The Full Wiki

Information Visualization: Wikis

Advertisements
  

Note: Many of our articles have direct quotes from sources you can cite, within the Wikipedia article! This article doesn't yet, but we're working on it! See more info or our list of citable articles.

Did you know ...


More interesting facts on Information Visualization

Include this on your site/blog:

Encyclopedia

(Redirected to Information graphics article)

From Wikipedia, the free encyclopedia

The Washington Metro subway map

Information graphics or infographics are visual representations of information, data or knowledge. These graphics are used where complex information needs to be explained quickly and clearly[1], such as in signs, maps, journalism, technical writing, and education. They are also used extensively as tools by computer scientists, mathematicians, and statisticians to ease the process of developing and communicating conceptual information.

Contents

Overview

Today information graphics surround us in the media, in published works both pedestrian and scientific, in road signs and manuals. They illustrate information that would be unwieldy in text form, and act as a visual shorthand for everyday concepts such as stop and go.

In newspapers, infographics are commonly used to show the weather, as well as maps and site plans for newsworthy events, and graphs for statistical data. Some books are almost entirely made up of information graphics, such as David Macaulay's The Way Things Work. Although they are used heavily in children's books, they are also common in scientific literature, where they illustrate physical systems, especially ones that cannot be photographed (such as cutaway diagrams, astronomical diagrams, and images of microscopic or sub-microscopic systems).

Modern maps, especially route maps for transit systems, use infographic techniques to integrate a variety of information, such as the conceptual layout of the transit network, transfer points, and local landmarks.

Traffic signs and other public signs rely heavily on information graphics, such as stylized human figures (the ubiquitous stick figure), icons and emblems to represent concepts such as yield, caution, and the direction of traffic. Public places such as transit terminals usually have some sort of integrated "signage system" with standardized icons and stylized maps.

Technical manuals make extensive use of diagrams and also common icons to highlight warnings, dangers, and standards certifications.

History

Advertisements

Early experiments

Coxcomb chart by Florence Nightingale illustrating causes of mortality during the Crimean War (1857)

In prehistory, early humans created the first information graphics: cave paintings and later maps. Map-making began several millennia before writing, and the map at Çatalhöyük dates from around 7500 BCE. Later icons were used to keep records of cattle and stock. The Indians of Mesoamerica used imagery to depict the journeys of past generations. Illegible on their own, they served as a supportive element to memory and storytelling.

Pie chart from Playfair's Statistical Breviary (1801)

In 1626 Christopher Scheiner published the Rosa Ursina sive Sol which used a variety of graphics to reveal his astronomical research on the sun. He used a series of images to explain the rotation of the sun over time (by tracking sunspots).

In 1786, William Playfair published the first data graphs in his book The Commercial and Political Atlas. The book is filled with statistical graphs, bar charts, line graph's and histograms, that represent the economy of 18th century England. In 1801 Playfair introduced the first area chart and pie chart in Statistical Breviary.[2]

In 1857, English nurse Florence Nightingale used information graphics persuading Queen Victoria to improve conditions in military hospitals, principally the Coxcomb chart, a combination of stacked bar and pie charts, depicting the number and causes of deaths during each month of the Crimean War.

1861 saw the release of a seminal information graphic on the subject of Napoleon's disastrous march on Moscow.

Charles Minard's information graphic of Napoleon's invasion of Russia

The creator, Charles Joseph Minard, captured four different changing variables that contributed to the failure, in a single two-dimensional image: the army's direction as they traveled, the location the troops passed through, the size of the army as troops died from hunger and wounds, and the freezing temperatures they experienced.

James Joseph Sylvester introduced the term "graph" in 1878 and published a set of diagrams showing the relationship between chemical bonds and mathematical properties. These were also the first mathematic graphs.

The development of a visual language in the 20th century

In 1936 Otto Neurath introduced a system of pictographs intended to function as an international visual or picture language. Isotype included a set of stylized human figures which were the basis for the ubiquitous modern stick figures.

In 1942 Isidore Isou published the Lettrist manifesto.

In 1958 Stephen Toulmin proposed a graphical argument model that became influential in argumentation theory and its applications.

