Bibliographic record
Abstract
Geospatial datasets are complex, difficult to understand, and hard to visualize. Although web maps have provided visualization of geospatial datasets using computer-generated 2D maps, such visualizations significantly deal with misinterpretations of areas and distances due to the mapping distortions. Digital Earth (DE) is an alternative solution for overcoming 2D map distortions and integrating various geospatial datasets. However, virtual 3D models of the Earth still suffer from common issues caused by projecting 3D scenes to 2D screens, such as losing one spatial dimension and inaccessibility for direct manual interaction. By providing tactile exploration and physical interaction, physical models facilitate cognition and understanding of data. This thesis argues that using 3D physical models supporting visualizations of geospatial datasets at different scales and resolutions can address challenges related to understanding and analyzing such datasets. To shed light on this hypothesis, we introduce a framework, GEOPHYS, to make tangible multi-resolution/multi-scale representations of geospatial data. Furthermore, GEOPHYS introduces a comprehensive, accurate, and repeatable physical rendering method for various applications and visualization scenarios, using accessible digital fabrication tools. A pipeline forms the core methodology of this thesis, which consists of data transformation, digital design, digital fabrication, evaluation, and the introduction of the final framework. In this thesis, the design and fabrication stages work hand-in-hand to transform the conceptual form into a visual representation, investigate the model for its manufacturability, and bring it into the physical world. Evaluations in the context of this thesis are technology-centred and human-centred. This thesis contributes to geovisualization and physicalization by introducing a method to visualize multi-resolution geospatial datasets in Large Areas, a physical rendering approach for creating landscape models, and a tactile representation of the RADAR imagery to facilitate the sea-ice travel for Inuit. We also provide an in-depth review of various methods by which physicalizations can be physically rendered. The results of our studies prove that models made using GEOPHYS are beneficial learning tools capable of creating interest and engagement to explore geospatial concepts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".