Title: Spatial data and Gaussian processes: A beautiful marriage
Abstract: In the past twenty years analysis of spatial data has become increasingly model-based. Full specification of stochastic models for the spatial process being investigated enables full inference and uncertainty assessment regarding the process. Gaussian processes on subsets of \(\mathbb R^2\) have become a fundamental specification for such modeling, particularly in settings where prediction is a primary goal. Therefore, focusing on the point-referenced case, we elaborate the substantial range of spatial settings where Gaussian processes have enabled rich and flexible modeling.