Spatial Analysis of Individual Activity Locations and Concentration Levels in Calgary
Bibliographic record
Abstract
Personal travel information available from household surveys helps urban transportation planners to better capture the real world situation at a much-detailed level. Individuals have a set of periodic activities and the resources that satisfy these activities are distributed across space and time. Individuals must distribute their limited time among these activities, and transportation is used to trade time for space changing. In this study, ArcGIS is used to analyze the time and location choices of several Calgarian activities, such as work, home, education and others. In particular, GIS is used to show activity locations and concentration levels over different time of the day, and visualize individual trip chains. Study results clearly show that the location choices of the work, home, and education are consistent with underlying land use patterns of desired zones. And time of the day analysis also indicates that work-shift results in significant changes in activity concentrations over different land use zones due to their own natures. Mapping of individual trip chains also provides evidences that daily travel patterns are much more complicated than those having been modeled in the traditional aggregated models.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".