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<p>This paper first determines the research method of rural tourism and the selection of research objects and carries out the corresponding pre-processing operation for the data. Secondly, the spatial distribution characteristics of key villages of rural tourism are analyzed by using the nearest neighbor index, standard deviation ellipse, geographic concentration index and geographic detector so as to understand the development of rural tourism in the five regions of east, west, south, north and central. The spatial clustering structure of key villages in rural tourism is calculated again using the fractal model to explore grid aggregation and correlation of high-quality development in rural tourism. From the spatial distribution characteristics, the nearest neighbor index of the southern region is 0.838, showing an aggregation-random distribution pattern, and the standard deviation ellipse of the southern region is 51.67°. From the spatial clustering structure, the aggregation dimension of the southern region is 0.485, and the correlation dimension of the four regions of east, west, south, north and south is relatively close to that of highway accessibility, for example. Thus, rural tourism in the context of the Internet needs to further strengthen infrastructure construction, enhance the means of training high-quality personnel, build diversified marketing channels, and realize the rural revitalization strategy to boost rural tourism.</p>
</abstract>

This paper first determines the research method of rural tourism and the selection of research objects and carries out the corresponding pre-processing operation for the data. Secondly, the spatial distribution characteristics of key villages of rural tourism are analyzed by using the nearest neighbor index, standard deviation ellipse, geographic concentration index and geographic detector so as to understand the development of rural tourism in the five regions of east, west, south, north and central. The spatial clustering structure of key villages in rural tourism is calculated again using the fractal model to explore grid aggregation and correlation of high-quality development in rural tourism. From the spatial distribution characteristics, the nearest neighbor index of the southern region is 0.838, showing an aggregation-random distribution pattern, and the standard deviation ellipse of the southern region is 51.67°. From the spatial clustering structure, the aggregation dimension of the southern region is 0.485, and the correlation dimension of the four regions of east, west, south, north and south is relatively close to that of highway accessibility, for example. Thus, rural tourism in the context of the Internet needs to further strengthen infrastructure construction, enhance the means of training high-quality personnel, build diversified marketing channels, and realize the rural revitalization strategy to boost rural tourism.

Analysis of the path to enhance the quality development of rural tourism with the help of rural revitalization strategy in the context of the Internet

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper first determines the research method of rural tourism and the selection of research objects and carries out the corresponding pre-processing...

Analysis of the path to enhance the quality development of rural tourism with the help of rural revitalization strategy in the context of the Internet

Publicado en línea: 06 dic 2023

Páginas: -

Recibido: 20 ene 2023

Aceptado: 16 jun 2023

DOI: https://doi.org/10.2478/amns.2023.2.01392

Palabras clave
Nearest neighbor index, Standard deviation ellipse, Geographic concentration index, Geodetector, Fractal modeling

© 2023 Shaolin Liu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Analysis of the path to enhance the quality development of rural tourism with the help of rural revitalization strategy in the context of the Internet

Publicado en línea: 06 dic 2023

Páginas: -

Recibido: 20 ene 2023

Aceptado: 16 jun 2023

DOI: https://doi.org/10.2478/amns.2023.2.01392

Palabras claveNearest neighbor index, Standard deviation ellipse, Geographic concentration index, Geodetector, Fractal modeling

© 2023 Shaolin Liu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Nearest neighbor index, Standard deviation ellipse, Geographic concentration index, Geodetector, Fractal modeling