Information Visualization and the Challenge of Universal Usability

Abstract

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.

The challenge of information visualization evaluation

“…Making visualization tools accessible to diverse users regardless of their backgrounds, technical disadvantages, or personal disabilities is necessary when the tools are to be used by the public, but it remains a challenge for designers [14]. Researchers work with high-end computers but they need to address the range of devices and network sp eeds available in people's homes and businesses.…”

“…demonstrating the use of sliders, or introducing the scattergram, so help needed to be provided to get users started. We explored alternative approaches to providing such help [14]. A "sticky note" technique was implemented into the Census Bureau version, and short narrated recordings of demonstrations may also be used.…”

Instrumentos de Visualização da Informação para Avaliação Diagnóstica em Curso de Programação a Distância

“…Dessa forma, uma apresentação gráfica e compacta para manipular um grande número de itens dá possibilidades de professores realizarem descobertas, tomarem decisões e explicarem padrões (como, por exemplo, tendências, clusters e outliers) de grupos de alunos e de cada aluno individualmente [Plaisant et al 2005].…”

Evaluating the data visualization for demanding marine operations

“…One major challenge in designing the evaluation methods is that the chosen method should be able to evaluate whether the visualization tools are accessible to diverse users regardless of their backgrounds and technical disadvantages [6]. For this reason, we use both quantitative and qualitative evaluation approaches [7].…”

“…Universality [6]: Users from diverse backgrounds must understand the visualization tools according to the guideline of evaluative tasks. For instance, the designer should considerate that the tasks are accessible to general participants from other background.…”