Computational steering requires the coupling of simulation and visualization elements, but if the latter is targetted at general requirements, little or no information about the calculation itself may survive in the final image. Consequently, changes made there cannot, in general, propagate back to the source. For example, in a study of a chemical reaction, a line on a graph simply consists of linked pairs of x and y coordinates, with no indication that these denote the concentrations of, say, oxygen or hydrogen at certain times. This paper will introduce a new visualization taxonomy and data structure which allow changes in the simulation to be accomplished by direct image manipulation, allowing more intuitive steering of a range of scientific applications.