Evaluating lossiness and fidelity in information visualization

Brath, Richard; Banissi, Ebad

doi:10.1117/12.2083444

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…When it comes to the usage of typography in visualization, two notable user studies [SOK*16,ACS*17] evaluate common guidelines. In addition, Brath and Banissi [BB15] presented an information‐theoretic approach quantifying the lossiness in visualizations based on evaluating the fidelity (number of unique levels perceivable for a particular visual attribute) in an encoding. Their approach measures the effectiveness of a particular set of visual encodings, which allows an efficient pruning of the design space of typographic dimensions.…”

Section: Text Datamentioning

confidence: 99%

Quality Metrics for Information Visualization

Behrisch

Blumenschein

Kim

et al. 2018

Computer Graphics Forum

101

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The visualization community has developed to date many intuitions and understandings of how to judge the quality of views in visualizing data. The computation of a visualization's quality and usefulness ranges from measuring clutter and overlap, up to the existence and perception of specific (visual) patterns. This survey attempts to report, categorize and unify the diverse understandings and aims to establish a common vocabulary that will enable a wide audience to understand their differences and subtleties. For this purpose, we present a commonly applicable quality metric formalization that should detail and relate all constituting parts of a quality metric. We organize our corpus of reviewed research papers along the data types established in the information visualization community: multi-and high-dimensional, relational, sequential, geospatial and text data. For each data type, we select the visualization subdomains in which quality metrics are an active research field and report their findings, reason on the underlying concepts, describe goals and outline the constraints and requirements. One central goal of this survey is to provide guidance on future research opportunities for the field and outline how different visualization communities could benefit from each other by applying or transferring knowledge to their respective subdomain. Additionally, we aim to motivate the visualization community to compare computed measures to the perception of humans.

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Section: Text Datamentioning

confidence: 99%

Quality Metrics for Information Visualization

Behrisch

Blumenschein

Kim

et al. 2018

Computer Graphics Forum

101

View full text Add to dashboard Cite

show abstract

“…in addition to the example shown, a sparse example started with a treemap where the area was large and almost all the initial headlines were readable; and a dense example started with a treemap where the area was small and most of the initial headlines were too small to read). Information density for the proportional encoding consistently outperformed the treemap by a factor of 2 while the font weight encoding could underperform as shown in Table 2 (see [11] for details).…”

Section: Query Results Lists and Proportional Encodingmentioning

confidence: 94%

“…Measurement of lossiness and fidelity is a general approach to measuring and comparing information content across alternative visualization techniques [11] as shown in Tables 2, 3 and 4. While these calculations have not been shown for the other visualizations herein, in general, the use of font attributes increases information density compared to plain text.…”

Section: Discussionmentioning

confidence: 99%

Font attributes enrich knowledge maps and information retrieval

Brath

Banissi

2016

Int J Digit Libr

Self Cite

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Typography is overlooked in knowledge maps (KM) and information retrieval (IR), and some deficiencies in these systems can potentially be improved by encoding information into font attributes. A review of font use across domains is used to itemize font attributes and information visualization theory is used to characterize each attribute. Tasks associated with KM and IR, such as skimming, opinion analysis, character analysis, topic modelling and sentiment analysis can be aided through the use of novel representations using font attributes such as skim formatting, proportional encoding, textual stem and leaf plots and multi-attribute labels.

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“…Information lossiness is an evaluation of the fidelity per visual attribute in an encoding; and an estimate of permutations across multiple attributes to compare relative lossiness (Brath and Banissi 2015). A simple example is shown in Figure 14: both maps use 5 levels of hue.…”

Section: Discussionmentioning

confidence: 99%

Multivariate label-based thematic maps

Brath

Banissi

2017

International Journal of Cartography

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The rich history of cartography and typography indicates that typographic attributes, such as bold, italic and size, can be used to represent data in labels on thematic maps. These typographic attributes are itemized and characterized for encoding literal, categorical and quantitative data. Label-based thematic maps are shown, including examples that scale to multiple data attributes, and a large number of entities. Multiple approaches to handle long labels are considered. Positional and proportional encoding apply attributes to portions of labels for encoding a large number of data attributes or quantitative values. Quantitative evaluation indicates label-based thematic maps may outperform choropleth maps for some tasks. Qualitative evaluation provides guidance for design considerations.

show abstract

Evaluating lossiness and fidelity in information visualization

Cited by 5 publications

References 14 publications

Quality Metrics for Information Visualization

Quality Metrics for Information Visualization

Font attributes enrich knowledge maps and information retrieval

Multivariate label-based thematic maps

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