Methods for descriptive factor analysis of multivariate geostatistical data: a case-study comparison

Samuel D. Oman, Bella Vakulenko-Lagun, Michael Zilberbrand

Research output: Contribution to journalArticlepeer-review


We consider the framework in which vectors of variables are observed at different points in a region. Such data are typically characterized by point-wise correlations among the variables, as well as spatial autocorrelation and cross-correlation. To help understand and model this dependence structure, one may define factors which operate at different spatial scales. We consider four such factor-analytic techniques: the linear model of coregionalization (LMC) and three recently proposed alternatives. We apply them to the same set of data, concentrations of major ions in water samples taken from springs in a carbonate mountain aquifer. The methods give quite different results for the spring chemistry, with those of the LMC being much more interpretable. We suggest some possible explanations for this, which may be relevant in other applications as well.

Original languageEnglish
Pages (from-to)1103-1116
Number of pages14
JournalStochastic Environmental Research and Risk Assessment
Issue number4
StatePublished - 1 May 2015
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2014, Springer-Verlag Berlin Heidelberg.


  • Carbonate dissolution
  • Factor analysis
  • Linear model of coregionalization
  • Major ions
  • Principal component analysis
  • Sea water
  • Semivariogram matrix
  • Sewage
  • Spatial correlation

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • Safety, Risk, Reliability and Quality
  • Water Science and Technology
  • General Environmental Science


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