Reasoning about socio-economic data: a visual analytics approach to Bayesian network

The visual analytics approach has become an important tool for analytical reasoning in the fields of information visualization, scientific visualization and cartography. Much research effort has been made to embed statistical methods in the interactive environment of visual analytics where analysts are supported by data visualization tools for the understanding and exploration of complex datasets. This has stimulated further demands on a more collaborative and co-creative human-computer reasoning. In this study, we have developed a model-based visual analytics prototype in which a probabilistic graphical model, namely Bayesian Network, is integrated within a geospatial visualization tool. Such a visual analytics environment aims to empower the joint human-computer reasoning under conditions of uncertainty, where domain experts may interactively assess multiple geospatial datasets. The proposed approach is demonstrated by means of a scenario with heterogeneous socio-economic data in Munich, Germany. By developing a Bayesian Network-enabled visual analytics, we establish a novel system that supports prior knowledge awareness and user involvement in socio-economic data discovery using interactive visualization. The proposed system uses data to uncover structure of the relationships and dependencies among demographic, social and economic factors.