Spatial Data Mining and Big Data Analysis of Tourist Travel Behavior

Received: 23 January 2019 Accepted: 18 March 2019 The user behavior and preference can be recognized by analyzing the spatial, temporal and semantic attributes of geographic data, making it possible to reconstruct the real-world travel trajectories of users. This paper collects and preprocesses the Weibo check-in data at A-level scenic spots in two Chinese provinces, namely, Jiangsu and Zhejiang, and analyzed the tourists’ travel behavior from the perspectives of time and space. From the angle of time, the author examined the interannual variations of the check-in data from 2016 to 2018, and explored how the data changed on holidays, weekends and workdays. From the angle of space, the kernel density analysis was performed on the collected data, and the hot spots were determined. Finally, the spatial and location flows and flow directions of holiday travels were investigated, and the travel mode and features on holidays were obtained. The research findings lay the basis for the development of wisdom tourism.