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Inthischapter, we highlight the importance of  social frontiers —sharp spatial divisions in the residential make-up of adjacent communities—as a potentially important form of segregation. The handful of studies estimating the impacts of social frontiers have been based in the USA and the UK, both of which are free-market democracies with a long history of immigration, ethnic mix and segregation. There are currently no studies of social frontiers in former socialist countries, for example, or in countries where immigration and ethnic mix are only a recent phenomenon or non-existent. This chapter aims to address this research gap by estimating the impacts of social frontiers on crime rates in a post-socialistcountry, Czechia. We demonstrate how a Bayesianspatial conditional autoregressive estimation can be used to detect social frontiers in this setting, and we use a fixed effect quasi-Poisson model to investigate the impact on crime. Our results suggest that in new immigration destinations, social frontiers may not be associated with higher rates of crime, at least in the short run. Moreover, our use of cultural distance measures helps to promote a more nuanced approach to studying the impact of segregation and highlights the role of cultural diversity in understanding the link between immigrant segregation and crime. We reflect on how this approach could contribute to the study of segregation and inequality in the Chinese context.

Social Frontiers: Estimating the Spatial Boundaries Between Residential Groups and Their Impacts on Crime