Mosaic Displays for Multi-Way Contingency Tables

Friendly, Michael

doi:10.2307/2291215

Cited by 141 publications

(159 citation statements)

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“…It has been used for categorical comparison to help people perceive differences between classification schemas (Graham and Kennedy, 2001;Munzner et al, 2003). The Mosaic display (Hartigan & Kleiner, 1981;Friendly 1994) visualizes n-way contingency tables. By portraying table entries as "tiles" whose areas are proportional to the value in the cell, it reveals pair-wise relationships between categorical variables (Friendly, 1999).…”

Section: Visualizing Relationships In Categorical Datamentioning

confidence: 99%

Using Category Information for Relationship Exploration in Textual Data

Yan

Furnas

Walstrum

2006

Proc of Assoc for Info

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Section: Visualizing Relationships In Categorical Datamentioning

confidence: 99%

Using Category Information for Relationship Exploration in Textual Data

Yan

Furnas

Walstrum

2006

Proc of Assoc for Info

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“…Less powerful but more accessible to novice users is the Table Lens (Rao and Card, 1994;Inxight Software Inc., 2002), which uses a spreadsheet metaphor. Other examples include VisDB for multi-dimensional database visualization (Keim and Kriegel, 1994), interactive mosaic displays (Friendly, 1994;Theus, 2002a,b), the Attribute Explorer (Tweedie et al, 1996) and the scatter plot or prosection matrices of Becker and Cleveland (1987). Interactive geovisualization software also utilizes multidimensional visualization techniques (Andrienko and Andrienko, 1999a-f;Gahegan et al, 2002a,b;MacEachren et al, 2003a,b) as emphasized in section B of this volume (see Andrienko et al,this volume (Chapter 5) and subsequent chapters).…”

Section: Multi-dimensional Datamentioning

confidence: 99%

Information Visualization and the Challenge of Universal Usability

Plaisant

2005

Exploring Geovisualization

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Information Visualization aims to provide compact graphical presentations and user interfaces for interactively manipulating large numbers of items. We present a simple "data by tasks taxonomy" then discuss the challenges of providing universal usability, with example applications using geo-referenced data. Information Visualization has been shown to be a powerful visual thinking or decision tool but it is becoming important for services to reach and empower every citizen. Technological advances are needed to deal with user diversity (age, language, disabilities, etc.) but also with the variety of technology used (screen size, network speed, etc.) and the gaps in user's knowledge (general knowledge, knowledge of the application domain, of the interface syntax or semantic). We present examples that illustrate how those challenges can be addressed. IntroductionDesigners are discovering how to use rapid and high-resolution color displays to present and manipulate large amounts of information in compact and user-controlled ways. Information Visualization can be defined as the use of computer-supported interactive visual representation of abstract data to amplify cognition (Card et al., 1999). The abstract characteristic of the data is what distinguishes Information Visualization from scientific visualization. Information Visualization is more likely to be used to display database content (for example, recorded stock values, health statistics) than.output of models or simulations, but this distinction is not always important. The display of geo-referenced data is often a hybrid visualization that combines abstract and concrete data. In fact several of the most famous examples of Information Visualization include maps, from the 1861 representation of the ill-fated Napoleon's Russian campaign by Minard (see Tufte (1983) and Kraak (undated)) to the interactive HomeFinder application shown in Figure 3.1 that introduced the concept of dynamic queries (Ahlberg et al., 1992). Information Visualization aims to provide compact graphical presentations and user interfaces for interactively manipulating large numbers of items (102-106), possibly extracted from far larger datasets (Card et al., 1999;Spence, 2001;Ware, 2000; Chen, 2002;Bederson and Shneiderman, 2003). Also sometimes called visual datamining, it uses the enormous visual bandwidth and the remarkable human visual system to enable users to make discoveries, take decisions, or propose explanations about patterns, groups of items, or individual items. Perceptual psychologists, statisticians, and graphic designers (Tufte, 1983) offer valuable advice about presenting static information, but advances in processor speed, graphic devices and dynamic displays takes user-interface designers well beyond current wisdom. This chapter presents a simple "data by tasks taxonomy" then discusses the challenges of providing "universal usability", with example applications using georeferenced data.

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“…The mosaic display, proposed by Hartigan & Kleiner (1981) and extended by Friendly (1994a), represents the counts in a contingency table directly by tiles whose area is proportional to the cell frequency. This display generalizes readily to n-way tables and can be used to display the residuals from various log-linear models.…”

Section: Mosaic Displays For N-way Tablesmentioning

confidence: 99%

“…The enhanced mosaic display (Friendly, 1992b(Friendly, , 1994a) achieves greater visual impact by using color and shading to reflect the size of the residual from independence and by reordering rows and columns to make the pattern more coherent. The resulting display shows both the observed frequencies and the pattern of deviations from a specified model.…”

Section: Enhanced Mosaicsmentioning

confidence: 99%