Visualizing Causality with Context using Animation

Candidate: Miao (Emily) Yao
Type: Master of Science (MSc), School of Interactive Arts and Technology
Date: December 10, 2007
Senior Supervisor: Dr. Lyn Bartram, Assistant Professor
Thesis: Download Thesis Document

Abstract

Visualizing causality is one of the most difficult problems in information visualization. In particular, visualizing causal relations within existing representations. (termed causal overlay) remains to be explored. The approach of a visual causal vector (VCV) holds promise as a perceptually efficient causal overlay technique. This thesis describes an empirical investigation of two initial issues of this technique: how to elicit and avoid causal impression and how to represent the strength of the causal effect. We examine the use of vector animation to produce the flow of causality and node animation to convey the strength of causal influence. The results of four experiments show that this approach has great potential to practically apply causal overlay and form an initial basis for a set of principled guidelines for designing causal overlay visualizations.