Spatial Cross-Validation Methods

Overview

This vignette demonstrates the different spatial cross-validation methods available in spatialcvR and when to use each approach.

Available Methods

spatialcvR implements four main spatial cross-validation methods:

Spatial Block CV: Divide space into rectangular blocks
Buffered CV: Exclude training observations within a buffer radius
Spatial Clustering CV: Group observations spatially using clustering
Random Split: Baseline random CV for comparison

Setup

library(spatialcvR)

# Load sample data
data(sample_spatial_data)
head(sample_spatial_data)

##   id longitude latitude variable1 variable2    target
## 1  1  287.5775 238.7260  86.36698  36.57333 110.40061
## 2  2  788.3051 962.3589 102.53939  49.52156 143.78578
## 3  3  408.9769 601.3657  67.79740  42.96299 101.66984
## 4  4  883.0174 515.0297  99.58281  45.22016 123.82676
## 5  5  940.4673 402.5733  92.87996  47.44277 129.43342
## 6  6   45.5565 880.2465  47.51536  43.20302  86.62062

Spatial Block Cross-Validation

Concept

Spatial block CV divides the study area into a grid of rectangular blocks. Observations are assigned to folds based on which block they fall into. This ensures spatial separation between training and test sets.

When to Use

Data with relatively uniform spatial distribution
When you want to ensure geographic coverage
When computational efficiency is important
As a default spatial CV method

Basic Usage

# Create spatial block folds
folds_block <- spatial_folds(
  data = sample_spatial_data,
  x = "longitude",
  y = "latitude",
  k = 5,
  method = "block",
  seed = 123
)

print(folds_block)

## Spatial Cross-Validation Folds
## ==============================
## Method: spatial_block 
## Number of folds: 5 
## Observations: 200 
## CRS: Not defined 
## Has duplicate coordinates: FALSE 
## Seed: 123 
## 
## Fold sizes:
##   Fold 1: 155 train, 45 test
##   Fold 2: 150 train, 50 test
##   Fold 3: 150 train, 50 test
##   Fold 4: 168 train, 32 test
##   Fold 5: 177 train, 23 test

Controlling Block Size

You can control the spatial resolution using either block size or number of blocks:

# Specify block size
folds_block_size <- spatial_block_folds(
  data = sample_spatial_data,
  x = "longitude",
  y = "latitude",
  k = 5,
  block_size = c(200, 200),  # 200x200 unit blocks
  seed = 123
)

# Specify number of blocks
folds_n_blocks <- spatial_block_folds(
  data = sample_spatial_data,
  x = "longitude",
  y = "latitude",
  k = 5,
  n_blocks = c(5, 5),  # 5x5 grid
  seed = 123
)

Assignment Strategies

Blocks can be assigned to folds using different strategies:

# Systematic assignment (default)
folds_systematic <- spatial_block_folds(
  data = sample_spatial_data,
  x = "longitude",
  y = "latitude",
  k = 5,
  assignment = "systematic",
  seed = 123
)

# Random assignment
folds_random_assign <- spatial_block_folds(
  data = sample_spatial_data,
  x = "longitude",
  y = "latitude",
  k = 5,
  assignment = "random",
  seed = 123
)

Buffered Cross-Validation

Concept

Buffered CV ensures that for each test observation, no training observation falls within a specified buffer radius. This provides strict control over the minimum spatial separation.

When to Use

When you need precise control over train/test distance
For point-based data with irregular sampling
When buffer distance has ecological/physical meaning
For conservative performance estimates

Basic Usage

# Create buffered folds
folds_buffer <- spatial_buffer_folds(
  data = sample_spatial_data,
  x = "longitude",
  y = "latitude",
  k = 5,
  buffer_radius = 100,  # 100 unit buffer
  seed = 123
)

print(folds_buffer)

## Spatial Cross-Validation Folds
## ==============================
## Method: spatial_buffer 
## Number of folds: 5 
## Observations: 
## CRS:  
## Has duplicate coordinates: 
## 
## Fold sizes:
##   Fold 1: 160 train, 40 test
##   Fold 2: 160 train, 40 test
##   Fold 3: 160 train, 40 test
##   Fold 4: 160 train, 40 test
##   Fold 5: 160 train, 40 test

Important Notes

Buffered CV requires a projected CRS for accurate distance calculations
Using geographic coordinates (longitude/latitude) will produce approximate distances
Larger buffer radii reduce the size of training sets
This method is computationally more intensive than block CV

Spatial Clustering Cross-Validation

Concept

Spatial clustering CV groups spatially proximate observations using clustering algorithms (k-means), then assigns clusters to folds. This is useful for data with complex spatial structure.