The 1972 Munich Olympics were the venue for Otl Aicher to introduce a new set of pictograms that proved to be extremely popular, and influenced the ubiquitous modern stick figures used in public signs.

Also in 1972 the Pioneer Plaque was launched into space with the Pioneer 10 probe. Inscribed into the plaque was an information graphic intended as a kind of interstellar message in a bottle, designed by Carl Sagan and Frank Drake. The message is unique in that it is intended to be understood by extraterrestrial beings who would share no common language with humans. It depicts a picture of a man and a woman standing in front of a simplified silhouette of the probe in order to give a sense of scale. It also contains a map locating the sun relative to a number of pulsars, and a simplified depiction of the solar system, with the probe's path from earth into outer space shown with an arrow.

Information graphics subjects

Visual devices

Information graphics are visual devices indended to communicate complex information quickly and clearly. The devices include, according to Doug Newsom (2004)[1], charts, diagrams, graphs, tables, maps and lists. Among the most common devices are horizontal bar charts, vertical column charts, and round or oval pie charts, that can summarize a lot of statistical information. Diagrams can be used to show how a system works, and may be an organizational chart that shows lines of authority, or a systems flowchart that shows sequential movement. Illustrated graphics use images to related data. The snapshots features used every day by USA Today are good examples of this technique. Tables are commonly used and may contain lots of numbers. Modern interactive maps and bulleted numbers are also infographic devices.[1]

Elements of information graphics

The basic material of an information graphic is the data, information, or knowledge that the graphic presents. In the case of data, the creator may make use of automated tools such as graphing software to represent the data in the form of lines, boxes, arrows, and various symbols and pictograms. The information graphic might also feature a key which defines the visual elements in plain English. A scale and labels are also common.

Interpreting information graphics

Many information graphics are specialised forms of depiction that represent their content in sophisticated and often abstract ways. In order to interpret the meaning of these graphics appropriately, the viewer requires a suitable level of graphicacy. In many cases, the required graphicacy involves comprehension skills that are learned rather than innate. At a fundamental level, the skills of decoding individual graphic signs and symbols must be acquired before sense can be made of an information graphic as a whole. However, knowledge of the conventions for distributing and arranging these individual components is also necessary for the building of understanding.

Interpreting with a common visual language

In contrast to the above, many other forms of infographics take advantage of innate visual language that is largely universal. The disciplined use of the color red, for emphasis, on an otherwise muted design, demands attention in a primal way even children understand. Many maps, interfaces, dials and gauges on instruments and machinery use icons that are easy to grasp and speed understanding for safe operation. The use of a rabbit and a turtle icon to represent fast and slow, respectively, is one such successful use by the John Deere company on the throttle of their tractors.

Modern practitioners

A statistician and sculptor, Edward Tufte has written a series of highly regarded books on the subject of information graphics. Tufte also delivers lectures and workshops on a regular basis. He describes the process of incorporating many dimensions of information into a two-dimensional image as 'escaping flatland' (alluding to the 2-dimensional world of the Victorian novella Flatland).

The work done by Peter Sullivan for The Sunday Times in the 1970s, 80s and 90s, was one of the key factors in encouraging newspapers to use more graphics. Sullivan is also one of the few authors who have written about information graphics in newspapers. Likewise the staff artists at USA Today, the colorful United States newspaper that debuted in 1982, firmly established the philosophy of using graphics to make information easier to comprehend. The paper received criticism for oversimplifying news and sometimes creating infographics that emphasized entertainment over respect for content and data, sometimes referred to as chartjunk. While some critics deride the graphic qualities of this work, its role in establishing infographics as a practice cannot be ignored.

Nigel Holmes is an established commercial creator of what he calls "explanation graphics". His works deal not only with the visual display of information but also of knowledge – how to do things. He created graphics for Time magazine for 16 years, and is the author of several books on the subject.

Close and strongly related to the field of information graphics, is information design. Actually, making infographics is a certain discipline within the information design world. Author and founder of the TED, Richard Saul Wurman, is considered the originator of the phrase, "information architect", and many of his books, such as Information Anxiety, helped propel the phrase, "information design", from a concept to an actual job category.

