Unveiling the Urban Spatial Structure from the Perspective of Job-Housing Relationship: A Case Study of Guangzhou, China

Commuting is a substantial component of urban transportation. Understanding the commuting patterns may contribute to the decision-making of urban spatial structure optimization and quality-of-life improvement. This paper addresses the urban spatial structure analysis at the macro and meso levels based on commuting demand. Mobile phone data is introduced to identify the commuting demand and construct the commuting flow network. The community detection method is applied to divide the basic spatial analysis units and further analyzes the urban spatial structure from the perspective of a job-housing relationship. Guangzhou is selected as a case to demonstrate the proposed methods. As a result, the spatial divisions based on community detection are proved to be applicable as basic spatial analysis units. The outcomes show the regional cohesiveness and the hierarchical levels in the spatial structure of Guangzhou and reveal the significant effect of commuting distance on the spatial structure. The methodology proposed in this paper is an operable macro- and meso-tool for an over knowledge of urban spatial structure.