Delineate migration patterns in China extracted from mobile positioning data, and then uncover the socio-economic influencing factors using geographically weighted regression model.
Migration patterns in China extracted from mobile positioning data.
Wang Yuxia, Dong Lei, Liu Ye, Huang Zhou, Liu Yu, 2019. Habitat International, 86, 71-80.
Nationwide migrations have drawn much attention from both geographical and social sciences. Compared to census-data-based studies, data collected from broadly used location-awareness devices enable us to describe migrant patterns with timely and fine spatial resolutions. Using a mobile positioning dataset, this paper first analyzes the spatial patterns of mobile-data-based active population estimation (MAPE) and aims to uncover the socioeconomic factors associated with migrant patterns based on the MAPE change around the Chinese Spring Festival of 2016. Time series analysis presents obvious regular patterns and characteristics of MAPE before and during the holiday. Results from a geographically weighted regression (GWR) model show that MAPE differences are significantly associated with the development of secondary and tertiary industries, wage levels and foreign investments. Spatial disparities of the GWR model coefficients reveal that areas in China have different degrees of association with the explanatory variables. Explanations of this spatial nonstationary phenomenon are further detailed with the perspective of a geographical background. Finally, associated social and economic development strategies among cities in China are analyzed, and policy implications regarding the newly emerged data and their insightful findings are discussed.
Active population distribution on New Year's Eve of 2016 and the difference between it and normal work days
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Time series curve of 9 typical cities, and the spatial distribution of per capita GRP of secondary and tertiary industry coefficients obtained from the GWR model
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