大数据应用视角下旅游流国际研究的进展与展望

Progress and Prospects of International Research on Tourism Flow from the Perspective of Big Data Application

旅游流是旅游研究的重点领域。当前,应用大数据技术对旅游流进行研究渐成趋势。本研究面向大数据应用视角下的旅游流研究前沿,以国际学术期刊发表的相关论文为研究对象,总结相关领域的研究进展,并提出研究展望。分析发现:(1)自2013年来,大数据应用视角下的旅游流研究呈快速发展态势,涉及旅游、管理、经济、信息技术等多个领域,成果呈多元化发展趋势。(2)基于关键词共现性网络分析,旅游流研究可划分为5个凝聚子集;研究主题方面,相关成果主要聚焦于旅游流的测量与刻画、旅游流的时空格局与效应、旅游流的影响因素及预测研究;应用数据方面,用户生成数据、设备数据、管理数据为主要数据源;研究方法方面,空间统计和分析法、网络分析法、模型构建法以及不同研究方法的交互融合是主要研究手段。(3)现有研究成果在主题、数据和方法等方面尚存在发展不平衡性,未来在研究主题拓展、大小数据融合、多学科方法集成等方面亟待继续探索和深化。

Tourism flow is one of the main focuses of tourism research.The application of big data technology to tourism flow research is gradually becoming a trend.This study aims to understand the state-of-the-art tourism flow research from the perspective of big data application.We selected articles published in international academic journals as the research objects,summarized the research progress in relevant fi elds,and proposed research prospects.The analysis indicates that(1)since 2013,research on tourism fl ow,from the perspective of big data application,has shown a rapid development trend.Relevant research involves tourism,management,economy,information technology,and other fi elds with a development trend of diversifi ed achievements.(2)based on keyword co-occurrence network analysis,it was divided into five condensed subsets.In terms of research themes,we found that achievements mainly focused on the measurement and characterization of tourism flow,the spatiotemporal pattern and effects of tourism flow,influencing factors,and prediction of tourism flow.In terms of application data,usergenerated equipment and management data were the main data sources for analysis.The main methods included spatial statistics and analysis,network analysis,model construction,and interactive integration of diff erent research methods.(3)existing research results show an imbalance in terms of themes,data,and methods.In the future,it is urgent to continue to explore and deepen the expansion of research themes,the fusion of big and small data,and the integration of multidisciplinary methods.