  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. We found spatial CV
approaches generally improved upon resubstitution and V-fold CV estimates,
particularly when approaches which combined assessment sets of spatially
conjunct observations with spatial exclusion buffers. To facilitate use of
these techniques, we introduce the `spatialsample` package which provides
tooling for performing spatial CV as part of the broader tidymodels modeling
framework.
