Spatiotemporal Patterns of Population Mobility and Its Determinants in Chinese Cities Based on Travel Big Data

Zhen Yang, Weijun Gao, Xueyuan Zhao, Chibiao Hao and Xudong Xie
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Zhen Yang: Innovation Institute for Sustainable Maritime Architecture Research and Technology, Qingdao University of Technology, Qingdao 266033, China
Weijun Gao: Innovation Institute for Sustainable Maritime Architecture Research and Technology, Qingdao University of Technology, Qingdao 266033, China
Xueyuan Zhao: Innovation Institute for Sustainable Maritime Architecture Research and Technology, Qingdao University of Technology, Qingdao 266033, China
Chibiao Hao: College of Architecture and Urban Planning, Qingdao University of Technology, Qingdao 266033, China
Xudong Xie: College of Architecture and Urban Planning, Qingdao University of Technology, Qingdao 266033, China

Sustainability, 2020, vol. 12, issue 10, 1-25

Abstract: Large-scale population mobility has an important impact on the spatial layout of China’s urban systems. Compared with traditional census data, mobile-phone-based travel big data can describe the mobility patterns of a population in a timely, dynamic, complete, and accurate manner. With the travel big dataset supported by Tencent’s location big data, combined with social network analysis (SNA) and a semiparametric geographically weighted regression (SGWR) model, this paper first analyzed the spatiotemporal patterns and characteristics of mobile-data-based population mobility (MBPM), and then revealed the socioeconomic factors related to population mobility during the Spring Festival of 2019, which is the most important festival in China, equivalent to Thanksgiving Day in United States. During this period, the volume of population mobility exceeded 200 million, which became the largest time node of short-term population mobility in the world. The results showed that population mobility presents a spatial structure dominated by two east–west main axes formed by Chengdu, Nanjing, Wuhan, Shanghai; and three north–south main axes formed by Guangzhou, Shenzhen, Shanghai, Wuhan, and Chengdu. The major cities in the four urban agglomerations in China occupy an absolute core position in the population mobility network hierarchy, and the population mobility network presents typical “small world” features and forms 11 closely related groups. Semiparametric geographically weighted regression model results showed that mobile-data-based population mobility variation is significantly related to the value-added of secondary and tertiary industries, foreign capital, average wage, urbanization rate, and value-added of primary industries. When the spatial heterogeneity and nonstationarity was considered, the socioeconomic factors that affect population mobility showed differences between different regions and cities. The patterns of population mobility and determinants explored in this paper can provide a new reference for the balanced development of regional economy.

Keywords: spatiotemporal patterns; population mobility; travel big data; socioeconomic factors; social network analysis (SNA); semiparametric geographically weighted regression (SGWR) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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