Detect Spatial Leakage in Cross-Validation Folds

Analyzes the proximity between training and test observations to detect potential spatial leakage, where training and test sets are too close spatially, leading to over-optimistic performance estimates.

Usage

detect_spatial_leakage(
  data,
  folds,
  x = NULL,
  y = NULL,
  threshold = NULL,
  risk_levels = 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)

threshold

Distance threshold for considering observations "too close" (default: NULL, auto-calculated)

risk_levels

Custom risk level thresholds as list(min, moderate, high) (default: NULL)

Value

An object of class "spatial_leakage_result" containing:

method

CV method used

fold_distances

Distance analysis for each fold

summary_statistics

Overall summary across all folds

risk_level

Overall risk assessment: "low", "moderate", or "high"

recommendations

Text recommendations based on analysis

threshold

Threshold used for risk assessment

Details

The function analyzes spatial distances between training and test observations:

• Calculates minimum, mean, and median distances per fold

• Identifies observations within threshold distance

• Assesses overall risk level

• Provides recommendations for improvement

Risk levels are based on the proportion of train/test pairs that are too close:

• "low": < 10% of pairs below threshold

• "moderate": 10-30% of pairs below threshold

• "high": > 30% of pairs below threshold

See also

Other spatial analysis functions: spatial_distance()

Examples

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