The Impact of Uncertainty Visualization on Trust in Thematic Maps

要旨

Thematic maps are widely used to communicate spatial patterns to non-expert audiences. Although uncertainty is inherent in thematic map data, it is rarely visualized, raising questions about how its inclusion affects trust. Prior work offers mixed perspectives: some argue that uncertainty fosters trust through transparency, while others suggest it may reduce trust by introducing confusion. Yet few empirical studies explicitly measure trust in thematic maps. We conducted a between-subjects experiment (N = 161) to evaluate how visualizing uncertainty at varying levels (low, medium, high) influences trust. We find that uncertainty visualization generally reduces trust, with greater reductions observed as uncertainty levels increase. However, maps dominated by low uncertainty do not significantly differ in trust from those with no uncertainty. Moreover, while uncertainty visualization tends to make readers question the accuracy of the data, it appears to have a weaker influence on perceptions of the mapmaker’s integrity.

著者
Varun Srivastava
Arizona State University, Tempe, Arizona, United States
Fan Lei
University of Waterloo, Waterloo, Ontario, Canada
Alan MacEachren
Pennsylvania State University, University Park, Pennsylvania, United States
Ross Maciejewski
Arizona State University, Tempe, Arizona, United States

会議: CHI 2026

ACM CHI Conference on Human Factors in Computing Systems

セッション: Perception & Cognition in Data Visualization

P1 - Room 123
6 件の発表
2026-04-14 18:00:00
2026-04-14 19:30:00