Scholarlink

<p>Traditional villages encompass abundant cultural resources, possessing significant humanistic and socio-economic value. This study used ArcGIS 10.8 and GeoDa software, took "Chinese Traditional Villages" as the research object, and analyzed their spatial autocorrelation and spatial heterogeneity through methods such as nearest neighbor analysis, Moran’s Index, and kernel density estimation. Additionally, by combining techniques like Geodetector, ordinary least squares (OLS), and geographically weighted regression (GWR), the study selected the 10 most correlated driving factors and examined the drivers of their spatial distribution characteristics. The results are as follows: (1) The spatial distribution of villages exhibits significant clustering characteristics. (2) Rainfall and GDP per capita are the two important explanatory variables influencing the spatial distribution of villages. (3)The clustering phenomenon of villages is positively correlated with temperature, rainfall, and tourism resources, while negatively correlated with population density and overall economic level.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A study on the spatial distribution and driving factors of traditional villages–a case study of the Beijing-Tianjin-Hebei region in China

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月5日

查看全文
  • Peng Peng,
  • Yuechen Zhang,
  • Jiajie Zhao,
  • Yan Rong,
  • Limin Zhao,
  • Jinyu Wu

摘要

Traditional villages encompass abundant cultural resources, possessing significant humanistic and socio-economic value. This study used ArcGIS 10.8 and GeoDa software, took "Chinese Traditional Villages" as the research object, and analyzed their spatial autocorrelation and spatial heterogeneity through methods such as nearest neighbor analysis, Moran’s Index, and kernel density estimation. Additionally, by combining techniques like Geodetector, ordinary least squares (OLS), and geographically weighted regression (GWR), the study selected the 10 most correlated driving factors and examined the drivers of their spatial distribution characteristics. The results are as follows: (1) The spatial distribution of villages exhibits significant clustering characteristics. (2) Rainfall and GDP per capita are the two important explanatory variables influencing the spatial distribution of villages. (3)The clustering phenomenon of villages is positively correlated with temperature, rainfall, and tourism resources, while negatively correlated with population density and overall economic level.