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Information Visualization: What is Information Visualization?

This guide describes various information visualization tools that can be used to create engaging and interactive charts, maps, diagrams, and more.

Getting Started with Visualization

Check out this interactive table for help in defining and differentiating various types of visualizations to best meet your needs. 

You will see that the table breaks visualization into six types:

data visualization

information visualization

concept visualization

strategy visualization

metaphor visualization

compound visualization

This tool is also useful in helping you determine which type of visualization is best for your needs. It asks you to consider whether the information you wish to visualize is:

simple 

complex or

quantitative

Welcome

Pellegrini, Valerio. (2013).The Atlas of Kant's Legacy. [Streamgraph representing the evolution of Kantian lexicon throughout his philosophical publications, 1747-1803]. Retrieved from http://www.informationisbeautifulawards.com/2013-shortlist/data-visualization/

 

Information visualization is “the use of computer-supported, interactive, visual representation of abstract data to amplify cognition (Card, Mackinlay, and Schneiderman, 1999).” Essentially, building an information visualization is you, the creator of the visualization, giving a computer program a set of instructions about how to represent a data set in an abstract, visual way.  

 

Building an information visualization can aid you in your research process at two different stages. Early in the research process, an information visualization can help you explore and understand patterns in your data. Later on, as you prepare to communicate the output of your research, an information visualization can help you communicate important aspects of your data set in a concise, easy-to-understand fashion.

 

To get started with information visualization, you will need to have a data set and some questions that you would like to explore using that data set. This guide contains a number of tools that can help you create visualizations in styles appropriate to your research work. 

 
 

References

Card, S. K., Mackinlay, J., and Schneiderman, B. (1999) Readings in information visualization: Using vision to think. San Francisco: Morgan Kaufmann.

Need help?

While DU's Center for Data Analysis and Information Visualization is no longer in operation, you can contact Meg Eastwood, the Science  and Engineering Reference Librarian, for basic advice on data visualization.