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This function generates spatially separated train and test folds by considering buffers of the specified distance (size parameter) around each observation point. This approach is a form of leave-one-out cross-validation. Each fold is generated by excluding nearby observations around each testing point within the specified distance (ideally the range of spatial autocorrelation, see cv_spatial_autocor). In this method, the testing set never directly abuts a training sample (e.g. presence or absence; 0s and 1s). For more information see the details section.


When working with presence-background (presence and pseudo-absence) species distribution data (should be specified by presence_bg = TRUE argument), only presence records are used for specifying the folds (recommended). Consider a target presence point. The buffer is defined around this target point, using the specified range (size). By default, the testing fold comprises only the target presence point (all background points within the buffer are also added when add_bg = TRUE). Any non-target presence points inside the buffer are excluded. All points (presence and background) outside of buffer are used for the training set. The methods cycles through all the presence data, so the number of folds is equal to the number of presence points in the dataset.

For presence-absence data (and all other types of data), folds are created based on all records, both presences and absences. As above, a target observation (presence or absence) forms a test point, all presence and absence points other than the target point within the buffer are ignored, and the training set comprises all presences and absences outside the buffer. Apart from the folds, the number of training-presence, training-absence, testing-presence and testing-absence records is stored and returned in the records table. If column = NULL and presence_bg = FALSE, the procedure is like presence-absence data. All other data types (continuous, count or multi-class responses) should be done by presence_bg = FALSE.

mlr3spatiotempcv notes

The 'Description' and 'Details' fields are inherited from the respective upstream function. For a list of available arguments, please see blockCV::cv_buffer.

blockCV >= 3.0.0 changed the argument names of the implementation. For backward compatibility, mlr3spatiotempcv is still using the old ones. Here's a list which shows the mapping between blockCV < 3.0.0 and blockCV >= 3.0.0:

  • theRange -> size

  • addBG -> add_bg

  • spDataType (character vector) -> presence_bg (boolean)


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 .

See also


Super class

mlr3::Resampling -> ResamplingSpCVBuffer

Active bindings


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


Inherited methods

Method new()

Create an "Environmental Block" resampling instance.

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


ResamplingSpCVBuffer$new(id = "spcv_buffer")



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.


ResamplingSpCVBuffer$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_buffer", theRange = 10000)

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

  # Internal storage:
  # rcv$instance
#> numeric(0)
# }