摘要
基于苏州古城区河道14个采样点的水质数据和兴趣点(POI)数据,分析古城河水质与周围POI类型之间的相关性。利用主成分分析计算每个采样点的综合水质指标;基于GIS空间分析技术,计算代表人类干扰强度的POI单类型点密度指数和POI多类型核密度指数;最后运用Pearson相关分析探讨POI数据对水质的影响。结果表明,水质高度依赖于POI类型的空间分布,古城区中部水质较好,北部和南部水质较差。缓冲半径为50 m、100 m、150 m、200 m、250 m和300 m的POI多类型核密度指数与水质综合指数的Pearson相关系数分别为0.559、0.607、0.573、0.521、0.466和0.422,在缓冲半径为100 m时相关性较为显著。
Based on the water quality data from 14 sampling sites in the rivers of Suzhou ancient city zone and the point of interest(POI)data,the correlation between the water quality of the rivers in ancient city zone and the surrounding POI types was analyzed.Principal component analysis was used to calculate the comprehensive water quality index of each sampling site.Based on GIS spatial analysis technology,POI single type point density index and POI multi type kernel density index representing human interference intensity were calculated.Finally,Pearson correlation analysis was used to explain the effect of POI data on water quality.The results showed that water quality was highly dependent on the spatial distribution of POI types.The water quality was good in central ancient city zone but was poor in the north and south.Pearson correlation coefficient between POI multi type kernel density index with buffer radius of 50 m,100 m,150 m,200 m,250 m,300 m and water quality comprehensive index were 0.559,0.607,0.573,0.521,0.466,0.422,respectively.The correlation was significant when the buffer radius was 100 m.
作者
杨权
杨羽佳
张怡
杨朝辉
YANG Quan;YANG Yu-jia;ZHANG Yi;YANG Zhao-hui(School of Environmental Science and Engineering,Suzhou University of Science and Technology,Suzhou,Jiangsu 215009,China;School of Geographical Science and Geomatics Engineering,Suzhou University of Science and Technology,Suzhou,Jiangsu 215009,China)
出处
《环境监测管理与技术》
CSCD
2022年第2期20-26,共7页
The Administration and Technique of Environmental Monitoring
基金
国家自然科学基金资助项目(41701477)
江苏省自然科学基金资助项目(BK20170379)
江苏省大学生创新创业训练计划基金资助项目(201910332081Y)。
关键词
水质
兴趣点数据
综合指数
主成分分析
河道
苏州古城区
Water quality
Point of interest(POI)data
Comprehensive index
Principal component analysis(PCA)
Rivers
Suzhou ancient city zone