Agenda
June
-
09:3012:00
Understanding the environmental impacts of everyday actions is a challenge: the data is complex and rarely communicated in an accessible way. However, improving public knowledge and awareness of unsustainable behaviors is essential to promoting more responsible choices, both individually and collectively. To achieve this, it is important to communicate environmental impacts using tools that spark interest and engage the general public.
In this thesis, we explore concrete and immersive visualizations as a means of communicating the environmental impacts of everyday actions. We first introduce a concrete visualization focusing on three planetary boundaries, represented through a virtual mini-world that mimics the Earth’s biophysical processes. We then present a controlled study examining the effects of augmented reality and concrete visualizations on the perceived credibility of environmental information as well as on user engagement—showing that concrete visualizations in augmented reality can substantially increase engagement without compromising credibility. Next, we report on a second controlled study examining the impact of enaction and gamification via quizzes on the retention of greenhouse gas emissions data in virtual reality—suggesting that gamification promotes retention while enaction increases intrinsic motivation and the state of flow. Finally, we introduce augmented reality visualizations spanning several tens of meters to represent datasets containing large orders of magnitude, and propose a design space exploring the use of movement to support progressive data exploration.
Taken as a whole, our contributions suggest that concrete and immersive visualizations represent a promising direction for environmental communication, offering an engaging and credible alternative to conventional graphic representations.
Salle Ada Lovelace (Inria)
July
-
14:0018:00
Nina Lassalle-Astis will defend her dissertation titled: “Development of a Hybrid Data Augmentation Methodology: Application to Supervised Defect Detection in X-ray Tomography.”
Amphi LaBRI