While the art form of infographics has its roots in print, by the year 2000, the use of Adobe Flash-based animations on the web has allowed to make mapping solutions and other products famous and addictive by using many key best practices of infographics.

Likewise, their use in television is relatively recent, for in 2002, two Norwegian musicians of Röyksopp issued a music video for their song Remind Me that was completely made from animated infographics. In 2004, a television commercial for the French energy company Areva used similar animated infographics and both of these videos and their high visibility have helped the corporate world recognize the value in using this form of visual language to describe complex information efficiently.

See also

References

  1. ^ a b c Doug Newsom and Jim Haynes (2004). Public Relations Writing: Form and Style. p.236.
  2. ^ H. Gray Funkhouser (1937) Historical Development of the Graphical Representation of Statistical Data. Osiris, Vol. 3. (1937), pp. 269–404.

Further reading

  • William S. Cleveland (1985). The Elements of Graphing Data. Summit, NJ: Hobart Press.
  • William S. Cleveland (1993). Visualizing Data. Summit, NJ: Hobart Press.
  • Paul Lewi (2006). "Speaking of Graphics".
  • Thomas L. Hankins (1999). "Blood, dirt, and nomograms: A particular history of graphs". In: Isis, 90:50–80.
  • Robert L. Harris (1999). Information Graphics: A Comprehensive Illustrated Reference. Oxford University Press.
  • Eric K. Meyer (1997). Designing Infographics. Hayden Books.
  • Edward R. Tufte (1983). The Visual Display of Quantitative Information. Edition, Cheshire, CT: Graphics Press.
  • Edward R. Tufte (1990). Envisioning Information. Cheshire, CT: Graphics Press.
  • Edward R. Tufte (1997). Visual Explanations: Images and Quantities, Evidence and Narrative. Cheshire,
  • Edward R. Tufte (2006). Beautiful Evidence. Cheshire. CT: Graphics Press.
  • John Wilder Tukey (1977). Exploratory Data Analysis. Addison-Wesley.

External links


Information visualization the interdisciplinary study of the visual representation of large-scale collections of non-numerical information, such as files and lines of code in software systems,[1] and the use of graphical techniques to help people understand and analyze data.[2] In contrast with scientific visualization, information visualization focuses on abstract data sets, such as unstructured text or points in high-dimensional space, that do not have an inherent 2D or 3D geometrical structure.[3][4]

Contents

Overview

The term Information visualization could be taken to subsume all developments in data visualization, information graphics, knowledge visualization, scientific visualization and visual design. At this level, almost anything, if sufficiently organized, is information of a sort: tables, graphs, maps and even text, whether static or dynamic, provide some means to see what lies within, determine the answer to a question, find relations, and perhaps apprehend things which could not be seen so readily in other forms. But today the term "information visualization" in scientific research is generally applied to the visual representation of large-scale collections of non-numerical information.[5]

Information visualization focused on the creation of approaches for conveying abstract information in intuitive ways. 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.[6]

Examples

Visualization of various data structures requires new user interface and visualization techniques, which is now evolving into a separate discipline.[7] This area of information visualization is different from the classical scientific visualization, although the two fields are related. In information visualization the data to be visualized is not the result of some mathematical models or large data set, but abstract data with their own, inherent structure. Examples of such data are:[7]

  • internal data structures of various programs, like compilers, or trace information for massively parallel programs;
  • WWW site contents;
  • operating system file spaces;
  • data returned from various database query engines, e.g., for digital libraries.

