Exploring the complexity of daily activity schedules using spatial statistics and machine learning methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it