Data and information visualization: Difference between revisions

Revision as of 07:07, 11 July 2025

Revision as of 07:12, 11 July 2025

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To communicate information clearly and efficiently, data visualization uses [[statistical graphics]], [[plot (graphics)|plots]], [[Infographic|information graphics]] and other tools. Numerical data may be encoded using dots, lines, or bars, to visually communicate a quantitative message.<ref name="ReferenceA">{{cite web|url=http://www.perceptualedge.com/articles/ie/the_right_graph.pdf|title=Stephen Few-Perceptual Edge-Selecting the Right Graph for Your Message-2004|access-date=2014-09-08|archive-url=https://web.archive.org/web/20141005080924/http://www.perceptualedge.com/articles/ie/the_right_graph.pdf|archive-date=2014-10-05|url-status=live}}</ref> Effective visualization helps users analyze and reason about data and evidence.<ref>{{Cite web|url=https://www.tableau.com/learn/articles/interactive-map-and-data-visualization-examples|title = 10 Examples of Interactive Map Data Visualizations| work=Tableau }}</ref> It makes complex data more accessible, understandable, and usable, but can also be reductive.<ref>{{Cite book|url=https://www.aup.nl/en/book/9789463722902

|title=Data Visualization in Society|date=2020-04-16|publisher=Amsterdam University Press|isbn=978-90-485-4313-7|editor-last=Engebretsen|editor-first=Martin |language=en|doi=10.5117/9789463722902_ch02|editor-last2=Helen|editor-first2=Kennedy}}</ref> Users may have particular analytical tasks, such as making comparisons or understanding [[causality]], and the design principle of the graphic (i.e., showing comparisons or showing causality) follows the task. Tables are generally used where users will look up a specific measurement, while charts of various types are used to show patterns or relationships in the data for one or more variables.

|___location=Nieuwe Prinsengracht 89 1018 VR Amsterdam Nederland|language=en|doi=10.5117/9789463722902_ch02|editor-last2=Helen|editor-first2=Kennedy}}</ref> Users may have particular analytical tasks, such as making comparisons or understanding [[causality]], and the design principle of the graphic (i.e., showing comparisons or showing causality) follows the task. Tables are generally used where users will look up a specific measurement, while charts of various types are used to show patterns or relationships in the data for one or more variables.

Data visualization refers to the techniques used to communicate data or information by encoding it as visual objects (e.g., points, lines, or bars) contained in graphics. The goal is to communicate information clearly and efficiently to users. It is one of the steps in [[data analysis]] or [[data science]]. According to Vitaly Friedman (2008) the "main goal of data visualization is to communicate information clearly and effectively through graphical means. It doesn't mean that data visualization needs to look boring to be functional or extremely sophisticated to look beautiful. To convey ideas effectively, both aesthetic form and functionality need to go hand in hand, providing insights into a rather sparse and complex data set by communicating its key aspects in a more intuitive way. Yet designers often fail to achieve a balance between form and function, creating gorgeous data visualizations which fail to serve their main purpose — to communicate information".<ref>Vitaly Friedman (2008) [http://www.smashingmagazine.com/2008/01/14/monday-inspiration-data-visualization-and-infographics/ "Data Visualization and Infographics"] {{Webarchive|url=https://web.archive.org/web/20080722172600/http://www.smashingmagazine.com/2008/01/14/monday-inspiration-data-visualization-and-infographics/ |date=2008-07-22 }} in: ''Graphics'', Monday Inspiration, January 14, 2008.</ref>

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# The result is readable.

Readability means that it is possible for a viewer to understand the underlying data, such as by making comparisons between proportionally sized visual elements to compare their respective data values; or using a legend to decode a map, like identifying coloured regions on a climate map to read temperature at that ___location. For greatest efficiency and simplicity of design and user experience, this readability is enhanced through the use of bijective mapping in that design of the image elements - where the mapping of representational element to data variable is unique.<ref name=StudiesComputIntell_2009>{{cite journalbook |last1=Ziemkiewicz |first1=C. |last2=Kosara |first2=R. |title=EmbeddingAdvances in Information Visualizationand withinIntelligent VisualSystems Representation (|chapter in Advances in=Embedding Information andVisualization Intelligentwithin Systems)Visual Representation |journalseries=Studies in Computational Intelligence |date=2009 |volume=251 |pages=307–326 |doi=10.1007/978-3-642-04141-9_15 |publisher=Springer |___location=Berlin, Heidelberg |isbn=978-3-642-04140-2 }}</ref>

Kosara (2007)<ref name=IEEExplore_2007/> also identifies the need for a visualisation to be "recognisable as a visualisation and not appear to be something else". He also states that recognisability and readability may not always be required in all types of visualisation e.g. "informative art" (which would still meet all three above criteria but might not look like a visualisation) or "artistic visualisation" (which similarly is still based on non-visual data to create an image, but may not be readable or recognisable).