The Factors Influencing China's Population Distribution and Spatial Heterogeneity: a Prefectural-Level Analysis using Geographically Weighted Regression

The study of population distribution and its influencing factors is one of the key fields within population geography. Most previous studies have used traditional global regression models to explore the factors influencing population distribution, but they neglect spatial heterogeneity. To overcome this weakness, we employed the Geographically Weighted Regression (GWR) model to identify spatially varying relationships between population density and potential influencing factors in mainland China. The results showed that road density, GDP, temperature and arable land proportion were the key factors influencing population distributions and that the influence of each factor varied in different regions. A lower road density significantly restricted population agglomeration in less developed regions such as Southwest China. The regression coefficients of GDP decreased from the more developed Southeast China to the less developed Northwest China. The regression coefficients of temperature were higher in southeastern coastal areas. Arable land proportion was a significant factor increasing population agglomeration in Xinjiang, Northwest China, but this relationship was weaker in other parts of China. We argue that regional population and development policy should be made according to the specific factors crucially influencing population distributions in various regions in order to promote orderly immigration and optimize population distribution.