffe, J. H., Taylor, R. B., & Fisher, R. (2019) · Policing & Society · doi:10.1080/10439463.2019.1577844 · Cited by 45 · see citing works
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Wheeler, A. P. (2019) · Justice Quarterly · doi:10.1080/07418825.2019.1630471 · Cited by 44 · see citing works
Improving social harm indices with a modulated Hawkes process
Mohler, G., Carter, J. G., & Raje, R. R. (2018) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2018.01.006 · Cited by 44 · see citing works
Self-citationTulumello, S., & Iapaolo, F. (2021) · Urban Geography · doi:10.1080/02723638.2021.1887634 · Cited by 44 · see citing works
Park, J., Schoenberg, F. P., Bertozzi, A. L., & Brantingham, P. J. (2021) · Journal of the American Statistical Association · doi:10.1080/01621459.2021.1898408 · Cited by 44 · see citing works
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Self-citationThe geography of crime and crime control
Vandeviver, C., & Bernasco, W. (2017) · Applied Geography · doi:10.1016/j.apgeog.2017.08.012 · Cited by 42 · see citing works
Enhancing short-term crime prediction with human mobility flows and deep learning architectures
Wu, J., Abrar, S. M., Awasthi, N., Frı́as-Martı́nez, E., & Frías-Martínez, V. (2022) · EPJ Data Science · doi:10.1140/epjds/s13688-022-00366-2 · Cited by 41 · see citing works
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Akpinar, N.-J., De‐Arteaga, M., & Chouldechova, A. (2021) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3442188.3445877 · Cited by 41 · see citing works
Talukder, K. A. (2025) · doi:10.63125/3v98q104 · Cited by 41 · see citing works
Weisburd, D. L., Majmundar, M. K., Aden, H., Braga, A. A., Bueermann, J., Cook, P. J., Goff, P. A., Harmon, R. A., Haviland, A. M., Lum, C. M., et al. (2019) · Asian Journal of Criminology · doi:10.1007/s11417-019-09284-1 · Cited by 37 · see citing works
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Rummens, A., & Hardyns, W. (2020) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2020.03.006 · Cited by 37 · see citing works
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Mohler, G., Brantingham, P. J., Carter, J. G., & Short, M. B. (2019) · Journal of Quantitative Criminology · doi:10.1007/s10940-019-09404-1 · Cited by 37 · see citing works
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Chohlas-Wood, A., Coots, M., Goel, S., & Nyarko, J. (2023) · Nature Computational Science · doi:10.1038/s43588-023-00485-4 · Cited by 36 · see citing works
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Mohler, G., Raje, R. R., Carter, J. G., Valasik, M. A., & Brantingham, P. J. (2018) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2018.00421 · Cited by 35 · see citing works
Self-citationPlatform Policing and the Real-Time Cop
Wilson, D. (2019) · Surveillance & Society · doi:10.24908/ss.v17i1/2.12958 · Cited by 35 · see citing works
Gerstner, D. (2018) · European Journal for Security Research · doi:10.1007/s41125-018-0033-0 · Cited by 33 · see citing works
Analyzing and Predicting Spatial Crime Distribution Using Crowdsourced and Open Data
Belesiotis, A., Papadakis, G., & Skoutas, D. (2017) · ACM Transactions on Spatial Algorithms and Systems · doi:10.1145/3190345 · Cited by 32 · see citing works
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Fitzpatrick, D., Gorr, W. L., & Neill, D. B. (2018) · Annual Review of Criminology · doi:10.1146/annurev-criminol-011518-024534 · Cited by 32 · see citing works
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Bowen, D. A., Kollar, L. M. M., Wu, D., Fraser, D. A., Flood, C. E., Moore, J. C., Mays, E. W., & Sumner, S. A. (2018) · International Journal of Injury Control and Safety Promotion · doi:10.1080/17457300.2018.1467461 · Cited by 31 · see citing works
Frontiers in the economics of crime: lessons for Latin America and the Caribbean
Jaitman, L. (2019) · Latin American Economic Review · doi:10.1186/s40503-019-0081-5 · Cited by 31 · see citing works
Exploring data assimilation and forecasting issues for an urban crime model
Lloyd, D. J. B., Santitissadeekorn, N., & Short, M. B. (2015) · European Journal of Applied Mathematics · doi:10.1017/s0956792515000625 · Cited by 31 · see citing works
Self-citationEvaluation of operation swordfish: a near-repeat target-hardening strategy
Johnson, S. D., Davies, T., Murray, A., Ditta, P., Belur, J., & BOWERS, K. J. (2017) · Journal of Experimental Criminology · doi:10.1007/s11292-017-9301-7 · Cited by 31 · see citing works
Egbert, S. (2018) · European Journal for Security Research · doi:10.1007/s41125-017-0027-3 · Cited by 31 · see citing works
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Duwe, G., Sanders, N. E., Rocque, M., & Fox, J. A. (2021) · Journal of Quantitative Criminology · doi:10.1007/s10940-021-09499-5 · Cited by 30 · see citing works
Smart Policing: A Critical Review of the Literature
Afzal, M. Y., & Panagiotopoulos, P. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-57599-1_5 · Cited by 30 · see citing works
Adolescent offenders' current whereabouts predict locations of their future crimes
