Data and information visualization: Difference between revisions

Revision as of 07:58, 3 March 2025

Revision as of 22:36, 4 March 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 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 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]].