Skip to contents

Spatially Constrained Hierarchical Clucstering (SCHC)

Source: R/spatial_cluster.R
st_schc.Rd

A wrapper function for rgeoda::schc().Spatially constrained hierarchical clustering is a special form of constrained clustering, where the constraint is based on contiguity (common borders). The method builds up the clusters using agglomerative hierarchical clustering methods: single linkage, complete linkage, average linkage and Ward's method (a special form of centroid linkage). Meanwhile, it also maintains the spatial contiguity when merging two clusters.

Usage

st_schc(
  sfj,
  varcol,
  k,
  wt = NULL,
  boundvar = NULL,
  method = "average",
  min_bound = 0,
  scale_method = "standardize",
  distance_method = "euclidean",
  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.

method

(optional) "single", "complete", "average"(default),"ward".

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".

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_schc(guerry,c('Crm_prs','Crm_prp','Litercy','Donatns','Infants','Suicids'),
4,method = "complete")
guerry_clusters
#> $Clusters
#>  [1] 1 1 1 1 1 1 1 1 1 1 1 1 4 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [39] 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [77] 1 1 1 1 1 1 1 1 1
#> 
#> $`Total sum of squares`
#> [1] 504
#> 
#> $`Within-cluster sum of squares`
#> [1] 78.13831 54.75868 81.49771 63.48675 80.60111 65.74389
#> 
#> $`Total within-cluster sum of squares`
#> [1] 79.77355
#> 
#> $`The ratio of between to total sum of squares`
#> [1] 0.1582809
#>