Bibliographic record
Abstract
Environmental psychology research and the emotional design boom have led to a series of theoretical studies and practical activities on emotional design in space. It includes interior design, architectural design, display design, and other space planning and design work. In order to conduct a deeper study and practice of emotional space design, a bibliometric and review approach was used to visualise and analyse data from the literature on emotional space design to understand the development trends of emotional space design. The literature data were sourced from the Web of Science (WoS), Science Citation Index Expanded (SCI Expanded), Social Science Citation Index (SSCI), and Arts Humanities Citation Index (AHCI) as the literature search source, enter TS=((emotion OR affective) AND (space design OR interior design OR landscape architecture )) in the advanced search formula generator, and set the search time to the whole year, i.e., 1991-2023. And 1146 documents were obtained. After excluding articles with missing field information, duplicate data, and those with no relevance to the selected topic, 629 valid documents were finally accepted. This paper uses two bibliometric tools, VOSviewer (Version 1.6.19) and CiteSpace (Version 5.6.R3), to obtain critical data such as keyword clustering profiles, Strongest Citation Bursts, highly cited literature, and visual images for this research topic. This information shows that the literature on emotional space design is generally rising. There is no downward trend in the literature output at this research stage, so it is judged that research in this area is in a high-growth phase. The United States, England, China, Australia, and Canada are the major research countries in this field; Cornell, Leeds, UCL, and other leading institutions in architecture and design-related disciplines are the primary subjects of research in emotional space design. International journals of environmental research and public health, Sustainability, and Building and environment are core journals in the field of environmental science and engineering. They all have a high volume of articles (≥20) and an impact factor (IF) of over 4.00, which shows the importance of emotional spatial design in this field of research. The 629 imported documents were imported into VOSviewer, and the co-occurrence frequency was set to 4. Finally, 204 keywords were obtained by merging synonyms. The four clusters formed were named #1 Affective Space Design; #2 Healthcare; #3 Virtual; and #4 Design Evaluation, based on the primary keyword information. In conjunction with the analysis of the Top 30 Keywords with the Strongest Citation Bursts generated by Citespace, future research on emotional space design is expected to focus on Virtual Environment, Healthcare, Difference Sustainable design, topical social issues, interdisciplinarity and user experience and Affective Response. The analysis of the top ten high-frequency references regarding co-citations shows that the underlying theoretical support for emotional space design comes mainly from research in environmental psychology, psychology, and colour psychology. Evidence-based healthcare design research repeatedly appears in the references to emotional space design, which is a research hotspot for emotional space design.
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 | 0.028 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.361 | 0.384 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".