Spatio-Temporal Distribution of Tourism Flows and Network Analysis of Traditional Villages in Western Hunan
Abstract
:1. Introduction
2. Literature Review
3. Study Area and Methods
3.1. Study Area
3.2. Collection, Sorting and Arrangement of Data
3.3. Study Methods
3.3.1. Kernel Density Estimation
3.3.2. Social Network Analysis
- (1)
- Relationship matrix construction. Before using UCINET to analyze the network tour data, the flow direction matrix is constructed according to the identified 16 tour nodes to establish the quantitative flow relationship between nodes. After comparing and testing the network structure under different flow controls, we used 3 as the breakpoint value for binarization and converted the matrix into 0 and 1. The 16 × 16 multi-valued directed relationship matrix is shown in Equation (2) [18,57]. This matrix was then used to characterize the tourism flow network structure for traditional villages.
- (2)
- Network characteristics analysis. The network scale, density, centrality, central potential, and other indicators were used to characterize the network structure and node linkage relationships of the tourism flow for traditional villages. The relevant calculation formulas are summarized in Table 2.
3.3.3. Core-Edge Model
4. Analysis of Spatio-Temporal Characteristics of Tourism Flow
4.1. Temporal Characteristics
4.2. Spatial Characteristics
5. Analysis of the Structural Features of the Tourism Flow Network
5.1. Flow Network Structure
5.2. Flow Network Characteristics
5.2.1. Scale and Density
5.2.2. Centrality Analysis
5.2.3. Cohesive Subgroups Analysis
5.2.4. Core-Edge Analysis
6. Conclusions and Recommendations
6.1. Conclusions
- (1)
- April to October is the peak period of tourism flow in traditional villages in Western Hunan, particularly in the months of April and May. The least passenger flow occurs during winter, while the other seasons have little overall variability. Largely influenced by holidays and weather conditions, short-term tourism is more preferred, with one-day and three-day tour packages accounting for the highest percentage.
- (2)
- The tourism flow in Xiangxi’s traditional villages is characterized by a “double core and multiple points” spatial distribution pattern, with Fenghuang Ancient Town and Zhangjiajie National Forest Park as core and pronounced concentration of high-grade tourism scenic spots.
- (3)
- In terms of network structure, the overall density of the tourism flow network of traditional villages in Western Hunan is low, with relatively few tourism routes between nodes. Eight cohesive subgroups were identified in the network. Four were found to be closely connected internally, providing clues for the organization of tourism routes, particularly the development of tourism products. The tourism flow network shows a “core-edge” hierarchical structure; Fenghuang Ancient City, Zhangjiajie National Forest Park, and Furong Town are prominent core nodes, and Hongjiang Ancient Commercial City and Morong Miao Village are edge nodes. The core nodes do not have a significant driving effect on the edge nodes.
6.2. Recommendations
- (1)
- Thematic tourism products should be cultivated and diversified to meet the needs of different seasons and holidays and to guide off-season consumption. Given the climate of Western Hunan, tourism officials and decision-makers should focus on developing short-distance flower viewing that would stimulate tourism flow in the spring lull period. They should also consider creating short-distance mountain fog and snow viewing to generate tourism in the winter lull period.
- (2)
- Accelerate the construction of core scenic spots and give full play to the role of radiation drive. Relying on Zhangjihuai high-speed railway, focus on the integration of the construction of Zhangjiajie National Forest Park, Tianmen Mountain, Furong Town, and Fenghuang Ancient City four core attractions, open up Zhangjiajie National Forest Park—Dayong Ancient City—Tianmen Mountain—Furong Town—Fenghuang Ancient City and Furong Town—Border Town—Shanjiang Miao Village—Fenghuang Ancient City—Hongjiang Ancient Commercial City, two traditional villages boutique tourism route, drive the development of the surrounding nodes such as Ai Village, Gouliang Miao Village, and Douluo Miao Village.
- (3)
- The transportation infrastructure and accessibility of marginal nodes, such as Morong Miao Village and Hongjiang Ancient Commercial City, should be upgraded and expanded to help stimulate tourism intensity. Broadcast media and different social media platforms should be tapped to advertise less-publicized nodes to improve their tourism intensities and highlight their unique appeal. Tourism resources should be allocated prudently in strategies and construction that would enable the integrated development of traditional village tourism in Western Hunan.
