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'{{Short description|Visual representation of data}} {{cleanup merge|Information visualization|discuss=Talk:Data visualization#Merger|date=February 2021}} {{Data Visualization}} {{InfoMaps}} '''Data and information visualization''' ('''data viz''' or '''info viz''')<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.com |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 an interdisciplinary field that deals with the [[Graphics|graphic]] [[Representation (arts)|representation]] of [[data]] and [[information]]. It is a particularly efficient way of communicating when the data or information is numerous as for example a [[time series]].<ref name="Nussbaumer Knaflic"/> It is also nigger are bad 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> 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}}</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"/> Data and information visualization has its roots in the field of [[statistics]] and is therefore generally considered a branch of [[descriptive Statistics]]. However, because both design skills and statistical and computing skills are required to visualize effectively, 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> 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}}</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> == Overview == [[File:Data visualization process v1.png|upright=1.5|thumb|Data visualization is one of the steps in analyzing data and presenting it to users.]] [[File:Internet map 1024.jpg|thumb|240px|Partial map of the Internet early 2005 represented as a graph, each line represents two [[IP addresses]], and some delay between those two nodes.]] The field of data and information visualization has emerged "from research in [[human–computer interaction]], [[computer science]], [[graphics]], [[visual design]], [[psychology]], and [[business methods]]. It is increasingly applied as a critical component in scientific research, [[digital libraries]], [[data mining]], financial data analysis, market studies, manufacturing [[production control]], and [[drug discovery]]".<ref name = "BBB03">Benjamin B. Bederson and [[Ben Shneiderman]] (2003). [http://www.cs.umd.edu/hcil/pubs/books/craft.shtml ''The Craft of Information Visualization: Readings and Reflections''], Morgan Kaufmann {{ISBN|1-55860-915-6}}.</ref> Data and information visualization presumes that "visual representations and interaction techniques take advantage of the human eye’s broad bandwidth pathway into the mind to allow users to see, explore, and understand large amounts of information at once. Information visualization focused on the creation of approaches for conveying abstract information in intuitive ways."<ref>James J. Thomas and Kristin A. Cook (Ed.) (2005). [http://nvac.pnl.gov/agenda.stm ''Illuminating the Path: The R&D Agenda for Visual Analytics''] {{webarchive|url=https://web.archive.org/web/20080929155753/http://nvac.pnl.gov/agenda.stm |date=2008-09-29 }}. National Visualization and Analytics Center. p.30</ref> Data analysis is an indispensable part of all applied research and problem solving in industry. The most fundamental data analysis approaches are visualization (histograms, scatter plots, surface plots, tree maps, parallel coordinate plots, etc.), [[statistics]] ([[hypothesis test]], [[regression analysis|regression]], [[Principal component analysis|PCA]], etc.), [[data mining]] ([[Association rule learning|association mining]], etc.), and [[machine learning]] methods ([[cluster analysis|clustering]], [[Statistical classification|classification]], [[decision trees]], etc.). Among these approaches, information visualization, or visual data analysis, is the most reliant on the cognitive skills of human analysts, and allows the discovery of unstructured actionable insights that are limited only by human imagination and creativity. The analyst does not have to learn any sophisticated methods to be able to interpret the visualizations of the data. Information visualization is also a hypothesis generation scheme, which can be, and is typically followed by more analytical or formal analysis, such as statistical hypothesis testing. 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}}</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 |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 14th, 2008.</ref> Indeed, [[Fernanda Viegas]] and [[Martin M. Wattenberg]] suggested that an ideal visualization should not only communicate clearly, but stimulate viewer engagement and attention.<ref>{{Cite news |first1= Fernanda |last1=Viegas|first2=Martin |last2=Wattenberg |title= How To Make Data Look Sexy |work= CNN.com |date= April 19, 2011 |url= http://articles.cnn.com/2011-04-19/opinion/sexy.data_1_visualization-21st-century-engagement?_s=PM:OPINION |url-status= dead |archive-date= May 6, 2011 |archive-url= https://web.archive.org/web/20110506065701/http://articles.cnn.com/2011-04-19/opinion/sexy.data_1_visualization-21st-century-engagement?_s=PM%3AOPINION |access-date= May 7, 2017 }}</ref> Data visualization is closely related to [[information graphics]], [[information visualization]], [[scientific visualization]], [[exploratory data analysis]] and [[statistical graphics]]. In the new millennium, data visualization has become an active area of research, teaching and development. According to Post et al. (2002), it has united scientific and information visualization.<ref name="FHP02">Frits H. Post, Gregory M. Nielson and Georges-Pierre Bonneau (2002). [http://visualisation.tudelft.nl/publications/post2003b.pdf ''Data Visualization: The State of the Art''. Research paper TU delft, 2002.] {{webarchive|url=https://web.archive.org/web/20091007134531/http://visualisation.tudelft.nl/publications/post2003b.pdf |date=2009-10-07 }}.</ref> In the commercial environment data visualization is often referred to as [[Dashboard (business)|dashboards]]. [[Infographic]]s are another very common form of data visualization. ==Principles== ===Characteristics of effective graphical displays=== [[File:Minard.png|thumb|upright=2|[[Charles Joseph Minard]]'s 1869 diagram of [[French invasion of Russia|Napoleonic France's invasion of Russia]], an early example of an information graphic]] {{quote box|width = 300px|quote=The greatest value of a picture is when it forces us to notice what we never expected to see. |source=[[John Tukey]]<ref name="Tukey1977">{{cite book | last = Tukey | first = John | author-link = John Tukey | year = 1977 | title = Exploratory Data Analysis | publisher = Addison-Wesley | isbn = 0-201-07616-0| title-link = Exploratory Data Analysis }}</ref> }} [[Edward Tufte]] has explained that users of information displays are executing particular ''analytical tasks'' such as making comparisons. The ''design principle'' of the information graphic should support the analytical task.<ref>{{cite web|url=https://www.youtube.com/watch?v=g9Y4SxgfGCg|title=Tech@State: Data Visualization - Keynote by Dr Edward Tufte|last=techatstate|date=7 August 2013|via=YouTube|access-date=29 November 2016|archive-url=https://web.archive.org/web/20170329102209/https://www.youtube.com/watch?v=g9Y4SxgfGCg|archive-date=29 March 2017|url-status=live}}</ref> As William Cleveland and Robert McGill show, different graphical elements accomplish this more or less effectively. For example, dot plots and bar charts outperform pie charts.<ref>{{Cite journal |title=Graphical perception and graphical methods for analyzing scientific data |year=1985 |doi=10.1126/science.229.4716.828 |pmid=17777913 |s2cid=16342041 |last1=Cleveland |first1=W. S. |last2=McGill |first2=R. |journal=Science |volume=229 |issue=4716 |pages=828–33 |bibcode=1985Sci...229..828C }}</ref> In his 1983 book ''The Visual Display of Quantitative Information'', [[Edward Tufte]] defines 'graphical displays' and principles for effective graphical display in the following passage: "Excellence in statistical graphics consists of complex ideas communicated with clarity, precision, and efficiency. Graphical displays should: *show the data *induce the viewer to think about the substance rather than about methodology, graphic design, the technology of graphic production, or something else * avoid distorting what the data has to say *present many numbers in a small space *make large data sets coherent *encourage the eye to compare different pieces of data *reveal the data at several levels of detail, from a broad overview to the fine structure *serve a reasonably clear purpose: description, exploration, tabulation, or decoration *be closely integrated with the statistical and verbal descriptions of a data set. Graphics ''reveal'' data. Indeed graphics can be more precise and revealing than conventional statistical computations."