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Enhancing Visual Encodings of Uncertainty Through Aesthetic Depictions in Line Graph Visualisations

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Human Interface and the Management of Information (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14015))

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Abstract

The method of representing uncertainty can drastically influence a user’s interpretation of the visualised data. Whilst reasons for the scarce adoption of accepting uncertainty visualisations has been extensively researched, exploring further intuitive depiction methods has taken a back seat. Currently, most visualisation methods for uncertainty revolve around the comprehension of grasping pre-existing techniques such as confidence intervals and error bars. Moreover, this anticipates that the intended audience will be proficient in obtaining the relevant information displayed. To help establish an accessible method for the visualisation of uncertainty, we adopt a novel cross-disciplinary approach to further understand and depict the more intuitive/affective dimensions of uncertainty. The field of aesthetics is mostly associated with the discipline of art and design, but it has been applied in this research to evaluate its effectiveness for uncertainty visualisation. In a recent study with one thousand one hundred and forty-two participants, the authors examined the influence of applying aesthetic dimensions to the visualisation of a line graph. We find that certain aesthetic renderings afford a higher degree of uncertainty and provide an intuitive approach to mapping uncertainty to the data. By analysing the participants’ responses to different aesthetic renderings, we aim to build a picture of how we might encourage the use of uncertainty visualisation for a lay audience.

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Acknowledgments

Supported by Knowledge Economy Skills Scholarships 2 (KESS2) which is an All Wales higher-level skills initiative led by Bangor University on behalf of the HE sectors in Wales. It is part-funded by the Welsh Government’s European Social Fund (ESF) competitiveness programme for East Wales.

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Authors and Affiliations

  1. Cardiff School of Technologies, Cardiff Metropolitan University Llandaff Campus, Western Avenue, Cardiff, CF5 2YB, UK

    Joel Pinney, Fiona Carroll & Esyin Chew

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  1. Joel Pinney
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  2. Fiona Carroll
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  3. Esyin Chew
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Correspondence to Joel Pinney .

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Editors and Affiliations

  1. Tokyo City University, Tokyo, Japan

    Hirohiko Mori

  2. Tokyo University of Science, Tokyo, Japan

    Yumi Asahi

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Pinney, J., Carroll, F., Chew, E. (2023). Enhancing Visual Encodings of Uncertainty Through Aesthetic Depictions in Line Graph Visualisations. In: Mori, H., Asahi, Y. (eds) Human Interface and the Management of Information. HCII 2023. Lecture Notes in Computer Science, vol 14015. Springer, Cham. https://doi.org/10.1007/978-3-031-35132-7_20

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  • DOI: https://doi.org/10.1007/978-3-031-35132-7_20

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