Liu, X; Yan, X; Wang, W; Titheridge, H; Wang, R; Liu, Y; (2021) Characterizing the polycentric spatial structure of Beijing Metropolitan Region using carpooling big data. Cities , 109 , Article 103040. 10.1016/j.cities.2020.103040.

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Abstract

Polycentric metropolitan regions are a high-level urbanization form characterized with dynamic layout, fuzzy boundary and various human activity performances. Owing to the complexity of polycentricity, it can be difficult to understand their spatial structure characteristics merely based on conventional survey data and method. This poses a challenge for authorities wishing to make effective urban land use and transport policies. Fortunately, the presence and availability of big data provides an opportunity for scholars to explore the complex metropolitan spatial structures, but there are still some research limitations in terms of data use and processing, unit scale, and method. To address these limitations, we proposed a three-step method to apply the carpooling big data in metropolitan analysis including: first, locating the metropolitan sub-centers; second, delimiting the metropolitan sphere; third, measuring the performance of polycentric structure. The developed method was tested in Beijing Metropolitan Region and the results show that the polycentric metropolitan region represents a hierarchical regional center system: one primary center interacting with seven surrounding secondary centers. These metropolitan centers have a strong attraction, which results in the continuous expansion beyond the administrative boundary to radiate more adjacent jurisdictions. Furthermore, the heterogeneity of human activity performance and role for each regional center is remarkable. It is necessary to consider the specific role of each sub-center when making metropolitan transport and land use policies. Compared with previous studies, the proposed method has the advantages of being more reliable, accurate and comprehensive in characterizing the polycentric spatial structure. The application of carpooling big data and the proposed method would provide a novel perspective for research on the other metropolitan regions.

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