Contrary to general belief, data visualization is not a modern development. Stellar data, or information such as location of stars were visualized on the walls of caves (such as those found in Lascaux cave in southern France) since the Pleistocene era. 19 Physical artefacts such as Mesopotamian clay tokens (5500 bc inca quipus (2600 BC) and summary Marshall Islands stick charts (n.d.) can also be considered as visualizing quantitative information. 20 21 First documented data visualization can be tracked back to 1160. With Turin Papyrus Map which accurately illustrates the distribution of geological resources and provides information about quarrying of those resources. 22 Such maps can be categorized as 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.
16 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. 17 Proper visualization provides a different approach to show potential connections, relationships, etc. Which needed are not as obvious in non-visualized quantitative data. Visualization can become a means of data exploration. History of data visualization edit 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. 18 Michael Friendly and Daniel j denis of York University are engaged in a project that attempts to provide a comprehensive history of visualization.
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 edit a human can distinguish differences in line length, shape, orientation, 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. 14 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). 14 Human perception/cognition and data visualization edit Almost all data visualizations are created for human consumption. Knowledge of human perception and cognition is necessary when designing intuitive visualizations. 15 Cognition refers to processes in human beings like perception, attention, learning, memory, thought, concept formation, reading, and problem solving.
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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 philosophy categorical subdivisions in no particular order, such as the sales volume by product code. A bar chart may be used for this comparison. 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. 2 13 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.
12 quantitative messages edit a time series illustrated with a line chart demonstrating trends. Federal spending and revenue over time. 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. A line chart may be used to demonstrate the trend. 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. 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.
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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." 11 Not applying these principles may result in misleading graphs, which distort the message or support an erroneous conclusion. According to tufte, chartjunk refers to extraneous interior decoration of the graphic that does not enhance the message, or gratuitous three dimensional or perspective effects. Needlessly separating the explanatory short 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. 11 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 context of the report; and c) Designing graphics that communicate the key messages in the report.
According to post. (2002 it has united scientific and information visualization. 8 Characteristics of effective graphical displays edit The greatest value of a picture is when it forces us to notice what we never expected to see. John tukey 9 Professor Edward Tufte explained that users of information displays are executing particular analytical tasks such as making comparisons or determining causality. The design principle of the information graphic should support the analytical task, showing the comparison or causality. 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. Indeed graphics can be more line precise and revealing than conventional statistical computations." 11 For example, the minard diagram shows the losses suffered by napoleon's army in the 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.
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". 6 Indeed, fernanda viegas and Martin. Wattenberg suggested that an ideal visualization should not only communicate clearly, but stimulate viewer engagement and attention. 7 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.
Data visualization is both an art and a plan science. 3, it is viewed as a branch of descriptive statistics by some, but also as a grounded theory development 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, internet of things. Processing, analyzing and communicating this data present ethical and analytical challenges for data visualization. 4, the field of data science and practitioners called data scientists help address this challenge. Contents, overview edit, data visualization is one of the steps in analyzing data and presenting it to users. 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.
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For the software company, see. Data visualization or data visualisation is viewed by many disciplines as a modern equivalent of visual communication. It involves the creation and study of the visual representation of data. 1, to communicate information clearly and efficiently, data visualization uses statistical graphics, plots, information graphics and other tools. Numerical data may be encoded using dots, lines, or bars, to visually communicate a quantitative message. 2, effective visualization helps users analyze and reason about data and evidence. It makes complex needed data more accessible, understandable and usable. 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.