spatial_clustering_cv {spatialsample}

Spatial Clustering Cross-Validation

Description

Spatial clustering cross-validation splits the data into V groups of disjointed sets by clustering points based on their spatial coordinates. A resample of the analysis data consists of V-1 of the folds/clusters while the assessment set contains the final fold/cluster.

Usage

spatial_clustering_cv(
  data,
  v = 10,
  cluster_function = c("kmeans", "hclust"),
  radius = NULL,
  buffer = NULL,
  ...,
  repeats = 1,
  distance_function = function(x) as.dist(sf::st_distance(x))
)

Arguments

data

An sf object (often from sf::read_sf() or sf::st_as_sf()) to split into folds.

v

The number of partitions of the data set.

cluster_function

Which function should be used for clustering? Options are either "kmeans" (to use stats::kmeans()) or "hclust" (to use stats::hclust()). You can also provide your own function; see Details.

radius

Numeric: points within this distance of the initially-selected test points will be assigned to the assessment set. If NULL, no radius is applied.

buffer

Numeric: points within this distance of any point in the test set (after radius is applied) will be assigned to neither the analysis or assessment set. If NULL, no buffer is applied.

...

Extra arguments passed on to stats::kmeans() or stats::hclust().

repeats

The number of times to repeat the clustered partitioning.

distance_function

Which function should be used for distance calculations? Defaults to sf::st_distance(), with the output matrix converted to a stats::dist() object. You can also provide your own function; see Details.

Details

Clusters are created based on the distances between observations if data is an sf object. Each cluster is used as a fold for cross-validation. Depending on how the data are distributed spatially, there may not be an equal number of observations in each fold.

You can optionally provide a custom function to distance_function. The function should take an sf object and return a stats::dist() object with distances between data points.

You can optionally provide a custom function to cluster_function. The function must take three arguments:

• dists, a stats::dist() object with distances between data points

• v, a length-1 numeric for the number of folds to create

• ..., to pass any additional named arguments to your function

The function should return a vector of cluster assignments of length nrow(data), with each element of the vector corresponding to the matching row of the data frame.

Value

A tibble with classes spatial_clustering_cv, spatial_rset, rset, tbl_df, tbl, and data.frame. The results include a column for the data split objects and an identification variable id. Resamples created from non-sf objects will not have the spatial_rset class.

Changes in spatialsample 0.3.0

As of spatialsample version 0.3.0, this function no longer accepts non-sf objects as arguments to data. In order to perform clustering with non-spatial data, consider using rsample::clustering_cv().

Also as of version 0.3.0, this function now calculates edge-to-edge distance for non-point geometries, in line with the rest of the package. Earlier versions relied upon between-centroid distances.

References

A. Brenning, "Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest," 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, 2012, pp. 5372-5375, doi: 10.1109/IGARSS.2012.6352393.

Examples

data(Smithsonian, package = "modeldata")

smithsonian_sf <- sf::st_as_sf(
  Smithsonian,
  coords = c("longitude", "latitude"),
  # Set CRS to WGS84
  crs = 4326
)

# When providing sf objects, coords are inferred automatically
spatial_clustering_cv(smithsonian_sf, v = 5)

# Can use hclust instead:
spatial_clustering_cv(smithsonian_sf, v = 5, cluster_function = "hclust")