Spatial Modelling

Spatial modeling focuses on analyzing and predicting phenomena distributed across geographical space, aiming to understand spatial patterns and relationships. Current research emphasizes improving model efficiency and accuracy through techniques like variational inference with Gaussian processes and advanced feature engineering (e.g., Fourier features), as well as addressing biases in data through importance reweighting. These advancements are crucial for diverse applications, including environmental monitoring, urban planning, and industrial process optimization, by enabling faster, more accurate, and better-explained analyses of spatially-distributed data. Furthermore, developments in visual analytics frameworks are improving the interpretability and communication of spatial model results.