摘要
研究集体建设用地级别划分方法,对丰富集体建设用地定级理论体系,规范集体建设用地流转具有重要意义。以江西省锦江镇为例,采用多因素综合评定法从繁华程度、交通条件等7个方面初选影响集体建设用地质量的指标,再结合主成分分析法进一步分析确定最终定级指标和权重,运用总分频率直方图法将研究区划分为4个土地级别。结果表明:(1)通过主成分分析法最终选取了最具代表性的18个因子;采用加权求和法计算单元综合分值,综合分值分布在24.96~87.63,二级评价单元面积最多;(2)本次土地级别评定结果符合实际情况,具有较强的应用性和操作性,对引导当地农村土地利用具有重要意义;(3)多因素综合评定法和主成分分析法相结合的定级方法提高了传统集体建设用地定级方法的准确性和客观性,为有关技术规范的制定奠定了基础。
The purpose of this study was to analyze the method for the gradation of collective construction land which is helpful to enrich the theoretical system of collective construction land gradation and standardizing the transformation of rural collective construction land.Jinjiang Town,Jiangxi Province was taven as an example,the method of multi-factor comprehensive evaluation was used to choose alternative grading factors from 7 aspects such as business prosperity and traffic condition.Then principal component analysis was used to determine the ultimate land grading factors and factor weight.With the frequency histogram method,the land of the town was divided into four land grades.The results showed that：（1）According to principal component analysis,18 representative factors were chosen at last;the method of weighting summation was used to calculate the comprehensive unit values,which were in the range of 24.96-87.63,most of the units belonged to the second grade.（2）The results of this study were in line with the regional realities,and had a strong practicability and operability,which was of great significance to guide the local rural land use.3）The land grading method,which combined multi-factor comprehensive evaluation with principal component analysis,improved the accuracy and objectivity of the traditional grading methods for rural collective construction land and established the basis for formulating relative technical norms.
出处
《江西农业大学学报》
CAS
CSCD
北大核心
2018年第1期198-205,共8页
Acta Agriculturae Universitatis Jiangxiensis
基金
国家自然科学基金项目(41361049)
江西省自然科学基金项目(20122BAB204012)~~
关键词
农村集体建设用地
土地定级
指标体系
权重
多因素综合评定法
主成分分析法
锦江镇
collective construction land
land gradation
index system
weight
multi-factor comprehensive evaluation
principal component analysis
Jinjiang Town
