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Posts tagged offender decision-making
Visiting Frequency on Crime Location Choice: Findings from an Online Self-Report Survey

By Barbara Menting, Marre Lammers, Stijn Ruiter*, Wim Bernasco

Crime pattern theory predicts that offenders commit offences in their activity spaces. We also propose that they most likely offend in the more frequently visited parts. Previous studies used offenders' residential areas or other activity space proxy measures but lacked data on other routinely visited places (e.g., work, school, and leisure activities). A major contribution of this study is the use of an online survey in which 78 offenders reported on their own activity spaces and committed offences (n = 140). Results show that offending is much more likely in offenders' activity spaces than elsewhere, and effects increase with visiting frequency. Although residential area is a good predictor, offenders' more extensive self-reported activity spaces predict much better where they commit offences.

The British Journal of Criminology, Volume 60, Issue 2, March 2020, Pages 303–322,

Learning from Criminals: Active Offender Research for Criminology

By Volkan Topalli, Timothy Dickinson, and Scott Jacques

Active offender research relies on the collection of data from noninstitutionalized criminals and has made significant contributions to our understanding of the etiology of serious crime. This review covers its history as well as its methodological, scientific, and ethical pitfalls and advantages. Because study subjects are currently and freely engaging in crime at the time of data collection, their memories, attitudes, and feelings about their criminality and specific criminal events are rich, detailed, and accurate. Contemporary approaches to active offender research employ systematized formats for data collection and analysis that improve the validity of findings and help illuminate the foreground of crime. Although active offender research has traditionally relied on qualitative techniques, we outline the potential for it to make contributions via mixed methods, experiments, and emerging computational and technological approaches, such as virtual reality simulation studies and agent-based modeling.

Annu. Rev. Criminol. 2020. 3:189–215