Implicit Error, Uncertainty and Confidence in Visualization: an Archaeological Case Study

Georgia Panagiotidou, Ralf Vandam, Jeroen Poblome, Andrew Vande Moere

View presentation:2021-10-29T13:15:00ZGMT-0600Change your timezone on the schedule page
Exemplar figure, described by caption below
SiteVis is a visualization tool for archaeological site data. Given that archaeological site data included implicit errors, SiteVis tried to account for them by using separate data views, by encoding the data collection methods as well as showing the sites in both time and era scales. We used the SiteVis visualization as a probe, and documented both qualitatively and quantitatively the insight-making process of 14 expert and novice archaeologists. We then analysed the origins of uncertainty in their process as well as documented how these archaeologists tried to cope with it.
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data uncertainty, data visualization, implicit error, qualitative study, digital humanities, design study, archaeology


While we know that the visualization of quantifiable uncertainty impacts the confidence in insights, little is known about whether the same is true for uncertainty that originates from aspects so inherent to the data that they can only be accounted for qualitatively. Being embedded within an archaeological project, we realized how assessing such qualitative uncertainty is crucial in gaining a holistic and accurate understanding of regional spatio-temporal patterns of human settlements over millennia. We therefore investigated the impact of visualizing qualitative implicit errors on the sense-making process via a probe that deliberately represented three distinct implicit errors, i.e. differing collection methods, subjectivity of data interpretations and assumptions on temporal continuity. By analyzing the interactions of 14 archaeologists with different levels of domain expertise, we discovered that novices became more actively aware of typically overlooked data issues and domain experts became more confident of the visualization itself. We observed how participants quoted social factors to alleviate some uncertainty, while in order to minimize it they requested additional contextual breadth or depth of the data. While our visualization did not alleviate all uncertainty, we recognized how it sparked reflective meta-insights regarding methodological directions of the data. We believe our findings inform future visualizations on how to handle the complexity of implicit errors for a range of user typologies and for highly data-critical application domains such as the digital humanities.