Spatial Structure

Spatial structure research focuses on understanding and modeling the arrangement of entities in space and how this arrangement influences various phenomena. Current research employs diverse approaches, including graph neural networks, diffusion models, and large language models, to analyze spatial relationships in diverse contexts such as human movement, disease spread, and image analysis. These analyses are crucial for improving applications ranging from urban planning and public health interventions to enhancing machine learning models' ability to understand and generate realistic spatial representations. The field is actively developing new methods to capture and utilize fine-grained spatial information, leading to more accurate and insightful models across numerous disciplines.