| 利用网络游记分析不同类型游客的旅游流网络特征差异——以云南省为例 |
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| 引用本文： | 李秋萍,陈宇,栾学晨.利用网络游记分析不同类型游客的旅游流网络特征差异——以云南省为例[J].武汉大学学报(信息科学版),2022,47(12):2143-2152. |
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| 作者姓名： | 李秋萍 陈宇 栾学晨 |
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| 作者单位： | 1.中山大学地理科学与规划学院, 广东 广州, 510275 |
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| 基金项目： | 国家自然科学基金41971345广东省自然科学基金2020A1515010695 |
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| 摘 要： | 旅游流网络分析对理解游客的目的地选择以及目的地在旅游流网络中承担的角色有重要意义。以中国云南省为例，基于网络游记数据挖掘游客的多维度偏好，并以此对游客聚类，进而划分出不同类型的游客群体。针对各类游客游记中的旅游目的地序列建立旅游流网络，并从多个角度分析各类游客旅游流网络的结构特征和各目的地节点的角色特征。结果表明，不同类别游客的旅游流网络在整体结构上各有特点，反映出旅游目的地不同的空间交互模式和网络中心化程度。此外，部分旅游目的地在不同类别旅游流网络中承担截然相反的角色。上述分析有助于优化旅游流网络中各节点的协作机制，辅助旅游目的地制定差异化的旅游产品。
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| 关 键 词： | 旅游流网络 文本挖掘 网络分析 游客偏好 游记数据 |
| 收稿时间： | 2021-09-01 |
Tourism Flow Network Structures of Different Types of Tourists Using Online Travel Notes: A Case Study of Yunnan Province |
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| Institution: | 1.School of Geography and Planning, Sun Yat?Sen University, Guangzhou 510275, China2.Guangdong Provincial Engineering Research Center for Public Security and Disaster, Guangzhou 510275, China3.School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China4.Guangdong Fundway Science and Technology Co. Ltd, Guangzhou 510275, China |
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| Abstract: | 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. |
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