  Data uniformity is a concept associated with several semantic data
characteristics such as lack of features, correlation and sample bias. This
article introduces a novel measure to assess data uniformity and detect uniform
pointsets on high-dimensional Euclidean spaces. Spatial uniformity measure
builds upon the isomorphism between hyperspherical chords and L2-normalised
data Euclidean distances, which is implied by the fact that, in Euclidean
spaces, L2-normalised data can be geometrically defined as points on a
hypersphere. The imposed connection between the distance distribution of
uniformly selected points and the hyperspherical chord length distribution is
employed to quantify uniformity. More specifically,, the closed-form expression
of hypersphere chord length distribution is revisited extended, before
examining a few qualitative and quantitative characteristics of this
distribution that can be rather straightforwardly linked to data uniformity.
The experimental section includes validation in four distinct setups, thus
substantiating the potential of the new uniformity measure on practical
data-science applications.
