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LI Qiuping, CHEN Yu, LUAN Xuechen. Tourism Flow Network Structures of Different Types of Tourists Using Online Travel Notes: A Case Study of Yunnan Province[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210045
Citation: LI Qiuping, CHEN Yu, LUAN Xuechen. Tourism Flow Network Structures of Different Types of Tourists Using Online Travel Notes: A Case Study of Yunnan Province[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210045

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

doi: 10.13203/j.whugis20210045
Funds:

The National Natural Science Foundation of China (41971345)

  • Received Date: 2021-09-01
  • Objectives: The structure of the tourism flow network is of great significance for understanding the choices of tourists and the role of attractions in the network. The previous studies mainly focused 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 analyzed 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. First, we use text mining to extract the multi-dimensional preferences of tourists, and cluster tourists into different groups. Second, 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 case 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:(1) 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; (2) 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. (3) 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. In the future work, we will focus on exploring the influencing factors of different tourism flow network characteristics, and applying the results to personalized tourism route recommendations.
  • [1] . SAINAGHI R, BAGGIO R. Complexity traits and dynamics of tourism destinations[J]. Tourism Management, 2017,63:368-382.
    [2] . Li, J., Xu, L., Tang, L., Wang, S., & Li, L. (2018). Big data in tourism research:A literature review. Tourism Management, 68, 301-323.
    [3] . Mou, N., Zheng, Y., Makkonen, T., Yang, T., Tang, J. J., & Song, Y. (2020). Tourists' digital footprint:The spatial patterns of tourist flows in Qingdao, China. Tourism Management, 81, 104151. doi: 10.1016/j.tourman.2020.104151.
    [4] . Xu, Y., Li, J., Belyi, A., & Park, S. (2021). Characterizing destination networks through mobility traces of international tourists-A case study using a nationwide mobile positioning dataset. Tourism Management, 82. https://doi.org/10.1016/j.tourman.2020.104195
    [5] . Xu, Y., Xue, J., Park, S., & Yue, Y. (2021). Towards a multidimensional view of tourist mobility patterns in cities:A mobile phone data perspective. Computers, Environment and Urban Systems, 86(January). https://doi.org/10.1016/j.compenvurbsys.2020.101593
    [6] . Leung, X., WangF, WuB., et al. A Social Network Analysis of Overseas Tourist Movement Patterns in Beijing:the Impact of the Olympic Games[J]. International Journal of Tourism Research, 2012, 14(5):469-484.
    [7] . Zhang, Y., Jin, R., Zhou, ZH. Understanding bag-of-words model:a statistical framework. International Journal of Machine Learning and Cybernetics. 2010, 1, 43-52.
    [8] . Salton G, buckley C. Term-weighting approaches in automatic text retrieval. Information Processing & Management, 1988,24(5):513-523.
    [9] . Laurens M, Hinton G. Visualizing Data using t-SNE. Journal of Machine Learning Research, 2008,9(2605):2579-2605.
    [10] . Huang Z. Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values. Data Mining and Knowledge Discovery, 1998,2(3):283-304.
    [11] . Calinski T, Harabasz J. A dendrite method for cluster analysis. Communications in Statistics-Theory and Methods, 1974,3(1):1-27.
    [12] . Rousseeuw J. Silhouettes:A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 1987,20:53-65.
    [13] . Freeman C. Centrality in social networks conceptual clarification. Social Networks, 1978,1(3):215-239.
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Tourism Flow Network Structures of Different Types of Tourists Using Online Travel Notes: A Case Study of Yunnan Province

doi: 10.13203/j.whugis20210045
Funds:

The National Natural Science Foundation of China (41971345)

Abstract: Objectives: The structure of the tourism flow network is of great significance for understanding the choices of tourists and the role of attractions in the network. The previous studies mainly focused 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 analyzed 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. First, we use text mining to extract the multi-dimensional preferences of tourists, and cluster tourists into different groups. Second, 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 case 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:(1) 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; (2) 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. (3) 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. In the future work, we will focus on exploring the influencing factors of different tourism flow network characteristics, and applying the results to personalized tourism route recommendations.

LI Qiuping, CHEN Yu, LUAN Xuechen. Tourism Flow Network Structures of Different Types of Tourists Using Online Travel Notes: A Case Study of Yunnan Province[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210045
Citation: LI Qiuping, CHEN Yu, LUAN Xuechen. Tourism Flow Network Structures of Different Types of Tourists Using Online Travel Notes: A Case Study of Yunnan Province[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210045
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