Stephen Law

Modelling daily activity patterns from mobile phone location data via deep representation learning

Passively collected mobile phone location data provide large-scale, longitudinal observations of human mobility but do not directly reveal activity purposes. The functional characteristics of visited locations offer useful contextual information, yet their relationship with activity purpose remains uncertain, particula...

LE4Mob: Towards Inductive, Distance-Aware and General-Purpose Location Embedding for Human Mobility Modelling

Location representations provide mobility models with fundamental information about the spatial position, functional characteristics, and relationships of places. However, existing embeddings are often dependent on mobility observations, unable to represent unseen locations, and weakly constrained to retain geographic...

UST-GNN: A unified spatial-topological graph neural network framework for urban analytics demonstrated through a case study on urban health prediction

UST-GNN is presented, a unified spatial--topological graph neural network framework that integrates neighbourhood connectivity, heterogeneous urban features, and positional/locational embeddings into a single representation and provides a unified GeoAI analytical pipeline that can be embedded into urban digital twin wo...

UST-GNN: A unified spatial-topological graph neural network framework for urban analytics demonstrated through a case study on urban health prediction