Mining travel patterns from GPS-tagged photos

Abstract

The phenomenal advances of photo-sharing services, such as Flickr TM , have led to voluminous community-contributed photos with socially generated textual, temporal and geographical metadata on the Internet. The photos, together with their time- and geo-references, implicitly document the photographers' spatiotemporal movement paths. This study aims to leverage the wealth of these enriched online photos to analyze the people's travel pattern at the local level of a tour destination. First, from a noisy pool of GPS-tagged photos downloaded from Internet, we build a statistically reliable database of travel paths, and mine a list of regions of attraction (RoA). We then investigate the tourist traffic flow among different RoAs, by exploiting Markov chain model. Testings on four major cities demonstrate promising results of the proposed system. © 2011 Springer-Verlag Berlin Heidelberg.