Quantifying spatial disparities and influencing factors of home, work, and activity space separation in Beijing

Due to rapid informatization and increasing urban sprawl, currently, metropolitan residents are facing challenges regarding spatial imbalance between their home, workplace, and activity locations, which seriously affects the quality of life and subjective well-being of urban residents. However, only a few existing studies have analyzed the relationship between home, workplace, and other activity spaces of urban residents and the influence mechanism of spatial separation of the three locations. In this study, we used the mobile phone signaling data of the residents of Beijing for May 2019 to explore the degree of mutual separation of the three locations, based on the identification of the homes, workplaces, and main activity spaces of over 2.3 million residents in Beijing. Leveraging these data, we discussed the influence mechanism of activity separation, in terms of the built environment and socioeconomic factors. The results indicated that the degree of the home-workplace separation was the highest, followed by the home-workplace-activity space separation, and the home-activity space separation was the lowest. Notably, the three types of spatial separations had significant spatial disparities. Attribution analysis indicated housing price as the dominant factor affecting the spatial separation of the residents, while the accessibility and location also having important effects. Moreover, the interaction of these factors had a stronger explanatory power for the spatial separation of the daily activity spaces of the urban residents. This study is the first to estimate the degree of spatial separation among the homes, workplaces, and other activity spaces of urban residents and analyzed their correlation using geographic context factors. Our results can provide useful scientific insights to explore the spatial characteristics of the home-work-activity relationship with respect to metropolitan residents and optimize the spatial distribution of urban functions.