Graph Database Solution for Higher Order Spatial Statistics in the Era of Big Data

Understand

We present an algorithm for the fast computation of the general $N$-point spatial correlation functions of any discrete point set embedded within an Euclidean space of $\mathbb{R}^n$.

• Utilizing the concepts of kd-trees and graph databases, we describe how to count all possible $N$-tuples in binned configurations within a given length scale, e.g.
• all pairs of points or all triplets of points with side lengths $<r_{max}$.
• Through bench-marking we show the computational advantage of our new graph based algorithm over more traditional methods.