Data and information visualization: Difference between revisions

Revision as of 15:38, 27 June 2025

Latest revision as of 22:19, 17 August 2025

(4 intermediate revisions by 3 users not shown)

Line 10:

'''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 journalbook|last1=Grandjean|first1=Martin|author1-linktitle=Handbook of Digital Public History |titlechapter=Data Visualization for History |journalauthor1-link=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, [[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 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> [[Visual analytics]] marries statistical data analysis, data and information visualization and human analytical reasoning through interactive visual interfaces to help users reach conclusions, gain actionable insights and make informed decisions which are otherwise difficult for computers to do. Research into how people read and misread types of visualizations helps to determine what types and features of visualizations are most understandable and effective.<ref name="Mason">{{Cite journal |first1=Betsy |last1=Mason |title=Why scientists need to be better at data visualization |url=https://knowablemagazine.org/article/mind/2019/science-data-visualization |journal=Knowable Magazine |date=November 12, 2019 |doi=10.1146/knowable-110919-1 |doi-access=free|url-access=subscription }}</ref><ref name="O'Donoghue">{{cite journal |last1=O'Donoghue |first1=Seán I. |last2=Baldi |first2=Benedetta Frida |last3=Clark |first3=Susan J. |last4=Darling |first4=Aaron E. |last5=Hogan |first5=James M. |last6=Kaur |first6=Sandeep |last7=Maier-Hein |first7=Lena |last8=McCarthy |first8=Davis J. |last9=Moore |first9=William J. |last10=Stenau |first10=Esther |last11=Swedlow |first11=Jason R. |last12=Vuong |first12=Jenny |last13=Procter |first13=James B. |title=Visualization of Biomedical Data |journal=Annual Review of Biomedical Data Science |date=2018-07-20 |volume=1 |issue=1 |pages=275–304 |doi=10.1146/annurev-biodatasci-080917-013424 |url=https://www.annualreviews.org/doi/full/10.1146/annurev-biodatasci-080917-013424 |access-date=25 June 2021|hdl=10453/125943 |s2cid=199591321 |hdl-access=free }}</ref> Unintentionally poor or intentionally misleading and deceptive visualizations can function as powerful tools which disseminate [[misinformation]], manipulate public perception and divert [[public opinion]].<ref>{{Citation |title=Misinformed by Visualization: What Do We Learn From Misinformative Visualizations? |author1=Leo Yu-Ho Lo |author2=Ayush Gupta |author3=Kento Shigyo |author4=Aoyu Wu |author5=Enrico Bertini |author6=Huamin Qu}}</ref> Thus data visualization literacy has become an important component of [[data literacy|data]] and [[information literacy]] in the [[information age]] akin to the roles played by [[literacy|textual]], [[numeracy|mathematical]] and [[visual literacy]] in the past.<ref>{{Citation |author1=Börner, K. |author2=Bueckle, A. |author3=Ginda, M. |year=2019 |title=Data visualization literacy: Definitions, conceptual frameworks, exercises, and assessments |journal=Proceedings of the National Academy of Sciences |volume=116 |issue=6 |pages=1857–1864|doi=10.1073/pnas.1807180116 |doi-access=free |pmid=30718386 |bibcode=2019PNAS..116.1857B |pmc=6369751 }}</ref>

== Overview ==

Line 27:

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>

Line 78 ⟶ 77:

# 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).

Line 117 ⟶ 116:

[[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 Observatory of Economic Complexity]]]]

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.

Line 549 ⟶ 548:

* [[List of mathematical art software]]

* [[Patent visualisation]]

* [[Pirouette: Turning Points in Design]]

* [[Statistical inference]]

{{Div col end}}

Line 562:

* {{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.