The network characteristics of classic red tourist attractions in Shaanxi province, China
<p>Tourism flow is a significant tourism phenomenon and a hot topic of tourism geography research. This study, based on the perspective of combining <span>'virtual' and 'reality'</span><span>,</span> takes 13 classic red tourism scenic areas in Shaanxi <span>p</span>rovince 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. Th<span>e</span> study shows that: (1) The case sites have formed a spatial layout <span>of the </span><span>'</span>dense in the north and sparse in the south'. Among them, the total number of attractions in <span>n</span>orthern Shaanxi is the largest and most are concentrated in Yan<span>'</span>an; the total number of attractions in <span>s</span>outhern Shaanxi is the smallest and mo<span>st</span> 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<span>'</span>an City is high. (3) The network structure of tourism flow in the case <span>has</span> the spatial characteristics of low density, <span>'</span>one level and multicore<span>'</span> and significant small network groups. (4) There are correlations and differences between network attention and tourism flow in the sites <span>in question</span>. 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 <span>'</span>precision identification and classification<span>'</span>, and proposes targeted development strategies such as <span>c</span>reating high-quality regional tourist routes<span>,</span> promoting the digital development of <span>tourist attractions</span>, and innovating <span>the </span>ways to promote attractions.</p>
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36/100
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These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 4
- Harmonization
- 12
- Access
- 12
- Reuse readiness
- 0
- Engagement
- 8