象限分析法在第二次全国污染源普查数据审核中的应用

刘孝富; 张志苗; 王莹; 刘柏音; 邱文婷; 罗镭

doi:10.12153/j.issn.1674-991X.20190194

象限分析法在第二次全国污染源普查数据审核中的应用

doi: 10.12153/j.issn.1674-991X.20190194

中国环境科学研究院环境信息研究所

详细信息

作者简介:
刘孝富(1983─),男,副研究员,博士,主要研究方向为环境数据挖掘、生态环境规划,liuxf@craes.org.cn

中图分类号: X501
计量
- 文章访问数: 523
- HTML全文浏览量: 177
- PDF下载量: 114
- 被引次数: 0
出版历程
- 收稿日期: 2019-11-15
- 刊出日期: 2020-07-20

Application of quadrant analysis to data audit in the Second National Pollution Source Census

Institute of Environmental Information Sciences, Chinese Research Academy of Environmental Sciences

摘要

摘要: 象限分析法是依据事物的2个重要属性进行趋势分类分析,从而找出解决问题的方法,是异常值筛选的重要方法。分别以工业源数量审核以及工业产量、燃煤使用量审核为例,分析了象限分析法在第二次全国污染源普查清查阶段和普查阶段的应用。清查阶段以识别数量漏报、少报为目标,将分布于象限右下的点位作为异常对象;普查阶段以识别数据填报异常为目标,将分布于象限左上和右下的点位作为异常对象。与直接对比法、专家经验法、排序法、占比法、平均值法、直方图法等常规方法相比,象限分析法具有客观性强、所需样本量少、操作简单的特点,特别适合污染源普查的数据审核,也可弥补普查数据审核中行业专家的不足。
- 污染源普查 /
- 象限分析法 /
- 异常值筛选 /
- 数据审核
Abstract: Quadrant analysis is an important method of outlier screening, which can find solutions to the problem through trend classification of two important attributes of things. Taking the industrial source quantity audit, and industrial output and coal usage audit as examples, the application process of quadrant analysis in the inventory and census stages of the Second National Pollution Source Census was analyzed. The target in the inventory stage was to identify the missing or underreported quantity, so the points distributed in the lower right quadrant were regarded as the abnormal objects. While in the census stage, the target was to report the outlier, so the points distributed in the upper left and lower right quadrants were regarded as the anomalous objects. Compared with the conventional methods such as direct comparison, expert experience judgment, ranking and proportion analysis, average and histogram method, quadrant analysis has the characteristics of strong objectivity, with fewer samples and simple operation. It is extremely suitable for the data audit of pollution source census, and can also make up for the lack of industry experts in the data audit.
- pollution source census /
- quadrant analysis /
- outlier screening /
- data audit

HTML全文

参考文献(16)

[1]	张嘉敏. 第二次全国污染源普查工作主要存在的问题及对策[J]. 环境与发展, 2019,31(5):37-38. ZHANG J M. The main problems and countermeasures of the second national pollution source census work[J]. Environment and Development, 2019,31(5):37-38.
[2]	张亚冉. 关于污染源普查数据处理阶段审核方法的研究[J]. 化工管理, 2019(11):63-64.
[3]	孟文玲. 基于二维四象限法对人才选拔中“德”与“才”的评价分析[J]. 智库时代, 2018(26):9-10.
[4]	于梅艳. 基于四象限法的中小型水库防洪预警方法研究[J]. 黑龙江水利, 2017,3(4):44-46. YU M Y. Study on flood early warning method of small and medium-sized reservoir based on integrated flood forecasting figure[J]. Heilongjiang Water Resources, 2017,3(4):44-46.
[5]	于梅艳. 基于四象限法的中小型水库防洪预警方法研究[C]// 大数据时代的信息化建设:2015(第三届)中国水利信息化与数字水利技术论坛论文集. 北京:水利部科技推广中心, 2015.
[6]	唐秀美, 潘瑜春, 刘玉, 等. 基于四象限法的县域高标准基本农田建设布局与模式[J]. 农业工程学报, 2014,30(13):238-246. TANG X M, PAN Y C, LIU Y, et al. Layout and mode partition of high-standard basic farmland construction at county level based on four-quadrant method[J]. Transactions of the Chinese Society of Agricultural Engineering, 2014,30(13):238-246.
[7]	毛建明. 基于象限法的主体功能区划与分区政策:以江苏省海安县为例[J]. 扬州职业大学学报, 2010,14(2):12-16. MAO J M. The subdivision and regional policies of main functional region based on the quadrant method[J]. Journal of Yangzhou Polytechnic College, 2010,14(2):12-16.
[8]	朱娟蓉, 谭晓东, 张建华, 等. 四象限法在健康知识和行为关联研究中的运用[J]. 中国热带医学, 2006(8):1502-1503. ZHU J R, TAN X D, ZHANG J H, et al. Application of four quadrants in the researches of health knowledge and behavioral linkage[J]. China Tropical Medicine, 2006(8):1502-1503.
[9]	姜艳艳. 时间四象限法在招标代理机构客户管理中的应用[J]. 招标采购管理, 2018,76(12):44-45.
[10]	温珂, 苏宏宇, SCOTT S. 走进巴斯德象限:中科院的论文发表与专利申请[J]. 中国软科学, 2016(11):32-43. WEN K, SU H Y, SCOTT S. China’s pasteur’s quadrant:scientific publication and patenting within the Chinese Academy of Sciences[J]. China Soft Science, 2016(11):32-43.
[11]	王健豪, 苏勇. 基于K-means算法的案件预测应用[J]. 计算机与数字工程, 2019,47(8):1999-2001. WANG J H, SU Y. Application in case prediction based on the K-means algorithm[J]. Computer & Digital Engineering, 2019,47(8):1999-2001.
[12]	马骞. 聚类算法概述与应用[J]. 中国新通信, 2018,20(14):225-226.
[13]	牛丽君. 基于层次和密度的任意形状聚类算法研究[D]. 焦作:河南理工大学, 2016.
[14]	张凤兰, 胡新文, 唐平, 等. 我国钢铁行业能耗状况分析[J]. 节能, 2019,38(10):106-108.
[15]	FUMIKI S, AKIHIRO S. Fitting discrete polynomial curve and surface to noisy data[J]. Annals of Mathematics and Artificial Intelligence, 2015,75(1/2):135-162. doi: 10.1007/s10472-014-9425-7
[16]	SHOHEI H, YUTA T, HISASHI K, et al. Statistical outlier detection using direct density ratio estimation[J]. Knowledge and Information Systems, 2011,26(2):309-336. doi: 10.1007/s10115-010-0283-2