R/0c_find_no_clusters.R

In dabrowskia/dspace: Dividing Space - Data Driven Segementation of Spatial Data

#' find_no_clusters
#'
#' Helps identify how many regions should be divided by analyzing the changes in modularity value
#'
#' @param x spatial data for regionalization
#' @param queen  if data is polygon and without disjoint polygons, should the
#'   neighborhood be treated by queen topology or rook topology
#' @param similarity.measure Character or function to declare distance method 
#'   transformed into similarity measure. If method
#'   is character, method must be "mahalanobis" or "euclidean", "maximum",
#'   "manhattan", "canberra", "binary" or "minkowisk". If method is one of
#'   "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski",
#'   see dist for details, because this function as used to compute the
#'   distance. If method="mahalanobis", the mahalanobis distance is computed
#'   between neighbor areas. If method is a function, this function is used to
#'   compute the distance.
#' @param data data to analyze similarity between regions
#' @param style style can take values “W”, “B”, “C”, “U”, “minmax” and “S”
#' @param n.neigh number of neighbors considered in the k-nearest neighbor
#'   algorithm that builds topology
#' @param disjoint if default settings generate error occurring to disjoint
#'   subgraphs it means, that in some places points or polygons are to disjoint
#'   to generate one connected graph. Use disjoint = T to enforce that one graph
#'   will be created. This is a slower option.
#' @param range number of divisions to test the modularity. THe bigger the
#'   numbers, the longer it will take to calculate plot
#'
#' @return A vector of modularity measures for given range of divisions
#' @export
#' @examples 
#' data(realEstate)
#' realEstate.modularity <- find_no_clusters(realEstate)
#' plot_modularity(realEstate.modularity)
#' 
#' 
find_no_clusters <- function(x,
                             queen = TRUE,
                             similarity.measure = "euclidean",
                             data = -grep(names(x), pattern = '^geom'),
                             style = "B",
                             n.neigh = 8,
                             disjoint = FALSE,
                             range = 2:30)
  {
  geometry <- sf::st_geometry(x)
  
    if (inherits(geometry, "sfc_POLYGON")) 
    {
      res <- prepare_polygons(
        x = x,
        queen = queen,
        disjoint = disjoint,
        n.neigh = n.neigh,
        plot = FALSE
      )
    } else {
      res <- prepare_points(
        x = x,
        n.neigh = n.neigh,
        plot = FALSE
      )
    }


    fg <-
      build_graph(
        x = res[["x"]],
        x.nb = res[["x.nb"]],
        data = data,
        similarity.measure = similarity.measure,
        style = style
      )
    graph <- fg[["graph"]]
    fg <- fg[["fg"]]
    modularities <- c()
    for (i in range) 
    {
      z <- igraph::cutat(fg, no = i)
      modularities <- append(modularities, igraph::modularity(graph, z))
    }
    names(modularities) <- range
    modularities
  }