Temporal variation in activity-space-based segregation: A case study of Beijing using location-based service data

Abstract

The literature on segregation is focused on the residential domain considered from a static perspective. In contrast, the purpose of our study is to examine temporal variations in the overall degree and spatial pattern of activity-space-based social segregation around the clock on weekday and at weekend in the central urban area of Beijing, China. Drawing on location-based service (LBS) big data, we measure the level of activity-space-based segregation at each hour of a weekday and a weekend day between groups of people who differ from each other in relation to formal educational achievements. Their comparisons with the segregation in major life domains such as residence and workplace are also made. At the global level, the extent of activity-space-based segregation fluctuates around the clock, with less segregation during the daytime than at night and less segregation on the weekend day than on the weekday. The segregation degrees for all groups are in descending order workplace segregation, residential segregation, and out-of-home non-employment segregation. At the local level, the highly segregated units centralize to city center in the morning and decentralize to suburban areas in the evening. The spatial segregation patterns at various times of the day change to a much greater extent on the weekday than during the weekend day, especially for employment centers and large-scale residential communities. Lastly, a spatial unit classification framework of real-time activity-space-based segregation is proposed to integrate multiple kinds of information pertaining to the segregation level and the dominant group in a given area at a given time with the extent and trend of the temporal variation identified presented as a concise map useful both to advancing further research and guiding policy formulation.