Section: New Results
Visualization in Specific Application Areas
Finally, we worked in collaboration with other researchers on projects aimed at investigating how visualization can support experts in different application areas.
In the area of geovisualization, we performed a comparison of visualization techniques to help analysts identify correlation between variables over space and time (TVCG/InfoVis 2019, ). Observing the relationship between two or more variables over space and time is essential in many application 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. We conducted a study comparing three techniques that are representative of different strategies to visualize geo-temporal multivariate data. 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. Our results showed that a visualization's effectiveness depends strongly on the task to be carried out. Based on this study's findings, we derived a set of design guidelines about geo-temporal visualization techniques for communicating correlation.
Together with researchers from INRA, we performed an exploratory study about the visual exploration of model simulations for a range of experts (CHI 2019, ). Experts in different domains rely increasingly on simulation models of complex processes to reach insights, make decisions, and plan future projects. These models are often used to study possible trade-offs, as experts try to optimize multiple conflicting objectives in a single investigation. Understanding all the model intricacies, however, is challenging for a single domain expert. This project introduced a simple approach to support multiple experts when exploring complex model results, working concurrently on a shared visualization surface. The results of an observational study focusing on the link between expertise and insight generation during the analysis process, revealed the different exploration strategies and multi-storyline approaches that domain experts adopt during trade-off analysis. This eventually led to recommendations for collaborative model exploration systems.
We collaborated with researchers in databases from Université Paris Descartes on progressive similarity search on time-series data (BigVis 2019 workshop, ). Time-series data are increasing at a dramatic rate, yet their analysis remains highly relevant in a wide range of human activities. Due to their volume, existing systems dealing with time-series data cannot guarantee interactive response times, even for fundamental tasks such as similarity search. This paper presented our vision to develop analytic approaches that support exploration and decision making by providing progressive results, before the final and exact ones have been computed. Findings from our experiment indicated that there is a gap between the time the most similar answer is found and the time when the search algorithm terminates, resulting in inflated waiting times without any improvement. These findings led to preliminary ideas about computing probabilistic estimates of the final results that could help users decide when to stop the search process.
In the field of Education, we contributed to EduClust, an online visualization application for teaching clustering algorithms (EuroGraphics 2019, ). EduClust combines visualizations, interactions, and animations to facilitate the understanding and teaching of clustering steps, parameters, and procedures. Traditional classroom settings aim for cognitive processes like remembering and understanding. We designed EduClust for expanded educational objectives like applying and evaluating. The application can be used by both educators to prepare teaching material and examples, and by students to explore clustering differences and discover algorithmic subtleties.