Applications of Interactive Topographic Maps: Tangibility with Improved Spatial Awareness and Readability
Bibliographic record
Abstract
Traditional flat topographic maps are difficult to understand due to the distortion and compromise of the 3-dimensional (3D) spatial representation when it is folded into lower-dimension media (e.g. 2D). During the process, the x-y coordinate of a location can be captured but its physical elevation must be transformed using some visualization techniques, resulting in noticeable cognitive effort in comprehending the original geometric and geographic properties of the original terrain. In this manuscript-based dissertation, I present a collection of my past publications that aim to increase the readability of topographic maps by restoring the original spatiality of the terrain - including the elevations - with a physical map representation and then superimpose additional data visualization on top of it. In this way, the entire terrain topology is kept in a scaled physical representation, allowing users to view it with natural human perceptions. Additionally, user gestures can be tracked in real-time as a sketch-based input to allow novel dynamic interaction of the map interface and data manipulation of the spatial information. Through the chapters, I present the aforementioned concept, named interactive topographic interface, along with a few applications of it in different academic and industrial environments. I also report the design and results of a user study that compares the interface with traditional flat topographic maps. In the long-term, I hope that research mentioned in this dissertation inspires future interactive physical cartography to not only improve map comprehension but also facilitate better spatial and situational awareness over the map interface, resulting in an evolved map usefulness.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".