Tourist Spatial-Temporal Behavior Model in Scenic Spot Based on Social Media Data

Abstract

In the paper, a method based on social media data for analyzing tourist temporal-spatial behavior was presented. The web crawler method, the synonym extended text information processing technology and the fuzzy clustering method were combined in order to collect the social media traveling data. The information was transformed into tourist flow data of scenic spots, and the spatial-temporal behavior model of tourists was established, which calculated the passenger flow coefficient inhomogeneity of the scenic spot, traveler tour routes and tour time. At the same time, taking Temple of Heaven Park in Beijing as an example, the popularity of each scenic spot in the park, the tourists' tour routes and the intensity of the distribution of the tourists' stay time are collected and analyzed. The results showed that the spatial-temporal behavior analysis of tourists based on social media data mining in this paper could provide a reference for the planning and management of scenic spots.