Different roads take me home: the nonlinear relationship between distance and flows during China’s Spring Festival

Abstract Human mobility modelling has attracted scholarly attention from physics-based methods and social science explanatory approaches. However, there is limited knowledge of the nonlinear relationship of flows and distance in intercity mobility and regional differences in the nonlinear relationship. Focusing on China’s long-distance and large-scale mobility during the Spring Festival, this paper develops a framework to explain the nonlinear relationship. Using the Gradient Boosting Decision Tree (GBDT) model and Tencent Big Data, we find that there are three types of nonlinear relationships, namely plateau (almost zero distance decay parameter), drop (decreasing distance decay parameter) and rebound (increasing distance decay parameter after decreasing). The provincial differences also reveal that the nonlinear relationships depend on the domestic relative location and the intra-provincial urban system. This result shows that the cities in the coastal province enjoy a more inclusive spatial structure, which supports the migration from the periphery of the province. In contrast, the inland cities are concerned with embracing the migrants and settling them down.