Data and information visualization: Difference between revisions

Revision as of 13:14, 28 June 2024

Revision as of 16:59, 29 June 2024

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[[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, [[Market data|business and financial 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>

Effective data visualization is properly sourced, contextualized, simple and uncluttered. The underlying data is accurate and up-to-date to make sure that insights are reliable. Graphical items are well-chosen for the given datasets 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 (labels and titles). These verbal and graphical components complement each other to ensure clear, quick and memorable understanding. Effective information visualization is aware of the needs and concerns and the level of expertise of the target audience, deliberately guiding them to the intended conclusion.<ref name=IBM>{{Cite web |title=What is data visualization? |url=https://www.ibm.com/topics/data-visualization |website=IBM |access-date=27 March 2023}}</ref><ref name="Nussbaumer Knaflic"/> Such effective visualization can be used not only for conveying specialized, complex, big data-driven ideas to a wider group of non-technical audience in a visually appealing, engaging and accessible manner, but also to ___domain experts and executives for making decisions, monitoring performance, generating new ideas and stimulating research.<ref name=IBM/><ref name=HDSR/> In addition, data scientists, data analysts and data mining specialists use data visualization to check the quality of data, find errors, unusual gaps and missing values in data, 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> In [[business]], data and information visualization can constitute a part of ''data storytelling'', where they are paired with a coherent [[narrative]] structure or [[Plot (narrative)|storyline]] to contextualize the analyzed data and communicate the insights gained from analyzing the data clearly and memorably with the goal of convincing the audience into making a decision or taking an action in order to create [[business value]].<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 the field of [[statistical graphics]], where complex statistical data are communicated graphically in an accurate and precise manner among researchers and analysts with statistical expertise to help them perform [[exploratory data analysis]] or to convey the results of such analyses, where visual appeal, capturing attention to a certain issue and storytelling are not as important.<ref>{{Citation |title=Statistics: Concepts and Applications for Science |author=David C. LeBlanc |publisher=Jones & Bartlett Learning |year=2004 |pagepages=35–36}}</ref>

The field of data and information visualization is of interdisciplinary nature as it incorporates principles found in the disciplines of [[descriptive statistics]] (as early as the 18th century),<ref>{{cite journal|last1=Grandjean|first1=Martin|author1-link= |title=Data Visualization for History|journal=Handbook of Digital Public History|date=2022|volume=|issue=|pages=291–300|doi=10.1515/9783110430295-024|isbn=9783110430295 |url=https://shs.hal.science/halshs-03775019/document}}</ref> [[visual communication]], [[graphic design]], [[cognitive science]] and, more recently, [[interactive computer graphics]] and [[human-computer interaction]].<ref>{{Citation |title=A Framework for Visualizing Information |author=E.H. Chi |publisher=Springer Science & Business Media |year=2013 |page=xxiii}}</ref> Since effective visualization requires design skills, statistical skills and computing skills, it is argued by authors such as Gershon and Page that it is both an art and a science.<ref name="Gershon">{{cite journal |last1=Gershon |first1=Nahum |last2=Page |first2=Ward |title=What storytelling can do for information visualization |journal=Communications of the ACM |date=1 August 2001 |volume=44 |issue=8 |pages=31–37 |doi=10.1145/381641.381653|s2cid=7666107 }}</ref> The neighboring field of [[visual analytics]] marries statistical data analysis, data and information visualization and human analytical reasoning through interactive visual interfaces to help human users reach conclusions, gain actionable insights and make informed decisions which are otherwise difficult for computers to do.

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*A ''graph'' is primarily used to show relationships among data and portrays values encoded as ''visual objects'' (e.g., lines, bars, or points). Numerical values are displayed within an area delineated by one or more ''axes''. These axes provide ''scales'' (quantitative and categorical) used to label and assign values to the visual objects. Many graphs are also referred to as ''charts''.<ref>{{cite web|url=http://www.perceptualedge.com/articles/ie/the_right_graph.pdf|title=Steven Few-Selecting the Right Graph for Your Message-September 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>

Eppler and Lengler have developed the "Periodic Table of Visualization Methods," an interactive chart displaying various data visualization methods. It includes six types of data visualization methods: data, information, concept, strategy, metaphor and compound.<ref>{{cite web|last1=Lengler|first1=Ralph|author-link1=Ralph Lengler|last2=Eppler|first2=Martin. J|author-link2=Martin J. Eppler|title=Periodic Table of Visualization Methods|url=http://www.visual-literacy.org/periodic_table/periodic_table.html|access-date=15 March 2013|publisher=www.visual-literacy.org|archive-url=https://web.archive.org/web/20130316073116/http://www.visual-literacy.org/periodic_table/periodic_table.html|archive-date=16 March 2013|url-status=live}}</ref> In "Visualization Analysis and Design" [[Tamara Munzner]] writes "Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively." Munzner agues that visualization "is suitable when there is a need to augment human capabilities rather than replace people with computational decision-making methods."<ref>{{Cite book |lastlast1=Munzner |firstfirst1=Tamara |title=Visualization analysis & design |last2=Maguire |first2=Eamonn |date=2015 |publisher=CRC Press |isbn=978-1-4665-0891-0 |series=A.K. Peters visualization series |___location=Boca Raton London New York}}</ref>

==Techniques==