Highlighting space-time patterns: Effective visual encodings for interactive decision-making

The research reported in this paper focuses on integrating analytical and visual methods in order to explore complex patterns in geo-related multivariate data sets and to understand changes of these patterns over time. The goal is to provide techniques that are able to analyze real-world Data Warehouses, a typical architecture to manage such geo-related multidimensional data sets, in order to support analyst’s decision making process. Challenges arise because real word applications usually have to deal with millions of records, with dozens of dimensions, and spatio-temporal context. Therefore, a tightly integration of automated analysis and interactive visualizations are integrated is needed (as proposed in the context of Visual Analytics). Our approach uses the well studied capabilities provided by Data Warehouses supporting knowledge discovery and decision making to analyze spatio-temporal behavior of pattern in high dimensional-spaces. The topic of the paper is to show possible interplays between automated analysis and geo-spatial visualization.