Geometric Interpretations of the $k$-Nearest Neighbour Distributions

TL;DR

This paper explores the geometric interpretations of $k$-Nearest Neighbour CDFs, establishing their equivalence to sphere volumes and intersections, and compares their information content and computational efficiency with Minkowski Functionals for analyzing spatial data.

Contribution

It provides a detailed geometric interpretation of $k$NN CDFs, linking them to sphere volumes and intersections, and demonstrates their comparable information content to Minkowski Functionals with faster computation.

Findings

$k$NN CDFs are equivalent to sphere volume intersections around data points.

The derivatives of $k$NN CDFs contain geometric information similar to Minkowski Functionals.

$k$NN CDFs are computationally more efficient while retaining similar information content.

Abstract

The $k$-Nearest Neighbour Cumulative Distribution Functions are measures of clustering for discrete datasets that are fast and efficient to compute. They are significantly more informative than the 2-point correlation function. Their connection to $N$-point correlation functions, void probability functions and Counts-in-Cells is known. However, the connections between the CDFs and other geometric and topological spatial summary statistics are yet to be fully explored in the literature. This understanding will be crucial to find optimally informative summary statistics to analyse data from stage 4 cosmological surveys. We explore quantitatively the geometric interpretations of the $k$NN CDF summary statistics. We establish an equivalence between the 1NN CDF at radius $r$ and the volume of spheres with the same radius around the data points. We show that higher $k$NN CDFs are equivalent to the volumes of intersections of $\ge k$ spheres around the data points. We present similar geometric interpretations for the $k$NN cross-correlation joint CDFs. We further show that the volume, or the CDFs, have information about the angles and arc lengths created at the intersections of spheres around the data points, which can be accessed through the derivatives of the CDF. We show this information is very similar to that captured by Germ Grain Minkowski Functionals. Using a Fisher analysis we compare the information content and constraining power of various data vectors constructed from the $k$NN CDFs and Minkowski Functionals. We find that the CDFs and their derivatives and the Minkowski Functionals have nearly identical information content. However, $k$NN CDFs are computationally orders of magnitude faster to evaluate. Finally, we find that there is information in the full shape of the CDFs, and therefore caution against using the values of the CDF only at sparsely sampled radii.