<ref name=Tufte1983>{{cite book|last=Tufte|first=Edward|title=The Visual Display of Quantitative Information|year=1983|publisher=Graphics Press|location=Cheshire, Connecticut|isbn=0-9613921-4-2|url=https://archive.org/details/visualdisplayofq00tuft|access-date=2019-08-10|archive-url=https://web.archive.org/web/20130114070823/http://archive.org/details/visualdisplayofq00tuft|archive-date=2013-01-14|url-status=live}}</ref> For example, the Minard diagram shows the losses suffered by Napoleon's army in the 1812–1813 period. Six variables are plotted: the size of the army, its location on a two-dimensional surface (x and y), time, the direction of movement, and temperature. The line width illustrates a comparison (size of the army at points in time), while the temperature axis suggests a cause of the change in army size. This multivariate display on a two-dimensional surface tells a story that can be grasped immediately while identifying the source data to build credibility. Tufte wrote in 1983 that: "It may well be the best statistical graphic ever drawn."<ref name=Tufte1983/> Not applying these principles may result in [[misleading graphs]], distorting the message, or supporting an erroneous conclusion. According to Tufte, [[chartjunk]] refers to the extraneous interior decoration of the graphic that does not enhance the message or gratuitous three-dimensional or perspective effects. Needlessly separating the explanatory key from the image itself, requiring the eye to travel back and forth from the image to the key, is a form of "administrative debris." The ratio of "data to ink" should be maximized, erasing non-data ink where feasible.<ref name=Tufte1983/> The [[Congressional Budget Office]] summarized several best practices for graphical displays in a June 2014 presentation. These included: a) Knowing your audience; b) Designing graphics that can stand alone outside the report's context; and c) Designing graphics that communicate the key messages in the report.<ref>{{cite web|url=https://www.cbo.gov/publication/45224|title=Telling Visual Stories About Data - Congressional Budget Office|website=www.cbo.gov|access-date=2014-11-27|archive-url=https://web.archive.org/web/20141204135630/https://www.cbo.gov/publication/45224|archive-date=2014-12-04|url-status=live}}</ref> ===Quantitative messages=== [[File:Total Revenues and Outlays as Percent GDP 2013.png|thumb|upright=1.75|A time series illustrated with a line chart demonstrating trends in U.S. federal spending and revenue over time]] [[File:U.S. Phillips Curve 2000 to 2013.png|thumb|upright=1.5|A scatterplot illustrating negative correlation between two variables (inflation and unemployment) measured at points in time]] Author Stephen Few described eight types of quantitative messages that users may attempt to understand or communicate from a set of data and the associated graphs used to help communicate the message: #Time-series: A single variable is captured over a period of time, such as the unemployment rate or temperature measures over a 10-year period. A [[line chart]] may be used to demonstrate the trend over time. #Ranking: Categorical subdivisions are ranked in ascending or descending order, such as a ranking of sales performance (the ''measure'') by sales persons (the ''category'', with each sales person a ''categorical subdivision'') during a single period. A [[bar chart]] may be used to show the comparison across the sales persons. #Part-to-whole: Categorical subdivisions are measured as a ratio to the whole (i.e., a percentage out of 100%). A [[pie chart]] or bar chart can show the comparison of ratios, such as the market share represented by competitors in a market. #Deviation: Categorical subdivisions are compared against a reference, such as a comparison of actual vs. budget expenses for several departments of a business for a given time period. A bar chart can show comparison of the actual versus the reference amount. #Frequency distribution: Shows the number of observations of a particular variable for given interval, such as the number of years in which the stock market return is between intervals such as 0-10%, 11-20%, etc. A [[histogram]], a type of bar chart, may be used for this analysis. A [[boxplot]] helps visualize key statistics about the distribution, such as median, quartiles, outliers, etc. #Correlation: Comparison between observations represented by two variables (X,Y) to determine if they tend to move in the same or opposite directions. For example, plotting unemployment (X) and inflation (Y) for a sample of months. A [[scatter plot]] is typically used for this message. #Nominal comparison: Comparing categorical subdivisions in no particular order, such as the sales volume by product code. A bar chart may be used for this comparison. #[[Geography|Geographic]] or [[geospatial]]: Comparison of a variable across a map or layout, such as the unemployment rate by state or the number of persons on the various floors of a building. A [[cartogram]] is a typical graphic used.<ref name="ReferenceA"/><ref>{{cite web|url=http://www.perceptualedge.com/articles/misc/Graph_Selection_Matrix.pdf|title=Stephen Few-Perceptual Edge-Graph Selection Matrix|access-date=2014-09-08|archive-url=https://web.archive.org/web/20141005080945/http://www.perceptualedge.com/articles/misc/Graph_Selection_Matrix.pdf|archive-date=2014-10-05|url-status=live}}</ref> Analysts reviewing a set of data may consider whether some or all of the messages and graphic types above are applicable to their task and audience. The process of trial and error to identify meaningful relationships and messages in the data is part of [[exploratory data analysis]]. ===Visual perception and data visualization=== A human can distinguish differences in line length, shape, orientation, distances, and color (hue) readily without significant processing effort; these are referred to as "[[Pre-attentive processing|pre-attentive attributes]]". For example, it may require significant time and effort ("attentive processing") to identify the number of times the digit "5" appears in a series of numbers; but if that digit is different in size, orientation, or color, instances of the digit can be noted quickly through pre-attentive processing.<ref name="perceptualedge.com">{{cite web|url=http://www.perceptualedge.com/articles/ie/visual_perception.pdf|title=Steven Few-Tapping the Power of Visual Perception-September 2004|access-date=2014-10-08|archive-url=https://web.archive.org/web/20141005080935/http://www.perceptualedge.com/articles/ie/visual_perception.pdf|archive-date=2014-10-05|url-status=live}}</ref> Compelling graphics take advantage of pre-attentive processing and attributes and the relative strength of these attributes. For example, since humans can more easily process differences in line length than surface area, it may be more effective to use a bar chart (which takes advantage of line length to show comparison) rather than pie charts (which use surface area to show comparison).<ref name="perceptualedge.com"/> ==== Human perception/cognition and data visualization ==== Almost all data visualizations are created for human consumption. Knowledge of human perception and cognition is necessary when designing intuitive visualizations.<ref name=":0">{{Cite book|title = Data Visualization for Human Perception|url = https://www.interaction-design.org/literature/book/the-encyclopedia-of-human-computer-interaction-2nd-ed/data-visualization-for-human-perception|website = The Interaction Design Foundation|access-date = 2015-11-23|archive-url = https://web.archive.org/web/20151123151958/https://www.interaction-design.org/literature/book/the-encyclopedia-of-human-computer-interaction-2nd-ed/data-visualization-for-human-perception|archive-date = 2015-11-23|url-status = live}}</ref> Cognition refers to processes in human beings like perception, attention, learning, memory, thought, concept formation, reading, and problem solving.