skater: Spatial 'K'luster Analysis by Tree Edge Removal In spdep: Spatial Dependence: Weighting Schemes, Statistics and Models

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

 1 2 3 skater(edges, data, ncuts, crit, vec.crit, method = c("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski", "mahalanobis"), p = 2, cov, inverted = FALSE)

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 scalar ow two dimensional vector with with criteria for groups. Examples: limits of group size or limits of population size. If scalar, is the minimum criteria for groups. vec.crit A vector for evaluating criteria. method Character or function to declare distance method. If method is character, method must be "mahalanobis" or "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowisk". If method is one of "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski", 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 is a function, this function is used to compute the distance. 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.

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