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Spatial C(K)luster Analysis by Tree Edge Removal(SKATER)

Source: R/spatial_cluster.R
st_skater.Rd

A wrapper function for rgeoda::skater().SKATER forms clusters by spatially partitioning data that has similar values for features of interest.

Usage

st_skater(
  sfj,
  varcol,
  k,
  wt = NULL,
  boundvar = NULL,
  min_bound = 0,
  scale_method = "standardize",
  distance_method = "euclidean",
  seed = 123456789,
  cpu_threads = 6,
  rdist = numeric()
)

Arguments

sfj

An sf (simple feature) object.

varcol

The variable selected to calculate spatial lag, which is a character.

k

The number of clusters.

wt

(optional) The spatial weights object,which can use st_weights() to construct,default is constructed by st_weights(sfj,'contiguity').

boundvar

(optional) A data frame / tibble with selected bound variable.

min_bound

(optional) A minimum bound value that applies to all clusters.

scale_method

(optional) 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 between observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan".

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".

Author

Wenbo Lv lyu.geosocial@gmail.com

Examples

library(sf)
guerry = read_sf(system.file("extdata", "Guerry.shp", package = "rgeoda"))
guerry_clusters = st_skater(guerry,c('Crm_prs','Crm_prp','Litercy','Donatns','Infants','Suicids'),4)
guerry_clusters
#> $Clusters
#>  [1] 3 2 3 1 1 1 2 1 2 1 1 1 2 1 1 3 3 3 2 4 3 1 2 1 2 2 4 1 1 1 1 1 4 3 4 1 2 1
#> [39] 4 3 3 4 2 1 1 1 4 4 2 2 4 2 2 4 2 3 2 2 4 2 3 1 1 1 2 2 1 2 3 4 2 2 2 2 3 2
#> [77] 1 1 1 1 3 3 3 2 2
#> 
#> $`Total sum of squares`
#> [1] 504
#> 
#> $`Within-cluster sum of squares`
#> [1] 57.89077 59.95242 28.72571 69.38030 62.30781 66.65809
#> 
#> $`Total within-cluster sum of squares`
#> [1] 159.0849
#> 
#> $`The ratio of between to total sum of squares`
#> [1] 0.3156447
#>