Mining co-location patterns from distributed spatial data

Abstract

Co-location patterns in spatial dataset are the interesting collection of dissimilar objects which are located in proximity. We keep similar objects in an entity set and maintain that no two objects in a co-location pattern belong to an entity set. Location proximity is based on Euclidean distance measure. However, algorithms for mining patterns in transactional datasets are not directly applicable to spatial datasets for mining co-location patterns. Conventional methods are not applicable to distributed temporal data and many applications generating spatial dataset are inherently distributive in nature. In this paper, a Map-Reduce based approach is proposed to find all co-location patterns from a spatial dataset distributed over nodes. This approach is modularized one and consists of four algorithms. With the first three algorithms in the first approach and by proposing an algorithm for dynamic datasets, this paper contains another approach for the co-location patterns set, that also updates in an incremental manner (not from scratch) whenever certain changes occur in the dataset. Experimental results on larger datasets are also presented.