Data and information visualization: Difference between revisions

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'''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 easy-to-communicate and easy-to-understand [[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 visually presenting sets of primarily quantitative raw data in a schematic form, using imagery. The visual formats used in data visualization include [[table (information)|table]]s, 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), [[animationradial_tree]]s, and other [[infographictree_structure]]s,; displays that prioritise ''relationships'' (Heer et al. 2010) such as [[Sankey diagram]]s, [[flownetwork chartdiagram]]s, [[networkvenn diagram]]s, [[semanticmind networkmap]]s, [[entity-relationshipsemantic diagramnetwork]]s, [[vennentity-relationship diagram]]s,; [[timelineflow chart]]s, [[mind maptimeline]]s, etc.

Useful '''criteria''' for a data or information visualization include: '''1. It is based on (non-visual) data '''- that is, a data/info viz is not image processing and collage;''' 2. it creates an image'''- specifically that the image plays the primary role in communicating meaning and is not an illustration accompanying the data in text form; and '''3. the result is readable'''<ref> Kosara, Robert (2007) Visualization Criticism – The Missing Link Between Information Visualization and Art, Proceedings of the 11th International Conference on Information Visualisation (IV), pp. 631–636, 2007. DOI: 10.1109/IV.2007.130</ref> . 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>Ziemkiewicz, Caroline, Kosara, Robert (2010) Embedding Information Visualization Within Visual Representation, in Ras, Ribarsky, Advances in Information and Intelligent Systems</ref>. Kosara (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.

[[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>