<ref>{{Cite web|url = https://www.sfu.ca/gis/geog_x55/web355/icons/11_lec_vweb.pdf|title = Visualization|access-date = 2015-11-22|website = SFU|publisher = SFU lecture|archive-url = https://web.archive.org/web/20160122203157/http://www.sfu.ca/gis/geog_x55/web355/icons/11_lec_vweb.pdf|archive-date = 2016-01-22|url-status = dead}}</ref> Human visual processing is efficient in detecting changes and making comparisons between quantities, sizes, shapes and variations in lightness. When properties of symbolic data are mapped to visual properties, humans can browse through large amounts of data efficiently. It is estimated that 2/3 of the brain's neurons can be involved in visual processing. Proper visualization provides a different approach to show potential connections, relationships, etc. which are not as obvious in non-visualized quantitative data. Visualization can become a means of [[data exploration]]. Studies have shown individuals used on average 19% less cognitive resources, and 4.5% better able to recall details when comparing data visualization with text.<ref>{{Cite news|last=Graham|first=Fiona|date=2012-04-17|title=Can images stop data overload?|language=en-GB|work=BBC News|url=https://www.bbc.com/news/business-17682294|access-date=2020-07-30}}</ref> == History == {{see also|Infographics#History}} [[File:50 years of datavisulization berengueres own work.png|thumb|Selected milestones and inventions]] The modern study of visualization started with [[computer graphics]], which "has from its beginning been used to study scientific problems. However, in its early days the lack of graphics power often limited its usefulness. The recent emphasis on visualization started in 1987 with the special issue of Computer Graphics on Visualization in ''[[Scientific Computing (magazine)|Scientific Computing]]''. Since then there have been several conferences and workshops, co-sponsored by the [[IEEE Computer Society]] and [[ACM SIGGRAPH]]".<ref>G. Scott Owen (1999). [http://www.siggraph.org/education/materials/HyperVis/visgoals/visgoal3.htm History of Visualization] {{Webarchive|url=https://web.archive.org/web/20121008032217/http://www.siggraph.org/education/materials/HyperVis/visgoals/visgoal3.htm |date=2012-10-08 }}. Accessed Jan 19, 2010.</ref> They have been devoted to the general topics of [[data visualization]], information visualization and [[scientific visualization]], and more specific areas such as [[volume visualization]]. In 1786, [[William Playfair]] published the first presentation graphics. [[File:ProductSpaceLocalization.png|thumb|[[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|250px|right|Tree Map of Benin Exports (2009) by product category. The Product Exports Treemaps are one of the most recent applications of these kind of visualizations, developed by the Harvard-MIT [[The Observatory of Economic Complexity|Observatory of Economic Complexity]]]] There is no comprehensive 'history' of data visualization. There are no accounts that span the entire development of visual thinking and the visual representation of data, and which collate the contributions of disparate disciplines.<ref name="Springer-Verlag">{{cite book|last1=Friendly|first1=Michael|chapter=A Brief History of Data Visualization|title=Handbook of Data Visualization|pages=15–56|publisher=Springer-Verlag |year=2006|doi=10.1007/978-3-540-33037-0_2|isbn=9783540330370}}</ref> Michael Friendly and Daniel J Denis of [[York University]] are engaged in a project that attempts to provide a comprehensive history of visualization. Contrary to general belief, data visualization is not a modern development. Since prehistory, stellar data, or information such as location of stars were visualized on the walls of caves (such as those found in [[Lascaux|Lascaux Cave]] in Southern France) since the [[Pleistocene]] era.<ref name="WhitehouseIce00">{{cite web |url=http://news.bbc.co.uk/2/hi/science/nature/871930.stm |title=Ice Age star map discovered |author=Whitehouse, D. |work=BBC News |date=9 August 2000 |access-date=20 January 2018 |archive-url=https://web.archive.org/web/20180106064810/http://news.bbc.co.uk/2/hi/science/nature/871930.stm |archive-date=6 January 2018 |url-status=live}}</ref> Physical artefacts such as Mesopotamian [[History of ancient numeral systems#Clay token|clay tokens]] (5500 BC), Inca [[quipu]]s (2600 BC) and Marshall Islands [[Marshall Islands stick chart|stick charts]] (n.d.) can also be considered as visualizing quantitative information.<ref name="Dragicevic 2012">{{cite web|url=http://www.dataphys.org/list|title=List of Physical Visualizations and Related Artefacts |date=2012 |access-date=2018-01-12 |last1=Dragicevic |first1=Pierre |last2=Jansen |first2=Yvonne |archive-url=https://web.archive.org/web/20180113194900/http://dataphys.org/list/ |archive-date=2018-01-13 |url-status=live}}</ref><ref>{{cite journal|url=https://hal.inria.fr/hal-01120152/document |first1=Yvonne |last1=Jansen |first2=Pierre |last2=Dragicevic |first3=Petra |last3=Isenberg |first4=Jason |last4=Alexander |first5=Abhijit |last5=Karnik |first6=Johan |last6=Kildal |first7=Sriram |last7=Subramanian |first8=Kasper |last8=Hornbæk |author8-link=Kasper Hornbæk |date=2015 |title=Opportunities and challenges for data physicalization |journal=Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems |pages=3227–3236 |access-date=2018-01-12 |archive-url=https://web.archive.org/web/20180113093035/https://hal.inria.fr/hal-01120152/document |archive-date=2018-01-13 |url-status=live}}</ref> The first documented data visualization can be tracked back to 1160 B.C. with [[Turin Papyrus Map]] which accurately illustrates the distribution of geological resources and provides information about quarrying of those resources.<ref name="Friendly 2001">{{cite web|url=http://www.datavis.ca/milestones/ |title=Milestones in the history of thematic cartography, statistical graphics, and data visualization |date=2001 |last=Friendly |first=Michael |archive-url=https://web.archive.org/web/20140414221920/http://www.datavis.ca/milestones/ |archive-date=2014-04-14 |url-status=dead}}</ref> Such maps can be categorized as [[thematic map|thematic cartography]], which is a type of data visualization that presents and communicates specific data and information through a geographical illustration designed to show a particular theme connected with a specific geographic area. Earliest documented forms of data visualization were various thematic maps from different cultures and ideograms and hieroglyphs that provided and allowed interpretation of information illustrated. For example, [[Linear B]] tablets of [[Mycenae]] provided a visualization of information regarding Late Bronze Age era trades in the Mediterranean. The idea of coordinates was used by ancient Egyptian surveyors in laying out towns, earthly and heavenly positions were located by something akin to latitude and longitude at least by 200 BC, and the map projection of a spherical earth into latitude and longitude by [[Claudius Ptolemy]] [c.85–c. 165] in Alexandria would serve as reference standards until the 14th century.<ref name="Friendly 2001"/> The invention of paper and parchment allowed further development of visualizations throughout history. Figure shows a graph from the 10th or possibly 11th century that is intended to be an illustration of the planetary movement, 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. [[File:Mouvement des planètes au cours du temps.png|thumb|upright=1.5|Planetary movements]] By the 16th century, techniques and instruments for precise observation and measurement of physical quantities, and geographic and celestial position were well-developed (for example, a “wall quadrant” constructed by [[Tycho Brahe]] [1546–1601], covering an entire wall in his observatory). Particularly important were the development of triangulation and other methods to determine mapping locations accurately.