Data and information visualization: Difference between revisions

Revision as of 11:28, 26 June 2025

Revision as of 13:38, 26 June 2025

Line 5:

{{Data Visualization}}

{{InfoMaps}}

'''Data and information visualization''' ('''data viz/vis''' or '''info viz/vis''') 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]], theseThese visualizations are intended forto help a broadertarget 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 andan engaging manner (''presentational'' or ''explanatory visualization''),<ref name=HDSR/> it is typically called [[information graphicsinfographic]]s.

'''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]].

'''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 chartflowchart]]s, [[timeline]]s, etc.

[[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 thatensure 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 verbalVerbal and graphical components complement each other to ensure clear, quick and memorable understanding. Effective information visualization is aware of the needs and concerns and theexpertise 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 effectiveEffective 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 toand ___domain experts and executives for making decisions, monitoring performance, generating new ideas and stimulating research.<ref name=IBM/><ref name=HDSR/> In addition, dataData scientists, data analysts and data mining specialists use data visualization to check thedata 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]], dataData and information visualization can constitute abe 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 theit datato clearly and memorably with the goal of convincingconvince 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 asless 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 dataData 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 [[visualVisual 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. 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>

Research into how people read and misread various types of visualizations is helping to determine what types and features of visualizations are most understandable and effective in conveying information.<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> On the other hand, unintentionally poor or intentionally misleading and deceptive visualizations (''misinformative visualization'') can function as powerful tools which disseminate [[misinformation]], manipulate public perception and divert [[public opinion]] toward a certain agenda.<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>

<!--

NOTE : The following is from an old lead and could be incorporated into the current lead in an appropriate manner:

From an academic point of view, this representation can be considered as a mapping between the original data (usually numerical) and graphic elements<ref>{{Cite web|url=https://www.whizlabs.com/blog/what-is-data-visualization/|title=What is Data Visualization? - Whizlabs Blog|date=22 October 2021 }}</ref> (for example, lines or points in a chart). The mapping determines how the attributes of these elements vary according to the data. In this light, a bar chart is a mapping of the length of a bar to a magnitude of a variable. Since the graphic design of the mapping can adversely affect the readability of a chart,<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--><!--}}</ref> mapping is a core competency of Data visualization.<ref name="Gershon"/>

It is also the study of [[visualization (graphics)|visual representation]]s of abstract data to reinforce human cognition. The abstract data include both numerical and non-numerical data, such as text and [[geographic information]]. It is related to [[infographics]] and [[scientific visualization]]. One distinction is that it's information visualization when the spatial representation (e.g., the [[page layout]] of a [[graphic design]]) is chosen, whereas it's [[scientific visualization]] when the spatial representation is given.<ref>{{cite web|url=http://www.cs.ubc.ca/labs/imager/tr/2008/pitfalls/|title=Process and Pitfalls in Writing Information Visualization Research Papers|author=Tamara Munzner|author-link=Tamara Munzner|website=www.cs.ubc.ca|access-date=9 April 2018}}</ref>

-->

== Overview ==