基于R和GeoDa软件的云南省人口信息GIS空间统计分析

发布时间：2018-04-26 07:56

本文选题：R软件 + GeoDa软件　；参考：《云南大学》2012年硕士论文

【摘要】：本文综合应用R软件、GeoDa软件、统计学、人口学、地图学、数据库、电子信息技术等相关科学和技术编程计算2010年云南省各州市人口数等人口信息的全局空间自相关和局部空间自相关指标来实现GIS技术及空间聚集性统计分析,为云南省人口信息科学研究和科学管理提供有益的探索。意义：(1)R软件和GeoDa软件均为国际承认的免费软件,从而解决了一般GIS软件较昂贵的弊端,有利于更广泛地推广与普及,具有较强的经济价值和应用前景。(2)将空间自相关聚集性统计分析引入到云南省各州市人口信息统计中,从而使人口信息研究更具科学性和广泛性,具有广泛的应用空间和实用价值。本文对从云南省统计年鉴中2010年16个州市抽取的人口数、性别比、出生率死亡率、自然增长率、人口密度六个指标,代表人口性别结构、数量结构、增速结构等人口信息,运用GeoDa软件构建空间地图、权重矩阵等空间文件,用R软件和GeoDa软件计算全局和局部的Moran'sⅠ值,并由专题地图或散点图来直观显示,从而分析是否存在空间聚集性,再结合人口信息指标的绝对数、相对排序地图的比较研究。结论：1、R软件和GeoDa软件都可以实现对空间自相关统计分析；2、都可以实现对人口信息中反映人口性别结构、人口数量结构和人口增速结构的六大指标的空间自相关统计分析；3、得出的全局自相关指标(全省)结果是一致的,如人口数、人口密度在全省范围内存在正向空间聚集性,即高高或低低聚集,而性别比、出生率、死亡率、自然增长率没有显著的空间聚集性；4、得出的局部相关指标(各州市)结果大部分一致的,但也存在少部分差异。建议：1、R和GeoDa两个软件应配合使用；2、将云南省人口信息特征划分为五个片区,将云南省人口数划分为三个片区,将各州市或片区进行“人口特征型”初步划型,建立“云南省人口空间示范区”,开展分片重点调控,发挥示范效应和提高地理辐射效益。
[Abstract]:This paper applies R software, GeoDa software, statistics, demography, cartography, database, This paper calculates the global spatial autocorrelation and local spatial autocorrelation index of population information such as population number of each city in Yunnan Province in 2010 to realize the GIS technology and spatial aggregation statistical analysis by programming related science and technology, such as electronic information technology, etc. It provides a useful exploration for the scientific research and management of population information in Yunnan Province. Meaning: 1 / R software and GeoDa software are internationally recognized free software, thus solving the problem of expensive GIS software, which is conducive to wider promotion and popularization. It has strong economic value and application prospect. (2) the statistical analysis of spatial autocorrelation agglomeration is introduced into the population information statistics of every city in Yunnan province, which makes the research of population information more scientific and extensive. It has extensive application space and practical value. In this paper, the population number, sex ratio, birth rate and death rate, natural growth rate, population density, representing population sex structure, quantity structure, growth rate, population information and so on are collected from 16 cities and towns in the Statistical Yearbook of Yunnan Province in 2010. Using GeoDa software to build spatial map, weight matrix and other spatial files, using R software and GeoDa software to calculate the global and local Moran's I values, and display them directly by thematic maps or scatter maps, and then analyze whether there is spatial aggregation. Combined with the absolute number of population information indicators, the comparative study of relative sorted maps. Conclusion both the software of 1 / 1 / R and the software of GeoDa can realize the statistical analysis of spatial autocorrelation and reflect the sex structure of population in the information of population. The spatial autocorrelation statistical analysis of the six major indexes of population quantity structure and population growth structure. 3. The global autocorrelation index (provincial) results are consistent. For example, population size and population density have positive spatial aggregation in the whole province. That is, high or low concentration, and sex ratio, birth rate, death rate, natural growth rate has no significant spatial aggregation. The results of local correlation index (each city) are mostly the same, but there are some differences. It is suggested that the two softwares: 1 / 1 / R and GeoDa should be used together to divide the population information characteristics of Yunnan Province into five regions, and to divide the population size of Yunnan Province into three regions, and to carry out a preliminary pattern of "population characteristic type" in each city or region. The population spatial demonstration area of Yunnan Province was established, and the key regulation was carried out to give play to the demonstration effect and improve the benefit of geographical radiation.
【学位授予单位】：云南大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：C924.2;TP319

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