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Visual analytics is an outgrowth of the fields of information visualization and scientific visualization that focuses on analytical reasoning facilitated by interactive visual interfaces. 〔Pak Chung Wong and J. Thomas (2004). "Visual Analytics". in: ''IEEE Computer Graphics and Applications'', Volume 24, Issue 5, Sept.-Oct. 2004 Page(s): 20–21.〕 == Overview == Visual analytics is "the science of analytical reasoning facilitated by interactive visual interfaces." 〔 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.4.〕 It can attack certain problems whose size, complexity, and need for closely coupled human and machine analysis may make them otherwise intractable.〔 Robert Kosara (2007). (''Visual Analytics'' ). ITCS 4122/5122, Fall 2007. Retrieved 28 june 2008.〕 Visual analytics advances science and technology developments in analytical reasoning, interaction, data transformations and representations for computation and visualization, analytic reporting, and technology transition. 〔Kielman, J. and Thomas, J. (Guest Eds.) (2009). "Special Issue: Foundations and Frontiers of Visual Analytics". in: ''Information Visualization'', Volume 8, Number 4, Winter 2009 Page(s): 239-314.〕 As a research agenda, visual analytics brings together several scientific and technical communities from computer science, information visualization, cognitive and perceptual sciences, interactive design, graphic design, and social sciences. Visual analytics integrates new computational and theory-based tools with innovative interactive techniques and visual representations to enable human-information discourse. The design of the tools and techniques is based on cognitive, design, and perceptual principles. This science of analytical reasoning provides the reasoning framework upon which one can build both strategic and tactical visual analytics technologies for threat analysis, prevention, and response. Analytical reasoning is central to the analyst’s task of applying human judgments to reach conclusions from a combination of evidence and assumptions.〔 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.〕 Visual analytics has some overlapping goals and techniques with information visualization and scientific visualization. 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 coupling interactive visual representations with underlying analytical processes (e.g., statistical procedures, data mining techniques) such that high-level, complex activities can be effectively performed (e.g., sense making, reasoning, decision making). Visual analytics seeks to marry techniques from information visualization with techniques from computational transformation and analysis of data. Information visualization forms part of the direct interface between user and machine, amplifying human cognitive capabilities in six basic ways:〔〔 Stuart Card, J.D. Mackinlay, and Ben Shneiderman (1999). "Readings in Information Visualization: Using Vision to Think". Morgan Kaufmann Publishers, San Francisco.〕 # by increasing cognitive resources, such as by using a visual resource to expand human working memory, # by reducing search, such as by representing a large amount of data in a small space, # by enhancing the recognition of patterns, such as when information is organized in space by its time relationships, # by supporting the easy perceptual inference of relationships that are otherwise more difficult to induce, # by perceptual monitoring of a large number of potential events, and # 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. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「visual analytics」の詳細全文を読む スポンサード リンク
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