Human Performance and Perception of Uncertainty Visualizations in Geospatial Applications: A Scoping Review

Area of Science:

• Geographic Information Science
• Human-Computer Interaction
• Data Visualization

Background:

• Geospatial data frequently contains uncertainty from measurement, spatial, or temporal constraints.
• A significant knowledge gap exists regarding the impact of geospatial uncertainty visualization techniques on human factors.

Purpose of the Study:

• To comprehensively review and synthesize existing literature on visual representations of uncertainty in geospatial data applications.
• To identify the range of techniques used and their relationship with human performance and perception outcomes.

Main Methods:

• A systematic literature review was conducted, including 40 eligible articles.
• Articles evaluated methods for representing uncertainty in diverse geospatial data (land, ocean, weather, climate, positioning).
• Visualizations included multivariate/univariate maps using color, shape, texture, symbols, and text.

Main Results:

• No single visualization method proved definitively superior; effects varied.
• Trends emerged in user understanding, preferences, confidence, decision-making, and response times.
• Key findings include impacts of color/shape categorizations, perception-performance mismatches, and novice vs. expert differences.

Conclusions:

• Effective uncertainty visualization design is influenced by contextual factors and user characteristics (task, type, desired outcomes).
• Future research should focus on standardized reporting, 3D/interactive visualizations, and real-world evaluations.