A Simple Spatial Method for Identifying Point Clusters by Neighbourhood Relationships

Abstract

Point events can be distributed regularly, randomly, or in clusters. A cluster of points is defined by the distance from which any point included in a cluster is farther from any other point outside the cluster. Many solutions and methods are possible to define clustering distance. I present here a simple method, nearest neighbour index clustering (NNIC), which separately identifies local clusters of points using only their neighbourhood relationships based on the nearest neighbour index (NNI). It computes a Delaunay triangulation among all points and calculates the length of each line, selecting the lines shorter than the expected nearest neighbour distance. The points intersecting the selected Delaunay lines are considered to belong to an independent cluster. I verified the performance of the NNIC method with a virtual and a real example. In the virtual example, I joined two sets of random point processes following a Poisson distribution and a Thomas cluster process. In the real example, I used a point process from the distribution of individuals of two species of Iberian lizards in a mountainous area. For both examples, I compared the results with those of the nearest neighbour cleaning (NNC) method. NNIC selected a different number of clustered points and clusters in each random set of point processes and included fewer points in clusters than the NNC method.