Revealing Dynamic Spatial Structures of Urban Mobility Networks and the Underlying Evolutionary Patterns

Abstract

Urban space exhibits rich and diverse organizational structures, which is difficult to characterize and interpret. Modelling urban spatial structures in the context of mobility and revealing their underlying patterns in dynamic networks are key to understanding urban spatial structures and how urban systems work. Most existing methods overlook its temporal dimension and oversimplify its spatial heterogeneity, and it is challenging to address these complex properties using one single method. Therefore, we propose a framework based on temporal networks for modeling dynamic urban mobility structures. First, we cast aggregated traffic flows into a compact and informative temporal network for structure representation. Then, we explore spatial cluster substructures and temporal evolution patterns to acquire evolution regularities. Last, the capability of the proposed framework is examined by an empirical analysis based on taxi mobility networks. The experiment results enable to quantitatively depict urban space dynamics and effectively detect spatiotemporal heterogeneity in mobility networks.

Exploring Public Transportation Supply–Demand Structure of Beijing from the Perspective of Spatial Interaction Network

“…By leveraging big data, including mobile phone, smart card, floating vehicle, and social media data, researchers can analyze user movement data as traffic flows on networks, thereby creating weighted networks that reveal the structure and related properties of UTNs [57][58][59]. Some studies have shown that complex network analysis based on various types of emerging traffic flow data can effectively reveal urban traffic demand and its dynamics [11,60]. For instance, Zhong et al (2014) constructed a weighted directed network of Singapore based on smart card datasets and identified the spatial structure of urban hubs, centers, and boundaries by integrating network and spatial analysis methods [33].…”

Spatiotemporal Patterns Evolution of Residential Areas and Transportation Facilities Based on Multi-Source Data: A Case Study of Xi’an, China

“…(3) Static analysis. Although some studies have analyzed urban dynamics, most research has explored urban spatial structures at a single time point [24]. Systematic analysis of the spatiotemporal evolution and trends of urban spaces is still lacking.…”