Geodetector-Based Analysis of Spatiotemporal Distribution Characteristics and Influencing Mechanisms for Rural Homestays in Beijing

Rural homestays have emerged as pivotal drivers of rural socioeconomic revitalization, particularly in metropolitan peripheries characterized by intensified urban–rural dynamics. However, their spatiotemporal distribution patterns and underlying mechanisms remain underexplored. This study employs Geodetector and related analytical methods to examine rural homestays in Beijing, aiming to decipher spatial heterogeneity and driving factors. The results reveal a distinct “large-scale dispersion with small-scale clustering” pattern marked by pronounced agglomeration in northern mountainous areas and sparse distributions in southern suburban regions. Temporally, the sector currently exhibits a notable expansion–contraction phase influenced by external factors, alongside spatial centroid migration toward resource-rich zones. Geodetector quantification identifies the proximity to transportation nodes and vegetation coverage as primary spatial determinants, while socioeconomic factors demonstrate comparatively limited influence—contrasting sharply with urban contexts. Rural homestay concentration zones are classified into high-, medium-, and low-intensity categories based on the homestay density, with high-intensity zones leveraging apex advantages of scenic resources, cultural heritage, and infrastructure. These findings underscore the interplay of natural environmental factors, tourism resources, transportation accessibility, and socioeconomic conditions in shaping agglomeration dynamics, providing actionable insights for optimizing spatial planning and promoting sustainable development in rural regions adjacent to megacities.

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Publication Details

Journal
Land
Published
2025-05-05
DOI
https://doi.org/10.3390/land14050997
Citations
4
Primary Topic
Sharing Economy and Platforms
Type
article
Field-Weighted Citation Impact
8.29
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article

Geodetector-Based Analysis of Spatiotemporal Distribution Characteristics and Influencing Mechanisms for Rural Homestays in Beijing

Hu Min, Weijun Gao, Ming Gao, Mei Zhao et al.
4 citations
Land
Sharing Economy and Platforms
8.29
article

Geodetector-Based Analysis of Spatiotemporal Distribution Characteristics and Influencing Mechanisms for Rural Homestays in Beijing

Hu Min, Weijun Gao, Ming Gao, Mei Zhao, Y. Hei, Yifan Sui
article en
4 citations

Abstract

Rural homestays have emerged as pivotal drivers of rural socioeconomic revitalization, particularly in metropolitan peripheries characterized by intensified urban–rural dynamics. However, their spatiotemporal distribution patterns and underlying mechanisms remain underexplored. This study employs Geodetector and related analytical methods to examine rural homestays in Beijing, aiming to decipher spatial heterogeneity and driving factors. The results reveal a distinct “large-scale dispersion with small-scale clustering” pattern marked by pronounced agglomeration in northern mountainous areas and sparse distributions in southern suburban regions. Temporally, the sector currently exhibits a notable expansion–contraction phase influenced by external factors, alongside spatial centroid migration toward resource-rich zones. Geodetector quantification identifies the proximity to transportation nodes and vegetation coverage as primary spatial determinants, while socioeconomic factors demonstrate comparatively limited influence—contrasting sharply with urban contexts. Rural homestay concentration zones are classified into high-, medium-, and low-intensity categories based on the homestay density, with high-intensity zones leveraging apex advantages of scenic resources, cultural heritage, and infrastructure. These findings underscore the interplay of natural environmental factors, tourism resources, transportation accessibility, and socioeconomic conditions in shaping agglomeration dynamics, providing actionable insights for optimizing spatial planning and promoting sustainable development in rural regions adjacent to megacities.

LandVol. 14(5)
Beijing Institute of Technology (CN), Beijing Jiaotong University (CN), Ministry of Housing and Urban-Rural Development (CN), China Academy of Building Research (CN)
Openalex Percentile: Top 3%
Sharing Economy and Platforms
8.29
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