Knowledge Maps of Tourism Big Data Research in China Based on Visualization Analysis

Abstract

OBJECTIVE:We scientifically analyze knowledge structure, development stages, research hotspots and research frontiers of tourism big data in China to provide practical and useful references for researchers to understand the research status and development trends of this field.METHODS:Published journal literatures were retrieved. A scientific collaboration analysis was conducted to visualize the relations of authors and institutions. A co-occurrence analysis was used to visualize the network of key words that was classified by the clustering analysis. Burst detection was conducted to visualize emerging words across the entire research field.RESULTS:We retrieved 964 literatures, from which 668 literatures were identified after screening. Wang Dong has published the most papers. A cooperative group of scientific research institutions with Beijing Union University as the core has been formed. The key words were classified into 6 clusters, and the frequency of "tourism industry" is the largest, and top 14 key words with the highest emergence intensity were detected.CONCLUSIONS:The literature of tourism big data research in China has been increasing rapidly since 2016. Three cooperative groups with Wang Dong, Liu Ligang and Pan Xinqin as the core respectively were formed, and a cooperative group of scientific research institutions with Beijing Union University as the core has been formed. The research hotspots of tourism big data in China mainly focus on six aspects: tourism industry development, key technologies of tourism big data, global tourism, tourism public service, tourists behavior, problems and countermeasures. The evolution of in this field can basically be divided into three stages: exploration (before 2012), start-up (2013-2016) and rapid development (from 2017 to present).