Spatial heterogeneity in spatial interaction of human movements—Insights from large-scale mobile positioning data

Distance decay is a primary characteristic of spatial interaction in human movements, and it has been incorporated into many spatial interaction models. Existing approaches mainly rely on travel survey datasets to fit the frictional coefficient of distance decay. However, limited sample size and spatiotemporal resolution make the determination of the spatial interaction characteristic from a comprehensive view difficult. Recently, this situation has been reversed due to emerging large human trajectory datasets, which have stimulated a body of literatures to re-examine the traditional issue of distance decay. However, these studies only focused on distance decay from a global perspective and neglected the spatial non-stationarity of spatial interaction. This study aims to reveal the spatial heterogeneity of distance decay of human movements extracted from massive mobile phone location data from Shenzhen, China. The power law function is utilized to fit the distance decay coefficients for inflow and outflow of each spatial analysis unit. Then, geographically weighted regression is employed to quantify the relationship between distance decay coefficients and land use distribution and between distance decay coefficients and traffic facilities. Results show that considerable spatial non-stationarity appears in the distance decay of spatial interaction, and the regression coefficients indicate the spatial variations of the influence of land use and traffic facilities on distance decay across urban space. These findings provide an in-depth insight into the distance decay characteristics of human movements in a more microcosmic space.