  In order to be able to process the increasing amount of spatial data,
efficient methods for their handling need to be developed. One major challenge
for big spatial data is access. This can be achieved through space-filling
curves, as they have the property that nearby points on the curve are also
nearby in space. They are able to handle higher dimensional data, too. Higher
dimensional data is widely used e.g. in CityGML and is becoming more and more
important. In a laboratory experiment on a tropical rain forest tree data set
of 2.5 million points taken from an 18-dimensional space, it is demonstrated
that the recently constructed scaled Gray-Hilbert curve index performs better
than its standard static version, saving a significant amount of space for a
projection of the data set onto 8 attributes. The implementation is based on a
binary tree in a data-driven process, in a similar way as e.g. the R-tree. Its
scalability allows the handling of different kinds of data distributions which
are reflected in the tree structure of the index. The relative efficiency of
the scaled Gray-Hilbert curve in comparison with the best static version is
seen to depend on the distribution of the point cloud. A local sparsity measure
derived from properties of the corresponding trees can distinguish point clouds
with different tail distributions. The different resulting binary trees are
visualised to illustrate the influences of the different tail distributions
they have been built on.
