基于自相关的我国农民人均纯收入的空间关联规则挖掘  

Autocorrelation Based Spatial Incident Rule Mining of Chinese Farmers' Per Capita Net Income

作　　者：吕曹芳[1] 庞宇[1] 罗永龙[1] 

机构地区：[1]安徽师范大学国土资源与旅游学院,安徽芜湖241003

出　　处：《国土资源信息化》2008年第2期33-37,共5页Land and Resources Informatization

基　　金：国家自然科学基金项目(编号:60573171);安徽省高校省级自然科学研究重点项目(编号:2006KJ024A);安徽省教学研究项目(编号:JYXM2005166)

摘　　要：空间自相关是空间统计的一种分析方法,能在设定显著性水平下研究相邻区域位置属性或现象之间的相关性,是分析区域变量的空间结构特征的有效手段。本文通过时2000～2004年我国农民人均纯收入进行全局和局部空间自相关分析,将区域分布划分为高收入地区、潜在高收入地区及低收入地区,揭示了全国范围内各省农民人均纯收入在整体上呈现出显著的空间正相关特性,其空间聚类趋势明显。The spatial autocorrelation is an analytical method of spatial statistics. It can study the correlativity of the location attribute and phenomenon between the neighboring regions under a given significance level, and is an efficient means for analyzing the spatial structure features of the area variable. Using the spatial autocorrelation technique, this paper conducts the overall and local analysis of the Chinese farmers＇ per capita net income from 2000 - 2004, and divides the study areas into three categories of high income area, potential high income area and low income area. This study shows that in the whole country, the spatial correlation of the farmer＇s per capita net income between different provinces is obviously positive, and there is a clear tendency of spatial clustering.

关 键 词：空间统计分析 空间自相关 农民人均纯收入 关联规则 GIS 

分 类 号：F323.8[经济管理—产业经济]