Multivariate analysis and geovisualization with an integrated geographic knowledge discovery approach

Diansheng Guo, Mark Gahegan, Alan M. MacEachren, Biliang Zhou

Research output: Contribution to journalArticle

101 Scopus citations

Abstract

The discovery, interpretation, and presentation of multivariate spatial patterns are important for scientific understanding of complex geographic problems. This research integrates computational, visual, and cartographic methods together to detect and visualize multivariate spatial patterns. The integrated approach is able to: (1) perform multivariate analysis, dimensional reduction, and data reduction (summarizing a large number of input data items in a moderate number of clusters) with the Self-Organizing Map (SOM); (2) encode the SOM result with a systematically designed color scheme; (3) visualize the multivariate patterns with a modified Parallel Coordinate Plot (PCP) display and a geographic map (GeoMap); and (4) support human interactions to explore and examine patterns. The research shows that such "mixed initiative" methods (computational and visual) can mitigate each other's weakness and collaboratively discover complex patterns in large geographic datasets, in an effective and efficient way.

Original languageEnglish (US)
Pages (from-to)113-132
Number of pages20
JournalCartography and Geographic Information Science
Volume32
Issue number2
DOIs
StatePublished - Apr 1 2005

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All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Geography, Planning and Development
  • Management of Technology and Innovation

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