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Journal of Visualization

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17.06.2019 | Regular Paper

Enhancing statistical charts: toward better data visualization and analysis

verfasst von: Xiaonan Luo, Yuan Yuan, Kaiyuan Zhang, Jiazhi Xia, Zhiguang Zhou, Liang Chang, Tianlong Gu

Erschienen in: Journal of Visualization | Ausgabe 4/2019

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Abstract

Conventional statistical charts are widely used in visual analysis. With the development of digital techniques, statistical charts are confronted with problems when data grow in scale and complexity. Accordingly, a huge amount of effort has been paid on the enhancement of standard charts, making the design space dramatically increased. It is cumbersome for naive users to choose appropriate design in a specific analysis scenario. In this paper, we survey the enhancement techniques for a compact set of statistical charts, and identify the types and usage scenarios. Motivated by the new problems, such as data volume and complexity, we present a challenge-and-task-driven framework to guide the understanding of the design space and the decision-making process.

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Titel: Enhancing statistical charts: toward better data visualization and analysis
verfasst von: Xiaonan Luo
Yuan Yuan
Kaiyuan Zhang
Jiazhi Xia
Zhiguang Zhou
Liang Chang
Tianlong Gu
Publikationsdatum: 17.06.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Journal of Visualization / Ausgabe 4/2019
Print ISSN: 1343-8875
Elektronische ISSN: 1875-8975
DOI: https://doi.org/10.1007/s12650-019-00569-2

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Abstract

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