空间插值视角下北京人口与空气质量指数的相关性研究
Examination of the Correlation between Beijing's Population and Air Quality Index through the Lens of Spatial Interpolation
任颐康 1刘芳1
作者信息
- 1. 北京建筑大学 测绘与城市空间信息学院,北京 100044;自然资源部城市空间信息重点实验室,北京 100044
- 折叠
摘要
随着全球城市化的加速,空气污染已经成为一个突出的公共健康问题,尤其在高度城市化的地区,如北京.鉴于北京的人口密度与社会经济层面存在的显著差异,传统的空气质量指数难以精确反映空气污染对居民的实际影响.通过对北京市的人口和空气质量指数(AQI)进行深入的空间插值分析,考察了普通克里金插值、简单克里金插值和反距离权重插值3 种方法.结果显示,普通克里金插值在核心统计指标上,如误差平均值和均方根误差,展现出了显著的优势.然而,在评估空间数据的相关性时,简单克里金插值对于北京市的人口与AQI数据显示了最强的正向关系.综合研究发现,虽然普通克里金插值在数据准确性和稳定性上具有优势,而简单克里金插值在秩次相关性评估上更为优越.
Abstract
As urbanization accelerates globally,air pollution has emerged as a prominent public health concern,particularly in highly urbanized areas such as Beijing.Given the significant disparities in population density and socio-economic factors in Beijing,conventional Air Quality Indexes(AQI)struggle to accurately reflect the actual impact of air pollution on its residents.This study delves into an in-depth spatial interpolation analysis of Beijing's population and AQI,examining three methods:Ordinary Kriging,Simple Kriging,and Inverse Distance Weighted(IDW)interpolation.The results reveal that Ordinary Kriging interpolation exhibits notable advantages in key statistical indicators,such as mean error and root mean square error.However,in assessing the correlation of spatial data,Simple Kriging interpolation demonstrates the strongest positive relationship between Beijing's population and AQI data.A comprehensive analysis indicates that while Ordinary Kriging interpolation excels in data accuracy and stability,Simple Kriging interpolation surpasses in the assessment of rank correlation.This nuanced examination contributes a refined understanding to the methodologies employed in air quality assessment,underscoring the complexity and variability inherent in urban environmental studies.
关键词
地统计学/空间插值/空气质量指数/人口密度Key words
geostatistics/spatial interpolation/atmospheric quality index/population density引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据