A bibliometric review of past trends and future prospects in urban heat island research from 1990 to 2017
Bibliographic record
Abstract
The urban heat island (UHI) phenomenon is among the most evident features of human impact on the Earth’s system. This phenomenon has been widely observed and documented in many cities around the world. UHI-related publications have increased rapidly over the last three decades. However, because of a refined methodology and widening scope, a holistic understanding of research patterns and issues related to UHI research is lacking. Although others have summarized developments in UHI studies, these publications have focused on describing the current state of research rather than uncovering research trends and prospects. In the present study, we examined the evolution of UHI-related research from 1990 to 2017 and applied a scientometrics approach to identify research trends. The characteristics of publication outputs, key scientific disciplines, and cooperation between countries and institutions were determined by a citation analysis. We also discuss research trends, including future directions, approaches, and expected data. We identified two potential directions for UHI research through the results of key co-word clustering and discriminant analyses: negative impacts of UHI on public health and strategies to mitigate and adapt to UHI effects. We provide a broad review of the development of UHI research that may inspire future studies on the UHI phenomenon by new researchers in this 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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.064 | 0.100 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| 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".