Another characteristics of the field is that the tools to be used are deliberately focused on widely available environments, such as general workstations, WWW, PC-s, etc. These are not tailored at high-end, expensive, and specialized computing equipment.[7]

Link with visual analytics

Information visualization has some overlapping goals and techniques with visual analytics. There is currently no clear consensus on the boundaries between these fields, but broadly speaking the three areas can be distinguished as follows. Scientific visualization deals with data that has a natural geometric structure (e.g., MRI data, wind flows). Information visualization handles abstract data structures such as trees or graphs. Visual analytics is especially concerned with sensemaking and reasoning.[8]

Human cognitive capabilities

Visual analytics seeks to marry techniques from information visualization with techniques from computational transformation and analysis of data. Information visualization itself forms part of the direct interface between user and machine. Information visualization amplifies human cognitive capabilities in six basic ways:[8][9]

  1. by increasing cognitive resources, such as by using a visual resource to expand human working memory,
  2. by reducing search, such as by representing a large amount of data in a small space,
  3. by enhancing the recognition of patterns, such as when information is organized in space by its time relationships,
  4. by supporting the easy perceptual inference of relationships that are otherwise more difficult to induce,
  5. by perceptual monitoring of a large number of potential events, and
  6. by providing a manipulable medium that, unlike static diagrams, enables the exploration of a space of parameter values.

These capabilities of information visualization, combined with computational data analysis, can be applied to analytic reasoning to support the sense-making process.[8]

History

Since the introduction of data graphics in the late 1700’s visual representations of abstract information have been used to demystify data and reveal otherwise hidden patterns. The recent advent of graphical interfaces in the 1990s has enabled direct interaction with visualized information, giving rise to over a decade of information visualization research. Information visualization seeks to augment human cognition by leveraging human visual capabilities to make sense of abstract information, providing means by which humans with constant perceptual abilities can grapple with increasing hordes of data.[10] The term "information visualization" itself was coined by Stuart K. Card, Jock D. Mackinlay and George G. Robertson in 1989.[11] The field of Information visualization which has emerged since the 1990s derives, according to Stuart K. Card in 1999, from several communities:

  • Work in information graphics dates from about the time of William Playfair end of the 18th century, who was among the earliest to use abstract visual properties such as line and area to represent data visually.[12] Ever since classical methods of plotting were developed In 1967 Jacques Bertin was the first to published a theory of graphics. This theory identified the basic elements of diagrams and describes a framework for their design. Edward Tufte in 1983 published a theory of data graphics that emphasized maximizing the density of useful information.[12] Both Bertin's and Tufte's theories became well known and influential in the various communities that led to the development of information visualization as a discipline.[13]
  • Within statistics in 1977 John Tukey began a movement with his work on "Exploring Data Analysis", which effected the data graphics community. The emphasis on this work was not on the quality of graphics but on the use of pictures to give rapid statistical insight into data. For example the Box and whisker plot allowed an analysis to see in an instant the most important four numbers that characterize a distribution. In the 1988 book "Dynamic Graphics for Statistics" William S. Cleveland explicated new visualizations of data in this area. A particular problem here is how to visualize data sets with many variables, see for example Inselberg's parallel coordinates method from 1990.[13]
  • In 1986 the National Science Foundation launched an important new initiative on scientific visualization with the work of H.B. McCormick. The first IEEE Visualization Conference was held in 1990, which initiated a community from earth resource scientists, physicists, to computer scientists in supercomputing.[13]
  • In the artificial intelligence community there was an interest in automatic design of visual presentation of data. The effort here was catalyzed by Jock D. Mackinlay thesis,[14] which formalized Bertin's design theory. added psychophysical data and used generated presentation.[13]
  • Finally the user interface community saw advances in graphics hardware opening the possibility of a new generation of user interfaces.[13]

In 2003 Ben Shneiderman stated that this field has emerging from research in slightly different direction:[15] He also mentions graphics, visual design, computer science and human-computer interaction, and newly psychology and business methods.

Topics

Visualization provide deep insight into the structure of data. There are graphical tools such as coplots, multiway dot plots, and the equal count algorithm. There are fitting tools such as loess and bisquare that fit equations, nonparametric curves, and nonparametric surfaces to data.[16]

Specific methods and techniques

Applications

Information visualization is increasingly applied as a critical component in different directions:[15]

See also:

