seg (version 0.3-3)

seg: Calculate Index of Dissimilarity

Description

seg calculates Duncan and Duncan's index of dissimilarity between two population groups. If nb is given, the index can be adjusted to reflect the spatial distribution of population.

Usage

seg(data, nb, tol = .Machine$double.eps)

Arguments

data

a numeric matrix or data frame with two columns that represent mutually exclusive population groups (e.g., Asians and non-Asians). If more than two columns are given, only the first two will be used for computing the index.

nb

an optional matrix object describing the intensity of interaction between geographic units.

tol

the tolerance for detecting differences between values. Differences in the input values that are smaller than tol should make no changes in the output index values. The default is .Machine$double.eps. See help(.Machine)

Value

• A single numeric value between 0 and 1, indicating the degree of segregation; 0 for no segregation, and 1 for complete segregation.

Details

nb must be a square matrix (same number of rows and columns) but does not have to be symmetric. When nb is not specified, seg calculates the traditional index of dissimilarity proposed by Duncan and Duncan (1955). If nb is a rook-based contiguity matrix standardised by the total number of neighbours, the calculation follows the Morrill's adjusted index of dissimilarity (1991). See the example code below and help(nb2mat) for more information regarding how to construct such a matrix. For Wong's D(w) and D(s), see https://sites.google.com/site/hongseongyun/seg. Note that the sum of all elements in nb should equal one.

References

Duncan, O. D., & Duncan, B. (1955). A methodological analysis of segregation indexes. American Sociological Review, 20, 210-217. Morrill, R. L. (1991). On the measure of geographic segregation. Geography Research Forum, 11, 25-36. Wong, D. W. S. (1993). Spatial indices of segregation. Urban Studies, 30, 559-572.

See Also

spseg, whiteseg

Examples

if (require(spdep)) {
  grd <- GridTopology(cellcentre.offset=c(0.5,0.5),
                      cellsize=c(1,1), cells.dim=c(10,10))
  grd.sp <- as.SpatialPolygons.GridTopology(grd)
  grd.nb <- nb2mat(poly2nb(grd.sp, queen = FALSE), style = "B")
  grd.nb <- grd.nb / sum(grd.nb)

  data(segdata)
  parval <- par()
  par(mfrow = c(2, 4), mar = c(0, 1, 0, 1))

  d <- numeric()
  m <- numeric()
  for (i in 1:8) {
    idx <- 2 * i
    d <- append(d, seg(segdata[,(idx-1):idx]))
    m <- append(m, seg(segdata[,(idx-1):idx], grd.nb))
    full <- segdata[,(idx-1)] == 100
    half <- segdata[,(idx-1)] == 50
    plot(grd.sp)
    plot(grd.sp[full,], col = "Black", add = TRUE)
    if (any(half))
      plot(grd.sp[half,], col = "Grey", add = TRUE)
    text(5, 11.5, labels = paste("D = ", round(d[i], 2), 
                                 ", D(adj) = ", round(m[i], 2), sep = ""))
  }
  par(mfrow = parval$mfrow, mar = parval$mar)
}