<ref name="Springer-Verlag"/> Very early, the measure of time led scholars to develop innovative way of visualizing the data (e.g. Lorenz Codomann in 1596, Johannes Temporarius in 1596<ref>{{Cite web|date=2020-12-09|title=Data visualization: definition, examples, tools, advice [guide 2020]|url=https://www.intotheminds.com/blog/en/data-visualization/|access-date=2020-12-09|website=Market research consulting|language=en-BE}}</ref>). French philosopher and mathematician [[René Descartes]] and [[Pierre de Fermat]] developed analytic geometry and two-dimensional coordinate system which heavily influenced the practical methods of displaying and calculating values. Fermat and [[Blaise Pascal]]'s work on statistics and probability theory laid the groundwork for what we now conceptualize as data.<ref name="Springer-Verlag"/> According to the Interaction Design Foundation, these developments allowed and helped William [[William Playfair|Playfair]], who saw potential for graphical communication of quantitative data, to generate and develop graphical methods of statistics.<ref name=":0" /> [[File:Playfair TimeSeries.png|thumb|upright=1.5|Playfair TimeSeries]] In the second half of the 20th century, [[Jacques Bertin]] used quantitative graphs to represent information "intuitively, clearly, accurately, and efficiently".<ref name=":0" /> John Tukey and Edward Tufte pushed the bounds of data visualization; Tukey with his new statistical approach of exploratory data analysis and Tufte with his book "The Visual Display of Quantitative Information" paved the way for refining data visualization techniques for more than statisticians. With the progression of technology came the progression of data visualization; starting with hand-drawn visualizations and evolving into more technical applications – including interactive designs leading to software visualization.<ref>{{Cite web|url=http://www.datavis.ca/papers/hbook.pdf |title=A Brief History of Data Visualization |date=2006 |access-date=2015-11-22 |website=York University |publisher=Springer-Verlag |last=Friendly |first=Michael |archive-url=https://web.archive.org/web/20160508232649/http://www.datavis.ca/papers/hbook.pdf |archive-date=2016-05-08 |url-status=live}}</ref> Programs like [[SAS (software)|SAS]], [[SOFA Statistics|SOFA]], [[R (programming language)|R]], [[Minitab]], Cornerstone and more allow for data visualization in the field of statistics. Other data visualization applications, more focused and unique to individuals, programming languages such as [[D3.js|D3]], [[Python (programming language)|Python]] and [[JavaScript]] help to make the visualization of quantitative data a possibility. Private schools have also developed programs to meet the demand for learning data visualization and associated programming libraries, including free programs like [[The Data Incubator]] or paid programs like [[General Assembly]].<ref>{{cite news |title=NY gets new boot camp for data scientists: It's free but harder to get into than Harvard |newspaper=Venture Beat |access-date=2016-02-21 |url=https://venturebeat.com/2014/04/15/ny-gets-new-bootcamp-for-data-scientists-its-free-but-harder-to-get-into-than-harvard/ |archive-url=https://web.archive.org/web/20160215235820/http://venturebeat.com/2014/04/15/ny-gets-new-bootcamp-for-data-scientists-its-free-but-harder-to-get-into-than-harvard/ |archive-date=2016-02-15 |url-status=live}}</ref> Beginning with the symposium "Data to Discovery" in 2013, ArtCenter College of Design, Caltech and JPL in Pasadena have run an annual program on interactive data visualization.<ref>[http://datavis.caltech.edu Interactive Data Visualization]</ref> The program asks: How can interactive data visualization help scientists and engineers explore their data more effectively? How can computing, design, and design thinking help maximize research results? What methodologies are most effective for leveraging knowledge from these fields? By encoding relational information with appropriate visual and interactive characteristics to help interrogate, and ultimately gain new insight into data, the program develops new interdisciplinary approaches to complex science problems, combining design thinking and the latest methods from computing, user-centered design, interaction design and 3D graphics. ==Terminology== Data visualization involves specific terminology, some of which is derived from statistics. For example, author Stephen Few defines two types of data, which are used in combination to support a meaningful analysis or visualization: *Categorical: Represent groups of objects with a particular characteristic. Categorical variables can either be nominal or ordinal. Nominal variables for example gender have no order between them and are thus nominal. Ordinal variables are categories with an order, for sample recording the age group someone falls into.<ref name=":1">{{Cite book|last=Bulmer|first=Michael|title=A Portable Introduction to Data Analysis|publisher=Publish on Demand Centre|year=2013|isbn=978-1-921723-10-0|location=The University of Queensland|pages=4–5}}</ref> *Quantitative: Represent measurements, such as the height of a person or the temperature of an environment. Quantitative variables can either be [[Continuous or discrete variable|continuous or discrete]]. Continuous variables capture the idea that measurements can always be made more precisely. While discrete variables have only a finite number of possibilities, such as a count of some outcomes or an age measured in whole years.<ref name=":1" /> The distinction between quantitative and categorical variables is important because the two types require different methods of visualization. Two primary types of [[Information graphics|information displays]] are tables and graphs. *A ''table'' contains quantitative data organized into rows and columns with categorical labels. It is primarily used to look up specific values. In the example above, the table might have categorical column labels representing the name (a ''qualitative variable'') and age (a ''quantitative variable''), with each row of data representing one person (the sampled ''experimental unit'' or ''category subdivision''). *A ''graph'' is primarily used to show relationships among data and portrays values encoded as ''visual objects'' (e.g., lines, bars, or points). Numerical values are displayed within an area delineated by one or more ''axes''. These axes provide ''scales'' (quantitative and categorical) used to label and assign values to the visual objects. Many graphs are also referred to as ''charts''.<ref>{{cite web|url=http://www.perceptualedge.com/articles/ie/the_right_graph.pdf|title=Steven Few-Selecting the Right Graph for Your Message-September 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> Eppler and Lengler have developed the "Periodic Table of Visualization Methods," an interactive chart displaying various data visualization methods. It includes six types of data visualization methods: data, information, concept, strategy, metaphor and compound.<ref>{{cite web|last1=Lengler|first1=Ralph|author-link1=Ralph Lengler|last2=Eppler|first2=Martin. J|author-link2=Martin J. Eppler|title=Periodic Table of Visualization Methods|url=http://www.visual-literacy.org/periodic_table/periodic_table.html|access-date=15 March 2013|publisher=www.visual-literacy.org|archive-url=https://web.archive.org/web/20130316073116/http://www.visual-literacy.org/periodic_table/periodic_table.html|archive-date=16 March 2013|url-status=live}}</ref> ==Techniques== {{See also|Diagram|Infographic#Data visualization}} {| cellpadding="20" ! width="220" | ! width="120" style="text-align:left;" | Name ! width="220" style="text-align:left;" | Visual dimensions ! width="300" style="text-align:left;" | Description / Example usages |- | [[File:Tips-day-barchart.pdf|thumb|Bar chart of tips by day of week]] | [[Bar chart]] | * length/count * category * color | * Presents [[Categorical variable|categorical data]] with [[Rectangle|rectangular]] bars with [[height]]s or [[length]]s proportional to the values that they represent. The bars can be plotted vertically or horizontally. *A bar graph shows comparisons among [[Discrete variable|discrete]] [[Categorical variable|categories]]. One axis of the chart shows the specific categories being compared, and the