March 2016 More informationNetworks are complex objects whose representation generates a large number of constraints and questions. If we add a temporal component it becomes even more difficult to produce a satisfactory overview for reporting the dynamic aspect of this type of data. So, why not try to represent this data dynamically?
December 2016 More informationWeather data are a good example of datasets for visualization experiments. They are temporal, spatialized and understandable by everyone. This type of datasets are rich but the number of dimension can make them difficult to visualize if we want to consider all their characteristics. This is an exploration of possible visualizations that can be considered, depending on the combinations of informations we want to highlight.