Institute for Data Analysis and Visualization

We extend the notion of multiresolution spatial data approximation of static datasets to spatio-temporal approximation of time-varying datasets. By including a temporal component, we allow a region from one time-step to approximate a congruent region at another time-step. While approximations of static datasets are generated by refining the representation if a given error-bound is not met, for approximations of time-varying datasets we use data from another time-step, if that data meets a given error-bound for the current time-step. Thus, our technique exploits the fact that time-varying datasets typically do not change uniformly over time, and by only loading data from rapidly changing regions, much less data need be loaded to generate an approximation. Regions that don't change, or change slowly, are not loaded and are approximated by regions from another time-step. Prior techniques only permit binary classifications between consecutive time-steps. Our technique allows a run-time error-criterion between non-temporally consecutive time-steps. Error between time-steps is calculated in a pre-processing step and stored in error-tables. These error-tables are used to calculate error at run-time, so no data is touched. User movements in time are not restricted to unidirectional, successive changes - movement can be forward, backward, or to any arbitrary time-step.