The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A Nested Model for Visualization Design and Validation
Abstract
We present a nested model for the visualization design process with four layers: characterize the problem domain, abstract into operations on data types, design visual encoding and interaction techniques, and create algorithms to execute techniques efficiently. The output from a level above is input to the level below, bringing attention to the design challenge that an upstream error inevitably cascades to all downstream levels. This model provides prescriptive guidance for determining appropriate evaluation approaches by identifying threats to validity unique to each level. We call attention to specific steps in the design and evaluation process that are often given short shrift. We also provide three recommendations motivated by this model:authors should distinguish between these levels when claiming contributions at more than one of them, authors should explicitly state upstream assumptions at levels above the focus of a paper, and visualization venues should accept more papers on domain characterization.
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Citations
Cites
A Framework of Interaction Costs in Information Visualization
Lam, H. (2008) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2008.109 · Cited by 133
Session Viewer: Visual Exploratory Analysis of Web Session Logs
Lam, H., Russell, D., Tang, D., & Munzner, T. (2007) · 2007 IEEE Symposium on Visual Analytics Science and Technology · doi:10.1109/vast.2007.4389008 · Cited by 62
Self-citation
Cited by
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems
Mohseni, S., Zarei, N., & Ragan, E. D. (2021) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3387166 · Cited by 785
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems
Mohseni, S., Zarei, N., & Ragan, E. D. (2018) · arXiv · doi:10.48550/arxiv.1811.11839 · Cited by 783
Visual learning analytics of educational data: A systematic literature review and research agenda
Vieira, C., Parsons, P., & Byrd, V. (2018) · Computers & Education · doi:10.1016/j.compedu.2018.03.018 · Cited by 316
Ranking Visualizations of Correlation Using Weber's Law
Harrison, L., Yang, F., Franconeri, S., & Chang, R. (2014) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2014.2346979 · Cited by 212
In Pursuit of Error: A Survey of Uncertainty Visualization Evaluation
Hullman, J., Qiao, X., Correll, M., Kale, A., & Kay, M. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2864889 · Cited by 187
Metadata
- Authors
- Munzner, T.
- Venue
- IEEE Transactions on Visualization and Computer Graphics
- Published
- 2009-11
- Volume
- 15
- Issue
- 6
- Pages
- 921-928
- ISSN
- 1077-2626 (ISSN-L)
- OpenAlex
- W2142493242
- Cited by
- 880
- References
- 2
- FWCI
- 23.09
- Type
- journal article
- Language
- en
- Open access
- closed
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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.