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Our paper on Quantifying tourist behavior patterns by travel motifs and geo-tagged photos from Flickr has been published on ISPRS International Journal of Geo-Information.

Abstract

With millions of people traveling to unfamiliar cities to spend holidays, travel recommendation becomes necessary to assist tourists in planning their trips more efficiently. Serving as a prerequisite to travel recommender systems, understanding tourist behavior patterns is therefore of great importance. Recently, geo-tagged photos on social media platform like Flickr provide a rich data source that captures location histories of tourists and reflects their preferences. This article utilizes geo-tagged photos from Flickr to extract trajectories of tourists, and then extends the concept of motifs from topological spaces, to temporal spaces, and to semantic spaces, for detecting tourist mobility patterns. By representing trajectories in terms of three distinct types of travel motif and further using them to measure user similarity, typical tourist travel behavior patterns associated with distinct sightseeing tastes/preferences are identified and analyzed for tourism recommendation. Our empirical results confirm that the proposed analytical framework is effective to uncover meaningful tourist behavior patterns.

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[conference website] [geospatial week 2017]

Archives

Our paper on An Analysis of Entropy of Human Mobility from Mobile Phone Data has been published on Geomatics and Information Science of Wuhan University.

Abstract

Quantifying human mobility patterns is intensively investigated by scientists from computational social science, statistical physics and complex science. In last decade, mobile phone data provide an unprecedented tool for capturing individuals’ travel activities in space and time. However, its nature of sparsity in time and imprecision in space imposes significant bias upon the derived mobility patterns. This research proposes two efficient techniques to cope with this issue. First, we implement an activity-location and travel-OD identification method to reconstruct reliable trajectories from call detailed records for mobile users. Second, we introduce the approximate entropy, which is superior to conditional entropy, for quantifying the regularity of individuals’ consecutively visited locations. With a case study in Harbin, the proposed approaches enable us to uncover meaningful patterns of urban mobility in terms of frequently and consecutively visited locations.

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