Performing Land-Capability Evaluation by Use of Numerical Taxonomy

Researchers have attempted to incorporate environmental variables into the land-use planning process by use of several ranking and mapping formulations. Most of these are based on some type of classification scheme. More recently, multivariate statistical techniques have been utilized to classify land areas into groups with similar suitability for urban development. A test was made of one of these numerical taxonomic techniques on a data set from Medford Township, New Jersey, and the results analyzed in terms of the pros and cons of these methods. A group of 484 forty-acre grid cells with forty-two environmental variables was collapsed into a ten variable set for ten groups of grid cells having like characteristics. The analysis involved two steps, factor analysis followed by a euclidean-distance-classification algorithm. The results show that numerical taxonomy can greatly facilitate the analysis of large environmental data sets and can help to identify the ecological relationships in quantitative terms. However, the complexity of the statistical methods involved greatly limits the wide application of these techniques, and the use of numerical taxonomic results in land-capability evaluation cannot release the researcher from making many judgmental decisions

Acre · Civil engineering · Data mining · Geography · Grid · Land use · Machine learning · Multivariate statistics · Numerical taxonomy · Operations research · Computer Science · Engineering · Environmental Science · Land Use and Ecosystem Services · Mathematics · Soil and Land Suitability Analysis · Ecology

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