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Showing content from https://github.com/rvalavi/blockCV below:

rvalavi/blockCV: The blockCV package creates spatially or environmentally separated training and testing folds for cross-validation to provide a robust error estimation in spatially structured environments. See

The package blockCV offers a range of functions for generating train and test folds for k-fold and leave-one-out (LOO) cross-validation (CV). It allows for separation of data spatially and environmentally, with various options for block construction. Additionally, it includes a function for assessing the level of spatial autocorrelation in response or raster covariates, to aid in selecting an appropriate distance band for data separation. The blockCV package is suitable for the evaluation of a variety of spatial modelling applications, including classification of remote sensing imagery, soil mapping, and species distribution modelling (SDM). It also provides support for different SDM scenarios, including presence-absence and presence-background species data, rare and common species, and raster data for predictor variables.

• There are four blocking methods: spatial, clustering, buffers, and NNDM (Nearest Neighbour Distance Matching) blocks
• Several ways to construct spatial blocks
• The assignment of the spatial blocks to cross-validation folds can be done in three different ways: random, systematic and checkerboard pattern
• The spatial blocks can be assigned to cross-validation folds to have evenly distributed records for binary (e.g. species presence-absence/background) or multi-class responses (e.g. land cover classes for remote sensing image classification)
• The buffering and NNDM functions can account for presence-absence and presence-background data types
• Using geostatistical techniques to inform the choice of a suitable distance band by which to separate the data sets

The latest major version of blockCV (v3.0) features significant updates and changes. All function names have been revised to more general names, beginning with cv_*. Although the previous functions (version 2.x) will continue to work, they will be removed in future updates after being available for an extended period. It is highly recommended to update your code with the new functions provided below.

Some new updates:

• Function names have been changed, with all functions now starting with cv_
• The CV blocking functions are now: cv_spatial, cv_cluster, cv_buffer, and cv_nndm
• Spatial blocks now support hexagonal (now, default), rectangular, and user-defined blocks
• A fast C++ implementation of Nearest Neighbour Distance Matching (NNDM) algorithm (Milà et al. 2022) is now added
• The NNDM algorithm can handle species presence-background data and other types of data
• The cv_cluster function generates blocks based on kmeans clustering. It now works on both environmental rasters and the spatial coordinates of sample points
• The cv_spatial_autocor function now calculates the spatial autocorrelation range for both the response (i.e. binary or continuous data) and a set of continuous raster covariates
• The new cv_plot function allows for visualization of folds from all blocking strategies using ggplot facets
• The terra package is now used for all raster processing and supports both stars and raster objects, as well as files on disk.
• The new cv_similarity provides measures on possible extrapolation to testing folds

To install the latest update of the package from GitHub use:

remotes::install_github("rvalavi/blockCV", dependencies = TRUE)

Or installing from CRAN:

install.packages("blockCV", dependencies = TRUE)

To see the practical examples of the package see:

1. blockCV introduction: how to create block cross-validation folds
2. Block cross-validation for species distribution modelling
3. Using blockCV with the caret and tidymodels (see here)

This code snippet showcases some of the package's functionalities, but for more comprehensive tutorials, please refer to the vignette included with the package (and above).

# loading the package
library(blockCV)
library(sf) # working with spatial vector data
library(terra) # working with spatial raster data

# load raster data; the pipe operator |> is available for R v4.1 or higher
myrasters <- system.file("extdata/au/", package = "blockCV") |>
  list.files(full.names = TRUE) |>
  terra::rast()

# load species presence-absence data and convert to sf
pa_data <- read.csv(system.file("extdata/", "species.csv", package = "blockCV")) |>
  sf::st_as_sf(coords = c("x", "y"), crs = 7845)

# spatial blocking by specified range and random assignment
sb <- cv_spatial(x = pa_data, # sf or SpatialPoints of sample data (e.g. species data)
                 column = "occ", # the response column (binary or multi-class)
                 r = myrasters, # a raster for background (optional)
                 size = 450000, # size of the blocks in metres
                 k = 5, # number of folds
                 hexagon = TRUE, # use hexagonal blocks - defualt
                 selection = "random", # random blocks-to-fold
                 iteration = 100, # to find evenly dispersed folds
                 biomod2 = TRUE) # also create folds for biomod2

Or create spatial clusters for k-fold cross-validation:

# create spatial clusters
set.seed(6)
sc <- cv_cluster(x = pa_data, 
                 column = "occ", # optionally count data in folds (binary or multi-class)
                 k = 5)

# now plot the created folds
cv_plot(cv = sc, # a blockCV object
        x = pa_data, # sample points
        r = myrasters[[1]], # optionally add a raster background
        points_alpha = 0.5,
        nrow = 2)

Investigate spatial autocorrelation in the landscape to choose a suitable size for spatial blocks:

# exploring the effective range of spatial autocorrelation in raster covariates or sample data
cv_spatial_autocor(r = myrasters, # a SpatRaster object or path to files
                   num_sample = 5000, # number of cells to be used
                   plot = TRUE)

Alternatively, you can manually choose the size of spatial blocks in an interactive session using a Shiny app.

# shiny app to aid selecting a size for spatial blocks
cv_block_size(r = myrasters[[1]],
              x = pa_data, # optionally add sample points
              column = "occ",
              min_size = 2e5,
              max_size = 9e5)

Please report issues at: https://github.com/rvalavi/blockCV/issues

To cite package blockCV in publications, please use:

Valavi R, Elith J, Lahoz-Monfort JJ, Guillera-Arroita G. blockCV: An R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models. Methods Ecol Evol. 2019; 10:225--232. https://doi.org/10.1111/2041-210X.13107

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