Understanding Spatiotemporal Patterns: Visual Ordering of Space and Time
Bibliographic record
Abstract
Deriving patterns and relations from large multivariate and multi-temporal data sets to acquire knowledge about real-world processes is not a trivial task. To understand the content of such data sets, current analytical tools do offer interesting solutions, but an approach combining the above different types of data is lacking. This article introduces a visual integrated solution that allows the user to explore and analyse the data at hand. The approach introduced consists of a dynamically linked multi-view environment that offers different interactive visual representations to “look at and play with” the data. For the time component, the temporal ordered space matrix (TOSM), which schematizes the temporal nature of the data set, is introduced. The rows of the matrix represent time and the columns the geographic units. A preliminary usability test has been conducted to see how the multi-view approach in general performs when considering specific tasks oriented toward the understanding of spatiotemporal patterns. The TOSM functions well for naturally ordered (linear) phenomena such as rivers and coastlines. The article also discusses the use of the TOSM for non-linear-ordered phenomena such as administrative units. The method is based on directional ordering and is compared with other ordering approaches, such as space-filling curves, the travelling salesman problem, and plane-sweeping algorithms.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 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".