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Spatial quantification of community resilience in contexts where quantitative data are scarce: The case of Muzarabani district in Zimbabwe

机译:在缺乏定量数据的情况下,社区复原力的空间量化:以津巴布韦的穆扎拉巴尼区为例

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There has been an upsurge in tools for measuring resilience of the past decade. Despite this progress, we argue, there are few studies focusing on the spatial quantification of resilience in the context of multiple hazards, particularly in developing countries. Placing a particular emphasis on the contribution of geography to resilience studies, this paper examines the spatial variation of community resilience to disasters in Muzarabani, Zimbabwe. Place‐specific resilience variables are selected from the 2012 national census report to develop a disaster resilience index for Muzarabani district. A principal component analysis technique was used to analyse the overall and subcomponents of resilience to identify wards that needed policy intervention. Using the Geographical Information Systems tool to model the spatial variation of community resilience and its subcomponents, we found a geographic variation in community resilience across Muzarabani district, with the majority of the wards scoring low to below low levels of overall resilience. Although we view this study as being complementary to qualitative studies, it would appear quantifying and visualising resilience provide possible explanations and actions required for decision‐makers to address the resilience gaps and disaster risk reduction broadly.
机译:在过去的十年中,用于测量弹性的工具迅速增加。我们认为,尽管取得了这一进展,但很少有研究集中在多重危害情况下的弹性空间量化上,特别是在发展中国家。本文特别强调了地理对复原力研究的贡献,研究了津巴布韦穆扎拉巴尼(Muzarabani)社区复原力对灾害的空间变化。从2012年国家人口普查报告中选择特定地点的复原力变量,以开发Muzarabani区的灾难复原力指数。主要成分分析技术用于分析弹性的整体和子成分,以确定需要政策干预的病房。使用地理信息系统工具来建模社区复原力及其子组件的空间变化,我们发现整个穆扎拉巴尼地区的社区复原力都存在地理差异,大多数病区的整体复原力都低至低水平。尽管我们认为本研究是对定性研究的补充,但似乎可以量化和可视化复原力,为决策者广泛解决复原力差距和减少灾害风险提供了可能的解释和行动。

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