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基于空间自相关的黎平县农村居民点分布与影响因素关系研究
引用本文:柴娇娇,杨柳,索萌萌.基于空间自相关的黎平县农村居民点分布与影响因素关系研究[J].国土与自然资源研究,2021(2):1-5.
作者姓名:柴娇娇  杨柳  索萌萌
作者单位:贵州大学公共管理学院,贵州贵阳550000;贵州大学公共管理学院,贵州贵阳550000;贵州大学公共管理学院,贵州贵阳550000
基金项目:国家自然科学基金项目“空间公平性视角下贵州贫困山区乡村聚落空间重构研究——以滇黔桂石漠化区为例”(D01024)。
摘    要:为科学精细分析云贵高原地区自然和社会经济因素对农村居民点空间分布的影响,本文以贵州省黎平县为研究对象,以行政村为研究单元,采用空间自相关与空间叠加相结合的方法,通过农村居民点分离度分析农村居民点分布与影响因素的关系。分析结果显示,(1)黎平县农村居民点分离度指标的全局Moran指数为0.089,Z值为2.164,大于1.96,农村居民点分布存在显著的空间自相关特征;(2)自然因素和社会经济因素对黎平县农村居民点的局部显著性空间聚集和异常特征影响明显,自然因素的影响程度大于社会经济因素,在自然因素的影响中,高程占主导地位。

关 键 词:农村居民点  全局空间自相关  局部空间自相关

Research on the Relationship between Distribution of Rural Residential Areas and Influencing Factors in Liping County Based on Spatial Autocorrelation
Institution:(School of Public Administration,Guizhou University,Guiyang 550000,China)
Abstract:In order to analyze the influence of natural and social and economic factors on the spatial distribution of rural residential areas scientifically and precisely in the Yunnan-Guizhou Plateau region,the paper takes Liping County,Guizhou Province as the research object and takes administrative villages as the research unit.In this paper,the spatial autocorrelation and spatial superposition are combined to the relationship between the distribution of rural residential areas and the influencing factors through the separation degree of rural residential areas.The analysis results show that,(1)the global Moran index and Z value of the separation degree of rural settlements in Liping County are 0.089 and 2.164 respectively,Z value is greater than 1.96,indicating that the distribution of rural residential areas has significant spatial autocorrelation;(2)natural factors and social and economic factors have obvious influence on the local significant spatial clustering and abnormal characteristics of rural settlements in Liping County.The influence of natural factors on the distribution of rural residential areas is greater than that of social and economic factors,and elevation plays a dominant role in the influence of natural factors on the distribution of rural residential areas.
Keywords:Rural residential area  Global spatial autocorrelation  Local spatial autocorrelation
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