Information visualization

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Information visualization or information visualisation is the study of (interactive) visual representations of abstract data to reinforce human cognition. The abstract data include both numerical and non-numerical data, such as text and geographic information. However, information visualization differs from scientific visualization: "it’s infovis [information visualization] when the spatial representation is chosen, and it’s scivis [scientific visualization] when the spatial representation is given".[1]

Overview

Partial map of the Internet early 2005, each line represents two IP addresses, and some delay between those two nodes.

The field of information visualization has emerged "from research in human-computer interaction, computer science, graphics, visual design, psychology, and business methods. It is increasingly applied as a critical component in scientific research, digital libraries, data mining, financial data analysis, market studies, manufacturing production control, and drug discovery".[2]

Information visualization presumes that "visual representations and interaction techniques take advantage of the human eye’s broad bandwidth pathway into the mind to allow users to see, explore, and understand large amounts of information at once. Information visualization focused on the creation of approaches for conveying abstract information in intuitive ways."[3]

Data analysis is an indispensable part of all applied research and problem solving in industry. The most fundamental data analysis approaches are visualization (histograms, scatter plots, surface plots, tree maps, parallel coordinate plots, etc.), statistics (hypothesis test, regression, PCA, etc.), data mining (association mining, etc.), and machine learning methods (clustering, classification, decision trees, etc.). Among these approaches, information visualization, or visual data analysis, is the most reliant on the cognitive skills of human analysts, and allows the discovery of unstructured actionable insights that are limited only by human imagination and creativity. The analyst does not have to learn any sophisticated methods to be able to interpret the visualizations of the data. Information visualization is also a hypothesis generation scheme, which can be, and is typically followed by more analytical or formal analysis, such as statistical hypothesis testing.

History

The modern study of visualization started with computer graphics, which "has from its beginning been used to study scientific problems. However, in its early days the lack of graphics power often limited its usefulness. The recent emphasis on visualization started in 1987 with the special issue of Computer Graphics on Visualization in Scientific Computing. Since then there have been several conferences and workshops, co-sponsored by the IEEE Computer Society and ACM SIGGRAPH".[4] They have been devoted to the general topics of data visualisation, information visualization and scientific visualisation, and more specific areas such as volume visualization.

Product Space Localization, intended to show the Economic Complexity of a given economy
Tree Map of Benin Exports (2009) by product category. The Product Exports Treemaps are one of the most recent applications of these kind of visualizations, developed by the Harvard-MIT Observatory of Economic Complexity

In 1786, William Playfair published the first presentation graphics.

Specific methods and techniques

Applications

Information visualization insights are being applied in areas such as:[2]

Organization

Notable academic and industry laboratories in the field are:

Conferences in this field, ranked by significance in data visualization research,[6] are:

For further examples, see: Category:Computer graphics organizations

See also

References

  1. ^ Tamara Munzner. "Process and Pitfalls in Writing Information Visualization Research Papers". www.cs.ubc.ca. Retrieved 9 April 2018.
  2. ^ a b Benjamin B. Bederson and Ben Shneiderman (2003). The Craft of Information Visualization: Readings and Reflections, Morgan Kaufmann ISBN 1-55860-915-6.
  3. ^ James J. Thomas and Kristin A. Cook (Ed.) (2005). Illuminating the Path: The R&D Agenda for Visual Analytics Archived 2008-09-29 at the Wayback Machine.. National Visualization and Analytics Center. p.30
  4. ^ G. Scott Owen (1999). History of Visualization. Accessed Jan 19, 2010.
  5. ^ doi:10.1177/1460458212465213
  6. ^ Kosara, Robert (11 November 2013). "A Guide to the Quality of Different Visualization Venues". eagereyes. Retrieved 7 April 2017.

Further reading

  • Ben Bederson and Ben Shneiderman (2003). The Craft of Information Visualization: Readings and Reflections. Morgan Kaufmann.
  • Stuart K. Card, Jock D. Mackinlay and Ben Shneiderman (1999). Readings in Information Visualization: Using Vision to Think, Morgan Kaufmann Publishers.
  • Jeffrey Heer, Stuart K. Card, James Landay (2005). "Prefuse: a toolkit for interactive information visualization". In: ACM Human Factors in Computing Systems CHI 2005.
  • Andreas Kerren, John T. Stasko, Jean-Daniel Fekete, and Chris North (2008). Information Visualization – Human-Centered Issues and Perspectives. Volume 4950 of LNCS State-of-the-Art Survey, Springer.
  • Riccardo Mazza (2009). Introduction to Information Visualization, Springer.
  • Spence, Robert Information Visualization: Design for Interaction (2nd Edition), Prentice Hall, 2007, ISBN 0-13-206550-9.
  • Colin Ware (2000). Information Visualization: Perception for design. San Francisco, CA: Morgan Kaufmann.
  • Kawa Nazemi (2014). Adaptive Semantics Visualization Eurographics Association.

External links

  • Media related to Information visualization at Wikimedia Commons
  • Information Visualization at Curlie
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