基于知识图谱的中国旅游大数据应用研究进展

旅游大数据的兴起为深化旅游研究与旅游应用实践提供了新的机遇和挑战。文章结合知识图谱分析与文献分析法,对中国旅游大数据的应用研究进展进行系统性综述。研究发现:中国旅游大数据的应用研究始于2007年,快速发展于2010年以后,具有明显的阶段性特征,相关成果主要刊发在旅游类、地理类和资源科学类期刊。作者群体与研究机构已有一定规模,整体呈现"全局分散、局部紧密"的合作网络格局,但合作强度均较弱。研究热点具有明显多元化趋势,且其更迭速度随政策导向不断加快。研究中常用的数据类型可分为UGC数据、位置数据、事务数据等类别,研究方法以内容分析、网络分析、空间分析与统计分析法为主,研究内容总体围绕旅游者、旅游流、旅游目的地、旅游数据四个方面展开,并形成若干主题方向。未来研究应在加强学术共同体合作、注重多源数据的交互验证、推动研究方法革新、避免重复研究等方面进一步探索。

Research Progress of Tourism Big Data Application in China Based on Knowledge Map

The rising of tourism big data provides new opportunities and challenges for deepening tourism research and tourism application practice. Based on the methods of knowledge map analysis and literature analysis, this paper systematically summarizes the research progress of tourism big data application in China. This research finds that: (1) The application research of tourism big data in China dated from 2007 and developed rapidly after 2010. It has obvious phase characteristics,and the related research findings are mainly published in tourism,geography and resource science journals.(2) Author groups and research institutions have a certain scale, showing a cooperative network pattern of "global dispersion and local tightness", but the cooperation intensity is weak. The diversification trend of research hotspots is obvious, and its replacement speed is accelerating with the policy orientation. (3) The data types commonly used in the application research of tourism big data consist of UGC data, location data, transaction data, etc. The main research methods include content analysis,network analysis,spatial analysis and statistical analysis. The relevant research contents generally focus on four aspects: tourist, tourist flow, tourist destination, and tourism data, which formed several thematic directions. In the future, tourism big data application research in China should be further explored in strengthening academic community cooperation,paying attention to the interactive verification of multi-source data,promoting research method innovation,and avoiding repeated research.