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A greedy algorithm to solve the max-p-region problem

Source: R/spatial_cluster.R
st_maxp_greedy.Rd

A wrapper function for rgeoda::maxp_greedy().The max-p-region problem is a special case of constrained clustering where a finite number of geographical areas are aggregated into the maximum number of regions (max-p-regions), such that each region is geographically connected and the clusters could maximize internal homogeneity.

Usage

st_maxp_greedy(
  sfj,
  varcol,
  wt = NULL,
  boundvar,
  min_bound,
  iterations = 99,
  initial_regions = vector("numeric"),
  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.

wt

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

boundvar

A numeric vector of selected bounding variable.

min_bound

A minimum value that the sum value of bounding variable int each cluster should be greater than.

iterations

(optional) The number of iterations of greedy algorithm. Defaults to 99.

initial_regions

(optional) The initial regions that the local search starts with. Default is empty. means the local search starts with a random process to "grow" 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 betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan"

seed

(optional) The seed for random number generator. Defaults to 123456789.

cpu_threads

(optional) The number of cpu threads used for parallel computation.Default is 6.

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_maxp_greedy(guerry,c('Crm_prs','Crm_prp','Litercy','Donatns',
'Infants','Suicids'),boundvar = 'Pop1831',min_bound = 3236.67)
guerry_clusters
#> $Clusters
#>  [1] 7 2 7 1 1 1 2 3 6 3 1 1 8 7 4 4 5 7 2 4 7 3 6 1 8 8 4 1 3 3 3 1 4 5 5 7 2 3
#> [39] 5 7 7 4 8 1 3 1 5 5 6 6 5 6 2 4 6 5 2 2 5 8 7 3 3 3 6 6 2 6 2 5 8 8 2 8 4 8
#> [77] 1 1 1 1 4 4 4 6 2
#> 
#> $`Total sum of squares`
#> [1] 504
#> 
#> $`Within-cluster sum of squares`
#> [1] 44.39253 37.80089 28.19168 53.09362 48.11754 48.21628
#> 
#> $`Total within-cluster sum of squares`
#> [1] 244.1875
#> 
#> $`The ratio of between to total sum of squares`
#> [1] 0.4844989
#>