A composite inherent resilience index for Zimbabwe

An adaptation of the disaster resilience of place model

Bibliographic Data

ID22028438
AuthorsEmmanuel Mavhura (0000-0003-3037-601X, Bindura University of Science Education, corresponding author), Tawanda Manyangadze (0000-0001-6140-5114, Bindura University of Science Education), Komal Aryal (0000-0001-9980-4516)
Year2021
Volume57
Pages102152
Publication date2021-04-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueInternational Journal of Disaster Risk Reduction (JOURNAL)
Journal identifiersISSN: 2212-4209
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.ijdrr.2021.102152
OpenAlexW3135137390
LanguageEN
Citations received24
References cited63

Building resilience to disasters has become a strategic goal of many risk reduction programs across the globe. This is because resilience ensures that communities develop capacities which prevent or minimise loses to disasters. In view of this, there is need to develop a baseline that tracks changes in resilience through time. This study responded to this gap in Zimbabwe by developing composite resilience indices (CRI) using 26 variables that reflected 5 subdomains of resilience: community capital, economic, infrastructure, social and health. The CRI were then used to map the spatial variation of resilience across 91 districts. The results show that the majority of the districts with below moderate resilience are mainly rural and marginalised, while the most resilient districts emerged in urban areas where service provision and infrastructure are better developed. These findings were further subjected to factor analyses which deconstructed the overall CRI and identified six latent factors behind resilience: infrastructure, health, household head, and income, access to maize and fortified food. These factors were mapped in a GIS environment to show their geographic variation in the country. Furthermore, Moran's Index and the Getis Ord Gi* statistical tests were applied to determine clusters of resilience across space. Results confirmed the spatial clustering of CRI. The results are therefore, useful in planning mitigation, response and preparedness measures across the country

Business · Community resilience · Composite index · Composite indicator · Disaster risk reduction · Economics · Environmental planning · Environmental resource management · Geography · Political science · Preparedness · Socioeconomics · Sociology · Climate Change, Adaptation, Migration · Computer Science · Disaster Management and Resilience · Flood Risk Assessment and Management