# spatialsample 0.2.0

Mike Mahoney

We’re positively electrified to announce the release of spatialsample 0.2.0. spatialsample is a package for spatial resampling, extending the rsample framework to help create spatial extrapolation between your analysis and assessment data sets.

You can install it from CRAN with:

install.packages("spatialsample")

This blog post will describe the highlights of what’s new. You can see a full list of changes in the release notes.

## New Features

This version of spatialsample includes a new data set, made up of 682 hexagons containing data about tree canopy cover change in Boston, Massachusetts:

library(spatialsample)
library(ggplot2)

ggplot(boston_canopy) +
geom_sf()


This data is stored as an sf object, and as such contains information about the proper coordinate reference system and units of measurement associated with the data.

This brings us to the first new feature in this release of spatialsample: spatial_clustering_cv() now supports sf objects, and will calculate distances in a way that respects coordinate reference systems (including using the s2 geometry library for geographic coordinate reference systems):

set.seed(123)
kmeans_clustering <- spatial_clustering_cv(boston_canopy, v = 5)
kmeans_clustering
#> #  5-fold spatial cross-validation
#> # A tibble: 5 × 2
#>   splits            id
#>   <list>            <chr>
#> 1 <split [524/158]> Fold1
#> 2 <split [493/189]> Fold2
#> 3 <split [517/165]> Fold3
#> 4 <split [605/77]>  Fold4
#> 5 <split [589/93]>  Fold5

This release also provides autoplot() methods to visualize resamples via ggplot2, making it easy to see how exactly your data is being divided. Just call autoplot() on the outputs from any spatial clustering function:

autoplot(kmeans_clustering) + labs(title = "kmeans()")


In addition to supporting more types of data, spatial_clustering_cv() has also been extended to support more types of clustering. Set the cluster_function argument to use "hclust" for hierarchical clustering via hclust() instead of the default kmeans()-based clusters:

set.seed(123)
spatial_clustering_cv(
boston_canopy,
v = 5,
cluster_function = "hclust"
) |>
autoplot() +
labs(title = "hclust()")


This argument can also accept functions, letting you plug in clustering methodologies from other packages or that you’ve written yourself:

set.seed(123)

custom_clusters <- function(dists, v, ...) {
rep(letters[1:v], length.out = nrow(boston_canopy))
}

spatial_clustering_cv(
boston_canopy,
v = 5,
cluster_function = custom_clusters
) |>
autoplot() +
labs(title = "custom_clusters()")


In addition to the clustering extensions, this version of spatialsample introduces new functions for other popular spatial resampling methods. For instance, spatial_block_cv() helps you perform block cross-validation, splitting your data into folds based on a grid of regular polygons. You can assign these polygons to folds at random:

set.seed(123)
spatial_block_cv(boston_canopy, v = 5) |>
autoplot()


Or systematically, either by assigning folds in order from the bottom-left and proceeding from left to right along each row by setting method = "continuous":

spatial_block_cv(boston_canopy, v = 5, method = "continuous") |>
autoplot()


Or by “snaking” back and forth up the grid by setting method = "snake":

spatial_block_cv(boston_canopy, v = 5, method = "snake") |>
autoplot()


This release of spatialsample also adds support for leave-location-out cross-validation through the new function spatial_leave_location_out_cv(). You can use this to create resamples when you already have a good idea of what data might be spatially correlated together – for instance, we can use it to split the Ames housing data from modeldata by neighborhood:

data(ames, package = "modeldata")

ames_sf <- sf::st_as_sf(ames, coords = c("Longitude", "Latitude"), crs = 4326)

set.seed(123)
spatial_leave_location_out_cv(ames_sf, Neighborhood) |>
autoplot()


## Buffering

The last major feature in this release is the introduction of spatial buffering. Spatial buffering enforces a certain minimum distance between your analysis and assessment sets, making sure that you’re spatially extrapolating when making predictions with a model.

While all spatial resampling functions in spatialsample can use spatial buffers, particularly interesting is the new spatial_buffer_vfold_cv() function. This function makes it easy to add spatial buffers around a standard V-fold cross-validation procedure. When we plot the object returned by this function, it just looks like a standard V-fold cross-validation setup:

set.seed(123)
blocks <- spatial_buffer_vfold_cv(
boston_canopy,
v = 15,
buffer = 100,
)

autoplot(blocks)


However, if we use autoplot() to visualize the splits themselves, we can see that we’ve created an exclusion buffer around each of our assessment sets. Data inside this buffer is assigned to neither the assessment or analysis set, so you can be sure your data is spatially separated:

blocks$splits |> purrr::walk(function(x) print(autoplot(x)))  In addition to exclusion buffers, spatialsample now lets you add inclusion radii to any spatial resampling. This will add any points within a certain distance of the original assessment set to the assessment set, letting you create clumped “discs” of data to assess your models against: set.seed(123) blocks <- spatial_buffer_vfold_cv( boston_canopy, v = 20, buffer = 100, radius = 100 ) blocks$splits |>
purrr::walk(function(x) print(autoplot(x)))


## …and more!

This is just scratching the surface of the new features and improvements in this release of spatialsample. You can see a full list of changes in the the release notes.

## Acknowledgments

We’d like to thank everyone that has contributed since the last release: @jennybc, @juliasilge, @mikemahoney218, @MxNl, @nipnipj, and @PathosEthosLogos.

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