Research progress of the urban built environment walkability: Using Citespace knowledge mapping analysis(城市建成环境步行性研究进展——基于Citespace知识图谱的分析)
Bibliographic record
Abstract
Walkability is a crucial metric for assessing the pedestrian environment since it shows how pedestrian-friendly an urban built environment is. Clarifying its research and development gives urban revitalization a foundation. This study employed bibliometric methods based on Citespace to find 3 202 applicable papers in the Web of Science core library. By building a knowledge map from the perspectives of research articles, research contents, and research fronts, it examined the progression of the study on the walkability of built environments. The findings indicate that: the countries with high number of relative research papers and citations on international journals in the areas of transportation, biomedicine, etc., number are the United States, China, Canada, Australia and England. Besides, the research focus was gradually transferred to social fairness and disadvantaged individuals rather than the effects of walkability on the built environment. The research can be expanded and further developed in terms of walkability assessment in the context of data integration, built environment governance to take into account different groups, and walkability optimization in the context of urban rejuvenation.步行性可反映城市建成环境的步行友好程度，是评估步行环境的重要指标，研究其发展脉络能够为城市更新提供依据。基于Web of Science核心合集数据库，筛选出3 202篇相关文献，运用Citespace文献计量方法，从文献特征、研究内容、研究前沿等方面绘制知识图谱，梳理城市建成环境步行性研究进展。结果表明，发文量和被引频次较高的国家主要有美国、中国、澳大利亚、加拿大和英国，且多发表于交通、生物医学等领域的国际性期刊。研究内容逐渐从建成环境对步行性的影响向考量弱势人群、重视社会公平等多维度转变。未来可在城市更新背景下的步行性优化、考虑群体差异的步行环境规划、数据融合背景下的步行性测度三方面进行进一步深化与扩展。
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.049 | 0.061 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".