other axis represents a measured value. *Some bar graphs present bars clustered in groups of more than one, showing the values of more than one measured variable. These clustered groups can be differentiated using color. *For example; comparison of values, such as sales performance for several persons or businesses in a single time period. |- | [[File:20210626 Variwide chart of greenhouse gas emissions per capita by country.svg|thumb|Variable-width bar chart relating (1) population, (2) per capita greenhouse gas emissions, and (3) total greenhouse gas emissions]] | Variable-width ("variwide") bar chart | * category (size/count/extent in first dimension) * size/count/extent in second dimension * size/count/extent as area of bar * color | * Includes most features of basic bar chart, above * Area of non-uniform-width bar explicitly conveys information of a third quantity that is implicitly related to first and second quantities from horizontal and vertical axes |- | |- | [[File:20220208 Projected temperature extremes for different degrees of global warming - orthogonal bar chart - IPCC AR6 WG1 SPM.svg|thumb|Projected (1) frequency and (2) intensity of extreme "10-year heat waves" are connected in pairs of horizontal and vertical bars, respectively. Bars are distinguished by (3) color-coded primary category (degree of global warming).]] | Orthogonal (orthogonal composite) bar chart | * numerical value of first variable (extent in first dimension; superimposed horizontal bars) * numerical value of second variable (extent in second dimension; like conventional vertical bar chart) * category for first and second variables (e.g., color-coded) | * Includes most features of basic bar chart, above * Pairs of numeric variables, usually color-coded, rendered by category * Variables need not be directly related in the way they are in "variwide" charts |- | [[File:Housingprice.png|thumb|Histogram of housing prices]] | [[Histogram]] | * bin limits * count/length * color | * An approximate representation of the [[Frequency distribution|distribution]] of numerical data. Divide the entire range of values into a series of intervals and then count how many values fall into each interval this is called [[Data binning|binning]]. The bins are usually specified as consecutive, non-overlapping [[Interval (mathematics)|intervals]] of a variable. The bins (intervals) must be adjacent, and are often (but not required to be) of equal size. *For example, determining frequency of annual stock market percentage returns within particular ranges (bins) such as 0-10%, 11-20%, etc. The height of the bar represents the number of observations (years) with a return % in the range represented by the respective bin. |- | [[File:Scatterplot5.pdf|thumb|Basic scatterplot of two variables]] | [[Scatter plot]] | * x position * y position * symbol/glyph * color * size | *Uses [[Cartesian coordinate system|Cartesian coordinates]] to display values for typically two [[Variable (mathematics)|variables]] for a set of data. *Points can be coded via color, shape and/or size to display additional variables. *Each point on the plot has an associated x and y term that determines its location on the cartesian plane. *Scatter plots are often used to highlight the correlation between variables (x and y). |- | [[File:Scatter plot.jpg|thumb|Scatter plot]] | Scatter plot (3D) | * position x * position y * position z * color *symbol *size | * Similar to the 2-dimensional scatter plot above, the 3-dimensional scatter plot visualizes the relationship between typically 3 variables from a set of data. * Again point can be coded via color, shape and/or size to display additional variables |- border="0" | [[File:Social Network Analysis Visualization.png|thumb|Network analysis]] | [[Network chart|Network]] | * nodes size * nodes color * ties thickness * ties color * [[spatialization]] | * Finding clusters in the network (e.g. grouping Facebook friends into different clusters). * Discovering bridges (information brokers or boundary spanners) between clusters in the network * Determining the most influential nodes in the network (e.g. A company wants to target a small group of people on Twitter for a marketing campaign). * Finding outlier actors who do not fit into any cluster or are in the periphery of a network. |- | [[File:English dialects1997.svg|thumb|Pie chart]] | [[Pie chart]] | * color | * Represents one categorical variable which is divided into slices to illustrate numerical proportion. In a pie chart, the [[arc length]] of each slice (and consequently its [[central angle]] and [[area]]), is [[Proportionality (mathematics)|proportional]] to the quantity it represents. * For example, as shown in the graph to the right, the proportion of [[English language|English]] native speakers worldwide |- | [[File:ScientificGraphSpeedVsTime.svg|thumb|Line chart]] | [[Line chart]] | * x position * y position * symbol/glyph * color * size | * Represents information as a series of data points called 'markers' connected by straight line segments. * Similar to a [[scatter plot]] except that the measurement points are ordered (typically by their x-axis value) and joined with straight line segments. * Often used to visualize a trend in data over intervals of time – a [[time series]] – thus the line is often drawn chronologically. |- | [[File:LastGraph example.svg|thumb|Streamgraph]] |[[Streamgraph]] | * width * color * time (flow) | * A type of stacked [[Area chart|area graph]] which is displaced around a [[Axis (mathematics)|central axis]], resulting in a flowing shape. * Unlike a traditional stacked area graph in which the layers are stacked on top of an axis, in a streamgraph the layers are positioned to minimize their "wiggle". * Streamgraphs display data with only positive values, and are not able to represent both negative and positive values. * For example, the right visual shows the music listened to by a user over the start of the year 2012 |- | [[File:Top100 states area treemap pop-density.svg|thumb|Treemap]] | [[Treemap]] | * size * color | * Is a method for displaying [[hierarchical]] data using [[Nesting (computing)|nested]] figures, usually rectangles. *For example, disk space by location / file type |- | [[File:GanttChartAnatomy.png|thumb|Gantt chart]] | [[Gantt chart]] | * color * time (flow) | * Type of [[bar chart]] that illustrates a [[Schedule (project management)|project schedule]] *Modern Gantt charts also show the [[Dependency (project management)|dependency]] relationships between activities and current schedule status. *For example, used in [[project planning]] |- | [[File:Heatmap.png|thumb|Heat map]] | [[heatmap|Heat map]] | * color *categorical variable | * Represents the magnitude of a phenomenon as color in two dimensions. *There are two categories of heat maps: **cluster heat map: where magnitudes are laid out into a matrix of fixed cell size whose rows and columns are categorical data. For example, the graph to the right. **spatial heat map: where no matrix of fixed cell size for example a heat-map. For example, a heat map showing population densities displayed on a geographical map |- |[[File:20190705 Warming stripes - Berkeley Earth (world) - avg above- and below-ice readings.png|thumb|Stripe graphic]] |[[Warming stripes|Stripe graphic]] | * x position * color | * A sequence of colored stripes visually portrays trend of a data series. * Portrays a single variable—prototypically ''temperature over time'' to portray [[global warming]] * Deliberately [[Minimalism|minimalist]]—with no technical indicia—to communicate intuitively with non-scientists<ref name="Gizmodo_20190617">{{cite news|last1=Kahn|first1=Brian|date=June 17, 2019|title=This Striking Climate Change Visualization Is Now Customizable for Any Place on Earth|work=Gizmodo|url=https://earther.gizmodo.com/this-striking-climate-change-visualization-is-now-custo-1835581866|url-status=live|archive-url=https://web.archive.org/web/20190626030105/https://earther.gizmodo.com/this-striking-climate-change-visualization-is-now-custo-1835581866|archive-date=June 26, 2019}} Developed in May 2018 by [[Ed Hawkins (scientist)|Ed Hawkins]], [[University of Reading]].