Experts

Stuart K. Card
Stuart K. Card is an American researcher. He is a Senior Research Fellow at Xerox PARC and one of the pioneers of applying human factors in human–computer interaction. The 1983 book The Psychology of Human-Computer Interaction, which he co-wrote with Thomas P. Moran and Allen Newell, became a very influential book in the field, partly for introducing the Goals, Operators, Methods, and Selection rules (GOMS) framework. His currently research is in the field of developing a supporting science of human–information interaction and visual-semantic prototypes to aid sensemaking.[17]
George W. Furnas
George Furnas is a professor and Associate Dean for Academic Strategy at the School of Information of the University of Michigan. Furnas has also worked with Bell Labs where he earned the moniker "Fisheye Furnas" while working with fisheye visualizations. He is a pioneer of Latent semantic analysis, Professor Furnas is also considered a pioneer in the concept of Mosaic of Responsive Adaptive Systems (MoRAS).
James D. Hollan
James D. Hollan directs the Distributed Cognition and Human-Computer Interaction Laboratory at University of California, San Diego. His research explores the cognitive consequences of computationally-based media. The goal is to understand the cognitive and computational characteristics of dynamic interactive representations as the basis for effective system design. His current work focuses on cognitive ethnography, computer-mediated communication, distributed cognition, human-computer interaction, information visualization, multiscale software, and tools for analysis of video data.
More related scientists

Organization

Organizations

See also

References

  1. ^ S.G. Eick (1994). "Graphically displaying text". In: Journal of Computational and Graphical Statistics, vol 3, pp. 127–142.
  2. ^ John Stasko 2004 syllabus for CS7450, "Information Visualization." http://www.cc.gatech.edu/classes/AY2004/cs7450_spring/ Spring 2004. Retrieved 1 September 2008.
  3. ^ Card, Mackinlay, and Shneiderman, "Readings in Information Visualization: Using Vision to Think," 1999.
  4. ^ Tamara Munzner, Guest Editor's Introduction IEEE Computer Graphics and Applications Special Issue on Information Visualization, Jan/Feb 2002
  5. ^ Michael Friendly (2008). "Milestones in the history of thematic cartography, statistical graphics, and data visualization".
  6. ^ James J. Thomas and Kristin A. Cook (Ed.) (2005). Illuminating the Path: The R&D Agenda for Visual Analytics. National Visualization and Analytics Center. p.30
  7. ^ a b c CWI Project Information Visualization (IV). Coordinator Dr. I. Herman. Startdate: 1997-07-01, Enddate: 2000-12-31. Retrieved 14 July 2008.
  8. ^ a b c James J. Thomas and Kristin A. Cook (Ed.) (2005). Illuminating the Path: The R&D Agenda for Visual Analytics. National Visualization and Analytics Center. p.3-33.
  9. ^ Stuart Card, J.D. Mackinlay, and Ben Shneiderman (1999). "Readings in Information Visualization: Using Vision to Think". Morgan Kaufmann Publishers, San Francisco.
  10. ^ Jeffrey Heer, Stuart K. Card, James Landay (2005). "Prefuse: a toolkit for interactive information visualization". In: ACM Human Factors in Computing Systems CHI 2005.
  11. ^ Stuart K. Card, Jock D. Mackinlay, and Ben Shneiderman (1999). Readings in Information Visualization: Using Vision to Think, Morgan Kaufmann Publishers. p.8.
  12. ^ a b Edward R. Tufte (1983). The Visual Display of Quantitative Information. Graphics Press.
  13. ^ a b c d e Stuart K. Card, Jock D. Mackinlay and Ben Shneiderman (1999). Readings in Information Visualization: Using Vision to Think, Morgan Kaufmann Publishers. pp.6-8.
  14. ^ Jock D. Mackinlay (1986)"Automating the Design of Graphical Presentations of Relational Information". In: ACM Transactions on Graphics 5 (2, April): 110-141.
  15. ^ a b Benjamin B. Bederson and Ben Shneiderman (2003). The Craft of Information Visualization: Readings and Reflections, Morgan Kaufmann ISBN 1-55860-915-6.
  16. ^ William S. Cleveland (1993). Visualizing Data. Hobart Press.
  17. ^ Stuart Card at PARC, 2004. Retrieved 1 July 2008.

Further reading

External links


Advertisements






Got something to say? Make a comment.
Your name
Your email address
Message