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Article published In: Information Visualization
Edited by Isabel Meirelles, Marian Dörk and Yanni Loukissas
[Information Design Journal 27:1] 2022
► pp. 52–63

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Communicating qualitative uncertainty in data visualization

Two cases from within the digital humanities

ORCiD logo with link | UCL Interaction Centre
 | KU Leuven
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Available under the Creative Commons Attribution-NonCommercial (CC BY-NC) 4.0 license.

For any use beyond this license, please contact the publisher at rights@benjamins.nl.

Published online: 7 November 2022
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https://doi.org/10.1075/idj.22014.pan
Abstract
Qualitative uncertainty refers to the implicit and underlying issues that are imbued in data, such as the circumstances of its collection, its storage or even biases and assumptions made by its authors. Although such uncertainty can jeopardize the validity of the data analysis, it is often overlooked in visualizations, due to it being indirect and non-quantifiable. In this paper we present two case studies within the digital humanities in which we examined how to integrate uncertainty in our visualization designs. Using these cases as a starting point we propose four considerations for data visualization research in relation to indirect, qualitative uncertainty: (1) we suggest that uncertainty in visualization should be examined within its socio-technological context, (2) we propose the use of interaction design patterns to design for it, (3) we argue for more attention to be paid to the data generation process in the humanities, and (4) we call for the further development of participatory activities specifically catered for understanding qualitative uncertainties. While our findings are grounded in the humanities, we believe that these considerations can be beneficial for other settings where indirect uncertainty plays an equally prevalent role.
Article outline
  • 1.Introduction
  • 2.Archaeological settlement data
  • 3.Synthesizing interdisciplinary socio-ecological data
  • 4.Towards communicating qualitative uncertainty in data visualization
  • 5.Conclusion
  • References
References (37)
References
Boukhelifa, N., Perrin, M. E., Huron, S., & Eagan, J. (2017). How Data Workers Cope with Uncertainty: A Task Characterisation Study. Proceedings of the 2017 ACM Conference on Human Factors in Computing Systems (CHI), 2017–May, 3645–3656. Google Scholar logo with link to Google Scholar
Boyd Davis, S., Vane, O., & Kräutli, F. (2021). Can I believe what I see? Data visualization and trust in the humanities. Interdisciplinary Science Reviews, 46(4), 522–546. Google Scholar logo with link to Google Scholar
Braun, S. (2018). Critically Engaging with Data Visualization through an Information Literacy Framework. DHQ: Digital Humanities Quarterly, 12(4).Google Scholar logo with link to Google Scholar
Correll, M. (2019). Ethical Dimensions of Visualization Research. Proceedings of the 2019 ACM Conference on Human Factors in Computing Systems (CHI), 1–13. Google Scholar logo with link to Google Scholar
D'Ignazio, C., & Klein, L. (2020). The Power Chapter. In Data Feminism (pp. 1–39). Retrieved from [URL].
D’Ignazio, C. (2017). Creative data literacy: Bridging the Gap Between the Data-haves and Data-have Nots. Information Design Journal, 23(1), 6–18.Google Scholar logo with link to Google Scholar
Deitrick, S. (2012). Evaluating Implicit Visualization of Uncertainty for Public Policy Decision Support. Proceedings AutoCarto 2012, (Abbasi 2005), 161.Google Scholar logo with link to Google Scholar
Dimara, E., & Perin, C. (2020). What is Interaction for Data Visualization? IEEE Transactions on Visualization and Computer Graphics, 26(1), 119–129. Google Scholar logo with link to Google Scholar
Dörk, M., Feng, P., Collins, C., & Carpendale, S. (2013). Critical InfoVis. CHI ’13 Extended Abstracts on Human Factors in Computing Systems on – CHI EA ’13, 2189. Google Scholar logo with link to Google Scholar
Drucker, J. (2011). Humanities Approaches to Graphical Display. DHQ: Digital Humanities Quarterly, 5(1).Google Scholar logo with link to Google Scholar
(2015). Graphical Approaches to the Digital Humanities. In A New Companion to Digital Humanities. Google Scholar logo with link to Google Scholar
Franke, M., Barczok, R., Koch, S., & Weltecke, D. (2019). Confidence as First-class Attribute in Digital Humanities Data. Proceedings of the Workshop on Visualization for the Digital Humanities (VIS4DH).Google Scholar logo with link to Google Scholar
Glinka, K., Pietsch, C., Dörk, M., & Marian Dörk. (2017). Past Visions and Reconciling Views: Visualizing Time, Texture and Themes in Cultural Collections. DHQ: Digital Humanities Quarterly, 11(2), 1–19.Google Scholar logo with link to Google Scholar
Greis, M., Avci, E., Schmidt, A., & Machulla, T. (2017). Increasing Users’ Confidence in Uncertain Data by Aggregating Data from Multiple Sources. Proceedings of the 2017 ACM Conference on Human Factors in Computing Systems (CHI), 2017– May, 828–840. Google Scholar logo with link to Google Scholar
