Multivariate Spatial Data Visualization: A Survey
It provides a review for researchers in computational science and engineering, but is incremental as it summarizes existing work without new methods or results.
This paper presents a comprehensive survey of state-of-the-art techniques for visualizing multivariate spatial data, covering tasks like feature classification and correlation analysis to help scientists understand scientific processes and discover new laws.
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.