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Titlebook: Data Representations, Transformations, and Statistics for Visual Reasoning; Ross Maciejewski Book 2011 Springer Nature Switzerland AG 2011

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發(fā)表于 2025-3-21 17:07:46 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Data Representations, Transformations, and Statistics for Visual Reasoning
編輯Ross Maciejewski
視頻videohttp://file.papertrans.cn/264/263036/263036.mp4
叢書名稱Synthesis Lectures on Visualization
圖書封面Titlebook: Data Representations, Transformations, and Statistics for Visual Reasoning;  Ross Maciejewski Book 2011 Springer Nature Switzerland AG 2011
描述Analytical reasoning techniques are methods by which users explore their data to obtain insight and knowledge that can directly support situational awareness and decision making. Recently, the analytical reasoning process has been augmented through the use of interactive visual representations and tools which utilize cognitive, design and perceptual principles. These tools are commonly referred to as visual analytics tools, and the underlying methods and principles have roots in a variety of disciplines. This chapter provides an introduction to young researchers as an overview of common visual representations and statistical analysis methods utilized in a variety of visual analytics systems. The application and design of visualization and analytical algorithms are subject to design decisions, parameter choices, and many conflicting requirements. As such, this chapter attempts to provide an initial set of guidelines for the creation of the visual representation, including pitfalls and areas where the graphics can be enhanced through interactive exploration. Basic analytical methods are explored as a means of enhancing the visual analysis process, moving from visual analysis to visua
出版日期Book 2011
版次1
doihttps://doi.org/10.1007/978-3-031-02599-0
isbn_softcover978-3-031-01471-0
isbn_ebook978-3-031-02599-0Series ISSN 2159-516X Series E-ISSN 2159-5178
issn_series 2159-516X
copyrightSpringer Nature Switzerland AG 2011
The information of publication is updating

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Data Representations, Transformations, and Statistics for Visual Reasoning978-3-031-02599-0Series ISSN 2159-516X Series E-ISSN 2159-5178
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發(fā)表于 2025-3-22 05:06:02 | 只看該作者
Managing Access to Nontimber Forest Productsearch for different components, discover different features, and analyze data in a variety of unique ways. Given these different representations and analyses, one can begin combining these techniques to create even more comprehensive tools and interactive visuals that can further aid the analysis process.
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https://doi.org/10.1007/978-3-319-27158-3e interfaces utilize combinations of statistical graphics, analysis, animation and interaction in order to enhance the cognitive process and provide users with an intuitive means to explore and understand their data. In this way, users can confirm expectations about their data, search for the unexpe
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發(fā)表于 2025-3-22 21:06:24 | 只看該作者
Julia Suderow,Amaya Angulo Garzaro choice that depends both on the data type, the problem domain and the applied visual representation. However, there is no ‘best’ color scale. Rheingans [115] notes that a choice of an appropriate color scale is influenced by several factors including the characteristics of the data being analyzed,
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Managing Access to Nontimber Forest Productsis also a key factor in determining not only the appropriate visual display parameters but also the underlying statistical analyses. Under the assumptions ofdata normality, choices ofdata grouping and axes scaling for visual analysis have been well studied(e.g., [19, 29, 30, 31, 36, 70, 103, 140]).
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發(fā)表于 2025-3-23 03:41:27 | 只看該作者
Susan J. Alexander,Roger D. Fight] coined the mantra of “Overview, zoom and filter, details on demand.” This idea of providing overviews of the data and then details on demand has been readily adopted in the visualization community; however, the notion of visual analytics extends this idea. Here, the overview step is now replaced b
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發(fā)表于 2025-3-23 05:40:07 | 只看該作者
Managing Access to Nontimber Forest Productsearch for different components, discover different features, and analyze data in a variety of unique ways. Given these different representations and analyses, one can begin combining these techniques to create even more comprehensive tools and interactive visuals that can further aid the analysis pr
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