This pore sampling and KDE analysis provides the portioning of space to different pores, however, does not provide local information for each pore, such as the pore centre. To achieve this the sampled points, labelled by size based on KDE, was clustered using the density-based spatial clustering of applications with noise (DBSCAN) approach. DBSCAN was applied using the sci-kit learn implementation. Clustering samples core points of high density and effectively expands clusters from them and is used for data that contain clusters of similar density. The clustering of sample points provided the number of pores within this size regime in the unit cell. Simply by computing the centre for cluster the coordinates of the pore centres were computed.