Exploring Data Analysts' Uncertainty Reasoning Strategies for Effective Uncertainty Visualization Design

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dc.contributor.authorKinkeldey, Christophen_US
dc.contributor.authorReljan-Delaney, Mirelaen_US
dc.contributor.authorPanagiotidou, Georgiaen_US
dc.contributor.authorDykes, Jasonen_US
dc.contributor.editorHunter, Daviden_US
dc.contributor.editorSlingsby, Aidanen_US
dc.date.accessioned2024-09-09T05:45:24Z
dc.date.available2024-09-09T05:45:24Z
dc.date.issued2024
dc.description.abstractDespite its proven positive effects, visual data analysis rarely includes information about data uncertainty. Building on past research, we explore the hypothesis that effective uncertainty visualizations must support reasoning strategies that enable data analysts to utilize uncertainty information ('uncertainty reasoning strategies', UnReSt). Through this work, we seek to gain insights into the reasoning strategies employed by domain experts for incorporating uncertainty into their visual analysis. Additionally, we aim to explore effective ways of designing uncertainty visualizations that support these strategies. For this purpose, we developed a methodology involving online meetings that included think-aloud protocols and interviews. We applied the methodology in a user study with five domain experts from the field of epidemiology. Our findings identify, describe, and discuss the UnReSt employed by our participants, allowing for initial recommendations as a foundation for future design guidelines: uncertainty visualization should (i) visually support data analysts in adapting or developing UnReSt, (ii) not facilitate ignoring the uncertainty, (iii) aid in the definition of acceptable levels of uncertainty, and (iv) not hide uncertain parts of the data by default. We reflect on the methodology we developed and applied in our study, addressing challenges related to the recruiting process, the examination of an existing tool along with familiar tasks and data, the design of bespoke prototypes in collaboration with visualization experts, and the timing of the meetings. We encourage visualization researchers to adapt this methodology to gain deeper insights into the UnReSt of data analysts and how uncertainty visualization can effectively support them. The supplemental materials can be found at https://osf.io/s2nwf/.en_US
dc.description.sectionheadersGeographic Visualisation
dc.description.seriesinformationComputer Graphics and Visual Computing (CGVC)
dc.identifier.doi10.2312/cgvc.20241232
dc.identifier.isbn978-3-03868-249-3
dc.identifier.pages9 pages
dc.identifier.urihttps://doi.org/10.2312/cgvc.20241232
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/cgvc20241232
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing → Empirical studies in visualization; Visualization design and evaluation methods
dc.subjectHuman centered computing → Empirical studies in visualization
dc.subjectVisualization design and evaluation methods
dc.titleExploring Data Analysts' Uncertainty Reasoning Strategies for Effective Uncertainty Visualization Designen_US
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