The case for inductive and visual techniques in the analysis of spatial data

Mark Gahegan
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Mark Gahegan: Department of Geography, The Pennsylvania State University, 302 Walker Building, University Park, PA 16802, USA (e-mail: mark@geog.psu.edu)

Journal of Geographical Systems, 2000, vol. 2, issue 1, 77-83

Abstract: Abstract. As the attribute spaces available to geography expand, new challenges are posed in comprehending and analysing data. This article introduces two complementary approaches to analysis that show promise in addressing data with high attribute dimensionality: inductive learning and visualisation. Whilst neither of these techniques are yet as robust or generally available as many accepted parametric techniques, they are nevertheless able to provide insight, and in the case of inductively-based classifiers and approximation methods, have been shown to outperform traditional approaches in some geographic settings. Some problems with parametric inferential statistics are briefly mentioned, followed by descriptions of inductive and visual analysis methods, and some of the important research that remains to be done before they can take a more prominent role.

Keywords: Key words: High-dimensionality; visualisation; machine learning; spatial analysis; classification (search for similar items in EconPapers)
Date: 2000
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s101090050033

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