</ref> * Can be "stacked" to represent plural series ([[:File:20190909_STACKED_country_warming_stripes_AND_global_average_(1901-_).png |example]]) |- |[[File:5 9 16 Andrea TempSpiralEdHawkins.gif|thumb|Animated spiral graphic]] |[[Climate spiral|Animated spiral graphic]] | * radial distance (dependent variable) * rotating angle (cycling through months) * color (passing years) | * Portrays a single dependent variable—prototypically ''temperature over time'' to portray [[global warming]] * Dependent variable is progressively plotted along a continuous "spiral" determined as a function of (a) constantly rotating angle (twelve months per revolution) and (b) evolving color (color changes over passing years)<ref name="WashPost_20160511">{{cite news|last1=Mooney|first1=Chris|date=11 May 2016|title=This scientist just changed how we think about climate change with one GIF|work=The Washington Post|url=https://www.washingtonpost.com/news/energy-environment/wp/2016/05/11/this-scientist-just-changed-how-we-think-about-climate-change-with-one-gif/|url-status=live|archive-url=https://web.archive.org/web/20190206213537/https://www.washingtonpost.com/news/energy-environment/wp/2016/05/11/this-scientist-just-changed-how-we-think-about-climate-change-with-one-gif/|archive-date=6 February 2019|quote=[[Ed Hawkins (scientist)|Ed Hawkins]] took these monthly temperature data and plotted them in the form of a spiral, so that for each year, there are twelve points, one for each month, around the center of a circle – with warmer temperatures farther outward and colder temperatures nearer inward.}}</ref> |- |[[File:Michelsonmorley-boxplot.svg|thumb|Box and whisker plot]] |[[Box plot|Box and Whisker Plot]] | * x axis * y axis | * A method for graphically depicting groups of numerical data through their [[quartile]]s. * Box plots may also have lines extending from the boxes (''whiskers'') indicating variability outside the upper and lower quartiles. * [[Outlier]]s may be plotted as individual points. * The two boxes graphed on top of each other represent the middle 50% of the data, with the line separating the two boxes identifying the median data value and the top and bottom edges of the boxes represent the 75th and 25th percentile data points respectively. * Box plots are [[non-parametric]]: they display variation in samples of a [[statistical population]] without making any assumptions of the underlying [[Probability distribution|statistical distribution]], thus are useful for getting an initial understanding of a data set. For example, comparing the distribution of ages between a group of people (e.g., male and females). |- |[[File:LampFlowchart.svg|thumb|Flowchart]] |[[Flowchart]] | * [[workflow]] or [[process]] | * Represents a [[workflow]], [[process]] or a step-by-step approach to solving a task. * The flowchart shows the steps as boxes of various kinds, and their order by connecting the boxes with arrows. * For example, outlying the actions to undertake if a lamp is not working, as shown in the diagram to the right. |- |[[File:MER Star Plot.gif|thumb|Radar chart]] |[[Radar chart]] | * attributes * value assigned to attributes | * Displays [[Multivariate statistics|multivariate]] [[data]] in the form of a two-dimensional [[chart]] of three or more quantitative variables represented on axes starting from the same point. * The relative position and angle of the axes is typically uninformative, but various heuristics, such as algorithms that plot data as the maximal total area, can be applied to sort the variables (axes) into relative positions that reveal distinct correlations, trade-offs, and a multitude of other comparative measures. * For example, comparing attributes/skills (e.g., communication, analytical, IT skills) learnt across different university degrees (e.g., mathematics, economics, psychology) |- |[[File:Venn diagram gr la ru.svg|thumb|Venn diagram]] |[[Venn diagram]] | * ''all'' possible [[logic]]al relations between a finite collection of different [[Set (mathematics)|sets]]. | * Shows ''all'' possible [[logic]]al relations between a finite collection of different [[Set (mathematics)|sets]]. * These diagrams depict [[Element (mathematics)|elements]] as points in the plane, and [[Set (mathematics)|sets]] as regions inside closed curves. * A Venn diagram consists of multiple overlapping closed curves, usually circles, each representing a set. * The points inside a curve labelled ''S'' represent elements of the set ''S'', while points outside the boundary represent elements not in the set ''S''. This lends itself to intuitive visualizations; for example, the set of all elements that are members of both sets ''S'' and ''T'', denoted ''S'' ∩ ''T'' and read "the intersection of ''S'' and ''T''", is represented visually by the area of overlap of the regions ''S'' and ''T''. In Venn diagrams, the curves are overlapped in every possible way, showing all possible relations between the sets. |- | [[File:AirMerIconographyCorrelation.jpg|thumb|Iconography of correlations]] | [[Iconography of correlations]] | * No axis * Solid line * dotted line * color | * Exploratory data analysis. * Replace a correlation matrix by a diagram where the “remarkable” correlations are represented by a solid line (positive correlation), or a dotted line (negative correlation). * Points can be coded via color. |- |} === Other techniques === * [[Cartogram]] * [[Cladistics|Cladogram]] (phylogeny) * [[Concept Map]]ping * [[Dendrogram]] (classification) * [[Information visualization reference model]] * [[Graph drawing]] * [[Heatmap]] * [[HyperbolicTree]] * [[Multidimensional scaling]] * [[Parallel coordinates]] * [[Problem solving environment]] * [[Treemapping]] ==Interactivity== {{Further|Interactive visualization}} '''Interactive data visualization''' enables direct actions on a graphical [[Plot (graphics)|plot]] to change elements and link between multiple plots.<ref>{{cite journal|last1=Swayne|first1=Deborah|author1-link= Deborah F. Swayne |title=Introduction to the special issue on interactive graphical data analysis: What is interaction?|journal=Computational Statistics|date=1999|volume=14|issue=1|pages=1–6|doi=10.1007/PL00022700|s2cid=86788346}}</ref> Interactive data visualization has been a pursuit of [[statisticians]] since the late 1960s. Examples of the developments can be found on the [[American Statistical Association]] video lending library.<ref>{{cite web|last1=American Statistics Association|first1=Statistical Graphics Section|title=Video Lending Library|url=http://stat-graphics.org/movies/}}</ref> Common interactions include: * '''[[Brushing and linking|Brushing]]''': works by using the [[Computer mouse|mouse]] to control a paintbrush, directly changing the color or glyph of elements of a plot. The paintbrush is sometimes a pointer and sometimes works by drawing an outline of sorts around points; the outline is sometimes irregularly shaped, like a lasso. Brushing is most commonly used when multiple plots are visible and some linking mechanism exists between the plots. There are several different conceptual models for brushing and a number of common linking mechanisms. Brushing [[scatterplots]] can be a transient operation in which points in the active plot only retain their new characteristics. At the same time, they are enclosed or intersected by the brush, or it can be a persistent operation, so that points retain their new appearance after the brush has been moved away. Transient brushing is usually chosen for linked brushing, as we have just described. * '''Painting''': Persistent brushing is useful when we want to group the points into clusters and then proceed to use other operations, such as the tour, to compare the groups. It is becoming common terminology to call the persistent operation painting, * '''Identification''': which could also be called labeling or label brushing, is another plot manipulation that can be linked. Bringing the cursor near a point or edge in a scatterplot, or a bar in a [[barchart]], causes a label to appear that identifies the plot element. It is widely available in many interactive graphics, and is sometimes called mouseover. * '''Scaling''': maps the data onto the window, and changes in the area of the. mapping function help us learn different things from the