李冰茹,孙文彬

• 1:北京农业质量标准与检测技术研究中心
• 2:农业部农产品质量安全风险评估实验室
• 3:中国矿业大学(北京)地球科学与测绘工程学院

摘要(Abstract)：

针对传统地学空间数据分析过程中,异常点数据影响范围过大甚至涵盖其他采样点的问题,该文提出了基于Voronoi图与变缓冲区分析相结合的异常点数据分析方法。以异常值点作为生长点构建Voronoi图,利用异常点数据与其一阶邻近Voronoi图生长点的数据差值与空间距离,计算出异常采样点的衰减系数;用衰减系数与异常采样点数据的乘积计算异常采样点的粗略影响范围,通过计算粗略影响范围与异常采样点Voronoi图的交集,最终确定异常采样点的有效影响范围。利用Voronoi图与变缓冲区相结合方法可以有效地控制采样点异常数据影响范围,提高空间数据分析准确度,更加客观地反映地学空间数据的分布特征。以云贵高原的刁江流域的土壤重金属含量为试点,以采样点作生长点、行政界为边界生成Voronoi图,分析刁江流域土壤重金属As元素的空间分布。利用该方法获得的As元素空间数据分布图,其空间特征更加鲜明。

关键词(KeyWords)： Voronoi图;数据异常;变缓冲区分析;土壤重金属

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目(41671383);; 北京市农林科学院创新建设项目(KJCX20170419);; 国家食品质量安全风险评估重大专项(GJFP201801501)

作者(Author): 李冰茹,孙文彬

DOI: 10.16251/j.cnki.1009-2307.2018.10.027

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