The impact of objective urban features on perception of neighbourhood environments
Bibliographic record
Abstract
Perceptions of the neighbourhood environment can play an important role in promoting public health, yet modifying perceptions is challenging. Adjusting the built environment may be a pathway to influence perceptions. In addition to the physical environment, intrapersonal factors may shape perceptions. This study analysed data from several Japanese major cities to explore the association between objective and perceived neighbourhood environment attributes, stratified by age and gender. Perceived neighbourhood environment measures were adapted from established scales, while objective measures were derived from participants' geographic address data. Multivariate linear regression was employed to assess these associations. All objective measures were positively associated with overall neighbourhood environment perception, and destination diversity presented the strongest association. Perceptions among those 65-69 were more strongly influenced by the physical environments of their neighbourhood, whether positively or negatively. Objective environmental measures have a greater positive impact on perception for females than for males, while males are more negatively affected in terms of perceptions of crime and traffic safety. These findings highlight how objective built environment attributes may shape residents' perceptions across different demographic groups.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".