Visualizing Multiple Variables Across Scale and Geography

Sarah Goodwin, Jason Dykes, Aidan Slingsby, Cagatay Turkay

Research output: Contribution to journalArticleResearchpeer-review

31 Citations (Scopus)

Abstract

Comparing multiple variables to select those that effectively characterize complex entities is important in a wide variety of domains - geodemographics for example. Identifying variables that correlate is a common practice to remove redundancy, but correlation varies across space, with scale and over time, and the frequently used global statistics hide potentially important differentiating local variation. For more comprehensive and robust insights into multivariate relations, these local correlations need to be assessed through various means of defining locality. We explore the geography of this issue, and use novel interactive visualization to identify interdependencies in multivariate data sets to support geographically informed multivariate analysis. We offer terminology for considering scale and locality, visual techniques for establishing the effects of scale on correlation and a theoretical framework through which variation in geographic correlation with scale and locality are addressed explicitly. Prototype software demonstrates how these contributions act together. These techniques enable multiple variables and their geographic characteristics to be considered concurrently as we extend visual parameter space analysis (vPSA) to the spatial domain. We find variable correlations to be sensitive to scale and geography to varying degrees in the context of energy-based geodemographics. This sensitivity depends upon the calculation of locality as well as the geographical and statistical structure of the variable.

Original languageEnglish
Article number7192660
Pages (from-to)599-608
Number of pages10
JournalIEEE Transactions on Visualization and Computer Graphics
Volume22
Issue number1
DOIs
Publication statusPublished - 31 Jan 2016

Keywords

  • Context
  • Correlation
  • Geography
  • Input variables
  • Prototypes
  • Spatial resolution
  • Visualization

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