Giving thieves the time of day: a temporal analysis of shoplifters in NSW

Mr Adam Marsden1

1Queensland University Of Technology, Brisbane, Australia

There is a distinct rise in retail thefts during the period; however, the data is limited in that the increase cannot be attributed to individual Local Government Areas (LGAs). Although more thefts were reported on Thursdays (late night shopping) than any other day (n=36,657), it did not have an effect on thieves’ preferred time of day for targeting retailers (before closing time on other days). Limitations also exist around accuracy of reporting, in particular the temporal factors, which may in turn have an impact on the findings. Because the data is generic in the sense that it represents an entire state and not LGAs, it would be impractical to suggest the same temporal analysis is accurate when analysing individual LGAs to address crime-prevention methods.


Adam Marsden is an AFP officer with over 12 years’ experience in complex investigations. He was part of the Identity Security Strike Team investigating misuse of identity documents and was case officer for an investigation resulting in the bringing down of one of Australia’s largest identity crime syndicates.

Mr Marsden has advanced training in Geographic Profiling Analysis through California State University USA and a Masters in Fraud & Financial Crime, Charles Sturt University. He is presently researching at Queensland University of Technology’s Crime and Justice Research Centre, examining the influence of geographic profiling in the retail crime environment.

This study analyses the temporal factors of shoplifters in the NSW region, and presents information for managers of store loss-prevention professionals to assist with allocating resources whilst cutting costs and ‘doing more with less’.

The research uses data from the Bureau of Crime Statistics and Research (BOCSAR), ‘steal from retail store’. The data categories are time of day, day of week, and month of year; data findings are visually represented in graphical format.

The study uses n=188,014 steal from retail store reports of the period January 2010–June 2018, and refers to a number of academic sources, including Rossmo’s (2014) and Muller’s (2011) explorations of reducing uncertainty in investigations by observing patterns in crime, and research around analyses into programs of crime-prevention in shoplifting.

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