Scale Effect on Principal Component Analysis for Vector Random Functions |
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| Authors: | J A Vargas-Guzmán A W Warrick and D E Myers |
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| Institution: | (1) Department of Soil, Water and Environmental Science, University of Arizona, 429 Shantz 38, Tucson, Arizona, 85721;(2) Department of Mathematics, University of Arizona, Tucson, Arizona, 85721 |
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| Abstract: | Principal component analysis (PCA) is commonly applied without looking at the  spatial support (size and shape, of the samples and the field), and the cross-covariance structure of the explored attributes. This paper shows that PCA can depend on such spatial features. If the spatial random functions for attributes correspond to largely dissimilar variograms and cross-variograms, the scale effect will increase as well. On the other hand, under conditions of proportional shape of the variograms and cross-variograms (i.e., intrinsic coregionalization), no scale effect may occur. The theoretical analysis leads to eigenvalue and eigenvector functions of the size of the domain and sample supports. We termed this analysis growing scale PCA, where spatial (or time) scale refers to the size and shape of the domain and samples. An example of silt, sand, and clay attributes for a second-order stationary vector random function shows the correlation matrix asymptotically approaches constants at two or three times the largest range of the spherical variogram used in the nested model. This is contrary to the common belief that the correlation structure between attributes become constant at the range value. Results of growing scale PCA illustrate the rotation of the orthogonal space of the eigenvectors as the size of the domain grows. PCA results are strongly controlled by the multivariate matrix variogram model. This approach is useful for exploratory data analysis of spatially autocorrelated vector random functions. |
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| Keywords: | dispersion covariances spatial support Pearson correlation spatial scales of variability PCA matrix variogram |
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