1. Optimization of sustainable development path of grassland ecotourism in Hulunbeier City based on data mining technology
- Author
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Zhao Yiding and Chang An
- Subjects
poi data mining ,nearest neighbor distance ,multi-distance spatial clustering ,grassland ecotourism ,68t42 ,Mathematics ,QA1-939 - Abstract
In recent years, the status of ecotourism in mass tourism has gradually increased, among which the grassland ecotourism market is booming. The study uses data mining techniques to extract POI data for grassland ecotourism in Hulunbeier City. The mined POI data of grassland ecotourism in Hulunbeier City are analyzed by using nearest-neighbor distance index analysis, multi-distance spatial clustering, kernel density estimation, standard deviation ellipse, and geodetector model to explore the spatial agglomeration characteristics of grassland ecotourism in Hulunbeier City, the grassland tourism development pattern, and the influencing factors of grassland ecotourism. Accordingly, the sustainable development of grassland tourism in Hulunbuir City is proposed. The nearest neighbor index (NNI) value of grassland tourism in Hulunbuir City is 0.64 (less than 1), and the NNI index of the 13 flags (cities and districts) under the jurisdiction of Hulunbuir City is less than 1, which indicates that the grassland tourism industry is characterized by agglomeration and distribution within the city of Hulunbuir. The grassland ecotourism industry in Hulunbuir City presents a spatial distribution pattern of “multi-core agglomeration” with large agglomeration and small dispersion. The high-density cores are located in New Barkhu Left Banner, Manzhouli, Hailar District, Chenbalhu Banner, and Ergunar City. There are eight subdivided types of grassland tourism development patterns in Hulunbeier City. The four major categories of single dominant, double dominant, strong integrated, and weak integrated accounted for 34.11%, 23.55%, 7.09%, and 35.25% of the total, respectively. The spatial distribution of tourism in Hulunbeier Grassland is more influenced by natural geographic factors than socio-economic factors.
- Published
- 2024
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