maxp_greedy function

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

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.

maxp_greedy(
  w,
  df,
  bound_variable,
  min_bound,
  iterations = 99,
  initial_regions = vector("numeric"),
  scale_method = "standardize",
  distance_method = "euclidean",
  random_seed = 123456789,
  cpu_threads = 6,
  rdist = numeric()
)

Arguments

• w: An instance of Weight class
• df: A data frame with selected variables only. E.g. guerry[c("Crm_prs", "Crm_prp", "Litercy")]
• bound_variable: 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"
• random_seed: (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)

Returns

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".

Examples

## Not run:

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
data <- guerry[c('Crm_prs','Crm_prp','Litercy','Donatns','Infants','Suicids')]
bound_variable <- guerry['Pop1831']
min_bound <- 3236.67 # 10% of Pop1831
maxp_clusters <- maxp_greedy(queen_w, data, bound_variable, min_bound, iterations=99)
maxp_clusters
## End(Not run)