RCEPolyK: A function to compute Constrained Relative Centralisation...

Description Usage Arguments Value References See Also Examples

View source: R/SegFunctions.R

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

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

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).

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

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

See Also

RCE, RCEPoly,

ACEDuncan, ACEDuncanPoly,

ACEDuncanPolyK, ACE, ACEPoly

Examples

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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)

OasisR documentation built on July 2, 2020, 3:14 a.m.

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