Exploring Spatial Nonstationarity in Determinants of Intercity Commuting Flows: A Case Study of Suzhou-Shanghai, China

The increasing popularity of intercity commuting is affecting regional development and people's lifestyles. A key approach to addressing the challenges brought about by intercity commuting is analyzing its determinants. Although spatial nonstationarity seems inevitable, or at least worth examining in spatial analysis and modeling, the global perspective was commonly employed to explore the determinants of intercity commuting flows in previous studies, which might result in inaccurate estimation. This paper aims to interpret intercity commuting flows from Suzhou to Shanghai in the Yangtze River Delta region. For this purpose, mobile signaling data was used to capture human movement trajectories, and multi-source big data was used to evaluate social-economic determinants. Negative binomial (NB) regression and spatially weighted interaction models (SWIM) were applied to select significant determinants and identify their spatial nonstationarity. The results show that the following determinants are significant: (1) commuting time, (2) scale of producer services in workplace, (3) scale of non-producer services in residence, (4) housing supply in residence, (5) year of construction in residence, and (6) housing price in residence. In addition, all six significant determinants exhibit evident spatial nonstationarity in terms of significance scope and coefficient level. Compared with the geographically weighted regression (GWR), SWIM reveals that the determinants of intercity commuting flows may manifest spatial nonstationarity in both residence and workplace areas, which might deepen our understanding of the spatial nonstationarity of OD flows.