Abstract:

The current studies on the influencing factors of the tourist flow network are more focused on the regional scale, thus Shanghai is taken as an example. Taking domestic tourists who traveled to Shanghai independently as study object and expanding the selection of the tourist nodes, this thesis extracted digital footprint of Shanghai independent travel itinerary during the year 2018 from online travel notes with data mining. Using social network analysis to construct the directed network, the study analyzed the network structure of Shanghai tourist flow. The influencing factors were analyzed through regression model. It is suggested that (1) the network density is quite low, forming a significant core-peripheral structure, and the core zone's effect on the periphery zone needs to be strengthened; (2) the core tourism nodes are the traditional well-known tourist attractions in the downtown area and the Shanghai Disneyland; (3) the influencing factors of the importance of tourism nodes in the network are the influence of the core nodes, the popularity of tourism nodes and the convenience of transportation. Based on these findings, the thesis could guide tourism nodes to strengthen their status in the network or to help them merge into the network.

Key words: tourist flow, digital footprint, social network, online travel notes, regression analysis, Shanghai

摘要：

目前对于旅游流网络形成的影响因素的研究多为区域尺度，因此以上海为例，将国内游客赴上海自由行作为对象，利用网络游记采集2018年上海自由行行程信息，扩充了对旅游节点类型的选取范围，结合社会网络分析法，构建有向旅游流网络，研究国内游客赴上海自由行所形成的旅游流网络结构特征，并通过回归分析研究其影响因素。结果表明，①网络密度较低，网络核心-边缘区结构分层明显，但核心区对边缘区的带动能力有待提高；②目前以核心城区的著名景点以及上海迪士尼乐园为核心旅游节点；③整体而言，网络中旅游节点重要程度的影响因素为核心节点的影响力、旅游节点自身的知名度以及交通便利程度。在此基础上提出了旅游节点应如何提高自身在网络中的重要程度或是融入网络。

关键词: 旅游流, 数字足迹, 社会网络, 网络游记, 回归分析, 上海市