This paper investigates the applicability of knowledge domain mapping for analyzing political science data. Utilizing metaphorical-space models grounded on political science theory, and applying sound cartographic visualization techniques, we demonstrate the construction and analysis of knowledge domain maps for exploring voting behaviour in Switzerland. We digitally transformed the results of Swiss popular referenda of the last twenty years to generate a 3-dimensional semantic space representing the current political landscape of Switzerland. The whole country is depicted in this semantic political space at various spatial scales. Locations in this spatialization represent aggregated voting outcomes from cities, regions, and provinces. Special attention was given to the interpretation of the resulting spatial configuration. This includes the assignment of meaning to the axes of the 3D space, depicted in two dimensions. Armed with political science theory locations in the voting behaviour space can be analyzed and the resulting political pattern can be interpreted meaningfully. The spatialized views were disseminated to the public after recent Swiss elections. The initial feedback from domain specialists and decision-makers alike has been very encouraging. Measured by high number of substantive reactions and wide-spread feedback on these spatializations of voting behaviour one could deduce that these abstract views were readily accepted and understood by public administrators, political party leaders, and the politically interested public. Based on these experiences we conclude the paper with a first attempt at identifying design recommendation for spatializing multidimensional political datasets.