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Abstract

Predictive analytics in policing is a data-driven approach to () characterizing crime patterns across time and space and () leveraging this knowledge for the prevention of crime and disorder. This article outlines the current state of the field, providing a review of forecasting tools that have been successfully applied by police to the task of crime prediction. We then discuss options for structured design and evaluation of a predictive policing program so that the benefits of proactive intervention efforts are maximized given fixed resource constraints. We highlight examples of predictive policing programs that have been implemented and evaluated by police agencies in the field. Finally, we discuss ethical issues related to predictive analytics in policing and suggest approaches for minimizing potential harm to vulnerable communities while providing an equitable distribution of the benefits of crime prevention across populations within police jurisdiction.

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/content/journals/10.1146/annurev-criminol-011518-024534
2019-01-13
2024-10-08
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Literature Cited

  1. Arial B, Weinborn C, Sherman LW 2016. “Soft” policing at hot spots: Do police community support officers work? A randomized controlled trial. J. Exp. Criminol. 12:277–317
    [Google Scholar]
  2. Bernasco W 2010. A sentimental journey to crime: effect of residential history on crime location choice. Criminology 48:389–416
    [Google Scholar]
  3. Bernasco W, Nieuwbeerta P 2005. How do residential burglars select target areas? A new approach to the analysis of criminal location choice. Br. J. Criminol. 45:296–315
    [Google Scholar]
  4. Blevins T, Kwiatkowski R, Macbeth J, McKeown K, Patton D, Rambow O 2016. Automatically processing tweets from gang-involved youth: towards detecting loss and aggression. Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers2196–206 Stroudsburg, PA: Assoc. Comp. Ling.
    [Google Scholar]
  5. Block RL, Block CR 1995. Space, place, and crime: hot spot areas and hot places of liquor-related crime Natl. Inst. Justice Tech. Rep. NCJ 160737 Off. Justice Programs Washington, DC:
    [Google Scholar]
  6. Blumstein A, Cohen J, Das S, Moitra SD 1988. Specialization and seriousness during adult criminal careers. J. Quant. Criminol. 4:303–45
    [Google Scholar]
  7. Bowers KJ, Johnson SD, Pease K 2004. Prospective hot-spotting: the future of crime mapping. ? Br. J. Criminol. 44:641–58
    [Google Scholar]
  8. Braga AA, Bond BJ 2008. Policing crime and disorder hot spots: a randomized controlled trial. Criminology 46:577–607
    [Google Scholar]
  9. Braga AA, Papachristos AV, Hureau DM 2014. The effects of hot spots policing on crime: an updated systematic review and meta-analysis. Justice Q 31:633–63
    [Google Scholar]
  10. Brantingham PL, Brantingham P 1999. Theoretical model on crime hot spot generation. Stud. Crime Crime Prevent. 8:7–26
    [Google Scholar]
  11. Bushman BJ, Newman K, Calvert SL, Downey G, Dredze M et al. 2016. Youth violence: what we know and what we need to know. Am. Psychol. 71:17–39
    [Google Scholar]
  12. Caplan JM, Kennedy LW, Miller J 2011. Risk terrain modeling: brokering criminological theory and GIS methods for crime forecasting. Justice Q 28:360–81
    [Google Scholar]
  13. Chainey S, Ratcliffe J 2005. GIS and Crime Mapping Hoboken, NJ: Wiley
    [Google Scholar]
