Urban Geography studies forms, social fabrics, and economic structures of cities from a
geographic perspective. Catalysed by the increasingly abundant spatial big data, Urban …

The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily
lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With …

With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …

To better handle real-world uncertainty, concepts such as fuzzy sets, neutrosophic sets,
rough sets, and soft sets have been introduced. For example, neutrosophic sets, which …

Deep learning models are modern tools for spatio-temporal graph (STG) forecasting.
Though successful, we argue that data scarcity is a key factor limiting their recent …

Traffic flow forecasting is a challenging task due to its spatio-temporal nature and the
stochastic features underlying complex traffic situations. Currently, Graph Convolutional …

Recent works demonstrate that capturing correlations between road network nodes is
crucial to improving traffic flow forecasting accuracy. In general, there are spatial, temporal …

Although generative AI has been successful in many areas, its ability to model geospatial
data is still underexplored. Urban flow, a typical kind of geospatial data, is critical for a wide …

Graph Neural Networks (GNNs) such as Graph Convolutional Networks (GCNs) can
effectively learn node representations via aggregating neighbors based on the relation …

Urban villages (UVs) refer to the underdeveloped informal settlement falling behind the
rapid urbanization in a city. Since there are high levels of social inequality and social risks in …