Bridging the gap: a bibliometric examination of the interdependency between pedestrian activity and built environment
Bibliographic record
Abstract
This bibliometric study comprehensively analyses the research landscape at the intersection of pedestrians and the built environment from 1975 to 2024, from the first study in the field till now. Several studies have published in recent years; however, lack a comprehensive approach to find out the themes and areas studied, and the analysis of the research gap remains unidentified (Revision 6.3). To address this, the extensive Scopus database was used, and 1126 research papers were selected based on relevance to map publication trends, identify dominant themes, pinpoint research gaps, and chart future research directions. The analysis reveals that research in this field has grown exponentially since 2007-08, with the United States leading in research output, followed by Canada, China, Australia, South Korea, and the United Kingdom. Global North highlights the lack of research in the region, but China and India are emerging as research hubs. Studies show the evolution of themes from thermal comfort to safety and security. It also addresses socio-demographic, sustainability, and universal inclusivity factors for growing urban spaces. This study helps stakeholders, such as industry experts, policymakers, urban planners, and researchers, by providing important evidence-based insights and a holistic overview of the field.
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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 | 0.020 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.241 | 0.324 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".