Does travel behavior matter in defining urban form? A quantitative analysis characterizing distinct areas within a region
Bibliographic record
Abstract
Research that attempts to characterize urban form is confronted with two key issues: criticism of the use of aggregate units of analysis, such as census tracts, and a general lack of consideration of variables related to elements other than the built environment, such as residents’ behavior. This methodological study explores the impact of travel behavior variables in the quantitative characterization of urban form at the census tract level for the Montreal region. Two separate factor-cluster analyses are performed: the first includes built-environment variables commonly used to typify areas within a region, and a second includes additional travel behavior variables. The results of both models are compared to satellite images to determine which analysis more accurately represents the reality on the ground. The results provide empirical evidence that travel behavior variables, in addition to built form, provide a more accurate representation of urban form at the census tract level. These variables refine the model output by moderating the effect of features that generally led to misleading results. This effect is particularly evident in areas represented by large census tracts. These results suggest that considering both built environment and behavioral characteristics in an analysis of urban form yields more precise results at the (aggregate) census tract level. The findings from this study could be helpful for engineers and planners when conducting property value studies, urban investment analysis, and policy intervention prioritization and when expanding the well-known land use classification of urban and rural categories.
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.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".