基于多源数字足迹的大陆赴台湾旅游流的时空特征及其成因分析

Spatial-temporal Characteristics and Cause of Mainland Tourist to Taiwan based on Multi-Source Digital Footprints

旅游数字足迹是解析旅游者群体行为特征的创新研究方法。本研究采用多数据源结构,采集和解析中国大陆赴台游客出游前和出游后两阶段产生的旅游数字足迹,以探索赴台旅游流的时空特征及其成因。研究发现:(1)游客赴台时间特征呈现季节性差异和公共假期差异;(2)在客源地分布空间上,区、省、市3个地域单元均有显著空间分异,呈现东部区域集中、省近域性明显、城市发展水平高等特征;(3)在目的地空间特征上,赴台旅游流呈现“节点—线路—区域”等三种空间特征,并以若干个核心旅游节点形成了多核心、多线状、多区域的空间分布形态;(4)在赴台旅游流网络中,整体网络密度不高,但网络的平均路径距离小,表明网络各节点间具有良好的通达性和便捷性。研究进一步发现:社会因素、自然因素以及节点旅游资源禀赋、经济发展水平、交通便利条件、旅游者基本特征(逗留时间和出行方式)等分别是旅游者群体时间和空间行为特征的影响因素。

Tourism digital footprint is an innovative research method that analyzes the behavioral characteristics of tourists. In this study, a multi-source structure was used to collect and analyze the digital tourism footprints generated by tourists before and after the trip to Taiwan to explore the temporal and spatial characteristics and causes of tourism to Taiwan. Results showed that:(1) The temporal characteristics of tourists visiting Taiwan presented seasonal differences and holiday differences. The seasonal feature presents off-season short, peak season and flat season are long. Golden week and winter and summer vacation have outstanding tourist flow.(2) The three spatial market characteristics of tourist flows to Taiwan were identified. At the regional level, tourists from the southeast of China are the main sources of tourists to Taiwan. At the provincial level, most of the tourists are from the eastern coastal provinces, which are obviously close to Taiwan. At the urban level, the economic development level of cities visiting to Taiwan is generally higher.(3) The three spatial destination characteristics of tourist flows to Taiwan were identified: node-line-area. At the node level, the tourist flow network to Taiwan presents obvious concentration and imbalance characteristics. Seven tourist areas, namely, Taipei, Hualien, Pingtung, Kaohsiung, Xinbei, Taitung and Nantou, are the core tourist nodes, serving as the aggregation point, core area and travel channel, with prominent centrality advantages. At the line level, the core nodes of tourism flow, Taipei, Kaohsiung and Pingtung, form a multi-linear spatial distribution pattern. At the regional level, the tourist flow to Taiwan has obvious structural stratification, mainly distributed in 10 core areas such as Taipei, Kaohsiung, Hualien, Pingtung, Tainan, Nantou, Taitung, Xinbei, Taitung, Chiayi. The nodes in the core areas are closely connected and interact frequently.(4) The overall network density of tourist flows is not high. Also, the average path distance of the network is small, indicating that the network nodes have good accessibility and convenience. The study further explores the factors influencing the spatio-temporal behavior of tourists to Taiwan. In terms of the causes of temporal characteristics, social factors and the natural environment are the important causes of tourists' temporal behavior characteristics. In terms of the causes of spatial characteristics, the regional economic development level, tourism resource endowment, convenient transportation conditions and tourist characteristics are the important factors for the spatial behavior characteristics of tourists visiting Taiwan. The spatial flow frequency of tourists in Taiwan is positively correlated with regional economic development level, resource endowment of tourist destinations and convenient transportation conditions. In terms of tourist characteristics, when the tourists stay for a shorter time and do not travel alone, the degree of tourist aggregation is high. The conclusion of this study plays an important role in the development of tourism to Taiwan and the accurate regulation of tourism flow to Taiwan by the mainland. It also plays a guiding role in the scientific establishment of tourism management policies.