City Research Online

Abstract

Visualizing big and complex multivariate data is challenging. To address this challenge, we propose flexible visual analytics (FVA) with the aim to mitigate visual complexity and interaction complexity challenges in visual analytics, while maintaining the strengths of multiple perspectives on the studied data. At the heart of our proposed approach are transitions that fluidly transform data between user-relevant views to offer various perspectives and insights into the data. While smooth display transitions have been already proposed, there has not yet been an interdisciplinary discussion to systematically conceptualize and formalize these ideas. As a call to further action, we argue that future research is necessary to develop a conceptual framework for flexible visual analytics. We discuss preliminary ideas for prioritizing multi-aspect visual representations and multi-aspect transitions between them, and consider the display user for whom such depictions are produced and made available for visual analytics. With this contribution we aim to further facilitate visual analytics on complex data sets for varying data exploration tasks and purposes based on different user characteristics and data use contexts.

Publication Type:	Article
Additional Information:	© 2021 The Author(s) Published by Elsevier B.V. on behalf of Zhejiang University and Zhejiang University Press. his is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/
Publisher Keywords:	Visual analytics, Animated transitions, Multi-faceted data
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Science & Technology > Computer Science
SWORD Depositor:	Symplectic Administrator

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