Data visualization in the time of coronavirus

Currently, we observe a proliferation of data visualizations about Covid-19 in the media, which makes it a convenient time to study the topic from the perspective of different disciplines, including information design and mathematics. If, on the one hand, the abundance of such pandemic representations would already be a legitimate reason to address the issue, on the other hand, it is not the central motivation of the present discussion. The uniqueness of the epidemiological phenomenon that we are experiencing highlights new aspects regarding the production and use of data visualizations, one of which is its diversification beyond counting and visual representation of events related to the virus spread. In this sense, the article discusses, through the analysis of examples, three different approaches for this type of schematic representation, namely: visualization of hypothetical data, visualizations based on secondary data, and visualization for social criticism and self-reflection. Ultimately, we can argue that design contributes to the production of data visualizations that can help people to understand the causes and implications involved in the new coronavirus and encourage civic responsibility through self-care and the practice of social distancing

Experiments in Situated Participatory Virtual Reality/Experiências em Realidade Virtual Participativa Situada

Virtual and Augmented Reality is arguably one of the areas of most rapid development in the industry nowadays. While some of the technologies have matured and different applications consolidated with market acceptance, the next frontier is still being pursued by researchers and the media.In this paper, we contribute to this quest of effective proposals for the new media with experiments of “Situated Participatory Virtual Reality” using the Holojam platform. They give a glimpse of what will be possible in future applications and guidelines regarding relevant authoring aspects

Surface Reconstruction from Noisy Point Clouds

We show that a simple modification of the power crust algorithm for surface reconstruction produces correct outputs in presence of noise. This is proved using a fairly realistic noise model. Our theoretical results are related to the problem of computing a stable subset of the medial axis. We demostrate the effectiveness of our algorithm with a number of experimental results