STAGE: A spatio-temporal attention and graph embedding framework for modeling human mobility trajectories

Human mobility trajectories are central to understanding social behavior, infrastructure use, and population response across a wide range of settings. Modeling these trajectories is inherently a spatial statistics problem, yet the data differ fundamentally from traditional spatial datasets. Unlike conventional areal or point-referenced observations, mobility trajectories introduce statistical challenges such as sparse and irregular sampling, temporally dependent movement decisions, spatial dependence shaped by transportation networks rather than Euclidean proximity, and heterogeneous semantic attributes of visited locations. These complexities exceed the assumptions of classical spatial statistical models and motivate the use of modern representation-learning tools. We introduce STAGE (Spatio-Temporal Attention and Graph Embedding), a general framework for modeling human mobility trajectories through spatially and temporally informed representation learning. STAGE integrates (i) graph-based embeddings of a spatial tessellation that capture connectivity patterns beyond nearest-neighbor adjacency, (ii) semantic embeddings of Points of Interest (POIs) summarizing the functional context of the built environment, and (iii) an attention-based autoencoder that explicitly models temporal dependence while accommodating sparsity through masked reconstruction. Together, these components yield latent representations that preserve key spatial, semantic, and temporal structure. The resulting latent space provides a low-dimensional, interpretable summary of mobility behavior suitable for diverse downstream statistical analyses. We further demonstrate the merit of the STAGE framework by clustering real mobility data following Hurricane Ian and identify five coherent behavioral regimes related to sheltering, medical access, evacuation, and localized recovery.