Household Activity Spaces and Neighborhood Typologies: \nA spatial and temporal comparative analysis of the effects of clustered land use indicators on the travel behaviour of households in three Quebec cities
Bibliographic record
Abstract
As the number of urban dwellers worldwide increases and as governments struggle to meet the pressure a concurrent rise in personal travel places on cities, the importance in understanding travel demand becomes vital. Traditional approaches to understanding the interaction between built form and travel have focused on individual indicators such as population density or land use mix, while measuring outputs such as vehicle kilometers travelled or mode share. Activity spaces, in contrast, are a relatively new and underexplored measure of travel demand which looks at the distribution of trips throughout space. These activity spaces are the focus of the following manuscript. \nA variety of land use and accessibility measures are described and calculated, the goal being to discern their effect on activity spaces in the Montreal, Sherbrooke and Quebec metropolitan regions. Clustering is used to find representative combinations of urban form indicator values, or neighborhoods, after which statistical analysis is employed to quantify the relationships between these clusters and the travel patterns of the households living in them. The primary data sources for mobility are origin-destination surveys conducted 5 years apart in each city; three such surveys were used for Montreal, two for Quebec and one for Sherbrooke. \nResults indicate that neighborhood type has a significant effect on the dispersion of travel, even after controlling for household size and type, number of trips and other demographic characteristics. Another key finding is that average activity space size is correlated with overall city size. Finally, the geometry of trip distribution is related to propensity for using specific transportation modes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.003 | 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".