# Multivariate Spatial Data Visualization: A Survey

arXiv: https://arxiv.org/abs/1908.11344 · HTML: https://mlsanity.com/paper/1908.11344/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2019.

## Built on by (most cited first, 1 of 1)

- [voxel2vec: A Natural Language Processing Approach to Learning Distributed Representations for Scientific Data](https://mlsanity.com/paper/2207.02565/) (2022)

## Abstract (arXiv)

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.