Emmanuel Pietriga's articles on arXiv 2019-11-15T00:00:00-05:00 http://arxiv.org/a/pietriga_e_1 http://arxiv.org/abs/1907.06399v2 2019-10-15T09:16:26-04:00 2019-07-15T05:49:47-04:00 A Comparison of Visualizations for Identifying Correlation over Space and Time 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. Vanessa Peña-Araya Emmanuel Pietriga Anastasia Bezerianos 10.1109/TVCG.2019.2934807 Accepted for presentation at IEEE VIS 2019, to be held October 20-25 in Vancouver, Canada; will be published in a special issue of IEEE Transactions on Visualization and Computer Graphics (TVCG)