Hall, K. W., Bradley, A. J., Hinrichs, U., Huron, S., Wood, J., Collins, C., & Carpendale, S. (2019). Design by Immersion: A Transdisciplinary Approach to Problem-Driven Visualizations. IEEE Transactions on Visualization and Computer Graphics, 26(1), 109–118. Google Scholar logo with link to Google Scholar
Kale, A., Kay, M., & Hullman, J. (2019). Decision-Making Under Uncertainty in Research Synthesis: Designing for the Garden of Forking Paths. Proceedings of the 2019 ACM Conference on Human Factors in Computing Systems (CHI), 1–14. Google Scholar logo with link to Google Scholar
Kale, A., Nguyen, F., Kay, M., & Hullman, J. (2019). Hypothetical Outcome Plots Help Untrained Observers Judge Trends in Ambiguous Data. IEEE Transactions on Visualization and Computer Graphics, 25(1), 892–902. Google Scholar logo with link to Google Scholar
Kennedy, H., Hill, R. L., Aiello, G., & Allen, W. (2016). The Work that Visualisation Conventions Do. Information Communication and Society, 19(6), 715–735. Google Scholar logo with link to Google Scholar
Kerzner, E., Goodwin, S., Dykes, J., Jones, S., & Meyer, M. (2018). A Framework for Creative VisualizationOpportunities Workshops. IEEE Transactions on Visualization and Computer Graphics, 25(1), 748–758. Google Scholar logo with link to Google Scholar
Kitchin, R. (2021). Data Lives: How Data Are Made and Shape Our World. Policy Press.Google Scholar logo with link to Google Scholar
Lee, C., Yang, T., Inchoco, G., Jones, G. M., & Satyanarayan, A. (2021). Viral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science Online. Proceedings of the 2021 ACM Conference on Human Factors in Computing Systems (CHI). Google Scholar logo with link to Google Scholar
Loukissas, Y. (2019). All Data are Local: Thinking Critically in a Data-Driven Society. MIT Press. Google Scholar logo with link to Google Scholar
Manovich, L. (2011). What is Visualisation? Visual Studies, 26(1), 36–49. Google Scholar logo with link to Google Scholar
Marai, G. E. (2018). Activity-Centered Domain Characterization for Problem-Driven Scientific Visualization. IEEE Transactions on Visualization and Computer Graphics, 24(1), 913–922. Google Scholar logo with link to Google Scholar
McAllister, J. W. (2018). Scientists’ Reuse of Old Empirical Data: Epistemological Aspects. Philosophy of Science, 85(5), 755–766. Google Scholar logo with link to Google Scholar
McCurdy, N., Gerdes, J., & Meyer, M. (2019). A Framework for Externalizing Implicit Error Using Visualization. IEEE Transactions on Visualization and Computer Graphics, 25(1), 925–935. Google Scholar logo with link to Google Scholar
Meyer, M., & Dykes, J. (2019). Criteria for Rigor in Visualization Design Study. IEEE Transactions on Visualization and Computer Graphics, 26(1), 87–97. Google Scholar logo with link to Google Scholar
Moretti, F. (2005). Graphs, Maps, Trees: Abstract Models for a Literary History.Google Scholar logo with link to Google Scholar
Padilla, L., Kay, M., & Hullman, J. (2020). Uncertainty Visualization. Handbook of Computational Statistics and Data Science. Google Scholar logo with link to Google Scholar
Panagiotidou, G., Poblome, J., Aerts, J., & Vande Moere, A. (2022). Designing a Data Visualisation for Interdisciplinary Scientists: How to Transparently Convey Data Frictions? Computer Supported Cooperative Work (CSCW). Google Scholar logo with link to Google Scholar
Panagiotidou, G., Vandam, R., Poblome, J., & Vande Moere, A. (2021). Implicit Error, Uncertainty and Confidence in Visualization: an Archaeological Case Study. IEEE Transactions on Visualization and Computer Graphics, 1–12. Google Scholar logo with link to Google Scholar
Sacha, D., Senaratne, H., Kwon, B. C., Ellis, G., & Keim, D. A. (2016). The Role of Uncertainty, Awareness, and Trust in Visual Analytics. IEEE Transactions on Visualization and Computer Graphics, 22(1), 240–249. Google Scholar logo with link to Google Scholar
Stoffel, F., Jentner, W., Behrisch, M., Fuchs, J., & Keim, D. (2017). Interactive Ambiguity Resolution of Named Entities in Fictional Literature. Computer Graphics Forum, 36(3), 189–200. Google Scholar logo with link to Google Scholar
Tak, S., Toet, A., & Van Erp, J. (2014). The Perception of Visual Uncertainty Representation by Non-experts. IEEE Transactions on Visualization and Computer Graphics, 20(6), 935–943. Google Scholar logo with link to Google Scholar
Van Der Bles, A. M., Van Der Linden, S., Freeman, A. L. J., Mitchell, J., Galvao, A. B., Zaval, L., & Spiegelhalter, D. J. (2019). Communicating Uncertainty about Facts, Numbers and Science. Royal Society Open Science, 6(5). Google Scholar logo with link to Google Scholar
Windhager, F., Salisu, S., & Mayr, E. (2019). Exhibiting Uncertainty: Visualizing Data Quality Indicators for Cultural Collections. Informatics, 6(3). Google Scholar logo with link to Google Scholar
Zimmerman, A. (2007). Not by metadata alone: The Use of Diverse Forms of Knowledge to Locate Data for Reuse. International Journal on Digital Libraries, 7(1–2), 5–16. Google Scholar logo with link to Google Scholar
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