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This function creates spatially separated folds based on a pre-specified distance. It assigns blocks to the training and testing folds randomly, systematically or in a checkerboard pattern. The distance (theRange) should be in metres, regardless of the unit of the reference system of the input data (for more information see the details section). By default, the function creates blocks according to the extent and shape of the study area, assuming that the user has considered the landscape for the given species and case study. Alternatively, blocks can solely be created based on species spatial data. Blocks can also be offset so the origin is not at the outer corner of the rasters. Instead of providing a distance, the blocks can also be created by specifying a number of rows and/or columns and divide the study area into vertical or horizontal bins, as presented in Wenger & Olden (2012) and Bahn & McGill (2012). Finally, the blocks can be specified by a user-defined spatial polygon layer.


To keep the consistency, all the functions use metres as their unit. In this function, when the input map has geographic coordinate system (decimal degrees), the block size is calculated based on deviding theRange by 111325 (the standard distance of a degree in metres, on the Equator) to change the unit to degree. This value is optional and can be changed by user via degMetre argument.

The xOffset and yOffset can be used to change the spatial position of the blocks. It can also be used to assess the sensitivity of analysis results to shifting in the blocking arrangements. These options are available when theRange is defined. By default the region is located in the middle of the blocks and by setting the offsets, the blocks will shift.

Roberts et. al. (2017) suggest that blocks should be substantially bigger than the range of spatial autocorrelation (in model residual) to obtain realistic error estimates, while a buffer with the size of the spatial autocorrelation range would result in a good estimation of error. This is because of the so-called edge effect (O'Sullivan & Unwin, 2014), whereby points located on the edges of the blocks of opposite sets are not separated spatially. Blocking with a buffering strategy overcomes this issue (see buffering).

mlr3spatiotempcv notes

By default blockCV::spatialBlock() does not allow the creation of multiple repetitions. mlr3spatiotempcv adds support for this when using the range argument for fold creation. When supplying a vector of length(repeats) for argument range, these different settings will be used to create folds which differ among the repetitions.

Multiple repetitions are not possible when using the "row & cols" approach because the created folds will always be the same.

The 'Description' and 'Details' fields are inherited from the respective upstream function.

For a list of available arguments, please see blockCV::spatialBlock.


Valavi R, Elith J, Lahoz-Monfort JJ, Guillera-Arroita G (2018). “blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models.” bioRxiv. doi:10.1101/357798 .

Super class

mlr3::Resampling -> ResamplingSpCVBlock

Public fields


sf | list of sf objects
Polygons (sf objects) as returned by blockCV which grouped observations into partitions.

Active bindings


Returns the number of resampling iterations, depending on the values stored in the param_set.


Inherited methods

Method new()

Create an "spatial block" resampling instance.

For a list of available arguments, please see blockCV::spatialBlock().


ResamplingSpCVBlock$new(id = "spcv_block")



Identifier for the resampling strategy.

Method instantiate()

Materializes fixed training and test splits for a given task.





A task to instantiate.

Method clone()

The objects of this class are cloneable with this method.


ResamplingSpCVBlock$clone(deep = FALSE)



Whether to make a deep clone.


# \donttest{
if (mlr3misc::require_namespaces(c("sf", "blockCV"), quietly = TRUE)) {
  task = tsk("ecuador")

  # Instantiate Resampling
  rcv = rsmp("spcv_block", range = 3000L, folds = 3)

  # Individual sets:
  intersect(rcv$train_set(1), rcv$test_set(1))

  # Internal storage:
#>      row_id fold
#>   1:      1    2
#>   2:      2    3
#>   3:      3    3
#>   4:      4    1
#>   5:      5    3
#>  ---            
#> 747:    747    1
#> 748:    748    2
#> 749:    749    2
#> 750:    750    1
#> 751:    751    2
# }