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Community vulnerability to hazards: introducing local expert knowledge into the equation

机译:危险的社区脆弱性:将本地专家知识引入等式

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摘要

Assessments of social vulnerability have gained importance over the years, evolving from their initial emphasis on environmental factors surrounding natural disasters to a conceptual framework in which human agency plays a more decisive role. Up to know, most approaches to vulnerability were developed using an equally weighted approach in which each component contributes the same to vulnerability. To improve and enrich the information needed by authorities and stakeholders, we believe that a participatory approach would enhance our current understanding of vulnerability. Therefore, as an alternative to equally weighted approaches we propose and test the introduction of an expert panel to provide deeper insights into the relative contribution of vulnerability drivers. Our methodology has been applied to Aragn (Spain) at a municipality scale. The core of the analysis is a principal component analysis (PCA) applied to a set of socioeconomic and demographic variables. PCA allows extracting the main drivers of vulnerability in the region. Then, we introduce the role of a local expert panel by means of an analytical hierarchical process. Results are mapped and analyzed to (1) outline the spatial distribution of Community Vulnerability Index (CoVI), (2) determine the extent and location of vulnerable areas and (3) identify their main drivers. Overall, the introduction of the panel improves the ability of the method to differentiate strong (low CoVI) and weak (high CoVI) positions, compared to the original equally weighted approach.
机译:多年来,对社会脆弱性的评估产生了重要性,从他们最初的重视对自然灾害周围的环境因素到人类代理发挥更具决定性作用的概念框架。众所周知,使用同样加权方法开发了大多数漏洞的方法,其中每个组件对漏洞产生相同的方法。为了改善和丰富当局和利益攸关方所需的信息,我们认为参与式方法将提升我们目前对脆弱性的理解。因此,作为同样加权方法的替代方案,我们提出并测试了专家面板的引入,以便对漏洞驱动因素的相对贡献提供更深入的见解。我们的方法已经应用于市政量表的Aragn(西班牙)。分析的核心是应用于一组社会经济和人口变量的主要成分分析(PCA)。 PCA允许提取该区域漏洞的主要驱动程序。然后,我们通过分析分层过程介绍当地专家面板的作用。结果映射并分析到(1)概述社区漏洞指数(CoVI)的空间分布,(2)确定弱势区域的范围和位置,(3)识别其主要司机。总体而言,与原始同样加权的方法相比,面板的引入改善了方法来区分强(低COVI)和弱(高COVI)位置的能力。

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