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Researching Tourism Space in China’s Great Bay Area: Spatial Pattern, Driving Forces and Its Coupling with Economy and Population

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  • Lingfeng Li

    (School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China
    Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China)

  • Quan Gao

    (School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China
    Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China)

Abstract

Analysis of the spatial patterns and dynamics of tourism services and facilities is crucial for tourism and land use planning. However, most studies in the spatial analysis of tourism rely on the city- or regional-level data; limited research has used POI (point of interest) data to accurately uncover the spatial distribution of tourism, especially its interactive and coupling relationship with the local economy and population. Based on POI data, this paper, therefore, investigates the spatial patterns and driving forces of tourism services distribution and how tourism space is coupled with the local economy and population in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) of China. The results show the following: (1) Different categories of tourism services (catering, shopping, scenic spots, leisure, and sports) exhibit diverse spatial patterns and agglomerations, but they tend to align with the variables of economic level and population in a grid of 1 km 2 . (2) The spatial econometric models further reveal that population density, transportation, and hospitality facilities are positively correlated with the spatial distribution of tourism services, but GDP in a grid of 1 km 2 shows a weak negative correlation with the POI of tourism services, which may be attributed to the incoordination between GDP and tourism in some areas. (3) The analysis of coupling degree further identifies the areas where tourism services have good interaction/coupling with the local GDP and population density, such that these areas can be viewed as hotspots suitable for tourism promotion. This paper thus offers meaningful policy implications by calling for an optimization of the coupling of tourism services with local social–economic factors in the GBA.

Suggested Citation

  • Lingfeng Li & Quan Gao, 2023. "Researching Tourism Space in China’s Great Bay Area: Spatial Pattern, Driving Forces and Its Coupling with Economy and Population," Land, MDPI, vol. 12(10), pages 1-24, October.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:10:p:1878-:d:1254641
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    References listed on IDEAS

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    3. Tang, Zi, 2015. "An integrated approach to evaluating the coupling coordination between tourism and the environment," Tourism Management, Elsevier, vol. 46(C), pages 11-19.
    4. Kai-Cheng Liao & Ming-Yue Yue & Si-Wei Sun & Hong-Bo Xue & Wei Liu & Sang-Bing Tsai & Jiang-Tao Wang, 2018. "An Evaluation of Coupling Coordination between Tourism and Finance," Sustainability, MDPI, vol. 10(7), pages 1-23, July.
    5. Jun Yuan & Yaokai Zhou & Yanhong Liu, 2022. "Convergence Evaluation of Sports and Tourism Industries in Urban Agglomeration of Guangdong–Hong Kong–Macao Greater Bay Area and Its Spatial-Temporal Evolution," Sustainability, MDPI, vol. 14(16), pages 1-20, August.
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    Cited by:

    1. Yuanfang Fu & Zhenrao Cai & Chaoyang Fang, 2024. "Hotspot Identification and Causal Analysis of Chinese Rural Tourism at Different Spatial and Temporal Scales Based on Tourism Big Data," Sustainability, MDPI, vol. 16(3), pages 1-24, January.

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