信息可视化

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

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

图形表示的一小部分WWW,演示超链接

Information visualization or information visualisation is the study of (interactive) 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. The naming of subfields is sometimes confusing. One accepted definition is that it's information visualization when the spatial representation is chosen, whereas it's scientific visualization when the spatial representation is given.[1]

Information visualization or information visualisation is the study of (interactive) 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. The naming of subfields is sometimes confusing. One accepted definition is that it's information visualization when the spatial representation is chosen, whereas it's scientific visualization when the spatial representation is given.

信息可视化或信息可视化是研究(交互式)视觉表征的抽象数据,以加强人类的认知。抽象数据包括数值数据和非数值数据,如文本和地理信息。子字段的命名有时令人困惑。一个公认的定义是,当选择空间表示时是信息可视化,而当给定空间表示时是科学可视化。


Overview

文件:Internet map 1024.jpg
Partial map of the Internet early 2005 represented as a graph, each line represents two IP addresses, and some delay between those two nodes.

Partial map of the Internet early 2005 represented as a graph, each line represents two IP addresses, and some delay between those two nodes.

互联网2005年初的部分地图用图表示,每一行代表两个IP 地址,以及这两个节点之间的一些延迟


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]

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".

信息可视化的研究领域已经从人机交互、计算机科学、图形学、视觉设计、心理学和商业方法的研究中脱颖而出。在科学研究、数字图书馆、数据挖掘、财务数据分析、市场研究、生产控制和药物发现等领域,它正日益成为一个关键组成部分。


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]

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."

信息可视化认为,“视觉表征和交互技术利用了人眼进入大脑的宽带通路,使用户能够一次性看到、探索和理解大量信息。信息可视化致力于创造用直观的方式传递抽象信息的方法


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.

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.

数据分析是所有工业应用研究和问题解决中不可缺少的一部分。最基本的数据分析方法是可视化(直方图、散点图、地面图、树图、平行坐标图等)。)、统计学(假设检验、回归分析、主成分分析等)等方法。数据挖掘(关联挖掘,关联挖掘等)是数据挖掘技术的一个重要组成部分。)和机器学习方法(聚类、分类、决策树等)。在这些方法中,信息可视化分析,或称视觉数据分析,最依赖于人类分析师的认知技能,并允许发现仅受人类想象力和创造力限制的非结构化的可操作的见解。分析师不需要学习任何复杂的方法就能够解释数据的可视化。信息可视化也是一个假设生成机制,它可以是,并且通常跟随着更多的分析或正式的分析,如统计假设检验。


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 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 visualisation, information visualization and scientific visualisation, and more specific areas such as volume visualization.

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". They have been devoted to the general topics of data visualisation, information visualization and scientific visualisation, and more specific areas such as volume visualization.

现代可视化研究始于计算机图形学,它“从一开始就被用于研究科学问题。然而,在早期,图形处理能力的缺乏常常限制了它的实用性。最近对可视化的重视始于1987年的《科学计算可视化计算机图形学》专刊。自那以后,已经举办了几次会议和研讨会,由 IEEE计算机协会和 ACM SIGGRAPH 协办。”。他们致力于数据可视化、信息可视化和科学可视化的一般主题,以及更具体的领域,如体视化。

Product Space Localization, intended to show the Economic Complexity of a given economy]]

产品空间本地化,旨在显示给定经济体的经济复杂性]

文件:Benin English.png
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

Observatory of Economic Complexity]]

经济复杂性天文台]


In 1786, William Playfair published the first presentation graphics.

In 1786, William Playfair published the first presentation graphics.

1786年,威廉·普莱费尔出版了第一份演示图表。


Specific methods and techniques


Applications

Information visualization insights are being applied in areas such as:[2]

Information visualization insights are being applied in areas such as:

信息可视化的洞察力正被应用于以下领域:

  • Scientific research
  • Financial data analysis
  • Market studies

Notable academic and industry laboratories in the field are:

该领域著名的学术和工业实验室有:


Organization

Notable academic and industry laboratories in the field are:

Conferences in this field, ranked by significance in data visualization research, are:

这个领域的会议,按照数据可视化研究的重要性排序,包括:


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.

For further examples, see: :Category:Computer graphics organizations

进一步的例子,请参阅: : 类别: 计算机图形学组织

  • 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.
  • 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


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Further reading

  • Spence, Robert Information Visualization: Design for Interaction (2nd Edition), Prentice Hall, 2007, .


< ! ——这里列出的外部链接只能与信息可视化有关,而不能与计算可视化、数据可视化、信息图形、知识可视化、信息可视化、视觉分析、可视化技术和其他特定的可视化主题有关。 -- >


External links


Category:Computational science

类别: 计算科学


Category:Computer graphics

类别: 计算机图形学

模板:Visualization

Category:Infographics

分类: 信息图表

模板:Authority control

Category:Scientific modeling

类别: 科学建模

nl:Datavisualisatie

nl:Datavisualisatie


This page was moved from wikipedia:en:Information visualization. Its edit history can be viewed at 信息可视化/edithistory

  1. Tamara Munzner. "Process and Pitfalls in Writing Information Visualization Research Papers". www.cs.ubc.ca. Retrieved 9 April 2018.
  2. 2.0 2.1 Benjamin B. Bederson and Ben Shneiderman (2003). The Craft of Information Visualization: Readings and Reflections, Morgan Kaufmann .
  3. James J. Thomas and Kristin A. Cook (Ed.) (2005). Illuminating the Path: The R&D Agenda for Visual Analytics -{zh-cn:互联网档案馆; zh-tw:網際網路檔案館; zh-hk:互聯網檔案館;}-存檔,存档日期2008-09-29.. National Visualization and Analytics Center. p.30
  4. G. Scott Owen (1999). History of Visualization -{zh-cn:互联网档案馆; zh-tw:網際網路檔案館; zh-hk:互聯網檔案館;}-存檔,存档日期2012-10-08.. 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. Unknown parameter |s2cid= ignored (help)
  6. Kosara, Robert (11 November 2013). "A Guide to the Quality of Different Visualization Venues". eagereyes. Retrieved 7 April 2017.