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.
Highlights
Since the reform and opening up in 1978, the rapid economic development and the process of social modernization in China have made population mobility between cities more common
The results showed that average wage, urbanization rate, foreign capital, value-added of primary industry, and value-added of secondary and tertiary industry are closely related to population mobility
Traditional census data cannot reveal the spatial patterns of population mobility and relevant socioeconomic factors within a specific period or even track people’s trajectories because of the slow updating frequency and other shortcomings
Summary
Since the reform and opening up in 1978, the rapid economic development and the process of social modernization in China have made population mobility between cities more common. It is a complex network based on nodes and connections to measure and map various aspects or relationships among people, organizations, and groups This method has emerged as a key technique in the modern social sciences—it is widely used in anthropology, biology, demography, communication studies, economics, geography, history, and information science—and has achieved remarkable results [34]. Based on the changes of net population mobility in different cities before and after the Spring Festival, this paper described the population activities in prefecture-level cities in China and analyzed the characteristics of the population mobility network through the social network analysis method, including the spatial structure, with key cities as nodes and typical “small world” features.
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