When to Use

Data with irregular or clustered spatial patterns
When natural spatial groupings exist
For heterogeneous spatial distributions
When block boundaries would be arbitrary

Basic Usage

# Create clustering folds
folds_cluster <- spatial_cluster_folds(
  data = sample_spatial_data,
  x = "longitude",
  y = "latitude",
  k = 5,
  n_clusters = 10,  # Number of spatial clusters
  seed = 123
)

print(folds_cluster)

## Spatial Cross-Validation Folds
## ==============================
## Method: spatial_cluster 
## Number of folds: 5 
## Observations: 
## CRS:  
## Has duplicate coordinates: 
## 
## Fold sizes:
##   Fold 1: 143 train, 57 test
##   Fold 2: 173 train, 27 test
##   Fold 3: 161 train, 39 test
##   Fold 4: 158 train, 42 test
##   Fold 5: 165 train, 35 test

Understanding Cluster Assignment

# Examine cluster centers
folds_cluster$parameters$cluster_centers

##    x_coords y_coords
## 1  532.7197 565.9710
## 2  887.9961 696.9285
## 3  434.2980 318.7487
## 4  233.6940 125.0067
## 5  148.7116 397.2902
## 6  608.2099 878.5329
## 7  843.4232 400.2357
## 8  886.7498 125.1667
## 9  619.4220 116.1783
## 10 235.5819 827.2978

Random Spatial Split (Baseline)

Concept

Random spatial split performs standard random k-fold cross-validation without spatial constraints. This serves as a baseline to demonstrate the impact of spatial dependence.

When to Use

As a baseline for comparison
When spatial dependence is minimal
For initial exploratory analysis
To demonstrate the value of spatial CV

Basic Usage

# Create random folds
folds_random <- spatial_split(
  data = sample_spatial_data,
  x = "longitude",
  y = "latitude",
  k = 5,
  seed = 123
)

print(folds_random)

## Spatial Cross-Validation Folds
## ==============================
## Method: random 
## Number of folds: 5 
## Observations: 
## CRS:  
## Has duplicate coordinates: 
## 
## Fold sizes:
##   Fold 1: 160 train, 40 test
##   Fold 2: 160 train, 40 test
##   Fold 3: 160 train, 40 test
##   Fold 4: 160 train, 40 test
##   Fold 5: 160 train, 40 test

Choosing the Right Method

Decision Flowchart

Start with spatial block CV - Good default choice
Need precise distance control? → Use buffered CV
Complex spatial patterns? → Use clustering CV
Compare with random CV → Always include baseline

Method Comparison

Method	Pros	Cons	Best For
Block CV	Fast, intuitive, geographic coverage	May split natural clusters	Most cases
Buffered CV	Precise distance control	Computationally intensive	Point data, strict requirements
Clustering CV	Handles complex patterns	Sensitive to cluster parameters	Heterogeneous data
Random CV	Fast, baseline	No spatial control	Comparison, minimal spatial dependence

Visualizing Folds

# Plot individual fold
plot_spatial_folds(folds_block, sample_spatial_data, "longitude", "latitude", 
                   fold = 1, main = "Block CV - Fold 1")

# Plot all folds
plot_spatial_folds(folds_block, sample_spatial_data, "longitude", "latitude", 
                   fold = "all", main = "Block CV - All Folds")

Practical Tips

Start Simple

Begin with spatial block CV using default parameters:

# Default spatial block CV
folds_default <- spatial_folds(
  data = sample_spatial_data,
  x = "longitude",
  y = "latitude",
  k = 5
)

