Perform spatial interpolation for missing data

Perform spatial interpolation using reliable methods to fill missing values in spatial datasets. Supports nearest neighbor, spline interpolation, and multivariate imputation with comprehensive error handling.

Usage

spatial_interpolation_comprehensive(
  spatial_data,
  target_variables,
  method = "NN",
  target_grid = NULL,
  region_boundary = NULL,
  power = 2,
  max_distance = Inf,
  min_points = 3,
  max_points = 50,
  cross_validation = FALSE,
  cv_folds = 5,
  handle_outliers = "none",
  outlier_threshold = 3,
  coord_cols = c("lon", "lat"),
  mice_method = "pmm",
  mice_iterations = 10,
  output_format = "sf",
  output_file = NULL,
  verbose = FALSE
)

Arguments

spatial_data

Spatial data to interpolate. Can be:

• sf object with point geometries

• data.frame with coordinate columns

• File path to spatial data (CSV, SHP, GeoJSON)

target_variables

Character vector of variables to interpolate

method

Interpolation method:

• "NN": Nearest neighbor (default)

• "simple": Simple distance weighting

• "spline": Thin plate spline interpolation

• "mice": Multivariate imputation (requires mice package)

• "auto": Automatically select best method based on data

target_grid

Target grid for interpolation. Can be:

• SpatRaster template for raster output

• sf object with target locations

• NULL for point-to-point interpolation only

region_boundary

Optional region boundary for clipping results

power

Power parameter for simple distance weighting (default: 2)

max_distance

Maximum distance for interpolation (map units)

min_points

Minimum number of points for interpolation

max_points

Maximum number of points to use for each prediction

cross_validation

Perform cross-validation for accuracy assessment

cv_folds

Number of folds for cross-validation (default: 5)

handle_outliers

Method for outlier handling: "none", "remove", "cap"

outlier_threshold

Z-score threshold for outlier detection (default: 3)

coord_cols

Coordinate column names for data.frame input

mice_method

MICE method for multivariate imputation

mice_iterations

Number of MICE iterations (default: 10)

output_format

Output format: "sf", "raster", "both"

output_file

Optional output file path

verbose

Print detailed progress messages

Value

Depending on output_format:

"sf"

sf object with interpolated values

"raster"

SpatRaster with interpolated surfaces

"both"

List containing both sf and raster results

Additional attributes include:

• interpolation_info: Method used, parameters, processing time

• cross_validation: CV results if performed

Details

Supported Interpolation Methods:

Distance-Based Methods:

• NN (Nearest Neighbor): Assigns nearest known value - Best for: Categorical data or when preserving exact values - Fast and creates Voronoi-like patterns - No assumptions about data distribution

• Simple (Simple distance weighting): Basic distance-based averaging - Best for: Quick estimates with minimal computation - Uses inverse distance weighting without external dependencies

Statistical Methods:

• Spline: Smooth surface interpolation using thin plate splines - Best for: Smooth, continuous phenomena - Creates smooth surfaces without sharp changes - Good for environmental data with gradual spatial variation

Multivariate Methods:

• MICE: Multivariate imputation by chained equations - Best for: Multiple correlated variables with missing values - Handles complex missing data patterns - Preserves relationships between variables - Requires mice package

Input Data Support:

• sf objects with point geometries

• data.frame with coordinate columns

• File paths (CSV, shapefile, GeoJSON)

• Target grids for raster output

Quality Control Features:

• Cross-validation for method comparison

• Outlier detection and handling

• Performance metrics calculation

• Robust error handling

Method Selection Guide

Dense, regular data

Simple distance weighting for good balance

Sparse, irregular data

Nearest neighbor for stability

Environmental data

Spline for smooth surfaces

Categorical data

Nearest neighbor

Multiple correlated variables

MICE for multivariate patterns

Unknown data characteristics

Auto-selection based on data properties

Performance Optimization

• For large datasets: Set max_points=50-100 for faster processing

• For high accuracy: Use cross_validation=TRUE to compare methods

• For memory efficiency: Process variables individually

• For smooth results: Use spline method

See also

• universal_spatial_join for spatial data integration

• calculate_spatial_correlation for spatial correlation analysis

• create_spatial_map for visualization

Examples

if (FALSE) { # \dontrun{
# These examples require external data files not included with the package
# Basic nearest neighbor interpolation
soil_interpolated <- spatial_interpolation_comprehensive(
  spatial_data = "soil_samples.csv",
  target_variables = c("nitrogen", "phosphorus", "ph"),
  method = "NN",
  target_grid = study_area_grid,
  region_boundary = "Iowa"
)

# Simple distance weighting
temp_interp <- spatial_interpolation_comprehensive(
  spatial_data = weather_stations,
  target_variables = "temperature",
  method = "simple",
  power = 2,
  cross_validation = TRUE,
  verbose = TRUE
)

# Multivariate imputation for environmental data
env_imputed <- spatial_interpolation_comprehensive(
  spatial_data = env_monitoring,
  target_variables = c("temp", "humidity", "pressure", "wind_speed"),
  method = "mice",
  mice_iterations = 15,
  handle_outliers = "cap"
)

# Auto-method selection with comparison
best_interp <- spatial_interpolation_comprehensive(
  spatial_data = precipitation_data,
  target_variables = "annual_precip",
  method = "auto",
  cross_validation = TRUE,
  cv_folds = 10,
  target_grid = dem_template
)

# Access results and diagnostics
plot(best_interp)  # Plot interpolated surface
best_interp$cross_validation$rmse  # Cross-validation RMSE
best_interp$interpolation_info$method_selected  # Method chosen
} # }