| 唐柜彪,朱庆伟,董士伟,高秉博,潘瑜春,王怡蓉,郜允兵.农业用地土壤重金属样本点数据精化方法——以北京市顺义区为例[J].农业环境科学学报,2020,39(10):2288-2296. |
| 农业用地土壤重金属样本点数据精化方法——以北京市顺义区为例 |
| Data refinement method for sampling sites of agricultural soil heavy metals: A case study in Shunyi district, Beijing， China |
| 投稿时间：2020-03-21 |
| DOI：10.11654/jaes.2020-0323 |
| 中文关键词: 样本点 数据精化 均匀变异指数 空间分布 偏离指数 |
| 英文关键词: sampling sites data refinement even variation index spatial distribution deviation index |
| 基金项目:国家重点研发计划课题（2016YFD0800904）；北京市自然科学基金项目（8192015）；北京市农林科学院青年科研基金项目（QNJJ201830）；国家自然科学基金项目（41801276） |
| 作者 | 单位 | E-mail | | 唐柜彪 | 西安科技大学测绘科学与技术学院, 西安 710054
北京农业信息技术研究中心, 北京 100097 | | | 朱庆伟 | 西安科技大学测绘科学与技术学院, 西安 710054 | | | 董士伟 | 北京农业信息技术研究中心, 北京 100097
国家农业信息化工程技术研究中心, 北京 100097 | dongsw@nercita.org.cn | | 高秉博 | 中国农业大学土地科学与技术学院, 北京 100193 | | | 潘瑜春 | 北京农业信息技术研究中心, 北京 100097
国家农业信息化工程技术研究中心, 北京 100097 | | | 王怡蓉 | 西安科技大学测绘科学与技术学院, 西安 710054
北京农业信息技术研究中心, 北京 100097 | | | 郜允兵 | 北京农业信息技术研究中心, 北京 100097
国家农业信息化工程技术研究中心, 北京 100097 | |
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| 中文摘要: |
| 样本点空间分布是样点数据检测评价和挖掘分析的关键因素。以北京市顺义区为例，研发了一种农业用地土壤重金属样本点数据精化方法：首先构建样本点均匀变异指数和均匀因子离散图来共同检测样本点数据均匀性，进一步将样本点类型划分为均匀样本点、聚集样本点和稀疏样本点并确定其数量；其次删除聚集样本点，基于研究区历史数据加密稀疏样本点；最后基于地理空间样本点均匀变异指数、特征空间偏离指数和插值误差共同评价数据精化效果。结果表明，研究区样本点的均匀变异指数为0.429，存在一个聚集样本点和一个稀疏样本点，空间偏离指数为0.327，空间属性插值误差为6.538；冗余数据精化后进行均匀性检测没有发现聚集样本点和稀疏样本点，均匀变异指数下降到0.406，特征空间偏离指数微弱下降，空间属性插值误差下降到6.357。研究表明该方法可以对提高采样数据的均匀性和代表性提供理论指导，可以服务于土壤污染防治行动计划（土十条）、土壤污染状况详查等，为更加精确研究土壤空间信息变化提供一定的基础条件。 |
| 英文摘要: |
| The spatial distribution of sampling sites is the key factor for the detection, evaluation, and mining analysis of sampling data. By selecting a case study in Shunyi district, Beijing, a data refinement method for the sampling sites of agricultural soil heavy metals was developed in this study. First, the even variation indices and even factor discrete graphs of sampling sites were constructed to detect their data uniformity. The sampling sites were then divided into even, aggregate, and sparse sampling sites, and their corresponding amounts were determined. Second, aggregate sampling sites were deleted and sparse sampling sites were densified based on the historical sites of the study area. Third, the data refinement effect was evaluated based on the even variation index in the geographical space, deviation index in the feature space, and spatial interpolation error. The results showed that the even variation index of sampling sites in the study area was 0.429, with aggregate and sparse sampling sites; the deviation index in the feature space and spatial interpolation error were 0.327 and 6.538, respectively. After redundant data refinement, aggregate sampling sites and sparse sampling sites were not found by uniformity detection. The even variation index and interpolation error were reduced to 0.406 and 6.357, respectively; the deviation index slightly decreased. This study suggests that the method can provide theoretical guidance for improving the uniformity and representativeness of sampling sites. It can support soil pollution prevention and control action plans, soil pollution situation detailed investigation, etc., providing some basic conditions for further accurate study of soil spatial information changes. |
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