Abstract

Near-repeat victimization (NRV) is a phenomenon in which there is a greater likelihood for a subsequent crime to occur within close spatiotemporal proximity of the last occurrence of a similar crime. This study investigated the NRV of sex crimes and threat incidents against women and girls in Tokyo. An analysis using the Knox ratio showed significant near-repeat patterns of sex crimes and two types of threat incidents, with the exception of threat incidents with physical contact against girls. Additionally, the tendency of NRV was revealed as being stronger when the victims were girls.

Highlights

  • Near-repeat victimization (NRV) is a phenomenon wherein it is likelier for a subsequent crime to occur within close spatiotemporal proximity of a similar crime’s last occurrence

  • The Knox ratios for all other sex crimes and threat incidents are maximal in an area less than 100 m and time of fewer than 7 days, and they tend to decay from the spatiotemporal range

  • This study extended the scope of NRV hypotheses to sex crimes and threat incidents against women and girls

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Summary

Introduction

Near-repeat victimization (NRV) is a phenomenon wherein it is likelier for a subsequent crime to occur within close spatiotemporal proximity of a similar crime’s last occurrence. NRV has consistently been verified since Townsley et al (2003) first empirically confirmed it. Earlier studies focused on the NRV of residential burglary, recent ones examined its relation to various crime types (Table 1). NRV presumes that offender(s) repeatedly select a space–time region where more vulnerable criminal targets are concentrated or where a similar crime had been successfully perpetrated in the past, avoiding the risk of being arrested and obtaining the maximum benefits as an “optimal forager” (Johnson and Bowers 2004; Townsley et al 2003). Optimal foraging is applicable to impersonal sexrelated crimes, and sex criminals tend to repeat sex previous studies have not verified NRV in sex-related crimes (Table 1), and this study aims to fill this gap.

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