Designing for Ambiguity: Visual Analytics in Avalanche Forecasting

TL;DR

This paper explores how visual analytics can support decision-making under ambiguity in avalanche forecasting, highlighting challenges and proposing glyph-based solutions for sensemaking in uncertain data environments.

Contribution

It introduces a case study on designing VA tools for avalanche forecasters, emphasizing the need for explicit ambiguity support in visualization.

Findings

Glyphs aid sensemaking under ambiguity

Ambiguity is prevalent in risk-based decision domains

Visualization research should address ambiguity explicitly

Abstract

Ambiguity, an information state where multiple interpretations are plausible, is a common challenge in visual analytics (VA) systems. We discuss lessons learned from a case study designing VA tools for Canadian avalanche forecasters. Avalanche forecasting is a complex and collaborative risk-based decision-making and analysis domain, demanding experience and knowledge-based interpretation of human reported and uncertain data. Differences in reporting practices, organizational contexts, and the particularities of individual reports result in a variety of potential interpretations that have to be negotiated as part of the forecaster's sensemaking processes. We describe our preliminary research using glyphs to support sensemaking under ambiguity. Ambiguity is not unique to public avalanche forecasting. There are many other domains where the way data are measured and reported vary in ways not accounted explicitly in the data and require analysts to negotiate multiple potential meanings. We argue that ambiguity is under-served by visualization research and would benefit from more explicit VA support.