Landscape Data Transformation: Categorical Descriptions to Numerical Descriptors
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.
[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.
[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.
@article{"International Journal of Biological, Life and Agricultural Sciences:50219", author = "Dennis A. Apuan", title = "Landscape Data Transformation: Categorical Descriptions to Numerical Descriptors", abstract = "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, numerical descriptors, principalcomponent analysis", volume = "5", number = "9", pages = "504-4", }