Journal of Applied Mathematics
Volume 2013 (2013), Article ID 531062, 7 pages
http://dx.doi.org/10.1155/2013/531062
Research Article

Generalized Linear Spatial Models to Predict Slate Exploitability

Angeles Saavedra,1,2 Javier Taboada,3 María Araújo,3 and Eduardo Giráldez3

1Department of Statistics, University of Vigo, 36310 Vigo, Spain
2E.T.S.I. MINAS, Universidad de Vigo, Campus Lagoas-Marcosende, Rúa Maxwell, 36310 Vigo, Spain
3Department of Natural Resources, University of Vigo, 36310 Vigo, Spain

Received 11 November 2012; Revised 3 May 2013; Accepted 26 May 2013

Academic Editor: Zhiping Qiu

Copyright © 2013 Angeles Saavedra et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The aim of this research was to determine the variables that characterize slate exploitability and to model spatial distribution. A generalized linear spatial model (GLSMs) was fitted in order to explore relationship between exploitability and different explanatory variables that characterize slate quality. Modelling the influence of these variables and analysing the spatial distribution of the model residuals yielded a GLSM that allows slate exploitability to be predicted more effectively than when using generalized linear models (GLM), which do not take spatial dependence into account. Studying the residuals and comparing the prediction capacities of the two models lead us to conclude that the GLSM is more appropriate when the response variable presents spatial distribution.