Articlegold2020

Spatiotemporal Exploration of Chinese Spring Festival Population Flow Patterns and Their Determinants Based on Spatial Interaction Model

Tao Zhou · Bo Huang · Xiaoqian Liu · Qiang Gou · Huang Zhi-hui · Cheng Xie

27 citations0/0 files storedISPRS International Journal of Geo-Information

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Human Mobility and Location-Based Analysis Regional Economics and Spatial Analysis Regional Economic and Spatial Analysis

Abstract

Large-scale population flow reshapes the economic landscape and is affected by unbalanced urban development. The exploration of migration patterns and their determinants is therefore crucial to reveal unbalanced urban development. However, low-resolution migration datasets and insufficient consideration of interactive differences have limited such exploration. Accordingly, based on 2019 Chinese Spring Festival travel-related big data from the AMAP platform, we used social network analysis (SNA) methods to accurately reveal population flow patterns. Then, with consideration of the spatial heterogeneity of interactive patterns, we used spatially weighted interactive models (SWIMs), which were improved by the incorporation of weightings into the global Poisson gravity model, to efficiently quantify the effect of socioeconomic factors on migration patterns. These SWIMs generated the local characteristics of the interactions and quantified results that were more regionally consistent than those generated by other spatial interaction models. The migration patterns had a spatially vertical structure, with the city development level being highly consistent with the flow intensity; for example, the first-level developments of Beijing, Shanghai, Chengdu, Guangzhou, Shenzhen, and Chongqing occupied a core position. A spatially horizontal structure was also formed, comprising 16 closely related city communities. Moreover, the quantified impact results indicated that migration pattern variation was significantly related to the population, value-added primary and secondary industry, the average wage, foreign capital, pension insurance, and certain aspects of unbalanced urban development. These findings can help policymakers to guide population migration, rationally allocate industrial infrastructure, and balance urban development.

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References

1996Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity 2010Migration and Development: A Theoretical Perspective 2003Rural–urban migration and urbanization in China: Evidence from time-series and cross-section analyses 2005Geographically weighted Poisson regression for disease association mapping 2007A gravity model of immigration 2017Universal model of individual and population mobility on diverse spatial scales 2003Spatial interaction modelling 2016The Impact of Internal Migration on Population Redistribution: an International Comparison

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