Spatial Clustering Cross-Validation

Creates spatial cross-validation folds by clustering observations spatially and assigning clusters to folds. This method is useful for data with complex spatial structure.

Usage

spatial_cluster_folds(
  data,
  x = NULL,
  y = NULL,
  k = 5,
  n_clusters = 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)

n_clusters

Number of spatial clusters (default: k)

seed

Random seed for reproducibility (default: NULL)

Value

An object of class "spatial_folds" containing fold assignments

Details

The clustering method groups spatially proximate observations into clusters using k-means clustering on coordinates, then assigns clusters to folds. This ensures spatial coherence within folds while maintaining separation between folds.

See also

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

Examples

if (FALSE) { # \dontrun{
data(sample_spatial_data)
folds <- spatial_cluster_folds(
  data = sample_spatial_data,
  x = "longitude",
  y = "latitude",
  k = 5,
  n_clusters = 10
)
} # }