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Multivariate spatial data visualization: a survey

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Abstract

Multivariate spatial data play an important role in computational science and engineering simulations. The potential features and hidden relationships in multivariate data can assist scientists to gain an in-depth understanding of a scientific process, verify a hypothesis, and further discover a new physical or chemical law. In this paper, we present a comprehensive survey of the state-of-the-art techniques for multivariate spatial data visualization. We first introduce the basic concept and characteristics of multivariate spatial data, and describe three main tasks in multivariate data visualization: feature classification, fusion visualization, and correlation analysis. Finally, we prospect potential research topics for multivariate data visualization according to the current research.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments. This work was supported by the National Key Research & Development Program of China (2017YFB0202203), National Natural Science Foundation of China (61472354, 61672452 and 61890954), NSFC-Guangdong Joint Fund (U1611263), and the Fundamental Research Funds for the Central Universities.

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Correspondence to Yubo Tao.

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He, X., Tao, Y., Wang, Q. et al. Multivariate spatial data visualization: a survey. J Vis 22, 897–912 (2019). https://doi.org/10.1007/s12650-019-00584-3

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