Multivariate Spatial Data Visualization: A Survey

TL;DR

This survey reviews current techniques for visualizing multivariate spatial data, emphasizing their roles in scientific understanding, hypothesis testing, and discovering new physical laws.

Contribution

It provides a comprehensive overview of state-of-the-art methods, clarifies key tasks, and discusses future research directions in multivariate spatial data visualization.

Findings

Identifies three main tasks: feature classification, fusion visualization, and correlation analysis.

Highlights the importance of visualization in scientific discovery and hypothesis verification.

Proposes potential future research topics in the field.

Abstract

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.