The Impact of Climate on Utilitarian Bicycling: Results of a Canadian Study
Bibliographic record
Abstract
P-807 Background: There is increasing interest to promote utilitarian bicycling (bicycling to school, work, or for errands) for its multiple benefits to environmental health, including reduced air and noise pollution. As the North American population cycles less than a fifth as much as the population in many European countries, there is a substantial potential to improve cycling rates. This research constructed environmental exposure variables to investigate the impact of regional climatic conditions on utilitarian bicycling patterns in Canada, a country with great variety in terms of climate, in order to inform evidence-based transportation and environmental policy. Methods: This cross-sectional study linked the 2003 Canadian Community Health Survey (CCHS) with 2001 Census data and 1971–2000 Environment Canada (EC) climate data. The study population included 60,004 respondents living in health regions with major cities. Key environmental climate variables were created from EC climate normals (long-term averages of >15 years of weather data) for weather stations located within main cities. Climate components included the annual number of days with rain, with snow, with precipitation, the average summer and winter temperatures, the average windspeed, and the number of days with freezing temperatures. Separate multilevel logistic regression models were developed for students and non-students to examine climate factors and individual traits influencing cycling while adjusting for unexplained differences between health regions. Results: The overall rate of reported utilitarian bicycling (yes/no in a typical week) was 8.1%, with students cycling more than non-students (17.2% versus 6.0%). Climate exposures differed significantly across Canadian regions, such as the observed number of days of precipitation (mean=152 days/year, range= 99–216) and with freezing temperatures (mean=156 days/year, range= 46–211). In multi-level models controlling for individual-level factors in the non-student population, a lower likelihood of cycling was associated with living in a region with a higher number of days of precipitation (OR for 30 days more=0.84, 95% CI 0.74–0.94), or with more days of freezing temperatures (OR=0.91, 95% CI 0.86–0.97). For students, only the number of days with freezing temperatures influenced bicycling (OR =0.94, 95% CI 0.89–1.00). Conclusions: Bicycling patterns are associated with the regional climate in which one lives. This evidence can guide policy initiatives for targeted transportation infrastructure (i.e. dedicated bike lanes and bike-friendly transit) to facilitate bicycling in adverse climate conditions, thus increasing overall bicycling rates and improving environmental and public health. While we concentrated on exposures to long-term climate, future studies may evaluate impacts of short-term weather.
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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 | 0.007 | 0.002 |
| 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 | 0.000 |
| 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 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".