RCE: A function to compute Relatice Centralisation Index (RCE)

Description

The relative centralisation index measures the proportion of a group that should change its localization to achieve the same level of centralization as another group. The function can be used in two ways: to provide a vector containing the distances between spatial units centroids or a external geographic information source (spatial object or shape file).

Usage

RCE(x, dc = NULL, center = 1, spatobj = NULL, folder = NULL, shape = NULL)

Arguments

- an object of class matrix (or which can be coerced to that class), where each column represents the distribution of a population group, within spatial units. The number of columns should be greater than 1 (at least 2 population groups are required). You should not include a column with total population in each unit, because this will be interpreted as a group.

- a numeric vector containing the distances between spatial units centroids and the central spatial unit

center

- a numeric value giving the number of the spatial unit that represents the centre in the table

spatobj

- a spatial object (SpatialPolygonsDataFrame) containing geographic information

folder

- a character vector with the folder (directory) name indicating where the shapefile with the geographic information is located.

shape

- a character vector with the name of the shapefile (without the .shp extension) which contains the geographic information

Value

a matrix containing relative centralisation index values

References

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

Examples

Run this code

# NOT RUN {
x <- segdata@data[ ,1:2]
distc<- distcenter(segdata, center = 45)
foldername <- system.file('extdata', package = 'OasisR')
shapename <- 'segdata'

RCE(x, dc=distc) 

RCE(x, spatobj = segdata, center = 45) 

RCE(x, folder = foldername, shape = shapename, center = 45) 

# }