Point2Hex: Higher-order Mobility Flow Data and Resources
Bibliographic record
Abstract
Research on trajectory data mining relies on appropriate datasets, including Gps-based geolocations, check-in data to points of interest (Pois), and synthetic datasets. Even though some data are accessible, the majority of mobility datasets are typically discovered through ad-hoc searches and lack comprehensive documentation of their generation process or source to reproduce curated or customized versions of them. At the same time, there has been a growing interest in a new type of mobility data, describing trajectories as sequences of higher-order geometric elements like hexagons that offer several benefits: (i) reduced sparsity and analysis at different granularity levels, (ii) compatibility with popular machine learning architectures, (iii) improved generalization and reduced overfitting, and (iv) efficient visualization. To this end, we present Point2Hex, a method and tool for generating higher-order mobility flow datasets from raw trajectory data. We used Point2Hex to create higherorder versions of seven popular mobility datasets typically employed in trajectory-related technical problems and downstream tasks, such as trajectory prediction, classification, clustering, imputation, and anomaly detection, to name a few. To promote reuse and encourage reproducibility, we provide the source code and documentation of Point2Hex, as well as the generated higher-order mobility flow datasets in publicly accessible repositories.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".