Bernasco, W. (2019) · PLoS ONE · doi:10.1371/journal.pone.0210733 · Cited by 30 · see citing works
Unfalsifiability of security claims
Herley, C. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1517797113 · Cited by 29 · see citing works
Machine Learning and the Police: Asking the Right Questions
Vestby, A., & Vestby, J. (2019) · Policing A Journal of Policy and Practice · doi:10.1093/police/paz035 · Cited by 29 · see citing works
van Sleeuwen, S. E. M., Ruiter, S., & Menting, B. (2018) · Journal of Research in Crime and Delinquency · doi:10.1177/0022427818766395 · Cited by 28 · see citing works
Near-repeat burglary patterns in Malmö: Stability and change over time
Hoppe, L., & Gerell, M. (2018) · European Journal of Criminology · doi:10.1177/1477370817751382 · Cited by 28 · see citing works
A Theory-Driven Algorithm for Real-Time Crime Hot Spot Forecasting
Lee, Y., SooHyun, O., & Eck, J. E. (2019) · Police Quarterly · doi:10.1177/1098611119887809 · Cited by 28 · see citing works
Rodrigues, A. L., González, J. A., & Mateu, J. (2023) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-023-02420-5 · Cited by 26 · see citing works
Crime modeling with truncated Lévy flights for residential burglary models
Pan, C., Li, B., Wang, C., Zhang, Y., Geldner, N., Wang, L., & Bertozzi, A. L. (2018) · Mathematical Models and Methods in Applied Sciences · doi:10.1142/s0218202518400080 · Cited by 26 · see citing works
Self-citationValues and Consequences in Predictive Machine Evaluation. A Sociology of Predictive PolicingPreprint
Benbouzid, B. (2018) · doi:10.31219/osf.io/kg3ex · Cited by 25 · see citing works
The Long-Term and System-Level Impacts of Institutionalizing Hot Spot Policing in a Small City
Koper, C. S., Lum, C. M., Wu, X., & Hegarty, T. (2020) · Policing A Journal of Policy and Practice · doi:10.1093/police/paaa096 · Cited by 24 · see citing works
The Limits of Reallocative and Algorithmic Policing
Hunt, L. W. (2022) · Criminal Justice Ethics · doi:10.1080/0731129x.2022.2056351 · Cited by 24 · see citing works
Roy, S., & Chowdhury, I. R. (2023) · The Professional Geographer · doi:10.1080/00330124.2023.2223250 · Cited by 24 · see citing works
Algorithmic Interactions in Open Source Work
Shaikh, M., & Vaast, E. (2022) · Information Systems Research · doi:10.1287/isre.2022.1153 · Cited by 23 · see citing works
Wu, J., Abrar, S. M., Awasthi, N., & Frías-Martínez, V. (2023) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2023.101967 · Cited by 23 · see citing works
Data and evidence challenges facing place-based policing
Hutt, O. K., BOWERS, K. J., Johnson, S. D., & Davies, T. (2018) · Policing An International Journal · doi:10.1108/pijpsm-09-2017-0117 · Cited by 23 · see citing works
A Recommendation Engine to Aid in Identifying Crime Patterns
Chohlas-Wood, A., & Levine, E. S. (2019) · INFORMS Journal on Applied Analytics · doi:10.1287/inte.2019.0985 · Cited by 23 · see citing works
Llinares, F. M. (2020) · IDP Revista de Internet Derecho y Política · doi:10.7238/idp.v0i30.3223 · Cited by 22 · see citing works
Learning to rank spatio-temporal event hotspots
Mohler, G., Porter, M. D., Carter, J. G., & Lafree, G. D. (2020) · Crime Science · doi:10.1186/s40163-020-00112-x · Cited by 22 · see citing works
Self-citationWhen Do Offenders Commit Crime? An Analysis of Temporal Consistency in Individual Offending Patterns
van Sleeuwen, S. E. M., Steenbeek, W., & Ruiter, S. (2020) · Journal of Quantitative Criminology · doi:10.1007/s10940-020-09470-w · Cited by 22 · see citing works
- Authors
- Mohler, G. O., Short, M. B., Malinowski, S., Johnson, M., Tita, G. E., Bertozzi, A. L., & Brantingham, P. J.
- Affiliations
- Santa Clara University (US)
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2015-10-02
- Volume
- 110
- Issue
- 512
- Pages
- 1399-1411
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2232870322
- Cited by
- 381
- References
- 54
- Citations by year
- 2026: 39 · 2025: 37 · 2024: 26 · 2023: 28 · 2022: 30 · 2021: 47 · 2020: 67 · 2019: 44 · 2018: 34 · 2017: 20 · 2016: 8 · 2015: 1
- FWCI
- 69.65
- Type
- journal article
- Language
- en
- Open access
- green
- escholarship.org
- Locations
- Journal of the American Statistical Association (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Science Foundation (0968309, 1343123)
- Multidisciplinary University Research Initiative (W911NF‐11‐1‐0332, FA9550-10-1-0569)
- Office of Naval Research (N000141210838, FA9550)
- Air Force Office of Scientific Research (FA9550-, FA9550-10-1-, FA9550-10-1-0569)
- Army Research Office (W911NF‐11‐1‐0332, W911NF, W911NF-11)
- Topics
- Social Sciences › Sociology and Political Science › Crime Patterns and Interventions
- Social Sciences › Political Science and International Relations › Policing Practices and Perceptions
- Social Sciences › Law › Criminal Justice and Penology
- Keywords
- predictive policing, epidemic-type aftershock sequence, crime prediction, crime hotspots, police patrol, crime reduction, hotspot mapping, criminal intelligence
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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