Visual Semiotics & Uncertainty Visualization: An Empirical Study.

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Show simple item record MacEachren, AM en Roth, RE en O'Brien, J en Li, B en Swingley, D en Gahegan, Mark en 2018-10-23T03:54:42Z en 2012-12 en
dc.identifier.issn 1077-2626 en
dc.identifier.uri en
dc.description.abstract This paper presents two linked empirical studies focused on uncertainty visualization. The experiments are framed from two conceptual perspectives. First, a typology of uncertainty is used to delineate kinds of uncertainty matched with space, time, and attribute components of data. Second, concepts from visual semiotics are applied to characterize the kind of visual signification that is appropriate for representing those different categories of uncertainty. This framework guided the two experiments reported here. The first addresses representation intuitiveness, considering both visual variables and iconicity of representation. The second addresses relative performance of the most intuitive abstract and iconic representations of uncertainty on a map reading task. Combined results suggest initial guidelines for representing uncertainty and discussion focuses on practical applicability of results. en
dc.format.medium Print en
dc.language eng en
dc.relation.ispartofseries IEEE transactions on visualization and computer graphics en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights.uri en
dc.title Visual Semiotics & Uncertainty Visualization: An Empirical Study. en
dc.type Journal Article en
dc.identifier.doi 10.1109/tvcg.2012.279 en
pubs.issue 12 en
pubs.begin-page 2496 en
pubs.volume 18 en
dc.rights.holder Copyright: The author en
dc.identifier.pmid 26357158 en
pubs.end-page 2505 en
pubs.publication-status Published en
dc.rights.accessrights en
pubs.subtype Research Support, U.S. Gov't, Non-P.H.S. en
pubs.subtype Journal Article en
pubs.elements-id 362897 en Science en School of Computer Science en
dc.identifier.eissn 1941-0506 en
pubs.record-created-at-source-date 2015-09-11 en
pubs.dimensions-id 26357158 en

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