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Regionalization with dynamically constrained agglomerative clustering and partitioning(REDCAP)

Source: R/spatial_cluster.R
st_redcap.Rd

A wrapper function for rgeoda::redcap().REDCAP (Regionalization with dynamically constrained agglomerative clustering and partitioning) is developed by D. Guo (2008). Like SKATER, REDCAP starts from building a spanning tree with 4 different ways (single-linkage, average-linkage, ward-linkage and the complete-linkage). The single-linkage way leads to build a minimum spanning tree. Then,REDCAP provides 2 different ways (first-order and full-order constraining) to prune the tree to find clusters. The first-order approach with a minimum spanning tree is exactly the same with SKATER. In GeoDa and pygeoda, the following methods are provided: \* First-order and Single-linkage \* Full-order and Complete-linkage \* Full-order and Average-linkage \* Full-order and Single-linkage \* Full-order and Ward-linkage.

Usage

st_redcap(
  sfj,
  varcol,
  k,
  wt = NULL,
  boundvar = NULL,
  method = "fullorder-averagelinkage",
  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.

method

(optional) "firstorder-singlelinkage", "fullorder-completelinkage", "fullorder-averagelinkage"(default),"fullorder-singlelinkage", "fullorder-wardlinkage"

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_redcap(guerry,c('Crm_prs','Crm_prp','Litercy','Donatns','Infants','Suicids'),
4,method = "fullorder-completelinkage")
guerry_clusters
#> $Clusters
#>  [1] 1 2 1 3 3 1 2 3 2 3 3 3 2 1 1 1 1 1 2 1 1 1 2 3 2 2 1 3 3 3 3 3 1 1 1 3 2 3
#> [39] 1 1 4 1 2 3 3 3 1 1 2 2 1 2 2 1 2 1 2 2 1 2 1 3 3 3 2 2 3 2 1 1 2 2 2 2 1 2
#> [77] 3 3 3 3 1 1 1 2 2
#> 
#> $`Total sum of squares`
#> [1] 504
#> 
#> $`Within-cluster sum of squares`
#> [1] 59.33033 55.01580 28.20272 68.58974 61.27232 54.63519
#> 
#> $`Total within-cluster sum of squares`
#> [1] 176.9539
#> 
#> $`The ratio of between to total sum of squares`
#> [1] 0.351099
#>