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

Author

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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))

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.

Suggested Citation

  • Mark Gahegan, 2000. "The case for inductive and visual techniques in the analysis of spatial data," Journal of Geographical Systems, Springer, vol. 2(1), pages 77-83, March.
    • Handle: RePEc:kap:jgeosy:v:2:y:2000:i:1:d:10.1007_s101090050033
    DOI: 10.1007/s101090050033
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    Cited by:

    1. Kamruzzaman, Md. & Hine, Julian & Gunay, Banihan & Blair, Neale, 2011. "Using GIS to visualise and evaluate student travel behaviour," Journal of Transport Geography, Elsevier, vol. 19(1), pages 13-32.
    2. Chen, Jie & Shaw, Shih-Lung & Yu, Hongbo & Lu, Feng & Chai, Yanwei & Jia, Qinglei, 2011. "Exploratory data analysis of activity diary data: a space–time GIS approach," Journal of Transport Geography, Elsevier, vol. 19(3), pages 394-404.
    3. Ying Song & Harvey Miller, 2012. "Exploring traffic flow databases using space-time plots and data cubes," Transportation, Springer, vol. 39(2), pages 215-234, March.

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