  The concepts of spread and spread dimension of a metric space were introduced
by Willerton in the context of quantifying biodiversity of ecosystems. This
paper develops practical applications of spread dimension in the context of
machine learning and manifold learning; we show that the topological dimension
of a Riemannian manifold can be accurately estimated by computing the spread
dimension of a finite subset. These results are presented as the theoretical
basis for a novel method of estimating the intrinsic dimension of data. The
practical applications of this method are demonstrated with empirical
computations using real and synthetic data.