Adjust Based on Data Characteristics

Dense data: Use larger blocks or more clusters
Sparse data: Use smaller blocks or fewer clusters
Strong spatial patterns: Consider clustering CV
Precise distance requirements: Use buffered CV

Always Compare with Random CV

# Compare spatial vs random
folds_spatial <- spatial_folds(sample_spatial_data, "longitude", "latitude", 
                               k = 5, method = "block", seed = 123)
folds_random <- spatial_folds(sample_spatial_data, "longitude", "latitude", 
                             k = 5, method = "random", seed = 123)

# Analyze spatial leakage for both
leakage_spatial <- detect_spatial_leakage(sample_spatial_data, folds_spatial, 
                                           "longitude", "latitude")
leakage_random <- detect_spatial_leakage(sample_spatial_data, folds_random, 
                                          "longitude", "latitude")

print(leakage_spatial)

## Spatial Leakage Detection
## =========================
## Method: spatial_block 
## Overall Risk Level: LOW 
## Distance Threshold: 53.04 
## 
## Summary Statistics:
##   Min distance: 11.24
##   Mean distance: 521.24
##   Median distance: 530.91
##   Proportion below threshold: 0.1%
## 
## Fold Analysis:
##   Fold 1: LOW risk (0.1% below threshold)
##   Fold 2: LOW risk (0.0% below threshold)
##   Fold 3: LOW risk (0.1% below threshold)
##   Fold 4: LOW risk (0.2% below threshold)
##   Fold 5: LOW risk (0.1% below threshold)
## 
## Recommendations:
##   - Spatial separation appears adequate. 
##   - Current cross-validation setup should provide reliable performance estimates. 
##   - Consider increasing spatial separation if you need more conservative estimates.

print(leakage_random)

## Spatial Leakage Detection
## =========================
## Method: random 
## Overall Risk Level: LOW 
## Distance Threshold: 51.27 
## 
## Summary Statistics:
##   Min distance: 2.21
##   Mean distance: 512.67
##   Median distance: 504.43
##   Proportion below threshold: 0.8%
## 
## Fold Analysis:
##   Fold 1: LOW risk (0.7% below threshold)
##   Fold 2: LOW risk (0.8% below threshold)
##   Fold 3: LOW risk (0.8% below threshold)
##   Fold 4: LOW risk (0.7% below threshold)
##   Fold 5: LOW risk (0.8% below threshold)
## 
## Recommendations:
##   - Spatial separation appears adequate. 
##   - Current cross-validation setup should provide reliable performance estimates. 
##   - Consider increasing spatial separation if you need more conservative estimates.

Common Issues and Solutions

Issue: Too few observations per fold

Solution: Reduce k or use fewer blocks/clusters

# Reduce number of folds
folds_k3 <- spatial_folds(sample_spatial_data, "longitude", "latitude", k = 3)

Issue: Uneven fold sizes

Solution: This is normal for spatial methods; consider using stratified approaches if class imbalance is severe

Issue: Geographic gaps in training data

Solution: Increase block size or number of clusters to improve coverage

Next Steps

Learn about spatial leakage detection
Explore model evaluation and comparison

Key Takeaways

Spatial block CV is a good default method for most cases
Buffered CV provides precise distance control when needed
Clustering CV handles complex spatial patterns
Always compare with random CV to assess spatial dependence impact
Visualize folds to understand spatial separation

Mamadou SOW

2026-09-15

Overview

Available Methods

Setup

Spatial Block Cross-Validation

Concept

When to Use

Basic Usage

Controlling Block Size

Assignment Strategies

Buffered Cross-Validation

Concept

When to Use

Basic Usage

Important Notes

Spatial Clustering Cross-Validation

Concept

When to Use

Basic Usage

Understanding Cluster Assignment

Random Spatial Split (Baseline)

Concept

When to Use

Basic Usage

Choosing the Right Method

Decision Flowchart

Method Comparison

Visualizing Folds

Practical Tips

Start Simple

Adjust Based on Data Characteristics

Always Compare with Random CV

Common Issues and Solutions

Issue: Too few observations per fold

Issue: Uneven fold sizes

Issue: Geographic gaps in training data

Next Steps

Key Takeaways