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干旱区地理 ›› 2026, Vol. 49 ›› Issue (6): 1288-1298.doi: 10.12118/j.issn.1000-6060.2025.596 cstr: 32274.14.ALG2025596

• 旅游地理 • 上一篇    下一篇

甘肃省民宿空间格局及影响因素研究

许可娟(), 何瑛(), 王军   

  1. 兰州文理学院甘肃 兰州 730010
  • 收稿日期:2025-09-25 修回日期:2025-11-12 出版日期:2026-06-25 发布日期:2026-06-29
  • 通讯作者: 何瑛(1976-),女,教授,主要从事乡村旅游、文化旅游融合等方面的研究. E-mail: 1001257@luas.edu.cn
  • 作者简介:许可娟(1987-),女,副教授,主要从事智慧旅游等方面的研究. E-mail: xr.1121@163.com
  • 基金资助:
    兰州文理学院校基金项目(202105);甘肃省教育厅2023年高校产业支撑计划项目(2023CYZC-73)

Spatial distribution and influencing factors of homestays in Gansu Province

XU Kejuan(), HE Ying(), WANG Jun   

  1. Lanzhou University of Arts and Science, Lanzhou 730010, Gansu, China
  • Received:2025-09-25 Revised:2025-11-12 Published:2026-06-25 Online:2026-06-29

摘要:

民宿在生态脆弱与欠发达地区的空间分布规律与形成机制,对优化区域旅游资源配置、推动文旅产业可持续协调发展具有重要意义。以甘肃省为例,运用最邻近指数、核密度估计、空间自相关分析与地理探测器,基于2667家民宿兴趣点数据及14项影响因子,探究民宿空间分布格局及驱动机制。结果表明:(1) 民宿空间分布高度聚集,呈现出“东密西疏、双核引领、‘勺子’状分布、边缘稀疏”的格局,兼具“集聚依赖”与“尺度敏感”双重特征。(2) 热点区集中于敦煌市、兰州市、陇南市沿线,次热点区环绕分布;天水市辖区、崆峒区、西峰区等区域内部差异显著;中东部多数县区为连片冷点区,区域发展不均衡。(3) 非民宿酒店密度是首要驱动因子,气候舒适度则作为关键的条件性调节因子,通过与核心因子的强交互作用显著驱动集聚。(4) 甘肃民宿发展遵循“设施-资源-环境”协同驱动的差异化逻辑,其空间格局既受顶级旅游资源宏观牵引,又在微观上高度依赖现有住宿设施与交通节点。

关键词: 民宿, 空间分析, 聚集特征, 甘肃省

Abstract:

Tourism resource endowment, traffic accessibility, and regional economic development are widely acknowledged as key factors shaping the spatial distribution of homestays. However, it remains uncertain whether the dominant influence and relative importance of these factors vary significantly across different regional contexts. In regions with distinct development stages, unique resource endowments, and diverse market conditions, whether the core mechanisms driving homestay agglomeration undergoes fundamental changes require further empirical investigation. To address this gap, this study examines Gansu Province—a less economically developed region in western China experiencing rapid growth in the homestay industry—as a case study. Utilizing the nearest neighbor index, kernel density estimation, spatial autocorrelation analysis, and geographical detector methods, this study analyzes the spatial distribution patterns and driving mechanisms of homestays under heterogeneous development conditions, based on POI data from 2667 homestays and 14 influencing factors. The results indicate that: (1) Homestays in Gansu Province exhibit a highly clustered spatial distribution, characterized by denser concentrations in the east and sparser distributions in the west, dual-core leadership, spoon-shaped layout, and peripheral rarity. Lanzhou and Dunhuang serve as primary agglomeration centers, with development axes along the Hexi Corridor and southeastern Gansu. Ecological barrier zones, alpine pastoral areas, desert Gobi regions, and mountainous valleys are largely devoid of homestays. (2) At the macro-municipal (county) scale, the global Moran’s I index suggests a random distribution, while at the micro scale (5-10 km), a significant short-distance agglomeration effect is observed, which has weak correlation with administrative divisions, demonstrating both agglomeration dependence and scale sensitivity. (3) Hotspots cluster in Dunhuang and along the Lanzhou-Longnan-Gannan belt, with sub-hotspots distributed peripherally. Anomalous zones, including Tianshui urban districts, Kongtong District, Xifeng District (high-low type), and Zhouqu County, Aksay County, and Min County (low-high type), surrounded by high or low values, show unbalanced development and strong growth potential. Coldspots are mainly found in central and eastern Gansu, anchored by Tongwei County and Zhuanglang County, forming a pattern of decreasing confidence from the core to the periphery. (4) Among influencing factors, non-homestay hotel density (q=0.375), scenic spot kernel density (q=0.264), and cultural/intangible cultural heritage density (q=0.188) are most significant, indicating that homestay locations depend heavily on existing accommodation infrastructure and high-quality tourism resources. While climate comfort has a limited independent impact, it acts as a latent amplifier notably enhancing the effects of other drivers (e.g., interaction q-value with non-homestay hotel density reaches 0.548). (5) In less economically developed regions with abundant ecological and cultural resources, such as Gansu, homestay development follows a synergistic logic of facility-resource-environment interaction rather than relying solely on market demand or capital input. This study offers empirical insights into the unique development patterns of the homestay industry in underdeveloped areas and provides guidance for optimizing regional homestay layout and formulating differentiated policy approaches.

Key words: homestay, spatial analysis, clustering characteristics, Gansu Province