- (4)
- Investments in tourism product research, development, innovation, and branding should be increased to address the changes in consumer preference and variations in market demands. For example, attractions such as Jidou Miao Village and Shanjiang Miao Village can repackage and redesign the ethnic diet and launch special Miao rice, and hold do-it-yourself competitions for Miao handicrafts to give traditional handicrafts new connotation and modern flavor and increase tourists’ sense of participation and experience.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Serial Number | Tourism Nodes | Visit Rate |
---|---|---|
1 | Fenghuang Ancient City | 36.67% |
2 | Zhangjiajie National Forest Park | 15.16% |
3 | Furong Town | 8.00% |
4 | Shanjiang Miao Village | 6.70% |
5 | Tianmen Mountain | 6.59% |
6 | Huangsiqiao Ancient City | 6.52% |
7 | Dehang Miao Village | 4.67% |
8 | Ai Village | 3.39% |
9 | Gouliang Miao Village | 2.58% |
10 | Dayong Ancient City | 2.14% |
11 | Douluo Village | 1.29% |
12 | Qianzhou Ancient City | 0.88% |
13 | The Frontier City | 0.48% |
14 | Hongjiang Ancient Commercial City | 0.29% |
15 | Jidou Miao Village | 0.22% |
16 | Morong Miao Village | 0.18% |
Indicator | Formula | Explanation |
---|---|---|
Network size | The number of all possible relation-ships in the travel network, is the number of travel nodes. | |
Density | The ratio of the number of relationships in the tourism network to the number of relationships that could theoretically exist [0, 1]. If there is a direct connec-tion between two nodes i and j, ; otherwise, 0. | |
Centrality | is the centrality of attraction is the centrality of the whole network, and are the in and out degrees of attraction i, respectively | |
Centralization |
Tourism Nodes | Degree Centrality | Closeness Centrality | Betweenness Centrality | ||
---|---|---|---|---|---|
Out-Degree | In-Degree | Out-Degree | In-Degree | ||
Zhangjiajie National Forest Park | 7 | 5 | 40.541 | 28.302 | 4.5 |
Furong Town | 8 | 6 | 41.667 | 28.846 | 9.05 |
Fenghuang Ancient City | 12 | 13 | 46.875 | 33.333 | 94.783 |
Biancheng | 0 | 1 | 6.25 | 34.884 | 0 |
Ai Village | 6 | 5 | 39.474 | 28.302 | 16.15 |
Tianmen Mountain | 6 | 4 | 38.462 | 27.778 | 2.983 |
Douluo Village | 3 | 2 | 34.884 | 26.786 | 0 |
Jidou Miao Village | 0 | 1 | 6.25 | 29.412 | 0 |
Dehang Miao Village | 5 | 7 | 38.462 | 29.412 | 7.467 |
Gouliang Miao Village | 2 | 4 | 34.091 | 27.778 | 0.25 |
Shanjiang Miao Village | 5 | 6 | 37.5 | 28.846 | 4.95 |
Qianzhou Ancient City | 3 | 3 | 36.585 | 25 | 0 |
Dayong City | 2 | 3 | 34.091 | 27.273 | 0 |
Huangsiqiao Ancient City | 5 | 5 | 37.5 | 28.302 | 3.867 |
Hongjiang Ancient Commercial City | 1 | 1 | 33.333 | 26.316 | 0 |
Morong Miao Village | 1 | 0 | 48.387 | 6.25 | 0 |
Mean | 4.125 | 4.125 | 34.647 | 27.301 | 9 |
S.D. | 3.16 | 3.059 | 11.484 | 5.92 | 22.576 |
Sum | 66 | 66 | 554.35 | 436.818 | 144 |
centralization/% | 56 | 63.11 | - | - | 43.57 |
Subgroup | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
---|---|---|---|---|---|---|---|---|
1 | 1 | 0 | 1 | 0.5 | 0.333 | 1 | 0.75 | 0.5 |
2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
3 | 0.75 | 0 | 1 | 1 | 0 | 0.25 | 0 | 0.75 |
4 | 0.75 | 0.5 | 0.75 | 1 | 0 | 0 | 0 | 0 |
5 | 0.333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
6 | 1 | 0 | 1 | 0.5 | 0.333 | 1 | 0.75 | 0.5 |
7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
8 | 0.75 | 0 | 1 | 1 | 0 | 0.25 | 0 | 0.75 |
Core | Edge | |
---|---|---|
Core | 0.762 | 0.270 |
Edge | 0.222 | 0.042 |
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Liu, C.; Qin, Y.; Wang, Y.; Yu, Y.; Li, G. Spatio-Temporal Distribution of Tourism Flows and Network Analysis of Traditional Villages in Western Hunan. Sustainability 2022, 14, 7943. https://doi.org/10.3390/su14137943
Liu C, Qin Y, Wang Y, Yu Y, Li G. Spatio-Temporal Distribution of Tourism Flows and Network Analysis of Traditional Villages in Western Hunan. Sustainability. 2022; 14(13):7943. https://doi.org/10.3390/su14137943
Chicago/Turabian StyleLiu, Chunla, Yingjie Qin, Yufei Wang, Yue Yu, and Guanghui Li. 2022. "Spatio-Temporal Distribution of Tourism Flows and Network Analysis of Traditional Villages in Western Hunan" Sustainability 14, no. 13: 7943. https://doi.org/10.3390/su14137943