Spatial Buffered Cross-Validation

Creates spatial cross-validation folds by excluding training observations within a specified buffer radius around test observations. This method ensures a minimum spatial separation between training and test sets.

Usage

spatial_buffer_folds(
  data,
  x = NULL,
  y = NULL,
  k = 5,
  buffer_radius = NULL,
  seed = NULL
)

Arguments

data

Spatial observations (data.frame or sf object)

x

Name of the x coordinate column (required for data.frame, ignored for sf)

y

Name of the y coordinate column (required for data.frame, ignored for sf)

k

Number of folds (default: 5)

buffer_radius

Buffer radius in coordinate units

seed

Random seed for reproducibility (default: NULL)

Value

An object of class "spatial_folds" containing fold assignments

Details

The buffered method ensures that for each test observation, no training observation falls within the specified buffer radius. This is particularly useful when you need strict control over the minimum distance between training and test observations.

Note: This method requires a projected CRS for accurate distance calculations. Using geographic coordinates (longitude/latitude) will produce warnings.

See also

Other spatial cross-validation functions: spatial_block_folds(), spatial_cluster_folds(), spatial_folds(), spatial_split()

Examples

if (FALSE) { # \dontrun{
data(sample_spatial_data)
folds <- spatial_buffer_folds(
  data = sample_spatial_data,
  x = "longitude",
  y = "latitude",
  k = 5,
  buffer_radius = 100
)
} # }