Data and information visualization: Difference between revisions

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Revision as of 10:51, 26 June 2025

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{{Data Visualization}}

{{InfoMaps}}

'''Data and information visualization''' ('''data viz/vis''' or '''info viz/vis''')<ref name=Biz2Comm_20161005>{{cite web |last1=Shewan |first1=Dan |title=Data is Beautiful: 7 Data Visualization Tools for Digital Marketers |url=https://www.business2community.com/online-marketing/data-beautiful-7-data-visualization-tools-digital-marketers-01668224 |website=Business2Community |archive-url=https://web.archive.org/web/20161112134851/https://www.business2community.com/online-marketing/data-beautiful-7-data-visualization-tools-digital-marketers-01668224 |archive-date=12 November 2016 |date=5 October 2016 |url-status=live }}</ref> is the practice of [[design]]ing and creating [[Graphics|graphic]] or visual [[Representation (arts)|representations]] of a large amount<ref name="Nussbaumer Knaflic">{{cite book |last1=Nussbaumer Knaflic |first1=Cole |title=Storytelling with Data: A Data Visualization Guide for Business Professionals |date=2 November 2015 |isbn=978-1-119-00225-3 |pages=<!--needed-->|publisher=John Wiley & Sons }}</ref> of complex quantitative and qualitative [[data]] and [[information]] with the help of static, dynamic or interactive visual items. Typically based on data and information collected from a certain [[___domain of expertise]], these visualizations are intended for a broader audience to help them visually explore and discover, quickly understand, interpret and gain important insights into otherwise difficult-to-identify structures, relationships, correlations, local and global patterns, trends, variations, constancy, clusters, outliers and unusual groupings within data (''exploratory visualization'').<ref name=HDSR/><ref>{{Citation |chapter=Managing and Visualizing Unstructured Big Data |author=Ananda Mitra |title=Encyclopedia of Information Science and Technology |year=2018 |edition=4th |publisher=IGI Global}}</ref><ref>{{Citation |author1=Bhuvanendra Putchala |author2=Lasya Sreevidya Kanala |author3=Devi Prasanna Donepudi |author4=Hari Kishan Kondaveeti |chapter=Applications of Big Data Analytics in Healthcare Informatics |title=Health Informatics and Patient Safety in Times of Crisis |editor1=Narasimha Rao Vajjhala |editor2=Philip Eappen |publisher=IGI Global |year= 2023 |pages=175–194}}</ref> When intended for the general public ([[mass communication]]) to convey a concise version of known, specific information in a clear and engaging manner (''presentational'' or ''explanatory visualization''),<ref name=HDSR/> it is typically called [[information graphics]].

'''Data visualization''' is concerned with presenting sets of primarily quantitative raw data in a schematic form, using imagery. The visual formats used in data visualization include charts and graphs (e.g. [[pie chart]]s, [[bar chart]]s, [[line chart]]s, [[area chart]]s, [[cone chart]]s, [[pyramid chart]]s, [[donut chart]]s, [[histogram]]s, [[spectrogram]]s, [[cohort chart]]s, [[waterfall chart]]s, [[funnel chart]]s, [[bullet graph]]s, etc.), [[diagram]]s, [[Plot (graphics)|plot]]s (e.g. [[scatter plot]]s, [[distribution plot]]s, [[box-and-whisker plot]]s), geospatial [[map]]s (such as [[proportional symbol map]]s, [[choropleth map]]s, [[isopleth map]]s and [[heat map]]s), figures, [[correlation matrix|correlation matrices]], percentage [[Gauge (instrument)|gauge]]s, etc., which sometimes can be combined in a [[Dashboard (business)|dashboard]].

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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, [[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>

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 |date=28 September 2021 |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 |pages=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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{{Div col|colwidth=20em}}

* [[Analytics]]

* [[Big data]]

* [[Climate change art]]

* [[Computational visualistics]]

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* [[Data management]]

* [[Data physicalization]]

* [[Data Presentation Architecture]]

* [[Data profiling]]

* [[Data warehouse]]

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== Further reading ==

* {{cite book |first=Kieran |last=Healy |author-link=Kieran Healy |title=Data VisualizationVisualisation: A Practical Introduction |publisher=Princeton University Press |year=2019 |isbn=978-0-691-18161-5}} — A modern, practical guide that balances technical skills with design principles, featuring R-based examples.

{{further cleanup|date=April 2022}}

* 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.

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* {{cite book|last1=Few|first1=Stephen|title=Show me the numbers : designing tables and graphs to enlighten|date=2012|edition=2|publisher=Analytics Press|isbn=9780970601971|oclc=795009632}} — Practical guide focusing on business applications of data visualisation.

Publications listed here should relate specifically only to data visualization, and not: Computational visualistics, Information graphics, information visualization, Knowledge visualization, Information visualization, and Visual analytics.

