soilsampling: Soil Sampling Design Methods • soilsampling

Soil Sampling Design Methods Including Spatial Coverage, Maxvol Optimal Design, Simple Random, and Stratified Sampling

Overview

The soilsampling package provides methods for designing soil sampling schemes. It implements:

Simple Random Sampling: Uniform random selection of sampling locations
Stratified Random Sampling: Random sampling within compact geographical strata
Spatial Coverage Sampling: Purposive sampling at stratum centroids for optimal coverage
Maxvol Optimal Design Sampling: Feature-based sampling using D-optimal experimental design
Composite Sampling: Sampling from equal-area strata for combined samples
Validation Metrics: Assess stratification quality through coverage efficiency and distance statistics

Installation

Install from GitHub (Recommended)

# Using pak (fastest)
pak::pak("ccarbajal16/soilsampling")

# Or using devtools
devtools::install_github("ccarbajal16/soilsampling")

Install from Local Source

# Install from local directory
install.packages(".", repos = NULL, type = "source")

# Or using devtools
devtools::install_local("path/to/soilsampling")

Dependencies

R (>= 4.1.0)
sf (>= 1.0-12)
ggplot2 (>= 3.3.0)

Quick Start

library(soilsampling)
library(sf)

# Create a study area
poly <- st_polygon(list(rbind(
  c(0, 0), c(100, 0), c(100, 50), c(0, 50), c(0, 0)
)))
study_area <- st_sf(geometry = st_sfc(poly))

# Set seed for reproducibility
set.seed(42)

# Spatial coverage sampling
samples <- ss_coverage(study_area, n_strata = 25, n_try = 5)
ss_plot(samples)

# Get coordinates
coords <- ss_to_data_frame(samples)
head(coords)

Sampling Methods

Simple Random Sampling

Select locations uniformly at random:

samples <- ss_random(study_area, n = 30)
ss_plot_samples(samples)

Stratified Random Sampling

Create strata first, then sample randomly within each:

# Create 25 compact strata
strata <- ss_stratify(study_area, n_strata = 25, n_try = 5)

# Take 1 random sample per stratum
samples <- ss_stratified(strata, n_per_stratum = 1)
ss_plot(strata, samples = samples)

Spatial Coverage Sampling

Place samples at stratum centroids for optimal spatial coverage:

# Direct approach
samples <- ss_coverage(study_area, n_strata = 25, n_try = 5)

# Or use pre-computed strata
strata <- ss_stratify(study_area, n_strata = 25, n_try = 5)
samples <- ss_coverage(strata)

ss_plot(samples)

Spatial Coverage with Prior Points

Incorporate existing sampling locations:

# Existing sample locations
prior_pts <- st_as_sf(
  data.frame(x = c(25, 75), y = c(25, 25)),
  coords = c("x", "y")
)

# Create stratification around prior points
strata <- ss_stratify(
  study_area, 
  n_strata = 20, 
  prior_points = prior_pts,
  n_try = 5
)

samples <- ss_coverage(strata)
ss_plot(samples)  # Prior points shown with different symbol

Equal-Area Stratification

Create strata of equal size:

samples <- ss_coverage_equal_area(study_area, n_strata = 25, n_try = 5)

Composite Sampling

For combining samples from multiple locations:

# Create 3 composite samples from 20 equal-area strata
samples <- ss_composite(study_area, n_strata = 20, n_composites = 3)
ss_plot(samples)  # Different symbols for each composite

Maxvol Optimal Design Sampling

Feature-based sampling using D-optimal experimental design to maximize diversity:

# Create a grid with terrain features
strata <- ss_stratify(study_area, n_strata = 100, n_try = 5)
cells_sf <- strata$cells

# Add terrain features (in practice, extract from DEM)
coords <- st_coordinates(cells_sf)
cells_sf$elevation <- coords[,2] + rnorm(nrow(coords), 0, 5)
cells_sf$slope <- abs(rnorm(nrow(coords), 5, 2))
cells_sf$twi <- rnorm(nrow(coords), 8, 1.5)

# Select 20 optimal samples based on features
samples <- ss_maxvol(
  cells_sf,
  n = 20,
  features = c("elevation", "slope", "twi"),
  min_dist = 5,        # Minimum distance between samples
  normalize = TRUE,    # Normalize features to equal scales
  add_coords = TRUE    # Include coordinates as features
)

ss_plot_samples(samples)

The maxvol algorithm selects locations that maximize the determinant (volume) of the feature submatrix, ensuring maximum diversity in feature space. This is particularly effective when: - Features explain soil-forming factors - You need deterministic (non-random) point selection - You want optimal coverage with few samples

Assessing Stratification Quality

The package provides validation metrics to assess the quality of your stratification:

Coverage Efficiency

Evaluate how uniformly cells are distributed across strata:

# Create stratification
strata <- ss_stratify(study_area, n_strata = 25, n_try = 5)

# Assess coverage efficiency
efficiency <- ss_coverage_efficiency(strata)
print(efficiency)

Output metrics: - CV (Coefficient of Variation): Lower values indicate more uniform distribution - Min/Max Ratio: Ratio of smallest to largest stratum (1.0 = perfect equality) - Gini Coefficient: Standard inequality measure (0 = perfect equality) - Efficiency Score: Qualitative assessment (Excellent/Good/Fair/Poor)

