A bibliometric analysis of the research on Sponge City: Current situation and future development direction
Bibliographic record
Abstract
Abstract With the rapid development of urbanization, more and more cities are facing the risk of flood disasters and the threat of water environment safety during the rainy season. Sponge City, as a new urban water resources management method, has attracted extensive attention in the academic circle. In order to promote the development of Sponge City, a bibliometric analysis method based on Web of Science (WoS) database and Bibliometrix tool is proposed in this study. After refining the retrieved 26,383 papers, 1456 papers were obtained. All the article information including author, keywords and publication time was downloaded. The bibliometric analysis model was established to analyse and discuss the development of Sponge City and related researches during the period 1998–020 (data up to 15 August 2020). Research performance, research focus and development trend were displayed by bibliometric measurement indicators and visual graphics. The results show that the number of research papers on Sponge City has been increased year by year in the past 10 years (2010–2020). Sponge City and related research are increasing rapidly, and the top five countries in terms of research volume are China, the United States, the United Kingdom, South Korea and Canada. China, the United States, the United Kingdom and Australia are the countries with the most extensive international cooperation in the field of Sponge City. Keywords such as ‘Sponge City’, ‘LID (Low Impact Development)’ and ‘SWMM (Storm Water Management Model)’ appeared frequently. In our opinions, interdisciplinary research methods, digital information management technology and comprehensive performance evaluation are the hot research directions for Sponge City in the future. This study aims to provide directions for future research on Sponge City, as well as scientific guidance and reference for government decision makers on Sponge City.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.108 | 0.226 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 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".