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基于地理加权模型的农村居民点空间格局及影响因子分析——以湖北省麻城市为例
引用本文:金晓,唐祥云. 基于地理加权模型的农村居民点空间格局及影响因子分析——以湖北省麻城市为例[J]. 测绘与空间地理信息, 2018, 0(3): 31-35. DOI: 10.3969/j.issn.1672-5867.2018.03.010
作者姓名:金晓  唐祥云
作者单位:武汉大学 资源与环境科学学院,湖北 武汉,430079
基金项目:中央高校基本科研业务费专项资金
摘    要:以湖北省麻城市2009年和2015年农村居民点为研究对象,采用区位指数和核密度测算法对农村居民点空间分布形态进行分析,运用传统最小二乘法和地理加权模型从全域和局部角度分析自然地貌、社会经济、生态限制等因素影响农村居民点空间演变的作用机制。研究得出:麻城市2009—2015年农村居民点用地面积增长率达6.92%,斑块主要以分散式外延扩张为主,空间结构演变总体上呈现沿沪汉蓉快速铁路和武麻公路向四周扩散的连续分布特征。从全域上看,农村居民点分布格局与坡度、公路、建制镇、风景名胜区、河流呈空间负相关关系;从局部影响程度来看,最大正向影响因子为人均收入和高程,负向影响因子主要是坡度和河流,其他影响因子随地理位置变化正负向影响均存在。

关 键 词:农村居民点演变  驱动力  地理加权模型  麻城市  rural residential areas  driving force  geographically weighted model  Macheng City

Spatial Pattern and Influencing Factors of Rural Residential Area Based on Geographical Weighting Model:A Case Study of Macheng City,Hubei Province
JIN Xiao,TANG Xiangyun. Spatial Pattern and Influencing Factors of Rural Residential Area Based on Geographical Weighting Model:A Case Study of Macheng City,Hubei Province[J]. Geomatics & Spatial Information Technology, 2018, 0(3): 31-35. DOI: 10.3969/j.issn.1672-5867.2018.03.010
Authors:JIN Xiao  TANG Xiangyun
Abstract:This paper takes the Macheng, Hubei Province in 2009 and 2015, rural residential areas as the research object, analyze the rural residential space form comparatively to characterize the evolution characteristics. And using Traditional least squares method and Geographically weighted model to explain the influence mechanism constituted by factors such as natural landscape, social economy and ecological limits. The results indicated that the rural residential land area growth rate was 6.92% from 2009 to 2015, and Plaque concentration was low, mainly expanding dispersedly. Rural residential spatial structure indicated continuous distribution characteristic along HuHanRong high-speed railway and Wuma road on the whole. The rural settlement distribution pattern indicated a negative rela-tionship with slope, road, town, river and scenic area from the analysis of the whole domain. From the perspective analysis of local level, the biggest positive impact factors were per capita income and elevation, negative impact factor were mainly slope and rivers, the others' influence varied with geographical position, existing positive influence and negative influence simultaneously.
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