Tourist behavior analysis through geotagged photographies: A method to identify the country of origin

Much information can be extracted from geotagged photographies posted on online image databases like Flickr or Panoramio. Recent works have demonstrated that some treatment of this data can provide a good estimation of tourism behavior. Tourism represents today and for several years an important factor in the regional economy. Understanding and analyzing the tourist behavior corresponds to a significant demand from institutions. For this purpose, many studies have been launched. Many specialists of tourism need to separate tourists according to their place of residence. In the context of two projects supported by territorial collectivities, this paper introduces a new paradigm to estimate photographer's country of residence. Each user will be described by his photographic timeline. This timeline allows to compute intermediate properties: travel time at a destination, number of trips, number of visited countries... This generation of symbolic data is essential and allows to synthesize the richness of the timeline in front of the recognition task to achieve. Classification algorithms will then be introduced, some sets with experts of science of tourism, others using data clustering and supervised learning techniques. We compared these methods for two distinct questions: firstly we classify photographers into two categories (French/non-French for example); secondly we find the country of residence of each user. It demonstrates that, using learning algorithms or expert-defined rules permits to identify users residence efficiently. We are thus able to meet the request of experts in tourism and refine even more the analysis of tourist behavior.