ACTA Scientiarum Naturalium Universitatis Pekinensis
Travel Movement Pattern Extraction Based on Social Media Data
SUN Qi, ZHANG Yi ZHAO Pengfei, WU Mengtong
Institute of Remote Sensing and Geographic Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871; † Corresponding author, E-mail: zy@pku.edu.cn
Abstract The authors propose a method to extract individual travel spatiotemporal behaviors from social media data, and then mine the group based on massive spatiotemporal behaviors. This study collects more than 40 million global geographic microblogs from users who have visited Suzhou, extracts 88270 tourism trajectories, and identifies 36 classes of inter-city tourism movement patterns in five categories. It is found that the extracted patterns conform to the LCF theoretical model; besides the simple movement patterns, there are more complex composite movement patterns. Based on big data, more accurate tourism movement patterns can be obtained, which helps tourism managers understand tourists’ trends and preferences, adjust destination marketing strategies, optimize tourism resource allocation, and provide better services for tourists. Key words tourism movement pattern; social media; geographic big data; LCF model