Information Visualization

Information visualization helps the human eye perceive huge amounts of data in an understandable way. It enables researchers to represent, draft, and display data visually in a way that allows humans to more efficiently identify, visualize, and remember it. Techniques of information visualization take advantage of the broad bandwidth pathway of the human eye. Information visualization is an indispensable part of applied research and problem solving, helping people graphically display, spatially organize, and locate huge amounts of data and information to facilitate fast and efficient categorization, classification, organization, and systematization of information.


Both quantitative and qualitative methods of research use information visualization techniques extensively. Graphs, charts, scatterplots, box plots, tables, diagrams, maps, three-dimensional figures, pictograms, schemes, tree diagrams, and coordinate plots are just few examples of information visualization techniques that help illustrate data. Information visualization reinforces human cognition by intuitively representing abstract information, including both numerical and nonnumerical data, textual information, and geographical dislocation.

A screenshot from QtiPlot 0.8.5 running on GNU/Linux.

A screenshot from QtiPlot 0.8.5 running on GNU/Linux.

Statistical research uses information visualization most vividly. The results of hypothesis testing, regression analysis, and instrumental variables analysis are all displayed via information visualization techniques to make the numbers and figures meaningful for data interpretation, analysis, and relevant decision making. Data grouping, clustering, classifying, and organizing are other examples of data mining and visual presentation.

Information visualization helps a human eye creatively structure and order seemingly unstructurable information. It yields unexpected insights into a vast array of data and opens up the orders and systems hidden from the human eye while gathering the information. Information visualization also facilitates the formation of a hypothesis-generating scheme, which is often followed by a more complex method of hypothesis testing.

A number of fields and disciplines have contributed to the formation of information visualization, including computer science, psychology, visual design, human-computer interaction, graphics, and business methods. Computer graphics gave rise to the emergence of information visualization and its extensive use in scientific problem solving and research; the 1987 special issue of Computer Graphics on visualization in scientific computing heralded the emergence of information visualization as a scientific graphical technique and opened up vast possibilities for researchers and explorers.

Information visualization is used in scientific research, data representation, digital libraries, data mining, financial data analysis, social data analysis, and many other fields. Cloud-based visualization services, digital media, computer-mediated communication, dynamic interactive representations, cognitive ethnography, multiscale software, and video data analysis are all fast-growing and developing fields that use information visualization extensively. In addition, effective system design is based on dynamic interactive representations, and scientists refer to information visualization in order to understand their cognitive and computational characteristics. Information visualization is also increasingly used in modern artworks, and its principles are applied to exhibit displays and configurations in modern art museums throughout the world.

—Mariam Orkodashvili, PhD

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(MLA 8th Edition)