Fine-Grained Spatiotemporal Dynamics of Inbound Tourists Based on Geotagged Photos: A Case Study in Beijing, China

Abstract

The aim of this study was to provide scientific support for the creation of policy on inbound tourism. A comprehensive and thorough understanding of fine-grained spatiotemporal dynamic patterns is crucial for tourism management, planning and policymaking. In spite of spatiotemporal pattern analysis on tourism movements, environment- and society-related topics have been developed to stimulate tourism. However, few studies have focused on the fine-grained spatiotemporal analysis of tourist behavior. Depending on a fine-grained Flickr data source, we investigated the spatiotemporal dynamic patterns of inbound tourism in the context of fine granularity in the spatial and temporal dimensions. The proposed approach based on fine-grained Flickr data and the emerging spatiotemporal analysis method was to first conduct a refined temporal variation analysis based on the annual, monthly, and daily variation; second, a thorough analysis of the seasonality of tourism was conducted with the kernel density estimation (KDE) method; third, the correlation between the attraction grade and popularity was complementarily exploited with both qualitative and quantitative methods; and finally, the patterns were identified and visualized with the space-time cube in the context of fine granularity. The results from the analysis revealed that the downtown region of Beijing was the most popular place throughout the year due to the many famous Chinese cultural heritage attractions. In contrast, the landscape sites and thematic parks were nearly cold spots because of their strong seasonality. Our approach can also be applied to other crowdsourced data, such as that from Twitter and Instagram. Spatiotemporal analysis and empirical research have interesting implications for other cities in China or other developing countries.