  14. Chainey S, Reid S, Stuart N 2002. When is a hotspot a hotspot? A procedure for creating statistically robust hotspot maps of crime. Innovations in GIS 9: Socio-Economic Applications of Geographic Information Science D Kidner, G Higgs, S White 21–36 London: Taylor and Francis
    [Google Scholar]
  15. Chainey S, Tompson L, Uhlig S 2008. The utility of hotspot mapping for predicting spatial patterns of crime. Secur. J. 21:4–28
    [Google Scholar]
  16. Chicago Police Dep. 2016. Strategic subject list (SSL) dashboard Spec. Order S09–11 Chicago Police Dep. Chicago, IL:
    [Google Scholar]
  17. Clemen R 1989. Combining forecasts: a review and annotated bibliography. Int. J. Forecast. 5:559–83
    [Google Scholar]
  18. Cohen LE, Felson M 1979. Social change and crime rate trends: a routine activity approach. Am. Sociol. Rev. 44:588
    [Google Scholar]
  19. DeLisi M 2001. Extreme career criminals. Am. J. Crim. Justice 25:239–52
    [Google Scholar]
  20. Doran BJ, Burgess MB 2012. Putting Fear of Crime on the Map: Investigating Perceptions of Crime Using Geographic Information Systems New York: Springer
    [Google Scholar]
  21. Drawve G, Barnum JD 2017. Place-based risk factors for aggravated assault across police divisions in Little Rock, Arkansas. J. Crime Justice 41:173–92
    [Google Scholar]
  22. Dumke M, Main F 2017. A look inside the watch list Chicago police fought to keep secret. Chicago Sun-Times May 18. https://chicago.suntimes.com/news/what-gets-people-on-watch-list-chicago-police-fought-to-keep-secret-watchdogs/
    [Google Scholar]
  23. Eck JE, Chainey S, Cameron JG, Leitner M, Wilson RE 2005. Mapping crime: understanding hot spots Natl. Inst. Justice Tech. Rep. NCJ 209393 Off. Justice Programs W: ashington, DC
    [Google Scholar]
  24. Eck JE, Weisburd D 1995. Crime places in crime theory. Crime and Place 4 JE Eck, D Weisburd 1–33 Monsey, NY: Crim. Justice Press
    [Google Scholar]
  25. Flaxman S, Wilson AG, Neill DB, Nickisch H, Smola AJ 2015. Fast Kronecker inference in Gaussian processes with non-Gaussian likelihoods. PMLR 37: Proceedings of the 32nd International Conference on Machine Learning F Bach, D Blei 607–16 Brookline, MA: PMLR
    [Google Scholar]
  26. Gorr W, Lee Y 2015. Early warning system for temporary crime hot spots. J. Quant. Criminol. 31:25–47
    [Google Scholar]
  27. Gorr W, Lee Y 2017. Chronic and temporary hot spots. Unraveling the Crime-Place Connection, Vol. 22: New Directions in Theory and Policy D Weisburd, JE Eck 41–63 Abingdon, UK: Taylor and Francis
    [Google Scholar]
  28. Green B, Horel T, Papachristos AV 2017. Modeling contagion through social networks to explain and predict gunshot violence in Chicago, 2006 to 2014. JAMA Intern. Med. 177:326–33
    [Google Scholar]
  29. Hart T, Zandbergen P 2014. Kernel density estimation and hotspot mapping: examining the influence of interpolation method, grid cell size, and bandwidth on crime forecasting. Polic. Int. J. Police Strateg. Manag. 37:305–23
    [Google Scholar]
  30. Herrmann CR 2015. The dynamics of robbery and violence hot spots. Crime Sci 4:33
    [Google Scholar]
  31. Hunt P, Saunders J, Hollywood JS 2014. Evaluation of the Shreveport Predictive Policing Experiment Santa Monica, CA: RAND
    [Google Scholar]
  32. Johnson SD, Bowers KJ 2004. The burglary as clue to the future: the beginnings of prospective hot-spotting. Eur. J. Criminol. 1:237–55
    [Google Scholar]
  33. Kennedy LW, Caplan JM, Piza E 2011. Risk clusters, hotspots, and spatial intelligence: risk terrain modeling as an algorithm for police resource allocation strategies. J. Quant. Criminol. 27:339–62
    [Google Scholar]
  34. Kouziokas GN 2017. The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transp. Res. Procedia 24:467–73
