Spatial Distribution Characteristics and Driving Factors of Traditional Villages in Henan Province: A Multi-Method Comprehensive Analysis

This study supports the preservation and sustainable development of traditional villages by examining their spatial distribution patterns and mechanisms underlying those patterns in Henan Province. The study utilizes data from six batches of Chinese traditional villages in the province, which are studied using kernel density estimation (KDE), spatial autocorrelation, optimal GeoDetector, and the geographically weighted regression (GWR) model, to explore the spatial differentiation pattern in depth and its mechanisms of influencing traditional villages in Henan Province. This study reveals that traditional villages in the province exhibit a “multi-core” clustering pattern, influenced by the natural environment, historical culture, location and transportation, and economic development. The Optimal Parameter GeoDetector indicates that 15 factors, including the average altitude, academy density, road density, and annual GDP, vary significantly in their impact. Furthermore, these factors exhibit a notable interactive, synergistic effect. Meanwhile, the GWR model indicates spatial heterogeneity in the influences of factors like the average rainfall, river density, road density, academy density, and GDP on the distribution of traditional villages. This study suggests developing tailored protection and development strategies for different clusters, enhancing inter-administrative joint protection, and building a radiation network centered on core areas to promote sustainable preservation and coordinated rural revitalization of traditional villages.

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Publication Details

Journal
Sustainability
Published
2025-12-03
DOI
https://doi.org/10.3390/su172310825
Primary Topic
Land Use and Ecosystem Services
Type
article
Field-Weighted Citation Impact
0.00

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article

Spatial Distribution Characteristics and Driving Factors of Traditional Villages in Henan Province: A Multi-Method Comprehensive Analysis

Jieun Kim, Mengru Song
Sustainability
Land Use and Ecosystem Services
article

Spatial Distribution Characteristics and Driving Factors of Traditional Villages in Henan Province: A Multi-Method Comprehensive Analysis

Jieun Kim, Mengru Song
article en

Abstract

This study supports the preservation and sustainable development of traditional villages by examining their spatial distribution patterns and mechanisms underlying those patterns in Henan Province. The study utilizes data from six batches of Chinese traditional villages in the province, which are studied using kernel density estimation (KDE), spatial autocorrelation, optimal GeoDetector, and the geographically weighted regression (GWR) model, to explore the spatial differentiation pattern in depth and its mechanisms of influencing traditional villages in Henan Province. This study reveals that traditional villages in the province exhibit a “multi-core” clustering pattern, influenced by the natural environment, historical culture, location and transportation, and economic development. The Optimal Parameter GeoDetector indicates that 15 factors, including the average altitude, academy density, road density, and annual GDP, vary significantly in their impact. Furthermore, these factors exhibit a notable interactive, synergistic effect. Meanwhile, the GWR model indicates spatial heterogeneity in the influences of factors like the average rainfall, river density, road density, academy density, and GDP on the distribution of traditional villages. This study suggests developing tailored protection and development strategies for different clusters, enhancing inter-administrative joint protection, and building a radiation network centered on core areas to promote sustainable preservation and coordinated rural revitalization of traditional villages.

SustainabilityVol. 17(23)
Kookmin University (KR), Henan University of Urban Construction (CN)
National Research Foundation, Ministry of Education, National Research Foundation of Korea
Openalex Percentile: Top 65%
Land Use and Ecosystem Services
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