Understanding Cybercrime Victimisation : Modelling The Local Area Variations in Routinely Collected Cybercrime Police Data Using Latent Class Analysis

Abstract

Numerous factors such as sociodemographic characteristics contribute to cybercrime victimisation. Previous research suggests that neighbourhood plays a role in cybercrime perpetration. However, despite the theoretical importance and particular interest to law enforcement agencies and policymakers, local area variations in cybercrime victimisation have rarely been examined. Drawing on data from recorded cybercrime incidents within one of the largest police forces in England from a three-year period with a victim dataset of 5,270 individuals enhanced by the Census data, this research untangles the relationships between demographics of cybercrime victims and their resident area characteristics. The research considers four types of cybercrime victimisation: ‘Harassment/Unwanted Contact’, ‘Fraud/Theft/Handling’, ‘Sexual/Indecent Images’ and ‘other types of cybercrime’ (classifications used by the participating police force). Latent Class Analysis (LCA) was applied to rigorously analyse the relationship among the four different types of cybercrime victimisation with victim demographics and resident area-level characteristics. This research finds that each type of cybercrime yielded statistically distinct victim profiles. Vulnerabilities to cybercrime varied among male and female of different age groups, and importantly, the types of residential areas of the victims. Specifically, it is evident that females were much more likely to become cybercrime victims than males for two types of cybercrime: ‘Harassment/Unwanted Contact’, and ‘Sexual/Indecent Images’. Vulnerabilities associated with these two types of cybercrime decreased with the increase of age. Cybercrime victims of ‘Sexual/Indecent Images’ were likely to be 5-14 year-olds living in areas with a higher number of Level 2, Level 4 qualifications and full-time students. Both males and females were vulnerable to ‘Fraud/Theft/Handling’ cybercrime and their resident areas had a higher number of full-time students, Level 4 qualifications and Asians. Finally, victims of ‘other types of cybercrime’ were most likely to be male and their resident areas had a high number of Asians and full-time students. Our work demonstrates that it is possible to apply statistical analysis to routinely collected police data to gain insight into the cybercrime victimisation that occurs across crime types in relation to demographics and area-level variations. These results provide valuable insights into policing cybercrime in England and beyond.