Multiple criteria analysis of the popularity and growth of research and practice of visual analytics, and a forecast of the future trajectory
Bibliographic record
Abstract
Abstract This study employs Google trends and data from the web of science to evaluate the popularity, growth, and impacts of visual analytics (VA) as a research field and data science technique. The paper undertakes quantitative analyses and visualization of the temporal trends, from VA's emergence in 2000 to the end of 2019. The trend analysis helps to forecast future growth in the research and practice of VA. The study highlights four outcomes. First, there is a robust direct relationship among the variables, including VA's growth on the Google trends, the scientific literature production (SLP), and usage of the published documents. Second, the SLP's growth pattern highlights VA's popularity as an emerging field with an overall annual increase of 17.4%. The high citation counts of the published scholarship indicate a significant impact and a continuous growth of the VA field. Third, VA contributes to diverse disciplines other than computer science and information systems, from business and economics to engineering, healthcare, biomedical and chemical sciences, and arts and humanities. VA helps researchers and practitioners in multidisciplinary fields analyze multidimensional data, enhance data visualization, knowledge discovery, generating insights, and make informed decisions. On the reverse, other disciplines contribute to propelling VA's popularity through research productivity, usage, and citation impacts. Finally, a trend analysis predicts sustained future growth of VA technology in research and practice to dissect and sensemaking of the increasingly massive and complex data structures, which is now the norm in many fields.
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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 | 0.010 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.030 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| 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".