The network characteristics of classic red tourist attractions in Shaanxi province, China

Tourism flow is a significant tourism phenomenon and a hot topic of tourism geography research. This study, based on the perspective of combining ‘virtual’ and ‘reality’, takes 13 classic red tourism scenic areas in Shaanxi province as examples. It constructs a multi-source data network attention evaluation index and adopts social network analysis method to explore the network attention and tourism flow of the study case, and further investigates the relationship between the two. The study shows that: (1) The case sites have formed a spatial layout of the ‘dense in the north and sparse in the south’. Among them, the total number of attractions in northern Shaanxi is the largest and most are concentrated in Yan’an; the total number of attractions in southern Shaanxi is the smallest and most scattered. (2) The overall network attention of the case sites is low, and there is variability in network attention of different types of tourist attractions, among which network attention of the attractions in Yan’an City is high. (3) The network structure of tourism flow in the case has the spatial characteristics of low density, ‘one level and multicore’ and significant small network groups. (4) There are correlations and differences between network attention and tourism flow in the sites in question. Based on the differences between them, the attractions are classified into four types: high-high, high-low, low-high and low-low. In response to the above findings, this study proposes the principle of ‘precision identification and classification’, and proposes targeted development strategies such as creating high-quality regional tourist routes, promoting the digital development of tourist attractions, and innovating the ways to promote attractions.

Methods
Based on the previous analysis, there are primarily two research methods for studying network attention. The first method is based on Baidu index, while the second method involves constructing network attention using multiple sources of data.However, when the research area encompasses multiple tourism resources, it is difficult to obtain comprehensive and reliable data solely through Baidu index. Moreover, due to the multitude of tourism resources in this study, the reliability and accuracy of using a single data source are relatively low.Therefore, it is necessary to construct a network attention index for case studies. Based on the reference to previous research and considering the comprehensiveness, accuracy, and availability of data, this study selected five Chinese social platforms, namely Ctrip, WeChat, Baidu, 360, and Mafengwo, as the sources of network attention data (all data were collected until January 1, 2023.).The specific steps are as follows: firstly, data collection is conducted using range retrieval methods on various platforms to establish a retrieval database (Table 1). Finally, the collected data is organized and categorized to obtain the raw data of network attention.The data on tourist flow is sourced from Mafengwo and Qunar platforms (all data retrieved as of January 1, 2023.). Building upon existing research [39], to ensure the integrity of the tourist flow network structure, non-red attractions in the tourist flow routes were retained, and the aforementioned data was transformed into directed flow data between attractions. Ultimately, 468 valid origin-destination (O-D) data were obtained.