Exploring the complexity of daily activity schedules using spatial statistics and machine learning methods
Bibliographic record
Abstract
Daily activity complexity—the diversity and sequence of activities individuals perform—is crucial for understanding travel behavior. However, the non-linear spatial interactions of socio-demographic and land use factors influencing this complexity remain less explored. This study integrates a complexity indicator (encompassing entropy and activity transitions), spatial clustering (Local Indicators of Spatial Association), and random forest modeling to address this gap. Using the 2018 Okanagan Travel Survey data, we identify distinct spatial clusters: High-High (areas where individuals and their neighbors both exhibit high complexity), High-Low, Low-Low, and Low-High complexity. Our results highlight significant non-linear associations between daily activity complexity and factors such as proximity to central business districts, amenities, transit accessibility, land use diversity, age, and income. This combined approach captures intricate spatial interactions, providing novel insights into how activity complexity varies across different geographic and socio-demographic contexts, emphasizing the importance of considering non-linear effects in travel behavior analysis.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".