The increasing popularity of social media services provides a promising opportunity for studying tourist behaviors. As more photos with geo-tagged information are uploaded by the general public to photo-sharing websites, such as Flickr, Instagram, and Panoramio, these public accessible geo-tagged photos can be used to study human dynamics and human mobility behaviors. This research aims to explore the feasibility of collecting data from photo-sharing services to map and analyze hot spots and tourist activities for various tourist attractions. Existing studies have employed various methods to obtain tourist data. Geo-tagged information, which can be revealed in pictures uploaded to social media platforms, allows us to extract accurate spatial and temporal data comparing to traditional methods. This research collected Flickr photos from the Grand Canyon area within 12 months (2014/12/01~2015/11/30) used Kernel Density Estimate mapping and Dynamic Time Warping to analyze the spatiotemporal patterns of tourist activities. The frequency of Flickr’s monthly photos is similar (but not identical) to the actual tourist total numbers in the Grand Canyon. During the summers, tourists went to more different locations and points of interests (POIs), and in winter their activities were limited in fewer locations. This study also found that the frequency of Flickr’s monthly photos is similar to the actual tourist numbers in the Grand Canyon. Tourists using high-end cameras are more active and explore more POIs than tourists using smart phones photos. For weekend tourists, they 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. In general, the spatiotemporal patterns of geo-tagged Flickr photos can provide valuable information about tourists’ activities and behaviors and can be used for the improvement of national park facility management, regional tourism, and local transportation plans.