Spatiotemporal Pattern

Spatiotemporal pattern analysis focuses on understanding and modeling how phenomena evolve across both space and time. Current research emphasizes developing robust methods for predicting these patterns, particularly using deep learning architectures like convolutional neural networks, graph convolutional networks, and transformers, often incorporating techniques like contrastive learning and attention mechanisms. These advancements are crucial for diverse applications, including traffic forecasting, urban planning, epidemic modeling, and even understanding human behavior and brain activity, by enabling more accurate predictions and improved resource allocation. The field is also actively exploring methods to handle incomplete or heterogeneous data, improving the generalizability and reliability of spatiotemporal models.