Factor analysis applied to regional geochemical data: problems and possibilities |
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| Institution: | 1. Department of Earth Sciences, University of Florence, Via G. La Pira 4, 50121 Firenze (I), Italy;2. CNR-IGG (Institute of Geosciences and Earth Resources), G. La Pira 4, 50121 Firenze, Italy;3. Department of Earth, Environment and Resources Sciences, University of Naples Federico II, Complesso Universitario Monte Sant''Angelo, Via Cintia snc, 80125 Napoli, Italy;4. Pegaso University, Piazza Trieste e Trento, 48, 80132 Napoli, Italy;5. Benecon Scarl, Dipartimento Ambiente e Territorio, Via S. Maria di Costantinopoli 104, 80138 Napoli, Italy;1. Faculty of Engineering, University of Mohaghegh Ardabili, Ardabil, Iran;2. Geochemistry, Department of Mining Engineering, University of Gonabad, Gonabad, Iran;1. Department of Earth Sciences, University of Florence, Via G. La Pira 4, 50121 Firenze, Italy;2. CNR-IGG (Institute of Geosciences and Earth Resources), G. La Pira 4, 50121 Firenze, Italy;3. Department of Earth, Environment and Resources Sciences, University of Naples Federico II, Via Mezzocannone 8, 80134 Napoli, Italy;4. Department of Geology, Universidad de Chile, Plaza Ercilla 803, Santiago, Chile;1. School of Geography, Archaeology and Palaeoecology, Queen''s University Belfast, BT7 1NN, UK;2. Dept. of Math. Anal. & Appl. of Math., Palacky University Olomouc, 17. listopadu 12, CZ-771 46 Olomouc, Czech Republic;3. Department of Earth and Environmental Sciences, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada;4. Geological Survey of Norway, PO Box 6315, Sluppen, N-7491 Trondheim, Norway;5. Geoscience Australia, GPO Box 378, Canberra, ACT 2601, Australia;6. Research School of Earth Sciences, Australian National University, Canberra, ACT 2601, Australia;7. Institute of Statistics & Mathematical Methods in Economics, Vienna University of Technology, Wiedner Hauptstr. 8-10, 1040 Vienna, Austria;8. Helmholtz Center Dresden Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Freiberg, Germany |
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| Abstract: | A large regional geochemical data set of C-horizon podzol samples from a 188,000 km2 area in the European Arctic, analysed for more than 50 elements, was used to test the influence of different variants of factor analysis on the results extracted. Due to the nature of regional geochemical data (neither normal nor log-normal, strongly skewed, often multi-modal data distributions), the simplest methods of factor analysis with the least statistical assumptions perform best. As a result of this test it can generally be suggested to use principal factor analysis with an orthogonal rotation for such data. Selecting the number of factors to extract is difficult, however, the scree plot provides some useful help. For the test data, a low number of extracted factors gave the most informative results. Deleting or adding just 1 element in the input matrix can drastically change the results of factor analysis. Given that selection of elements is often rather based on availability of analytical packages (or detection limits) than on geochemical reasoning this is a disturbing result. Factor analysis revealed the most interesting data structures when a low number of variables were entered. A graphical presentation of the loadings and a simple, automated mapping technique allows extraction of the most interesting results of different factor analyses in one glance. Results presented here underline the importance of careful univariate data analysis prior to entering factor analysis. Outliers should be removed from the dataset and different populations present in the data should be treated separately. Factor analysis can be used to explore a large data set for hidden multivariate data structures. |
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