From Data-Centered to Activity-Centered Geospatial Visualizations
Bibliographic record
Abstract
As geospatial visualizations grow in popularity, their role in human activities is also evolving. While maps have been used to support higher-level cognitive activities such as decision-making, sense making, and knowledge discovery, traditionally their use in such activities has been partial. Nowadays they are being used at various stages of such activities. This trend is simultaneously being accompanied with another shift: a movement from the design and use of data-centered geospatial visualizations to activity-centered visualizations. Data-centered visualizations are primarily focused on representation of data from data layers; activity-centered visualizations, not only represent the data layers, but also focus on users' needs and real-world activities—such as storytelling and comparing data layers with other information. Examples of this shift are being seen in some mashup techniques that deviate from standard data-driven visualization designs. Beyond the discussion of the needed shift, this chapter presents ideas for designing human-activity-centered geospatial visualizations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".