Spatial Interaction

Spatial interaction research focuses on understanding and modeling the relationships between entities in space, aiming to quantify and predict these interactions for various applications. Current research emphasizes developing sophisticated models, including graph neural networks, transformers, and convolutional neural networks, to capture complex spatial dependencies and high-order interactions, often incorporating both spatial and frequency domain information. These advancements are improving the accuracy and efficiency of tasks ranging from image analysis and object detection to urban planning and causal inference in environmental studies, ultimately leading to more nuanced insights and better decision-making across diverse fields.