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Spatial Distribution and Accessibility of High Level Scenic Spots in Inner Mongolia

Author

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  • Xinyang Wu

    (School of Economics and Management, Northwest University, Xi’an 710127, China)

  • Chuying Chen

    (School of Tourism and Culture, Guangdong Eco-engineering Polytechnic, Guangzhou 510520, China)

Abstract

Based on the data of high-level scenic spots in Inner Mongolia, the methods of the nearest neighbor index, kernel density, accessibility, and spatial autocorrelation are used to systematically sort out the spatial distribution pattern, accessibility, and influencing factors of various types of scenic spots. The following conclusions are drawn from the analysis. The spatial distribution of different types of tourist attractions in Inner Mongolia is in a state of “small agglomeration and large dispersion”. The spatial accessibility of different types of tourist attractions in Inner Mongolia is generally poor, and the temporal accessibility presents an inverted U-shaped distribution over time. The county-level accessibility of different types of scenic spots in Inner Mongolia is relatively poor, basically showing an oblique distribution pattern of low in the west and high in the east. The influencing factors of the spatial distribution pattern and accessibility of various scenic spots in Inner Mongolia mainly include the natural environment, transportation network, resource endowment, and economic level. This study proposes an optimal path for accessibility according to the aspects of the design of tourism scenic areas in a circle and the construction of tourist traffic and facilities, as well as the linkage design of tourist routes.

Suggested Citation

  • Xinyang Wu & Chuying Chen, 2022. "Spatial Distribution and Accessibility of High Level Scenic Spots in Inner Mongolia," Sustainability, MDPI, vol. 14(12), pages 1-13, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:12:p:7329-:d:839399
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    References listed on IDEAS

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    1. Yuewei Wang & Hang Chen & Xinyang Wu, 2021. "Spatial Structure Characteristics of Tourist Attraction Cooperation Networks in the Yangtze River Delta Based on Tourism Flow," Sustainability, MDPI, vol. 13(21), pages 1-16, October.
    2. Yena Song & Keumsook Lee & William Anderson & T. Lakshmanan, 2012. "Industrial agglomeration and transport accessibility in metropolitan Seoul," Journal of Geographical Systems, Springer, vol. 14(3), pages 299-318, July.
    3. Sophie Masson & Romain Petiot, 2009. "Can the high speed rail reinforce tourism attractiveness ? The case of the hagh speed rail between Perpignan (france) and Barcelona (Spain)," Post-Print hal-03062650, HAL.
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    5. Sophie Masson & Romain Petiot, 2009. "Can the High Speed Rail reinforce tourism attractiveness? The case of the High Speed Railway section between Perpignan (France) and Barcelona (Spain)," Post-Print hal-02422659, HAL.
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    Cited by:

    1. Juchen Li & Xiucheng Guo & Ruiying Lu & Yibang Zhang, 2022. "Analysing Urban Tourism Accessibility Using Real-Time Travel Data: A Case Study in Nanjing, China," Sustainability, MDPI, vol. 14(19), pages 1-18, September.
    2. Xiaodong Zhang & Haoying Han & Yongjun Tang & Zhilu Chen, 2023. "Spatial Distribution Characteristics and Driving Factors of Tourism Resources in China," Land, MDPI, vol. 12(5), pages 1-16, May.
    3. Shengrui Zhang & Lei Chi & Tongyan Zhang & Yingjie Wang, 2022. "Spatial Pattern and Influencing Factors of Tourism Resources in Northwestern Ethnic Areas in China—A Case Study of Longde County," IJERPH, MDPI, vol. 19(24), pages 1-20, December.
    4. Bo Zhang & Liangyu Zhou & Zhiwen Yin & Ao Zhou & Jue Li, 2023. "Study on the Correlation Characteristics between Scenic Byway Network Accessibility and Self-Driving Tourism Spatial Behavior in Western Sichuan," Sustainability, MDPI, vol. 15(19), pages 1-24, September.
    5. Yuewei Wang & Xinyang Wu & Zhizheng Liu & Hang Chen & Yuyan Zhao, 2022. "Spatial Patterns of Tourist Attractions in the Yangtze River Delta Region," Land, MDPI, vol. 11(9), pages 1-22, September.

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