Information Visualization

What Is Information Visualization?

Considerable variation exists in what is included under the term information visualization. For example, some resources distinguish little between InfoVis and established visual disciplines such as graphic design, information graphics, and scientific visualizations.

Four criteria can help distinguish InfoVis from other graphics disciplines. First, it is data driven: InfoVis involves the conversion of data (whether numeric, textual, or multimedia) that is not inherently visual into visual representations. In contrast, graphic illustrations of analog processes are not included because they are not based on raw data transformation. Second, InfoVis data transformations can be classified as bijective data mappings: each point in a data set is mapped into a unique representation. Although this criterion may appear to be minor, the implication is that each InfoVis element can be made interactive and is capable of displaying related information. Third, InfoVis includes nontrivial interactivity: InfoVis images are interactive and encourage exploration and hypothesis testing by allowing users to find or manipulate personally relevant data. Finally, InfoVis data representations are syntactically notational. In other words, data are converted into forms that differ from their original modality (e.g., musical notation is notational because it represents sounds as symbols).

Visual representations also differ in their epistemological orientations. Static visuals tend to be associated with a knowledge delivery learning paradigm. They are concerned primarily with how ideas or data can be communicated effectively through visual presentation. In contrast, the interactive images used in InfoVis are closely associated with constructivism (because they encourage knowledge building, testing, refining, and sharing) and distributed cognition (because cognition is viewed as the result of the interaction between a person and a software interface).

[Page 382]InfoVis Design

Several benefits of interacting with InfoVis systems have been described. They help users make sense of, or gain insight into, large (or even massive) data sets by finding patterns, trends, or problems in data, reducing the size of an information space that needs to be searched, and serving as a memory aid by temporarily expanding the space available to analyze data. By using InfoVis tools to manipulate data, users often learn to ask questions that lead to deep understanding or insight, resulting in the creation of rich and flexible mental models.

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