Tourism Flow Network Structures of Different Types of Tourists Using Online Travel Notes: A Case Study of Yunnan Province

  Objectives  The structure of the tourism flow network is significant for understanding the choices of tourists and the role of attractions in the network. The previous studies mainly focus on the structure of the tourism flow network of all tourists. However, the analysis on the disparities of tourism flow network for different tourists is still lack in the thorough research. Therefore, we analyze the tourism flow networks constructed by different types of tourism routes from online travel notes.

  Methods  Based on the online travel notes, the text mining and social network analysis methods are used to construct and analyze tourism flow networks. Firstly, we use text mining to extract the multi-dimensional preferences of tourists, and cluster tourists into different groups. Secondly, the destination sequences of different tourist groups are used to construct various tourist flow networks. Finally, the structural characteristics of these tourism flow networks and the role of each destination node are analyzed from multiple perspectives.

  Results  The experiment takes Yunnan Province as the study area, and the tourists travelled in Yunnan in 2019 are clustered into five groups, then five travel flow networks are constructed. The results show that the tourism flow network structures of five clusters of tourists are distinct, demonstrating the disparities of spatial interaction patterns among travel destinations and different degrees of network centralization. The travel destinations of cost sensitive and time sensitive tourists are primarily a few popular attractions and some attractions around them. The networks of these two types of tourists show a single-core structure. As for other types of tourists, their travel destinations are more diverse and their travel routes have a larger spatial span. The networks of these types of tourists present a typical multi-core structure. Some travel destinations like Lugu Lake, Xizhou and Dian Lake take opposite roles in the tourism flow networks of different clusters of tourists.

  Conclusions  Our research is helpful for tourism management department to clarify the characteristics of tourism flows and optimize the cooperation mechanism of travel destinations in the tourism network.