Mapping Spatiotemporal Tourist Behaviors and Hotspots Through Location-Based Photo-Sharing Service (Flickr) Data

Abstract

Social media services and location-based photo-sharing applications, such as Flickr, Twitter, and Instagram, provide a promising opportunity for studying tourist behaviors and activities. Researchers can use public accessible geo-tagged photos to map and analyze hotspots and tourist activities in various tourist attractions. This research studies geo-tagged Flickr photos collected from the Grand Canyon area within 12 months (2014/12/01–2015/11/30) using kernel density estimate (KDE) mapping, Exif (Exchangeable image file format) data, and dynamic time warping (DTW) methods. Different spatiotemporal movement patterns of tourists and popular points of interests (POIs) in the Grand Canyon area are identified and visualized in GIS maps. The frequency of Flickr’s monthly photos is similar (but not identical) to the actual tourist total numbers in the Grand Canyon. We found that winter tourists in the Grand Canyon explore fewer POIs comparing to summer tourists based on their Flickr data. Tourists using high-end cameras are more active and explore more POIs than tourists using smart phones photos. Weekend tourists are more likely to stay around the lodge area comparing to weekday tourists who have visited more remote areas in the park, such as the north of Pima Point. These tourist activities and spatiotemporal patterns can be used for the improvement of national park facility management, regional tourism, and local transportation plans.