spatial c(k)luster analysis by tree edge removal

SKATER forms clusters by spatially partitioning data that has similar values for features of interest.

Usage

spdep_skater(sfj, k = 6, nb = NULL, ini = 5, ...)

sfj: An sf object of observation data. Please ensure that the attribute columns are included in the SKATER analysis.
k: (optional) The number of clusters. Default is 6.
nb: (optional) A neighbours list with class nb. If the input nb is NULL, it will be constructed automatically using spdep_nb().
ini: (optional) The initial node in the minimal spanning tree. Defaul is 5.
...: (optional) Other parameters passed to spdep::skater().

A numeric vector of clusters.

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
gzma_c = spdep_skater(gzma,8)
gzma$group = gzma_c
plot(gzma["group"])