Emmanuel Pietriga

Mengfei Gao, Caroline Appert, Ludovic David et al.

Browsing the Web on mobile devices is often cumbersome due to their limited screen space. We investigate a phone+AR Web browsing approach, AiRWeb, that leverages the structural properties of Web pages to allow users to seamlessly select and offload arbitrary Web content into the space surrounding them. Focusing on flexibility, AiRWeb lets users decide what to offload, when to do so, and how offloaded content is arranged, enabling personalized organization tailored to the task at hand. We developed a fully functional prototype using standard Web technologies, that covers the complete interaction workflow, from the selection of elements to offload from the phone to their manipulation in the air. Results from a preliminary study conducted using this prototype suggest that AiRWeb is learnable and usable, while also revealing open design challenges around offload mode activation in particular.

Vanessa Peña-Araya, Emmanuel Pietriga, Anastasia Bezerianos

Observing the relationship between two or more variables over space and time is essential in many domains. For instance, looking, for different countries, at the evolution of both the life expectancy at birth and the fertility rate will give an overview of their demographics. The choice of visual representation for such multivariate data is key to enabling analysts to extract patterns and trends. Prior work has compared geo-temporal visualization techniques for a single thematic variable that evolves over space and time, or for two variables at a specific point in time. But how effective visualization techniques are at communicating correlation between two variables that evolve over space and time remains to be investigated. We report on a study comparing three techniques that are representative of different strategies to visualize geo-temporal multivariate data: either juxtaposing all locations for a given time step, or juxtaposing all time steps for a given location; and encoding thematic attributes either using symbols overlaid on top of map features, or using visual channels of the map features themselves. Participants performed a series of tasks that required them to identify if two variables were correlated over time and if there was a pattern in their evolution. Tasks varied in granularity for both dimensions: time (all time steps, a subrange of steps, one step only) and space (all locations, locations in a subregion, one location only). Our results show that a visualization's effectiveness depends strongly on the task to be carried out. Based on these findings we present a set of design guidelines about geo-temporal visualization techniques for communicating correlation.