• Danijela V. Spasić University of Criminal Investigation and Police Studies
Keywords: predictive policing, safe community, GIS, law enforcement agencies


The availability of large datasets and the rapid development of sophisticated tools that allow fast processing of vast quantities of information have been the key drivers behind the increasing use of algorithmic technologies in policing since the early 2000ends. “Predictive policing” became an umbrella term for a variety of models, software and applications. All location based predictive policing programs however have the same aim: Sending police officers to the right place at the right time. For decades, police action has been rather reactive than proactive, focused on arrest and failing to see incidents as indicators of continuing underlying problems. Predictive policing has been praised as a turnaround of this approach, a “panacea” for the optimization of resources and the creation of a safer society, where the police can stop breaches of law, before they happen. Although lately more critical voices have been raised from civil society and research, questioning the effectiveness of the tools as well as their compatibility with human rights, there is still a lack of objective research on the issue.


Aldous, C. & Leishman, F. (1999). Police and Community Safety in Japan: Model or Myth? Crime Prevention & Community Safety, 1, 25–39.
Anselin, L. (2004). Review of Cluster Analysis Software. Springfield (IL): North American Association of Central Cancer Registries. Accessed on June 14, 2020.
Bachner, J. (2013) Predictive Policing: Preventing Crime with Data and Analytics, IBM Centre for the Business of Government.
Accessed on 30 June, 2020.
Berk, R., Sherman, L., Barnes, G., Kurtz, E. & Ahlman, L. (2009). Forecasting murder within a population of probationers and parolees: a high stakes application of statistical learning. Journal of the Royal Statistical Society, 172(1), 191-211.
Boehm, A. & Cnaan, R. A. (2012). Towards a Practice-based Model for Community Practice: Linking Theory and Practice. Journal of Sociology & Social Welfare, 39(1), 141-168.
Checkoway, B. (1995). Six strategies of community change. Community Development Journal, 30(1), 2-20.
Cordella, A., Willcocks, L. (2010). Outsourcing, bureaucracy and public value: Reappraising the notion of the “contract state”. Government Information Quarterly, 27(1), 82-88.
Curtin, K.M.,Qui, F., Hayslett-McCall, K. & Bray, T.M. (2005). Integrating GIS and maximal covering models to determine optimal police patrol areas. In: F. Wang, ed. Geographic information systems and crime analysis. Hershey, PA: Idea Group Publishing, 214–235.
Groff, E.R. (2007). Simulation for theory testing and experimentation: an example using routine activity theory and street robbery. Journal of Quantitative Criminology, 23, 75–103.
Grubesic, T.H., Mack, E. & Murray, A.T. (2007). Geographic exclusion: spatial analysis for evaluating the implications of Megan’s Law. Social Science Computing Review, 25, 143–162.
BM Source, Memphis PD: Keeping ahead of criminals by finding the “hot spots”, Accessed on 30 June, 2020.
Ife, J. & Fiske, L. (2006). Human rights and community work: Complementary theories and practices. International Social Work, 49(3), 297-308.
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.
Jouvenal, J. (2016). The new way police are surveilling you: calculating your threat ‘score’. The Washington Post, January 10. Accessed on June 26, 2020. story.html
Karlović, R., Babić, J., Sučić, I., Šimunić, N. & Bartoš, V. (2019). Prostorna i vremenska distribucija seksualnog nasilja – studija slučaja Grada Zagreba. Zbornik radova Međunarodne znanstveno-stručne konferencije Visoke policijske škole: 6. Istraživački dani Visoke policijske škole u Zagrebu – „Idemo li ukorak s novim sigurnosnim izazovima?” , Zagreb, Hrvatska, 5.04. 2019.
Krug, E., Dahlberg, L. & Mercy, J. (2002). World report on violence and health. Geneva: World Health Organization.
McCarthy, T. & Ratcliffe, J.H. (2005). Garbage in garbage out. In: F. Wang, ed. Geographic information systems and crime analysis. Hershey, PA: Idea Group Publishing, 45–59.
Mendola, M. (2016). One Step Further in the ‘Surveillance Society’: The Case of Predictive Policing. Milano: Tech and Law Center.
Moore, M. H. (1992). Problem-solving and Community Policing. In: M. Tonry & N. Morris (Eds.). Crime and Justice: A Review of Research, 15, 99-158. Chicago: University of Chicago Press.
National Institute of Justice (2010). How to Identify Hotspots and Read a Crime Map?
Accessed on June 16, 2020.
National Institute of Justice (2014). Predictive Policing, Accessed on June 16, 2020.
Pearsall, B. (2010). Predictive Policing: The Future of Law Enforcement? National Institute of Justice Journal, No. 266, May 2010.
Perry, W., McInnis, B., Price, C., Smith, S. & Hollywood, J. (2013). Predictive Policing. The Role of Crime Forecasting in Law Enforcement Operations. Santa Monica: RAND.
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.
Stoffel, F., Post, H., Stewen, M. & Keim, D. (2018): Polimaps: Supporting Predictive Policing with Visual Analytics. In C. Tominski & T. von Landesberger (Eds.), EuroVis Workshop on Visual Analytics. Accessed on June 26, 2020.
Uchida, C. D. (2014). Predictive Policing. In G. Bruinsma & D. Weisburd (Eds.), Encyclopedia of Criminology and Criminal Justice (pp. 3871-3880). NY: Springer New York.
Willems, D., & Doeleman, W. (2014). Predictive Policing. Wens of Werkelijkheid. Tijdschrift voor de Politie, 76(4/5), 39-42.
Van Koppen, P.J. & De Keijser, J.W. (1997). Desisting distance decay: on the aggregation of individual crime trips. Criminology, 35, 505–515.
Vlahos, J. (2012). Can Machines Predict Where Crimes Are about to Happen? American Scientific ( January 2012), Accessed on June 26, 2020.
Police Organization – Structure and Functioning