skater: SKATER Spatial Clustering

In spopt: Spatial Optimization for Regionalization, Facility Location, and Market Analysis

SKATER Spatial Clustering

Description

Performs spatial clustering using the SKATER algorithm (Spatial 'K'luster Analysis by Tree Edge Removal). The algorithm builds a minimum spanning tree from the spatial contiguity graph, then iteratively removes edges to create spatially contiguous clusters.

Usage

skater(
  data,
  attrs = NULL,
  n_regions,
  weights = "queen",
  bridge_islands = FALSE,
  floor = NULL,
  floor_value = 0,
  scale = TRUE,
  seed = NULL,
  verbose = FALSE
)

Arguments

data	An sf object with polygon or point geometries.
attrs	Character vector of column names to use for clustering (e.g., c("var1", "var2")). If NULL, uses all numeric columns.
n_regions	Integer. Number of regions (clusters) to create.
weights	Spatial weights specification. Can be: "queen" (default): Polygons sharing any boundary point are neighbors "rook": Polygons sharing an edge are neighbors An nb object from spdep or created with sp_weights() A list for other weight types: list(type = "knn", k = 6) for k-nearest neighbors, or list(type = "distance", d = 5000) for distance-based weights
bridge_islands	Logical. If TRUE, automatically connect disconnected components (e.g., islands) using nearest-neighbor edges. If FALSE (default), the function will error when the spatial weights graph is disconnected.
floor	Optional. Column name specifying a floor constraint variable.
floor_value	Numeric. Minimum sum of floor variable required per region. Only used if floor is specified.
scale	Logical. If TRUE (default), standardize attributes before clustering.
seed	Optional integer for reproducibility.
verbose	Logical. Print progress messages.

Value

An sf object with a .region column containing cluster assignments. Metadata is stored in the "spopt" attribute.

References

Assuncao, R. M., Neves, M. C., Camara, G., & Freitas, C. da C. (2006). Efficient regionalization techniques for socio-economic geographical units using minimum spanning trees. International Journal of Geographical Information Science, 20(7), 797-811.

Examples

library(sf)
nc <- st_read(system.file("shape/nc.shp", package = "sf"))

# Cluster into 5 regions based on SIDS rates
result <- skater(nc, attrs = c("SID74", "SID79"), n_regions = 5)

# With floor constraint: each region must have at least 100,000 births
result <- skater(nc, attrs = c("SID74", "SID79"), n_regions = 5,
                 floor = "BIR74", floor_value = 100000)

# View results
plot(result[".region"])

spopt documentation built on April 22, 2026, 9:07 a.m.