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

Pre-training location embeddings from spatial-temporal trajectories is a fundamental
procedure and very beneficial for user next location prediction. In the real world, a location …

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 …

Spatial-temporal graph learning has emerged as the state-of-the-art solution for modeling
structured spatial-temporal data in learning region representations for various urban sensing …

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

Mobility trajectory data is of great significance for mobility pattern study, urban computing,
and city science. Self-driving, traffic prediction, environment estimation, and many other …

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 …

Understanding intrinsic patterns and predicting spatiotemporal characteristics of cities
require a comprehensive representation of urban neighborhoods. Existing works relied on …

The prevalence of region-based urban data has opened new possibilities for exploring
correlations among regions to improve urban planning and smart-city solutions. Region …