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Journal of Visualization

, Volume 22, Issue 5, pp 897–912 | Cite as

Multivariate spatial data visualization: a survey

  • Xiangyang He
  • Yubo TaoEmail author
  • Qirui Wang
  • Hai Lin
Regular Paper

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.

Graphic abstract

Keywords

Multivariate spatial data Feature classification Fusion visualization Correlation analysis 

Notes

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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Copyright information

© The Visualization Society of Japan 2019

Authors and Affiliations

  1. 1.State Key Laboratory of CAD and CGZhejiang UniversityHangzhouChina

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