An Evaluation of a Visual Analytics Prototype for Calendar-Related Spatiotemporal Periodicity Detection and Analysis
Bibliographic record
Abstract
Whether it is sunrise, the weekend, or Christmas, some form of temporal structure or periodic pattern governs our daily activities. Understanding them is essential to making sense of human activity, because they frame normality and allow us to identify abnormalities. However, cultural heterogeneity and scale greatly complicate our ability to uncover and understand human activity at a given time within a region. Current research in the field of visual analytics and geography provide methods of addressing spatiotemporal periodicity, but they fall short in providing access to multiple spatial and temporal scales via a relevant calendar. In response to these shortcomings, we developed PerSE (periodicity in spatiotemporal events), a coordinated-view Web application designed to aid users in the detection and analysis of calendar-related periodicity in spatiotemporal event data sets. Given the complexity of such a visualization tool, this paper focuses on the usability and learnability of PerSE. We evaluated the tool through a 20-participant study that consisted of training, a multiple-choice test, and the System Usability Scale. Our analysis of the results shows that the complex combination of visual tools and multi-scale, multi-calendar capability used within PerSE is effective, but still in need of usability improvements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".