Data visualization\u00c2\u00a0is the\u00c2\u00a0graphic\u00c2\u00a0representation\u00c2\u00a0of\u00c2\u00a0data. It involves producing images that communicate relationships among the represented data to viewers of the images. This communication is achieved through the use of a systematic\u00c2\u00a0mapping\u00c2\u00a0between graphic marks and data values in the creation of the visualization. This mapping establishes how data values will be represented visually, determining how and to what extent a property of a graphic mark, such as size or color, will change to reflect changes in the value of a datum.<\/p>\n
To communicate information clearly and efficiently, data visualization uses\u00c2\u00a0statistical graphics,\u00c2\u00a0plots,\u00c2\u00a0information graphics\u00c2\u00a0and other tools. Numerical data may be encoded using dots, lines, or bars, to visually communicate a quantitative message.\u00c2\u00a0Effective visualization helps users analyze and reason about data and evidence. It makes complex data more accessible, understandable and usable. Users may have particular analytical tasks, such as making comparisons or understanding\u00c2\u00a0causality, 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.<\/p>\n
Data visualization is both an art and a science.It is viewed as a branch of\u00c2\u00a0descriptive statistics\u00c2\u00a0by some, but also as a\u00c2\u00a0grounded theory\u00c2\u00a0development tool by others. Increased amounts of data created by Internet activity and an expanding number of sensors in the environment are referred to as “big data” or\u00c2\u00a0Internet of things. Processing, analyzing and communicating this data present ethical and analytical challenges for data visualization. The field of\u00c2\u00a0data science\u00c2\u00a0and practitioners called data scientists help address this challenge.<\/p>\n
Professor\u00c2\u00a0Edward Tufte\u00c2\u00a0explained that users of information displays are executing particular\u00c2\u00a0analytical tasks<\/i>\u00c2\u00a0such as making comparisons. The\u00c2\u00a0design principle\u00c2\u00a0of the information graphic should support the analytical task.\u00c2\u00a0As 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.<\/p>\n
In his 1983 book\u00c2\u00a0The Visual Display of Quantitative Information,\u00c2\u00a0Edward Tufte\u00c2\u00a0defines ‘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:<\/p>\n
Graphics\u00c2\u00a0reveal<\/i>\u00c2\u00a0data. Indeed graphics can be more precise and revealing than conventional statistical computations.”<\/p>\n
For example, the Minard diagram shows the losses suffered by Napoleon’s army in the 1812\u00e2\u20ac\u201c1813 period. Six variables are plotted: the size of the army, its location on a two-dimensional surface (x and y), time, 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.”<\/p>\n
Not applying these principles may result in\u00c2\u00a0misleading graphs, which distort the message or support an erroneous conclusion. According to Tufte,\u00c2\u00a0chartjunk\u00c2\u00a0refers to 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.<\/p>\n
The\u00c2\u00a0Congressional Budget Office\u00c2\u00a0summarized 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 context of the report; and c) Designing graphics that communicate the key messages in the report.<\/p>\n
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 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.<\/p>\n
Effective 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).<\/p>\n
Almost all data visualizations are created for human consumption. Knowledge of human perception and cognition is necessary when designing intuitive visualizations.\u00c2\u00a0Cognition refers to processes in human beings like perception, attention, learning, memory, thought, concept formation, reading, and problem solving.\u00c2\u00a0Human 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\u00c2\u00a0data exploration.<\/p>\n
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:<\/p>\n
Two primary types of\u00c2\u00a0information displays\u00c2\u00a0are tables and graphs.<\/p>\n
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.<\/p>\n
The above is a brief about\u00c2\u00a0Data Visualization. Watch this space for more updates on the latest trends in Technology.<\/p>\n","protected":false},"excerpt":{"rendered":"
Data visualization\u00c2\u00a0is the\u00c2\u00a0graphic\u00c2\u00a0representation\u00c2\u00a0of\u00c2\u00a0data. It involves<\/p>\n","protected":false},"author":1,"featured_media":1835,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[676,677,7],"tags":[678,679,18],"class_list":["post-1833","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-visualization","category-graphic-representation","category-techtrends","tag-data-visualization","tag-graphic-representation","tag-technology"],"_links":{"self":[{"href":"https:\/\/blog.amt.in\/index.php\/wp-json\/wp\/v2\/posts\/1833","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog.amt.in\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.amt.in\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.amt.in\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.amt.in\/index.php\/wp-json\/wp\/v2\/comments?post=1833"}],"version-history":[{"count":1,"href":"https:\/\/blog.amt.in\/index.php\/wp-json\/wp\/v2\/posts\/1833\/revisions"}],"predecessor-version":[{"id":1834,"href":"https:\/\/blog.amt.in\/index.php\/wp-json\/wp\/v2\/posts\/1833\/revisions\/1834"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blog.amt.in\/index.php\/wp-json\/wp\/v2\/media\/1835"}],"wp:attachment":[{"href":"https:\/\/blog.amt.in\/index.php\/wp-json\/wp\/v2\/media?parent=1833"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.amt.in\/index.php\/wp-json\/wp\/v2\/categories?post=1833"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.amt.in\/index.php\/wp-json\/wp\/v2\/tags?post=1833"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}