基于地理探测器的传统村落空间分异及影响因素研究——以重庆市为例
Research on Spatial Differentiation and Influencing Factors of Traditional Villages Based on Geographical Detector——Through Taking Chongqing City as an Example
张孝帝1
作者信息
- 1. 重庆师范大学 地理与旅游学院,重庆 400000
- 折叠
摘要
为探究重庆市传统村落空间分异及影响因素,运用多种空间方法,结合GIS软件探讨其空间分异,利用最优参数的地理探测器分析其空间分异的影响因素.研究发现,重庆市传统村落空间分布呈现显著集聚分布特征,主要集中在渝东南的酉阳、秀山与石柱等区县,形成主城九区低密度区与渝东南显著密集区.传统村落空间分布密度在空间上表现为高值区县相邻、低值区县相连的"大集聚、小分散"空间格局.重庆市传统村落空间分异主要受非物质文化遗产规模、地形起伏度、高程、农业化率与城镇化率等因素影响,其中单因子探测存在明显的差异,因子间的交互作用存在明显协同增强效应,说明重庆市传统村落空间分异是自然地理与经济、社会、文化等多因素共同作用的结果.
Abstract
In order to explore the spatial differentiation and influencing factors of traditional villages in Chongqing City,the study conducts a variety of spatial methods and GIS software,explores the spatial differentiation and the influencing factors with the geographic detector based on optimal parameters.It is found that the spatial distribution of traditional villages in Chongqing City presents a significant clustering distribution,which is mainly concentrated in Youyang,Xiushan and Shizhu District in Southeast Chongqing,and forms a low-density area in the ninth district of the main city and a significant dense area in Southeast Chongqing City.The spatial distribution density of traditional villages shows a spatial pattern of"large agglomeration and small dispersion",in which counties with high values are adjacent and counties with low values are connected.The spatial differentiation of traditional villages in Chongqing is mainly influenced by the scale of intangible cultural heritage,topographic relief,elevation,agricultural rate and urbanization rate,among which there are obvious differences in single factor detection and obvious synergistic enhancement effect in the interaction between factors,indicating that the spatial differentiation of traditional villages in Chongqing is the result of multiple factors,such as natural geography,economy,society and culture.
关键词
传统村落/空间分异/地理探测器/重庆市Key words
Traditional villages/Spatial differentiation/Geographical detector/Chongqing City引用本文复制引用
出版年
2024
NETL
NSTL
万方数据