What question did this study set out to answer?

This research aims to understand how social vulnerability to flooding varies spatially among communities in Delta State, Nigeria.

May 1, 2026Open Access

Spatial Patterns of Social Vulnerability to Flooding in Delta State, Nigeria: A Geographically Weighted Regression Approach to Identifying Priority Intervention Zones

Key Points

This research aims to understand how social vulnerability to flooding varies spatially among communities in Delta State, Nigeria.
Mixed-methods design including household surveys (n=761), key informant interviews, and focus group discussions.
Applied Geographically Weighted Regression (GWR) for spatial analysis of vulnerability.
Principal Component Analysis to extract economic, demographic, and infrastructure dimensions of vulnerability.
Identified five priority communities that represent 38% of the population but 72% of the modeled flood risk.
GWR showed better explanatory power (R² = 0.457) than ordinary least squares regression (R² = 0.412).
Significant clustering of vulnerability along the Niger River, with extreme vulnerability observed in specific areas.

Abstract

Flooding in Nigeria's Niger Delta disproportionately affects communities based on their socio-economic characteristics, yet spatial patterns of social vulnerability remain poorly understood. This research investigates the spatial dimensions of social vulnerability to flooding across 18 communities in Delta State, employing Geographically Weighted Regression (GWR) to identify spatially varying relationships and priority intervention zones. A mixed-methods design integrated household surveys (n=761, 94.0% response rate), 36 key informant interviews, and 18 focus group discussions with geospatial analysis. Principal Component Analysis extracted three vulnerability dimensions—economic (34.2% variance), demographic (22.4%), and infrastructure (16.8%)—collectively explaining 73.4% of total variance. Social Vulnerability Index (SVI) rankings ranged from 43.2 (Okwe) to 82.1 (Abuator), with four communities forming an extreme vulnerability cluster. Spatial analysis revealed significant clustering along the Niger River corridor (Global Moran's I = 0.342, p < 0.001), with Local Indicators of Spatial Association (LISA) identifying six High-High hotspots. GWR analysis demonstrated that GWR (R² = 0.457) substantially outperforms ordinary least squares regression (R² = 0.412). Critically, poverty exhibited near-uniform exacerbating effects (low spatial variation, 100% significant) while infrastructure access showed significant spatial variation, with stronger protective effects where facilities remain accessible during floods. Integrated risk modelling identified five priority communities—Abuator, Patani, Koloware II, Odorubu, and Uzere—accounting for 38% of the study population but 72% of modeled risk. Qualitative findings documented severe livelihood impacts (average crop loss 54.2%), coping strategies with negative consequences (76% borrowing creating debt cycles), and institutional gaps (only 17% with early warning systems). This research advances vulnerability assessment methodology through spatially explicit analysis and provides actionable intelligence for targeted disaster risk reduction interventions in the Niger Delta.

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