spdep (version 0.5-3)

skater: Spatial 'K'luster Analysis by Tree Edge Removal

Description

This function implements a SKATER procedure for spatial clustering analysis. This procedure essentialy begins with an edges set, a data set and a number of cuts. The output is an object of 'skater' class and is valid for input again.

Usage

skater(edges, data, ncuts, crit, vec.crit, method = c("euclidean", 
    "maximum", "manhattan", "canberra", "binary", "minkowski", 
    "mahalanobis", "other"), p = 2, cov, inverted = FALSE, otherfun)

Arguments

edges

A matrix with 2 colums with each row is an edge

data

A data.frame with data observed over nodes.

ncuts

The number of cuts

crit

A numeric or integer with criteria for groups. Example: minimum population size.

vec.crit

A vector for evaluating critera.

method

Character for declare the distance method. For "euclidean", "maximum", "manhattan", "canberra", "binary" and "minkowski", see dist for details, because this function as used to compute the dista

p

The power of the Minkowski distance.

cov

The covariance matrix used to compute the mahalanobis distance.

inverted

logical. If 'TRUE', 'cov' is supposed to contain the inverse of the covariance matrix.

otherfun

A user defined function to compute the distance

Value

• A object of skater class with:
• groups
  A vector with length equal the number of nodes. Each position identifies the group of node
• edges.groups
  A list of length equal the number of groups with each element is a set of edges
• not.prune
  A vector identifying the groups with are not candidates to partition.
• candidates
  A vector identifying the groups with are candidates to partition.
• ssto
  The total dissimilarity in each step of edge removal.

Details

to do

References

Assuncao, R.M., Lage J.P., and Reis, E.A. (2002). Analise de conglomerados espaciais via arvore geradora minima. Revista Brasileira de Estatistica, 62, 1-23.

Assuncao, R. M, Neves, M. C., Camara, G. and Freitas, C. da C. (2006). Efficient regionalization techniques for socio-economic geographical units using minimum spanning trees. International Journal of Geographical Information Science Vol. 20, No. 7, August 2006, 797-811

See Also

See Also as mstree

Examples

### loading data
bh <- readShapePoly(system.file("etc/shapes/bhicv.shp",
      package="spdep")[1])
### data padronized
dpad <- data.frame(scale(bh@data[,5:8]))

### neighboorhod list
bh.nb <- poly2nb(bh)

### calculing costs
lcosts <- nbcosts(bh.nb, dpad)

### making listw
nb.w <- nb2listw(bh.nb, lcosts, style="B")

### find a minimum spanning tree
mst.bh <- mstree(nb.w,5)

### the mstree plot
par(mar=c(0,0,0,0))
plot(mst.bh, coordinates(bh), col=2,       
     cex.lab=.7, cex.circles=0.035, fg="blue")
plot(bh, border=gray(.5), add=TRUE)

### three groups with no restriction
res1 <- skater(mst.bh[,1:2], dpad, 2)

### thee groups with minimum population 
res2 <- skater(mst.bh[,1:2], dpad, 2, 200000, bh@data$Pop)

### thee groups with minimun number of areas
res3 <- skater(mst.bh[,1:2], dpad, 2, 3, rep(1,nrow(bh@data)))

### groups frequency
table(res1$groups)
table(res2$groups)
table(res3$groups)

### the skater plot
par(mar=c(0,0,0,0))
plot(res1, coordinates(bh), cex.circles=0.035, cex.lab=.7)

### more one partition
res1b <- skater(res1, dpad, 1)

### length groups frequency
table(res1$groups)
table(res1b$groups)

### the skater plot, using other colors
plot(res1b, coordinates(bh), cex.circles=0.035, cex.lab=.7,
     groups.colors=colors()[(1:length(res1b$ed))*10])

### the Spatial Polygons plot
plot(bh, col=heat.colors(4)[res1b$groups])