Predictive policing in an Australian context: Assessing viability and utility

Photo of two police officers

Studies in the United States and Europe have demonstrated that burglary and vehicle crime exhibit consistent patterns, supporting the application of crime prediction techniques to proactively deploy police resources to reduce incidents of crime. Research into whether these techniques are applicable in an Australian context is currently limited.

Using crime data from the Queensland Police Service, this study assessed the presence of spatio-temporal patterns in burglary, theft of motor vehicle and theft from motor vehicle offences in three distinct local government areas. After establishing the presence of spatiotemporal clustering, the forecasting performance of two predictive algorithms and a retrospective crime mapping technique was evaluated.

Forecasting performance varied across study regions; however, the prediction algorithms performed as well as or better than the retrospective method, while using less data. The next step in evaluating predictive policing within Australia is to consider and design effective tactical responses to prevent crime based on the forecast locations and identified patterns.


Anselin L 1995. Local indicators of spatial association—LISA. Geographical Analysis 27(2): 93–115.

Australian Bureau of Statistics nd. Data by region.

Australian Bureau of Statistics 2019. Crime victimisation, Australia, 2017–18. ABS cat. no. 4530.0. Canberra: ABS

Borrion H et al. 2020. The problem with crime problem-solving: Towards a second generation POP? The British Journal of Criminology 60(1): 219–240.

Bowers KJ, Johnson SD & Pease K 2004. Prospective hot-spotting: The future of crime mapping? The British Journal of Criminology 44: 641–658.

Braga AA, Papachristos AV & Hureau DM 2014. The effects of hot spots policing on crime: An updated systematic review and meta-analysis. Justice Quarterly 31(4): 633–663.

Brantingham PJ 2016. Crime diversity. Criminology 54(4): 553–586.

Bullock K et al. 2022. Problem-oriented policing in England and Wales: Barriers and facilitators. Policing and Society 32(9): 1087–1102.

Chainey S & Ratcliffe JH 2005. GIS and crime mapping. San Francisco, CA: Wiley.

Chainey S, Tompson L & Uhlig S 2008. The utility of hotspot mapping for predicting patterns of crime. Security Journal 21: 4–28.

Eck JE, Chainey S, Cameron JG, Leitner M & Wilson RE 2005. Mapping crime: Understanding hotspots. Washington DC: National Institute of Justice Special Report

Eck JE & Spelman W 1987. Problem-solving: Problem-oriented policing in Newport News. Washington DC: Police Executive Research Forum

Farrell G, Chenery S & Pease K 1998. Consolidating police crackdowns: Findings from an anti-burglary project. London: Home Office, Policing and Reducing Crime Unit, Research, Development and Statistics Directorate

Fielding M & Jones V 2012. Disrupting the optimal forager: Predictive risk mapping and domestic burglary reduction in Trafford, Greater Manchester. International Journal of Police Science & Management 14(1): 30–41.

Getis A & Ord JK 1992. The analysis of spatial association by use of distance statistics. Geographical Analysis 24(3): 189–206.

Goldstein H 2003. On further developing problem-oriented policing. In J Knutsson (ed), Problem-oriented policing: From innovation to mainstream. Crime prevention studies vol 15. Devon, UK: Willan Publishing: 13–48

Gorr WL & Lee Y 2015. Early warning system for temporary crime hot spots. Journal of Quantitative Criminology 31: 25–47.

Hartigan JA 1975. Clustering algorithms. New York: John Wiley & Sons

Hunt P, Saunders J & Hollywood JS 2014. Evaluation of the Shreveport Predictive Policing Experiment. RAND Corporation.

Johnson SD, Bernasco W et al. 2007. Space-time patterns of risk: A cross national assessment of residential burglary victimization. Journal of Quantitative Criminology 23(3): 201–219.