same plot. Scaling is commonly used to zoom in on crowded regions of a scatterplot, and it can also be used to change the aspect ratio of a plot, to reveal different features of the data. * '''[[Brushing and linking|Linking]]''': connects elements selected in one plot with elements in another plot. The simplest kind of linking, one-to-one, where both plots show different projections of the same data, and a point in one plot corresponds to exactly one point in the other. When using area plots, brushing any part of an area has the same effect as brushing it all and is equivalent to selecting all cases in the corresponding category. Even when some plot elements represent more than one case, the underlying linking rule still links one case in one plot to the same case in other plots. Linking can also be by categorical variable, such as by a subject id, so that all data values corresponding to that subject are highlighted, in all the visible plots. == Other perspectives == There are different approaches on the scope of data visualization. One common focus is on information presentation, such as Friedman (2008). Friendly (2008) presumes two main parts of data visualization: [[statistical graphics]], and [[Thematic map|thematic cartography]].<ref name = "MF08">[[Michael Friendly]] (2008). [http://www.math.yorku.ca/SCS/Gallery/milestone/milestone.pdf "Milestones in the history of thematic cartography, statistical graphics, and data visualization"] {{Webarchive|url=https://web.archive.org/web/20080911042504/http://www.math.yorku.ca/SCS/Gallery/milestone/milestone.pdf |date=2008-09-11 }}.</ref> In this line the "Data Visualization: Modern Approaches" (2007) article gives an overview of seven subjects of data visualization:<ref>[http://www.smashingmagazine.com/2007/08/02/data-visualization-modern-approaches/ "Data Visualization: Modern Approaches"] {{Webarchive|url=https://web.archive.org/web/20080722233419/http://www.smashingmagazine.com/2007/08/02/data-visualization-modern-approaches/ |date=2008-07-22 }}. in: ''Graphics'', August 2nd, 2007</ref> * [[Article (publishing)|Articles]] & [[resources]] * Displaying [[:wikt:connection|connection]]s * Displaying [[data]] * Displaying [[news]] * Displaying [[website]]s * [[Mind map]]s * Tools and services All these subjects are closely related to [[graphic design]] and information representation. <!-- This is hardly a reliable source and this list should maybe be moved to Information graphics --> On the other hand, from a [[computer science]] perspective, Frits H. Post in 2002 categorized the field into sub-fields:<ref name= "FHP02"/><ref name="FHP03">Frits H. Post, Gregory M. Nielson and Georges-Pierre Bonneau (2002). [https://web.archive.org/web/20091007134531/http://visualisation.tudelft.nl/publications/post2003b.pdf ''Data Visualization: The State of the Art''] {{webarchive|url=https://web.archive.org/web/20091007134531/http://visualisation.tudelft.nl/publications/post2003b.pdf |date=2009-10-07 }}.</ref> * [[Information visualization]] * [[Interaction techniques]] and architectures * Modelling techniques * Multiresolution methods * Visualization [[algorithm]]s and techniques * [[Volume visualization]] Within The Harvard Business Review, Scott Berinato developed a framework to approach data visualisation.<ref name=":2">{{Cite journal|last=Berinato|first=Scott|date=June 2016|title=Visualizations That Really Work|url=https://hbr.org/2016/06/visualizations-that-really-work|journal=Harvard Business Review|pages=92–100}}</ref> To start thinking visually, users must consider two questions; 1) What you have and 2) what you're doing. The first step is identifying what data you want visualised. It is data-driven like profit over the past ten years or a conceptual idea like how a specific organisation is structured. Once this question is answered one can then focus on whether they are trying to communicate information (declarative visualisation) or trying to figure something out (exploratory visualisation). Scott Berinato combines these questions to give four types of visual communication that each have their own goals.<ref name=":2" /> These four types of visual communication are as follows; * idea illustration (conceptual & declarative).<ref name=":2" /> ** Used to teach, explain and/or simply concepts. For example, organisation charts and decision trees. * idea generation (conceptual & exploratory).<ref name=":2" /> ** Used to discover, innovate and solve problems. For example, a whiteboard after a brainstorming session. * visual discovery (data-driven & exploratory).<ref name=":2" /> ** Used to spot trends and make sense of data. This type of visual is more common with large and complex data where the dataset is somewhat unknown and the task is open-ended. * everyday data-visualisation (data-driven & declarative).<ref name=":2" /> ** The most common and simple type of visualisation used for affirming and setting context. For example, a line graph of GDP over time. == Applications == Data and information visualization insights are being applied in areas such as:<ref name = "BBB03"/> * Scientific research * [[Digital libraries]] * [[Data mining]] * [[Information graphics]] * Financial data analysis * [[Health care]]<ref>{{cite journal| doi =10.1177/1460458212465213 | volume=19 | title=Making sense of personal health information: Challenges for information visualization | year=2013 | journal=Health Informatics Journal | pages=198–217 | last1 = Faisal | first1 = Sarah | last2 = Blandford | first2 = Ann | last3 = Potts | first3 = Henry WW| issue=3 | pmid=23981395 | s2cid=3825148 | url=http://discovery.ucl.ac.uk/1416283/1/VisPatientData_preprint.pdf }}</ref> * Market studies * Manufacturing [[production control]] * [[Crime mapping]] * [[eGovernance]] and [[Policy Modeling]] == Organization == Notable academic and industry laboratories in the field are: * [[Adobe Systems|Adobe Research]] * [[IBM Research]] * [[Google|Google Research]] * [[Microsoft Research]] * [[Panopticon Software]] * [[Scientific Computing and Imaging Institute]] * [[Tableau Software]] * [[University of Maryland Human-Computer Interaction Lab]] * [[VVI (company)|Vvi]] Conferences in this field, ranked by significance in data visualization research,<ref>{{cite web|last1=Kosara|first1=Robert|title=A Guide to the Quality of Different Visualization Venues|url=https://eagereyes.org/blog/2013/a-guide-to-the-quality-of-different-visualization-venues|website=eagereyes|access-date=7 April 2017|date=11 November 2013}}</ref> are: * [[IEEE Visualization]]: An annual international conference on scientific visualization, information visualization, and visual analytics. Conference is held in October. * [[SIGGRAPH|ACM SIGGRAPH]]: An annual international conference on computer graphics, convened by the ACM SIGGRAPH organization. Conference dates vary. * [[EuroVis]]: An annual Europe-wide conference on data visualization, organized by the Eurographics Working Group on Data Visualization and supported by the IEEE Visualization and Graphics Technical Committee (IEEE VGTC). Conference is usually held in June. * [[Conference on Human Factors in Computing Systems|Conference on Human Factors in Computing Systems (CHI)]]: An annual international conference on human–computer interaction, hosted by [[Association for Computing Machinery|ACM]] [[SIGCHI]]. Conference is usually held in April or May. * [[Eurographics]]: An annual Europe-wide computer graphics conference, held by the European Association for Computer Graphics. Conference is usually held in April or May. * [[PacificVis]]: An annual visualization symposium held in the Asia-Pacific region, sponsored by the IEEE Visualization and Graphics Technical Committee (IEEE VGTC). Conference is usually held in March or April. For further examples, see: [[:Category:Computer graphics organizations]] == Data presentation architecture == {{undue weight section|date=February 2021}} {{unreferenced section|date=March 2022}} [[File:Kencf0618FacebookNetwork.jpg|right|thumb|A data visualization from [[social media]]]] '''Data presentation architecture''' ('''DPA''') is a skill-set that seeks to identify, locate, manipulate, format and present data in such a way as to optimally communicate meaning and proper knowledge. Historically, the term ''data presentation architecture'' is attributed to Kelly Lautt:{{efn|The first formal, recorded, public usages of the term data presentation architecture were at the three formal Microsoft Office 2007 Launch events in Dec, Jan and Feb of 2007–08 in Edmonton, Calgary and Vancouver (Canada) in a presentation by Kelly Lautt describing a business intelligence system designed to improve service quality in a pulp and paper company. The term was further used and recorded in public usage on December 16, 2009 in a Microsoft Canada presentation on the value of merging Business Intelligence with corporate collaboration processes.