spdep_skater: spatial c(k)luster analysis by tree edge removal

In sdsfun: Spatial Data Science Complementary Features

spatial c(k)luster analysis by tree edge removal

Description

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, ...)

Arguments

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().

Value

A numeric vector of clusters.

Examples

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

sdsfun documentation built on April 3, 2025, 8:39 p.m.