Johnson SD, Birks DJ, McLaughlin L, Bowers KJ & Pease K 2007. Prospective crime mapping in operational context. Home Office online report 19/07. London, UK: Home Office

Knutsson J 2003. Introduction. In J Knutsson (ed), Problem-oriented policing: From innovation to mainstream. Crime prevention studies vol 15. Devon, UK: Willan Publishing: 1–12

Lee YJ, O SH & Eck JE 2020. A theory-driven algorithm for real-time crime hot spot forecasting. Police Quarterly 23(2): 174–201.

Levine N 2008. The ‘hottest’ part of a hotspot: Comments on ‘the utility of hotspot mapping for predicting spatial patters of crime’. Security Journal 21: 295–302.

McBratney AB & deBruijter JJ 1992. A continuum approach to soil classification by modified fuzzy k-means with extragrades. Journal of Soil Science 43(1): 159–178.

Milic N, Popovic B, Mijalkaovic S & Marinkovic D 2019. The influence of data classification methods on predictive accuracy of kernel density estimation hotspot maps. International Arab Journal of Information Technology 16(6): 1053–1062

Mohler G & Porter MD 2018. Rotational grid, PAI-maximizing crime forecasts. Statistical Analysis and Data Mining: The ASA Data Science Journal 11(5): 227–236.

Mohler G, Porter M, Carter J & LaFree G 2020. Learning to rank spatio-temporal event hotspots. Crime Science 9(3): 1–12.

Mohler GO et al. (2015). Randomized control field trials of predictive policing. Journal of the American Statistical Association 110(512): 1399–1411.

O’Donnell RM 2019. Challenging racist predictive policing algorithms under the equal protection clause. New York University Law Review 94(3): 544–580

Ord JK & Getis A 1995. Local spatial autocorrelation statistics: Distributional issues and an application. Geographical Analysis 27(4): 286–306.

Perry WL, McInnis B, Price CC, Smith SC & Hollywood JS 2013. Predictive policing: The role of crime forecasting in law enforcement operations. Washington DC: Rand Corporation.

Prins SJ & Reich A 2018. Can we avoid reductionism in risk reduction? Theoretical Criminology 22(2): 258–278.

Queensland Government Statistician’s Office nd. Queensland Thematic Maps – Crime and Justice – General.

Queensland Government Statistician’s Office 2018. SEIFA Index of Advantage/Disadvantage, Census 2016 – LGA. Queensland Government Statistician’s Office.

Queensland Police Service 2017. Annual Statistical Review 2016–2017. Queensland Government.

Ratlciffe JH 2016. Intelligence-led policing, 2nd ed. London: Routledge.

Ratcliffe JH et al. 2021. The Philadelphia predictive policing experiment. Journal of Experimental Criminology 17(1):15–41.

Richardson R, Schultz JM & Crawford K 2019. Dirty data, bad predictions: How civil rights violations impact police data, predictive policing systems, and justice. New York University Law Review 94: 15–55

Scott MS 2003. Getting the police to take problem-oriented policing seriously. In J Knutsson (ed), Problem-oriented policing: From innovation to mainstream. Crime prevention studies vol 15. Devon, UK: Willan Publishing: 49–78

Spring JV & Block CR 1989. STAC user’s manual. Chicago, IL: Criminal Justice Information Authority

Townsley M, Homel R & Chaseling J 2003. Infectious burglaries: A test of the near repeat hypothesis. The British Journal of Criminology 43(1): 615–633.

Townsley M & Oliveira A 2015. Space-time dynamics of maritime piracy. Security Journal 28: 217–229.

Ugwudike P 2020. Digital predictive technologies in the justice system: The implications of a ‘race-neutral’ agenda. Theoretical Criminology 24(3): 482–501.

Weisburd D & Majmundar MK 2018. Proactive policing: Effects on crime and communities. Washington, DC: National Academies Press.

Williamson D, McLafferty S, Goldsmith V, Mollenkopf J & McGuire P 1999. A better method to smooth crime incident data. ESRI ArcUser Magazine.

Završnik A 2021. Algorithmic justice: Algorithms and big data in criminal justice settings. European Journal of Criminology 18(5): 623-642.