Unveiling and modelling the flood risk and multidimensional poverty determinants using geospatial multi-criteria approach: Evidence from Jigawa, Nigeria - Gambo, Jibrin; Roslan, Siti Nur Aliaa; Zulhaidi Mohd Shafri, Helmi; Che Ya, Nik Norasma; Ahmed Yusuf, Yusuf; Ang, Yuhao (Elsevier, 2024)
Floods are recurring natural disasters that pose major challenges to communities worldwide, especially in regions where poverty is widespread. This research focuses on analysing the relationship between flood risk and poverty factors in Jigawa. It uses geospatial data, historical flood records and remote sensing to locate flood-prone areas. The integration of these data sets creates a comprehensive model for measuring flood-prone zones and their impact on multidimensional poverty. The flood risk zone included 3% and 13% as extremely high and high. In addition, 40% of the population is particularly vulnerable to flood disasters, leading to multidimensional poverty in the region. At the same time, poverty determinants such as agricultural activities, income level, education, access to health care, housing conditions, and road networks were analysed using statistical methods of principal component analysis (PCA). Findings from this research will go a long way toward understanding the complex interactions between natural disasters (flood) and poverty and will shed light on the mechanisms that perpetuate vulnerability in impoverished regions. By identifying the specific determinants of poverty that exacerbate the impact of flooding, this study provides evidence-based insights into possible avenues for targeted interventions and risk-reduction strategies. In addition, this study underscores the importance of using geospatial technology for disaster risk management and poverty alleviation. These findings will help policymakers and local authorities implement more efficient and adaptive disaster preparedness and response policies, thereby building the resilience of communities facing the intersecting challenges of poverty and flood risk.