4.1 Visualization matters

Key Concepts

• Themes in the science of visualization
• Simulation models
• Visualization tools – graphs created using Excel and MATLAB
• Distributed visualization
• Metadata and Paradata for scientific visualization

Introduction

"We don’t see with our eyes. We see with our brains", Paul Bach-y-Rita.

In the last thirty years computer-based visualization has moved from an informal ad hoc tool designed to create particular results, to becoming a proper science in its own right. Universal generalisations and specifications as well as best practice guidelines are now available. Visualization methods are now being studied as an individual topic within various courses and modules; at all levels from undergraduate to postgraduate. Visualization is now the basis of numerous PhD titles and further research projects and programmes, funded across all the research councils and the infrastructure HE/FE funding agencies. This research and development has created a large toolkit for general use as well as individual methodologies for specialist user data sets, and has helped in understanding the barriers between the computer display and the human visual system. Visualization, it should be emphasised, is as much about gaining investigative insight as it is about enhancing presentations to tell a clearly specified story.

The science of visualization has been split into three themes; information visualization that studies methods for the representation of large-scale collections of often non-numerical information as well as the recommendations for use of graphical techniques to aid in the analysis of data. Scientific visualization, the second theme, was developed from previous often natural and experimental methods of displaying data, which has seen an explosion of users due to the deluge of in-silico experimental data (e.g. supercomputing and high throughput computing results) as well as real experimental capture equipment (e.g. 3D medical scanners, climate sensor data and astrophysical telescopes). Results often mimic reality, for example creating virtual wind-tunnel visualizations, but can be abstract, for example visualizing 6-dimensional tensor components using different geometric shapes (as in Figure 1). Visual analytics is the third theme. This merges both of these fields to focus on the user’s analytical reasoning, which often involves interactive visual interfaces and commonly employs various data-mining techniques as well as combining data across different databases.

This chapter introduces examples within these visualization themes, first providing an overview of simulation models and then specific examples from the creation of graphs using popular tools such as Excel and MATLAB. It then moves on to present the complexities of distributed visualization, as well as the role of adding metadata and paradata.