Angela Zoss's LibGuide on Data Visualization describes data visualization as an umbrella term containing the subsets of scientific visualization, information visualization, infographics, and visual analytics.
Manuel Lima, author and presenter, states that information visualization is “intrinsic aspiration for sense-making.” He is the author of Visual Complexity: Mapping Patterns of Information (2011), The Book of Trees: Visualizing Branches of Knowledge (2014), and The Book of Circles: Visualizing Spheres of Knowledge (2017).
The simplest definition for data visualization is found in Data Visualization: A Handbook for Data Driven Design by Andy Kirk. In nine words, Kirk offers this definition: “The representation and presentation of data to facilitate understanding.”
More examples are provided on the web, in Lima's “Visual Complexity” (http://www.visualcomplexity.com) and Josh On's, “They Rule” (http://www.theyrule.net/about).
The Addlestone Library has a very limited physical collection, location is TK 7882.I6 W37.
Kirk, Andy. Data Visualization: A Handbook for Data Driven Design. California: Sage Publications, 2016.
Lima, Manuel. Visual Complexity: Mapping Patterns of Information. New York: Princeton Architectural Press, 2013.
Zoss, Angela. "Data Visualization: About Data Visualization." Duke University Libraries. Accessed July 2017. http://guides.library.duke.edu/datavis.
As a student, it is useful to know some basics of data visualization in order to:
If you are creating, then knowing good design concepts can make your presentations more powerful.
If you are consuming, then knowing good design concepts will alert you to misinformation that is accidental or deliberate.
Including data in your designs requires you to know how to access reliable sources of information.
Interpreting the data used in others’ designs requires you to identify the source of data and mine it for yourself to assess its reliability.
Data Visualization is important in many different disciplines and job descriptions.
What is emphasized is reflected by the type of field. For example, in computer science, data visualization involves coding languages and can include making interactive web-based visualizations.
Within journalism, data visualization may include graphic arts, and an emphasis on data sources.
Within psychology or social science research, data visualization may emphasize patterns in human behavior, as shown in large data sets (census), or in small data sets (focus groups).
How you view and define data visualization is influenced by what field you are studying or working within.