Uncovering Organized Shoplifting and Theft Networks

Abstract

This article explores the role of organized crime groups in persistent theft and shop theft. It is based on an analysis of police data using exploratory analytical techniques assessing the extent of connectivity between acquisitive offenders. The research also draws on qualitative data collected from interviews. The findings suggest that shop theft was predominantly committed by UK nationals; although foreign nationals were proportionately more likely to be involved in organized crime, there is some evidence that they may themselves be being exploited. The findings are discussed and suggestions for further research are made.

Yours truly: the role of organizational commitment in shoplifting prevention

“…Retail crime that includes shoplifting, burglary, robbery, etc., results in an average of 1.3 percent of erosion of annual sales revenues within New Zealand (Guthrie and Mulder, 2017). Additionally, shoplifting has an adverse effect on tax revenues that amounts to billions of dollars per annum (Smith A.D., 2014) that could be utilized for national welfare (Crocker et al , 2017; Welsh and Farrington, 2018). Taken holistically, this suggests more effective measures for the deterrence of shoplifting are required (Hayes et al , 2012).…”

Themes in data mining, big data, and crime analytics

“…Example SNA applications include: Campana and Giovannetti (2020) use a network‐based approach for prediction, using information on previous violent acts, possession of weapons, and that of associates also, to predict violent attacks with injury; Grassi et al (2019) use various centrality measures to assess leaders in criminal networks (Italian mafia), with significant brokerage values indicating they favored face‐to‐face meetings rather than telephone use with the danger of wiretaps (Calderoni & Superchi, 2019); Ariel et al (2019) use SNA to identify prolific offenders and subsequently conducted a randomized controlled trial to measure the effect of a “specific deterrence” message on prolific offenders and their co‐offenders; Bright et al (2019) employed a longitudinal analysis to investigate structural and functional changes in an Australian drug trafficking network over time; Crocker et al (2019) investigate the structure of persistent theft and shop theft gangs using SNA; Ünal (2019) explores the balance between security and efficiency in terrorism and criminal networks with SNA measures such as path length and clustering of sub‐groups; and finally, Lim et al (2019) use deep reinforcement learning to infer missing nodes and links in incomplete and inconsistent criminal activities data.…”

Trafficking in human beings in Spain: What do the data on detected victims tell us?

“…The next most frequently chosen response was that the respondent did not know what form this type of exploitation took (22 percent), indicating that not even the professionals closest to this form of THB and exploitation have detailed knowledge of it. Third, the detected victims of THB for criminal exploitation were normally exploited by being made to participate in property-related street crime (31.4 percent) -which has been identified as a growing trend, closely linked to the activity of organized crime groups (Crocker et al, 2017) -and in forced begging (31.4 percent) (Figure 12). To a lesser extent, and in contrast with previous case studies (RACE in Europe, 2014;Villacampa and Torres, 2015), they were compelled to engage in behaviours related to drug trafficking, including both production or cultivation and as drug couriers or mules.…”