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

Xiping Yang, Zhixiang Fang, Ling Yin, Junyi Li, Yang Zhou and Shiwei Lu
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Xiping Yang: School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China
Zhixiang Fang: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Ling Yin: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Road, Shenzhen 518005, China
Junyi Li: School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China
Yang Zhou: College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
Shiwei Lu: School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China

Sustainability, 2018, vol. 10, issue 5, 1-14

Abstract: 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.

Keywords: commuting; mobile phone data; spatial structure (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (10)

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