  Space filling curves are widely used in Computer Science. In particular
Hilbert curves and their generalisations to higher dimension are used as an
indexing method because of their nice locality properties. This article
generalises this concept to the systematic construction of p-adic versions of
Hilbert curves based on affine transformations of the p-adic Gray code, and
develops an efficient scaled indexing method for data taken from
high-dimensional spaces based on these new curves, which with increasing
dimension is shown to be less space consuming than the optimal standard static
Hilbert curve index. A measure is derived which allows to assess the local
sparsity of a data set, and is tested on some data.
