A two-layer graph-convolutional network for spatial interaction imputation from hierarchical functional regions

Understanding spatial interactions in urban environments has become critical in the context of spatio-temporal big data. However, Spatial–temporal big data often exhibit non-uniformity, necessitating the imputation of spatial interaction relationships derived from the analysis of such data. Previous studies often used simplified grid-based or TAZ approaches that ignore the complex interactions for spatial interaction imputation, leading to limitations in accuracy. In this paper, we proposed a two-layer spatial interaction imputation framework (SIF) for accurate multi-scale spatial interaction imputation. To our knowledge, this is the first time that we impute spatial interactions in multi-scale urban areas. In the first layer, it utilised a hierarchical spatial units division algorithm inspired by Shannon’s information entropy to hierarchically classify study area using point of interest (POI) data; In the second layer, it integrates the classified areas and travel flow data into a spatial interaction graph convolutional network (SI-GCN) for spatial interaction imputation. Two case studies were conducted in Beijing, China and New York City, USA, using over eight million taxi data and one million bike-sharing data. The results showed the superior performance of SIF compared to baseline models. The results also analysed the travel behaviours in both Cities, as well as the impact of social, economic and environmental factors on passengers’ spatial choices when travelling. •A two-layer framework for accurately imputing spatial interaction.•Integrating POI data and taxi data for spatial interaction imputation.•Hierarchical improves the imputation accuracy over the regular grid method by about 9% in Beijing and NYC.•Revealing the travel patterns of different urban functional regions under different time periods.•Activity transitions are discovered spatially and temporally.