A tabu-search algorithm to solve the max-p-region problem

A wrapper function for rgeoda::maxp_tabu().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_tabu(
  sfj,
  varcol,
  wt = NULL,
  boundvar,
  min_bound,
  tabu_length = 10,
  conv_tabu = 10,
  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.
tabu_length: (optional) The length of a tabu search heuristic of tabu algorithm. Defaults to 10.
conv_tabu: (optional): The number of non-improving moves. Defaults to 10.
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_tabu(guerry,c('Crm_prs','Crm_prp','Litercy','Donatns',
'Infants','Suicids'),boundvar = 'Pop1831',min_bound = 3236.67)
guerry_clusters
#> $Clusters
#>  [1] 2 5 1 3 3 1 5 1 4 1 1 3 6 1 2 7 2 7 5 8 2 7 4 3 6 6 8 3 1 1 7 3 8 2 7 3 5 7
#> [39] 2 2 1 8 6 1 7 3 2 2 4 5 8 4 4 8 4 2 5 5 2 6 1 1 1 1 4 4 3 4 2 2 6 6 5 6 2 6
#> [77] 1 1 3 3 8 7 7 4 5
#> 
#> $`Total sum of squares`
#> [1] 504
#> 
#> $`Within-cluster sum of squares`
#> [1] 37.71292 53.00122 24.16448 61.51004 53.23544 33.45476
#> 
#> $`Total within-cluster sum of squares`
#> [1] 240.9211
#> 
#> $`The ratio of between to total sum of squares`
#> [1] 0.4780181
#>