Visualization Analysis and Design

By Tamara Munzner. 2014

1,737 citations recorded by the source corpus.

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Citations in the corpus

Towards Better Analysis of Deep Convolutional Neural Networks (2016, methodology): “We use the size of an image patch to encode the importance of the corresponding neuron because size is among the most effective visual channels [44].”
Decision making with visualizations: a cognitive framework across disciplines (2018, background): “Consistent with assertions from cognitive science and scientific visualization (Munzner, 2014), we propose that visualization designers should identify the critical information needed for a task and use a visual encoding technique that directs participants’ attention to this information.”
A Framework for Considering Comprehensibility in Modeling (2016, unknown): “The idea of using the basic question words to create facets is owed to its famous application in journalism, where it is immortalized as ‘‘the ﬁve Ws.’’ This was recently applied for analyzing data visualization tasks by Schulz et al., 1 and a similar set of questions is used as a framework for visu”
iForest: Interpreting Random Forests via Visual Analytics (2019, methodology): “We chose line charts over heatmap, because users may feel difficult to perceive small differences in heatmap [39].”
VizLinter: A Linter and Fixer Framework for Data Visualization (2021, background): “Consequently, four common issues are categorized from the expert interviews and previous literature related to visualization principles [14,33,39,58] and visualization grammar [60].”
Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco (2018, methodology): “If activity-oriented [40] or low-level [2] task taxonomies were expressed as constraints, they could be used in automated design systems.”
Embedded Data Representations (2017, background): “As with scientific visualizations [36], the spatial representation of an embedded visualization is to a large extent predetermined.”
Visualizations of Deep Neural Networks in Computer Vision: A Survey (2017, methodology): “Only a few visualization methods [51] have been applied to DNNs.”
The Science of Visual Data Communication: What Works (2021, background): “Visual channels translate numbers into images Visualizations rely on several visual channels to transform numbers into images that the visual system can efficiently process (Bertin, 1983; Mackinlay, 1986; see Munzner, 2014, for a more complete list).”
VLAT: Development of a Visualization Literacy Assessment Test (2017, methodology): “In order to do so, we considered (1) task taxonomies in Information Visualization [4] [11] [14] and (2) the associated dataset types of the 12 visualizations [37].”