Evaluating web-based static, animated and interactive maps for injury prevention
Bibliographic record
Abstract
Public health planning can benefit from visual exploration and analysis of geospatial data. Maps and geovisualization tools must be developed with the user-group in mind. User-needs assessment and usability testing are crucial elements in the iterative process of map design and implementation. This study presents the results of a usability test of static, animated and interactive maps of injury rates and socio-demographic determinants of injury by a sample of potential end-users in Toronto, Canada. The results of the user-testing suggest that different map types are useful for different purposes and for satisfying the varying skill level of the individual user. The static maps were deemed to be easy to use and versatile, while the animated maps could be made more useful if animation controls were provided. The split-screen concept of the interactive maps was highlighted as particularly effective for map comparison. Overall, interactive maps were identified as the preferred map type for comparing patterns of injury and related socio-demographic risk factors. Information collected from the user-tests is being used to expand and refine the injury web maps for Toronto, and could inform other public health-related geo-visualization projects.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".