A multiple window-based co-location pattern mining approach for various types of spatial data

Co-location pattern analysis represents the subsets of spatial events whose instances are found in close geographic proximity. Given a collection of Boolean spatial features, the co-location pattern discovery process finds the subsets of features frequently located together. Key challenges in co-location pattern analysis are modelling of neighbourhood in spatial domain, minimum prevalent threshold to generate collocation patterns and analysing extended spatial objects. We discuss the above key challenges using event centric approach and N-most prevalent co-location patterns approach. We propose a window-based model to find the neighbourhood for point spatial datasets and the multiple window model for extended spatial data objects. We also use N-most prevalent co-location patterns approach to filter the number of co-location pattern generation. We propose a more generic and efficient window-based model algorithm to find co-location patterns. Towards the end, we have done a comparative study of the existing approaches with our proposed approach.