From Spatial-Temporal Cluster Relationships to Lifecycles: Framework and Mobility Applications
Bibliographic record
Abstract
Spatial-temporal data analysis relates to the application of data analysis techniques to data where space and time are both relevant. Usually, the results of these techniques are used to classify or predict a phenomenon, but little attention is given to the explanation of how such phenomenon happened. For example, one may predict that a sporting event will happen at a particular location and date, but little is known about the indications that such event will happen (e.g. a higher number of vehicles on certain streets, parking lots becoming full, large number of vehicles going to supermarkets, or a sudden drop in pedestrian and vehicular traffic movement when the match starts). In this paper, we report on our ongoing work on using spatial-temporal cluster relationships to identify cluster lifecycles. These lifecycles are a series of stages through which a cluster passes during its lifetime, much like a human lifecycle of birth, growth, reproduction, and death. We focus on the identification of cluster lifecycle stages, namely start, expand, shrink, and end, and on their use to predict spatial-temporal phenomena, such as traffic congestion, human events, or animal movement.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".