Data analysts must often attend to several perspectives on a dataset concurrently. A common example is when the data have many attributes that carry spatial, temporal and other descriptive characteristics. Analysts need approaches that enable these many perspectives to be considered concurrently, so that they can build a comprehensive, multi-faceted understanding of phenomena. In this work, we propose a design framework for producing composite faceted views that incorporate different levels of visual abstraction for multiple perspectives. Fluid transitions and selective varying of these abstractions encourages concurrent analysis across perspectives. The software, which can be seen in the video below, was developed in Java using the Processing.org graphical library.