Suzuki, Ai; (2023) Patterns and Predictors of Crime and Fear of Crime during the Crime Drop: A Multilevel Analysis of Repeated Cross-Sectional Data in Japan, 2007-2018. Doctoral thesis (Ph.D), UCL (University College London).

Preview

Text revised_thesis_aisuzuki_final_upload.pdf - Accepted Version Download (2MB) | Preview

Abstract

Many attempts have been made to examine the determinants of victimisation and the fear of crime, often guided by social disorganisation theory and environmental criminology. However, there have been only a handful of studies that have been carried out in East Asia so far. Consequently, it is unclear whether those factors which are reliably associated with higher or lower levels of crime and fear of crime in Western societies are generalisable to the dissimilar context of Japan. Against this backdrop, this thesis is concerned with the patterns and predictors of victimisation, repeat victimisation, fear of crime and perceived risk of victimisation in Japan, drawing on repeated cross-sectional data collected as part of a nationally representative household survey “the Japanese Public Safety Survey” (JPSS) and the census. Study 1 is concerned with the patterns and predictors of household property crime. Exploratory factor analysis was first performed to reveal the factor structure of eleven perceived neighbourhood disorder variables used in the JPSS. A series of multilevel logistic regression models demonstrated that the year variables were found to be negatively associated with household property crime risk. Detached house, homeownership, social support, and the presence of community policing were found to be associated with household property crime risk. Study 2 examined the patterns and predictors of repeat victimisation of residential burglary and vandalism. In contrast with what was found in Study 1, the survey year variables were not correlated with the risk of repeat residential burglary and vandalism victimisation. Social support and university degree were found to be the factors which distinguish repeat residential burglary victims from other groups. Social support and social disorder were found to be the factors that distinguish repeat vandalism victims from other groups. Social support and high ratio of manufacturing industry were found to be the factors that distinguish repeat residential burglary victims from single victims. Social disorder was found to be the factor that distinguishes repeat vandalism victims from single victims. Study 3 examined the patterns and predictors of fear and perceived risk of household property crime. The results of multilevel regression models revealed that, at the individual/householdlevel, experiencing previous victimisation, being older, living in a detached house and having higher annual household income were associated with increased fear of household property crime. At the neighbourhood-level, the presence of social disorder and community policing were statistically related to the levels of fear of household property crime. There was a statistical association between prior victimisation and perceived risk of victimisation, and different predictors were found to be associated with fear of crime and perceived risk of victimisation. The survey year variables were not found to be associated with fear of and the perceived risk of household property victimisation. The findings from the analysis furthered support the three models of fear of crime. In summary, the findings of three empirical studies yielded both consistencies and inconsistencies with the relevant literature derived mainly from studies conducted in Western industrialised countries, showing some applicability of the criminological theories to Japan. The thesis demonstrated the usefulness of multilevel modelling and multiple secondary data sources, and the importance of introducing measures dealing with neighbourhood social disorder, and crime prevention measures which reflect the crime trends or related problems of each municipality.

Download activity - last month

Export as JSONXMLCSV

Download activity - last 12 months

Export as XMLCSVJSON

Downloads by country - last 12 months

Export as XMLCSVJSON

Archive Staff Only

View Item