Abstract：Studies of urban socio-spatial segregation has been increasingly benefited from the availability of space-time behavioral big data. The latter can well enrich the existing insights of activity-space segregation, which were predominantly based on conventional datasets such as activity diary data with substantially smaller sample sizes. Specifically, while studies with conventional datasets only adopted residential population as a proxy of the socioeconomic structures in the activity places, the use of space-time behavioral big data can effectively unravel the temporal variation of the socioeconomic structures by tracing the changing locations of the population over different time periods. Echoing the emerging strand of studies that utilized space-time behavioral big data to examine socio-spatial segregation, this paper presents a review of the existing studies through summarizing the types of data and analytical approaches used in those studies as well as the relevant contributions. Most of the existing studies were found being derived from three types of spacetime behavioral big datasets, namely cellphone data, social media data and transportation-derived data. Among the existing studies, three major contributions can be identified. First, the existing studies had unraveled the temporal variations and periodic patterns of socio-spatial segregation that were manifested at multiple temporal scales. Second, facilitated by space-time behavioral big data, the existing studies managed to measure individual socio-contextual exposure (i.e., co-presence) in a real-time manner. Third, the existing studies extended the analytical lens of socio-spatial segregation by including data of social network and tweet-based subjective attitudes. The present paper thus draws attention to the underrated potentials of spacetime behavioral big data of conducting critical-quantitative analyses.
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