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Visualizing Multiple Variables Across Scale and Geography

Goodwin, S., Dykes, J., Slingsby, A. & Turkay, C. (2015). Visualizing Multiple Variables Across Scale and Geography. IEEE Transactions on Visualization and Computer Graphics (Proceedings of the Visual Analytics Science and Technology / Information Visualization / Scientific Visualization 2015), 22(1), pp. 599-608. doi: 10.1109/tvcg.2015.2467199


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.

Publication Type: Article
Additional Information: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords: Scale, Geography, Multivariate, Sensitivity Analysis, Variable Selection, Local Statistics, Geodemographics, Energy
Subjects: G Geography. Anthropology. Recreation > GA Mathematical geography. Cartography
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Science & Technology > Computer Science
School of Science & Technology > Computer Science > giCentre
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