Exploring the Associations Between the Visual Built Environment and Active Transport
Bibliographic record
Abstract
Health Geography emphasizes the importance of place in shaping health. The built environment—the human-made physical environment for human activity and travel—is an important physical context in place. The built environment has been shown to impact health behaviors, including Active Transport (AT), which refers to physical activity during travel. AT has been shown to reduce the risk of chronic disease and is suggested to have the potential to make cities more sustainable. When engaging in AT, how people observe and evaluate the visual built environment (VBE) may affect their decisions to participate in AT. However, researchers face a challenge in collecting high-quality VBE data in support of large-scale AT studies. The dual availability of extensive street view imagery (SVI) and advanced scene understanding techniques presents the potential to collect high-quality VBE data on a large scale for AT studies. The overarching research question of this thesis is: How is the street view-derived VBE in travel associated with AT? This dissertation uses SVIs as well as individual health and mobility data to examine how the objective elements of the VBE are associated with AT. Results show that streetscape diversity (serving as a proxy to measure the overall design of VBE), road, and vertical greenness, are positively related to AT, while traffic light makes a negative association. Additionally, this dissertation uses SVIs and a human-audited SVI dataset to measure AT-related subjective perceptions. Results show that SVI-based perceptions can vary by gender, and model-predicted perceptions based on SVIs are close to onsite observed perceptions but still could not replace onsite perceptions. Through using SVIs, this dissertation not only elucidates the relationship between the VBE factors and AT, but also demonstrates the potential of SVIs to model human perceptions. Furthermore, this thesis provides scalable methods to study and enhance AT behaviors from the perspective of VBE. Findings also provide evidence to guide urban planning and policy in creating healthier, more sustainable cities by integrating features that promote AT and improving the understanding of large-scale human perceptions of VBE.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".