Revealing Urban Community Structures by Fusing Multisource Transportation Data

Abstract

Revealing urban community structures of a city is of great importance for investigating urban development and sprawl behind the movement dynamics. However, most studies focus on delineating urban community structure and its variations with a single transit mode without covering hierarchical travel distances. This paper proposes an overarching framework to reveal urban community structures by fusing multisource spatiotemporal transportation data. Network science methods and community detection are applied to construct spatially embedded networks and uncover the urban structure from different perspectives, using 1-week transportation data derived from the metro, taxi, and dockless bike-sharing systems (BSSs) of Shanghai, China, in year 2016. Our finding shows that Shanghai can be clustered into six primary communities and exhibits polycentric patterns with strong monocentric characteristics. Shanghai’s urban structure moves toward an embedded hierarchical pattern: the dispersed monocentric structure and the centralized polycentric structure. It reflects poor functional interdependence and horizontal connectivity between communities. Beneath the complex and coupled travel-flow system, the metro dominants the basic framework of the urban community structure and contributes to form the prototype of the core community, while the taxi and BSS tend to play complementary roles like expanding, enhancing, and refining the structure. This research not only provides a promising bridge from the complex urban transportation networks to urban community structures, but also implies potential urban planning policies from an internal and comprehensive perspective.