Original Research

Modelling social vulnerability in sub-Saharan West Africa using a geographical information system

Olanrewaju Lawal, Samuel B. Arokoyu
Jàmbá: Journal of Disaster Risk Studies | Vol 7, No 1 | a155 | DOI: https://doi.org/10.4102/jamba.v7i1.155 | © 2015 Olanrewaju Lawal, Samuel B. Arokoyu | This work is licensed under CC Attribution 4.0
Submitted: 07 September 2014 | Published: 28 May 2015

About the author(s)

Olanrewaju Lawal, Department of Geography and Environmental Management, Centre for Disaster Risk Management and Development Studies, University of Port Harcourt, Nigeria
Samuel B. Arokoyu, Department of Geography and Environmental Management, Centre for Disaster Risk Management and Development Studies, University of Port Harcourt, Nigeria


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Abstract

In recent times, disasters and risk management have gained significant attention, especially with increasing awareness of the risks and increasing impact of natural and other hazards especially in the developing world. Vulnerability, the potential for loss of life or property from disaster, has biophysical or social dimensions. Social vulnerability relates to societal attributes which has negative impacts on disaster outcomes. This study sought to develop a spatially explicit index of social vulnerability, thus addressing the dearth of research in this area in sub-Saharan Africa. Nineteen variables were identified covering various aspects. Descriptive analysis of these variables revealed high heterogeneity across the South West region of Nigeria for both the state and the local government areas (LGAs). Feature identification using correlation analysis identified six important variables. Factor analysis identified two dimensions, namely accessibility and socioeconomic conditions, from this subset. A social vulnerability index (SoVI) showed that Ondo and Ekiti have more vulnerable LGAs than other states in the region. About 50% of the LGAs in Osun and Ogun have a relatively low social vulnerability. Distribution of the SoVI shows that there are great differences within states as well as across regions. Scores of population density, disability and poverty have a high margin of error in relation to mean state scores. The study showed that with a geographical information system there are opportunities to model social vulnerability and monitor its evolution and dynamics across the continent.

Keywords

Social Vulnerability; GIS; disaster risk management; South West Geopolitical Zone; spatial modelling; vulnerability assessment; Social vulnerability index

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