| skater {spdep} | R Documentation |
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.
skater(edges, data, ncuts, crit, vec.crit, method = c("euclidean",
"maximum", "manhattan", "canberra", "binary", "minkowski",
"mahalanobis", "other"), p = 2, cov, inverted = FALSE, otherfun)
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 |
to do
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. |
Renato M. Assuncao and Elias T. Krainski
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 as mstree
### 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])