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{{Short description|Visual representation of data}}
{{Redirect|Dataviz|the software company|DataViz}}
[[File:Minard.png|thumb|upright=
▲[[File:Minard.png|thumb|upright=2|Statistician professor [[Edward Tufte]] described [[Charles Joseph Minard]]'s 1869 graphic of [[French invasion of Russia|Napoleonic France's invasion of Russia]] as what "may well be the best statistical graphic ever drawn", noting that it captures six variables in two dimensions.<ref name=CorbettCSISS>{{cite web |last1=Corbett |first1=John |title=Charles Joseph Minard: Mapping Napoleon's March, 1861 |url=http://www.csiss.org/classics/content/58 |publisher=Center for Spatially Integrated Social Science |archive-url=https://web.archive.org/web/20030619011958/http://www.csiss.org/classics/content/58 |archive-date=19 June 2003 |url-status=usurped}} ([http://csiss.ncgia.ucsb.edu/ CSISS website has moved]; use archive link for article)</ref>]]
{{Data Visualization}}
{{InfoMaps}}
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'''Information visualization''' deals with multiple, large-scale and complicated datasets which contain quantitative data, as well as qualitative, and primarily abstract information, and its goal is to add value to raw data, improve the viewers' comprehension, reinforce their cognition and help derive insights and make decisions as they navigate and interact with the graphical display. Visual tools used 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; displays that prioritise ''relationships'' such as [[Sankey diagram]]s; [[flowchart]]s, [[timeline]]s.
[[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 |article-number=22 |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]], which is different from ''[[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]].<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>
Effective data visualization is properly sourced, contextualized, simple and uncluttered. The underlying data is accurate and up-to-date to ensure insights are reliable. Graphical items are well-chosen and aesthetically appealing, with shapes, colors and other visual elements used deliberately in a meaningful and non-distracting manner. The visuals are accompanied by supporting texts. Verbal and graphical components complement each other to ensure clear, quick and memorable understanding. Effective information visualization is aware of the needs and expertise level of the target audience.<ref name=IBM>{{Cite web |title=What is data visualization? |url=https://www.ibm.com/topics/data-visualization |website=IBM |date=28 September 2021 |access-date=27 March 2023}}</ref><ref name="Nussbaumer Knaflic"/> Effective visualization can be used for conveying specialized, complex, [[big data]]-driven ideas to a non-technical audience in a visually appealing, engaging and accessible manner, and ___domain experts and executives for making decisions, monitoring performance, generating ideas and stimulating research.<ref name=IBM/><ref name=HDSR/> Data scientists, analysts and data mining specialists use data visualization to check data quality, find errors, unusual gaps, missing values, clean data, explore the structures and features of data, and assess outputs of data-driven models.<ref name=HDSR>{{Cite journal |title=Why Is Data Visualization Important? What Is Important in Data Visualization? |author=Antony Unwin |journal=Harvard Data Science Review |url=https://hdsr.mitpress.mit.edu/pub/zok97i7p/release/4 |date=31 January 2020 |volume=2 |issue=1 |doi=10.1162/99608f92.8ae4d525 |access-date=27 March 2023|doi-access=free }}</ref> Data and information visualization can be part of ''data storytelling'', where they are paired with a [[narrative]] structure, to contextualize the analyzed data and communicate insights gained from analyzing it to convince the audience into making a decision or taking action.<ref name="Nussbaumer Knaflic"/><ref>{{Citation |title=Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals |author=Brent Dykes |publisher=John Wiley & Sons |page=16 |year=2019}}</ref> This can be contrasted with [[statistical graphics]], where complex data are communicated graphically among researchers and analysts to help them perform [[exploratory data analysis]] or convey results of such analyses, where visual appeal, capturing attention to a certain issue and storytelling are less important.<ref>{{Citation |title=Statistics: Concepts and Applications for Science |author=David C. LeBlanc |publisher=Jones & Bartlett Learning |year=2004 |pages=35–36}}</ref>
Data and information visualization is interdisciplinary, it incorporates principles found in [[descriptive statistics]],<ref>{{cite
== Overview ==
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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.
