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Indices of social vulnerability to hazards: Model uncertainty and sensitivity.

机译:社会对危害的脆弱性指标:模型的不确定性和敏感性。

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摘要

Social vulnerability indices have emerged over the past decade as an approach to quantitatively measure the social dimensions of natural hazards vulnerability. Validation of the metrics with external reference data has posed a persistent challenge in large part because social vulnerability is not directly observable. This research applies global uncertainty and sensitivity analyses to internally validate the methods used in the most common social vulnerability index designs. Global uncertainty analysis is performed to assess the robustness of index ranks when reasonable alternative index configurations are modeled. Global sensitivity analysis is employed to evaluate which index construction decisions have the greatest influence on the output rank variability. The research is guided by the following questions: (1) What is the uncertainty associated with social vulnerability index ranks? (2) What is the spatial relationship between social vulnerability and uncertainty? (3) Which modeling decisions contribute the most to variability in index ranks? (4) Do the uncertainty and sensitivity analyses findings vary by study area location?;All of the evaluated social vulnerability indices were imbued with a high level of epistemic uncertainty, which results from subjective decisions made during each phase of index construction. This uncertainty was represented cartographically on output social vulnerability maps. Spatial autocorrelation between social vulnerability and epistemic uncertainty was found to be low. The most common index designs were replicated, with hierarchical models found to be the most robust, and inductive models the least.;The variation in deductive vulnerability index ranks is most sensitive to the choice of transformation method, hierarchical models to the selection of weighting scheme, and inductive indices to the indicator set and scale of analysis. The findings demonstrate why global uncertainty and sensitivity analyses should be a standard element in social vulnerability index development. Specific recommendations for each stage of index construction are provided so that the next generation of social vulnerability indices can be developed with a greater degree of robustness and transparency.
机译:在过去的十年中,社会脆弱性指数作为一种定量测量自然灾害脆弱性的社会维度的方法而出现。使用外部参考数据对指标进行验证一直是一项持续的挑战,这在很大程度上是因为无法直接观察到社会脆弱性。这项研究运用全球不确定性和敏感性分析来内部验证最常见的社会脆弱性指数设计中使用的方法。当对合理的替代指标配置进行建模时,将执行全局不确定性分析以评估指标等级的稳健性。全局敏感性分析用于评估哪些指标构建决策对输出等级变异性有最大影响。该研究受到以下问题的指导:(1)与社会脆弱性指数等级相关的不确定性是什么? (2)社会脆弱性与不确定性之间的空间关系是什么? (3)哪些建模决策对索引等级的变化贡献最大? (4)不确定性和敏感性分析结果是否随研究区域的位置而变化?;所有评估的社会脆弱性指标都充满了较高的认知不确定性,这是在指标构建的每个阶段做出的主观决定所致。这种不确定性在输出的社会脆弱性地图上用制图法表示。发现社会脆弱性与认知不确定性之间的空间自相关性较低。复制了最常见的索引设计,发现层次模型最健壮,归纳模型最少;演绎脆弱性指标等级的变化对转换方法的选择最敏感,而层次模型对加权方案的选择最敏感,以及指标集和分析规模的归纳指标。研究结果表明,为什么全球不确定性和敏感性分析应成为社会脆弱性指数发展的标准要素。为索引构建的每个阶段提供了具体建议,以便可以更高程度地增强健壮性和透明度来开发下一代社会脆弱性索引。

著录项

  • 作者

    Tate, Eric C.;

  • 作者单位

    University of South Carolina.;

  • 授予单位 University of South Carolina.;
  • 学科 Geography.;Environmental Sciences.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 400 p.
  • 总页数 400
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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