Predictive Policing: Review of Benefits and Drawbacks

References

• Andrejevic, M. (2017). To Pre-Empt A Thief. International Journal of Communication, 8, 879–896.
   Google Scholar
• Bennett Moses, L., & Chan, J. (2016). Algorithmic prediction in policing: Assumptions, evaluation, and accountability. Policing and Society. doi:10.1080/10439463.2016.1253695
   PubMed Web of Science ®Google Scholar
• Brannon, M. M. (2017). Datafied and divided: Techno‐dimensions of inequality in American cities. City & Community, 16(1), 20–24. doi:10.1111/cico.12220
   Web of Science ®Google Scholar
• Camacho-Collados, M., & Liberatore, F. (2015). A decision support system for predictive police patrolling. Decision Support Systems, 75, 25–37. doi:10.1016/j.dss.2015.04.012
   Web of Science ®Google Scholar
• Chan, J., & Bennett Moses, L. (2016). Is big data challenging criminology? Theoretical Criminology, 20(1), 21–39. doi:10.1177/1362480615586614
   Web of Science ®Google Scholar
• Costanzo, P., D’Onofrio, F., & Friedl, J. (2015). Big data and the Italian legal framework: Opportunities for police forces. In B. Akhgar, G. Saathoff, H. R. Arabnia, R. Hill, A. Staniforth, & P. S. Bayerl (Eds.), Application of big data for national security (pp. 238–249). Oxford, UK: Butterworth-Heinemann.
   Google Scholar
• Dario, L. M., Morrow, W. J., Wooditch, A., & Vickovic, S. G. (2015). The point break effect: An examination of surf, crime, and transitory opportunities. Criminal Justice Studies, 28(3), 257–279. doi:10.1080/1478601X.2015.1032409
   Web of Science ®Google Scholar
• Datta, A., Sen, S., & Zick, Y. (2016). Algorithmic transparency via quantitative input influence: Theory and experiments, with learning systems. In 2016 IEEE symposium on security and privacy (pp. 598–617). IEEE. doi:10.2116/analsci.32.598
   Google Scholar
• De Hert, P., & Lammerant, H. (2016). Predictive profiling and its legal limits: Effectiveness gone forever? In B. Van der Sloot, D. Broeders, & E. Schrijvers. (Eds.), Exploring the boundaries of big data (pp. 145–176). Amsterdam: University Press Amsterdam.
   Google Scholar
• Downs, J. A. (2016). Mapping sex offender activity spaces relative to crime using time-geographic methods. Annals of GIS, 22(2), 141–150. doi:10.1080/19475683.2016.1147495
   Google Scholar
• Edwards, L., & Urquhart, L. (2016). Privacy in public spaces: What expectations of privacy do we have in social media intelligence? International Journal of Law and Information Technology, 24(3), 279–310. doi:10.1093/ijlit/eaw007
   Web of Science ®Google Scholar
• Haberman, C. P., & Ratcliffe, J. H. (2012). The predictive policing challenges of near repeat armed street robberies. Policing: A Journal of Policy and Practice, 6(2), 151–166. doi:10.1093/police/pas012
   Google Scholar
• Hunt, P., Saunders, J., & Hollywood, J. S. (2014). Evaluation of the shreveport predictive policing experiment. Santa Monica, CA: Rand Corporation.
   Google Scholar
• Inayatullah, S. (2013). The futures of policing: Going beyond the thin blue line. Futures, 49, 1–8. doi:10.1016/j.futures.2013.01.007
   Web of Science ®Google Scholar
• Innes, M., Fielding, N., & Cope, N. (2005). ’The appliance of science?’: The theory and practice of crime intelligence analysis. The British Journal of Criminology, 45, 39–57. doi:10.1093/bjc/azh053
   Web of Science ®Google Scholar
• Kitchin, R. (2014). Big data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 1–12. doi:10.1177/2053951714528481
   Google Scholar
• Kump, P., Alonso, D. H., Yang, Y., Candella, J., Lewin, J., & Wernick, M. N. (2016). Measurement of repeat effects in Chicago’s criminal social network. Applied Computing and Informatics, 12(2), 154–160. doi:10.1016/j.aci.2016.01.002
   Google Scholar
• Levine, E. S., Tisch, J., Tasso, A., & Joy, M. (2017). The New York City police department’s domain awareness system. Interfaces, 47(1), 70–84. doi:10.1287/inte.2016.0860
   Web of Science ®Google Scholar
• Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., & Ioannidis, J. P. A. (2009). The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: Explanation and elaboration. PLoS Medicine, 6(7), e1000100: 1–28. doi:10.1371/journal.pmed.1000100
   Web of Science ®Google Scholar
• McCue, C. (2014). Data mining and predictive analysis: Intelligence gathering and crime analysis. Oxford, UK: Butterworth-Heinemann.
