Elevating Streets in Urban Topographic Maps Improves the Speed of Map-Reading
Bibliographic record
Abstract
A fast and accurate reading of maps is relevant for many human orientation, navigation, and wayfinding tasks. Autostereoscopic displays can visualize depth illusions in True-3D and allow map-makers to use the depth axis as an additional cartographic design parameter. This new design parameter has hardly been considered in empirical investigations in cartography. A previous study provided initial evidence that distribution of map information over different depth layers could bring advantages for the speed of map-reading. These results require further investigation. Research from cognitive psychology has demonstrated that the cognitive processing of map information could be enhanced by using linear features of the map graphics that subdivide the map into different sections (“spatial chunks”). These spatial chunks provide map readers an additional orientation pattern that supports information processing. Spatial judgements, for instance, can be made faster when spatial chunks are present in a map. It still remains an open question whether the True-3D accentuation of chunking features, such as dominant street representations in maps, can lead to additional advantages for an efficient transfer of map information. The aim of the present study is to investigate the effects of True-3D–accentuated streets for map-reading efficiency. To achieve this, an empirical study of the performances of 66 participants was conducted. In this study, a streets-in-True-3D condition (3D-Streets) was compared to a streets-in-2D condition (2D-Streets). Following previous research, map-reading efficiency was measured as both the mean percentage of correct counting tasks (hit rate) and the mean response time to solve a counting task correctly (speed). It was shown that the 3D-Streets condition significantly improved the speed but not the hit rate.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".