  We review the concepts of the Voronoi binning technique (Cappellari & Copin
2003), which optimally solves the problem of preserving the maximum spatial
resolution of general two-dimensional data, given a constraint on the minimum
signal-to-noise ratio (S/N). This is achieved by partitioning the data in an
adaptive fashion using a Voronoi tessellation with nearly hexagonal lattice. We
review astrophysical applications of the method to X-ray data, integral-field
spectroscopy, Fabry-Perot interferometry, N-body simulations, standard images
and other regularly or irregularly sampled data. Voronoi binning, unlike
adaptive smoothing, produces maps where the noise in the data can be visually
assessed and spurious artifacts can be recognized. The method can be used to
bin data according to any general criterion and not just S/N. It can be applied
to higher dimensions and it can be used to generate optimal adaptive meshes for
numerical simulations.