    [Google Scholar]
  35. Kulldorff M 1997. A spatial scan statistic. Commun. Stat. Theory Methods 26:1481–96
    [Google Scholar]
  36. Lersch KM 2017. Risky places: an analysis of carjackings in Detroit. J. Crim. Justice 52:34–40
    [Google Scholar]
  37. McGuire P, Williamson D 1999. Mapping tools for management and accountability Paper presented at the Third International Crime Mapping Research Conference Orlando, FL:
    [Google Scholar]
  38. Mohler G, Short M, Brantingham P, Schoenberg F, Tita G 2011. Self-exciting point process modeling of crime. J. Am. Stat. Assoc. 106:100–8
    [Google Scholar]
  39. Mohler G, Short M, Malinowski S, Johnson M, Tita G et al. 2015. Randomized controlled field trials of predictive policing. J. Am. Stat. Assoc. 110:1399–411
    [Google Scholar]
  40. Moses LB, Chan J 2016. Algorithmic prediction in policing: assumptions, evaluation, and accountability. Polic. Soc. https://doi.org/10.1080/10439463.2016.1253695
    [Crossref] [Google Scholar]
  41. Natl. Res. Counc. 2004. Fairness and Effectiveness in Policing: The Evidence W Skogan, K Frydl Washington, DC: Natl. Acad. Press
    [Google Scholar]
  42. Neill DB 2012. Fast subset scan for spatial pattern detection. J. R. Stat. Soc. Ser. B 74:337–60
    [Google Scholar]
  43. Neill DB, Gorr W 2007. Detecting and preventing emerging epidemics of crime. Adv. Dis. Surveill. 4:13
    [Google Scholar]
  44. Newbold P 1983. ARIMA model building and the time series analysis approach to forecasting. J. Forecast. 2:23–35
    [Google Scholar]
  45. Papachristos AV, Wildeman C, Roberto E 2015. Tragic, but not random: the social contagion of nonfatal gunshot injuries. Soc. Sci. Med. 125:139–50
    [Google Scholar]
  46. Perry WL, McInnis B, Price CC, Smith S, Hollywood JS 2013. Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations Santa Monica, CA: RAND
    [Google Scholar]
  47. Phillips P, Lee I 2009. Criminal cross correlation mining and visualization Presented at Pacific-Asia Workshop on Intelligence and Security Informatics Bangkok: Apr. 27
    [Google Scholar]
  48. Piquero A 2000. Frequency, specialization, and violence in offending careers. J. Res. Crime Delinq. 37:392–418
    [Google Scholar]
  49. Rasmussen CE, Williams CK 2005. Gaussian Processes for Machine Learning Cambridge, MA: MIT Press
    [Google Scholar]
  50. Ratcliffe J, Taniguchi T, Groff ER, Wood JD 2011. The Philadelphia foot patrol experiment: a randomized controlled trial of police patrol effectiveness in violent crime hotspots. Criminology 49:795–831
    [Google Scholar]
  51. Rengert GF, Piquero AR, Jones PR 1999. Distance decay reexamined. Criminology 37:427–46
    [Google Scholar]
  52. Sherman LW, Gartin PR, Buerger ME 1989. Hot spots of predatory crime: routine activities and the criminology of place. Criminology 27:27–56
    [Google Scholar]
  53. Tita G, Ridgeway G 2007. The impact of gang formation on local patterns of crime. J. Res. Crime Delinq. 44:208–37
    [Google Scholar]
  54. Wang X, Brown DE 2012. The spatio-temporal modeling for criminal incidents. Secur. Inform. 1:2
    [Google Scholar]
  55. Weisburd D 2015. The law of crime concentration and the criminology of place. Criminology 53:133–57
    [Google Scholar]
  56. Weisburd D, Groff ER, Yang SM 2012. The Criminology of Place: Street Segments and Our Understanding of the Crime Problem Oxford, UK: Oxford Univ. Press
    [Google Scholar]
  57. Yu H, Wu Z, Wang S, Wang Y, Ma X 2017. Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks. Sensors 17:1501
    [Google Scholar]
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