Calculate Spatial Distances Between Train and Test Observations

Computes distances between training and test observations for each fold in a spatial cross-validation setup. This helps assess the spatial separation between training and test sets.

Usage

spatial_distance(data, folds, x = NULL, y = NULL)

Arguments

data

Spatial observations (data.frame or sf object)

folds

A spatial_folds 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)

Value

A list containing distance information for each fold:

fold

Fold number

min_distance

Minimum distance between train and test observations

mean_distance

Mean distance between train and test observations

median_distance

Median distance between train and test observations

max_distance

Maximum distance between train and test observations

sd_distance

Standard deviation of distances

quantiles

Distance quantiles (25%, 50%, 75%)

Details

For each fold, the function calculates Euclidean distances between all pairs of training and test observations. This provides a comprehensive view of spatial separation.

Note: Distance calculations use Euclidean distance on the provided coordinates. For accurate metric distances, ensure data is in a projected CRS. Geographic coordinates (longitude/latitude) will produce approximate distances.

See also

Other spatial analysis functions: detect_spatial_leakage()

Examples

if (FALSE) { # \dontrun{
data(sample_spatial_data)
folds <- spatial_folds(sample_spatial_data, "longitude", "latitude", k = 5)
distances <- spatial_distance(sample_spatial_data, folds, "longitude", "latitude")
print(distances)
} # }