Stephen Law

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

This work conceptualises activity pattern mining as an integrated process of representation, clustering, and interpretation, and proposes the Activity Chain Encoder (ACE) for the representation stage, a self-supervised model that combines pre-trained urban embeddings with visit timing and duration and uses a Transforme...

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

LE4Mob is proposed, an inductive, distance-aware, and geography-derived location embedding framework for mobility modelling that extends contrastive language-location pre-training while introducing a distance-aware regularisation objective that encourages the embedding space to preserve spatial relationships.

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