  We propose a data reduction technique for scattered data based on statistical
sampling. Our void-and-cluster sampling technique finds a representative subset
that is optimally distributed in the spatial domain with respect to the blue
noise property. In addition, it can adapt to a given density function, which we
use to sample regions of high complexity in the multivariate value domain more
densely. Moreover, our sampling technique implicitly defines an ordering on the
samples that enables progressive data loading and a continuous level-of-detail
representation. We extend our technique to sample time-dependent trajectories,
for example pathlines in a time interval, using an efficient and iterative
approach. Furthermore, we introduce a local and continuous error measure to
quantify how well a set of samples represents the original dataset. We apply
this error measure during sampling to guide the number of samples that are
taken. Finally, we use this error measure and other quantities to evaluate the
quality, performance, and scalability of our algorithm.
