Information visualization

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Information visualization (or visualisation) is the study of 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. It is related to data visualization, infographics, and scientific visualization. One definition is that it's information visualization when the spatial representation (e.g., the page layout of a graphic design) is chosen, whereas it's scientific visualization when the spatial representation is given.[1]

Overview[]

Partial map of the Internet early 2005 represented as a graph, 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[]

See also: Infographics § History, and Data visualization § 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 . 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 visualization, information visualization and scientific visualization, 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.

Techniques[]

See also: Data visualization § 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:

  • IEEE Visualization: An annual international conference on scientific visualization, information visualization, and visual analytics. Conference is held in October.
  • ACM SIGGRAPH: An annual international conference on computer graphics, convened by the ACM SIGGRAPH organization. Conference dates vary.
  • : An annual Europe-wide conference on data visualization, organized by the Eurographics Working Group on Data Visualization and supported by the IEEE Visualization and Graphics Technical Committee (IEEE VGTC). Conference is usually held in June.
  • Conference on Human Factors in Computing Systems (CHI): An annual international conference on human–computer interaction, hosted by ACM SIGCHI. Conference is usually held in April or May.
  • Eurographics: An annual Europe-wide computer graphics conference, held by the European Association for Computer Graphics. Conference is usually held in April or May.
  • : An annual visualization symposium held in the Asia-Pacific region, sponsored by the IEEE Visualization and Graphics Technical Committee (IEEE VGTC). Conference is usually held in March or April.

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. ^ Jump up to: 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 Archived 2012-10-08 at the Wayback Machine. Accessed Jan 19, 2010.
  5. ^ Faisal, Sarah; Blandford, Ann; Potts, Henry WW (2013). "Making sense of personal health information: Challenges for information visualization" (PDF). Health Informatics Journal. 19 (3): 198–217. doi:10.1177/1460458212465213. PMID 23981395. S2CID 3825148.
  6. ^ Kosara, Robert (11 November 2013). "A Guide to the Quality of Different Visualization Venues". eagereyes. Retrieved 7 April 2017.

Further reading[]

External links[]

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