}} "Data Presentation Architecture (DPA) is a rarely applied skill set critical for the success and value of [[Business intelligence|Business Intelligence]]. Data presentation architecture weds the science of numbers, data and statistics in [[information discovery|discovering valuable information]] from data and making it usable, relevant and actionable with the arts of data visualization, communications, [[organizational psychology]] and [[change management]] in order to provide business intelligence solutions with the data scope, delivery timing, format and visualizations that will most effectively support and drive operational, tactical and strategic behaviour toward understood business (or organizational) goals. DPA is neither an IT nor a business skill set but exists as a separate field of expertise. Often confused with data visualization, data presentation architecture is a much broader skill set that includes determining what data on what schedule and in what exact format is to be presented, not just the best way to present data that has already been chosen. Data visualization skills are one element of DPA." === Objectives === DPA has two main objectives: * To use data to provide knowledge in the most efficient manner possible (minimize noise, complexity, and unnecessary data or detail given each audience's needs and roles) * To use data to provide knowledge in the most effective manner possible (provide relevant, timely and complete data to each audience member in a clear and understandable manner that conveys important meaning, is actionable and can affect understanding, behavior and decisions) === Scope === With the above objectives in mind, the actual work of data presentation architecture consists of: * Creating effective delivery mechanisms for each audience member depending on their role, tasks, locations and access to technology * Defining important meaning (relevant knowledge) that is needed by each audience member in each context * Determining the required periodicity of data updates (the currency of the data) * Determining the right timing for data presentation (when and how often the user needs to see the data) * Finding the right data (subject area, historical reach, breadth, level of detail, etc.) * Utilizing appropriate analysis, grouping, visualization, and other presentation formats === Related fields === DPA work shares commonalities with several other fields, including: * [[Business analysis]] in determining business goals, collecting requirements, mapping processes. * Business process improvement in that its goal is to improve and streamline actions and decisions in furtherance of business goals * Data visualization in that it uses well-established theories of visualization to add or highlight meaning or importance in data presentation. * [[Digital humanities]] explores more nuanced ways of visualising complex data. * [[Information architecture]], but information architecture's focus is on [[unstructured data]] and therefore excludes both analysis (in the statistical/data sense) and direct transformation of the actual content (data, for DPA) into new entities and combinations. * [[Human–computer interaction|HCI]] and [[interaction design]], since many of the principles in how to design interactive data visualisation have been developed cross-disciplinary with HCI. * [[Visual journalism]] and [[data-driven journalism]] or [[data journalism]]: Visual journalism is concerned with all types of graphic facilitation of the telling of news stories, and data-driven and data journalism are not necessarily told with data visualisation. Nevertheless, the field of journalism is at the forefront in developing new data visualisations to communicate data. * [[Graphic design]], conveying information through styling, typography, position, and other aesthetic concerns. == See also == {{Div col|colwidth=20em}} * [[Analytics]] * [[Big Data]] * [[Climate change art]] * [[Color coding technology for visualization]] * [[Computational visualistics]] * [[Data art]] * [[Data Presentation Architecture]] * [[Data profiling]] * [[Data warehouse]] * [[Geovisualization]] * [[Grand Tour (data visualisation)]] * [[imc FAMOS]] (1987)]], graphical data analysis * [[Infographics]] * [[Information design]] * [[List of information graphics software]] * [[List of countries by economic complexity]], example of Treemapping * [[Patent visualisation]] <!-- -ization form is red as of 1 Aug 21 --> * [[Software visualization]] * [[Statistical analysis]] * [[Visual analytics]] * [[Warming stripes]]{{Div col end}} == Notes == {{Notelist}} == References == {{Reflist}} == Further reading == {{further cleanup|date=April 2022}} <!-- 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. 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. --> * {{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 }} * {{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 |first=Kieran |last=Healy |author-link=Kieran Healy |title=Data Visualization: A Practical Introduction |location=Princeton |publisher=Princeton University Press |year=2019 |isbn=978-0-691-18161-5 }} * {{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 |location=New York |publisher=Springer |isbn=978-1-4613-5430-7 }} *{{Cite book |last1=Rosling |first1=H. |author-link1=Hans Rosling |last2=Rosling |first2=O. |author-link2=Ola Rosling |last3=Rosling Rönnlund |first3=A. |author-link3=Anna Rosling Rönnlund |title=[[Factfulness: Ten Reasons We're Wrong About the World – and Why Things Are Better Than You Think]] |publisher=Flatiron Books |pages=288 |year=2018 |isbn=9781250123817}} * {{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 |last=Wilkinson |first=Leland |author-link=Leland Wilkinson |title=Grammar of Graphics |publisher=Springer |location=New York |year=2012 |isbn=978-1-4419-2033-1 }} * [[Ben Bederson]] and [[Ben Shneiderman]] (2003). [https://books.google.com/books?id=TrZZQ5I76BcC&dq=the+craft+of+information+visualization+readings+and+reflections&psp=1&source=gbs_summary_s&cad=0 ''The Craft of Information Visualization: Readings and Reflections'']. Morgan Kaufmann. * [[Stuart K. Card]], [[Jock D. Mackinlay]] and [[Ben Shneiderman]] (1999). [https://books.google.com/books?id=wdh2gqWfQmgC&dq=readings+in+information+visualization+using+vision+to+think&psp=1&source=gbs_summary_s&cad=0 ''Readings in Information Visualization: Using Vision to Think''], Morgan Kaufmann Publishers. * 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"]. In: ''ACM Human Factors in Computing Systems'' CHI 2005. * 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. * Riccardo Mazza (2009). [https://www.amazon.com/Introduction-Information-Visualization-Riccardo-Mazza/dp/1848002181 ''Introduction to Information Visualization''], Springer. * [[Robert Spence (engineer)|Spence, Robert]] ''Information Visualization: Design for Interaction (2nd Edition)'', Prentice Hall, 2007, {{ISBN|0-13-206550-9}}. * Colin Ware (2000). [https://www.amazon.com/dp/3835060155 ''Information Visualization: Perception for design'']. San Francisco, CA: Morgan Kaufmann. * Kawa Nazemi (2014). [https://diglib.eg.org/handle/10.2312/12076 Adaptive Semantics Visualization] Eurographics Association. ==External links== {{Commons category}} *[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}} {{Authority control}} {{DEFAULTSORT:Data Visualization}} [[Category:Data visualization| ]] [[Category:Visualization (graphics)]] [[Category:Statistical charts and diagrams]] [[Category:Information technology governance]] [[Category:Data|Visualization]] [[de:Informationsvisualisierung]]'