Modeling Dynamic Spatial-Temporal Cluster Relationships
Bibliographic record
Abstract
Spatial-temporal data refers to potentially massive amounts of data gathered across both space and time. Spatial-temporal data analysis helps uncover the value that this type of data holds to domains such as transportation operations, traffic management, service demand, and trip planning. Specifically, cluster analysis groups data into sets known as clusters such that elements inside a cluster are more similar to each other than elements in other clusters. Cluster analysis has been successfully applied in domains such as transportation, ecology, medicine, and astronomy. However, current cluster analysis techniques limit themselves to static cluster analysis, thereby missing the identification of interesting insights and patterns related to the evolution of clusters over time. In this paper, we clarify the concept of dynamic clusters and support new forms of cluster analyses by introducing, describing, and formalizing cluster relationships that represent important events, such as split or merge, that a cluster may go through from its start to its end. These relationships provide a foundation for investigating cluster evolution and providing novel insights for better operational and business decision making.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".