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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Indeed, [[Fernanda Viegas]] and [[Martin M. Wattenberg]] suggested that an ideal visualization should not only communicate clearly, but stimulate viewer engagement and attention.<ref>{{Cite news |first1= Fernanda |last1=Viegas|first2=Martin |last2=Wattenberg |title= How To Make Data Look Sexy |work= CNN |date= April 19, 2011 |url= https://edition.cnn.com/2011/OPINION/04/19/sexy.data/ |url-status= live |archive-date= May 6, 2011 |archive-url= https://web.archive.org/web/20110506065701/http://articles.cnn.com/2011-04-19/opinion/sexy.data_1_visualization-21st-century-engagement?_s=PM%3AOPINION |access-date= May 7, 2017 }}</ref>
Data visualization is closely related to [[information graphics]],
In the commercial environment data visualization is often referred to as [[Dashboard (business)|dashboards]]. [[Infographic]]s are another very common form of data visualization.
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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
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).
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===Visual perception and data visualization===
[[File:Grafana dashboard for MusicBrainz Hetzner Yamaoka server screenshot.webp|thumb|Example of data visualization ([[website monitoring]] for [[MusicBrainz]] wirh [[Grafana]]).]]
A human can distinguish differences in line length, shape, orientation, distances, and color (hue) readily without significant processing effort; these are referred to as "[[Pre-attentive processing|pre-attentive attributes]]". For example, it may require significant time and effort ("attentive processing") to identify the number of times the digit "5" appears in a series of numbers; but if that digit is different in size, orientation, or color, instances of the digit can be noted quickly through pre-attentive processing.<ref name="perceptualedge.com">{{cite web|url=http://www.perceptualedge.com/articles/ie/visual_perception.pdf|title=Steven Few-Tapping the Power of Visual Perception-September 2004|access-date=2014-10-08|archive-url=https://web.archive.org/web/20141005080935/http://www.perceptualedge.com/articles/ie/visual_perception.pdf|archive-date=2014-10-05|url-status=live}}</ref>
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[[File:50 years of datavisulization berengueres own work.png|thumb|upright=1.5|Selected milestones and inventions]]
[[File:ProductSpaceLocalization.png|thumb|upright=.7|[[The Product Space|Product Space Localization]], intended to show the [[List of countries by economic complexity|Economic Complexity]] of a given economy]]
[[File:Benin English.png|thumb|Tree map of Benin exports (2009) by product category,
The invention of paper and parchment allowed further development of visualizations. One graph from the 10th or possibly 11th century is an illustration of planetary movements, used in an appendix of a textbook in monastery schools.<ref name="FUNKHOUSER">{{cite journal|last1=Funkhouser |first1=Howard Gray |title=A Note on a Tenth Century Graph |journal=Osiris |date=January 1936 |volume=1 |pages=260–262 |jstor=301609 |doi=10.1086/368425 |s2cid=144492131}}</ref> The graph apparently was meant to represent a plot of the inclinations of the planetary orbits as a function of the time. For this purpose, the zone of the zodiac was represented on a plane with a horizontal line divided into thirty parts as the time or longitudinal axis. The vertical axis designates the width of the zodiac. The horizontal scale appears to have been chosen for each planet individually for the periods cannot be reconciled. The accompanying text refers only to the amplitudes. The curves are apparently not related in time.
By the 16th century, techniques and instruments for precise observation and measurement of physical quantities, and geographic and celestial position were well-developed (for example, a "wall quadrant" constructed by [[Tycho Brahe]] [1546–1601], covering an entire wall in his observatory). Particularly important were the development of triangulation and other methods to determine mapping locations accurately.<ref name="Springer-Verlag"/> Very early, the measure of time led scholars to develop innovative way of visualizing the data (e.g. Lorenz Codomann in 1596, Johannes Temporarius in 1596<ref>{{Cite web|date=2020-12-09|title=Data visualization: definition, examples, tools, advice [guide 2020]|url=https://www.intotheminds.com/blog/en/data-visualization/|access-date=2020-12-09|website=Market research consulting|language=en-BE}}</ref>).
