DOI: 10.5061/dryad.bvq83bkgn
title: The network characteristics of classic red tourist attractions in Shaanxi province, China

description: 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.
description: 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
 <sup>[39]</sup>, 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.
description: # **The network characteristics of classic red tourist attractions in
 Shaanxi province, China** 
 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 the 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 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,
 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. Research shows that: (1) Overall, the network attention to
 case-based destinations is relatively low, and there are significant
 differences in network attention among different attractions. Spatially,
 the distribution of network attention is uneven. This is manifested by
 higher network attention to attractions in Yan’an City and lower network
 attention to attractions in other regions. (2) There are differences in
 the network attention of different types of attractions. High-level
 attractions have a higher level of online attention, while low-level
 attractions have a lower level of network attention. Additionally,
 archaeological sites tend to receive a higher level of online attention.
 (3) The network density of tourist flow is low, and the tourism
 connections between nodes are not closely linked. The linkage between core
 nodes and edge nodes in tourism is poor. Developed tourism routes only
 exist in core nodes. (4) Nodes such as Zaoyuan revolution site,
 Yangjialing revolution site, and Wangjiaping revolution site have a
 significant influence in the network structure. In addition, the
 integration and development between red nodes and non-red nodes have been
 achieved. (5) There is a correlation between network attention and tourist
 flow, as well as a ‘misplacement’ feature. Based on the characteristics of
 attractions, they can be divided into four types:bright-star attractions,
 cash-cow attractions, thin-dog attractions, and question attractions.Based
 on the above conclusions, this study proposes targeted development
 recommendations. ## Description of the data and file structure See
 README.docx for more information on the files and calculations. ##
 Sharing/Access information Data was derived from the following
 sources:Ctrip, WeChat, Baidu, 360, and Mafengwo. ## Code/Software In this
 study, travelogue texts from tourists are used as data, and a total of
 1413 data points are acquired through the Octopus collection tool. In this
 study, the weighted point-in-degree, weighted point-out-degree, weighted
 degree, the comprehensive ranking value, and network modularity in Gephi
 software are used to study tourism flow.