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Information visualization

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Graphic representation of a minute fraction of the WWW, demonstrating hyperlinks

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

This section is an excerpt from Data and information visualization § History.[edit]
Selected milestones and inventions

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 visualization, information visualization and scientific visualization, and more specific areas such as volume visualization. In 1786, William Playfair published the first presentation graphics.

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.

There is no comprehensive 'history' of data visualization. There are no accounts that span the entire development of visual thinking and the visual representation of data, and which collate the contributions of disparate disciplines.[5] Michael Friendly and Daniel J Denis of York University are engaged in a project that attempts to provide a comprehensive history of visualization. Contrary to general belief, data visualization is not a modern development. Since prehistory, stellar data, or information such as location of stars were visualized on the walls of caves (such as those found in Lascaux Cave in Southern France) since the Pleistocene era.[6] Physical artefacts such as Mesopotamian clay tokens (5500 BC), Inca quipus (2600 BC) and Marshall Islands stick charts (n.d.) can also be considered as visualizing quantitative information.[7][8]

The first documented data visualization can be tracked back to 1160 B.C. with Turin Papyrus Map which accurately illustrates the distribution of geological resources and provides information about quarrying of those resources.[9] Such maps can be categorized as thematic cartography, which is a type of data visualization that presents and communicates specific data and information through a geographical illustration designed to show a particular theme connected with a specific geographic area. Earliest documented forms of data visualization were various thematic maps from different cultures and ideograms and hieroglyphs that provided and allowed interpretation of information illustrated. For example, Linear B tablets of Mycenae provided a visualization of information regarding Late Bronze Age era trades in the Mediterranean. The idea of coordinates was used by ancient Egyptian surveyors in laying out towns, earthly and heavenly positions were located by something akin to latitude and longitude at least by 200 BC, and the map projection of a spherical Earth into latitude and longitude by Claudius Ptolemy [c. 85–c. 165] in Alexandria would serve as reference standards until the 14th century.[9]

The invention of paper and parchment allowed further development of visualizations throughout history. Figure shows a graph from the 10th or possibly 11th century that is intended to be an illustration of the planetary movement, used in an appendix of a textbook in monastery schools.[10] The graph apparently was meant to represent a plot of the inclinations of the planetary orbits as a function of the time. For this purpose, the zone of the zodiac was represented on a plane with a horizontal line divided into thirty parts as the time or longitudinal axis. The vertical axis designates the width of the zodiac. The horizontal scale appears to have been chosen for each planet individually for the periods cannot be reconciled. The accompanying text refers only to the amplitudes. The curves are apparently not related in time.

Planetary movements

By the 16th century, techniques and instruments for precise observation and measurement of physical quantities, and geographic and celestial position were well-developed (for example, a "wall quadrant" constructed by Tycho Brahe [1546–1601], covering an entire wall in his observatory). Particularly important were the development of triangulation and other methods to determine mapping locations accurately.[5] Very early, the measure of time led scholars to develop innovative way of visualizing the data (e.g. Lorenz Codomann in 1596, Johannes Temporarius in 1596[11]).

French philosopher and mathematician René Descartes and Pierre de Fermat developed analytic geometry and two-dimensional coordinate system which heavily influenced the practical methods of displaying and calculating values. Fermat and Blaise Pascal's work on statistics and probability theory laid the groundwork for what we now conceptualize as data.[5] According to the Interaction Design Foundation, these developments allowed and helped William Playfair, who saw potential for graphical communication of quantitative data, to generate and develop graphical methods of statistics.[12]
Playfair TimeSeries
In the second half of the 20th century, Jacques Bertin used quantitative graphs to represent information "intuitively, clearly, accurately, and efficiently".[12]

John Tukey and Edward Tufte pushed the bounds of data visualization; Tukey with his new statistical approach of exploratory data analysis and Tufte with his book "The Visual Display of Quantitative Information" paved the way for refining data visualization techniques for more than statisticians. With the progression of technology came the progression of data visualization; starting with hand-drawn visualizations and evolving into more technical applications – including interactive designs leading to software visualization.[13]

Programs like SAS, SOFA, R, Minitab, Cornerstone and more allow for data visualization in the field of statistics. Other data visualization applications, more focused and unique to individuals, programming languages such as D3, Python and JavaScript help to make the visualization of quantitative data a possibility. Private schools have also developed programs to meet the demand for learning data visualization and associated programming libraries, including free programs like The Data Incubator or paid programs like General Assembly.[14]

Beginning with the symposium "Data to Discovery" in 2013, ArtCenter College of Design, Caltech and JPL in Pasadena have run an annual program on interactive data visualization.[15] The program asks: How can interactive data visualization help scientists and engineers explore their data more effectively? How can computing, design, and design thinking help maximize research results? What methodologies are most effective for leveraging knowledge from these fields? By encoding relational information with appropriate visual and interactive characteristics to help interrogate, and ultimately gain new insight into data, the program develops new interdisciplinary approaches to complex science problems, combining design thinking and the latest methods from computing, user-centered design, interaction design and 3D 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,[17] 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.
  • EuroVis: 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.
  • PacificVis: 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. ^ 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. ^ a b c Friendly, Michael (2008). "A Brief History of Data Visualization". Handbook of Data Visualization. Springer-Verlag. pp. 15–56. doi:10.1007/978-3-540-33037-0_2. ISBN 9783540330370. S2CID 62626937.
  6. ^ Whitehouse, D. (9 August 2000). "Ice Age star map discovered". BBC News. Archived from the original on 6 January 2018. Retrieved 20 January 2018.
  7. ^ Dragicevic, Pierre; Jansen, Yvonne (2012). "List of Physical Visualizations and Related Artefacts". Archived from the original on 2018-01-13. Retrieved 2018-01-12.
  8. ^ Jansen, Yvonne; Dragicevic, Pierre; Isenberg, Petra; Alexander, Jason; Karnik, Abhijit; Kildal, Johan; Subramanian, Sriram; Hornbæk, Kasper (2015). "Opportunities and challenges for data physicalization". Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems: 3227–3236. Archived from the original on 2018-01-13. Retrieved 2018-01-12.
  9. ^ a b Friendly, Michael (2001). "Milestones in the history of thematic cartography, statistical graphics, and data visualization". Archived from the original on 2014-04-14.
  10. ^ Funkhouser, Howard Gray (January 1936). "A Note on a Tenth Century Graph". Osiris. 1: 260–262. doi:10.1086/368425. JSTOR 301609. S2CID 144492131.
  11. ^ "Data visualization: definition, examples, tools, advice [guide 2020]". Market research consulting. 2020-12-09. Retrieved 2020-12-09.
  12. ^ a b "Data Visualization for Human Perception". The Interaction Design Foundation. Archived from the original on 2015-11-23. Retrieved 2015-11-23.
  13. ^ Friendly, Michael (2006). "A Brief History of Data Visualization" (PDF). York University. Springer-Verlag. Archived (PDF) from the original on 2016-05-08. Retrieved 2015-11-22.
  14. ^ "NY gets new boot camp for data scientists: It's free but harder to get into than Harvard". Venture Beat. Archived from the original on 2016-02-15. Retrieved 2016-02-21.
  15. ^ Interactive Data Visualization
  16. ^ 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.
  17. ^ Kosara, Robert (11 November 2013). "A Guide to the Quality of Different Visualization Venues". eagereyes. Retrieved 7 April 2017.

Further reading

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