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Cited by 880

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

Munzner, Tamara·2009·IEEE Transactions on Visualization and Computer Graphics

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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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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CoChat. (2026). Citation check: A Nested Model for Visualization Design and Validation. https://app.cochat.ai/papers/10.1109/tvcg.2009.111
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Munzner, T. (2009). A Nested Model for Visualization Design and Validation. IEEE Transactions on Visualization and Computer Graphics. https://doi.org/10.1109/tvcg.2009.111

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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.