  Multivariate spatial data plays 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.
