Spatial Nonstationarity

Spatial nonstationarity describes the variability of statistical properties across different locations within a spatial dataset, a common challenge in fields like geoscience and environmental modeling where traditional stationary assumptions often fail. Current research focuses on developing advanced methods to account for this nonstationarity, employing techniques like convolutional neural networks (CNNs) to identify and model spatially varying patterns, and vision transformers to capture complex, large-scale spatial relationships more effectively than CNNs. These improved modeling approaches lead to more accurate predictions and better understanding of spatially varying phenomena, impacting diverse applications from resource estimation to climate modeling.