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协同遥感信息与统计信息的人口空间格局分析
引用本文:董珍珍,王亮,仇阿根. 协同遥感信息与统计信息的人口空间格局分析[J]. 测绘通报, 2017, 0(5): 88-94. DOI: 10.13474/j.cnki.11-2246.2017.0162
作者姓名:董珍珍  王亮  仇阿根
作者单位:中国测绘科学研究院, 北京 100830
基金项目:测绘地理公益性行业科研专项,基础测绘
摘    要:针对人口空间格局分布分析不足的问题,借助遥感信息与基本统计信息,引入人口地理集中度、探索性空间数据分析和地理加权回归模型,定量地分析了特征因子在市域范围上的空间异质性。人口集中指数与Moran指数能有效地反映空间事物之间的密切作用关系,在产业空间分布中能较好地评价集聚效应,反映空间分布状态。以长江经济带为研究区域,对人口空间分布进行了研究,得出了“圈层集中-东西对立-南北差异-四周分散”的分布特征,并从经济、社会、环境、土地利用方面分析原因,为人口合理增长和资源分配提供了参考。

关 键 词:人口  空间格局分析  探索性分析  地理加权回归  
收稿时间:2016-10-12
修稿时间:2016-12-05

Analysis of Population Spatial Pattern of Cooperative Remote Sensing Information and Statistical Information
DONG Zhenzhen,WANG Liang,QIU Agen. Analysis of Population Spatial Pattern of Cooperative Remote Sensing Information and Statistical Information[J]. Bulletin of Surveying and Mapping, 2017, 0(5): 88-94. DOI: 10.13474/j.cnki.11-2246.2017.0162
Authors:DONG Zhenzhen  WANG Liang  QIU Agen
Affiliation:Chinese Academy of Surveying and Mapping, Beijing 100830, China
Abstract:The article analyses the population spatial distribution pattern of the problem of insufficiency.With the aid of remote sensing information and basic information,we quantitatively analyse the spatial heterogeneity of the range area with the introduction of the population geographical concentration,exploratory spatial data analysis and quantitative analysis of the characteristics of geographical weighted regression model.The population concentration index and Moran index effectively reflect the close relationship among the space objects,better evaluate the agglomeration effect in industrial spatial distributron,and reflect the space distribution state.Based on the study area of the Yangtze River economic belt,with the study of spatial distribution of population,it concluded the layers of focus-opposites-the north-south differences-scattered around distribution characteristics,and analyzed the causes from the aspects of economy,society,environment,land use analysis.Thus,it provided a reference for reasonable population growth and resources distribution.
Keywords:population  spatial pattern analysis  exploratory spatial data analysis  GWR
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