   Google Scholar
• Mohler, G. O., Short, M. B., Malinowski, S., Johnson, M., Tita, G. E., Bertozzi, A. L., & Brantingham, P. J. (2015). Randomized controlled field trials of predictive policing. Journal of the American Statistical Association, 110(512), 1399–1411. doi:10.1080/01621459.2015.1077710
   Web of Science ®Google Scholar
• National Institute of Justice (NIJ). (2014). Predictive policing. Retrieved from https://www.nij.gov/topics/law-enforcement/strategies/predictive-policing/Pages/welcome.aspx.
   Google Scholar
• Norton, A. A. (2013). Predictive policing: The future of law enforcement in the trinidad and tobago police service (TTPS). International Journal of Computer Applications, 62(4), 32–36. doi:10.5120/10070-4680
   Google Scholar
• Pearsall, B. (2010). Predictive policing: The future of law enforcement. National Institute of Justice Journal, 266(1), 16–19.
   Google Scholar
• Perry, W. L. (2013). Predictive policing: The role of crime forecasting in law enforcement operations. Santa Monica, CA: Rand Corporation.
   Google Scholar
• Ratcliffe, J. H. (2004). The hotspot matrix: A framework for the spatio‐temporal targeting of crime reduction. Police Practice and Research, 5(1), 5–23. doi:10.1080/1561426042000191305
   Google Scholar
• Santos, R. B. (2014). The effectiveness of crime analysis for crime reduction: Cure or diagnosis? Journal of Contemporary Criminal Justice, 30(2), 147–168. doi:10.1177/1043986214525080
   Web of Science ®Google Scholar
• Saunders, J., Hunt, P., & Hollywood, J. S. (2016). Predictions put into practice: A quasi-experimental evaluation of Chicago’s predictive policing pilot. Journal of Experimental Criminology, 12(3), 347–371. doi:10.1007/s11292-016-9272-0
   Web of Science ®Google Scholar
• Schlehahn, E., Aichroth, P., Mann, S., Schreiner, R., Lang, U., Shepherd, I. D., & Wong, B. W. (2015). Benefits and pitfalls of predictive policing. In Intelligence and Security Informatics Conference (EISIC) (pp. 145–148). IEEE. Manchester, UK
   Google Scholar
• Tayebi, M. A., & Glässer, U. (2016). Social network analysis in predictive policing: Concepts, models and methods. Cham, Switzerland: Springer International Publishing.
   Google Scholar
• Townsley, M. (2017). Crime mapping and spatial analysis. In B. Leclerc & E. Savona (Eds.), Crime prevention in the 21st century (pp. 101–112). Cham, Switzerland: Springer International Publishing.
   Google Scholar
• Van Brakel, R., & De Hert, P. (2011). Policing, surveillance and law in a pre-crime society: Understanding the consequences of technology based strategies. In E. De Pauw, P. Ponsaers, W. Bruggeman, P. Deelman, & K. Van der Vijver (Eds.), Technology-led policing (pp. 65–94). Antwerp, Belgium: Maklu.
   Google Scholar
• Vlahos, J. (2012). The department of pre-crime. Scientific American, 306(1), 62–67.
   PubMed Web of Science ®Google Scholar
• Williams, M. L., Burnap, P., & Sloan, L. (2016). Crime sensing with big data: The affordances and limitations of using open source communications to estimate crime patterns. British Journal of Criminology, 57, 320–340.
   Web of Science ®Google Scholar