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SKATER Spatial Clustering

Source: R/region_skater.R
skater.Rd

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

if (FALSE) { # \dontrun{
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"])
} # }