Spatial Heterogeneity in the Nonlinear Impact of Built Environment on Commuting Time of Active Users: A Gradient Boosting Regression Tree Approach
Bibliographic record
Abstract
Many studies provided evidence regarding the influence of built environment (BE) on commuting time. However, few studies have considered the spatial heterogeneity of such impacts. Using data from Nanjing, China, this study employs two-step clustering and gradient boosted regression trees (GBRT) to segment the neighborhoods into different types and investigate the effects of BE characteristics on the commuting time of active users. The results show a strong effect of BE characteristics on commuting time, involving active modes. The importance of BE characteristics varies among neighborhood types. For active commuters in the internal region of Nanjing, commuting time is affected mostly by the land use mix at the work end. The lowest impact of BE in internal regions is associated with metro station density. For active commuters in external region of the city, the relative importance of intersection density at the home end is the largest (as high as 5.76%). Moreover, other significant differences are found in the associations between BE characteristics and active commuting time in the two regions.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".