The DOI matches its Crossref record.
Visual Analytics
Abstract
The information revolution is upon us, and it is guaran-teed to change our lives and the way we conduct our daily business. The fact that we have to deal with not just the size but also the variety and complexity of this in-formation makes it a real challenge to survive the revolu-tion. Enter Visual Analytics, a contemporary and proven approach to combine the art of human intuition and the science of mathematical deduction to directly perceive patterns and derive knowledge and insight from them.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Social big data: recent achievements and new challenges
Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016) · Information Fusion · doi:10.1016/j.inffus.2015.08.005 · Cited by 846
“• The open problems and challenges related to visual analytics [145] , especially related to the capacity to collect and store new data, are rapidly increasing in number, including the ability to analyse these data volumes [150] , to record data about the movement of people and objects at a large scale [151] , and to…”
BackgroundRaghupathi, W., & Raghupathi, V. (2018) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph15030431 · Cited by 531
Jigsaw: Supporting Investigative Analysis through Interactive Visualization
Stasko, J., Görg, C., & Liu, Z. (2008) · Information Visualization · doi:10.1057/palgrave.ivs.9500180 · Cited by 280
Archaeological Visualization: Towards an Archaeological Information Science (AISc)
Llobera, M. (2011) · Journal of Archaeological Method and Theory · doi:10.1007/s10816-010-9098-4 · Cited by 110
neuroVIISAS: Approaching Multiscale Simulation of the Rat Connectome
Schmitt, O., & Eipert, P. (2012) · Neuroinformatics · doi:10.1007/s12021-012-9141-6 · Cited by 51
Metadata
- Authors
- Wong, P. C., & Thomas, J.
- Venue
- IEEE Computer Graphics and Applications
- Published
- 2004-09
- Volume
- 24
- Issue
- 5
- Pages
- 20-21
- ISSN
- 0272-1716 (ISSN-L)
- 1558-1756
- OpenAlex
- W2294316503
- Cited by
- 214
- References
- 0
- FWCI
- 3.40
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Visual Analytics. https://app.cochat.ai/papers/10.1109/mcg.2004.39
- The paper
- Wong, P. C., & Thomas, J. (2004). Visual Analytics. IEEE Computer Graphics and Applications. https://doi.org/10.1109/mcg.2004.39
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
