skater: Spatial C(K)luster Analysis by Tree Edge Removal

In GeoDaCenter/rgeoda: R Library for Spatial Data Analysis

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

skater(
  k,
  w,
  df,
  bound_variable = data.frame(),
  min_bound = 0,
  scale_method = "standardize",
  distance_method = "euclidean",
  random_seed = 123456789,
  cpu_threads = 6,
  rdist = numeric()
)

Arguments

k	The number of clusters
w	An instance of Weight class
df	A data frame with selected variables only. E.g. guerry[c("Crm_prs", "Crm_prp", "Litercy")]
bound_variable	(optional) A data frame with selected bound variable
min_bound	(optional) A minimum bound value that applies to all clusters
scale_method	One of the scaling methods ('raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust') to apply on input data. Default is 'standardize' (Z-score normalization).
distance_method	(optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan"
random_seed	(int,optional) The seed for random number generator. Defaults to 123456789.
cpu_threads	(optional) The number of cpu threads used for parallel computation
rdist	(optional) The distance matrix (lower triangular matrix, column wise storage)

Value

A names list with names "Clusters", "Total sum of squares", "Within-cluster sum of squares", "Total within-cluster sum of squares", and "The ratio of between to total sum of squares".

Examples

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
data <- guerry[c('Crm_prs','Crm_prp','Litercy','Donatns','Infants','Suicids')]
guerry_clusters <- skater(4, queen_w, data)
guerry_clusters

GeoDaCenter/rgeoda documentation built on Dec. 20, 2024, 11:18 p.m.