RCEPolyK: A function to compute Constrained Relative Centralisation Index

Description

The constrained (local) version of relative centralization index. The function can be used in two ways: to provide a matrix containing the distances between spatial unit centroids or a external geographic information source (spatial object or shape file).

Usage

RCEPolyK(x, dc = NULL,  K = NULL, kdist = NULL, center = 1,
                spatobj = NULL, folder = NULL, shape = NULL)

Value

a matrix containing relative centralisation index values

Arguments

x: - an object of class matrix (or which can be coerced to that class), where each column represents the distribution of a group within spatial units. The number of columns should be greater than 1 (at least 2 groups are required). You should not include a column with total population, because this will be interpreted as a group.
dc: - a numeric matrix/vector containing the distances between spatial units centroids and the central spatial unit(s).
K: - the number of neighbourhoods under the influence of a center
kdist: - the maximal distance that defines the neighbourhoods influenced by a center
center: - a numeric vector giving the number of the spatial units that represent the centers in the table
spatobj: - a spatial object (SpatialPolygonsDataFrame) with geographic information
folder: - a character vector with the folder (directory) name indicating where the shapefile is located on the drive
shape: - a character vector with the name of the shapefile (without the .shp extension).

References

Duncan O. D. and Duncan B. (1955) A Methodological Analysis of Segregation Indexes. American Sociological Review 41, pp. 210-217

Folch D.C and Rey S. J (2016) The centralization index: A measure of local spatial segregation. Papers in Regional Science 95 (3), pp. 555-576

Examples

Run this code

x <- segdata@data[ ,1:2]
foldername <- system.file('extdata', package = 'OasisR')
shapename <- 'segdata'

RCEPolyK(x, spatobj = segdata, center = c(28, 83))

RCEPolyK(x, folder = foldername, shape = shapename, center = c(28, 83), K = 3)

center <- c(28, 83)
polydist <- matrix(data = NA, nrow = nrow(x), ncol = length(center))
for (i in 1:ncol(polydist))
  polydist[,i] <- distcenter(spatobj = segdata, center = center[i])
RCEPolyK(x, dc = polydist, kdist = 2)