Comparing stratification methods:

# Regular stratification
strata_regular <- ss_stratify(study_area, n_strata = 20, n_try = 5)

# Equal-area stratification
strata_equal <- ss_stratify(study_area, n_strata = 20,
                             equal_area = TRUE, n_try = 5)

# Compare efficiency
eff_regular <- ss_coverage_efficiency(strata_regular)
eff_equal <- ss_coverage_efficiency(strata_equal)

cat("Regular CV:", eff_regular$cv, "\n")
cat("Equal-area CV:", eff_equal$cv, "\n")
# Equal-area should have much lower CV (< 0.05 vs 0.15-0.30)

Distance Statistics

Compute distance from cells or samples to stratum centroids:

# For stratification
dist_summary <- ss_distance_summary(strata)
print(dist_summary)

# For samples (works with coverage or stratified samples)
samples <- ss_coverage(strata)
dist_samples <- ss_distance_summary(samples, actual_distance = TRUE)
print(dist_samples)

Output (per stratum): - Mean, SD, min, max, median distances to centroid - Helps identify dispersed or compact strata - Default: squared distances (consistent with MSSD metric)

Use cases: - Compare different stratification runs (n_try values) - Verify equal-area maintains compactness - Identify strata with unusual geometry

Working with Shapefiles

# Read study area
study_area <- st_read("path/to/study_area.shp")

# Create sampling design
set.seed(42)
samples <- ss_coverage(study_area, n_strata = 50, n_try = 10)

# Export as CSV
coords <- ss_to_data_frame(samples)
write.csv(coords, "sampling_points.csv", row.names = FALSE)

# Export as GeoPackage
samples_sf <- ss_to_sf(samples)
st_write(samples_sf, "sampling_points.gpkg")

Function Reference

Stratification

Function	Description
`ss_stratify()`	Create compact geographical strata using k-means

Sampling

Function	Description
`ss_random()`	Simple random sampling
`ss_stratified()`	Stratified random sampling
`ss_coverage()`	Spatial coverage sampling (centroids)
`ss_coverage_equal_area()`	Coverage sampling with equal-area strata
`ss_maxvol()`	Maxvol optimal design sampling (feature-based)
`ss_composite()`	Composite sampling

Visualization

Function	Description
`ss_plot()`	Plot stratification and/or samples
`ss_plot_samples()`	Plot sampling points only

Validation

Function	Description
`ss_coverage_efficiency()`	Assess uniformity of cell distribution across strata
`ss_distance_summary()`	Compute distance statistics from cells/samples to centroids

Utilities

Function	Description
`ss_to_sf()`	Convert to sf object
`ss_to_data_frame()`	Convert to data frame with coordinates
`ss_summary()`	Get summary statistics
`ss_get_samples()`	Extract samples sf object
`ss_n_strata()`	Get number of strata
`ss_n_samples()`	Get number of samples
`ss_area()`	Get stratum areas
`ss_relative_area()`	Get relative stratum areas

When to Use Each Method

Method	Best For	Inference Type
`ss_coverage()`	Interpolation (kriging)	Model-based
`ss_maxvol()`	Feature diversity, D-optimal design	Model-based
`ss_stratified()`	Estimating means/totals	Design-based
`ss_random()`	Simple design-based analysis	Design-based
`ss_composite()`	Reducing laboratory costs	Design-based

Algorithm Details

The package uses pure R implementations of k-means algorithms:

Transfer Algorithm (default): Standard k-means that iteratively transfers cells to nearer cluster centers. Creates compact but potentially unequal-sized strata.
Swop Algorithm (equal_area = TRUE): Modified k-means that swaps cells between clusters to maintain equal sizes while optimizing compactness.

Both algorithms minimize the Mean Squared Shortest Distance (MSSD) between cells and their assigned cluster centroids.

Tips for Best Results

Use multiple tries: Set n_try = 10 or higher to avoid local optima
Set a seed: Use set.seed() for reproducible results
Adjust resolution: Use n_cells parameter to control grid density
Check convergence: The output includes a converged flag
Validate your stratification: Use ss_coverage_efficiency() to ensure uniform strata
Compare alternatives: Test different n_strata values and assess efficiency

References

de Gruijter, J.J., Brus, D.J., Bierkens, M.F.P., and Knotters, M. (2006). Sampling for Natural Resource Monitoring. Springer, Berlin.
Walvoort, D.J.J., Brus, D.J., and de Gruijter, J.J. (2010). An R package for spatial coverage sampling and random sampling from compact geographical strata by k-means. Computers & Geosciences 36, 1261-1267. DOI: 10.1016/j.cageo.2010.04.005
Petrovskaia, N., Korveh, K., and Maas, E. (2021). Optimal soil sampling design based on the maxvol algorithm. Geoderma 381, 114733. DOI: 10.1016/j.geoderma.2020.114733

License

GPL (>= 3)