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Jenseevinck (talk | contribs) (1) Removed 'tables' as an example of data visualization; added (2) criteria for data & info visualisation with (3) reference, that explain why (Table is not a visualisation, it requires an image). Corrected error lumping Tree maps in with Location maps. Added detail on diff kinds of information visualization with reference. |
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'''Information visualization''', on the other hand, deals with multiple, large-scale and complicated datasets which contain quantitative (numerical) data as well as qualitative (non-numerical, i.e. verbal or graphical) and primarily abstract information and its goal is to add value to raw data, improve the viewers' comprehension, reinforce their cognition and help them derive insights and make decisions as they navigate and interact with the computer-supported graphical display. Visual tools used in information visualization include [[map]]s for ___location based data; ''hierarchical''<ref>Heer, Jeffrey, Bostock, Michael, Ogievetsky, Vadim (2010) A tour through the visualization zoo, Communications of the ACM, Volume 53, Issue 6 Pages 59 - 67 https://doi.org/10.1145/1743546.1743567</ref> organisations of data such as [[tree map]]s, [[radial_tree]]s, and other [[tree_structure]]s; displays that prioritise ''relationships'' (Heer et al. 2010) such as [[Sankey diagram]]s, [[network diagram]]s, [[venn diagram]]s, [[mind map]]s, [[semantic network]]s, [[entity-relationship diagram]]s; [[flow chart]]s, [[timeline]]s, etc.
Useful '''criteria''' for a data or information visualization include: '''1. It is based on (non-visual) data '''- that is, a data/info viz is not image processing and collage;''' 2. it creates an image'''- specifically that the image plays the primary role in communicating meaning and is not an illustration accompanying the data in text form; and '''3. the result is readable'''<ref> Kosara, Robert (2007) Visualization Criticism – The Missing Link Between Information Visualization and Art, Proceedings of the 11th International Conference on Information Visualisation (IV), pp. 631–636, 2007. DOI: 10.1109/IV.2007.130</ref> . 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>Ziemkiewicz, Caroline, Kosara, Robert (2010) Embedding Information Visualization Within Visual Representation, in Ras, Ribarsky, Advances in Information and Intelligent Systems</ref>. Kosara (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. ▼
[[Emerging technologies]] like [[virtual reality|virtual]], [[augmented reality|augmented]] and [[mixed reality]] have the potential to make information visualization more immersive, intuitive, interactive and easily manipulable and thus enhance the user's [[visual perception]] and [[cognition]].<ref>{{Citation |title=Visualizing Big Data with augmented and virtual reality: challenges and research agenda. |last1=Olshannikova |first1=Ekaterina |last2=Ometov |first2=Aleksandr |last3=Koucheryavy |first3=Yevgeny |last4=Ollson |first4=Thomas |journal=[[Journal of Big Data]] |volume=2 |issue=22 |year=2015 |doi=10.1186/s40537-015-0031-2|doi-access=free }}</ref> In data and information visualization, the goal is to graphically present and explore abstract, non-physical and non-spatial data collected from [[database]]s, [[information system]]s, [[file system]]s, [[document]]s, [[Marketing information system|business data]], etc. (''presentational and exploratory visualization'') which is different from the field of ''[[scientific visualization]]'', where the goal is to render realistic images based on physical and [[Geographic_data_and_information|spatial]] [[scientific data]] to confirm or reject [[hypotheses]] (''confirmatory visualization'').<ref>{{Citation |author=Card, Mackinlay, and Shneiderman |title=Readings in Information Visualization: Using Vision to Think |pages=6–7 |year=1999 |publisher=Morgan Kaufmann}}</ref>
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===Characteristics of effective graphical displays===
{{quote box|width = 300px|quote=The greatest value of a picture is when it forces us to notice what we never expected to see.
|source=[[John Tukey]]<ref name="Tukey1977">{{cite book
| last = Tukey
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The [[Congressional Budget Office]] summarized several best practices for graphical displays in a June 2014 presentation. These included: a) Knowing your audience; b) Designing graphics that can stand alone outside the report's context; and c) Designing graphics that communicate the key messages in the report.<ref>{{cite web|url=https://www.cbo.gov/publication/45224|title=Telling Visual Stories About Data - Congressional Budget Office|website=www.cbo.gov|access-date=2014-11-27|archive-url=https://web.archive.org/web/20141204135630/https://www.cbo.gov/publication/45224|archive-date=2014-12-04|url-status=live}}</ref>
▲Useful '''criteria''' for a data or information visualization include: '''1. It is based on (non-visual) data '''- that is, a data/info viz is not image processing and collage;''' 2. it creates an image'''- specifically that the image plays the primary role in communicating meaning and is not an illustration accompanying the data in text form; and '''3. the result is readable'''<ref> Kosara, Robert (2007) Visualization Criticism – The Missing Link Between Information Visualization and Art, Proceedings of the 11th International Conference on Information Visualisation (IV), pp. 631–636, 2007. DOI: 10.1109/IV.2007.130</ref> . 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>Ziemkiewicz, Caroline, Kosara, Robert (2010) Embedding Information Visualization Within Visual Representation, in Ras, Ribarsky, Advances in Information and Intelligent Systems</ref>. Kosara (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.
===Quantitative messages===
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