Mining Highly Visited Co-Location Patterns Based on Minimum Visitor Similarity Constraints

Abstract

Spatial co-location pattern is a subset of spatial features which shows association relationships based on the spatial neighborhoods. Because the previous prevalence measurements of a co-location pattern have not considered the visited information of spatial instances, co-location patterns do not reflect the social connections (such as their spatial instances are constantly visited by common or similar moving objects) between spatial features. In this paper, a special type of co-location pattern, “Highly visited co-location patterns”, is proposed, which considers the spatial proximity and visitor similarity of spatial features at the same time. A new measurement, “Minimum visitor similarity”, has been proposed to reflect the visitor similarity of co-location patterns. By discussing the properties of the minimum visitor similarity, we propose an efficient algorithm to mine the highly visited co-locations and give two pruning strategies to improve the efficiency of the algorithm. Finally, extensive experiments on YELP and Foursquare datasets prove the practicability and efficiency of the proposed algorithm, and we define a “Social Entropy” to prove that spatial features in the co-locations we mined have stronger social connections.