Exploring Evolution Effects of Neighborhood Typologies on Cycling
Bibliographic record
Abstract
This paper presents a methodology to investigate the temporal evolution of cycling and the role of built environment. More specifically, the authors aim at exploring how commuting cycling modal share has evolved across neighborhood types between 1998 and 2008 in Montreal. The authors use two main approaches to explore the effects of neighborhood characteristics on cycling and its growth: (i) a binary logit model; and (ii) a simultaneous equation model. One of the key findings is the general increase in the likelihood to cycle over time in the study region. Specifically, urban and urban-suburb areas have been experiencing the greatest growth with increases of 2.5% and 1.6% from 1998 to 2008, respectively. The built environment of the study region has not evolved significantly during the 10-year study period. As a result, The authors conclude that the observed change in cycling activity is explained by attitudinal and cultural changes in the population over time. However, the choice of neighborhood type significantly affects cycling. In fact, living in downtown, inner suburb, and outer suburb in comparison to living in urban-suburb decreases the likelihood to cycle to work by 58, 20, and 11 percent, respectively. Promotional efforts by local municipalities and agencies, such as improving safety, campaigns, etc., appear to have positively influenced cycling activity. In terms of individual-level socio-demographics, the authors found that gender, age and employment status influenced cycling levels. As expected, an increase in the distance to nearest cycling facilities from residence reduces the probability of individuals choosing to cycle to work.
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 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 | 0.011 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".