Human Performance and Perception of Uncertainty Visualizations in Geospatial Applications: A Scoping Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Geospatial data are often uncertain due to measurement, spatial, or temporal limitations. A knowledge gap exists about how geospatial uncertainty visualization techniques influence human factors measures. This comprehensive review synthesized the current literature on visual representations of uncertainty in geospatial data applications, identifying the breadth of techniques and the relationships between strategies and human performance and perception outcomes. Eligible articles described and evaluated at least one method for representing uncertainty in geographical data with participants, including land, ocean, weather, climate, and positioning data. Forty articles were included. Uncertainty was visualized using multivariate and univariate maps through colours, shapes, boundary regions, textures, symbols, grid noise, and text. There were varying effects, and no definitive superior method was identified. The predominant user focus was on novices. Trends were observed in supporting users understand uncertainty, user preferences, confidence, decision-making performance, and response times for different techniques and application contexts. The findings highlight the impacts of different categorizations within colour and shape techniques, heterogeneity in perception and performance evaluation, performance and perception mismatch, and differences and similarities between novices and experts. Contextual factors and user characteristics, including understanding the decision-maker's tasks, user type, and desired outcomes for decision-support appear to be important factors influencing the design of effective uncertainty visualizations. Future research on geospatial applications of uncertainty visualizations can expand on the observed trends with consistent and standardized measurement and reporting, further explore human performance and perception impacts with 3-dimensional and interactive uncertainty visualizations, and perform real-world evaluations within various contexts.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it