Mathematicians [[René Descartes]] and [[Pierre de Fermat]] developed analytic geometry and two-dimensional coordinate system which heavily influenced the practical methods of displaying and calculating values. Fermat and [[Blaise Pascal]]'s work on statistics and probability theory laid the groundwork for what we now conceptualize as data.<ref name="Springer-Verlag"/> These developments helped [[William Playfair]], who saw potential for graphical communication of quantitative data, to generate and develop [[List of graphical methods|graphical methods]] of statistics.<ref name=":0" /> In 1786, Playfair published the first presentation graphics.
In the second half of the 20th century, [[Jacques Bertin]] used quantitative graphs to represent information "intuitively, clearly, accurately, and efficiently".<ref name=":0" /> John Tukey and Edward Tufte pushed the bounds of data visualization; Tukey with his new statistical approach of exploratory data analysis and Tufte with his book "The Visual Display of Quantitative Information" paved the way for refining data visualization techniques for more than statisticians. With the progression of technology came the progression of data visualization; starting with hand-drawn visualizations and evolving into more technical applications – including interactive designs leading to [[software visualization]].<ref>{{Cite web|url=http://www.datavis.ca/papers/hbook.pdf |title=A Brief History of Data Visualization |date=2006 |access-date=2015-11-22 |website=York University |publisher=Springer-Verlag |last=Friendly |first=Michael |archive-url=https://web.archive.org/web/20160508232649/http://www.datavis.ca/papers/hbook.pdf |archive-date=2016-05-08 |url-status=live}}</ref>
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 ''[[Computational science|Scientific Computing]]''. Since then there have been several conferences and workshops, co-sponsored by the [[IEEE Computer Society]] and [[ACM SIGGRAPH]]".<ref>G. Scott Owen (1999). [http://www.siggraph.org/education/materials/HyperVis/visgoals/visgoal3.htm History of Visualization] {{Webarchive|url=https://web.archive.org/web/20121008032217/http://www.siggraph.org/education/materials/HyperVis/visgoals/visgoal3.htm |date=2012-10-08 }}. Accessed Jan 19, 2010.</ref> They have been devoted to the general topics of data visualization, information visualization and [[scientific visualization]], and more specific areas such as [[volume visualization]].
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<!-- This is hardly a reliable source and this list should maybe be moved to Information graphics -->
*
* [[Interaction techniques]] and architectures
* Modelling techniques
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== See also ==
{{Div col|colwidth=
* [[Analytics]]
* [[Climate change art]]
* [[Computational visualistics]]
* [[Data management]]
* [[Data physicalization]]
* [[Data profiling]]
* [[Data warehouse]]
▲* [[imc FAMOS]] (1987), graphical data analysis
* [[Information management]]
* [[List of information graphics software]]
* [[List of countries by economic complexity]], example of Treemapping
* [[List of mathematical art software]]
* [[Patent visualisation]]
* [[Pirouette: Turning Points in Design]]
* [[Statistical
▲* [[Warming stripes]]{{Div col end}}
== Notes ==
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* {{cite book |first=Kieran |last=Healy |title=Data Visualisation: A Practical Introduction |publisher=Princeton University Press |year=2019 |isbn=978-0-691-18161-5}} — Modern guide that balances technical skills with design principles, featuring R-based examples.
* Schwabish, Jonathan A. 2014. "[https://www.aeaweb.org/articles?id=10.1257/jep.28.1.209 An Economist's Guide to Visualizing Data]." ''Journal of Economic Perspectives'', 28 (1): 209–34. — Specialised guide for economic data visualisation with principles applicable across domains.
* {{cite book |first=Claus O. |last=Wilke |title=Fundamentals of Data Visualisation |publisher=O'Reilly |year=2018 |isbn=978-1-4920-3108-6 |url=https://serialmentor.com/dataviz/ |archive-date=2019-10-19 |access-date=2018-09-22 |archive-url=https://web.archive.org/web/20191019105107/https://serialmentor.com/dataviz/ |url-status=dead }} — Comprehensive guide focusing on principles of effective visualisation with real-world examples, available in print and open-access online resource.
* {{cite book|title=The visual display of quantitative information|last1=Tufte|first1=Edward R.|author-link1=Edward Tufte|date=2015|publisher=Graphics Press|edition=2|isbn=9780961392147}} — Classic foundational text on visualisation principles that remains influential decades after its first publication.
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