Constructing knowledge from multivariate spatiotemporal data: Integrating geographical visualization with knowledge discovery in database methods

Alan M. Maceachren, Monica Wachowicz, Robert Edsall, Daniel Haug, Raymon Masters

Research output: Contribution to journalArticlepeer-review

150 Scopus citations

Abstract

We present an approach to the process of constructing knowledge through structured exploration of large spatiotemporal data sets. First, we introduce our problem context and define both Geographic Visualization (GVis) and Knowledge Discovery in Databases (KDD), the source domains for methods being integrated. Next, we review and compare recent GVis and KDD developments and consider the potential for their integration, emphasizing that an iterative process with user interaction is a central focus for uncovering interest and meaningful patterns through each. We then introduce an approach to design of an integrated GVis-KDD environment directed to exploration and discovery in the context of spatiotemporal environmental data. The approach emphasizes a matching of GVis and KDD meta-operations. Following description of the GVis and KDD methods that are linked in our prototype system, we present a demonstration of the prototype applied to a typical spatiotemporal dataset. We conclude by outlining, briefly, research goals directed toward more complete integration of GVis and KDD methods and their connection to temporal GIS.

Original languageEnglish (US)
Pages (from-to)311-334
Number of pages24
JournalInternational Journal of Geographical Information Science
Volume13
Issue number4
DOIs
StatePublished - Jan 1 1999

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Geography, Planning and Development
  • Library and Information Sciences

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