Modelling social vulnerability in sub-Saharan West Africa using a geographical information system.
Modelling social vulnerability in sub-Saharan West Africa using a geographical information system.
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DOI：
10.4102/jamba.v7i1.155
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Arokoyu SB
中科院分区：
文献类型：
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作者：
Lawal O;Arokoyu SB
近年来，灾害和风险管理得到了极大的重视，特别是在发展中国家，人们对风险的认识不断提高，自然灾害和其他灾害的影响也越来越大。脆弱性，即灾害造成生命或财产损失的可能性，具有生物物理或社会层面。社会脆弱性与对灾害后果有负面影响的社会属性有关。这项研究试图开发一个空间上明确的社会脆弱性指数，从而解决撒哈拉以南非洲在这一领域缺乏研究的问题。确定了19个变量，涵盖了不同的方面。对这些变量的描述性分析表明，尼日利亚西南部地区的州和地方政府地区(LGA)具有高度的异质性。利用相关分析进行特征识别，确定了六个重要变量。因子分析从这一子集中确定了两个维度，即可获得性和社会经济条件。社会脆弱性指数(SOVI)显示，翁多州和埃基提州的LGA比该地区其他州更脆弱。在奥桑和奥贡，大约50%的LGA具有相对较低的社会脆弱性。苏维埃的分布表明，州内和地区之间都存在很大的差异。人口密度、残疾和贫困的分数相对于各州的平均分数有很高的误差。这项研究表明，有了地理信息系统，就有机会对整个非洲大陆的社会脆弱性进行建模和监测其演变和动态。
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.