Characterizing the polycentric spatial structure of Beijing Metropolitan Region using carpooling big data

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