Changing your perspective: the impact of different visualisation methods on seismic hazard maps
Bibliographic record
Abstract
A number of widely used colour palettes applied to display critical scientific results not only distort data but are also inaccessible to a proportion of the population. An issue with the rainbow palette (and variants such as “jet”) is that the gradients between the colours are not even. The impact of an uneven colour gradient is that certain colours are highlighted over others, distorting the underlying data. Furthermore, an uneven colour palette like rainbow may be inaccessible for people with colour vision deficiencies or colour blindness. When communicating scientific results, data should always be presented without distortion and be universally accessible. This is particularly important when communicating public-facing and time-critical information such as hazards. Here, we show the impact of changing the visualisation profile of seismic hazard maps on the perception of risk, as well as qualifying the public accessibility of this information. Using Canadian seismic hazard as an example, our results reveal that an uneven colour map applied to seismic hazard data can exaggerate lower hazard values and reduce the perception of extremely high hazard values. Applying an even colour gradient to our sample data not only allows this essential public resource to be universally accessible but was found to lead to the greatest visual change in regions with the most populated cities. The choice of colour map and subsequent data interpretations also holds relevance for considerations such as insurance. We highlight potential next steps to promote inclusiveness in data visualisation and welcome discussion on science communication best practices.
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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 | 0.024 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".