Spatial and Temporal Analysis and Visualization

Geo-visualization: Facing Meaning, Imperfection and Big Data

Geo-visualization can be seen has the part of Information Visualization dedicated to Geographic Information. The design and development of geo-visualization interfaces are complex and tedious tasks that often address conceptual and cognitive questions. From the designer’s point of view, the challenge is to use relevant visual and dynamic variables, according to some context of use, to some ob jectives, or to the animation and interaction properties associated with the displayed numerical maps (C. Saint-Marc’s PhD, started in 2014). Here, new semiological rules must be defined. Also, methodological and conceptual frameworks must be developed, as well as reusable software components, for building efficient geo-visualization interfaces. This requires also analysing the user’s cognitive processes involved in the perception of such animated, interactive and dynamic maps, and querying about the adequacy between the generated map and the initial message that it was supposed to convey. For this, new protocols of assessment must be proposed. Furthermore, even if the map correctly conveys the message, data it displays might be questionable in terms of quality.

As a matter of fact, imperfection and its various forms (uncertainty, incompleteness, vagueness, …) are studied since more than one decade in Geomatics. While new formalisms for representing and reasoning with imperfect geographic information are paid much attention, the way imperfection is visual ly handled still requires to investigate some new adapted semiology and, consequently, new tools to visualize imperfection, and new protocols and tests to assess map reader’s perception. Finally, day after day, the avalanche of spatiotemporal data (whether such data come from continuous flows – satellites, sensors, Web, social networks, VGI, etc. – or from accumulated long-term stocks – legacy authoritative and official databases) pauses the very acute issue of visualizing such data. Regarding Big Data, geo-visualization here has not only the purpose of showing data by relying on various spatial analysis, geo-statistical, or clustering methods, but also the purpose of facilitating the exploration and the selection of relevant datasets.