6 papers
Modelling daily activity patterns from mobile phone location data via deep representation learning
Xinglei Wang, Junyuan Liu, Guangsheng Dong +4
Passively collected mobile phone location data provide large-scale, longitudinal observations of human mobility but do not directly reveal activity purposes. The functional charact…
LE4Mob: Towards Inductive, Distance-Aware and General-Purpose Location Embedding for Human Mobility Modelling
Xinglei Wang, Stephen Law, Zichao Zeng +3
Location representations provide mobility models with fundamental information about the spatial position, functional characteristics, and relationships of places. However, existing…
AI vs Human Expert Reasoning: Assessing Agreements in Building Typology Predictions based on Street View Imagery
Zahratu Shabrina, Muhammad Asa, Jin Rui +2
This research investigates the potential of Vision-Language Models (VLMs) to infer building typologies: Construction, Current Use, and Storeys from Google Street View (GSV) images.…
Into the Unknown: Applying Inductive Spatial-Semantic Location Embeddings for Predicting Individuals' Mobility Beyond Visited Places
Xinglei Wang, Tao Cheng, Stephen Law +6
Predicting individuals' next locations is a core task in human mobility modelling, with wide-ranging implications for urban planning, transportation, public policy and personalised…
UST-GNN: A Unified Spatial--Topological Graph Neural Network Framework for Urban Analytics--Demonstrated through a Case Study on Urban Health Prediction
Minwei Zhao, Sanja Scepanovic, Stephen Law +3
Understanding how social, demographic, environmental, and spatial factors jointly shape urban outcomes is essential for sustainable urban development and evidence-based policy. Tra…
Multimodal Contrastive Learning of Urban Space Representations from POI Data
Xinglei Wang, Tao Cheng, Stephen Law +3
Existing methods for learning urban space representations from Point-of-Interest (POI) data face several limitations, including issues with geographical delineation, inadequate spa…