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Mapping Human Vulnerability to Extreme Heat: A Critical Assessment of Heat Vulnerability Indices Created Using Principal Components Analysis.
Environmental Health Perspectives ( IF 10.4 ) Pub Date : 2020-9-2 , DOI: 10.1289/ehp4030
Kathryn C Conlon 1, 2 , Evan Mallen 3, 4 , Carina J Gronlund 1, 5 , Veronica J Berrocal 6 , Larissa Larsen 3 , Marie S O'Neill 1
Affiliation  

Abstract

Background:

Extreme heat poses current and future risks to human health. Heat vulnerability indices (HVIs), commonly developed using principal components analysis (PCA), are mapped to identify populations vulnerable to extreme heat. Few studies critically assess implications of analytic choices made when employing this methodology for fine-scale vulnerability mapping.

Objective:

We investigated sensitivity of HVIs created by applying PCA to input variables and whether training input variables on heat–health data produced HVIs with similar spatial vulnerability patterns for Detroit, Michigan, USA.

Methods:

We acquired 2010 Census tract and block group level data, land cover data, daily ambient apparent temperature, and all-cause mortality during May–September, 2000–2009. We used PCA to construct HVIs using: a) “unsupervised”—PCA applied to variables selected a priori as risk factors for heat-related health outcomes; b) “supervised”—PCA applied only to variables significantly correlated with proportion of all-cause mortality occurring on extreme heat days (i.e., days with 2-d mean apparent temperature above month-specific 95th percentiles).

Results:

Unsupervised and supervised HVIs yielded differing spatial vulnerability patterns, depending on selected land cover input variables. Supervised PCA explained 62% of variance in the input variables and was applied on half the variables used in the unsupervised method. Census tract–level supervised HVI values were positively associated with increased proportion of mortality occurring on extreme heat days; supervised PCA could not be applied to block group data. Unsupervised HVI values were not associated with extreme heat mortality for either tracts or block groups.

Discussion:

HVIs calculated using PCA are sensitive to input data and scale. Supervised HVIs may provide marginally more specific indicators of heat vulnerability than unsupervised HVIs. PCA-derived HVIs address correlation among vulnerability indicators, although the resulting output requires careful contextual interpretation beyond generating epidemiological research questions. Methods with reliably stable outputs should be leveraged for prioritizing heat interventions. https://doi.org/10.1289/EHP4030



中文翻译:

将人的脆弱性映射到极热:使用主成分分析创建的热脆弱性指标的重要评估。

摘要

背景:

极热会对人类健康构成当前和将来的风险。通常使用主成分分析(PCA)制定的热脆弱性指数(HVI),用于确定易受极端高温影响的人群。很少有研究能严格评估使用这种方法进行精细规模的漏洞映射时做出的分析选择的含义。

目的:

我们调查了通过将PCA应用于输入变量而创建的HVI的敏感性,以及在美国密歇根州底特律的热健康数据上训练输入变量是否产生了具有相似空间脆弱性模式的HVI。

方法:

我们获取了2010年人口普查区和地块组水平的数据,土地覆盖数据,每日环境表观温度以及2000-2009年5月至9月的全因死亡率。我们使用PCA通过以下方式构建HVI:a)“无监督” — PCA应用于先验选择变量,作为与热相关的健康结果的危险因素;b)“监督” — PCA仅适用于与极端高温天(即,平均表观温度的二维平均温度高于特定月份的第95个百分位数的天)发生的与全因死亡率的比例显着相关的变量。

结果:

无监督和有监督的HVI产生不同的空间脆弱性模式,具体取决于选定的土地覆盖物输入变量。有监督的PCA解释了输入变量中62%的方差,并将其应用于无监督方法中使用的一半变量。人口普查级别的监督HVI值与极端高温日死亡率的增加呈正相关。受监督的PCA无法应用于块组数据。区域或区域组的无监督HVI值均与极高的热死亡率无关。

讨论:

使用PCA计算的HVI对输入数据和规模敏感。受监督的HVI比未受监督的HVI可能会提供更具体的热脆弱性指标。来自PCA的HVI解决了脆弱性指标之间的相关性,尽管最终的结果需要仔细的上下文解释,而不是产生流行病学研究问题。应该利用具有可靠稳定输出的方法来优先进行热干预。https://doi.org/10.1289/EHP4030

更新日期:2020-09-02
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