Sonifying for Public Engagement: A Context-Based Model for Sonifying Air Pollution Data

Abstract

In this paper we report on a unique and contextually-sensitive approach to sonification of a subset of climate data: urban air pollution for four Canadian cities. Similarly to other datadriven models for sonification and auditory display, this model details an approach to data parameter mappings, however we specifically consider the context of a public engagement initiative and a reception by an 'everyday' listener, which informs our design. Further, we present an innovative model for FM index-driven sonification that rests on the notion of 'harmonic identities' for each air pollution data parameter sonified, allowing us to sonify more datasets in a perceptually 'economic' way. Finally, we briefly discuss usability and design implications and outline future work.