Laboratory of Informatics of Grenoble Engineering Human-Computer Interaction Research Group

Engineering Human-Computer Interaction
Research Group

Composite Visual Mapping for Time Series Visualization

In Proceedings of the 11th IEEE Pacific Visualization Symposium (PacificVis 2018). pages 116-124. 2018.

Ali Jabbari, Renaud Blanch, Sophie Dupuy-Chessa

Abstract

In the information visualization reference model, visual mapping is the most crucial step in producing a visualization from a data set. The conventional visual mapping maps each data attribute onto a single visual channel (e.g. the year of production of a car to the position on the horizontal axis). In this work, we investigate composite visual mapping: mapping single data attributes onto several visual channels, each one representing one aspect of the data attribute (e.g. its order of magnitude, or its trend component). We first propose a table which allows us to explore the design space of composite mappings by offering a systematic overview of channel combinations. We expect that using more than one visual channel for communicating a data attribute increases the bandwidth of information presentation by displaying separable information on different aspects of data. In order to evaluate this point, we compare horizon graph, an existing technique which successfully adopts a composite visual mapping, with a selection of alternative composite mappings. We show that some of those mappings perform as well as –and in some cases even better than– horizon graph in terms of accuracy and speed. Our results confirm that the benefits of composite visual mapping are not limited to horizon graph. We thus recommend the use of composite visual mapping when users are simultaneously interested in several aspects of data attributes.