Exploring and evaluating the spatial association between commercial and residential spaces using Baidu trajectory data

The spatial association between commercial and residential spaces strongly influences residents' daily lives, social equity, and economic performance. Based on Baidu trajectory dataset of Zhuhai and the dynamic consumption travel flow perspective, this study proposes a research framework to explore the spatial association patterns between commercial and residential spaces and to evaluate the commercial accessibility of different social groups. Findings indicate that the formation of spatial association communities is partially limited by natural barriers and administrative boundaries. The spatial association patterns follow a paradigm: a polycentric spatial association formed in the main urban area with medium travel distance, a monocentric spatial association formed in the suburbs with short travel distance, and a bicentric spatial association formed in the outer suburbs with long travel distance. For social justice in consumption, the mid-high-level income social group has the highest commercial accessibility, whereas the low-level income social group suffers from long consumption travel distance. Moreover, social disparities in commercial accessibility are also found among urban circles and spatial association patterns. This study has implications for governments in terms of designing policies to enhance commercial accessibility, mitigate social inequality in consumption, and achieve their goal of self-containment during urbanization.