I Don't Know What's Going on There: The Use of Spatial Transformations to Deal With and Resolve Uncertainty in Complex Visualizations (Preprint).

摘要

Imagine a meteorologist preparing a weather forecast. In addition to years of experience and a vast store of domain knowledge, the forecaster has access to satellite images, to computer generated weather models and programs to display them in a variety of ways, and to an assortment of special-purpose tools that provide additional task-relevant data. There is no shortage of data, yet despite this array of resources, the task remains very challenging. One source of complexity is the uncertainty inherent in these data. To complicate matters further, the uncertainty in the data is not explicitly represented; rather, the visualizations indicate that the data are exactly as they appear. The visualizations thus invite the forecaster to map uncertain data to certain values, yet to do so would most likely lead to erroneous predictions. This example illustrates the basic question we investigate in this paper: how do people, especially experts, deal with uncertainty in highly spatial domains, when the data are inherently uncertain but the tools actually display very little uncertainty. We first examine how uncertainty affects operations in three representative domains, submarine operations (military), meteorology (geoscience), and fMRI research (scientific visualization), in which dealing with uncertainty is a critical component of the task. We then investigate how experts in two of these domains, meteorology and fMRI, manage uncertainty as they perform problem-solving activities and make decisions as part of their regular task performance.