Among various region embedding methods, graph-based region relation learning models
stand out, owing to their strong structure representation ability for encoding spatial …

Deep learning models have been demonstrated powerful in modeling complex spatio-
temporal data for traffic prediction. In practice, effective deep traffic prediction models rely on …

The prosperity of crowdsourcing geospatial data provides increasing opportunities to
understand our cities. In particular, OpenStreetMap (OSM) has become a prominent vault of …

The increasing amount of urban data enables us to investigate urban dynamics, assist urban
planning, and, eventually, make our cities more livable and sustainable. In this paper, we …

Socioeconomic indicators reflect location status from various aspects such as
demographics, economy, crime and land usage, which play an important role in the …

Learning the embeddings for urban regions from human mobility data can reveal the
functionality of regions, and then enables the correlated but distinct tasks such as crime …

Graph neural networks (GNNs) provide a powerful and scalable solution for modeling
continuous spatial data. However, they often rely on Euclidean distances to construct the …

Unsupervised spatial representation learning aims to automatically identify effective features
of geographic entities (ie, regions) from unlabeled yet structural geographical data. Existing …

Mobile user profiles are a summary of characteristics of user-specific mobile activities.
Mobile user profiling is to extract a user's interest and behavioral patterns from mobile …

Points-of-interests (POIs) have been proven to be indicative for sensing urban land use in
numerous studies. However, recent progress mainly relies on spatial co-occurrence patterns …