spatial_interpolation_comprehensive: Perform spatial interpolation for missing data

In geospatialsuite: Comprehensive Geospatiotemporal Analysis and Multimodal Integration Toolkit

Perform spatial interpolation for missing data

Description

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

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

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

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

## Not run: 
# 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

## End(Not run)

geospatialsuite documentation built on Nov. 6, 2025, 1:06 a.m.