Spatialization Design: Comparing Points and Landscapes
Bibliographic record
Abstract
Spatializations represent non-spatial data using a spatial layout similar to a map. We present an experiment comparing different visual representations of spatialized data, to determine which representations are best for a non-trivial search and point estimation task. Primarily, we compare point-based displays to 2D and 3D information landscapes. We also compare a colour (hue) scale to a grey (lightness) scale. For the task we studied, point-based spatializations were far superior to landscapes, and 2D landscapes were superior to 3D landscapes. Little or no benefit was found for redundantly encoding data using colour or greyscale combined with landscape height. 3D landscapes with no colour scale (height-only) were particularly slow and inaccurate. A colour scale was found to be better than a greyscale for all display types, but a greyscale was helpful compared to height-only. These results suggest that point-based spatializations should be chosen over landscape representations, at least for tasks involving only point data itself rather than derived information about the data space.
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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 | 0.002 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".