Toward measuring visualization insight
Abstract
Recent visualization research literature has paid an increasing amount of attention to evaluating visualizations. It seems an appropriate time to reopen the question about what the ultimate purpose of visualization is and how it should be evaluated. One potential claim is: the purpose of visualization is insight. The purpose of visualization evaluation is to determine to what degree visualizations achieve this purpose. If this claim is true, then evaluating visualizations should seek to determine how well visualizations generate insight. Measuring insight would enable the direct comparison of visualization design alternatives, or the comparison against an insight goal. This article examines the capability of the controlled experiment method to measure insight
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Audio interactions can be powerfully expressive, spatially situated, help users understand semantic content, and complement the spatial limitations inherent to haptic interfaces.
“Audio interactions can also be powerfully expressive [42], spatially situated [88], help users understand semantic content [76], and complement the spatial limitations inherent to haptic interfaces.”
From The Accessibility of Data Visualizations on the Web for Screen Reader Users: Practices and Experiences During COVID-19 (2023) · cites this paper as [88]
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- Authors
- North, C.
- Affiliations
- Virginia Tech (US)
- Corresponding author
- North, C.
- ORCID
- North, C.
- Venue
- IEEE Computer Graphics and Applications
- Published
- 2006-05
- Volume
- 26
- Issue
- 3
- Pages
- 6-9
- ISSN
- 0272-1716 (ISSN-L)
- OpenAlex
- W1987135115
- Cited by
- 456
- References
- 6
- Citations by year
- 2026: 7 · 2025: 19 · 2024: 18 · 2023: 22 · 2022: 16 · 2021: 23 · 2020: 34 · 2019: 30 · 2018: 30 · 2017: 36 · 2016: 30 · 2015: 32 · 2014: 25 · 2013: 20 · 2012: 21
- FWCI
- 21.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Graphics and Applications (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computer Graphics and Visualization Techniques
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Keywords
- visualization evaluation, controlled experiments
- MeSH terms
- Computer Graphics, Data Display, Humans, Image Interpretation, Computer-Assisted, User-Computer Interface, Vision Tests, Visual Perception
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 ›
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