skater {spdep} | R Documentation

## 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 "minkowisk", see `dist` for details, because this function as used to compute the distance. If `method="mahalanobis"`, the mahalanobis distance is computed between neighbour areas. If `method="other"`, any function must be informed in `otherfun` argument.
`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

### Details

to do

### 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.

### Author(s)

Renato M. Assuncao and Elias T. Krainski

### 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])

[Package spdep version 0.4-56 Index]