* {{cite book |first=Claus O. |last=Wilke |title=Fundamentals of Data VisualizationVisualisation |publisher=O'Reilly |year=2018 |isbn=978-1-4920-3108-6 |url=https://serialmentor.com/dataviz/ }} — Comprehensive guide focusing on principles of effective visualisation with real-world examples, available both in print and as an 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.

There are some links added here to check the content of every publication. Later on these links should be removed or moved to the talk page.

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* {{cite book |first=Kieran |last=Healy |author-link=Kieran Healy |title=Data Visualization: A Practical Introduction |publisher=Princeton University Press |year=2019 |isbn=978-0-691-18161-5}}

* {{cite book |first=Claus O. |last=Wilke |title=Fundamentals of Data Visualization |publisher=O'Reilly |year=2018 |isbn=978-1-4920-3108-6 |url=https://serialmentor.com/dataviz/ }}

* {{cite book |first=Stephanie |last=Evergreen |title=Effective Data Visualization: The Right Chart for the Right Data |publisher=Sage |year=2016 |isbn=978-1-5063-0305-5 }}

* {{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}}

* Kawa Nazemi (2014). [https://diglib.eg.org/handle/10.2312/12076 Adaptive Semantics Visualization] Eurographics Association.

* {{cite book|last1=Few|first1=Stephen|title=Show me the numbers : designing tables and graphs to enlighten|date=2012|edition=2|publisher=Analytics Press|isbn=9780970601971|oclc=795009632}}

* {{cite book |last=Wilkinson |first=Leland |author-link=Leland Wilkinson |title=Grammar of Graphics |publisher=Springer |year=2012 |isbn=978-1-4419-2033-1}}

* {{cite book|last1=Mazza|first1=Riccardo|title=Introduction to Information Visualization|publisher=Springer|date=2009|isbn=9781848002180|oclc=458726890}}

* Andreas Kerren, John T. Stasko, [[Jean-Daniel Fekete]], and Chris North (2008). [https://www.springer.com/computer/user+interfaces/book/978-3-540-70955-8 ''Information Visualization&nbsp;– Human-Centered Issues and Perspectives'']. Volume 4950 of LNCS State-of-the-Art Survey, Springer.

* [[Robert Spence (engineer)|Spence, Robert]] ''Information Visualization: Design for Interaction (2nd Edition)'', Prentice Hall, 2007, {{ISBN|0-13-206550-9}}.

* Jeffrey Heer, [[Stuart K. Card]], [[James Landay]] (2005). [http://bid.berkeley.edu/files/papers/2005-prefuse-CHI.pdf "Prefuse: a toolkit for interactive information visualization"] {{Webarchive|url=https://web.archive.org/web/20070612082611/http://bid.berkeley.edu/files/papers/2005-prefuse-CHI.pdf |date=2007-06-12 }}. In: ''ACM Human Factors in Computing Systems'' CHI 2005.

* {{cite book |first1=Frits H. |last1=Post |first2=Gregory M. |last2=Nielson |first3=Georges-Pierre |last3=Bonneau |year=2003 |title=Data Visualization: The State of the Art |publisher=Springer |isbn=978-1-4613-5430-7}}

* [[Ben Bederson]] and [[Ben Shneiderman]] (2003). [https://books.google.com/books?id=TrZZQ5I76BcC&psp=1&cad=0 ''The Craft of Information Visualization: Readings and Reflections'']. Morgan Kaufmann.

* Colin Ware (2000). [https://www.amazon.com/dp/3835060155 ''Information Visualization: Perception for design'']. Morgan Kaufmann.

* [[Stuart K. Card]], [[Jock D. Mackinlay]] and [[Ben Shneiderman]] (1999). [https://books.google.com/books?id=wdh2gqWfQmgC&psp=1&cad=0 ''Readings in Information Visualization: Using Vision to Think''], Morgan Kaufmann Publishers.

* {{cite book |first=William S. |last=Cleveland |year=1993 |title=Visualizing Data |publisher=Hobart Press |isbn=0-9634884-0-6 |url=https://archive.org/details/visualizingdata00will }}

* 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.

==External links==

{{Sister project links|auto=y|d=y}}

*[http://www.math.yorku.ca/SCS/Gallery/ Milestones in the History of Thematic Cartography, Statistical Graphics, and Data Visualization], An illustrated chronology of innovations by Michael Friendly and Daniel J. Denis.

*[http://compsci.capture.duke.edu/Panopto/Pages/Viewer.aspx?id=ee45ebd7-da62-4d27-8d16-5647aa167946 Duke University-Christa Kelleher Presentation-Communicating through infographics-visualizing scientific & engineering information-March 6, 2015]

{{Visualization}}