Low-Complexity Detection of POI Boundaries Using Geo-Tagged Tweets: A Geographic Proximity Based Approach

Abstract

Users tend to check in and post their statuses in location-based social networks (LBSNs) to describe that their interests are related to a point-of-interest (POI). Since the relevance of the data to the POI varies according to the geographic distance between the POI and the locations where the data are generated, it is important to characterize an area-of-interest (AOI) that enables to utilize the location information in a variety of businesses, services, and place advertisements. While previous studies on discovering AOIs were conducted based mostly on density-based clustering methods with the collection of geo-tagged photos from LBSNs, we focus on detecting a POI boundary, which corresponds to only one cluster containing its POI center. Using geo-tagged tweets recorded from Twitter users, this paper introduces a low-complexity two-phase strategy to detect a POI boundary by finding a suitable radius reachable from the POI center. We detect a polygon-type boundary of the POI as the convex hull (i.e., the outermost region) of selected geo-tags through our two-phase approach, where each phase proceeds on with different sizes of radius increment, thus yielding a more precise boundary. It is shown that our approach outperforms the conventional density-based clustering method in terms of runtime complexity.