A systematic review of urban green space research over the last 30 years: A bibliometric analysis
Bibliographic record
Abstract
Worldwide, due to rapid urbanization, the provision of urban green spaces (UGSs) has become a primary goal of urban planning. As such, research on the benefits, effects, and challenges of UGSs has gained widespread attention among scholars. This paper comprehensively analyzes three decades of UGS research and its evolution; it conducts a bibliometric analysis of approximately 4000 articles and reviews from the Web of Science platform to discover the patterns and trends characterizing UGS research over time. We found that the pioneers of initial UGS research were the United States and Canada, whereas recently the European Union and China have become the global engines of research in the field. UGS research initially focused on studying urban forests, gradually shifting toward green spaces located in inner urban areas. Early on, researchers investigated UGSs (i.e., urban forests) from an ecological perspective. However, the most current research phase focuses on the social aspects of UGSs, characterized by such keywords as environmental justice and accessibility. Furthermore, the introduction of geographic information systems (GIS) has given new impetus to the evolution of UGS research and has remained the most used technological advancement besides remote sensing techniques. As the social aspects of UGS research have gained importance, new research methods have been employed, such as machine learning, big data and social media data analysis, and artificial intelligence, most recently.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.062 | 0.072 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".