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A review of multivariate social vulnerability methodologies: a case study of the River Parrett catchment, UK

机译:多元社会脆弱性方法论综述:英国帕雷特河流域的案例研究

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摘要

In the field of disaster risk reduction (DRR), there exists a proliferation of research into different ways to measure, represent, and ultimately quantify a population's differential social vulnerability to natural hazards. Empirical decisions such as the choice of source data, variable selection, and weighting methodology can lead to large differences in the classification and understanding of the "at risk" population. This study demonstrates how three different quantitative methodologies (based on Cutter et al., 2003; Rygel et al., 2006; Willis et al., 2010) applied to the same England and Wales 2011 census data variables in the geographical setting of the 2013/2014 floods of the River Parrett catchment, UK, lead to notable differences in vulnerability classification. Both the quantification of multivariate census data and resultant spatial patterns of vulnerability are shown to be highly sensitive to the weighting techniques employed in each method. The findings of such research highlight the complexity of quantifying social vulnerability to natural hazards as well as the large uncertainty around communicating such findings to stakeholders in flood risk management and DRR practitioners.
机译:在减少灾害风险(DRR)领域中,研究正在以不同的方式进行衡量,表示和最终量化人口对自然灾害的不同社会脆弱性的研究。诸如源数据选择,变量选择和加权方法之类的经验性决策可能会导致对“处于风险中”的人群的分类和理解存在很大差异。这项研究证明了三种不同的定量方法(基于Cutter等,2003; Rygel等,2006; Willis等,2010)如何将相同的英格兰和威尔士2011年人口普查数据变量应用于2013年的地理环境/ 2014年英国Parrett河流域的洪水导致脆弱性分类上的显着差异。多元人口普查数据的量化和由此产生的脆弱性空间模式都显示出对每种方法中使用的加权技术高度敏感。此类研究的结果突显了量化社会对自然灾害的脆弱性的复杂性,以及将这些发现传达给洪水风险管理的利益相关者和DRR实践者的巨大不确定性。

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