CO-424 COGNITIVELY PLAUSIBLE VISUALIZATION OF NETWORK DATA

We present a network visualization approach based on the spatialization framework proposed by Fabrikant and Skupin (2005) which allows the perceptually salient and cognitively plausible information visualization of large relational datasets at high aggregation level. The framework is put to a rigorous test in a case study aimed at uncovering the latent relational structure of global cities, based on air passenger volumes. A force-directed placement algorithm is employed to depict cities (i.e., nodes) and respective passenger flows between the cities (i.e., edges) as a network in relational data space. The relational dataset is further semantically and cartographically generalized by means of common network clustering methods coupled with the adaptive multiplicative weighted Voronoi algorithm (AMWVD). Qualitative expert interviews have been carried out for evaluating the resulting generalized network visualizations. The evaluation confirms the overall validity of methodological framework and suggests further steps for refinement