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Understanding commuting patterns has been a classic research topic in the fields of geography, transportation and urban planning, and it is significant for handling the increasingly serious urban traffic congestion and air pollution and their impacts on the quality of life. Traditional studies have used travel survey data to investigate commuting from the aspects of commuting mode, efficiency and influence factors. Due to the limited sample size of these data, it is difficult to examine the large-scale commuting patterns of urban citizens, especially when exploring the spatial structure of commuting. This study attempts to understand the spatial structure characteristics generated by human commutes to work by using massive mobile phone datasets. A three-step workflow was proposed to accomplish this goal, which includes extracting the home and work locations of phone users, detecting the communities from the commuting network, and identifying the commuting convergence and divergence areas for each community. A case study of Shenzhen, China was implemented to determine the commuting structure. We found that there are thirteen communities detected from the commuting network and that some of the communities are in accordance with urban planning; moreover, spatial polycentric polygons exist in each community. These findings can be referenced by urban planners or policy-makers to optimize the spatial layout of the urban functional zones.

Understanding the Spatial Structure of Urban Commuting Using Mobile Phone Location Data: A Case Study of Shenzhen, China