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Modeling spatial distribution of population for environmental epidemiological studies: Comparing the exposure estimates using choropleth versus dasymetric mapping
Environment International ( IF 11.8 ) Pub Date : 2018-06-26 , DOI: 10.1016/j.envint.2018.06.021
Weeberb J. Requia , Petros Koutrakis , Altaf Arain

Precise population information is critical for identifying more accurate environmental exposures for air pollution impacts analysis. Basically, there are two methods for estimating spatial distribution of population, choropleth and dasymetric mapping. While the choropleth approach accounts for linear distribution of population over area based on census tract units, the dasymetric model accounts for a more heterogeneous population density by quantifying the association between the area-class map data categories and values of the statistical surface as encoded in the census dataset. Environmental epidemiological studies have indicated the dasymetric mapping as a more accurate approach to estimate and characterize population densities in large urban areas. However, investigations that have attempted to compare the exposure estimates from choropleth versus dasymetric mapping in environmental health analysis are still missing. This paper addresses this gap and compares the impact of using choropleth and dasymetric mapping in different exposure metrics. We compare the impact of using choropleth and dasymetric mapping in three case studies, defined here as case study A (relationship between urban structure types and health), case study B (PM2.5 emissions and human exposure), and case study C (distance-decays of mortality risk related to PM2.5 emitted by traffic along major highways). These case studies represent previous investigations performed by our research group where spatial distribution of population was an essential input for analysis. Our findings indicate that the method used to estimate spatial distribution of population impacts significantly the exposure estimates. We observed that the choropleth mapping overestimated exposure for the case study A and B, while for the case study C the exposure was underestimated by the choropleth approach. Our findings show that the dasymetric model is a preferred method for creating spatially-explicit information about population distribution for health exposure studies. The results presented here can be useful for the environmental health community to more accurately assess the relationship between environmental factors and health risks.



中文翻译:

为环境流行病学研究建模人口的空间分布:使用色谱法和幅射图法比较暴露估计值

准确的人口信息对于识别更准确的环境暴露以进行空气污染影响分析至关重要。基本上,有两种估算空间分布的方法人口,合唱团和等轴测图。虽然Choropleth方法基于普查单位来考虑人口在区域上的线性分布,但dasymetric模型通过量化区域类地图数据类别和统计表面中编码的统计表面值之间的关联来说明更不均匀的人口密度。人口普查数据集。环境流行病学研究表明,大地测量法制图是估计和表征大城市地区人口密度的一种更准确的方法。但是,在环境健康分析中,试图比较通过脉络线描图法和幅射图法测得的暴露估计的研究仍然缺失。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。本文解决了这一差距,并比较了在不同的曝光指标中使用色度和幅值映射的影响。我们在三个案例研究(在此定义为案例研究A(两者之间的关系)城市结构类型和健康),案例研究B(PM 2.5排放和人体暴露)和案例研究C(与PM 2.5相关的死亡风险的距离衰减)沿主要高速公路的交通所产生的排放)。这些案例研究代表了我们研究小组以前进行的调查,其中人口的空间分布是进行分析的重要输入。我们的发现表明,用于估算人口空间分布的方法会显着影响暴露估算。我们观察到,案例研究A和B的脉络线图高估了暴露量,而案例研究C的脉络膜法则低估了暴露量。我们的发现表明,dasymetric模型是用于创建有关健康暴露研究的人口分布的空间明晰信息的首选方法。这里介绍的结果对于环境健康界更准确地评估环境因素与健康风险之间的关系可能很有用。

更新日期:2018-07-12
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