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Landscape Data Transformation: Categorical Descriptions to Numerical Descriptors

Authors: Dennis A. Apuan


Categorical data based on description of the agricultural landscape imposed some mathematical and analytical limitations. This problem however can be overcome by data transformation through coding scheme and the use of non-parametric multivariate approach. The present study describes data transformation from qualitative to numerical descriptors. In a collection of 103 random soil samples over a 60 hectare field, categorical data were obtained from the following variables: levels of nitrogen, phosphorus, potassium, pH, hue, chroma, value and data on topography, vegetation type, and the presence of rocks. Categorical data were coded, and Spearman-s rho correlation was then calculated using PAST software ver. 1.78 in which Principal Component Analysis was based. Results revealed successful data transformation, generating 1030 quantitative descriptors. Visualization based on the new set of descriptors showed clear differences among sites, and amount of variation was successfully measured. Possible applications of data transformation are discussed.

Keywords: data transformation, PrincipalComponent Analysis, numerical descriptors

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[1] O. P. K. Sarkar, O. W. Bidwell, L. F. Marcus, "Selection of Characteristics for numerical classification of soils". Soil Science Society of America Proceedings, 30:269-272, 1966.
[2] J. H. Rayner, "Classification of Soils by Numerical Taxonomy" Journal of Soil Science, 17:79-92, 1966.
[3] D. F. Grigal, H. F. Arneman, "Numerical Classification of Some Forested Minnesota Soils", Soil Science Society of America Proceedings, 33: 433-438, 1969.
[4] D. W. Goodall, "Objective methods for the classification of vegetation. III. An essay in the use of factor analysis", Australian Journal of Botany, 2(3): 304 - 324, 1954.
[5] A. Field, "Discovering Statistics using SPSS", Sage Publication, London, pp. 619-679, 2005.
[6] O. Hammer, D. A. T. Harper, P. D. Ryan, "PAST: Paleontological Statistics for Educational and Data Analysis", Paleontologia Electronica 4 (1):9, 2001.
[7] I. H. Dixon, M. M. Douglas, J. L. Dowe, D. W. Burrows, S. A. Townsend, "A Rapid Method for Assessing the Condition of Riparian Zones in the Wet/Dry Tropics of Northern Australia", In: I. D. Rutherfurd, I. Wiszniewski, M. A. Askey-Doran, R. Glazik (eds.), Proceedings of the 4th Australian Stream Management Conference; Linking Rivers to Landscapes, Launceston, Tasmania, Department of Primary Industries, Water and Environment, Hobart, pp. 173-178, 2005.
[8] A. Jansen, A. Robertson, L. Thompson, A. Wilson, K. Nicholls, "Rapid Appraisal of Riparian Condition" In: Technical Guide for the Mid North of South Australia. Land, Water and Wool. pp. 1-17, 2006.