An approach for measuring social vulnerability in context: The case of flood hazards in Muzarabani district, Zimbabwe

Emmanuel Mavhura, Bernard Manyena, Andrew Collins

Research output: Contribution to journalArticlepeer-review

104 Citations (Scopus)
15 Downloads (Pure)


Understanding the complexity of vulnerability to disasters, including those triggered by floods, droughts and epidemics is at the heart of disaster risk reduction. Despite its importance in disaster risk reduction, there remains a paucity of approaches that contribute to our understanding of social vulnerability that is hidden in dynamic contextual conditions. The study demonstrates an accessible means to assessing the spatial variation of social vulnerability to flood hazards and related for the context of Muzarabani district in northeast Zimbabwe. The study facilitated local identification with residents of variables contributing to social vulnerability and used the principal component analysis (PCA) technique to develop a social vulnerability index (SoVI). Using ArcMap10.2 geographic information systems (GIS) tool, the study mapped composite SoVI at the ward level. The results showed that Muzarabani district is socially vulnerable to hazards. The social vulnerability is influenced by a variety of economic, social and institutional factors that vary across the wards. Quantifying and visualising social vulnerability in Muzarabani provides useful information for decision makers to support disaster preparedness and mitigation programmes. The approach shows how spatially distributed multivariate vulnerability, as grounded in interpretations at local level, can be quantitatively derived for contexts such as those of Muzarabani. The study findings can inform disaster risk reduction communities and cognate disciplines on quantitative assessments for managing hazard vulnerability where these have hitherto not been developed.
Original languageEnglish
Pages (from-to)103-117
Number of pages15
Early online date5 Oct 2017
Publication statusPublished - Nov 2017


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