Delineating a hierarchical organization of ranked urban clusters using a spatial interaction network

Abstract How to reveal the hierarchical structures of urban space has long posed a challenge for researchers. Yet, revealing such structures is of fundamental importance to our understanding of multiscale urban organization. This study, therefore, built a spatial interaction network based on a massive body of human movement data to derive the hierarchical organization of ranked urban clusters. These urban clusters were statistically robust and significant. They were ranked at different levels by fusing network community metrics, urban functional metrics, and road network topological metrics. To verify our methods, experiments were conducted in downtown Wuhan, China, using taxicab trajectory data to construct a spatial interaction network. We derived three levels of urban clusters: (1) geographically cohesive, (2) observable in forms different from government-defined boundaries, and (3) constrained or affected by natural boundaries. We also calculated ranking scores for the urban clusters at different levels based on their structural and functional roles in urban space. Finally, we demonstrate that our ranking scores correlate better with socioeconomic indicators than is the case with other methods. Thus, our method can enrich research on delineating ranked hierarchical urban clusters while the results can provide decision-making support for urban planning and management.