The CAST Package for Training and Assessment of Spatial Prediction Models

Meyer H; Ludwig M; Milà C; Linnenbrink J; Schumacher F

Abstract

One key task in environmental science is to map environmental variables continuously in space or even in space and time. Machine learning algorithms are frequently used to learn from local field observations to make spatial predictions by estimating the value of the variable of interest in places where it has not been measured. However, the application of machine learning strategies for spatial mapping involves additional challenges compared to ``nonspatial'' prediction tasks that often originate from spatial autocorrelation and from training data that are not independent and identically distributed.