Assessing the performance of spatial cross-validation approaches for models of spatially structured data

Understand

Evaluating models fit to data with internal spatial structure requires specific cross-validation (CV) approaches, because randomly selecting assessment data may produce assessment sets that are not truly independent of data used to train the model.

• Many spatial CV methodologies have been proposed to address this by forcing models to extrapolate spatially when predicting the assessment set.
• However, to date there exists little guidance on which methods yield the most accurate estimates of model performance.
• We conducted simulations to compare model performance estimates produced by five common CV methods fit to spatially structured data.