Supporting the process of exploring and interpreting space - Time multivariate patterns: The visual inquiry toolkit

Jin Chen, Alan M. MacEachren, Diansheng Guo

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

While many data sets carry geographic and temporal references, our ability to analyze these datasets lags behind our ability to collect them because of the challenges posed by both data complexity and tool scalability issues. This study develops a visual analytics approach that leverages human expertise with visual, computational, and cartographic methods to support the application of visual analytics to relatively large spatio-temporal, multivariate data sets. We develop and apply a variety of methods for data clustering, pattern searching, information visualization, and synthesis. By combining both human and machine strengths, this approach has a better chance to discover novel, relevant, and potentially useful information that is difficult to detect by any of the methods used in isolation. We demonstrate the effectiveness of the approach by applying the Visual Inquiry Toolkit we developed to analyze a data set containing geographically referenced, time-varying and multivariate data for U.S. technology industries.

Original languageEnglish (US)
Pages (from-to)33-50
Number of pages18
JournalCartography and Geographic Information Science
Volume35
Issue number1
DOIs
StatePublished - Jan 2008

All Science Journal Classification (ASJC) codes

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

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