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Color is widely and reliably used to display the value of a single scalar variable. It is more rarely, and far less reliably, used to display multivariate data. Dynamic control over the parameters of the color mapping results in a more effective environment for the exploration of multivariate spatial distributions. The paper describes an empirical study comparing the effectiveness of static versus dynamic representations for the exploration of qualitative aspects of bivariate distributions. In this experiment, subjects made judgments about the correspondence of the shape, location, and magnitude of two patterns under conditions with varying amounts of random noise. Subjects made significantly more correct judgements (p<0.001) about feature shape and relative positions using the dynamic representation, on average forty-five percent more. The differences between static and dynamic representations were greater in the presence of noise.