Journal article Open Access

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

Mohammad Shan-A-Khuda; Z. Cliffe Schreuders

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. 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. The results of the study will provide valuable insights into policing cybercrime in England and beyond.

 

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