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Data visualization

Data visualization
Data visualization or data visualisation is viewed by many disciplines as a modern equivalent of visual communication. It is not owned by any one field, but rather finds interpretation across many (e.g. it is viewed as a modern branch of descriptive statistics by some, but also as a grounded theory development tool by others). It involves the creation and study of the visual representation of data, meaning "information that has been abstracted in some schematic form, including attributes or variables for the units of information".[1] A primary goal of data visualization is to communicate information clearly and efficiently to users via the information graphics selected, such as tables and charts. Effective visualization helps users in analyzing and reasoning about data and evidence. It makes complex data more accessible, understandable and usable. Data visualization is both an art and a science. Overview[edit] Indeed, Fernanda Viegas and Martin M. Graphics reveal data. Terminology[edit]

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Mind Mapping This article is about the diagram. For the geographical concept, see Mental mapping. Hand-drawn and computer-drawn variations of a mind map. A mind map is a diagram used to visually organize information. Statistical graphics Statistical graphics, also known as graphical techniques, are information graphics in the field of statistics used to visualize quantitative data. Overview[edit] Exploratory data analysis (EDA) relies heavily on such techniques. Data Visualization: Modern Approaches - Smashing Magazine Data presentation can be beautiful, elegant and descriptive. There is a variety of conventional ways to visualize data – tables, histograms, pie charts and bar graphs are being used every day, in every project and on every possible occasion. However, to convey a message to your readers effectively, sometimes you need more than just a simple pie chart of your results.

Pulse of the Nation: U.S. Mood Throughout the Day inferred from Twitter Click for high-resolution PDF version (11MB) Video A time-lapse video of the maps, cycled twice, is available below (best viewed at 720p): Mood Variations A number of interesting trends can be observed in the data. Latent class model In statistics, a latent class model (LCM) relates a set of observed (usually discrete) multivariate variables to a set of latent variables. It is a type of latent variable model. It is called a latent class model because the latent variable is discrete. A class is characterized by a pattern of conditional probabilities that indicate the chance that variables take on certain values.

Crowdmap Crowdmap allows you to set up your own deployment of the Ushahidi Platform without having to install it on your own web server. Crowdmap is the fastest, simplest installation of the Ushahidi platform. Within minutes you'll be up and running with your own installation, mapping reports events and visualizing information. Information graphics Information graphics or infographics are graphic visual representations of information, data or knowledge intended to present complex information quickly and clearly.[1][2] They can improve cognition by utilizing graphics to enhance the human visual system’s ability to see patterns and trends.[3][4] The process of creating infographics can be referred to as data visualization, information design, or information architecture.[2] Overview[edit] Infographics have been around for many years and recently the proliferation of a number of easy-to-use, free tools have made the creation of infographics available to a large segment of the population. Social media sites such as Facebook and Twitter have also allowed for individual infographics to be spread among many people around the world. In newspapers, infographics are commonly used to show the weather, as well as maps, site plans, and graphs for statistical data. "Graphical displays should:

Principal Manifolds for Data Visualization and Dimension Reduction New approaches to NLPCA, principal manifolds, branching principal components and topology preserving mappings are described Presentation of algorithms is supplemented by case studies In 1901 Karl Pearson invented Principal Component Analysis (PCA). Since then, PCA serves as a prototype for many other tools of data analysis, visualization and dimension reduction: Independent Component Analysis (ICA), Multidimensional Scaling (MDS), Nonlinear PCA (NLPCA), Self Organizing Maps (SOM), etc. The book starts with the quote of the classical Pearson definition of PCA and includes reviews of various methods: NLPCA, ICA, MDS, embedding and clustering algorithms, principal manifolds and SOM. New approaches to NLPCA, principal manifolds, branching principal components and topology preserving mappings are described as well. Presentation of algorithms is supplemented by case studies, from engineering to astronomy, but mostly of biological data: analysis of microarray and metabolite data.

Physics Simulations and Artwork Here is a 3D view of a hydrogren atom in the 4f state. The left image was made in C++ using a technique described by Krzysztof Marczak to make it volumetric like a cloud of smoke. The right image was made in Mathematica by adding 2D cross-sectional layers. Dashboard (business) Business Dashboards. Simple, communicates easilyMinimum could cause confusionSupports organized business with meaning and useful dataApplies human visual perception to visual presentation of information In management information systems, a dashboard is "An easy to read, often single page, real-time user interface, showing a graphical presentation of the current status (snapshot) and historical trends of an organization’s key performance indicators (KPIs) to enable instantaneous and informed decisions to be made at a glance."[3]

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