Effects of data distribution and granularity on color semantics for colormap data visualizations

Zimnicki, Clementine; Tseng, Chin; Szafir, Danielle Albers; Schloss, Karen B

doi:10.1109/vis54172.2023.00011

Cited by 2 publications

(2 citation statements)

References 33 publications

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“…Experts acknowledge that chromatic colormaps should also be aesthetically pleasing [48]. But the aesthetic utility of a colormap will vary with the characteristics of the audience (e.g., personal preference or past experience [2]), the specific dataset (e.g., semantics [61] and distributions [79]), and the problem space (e.g., the target visualization [67] or the data domain [13]). For example, how we see colors on a chart varies depending on the sizes, the shapes, and the spatial distribution of the colored marks [67,79].…”

Section: Quantifying Colormap Utilitymentioning

confidence: 99%

“…Beyond these perceptual guidelines, colormaps should also have aesthetic appeal [48], which can depend on color-data semantics (e.g., cool-warm, positive-negative affects) [4], branding, domain conventions, or personal preferences [54]. Furthermore, how we see colors applied on a chart will vary with the sizes and shapes of the colored marks [67], their spatial distribution [79], and the chart's background color [54]. These factors and their interactions make colormap design challenging for a typical data analyst.…”

Section: Introductionmentioning

confidence: 99%

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Cieran: Designing Sequential Colormaps via In-Situ Active Preference Learning

Hong,

Sunberg,

Szafir

2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Figure 1: Cieran's preference learning interface used with Altair [71]. Cieran supports efficient colormap selection within an analyst's workflow through integration with Jupyter Notebooks. Cieran first interpolates example colormaps through a chosen color (e.g., #186E8D, a teal blue shown in the progress bar). People iteratively input preferences to Cieran by making value judgements across pairs of examples, and Cieran uses the preference data to induce a context-specific model of aesthetic utility. This model used to rank and create colormaps.

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Section: Quantifying Colormap Utilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cieran: Designing Sequential Colormaps via In-Situ Active Preference Learning

Hong,

Sunberg,

Szafir

2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

Self Cite

View full text Add to dashboard Cite

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Open Your Ears and Take a Look: A State‐of‐the‐Art Report on the Integration of Sonification and Visualization

Enge,

Elmquist,

Caiola

et al. 2024

Computer Graphics Forum

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The research communities studying visualization and sonification for data display and analysis share exceptionally similar goals, essentially making data of any kind interpretable to humans. One community does so by using visual representations of data, and the other community employs auditory (non‐speech) representations of data. While the two communities have a lot in common, they developed mostly in parallel over the course of the last few decades. With this STAR, we discuss a collection of work that bridges the borders of the two communities, hence a collection of work that aims to integrate the two techniques into one form of audiovisual display, which we argue to be “more than the sum of the two.” We introduce and motivate a classification system applicable to such audiovisual displays and categorize a corpus of 57 academic publications that appeared between 2011 and 2023 in categories such as reading level, dataset type, or evaluation system, to mention a few. The corpus also enables a meta‐analysis of the field, including regularly occurring design patterns such as type of visualization and sonification techniques, or the use of visual and auditory channels, showing an overall diverse field with different designs. An analysis of a co‐author network of the field shows individual teams without many interconnections. The body of work covered in this STAR also relates to three adjacent topics: audiovisual monitoring, accessibility, and audiovisual data art. These three topics are discussed individually in addition to the systematically conducted part of this research. The findings of this report may be used by researchers from both fields to understand the potentials and challenges of such integrated designs while hopefully inspiring them to collaborate with experts from the respective other field.

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Effects of data distribution and granularity on color semantics for colormap data visualizations

Cited by 2 publications

References 33 publications

Cieran: Designing Sequential Colormaps via In-Situ Active Preference Learning

Cieran: Designing Sequential Colormaps via In-Situ Active Preference Learning

Open Your Ears and Take a Look: A State‐of‐the‐Art Report on the Integration of Sonification and Visualization

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