Human Performance and Perception of Uncertainty Visualizations in Geospatial Applications: A Scoping Review
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".