Citywide spatio-temporal (ST) forecasting is a fundamental task for many urban applications,
including traffic accident prediction, taxi demand planning, and crowd flow forecasting. The …

Graph neural networks have been widely used to learn node representations for many real-
world static graphs. In general, they learn node representations by recursively aggregating …

The proliferation of trajectory data has facilitated various applications in urban spaces, such
as travel time estimation, traffic monitoring, and flow prediction. These applications require a …

The proliferation of geospatial data in urban and territorial environments has significantly
facilitated the development of geospatial artificial intelligence (GeoAI) across various urban …

Graph Neural Networks (GNNs) have been broadly applied in many urban applications
upon formulating a city as an urban graph whose nodes are urban objects like regions or …

Geo-textual objects with both geographical location and textual description are gaining in
prevalence. Over the past decades, substantial research has been conducted on spatial …

Recently, graph contrastive learning proposes to learn node representations from the
unlabeled graph to alleviate the heavy reliance on node labels in graph neural networks …

Finding multiple temporal relationships among locations can benefit a bunch of urban
applications, such as dynamic offline advertising and smart public transport planning. While …

With the proliferation of spatio-textual data, Top-k KNN spatial keyword queries (TkQs),
which return a list of objects based on a ranking function that considers both spatial and …

Identifying strategic groups is crucial for businesses to understand the local competitive
landscape and develop successful strategies. With the advancements in location-based …