Spatial econometric modeling of socioeconomic vulnerability and flood impact

Towards a risk-layering approach in southern Malawi

Bibliographic Data

ID22025541
AuthorsCarolina Ferman Carral (Vrije Universiteit Amsterdam), Marija Bočkarjova (University of Twente), Marc Van Den Homberg (0000-0003-1436-254X, Red Cross Hospital, corresponding author), Frank Osei (0000-0003-2206-3662, University of Twente), Norman Kerle (0000-0002-4513-4681, University of Twente)
Year2025
Volume121
Pages105433
Publication date2025-04-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueInternational Journal of Disaster Risk Reduction (JOURNAL)
Journal identifiersISSN: 2212-4209
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.ijdrr.2025.105433
OpenAlexW4408922815
LanguageEN
Citations received1
References cited51

As climate-related disasters escalate, particularly in vulnerable communities in the Global South effective risk management strategies become necessary. The objective of this work was to examine the spatial dependencies between socioeconomic vulnerability and flood impacts in Southern Malawi, merging geospatial methods with econometric modeling. The analysis revealed significant spatial dependencies and spillover effects from data in the Unified Beneficiary Register, Malawi Census, and Rapid Damage Assessments from the 2020 and 2022 floods. Moran's I analysis emphasized the need to account for those spatial spillover effects. The spatial econometric framework, represented by a spatial lag variable in the Spatial Generalized Linear Model, captured these dependencies effectively. Expected associations emerged between flood impacts and socioeconomic indicators such as wealth, education, and household savings, suggesting that economically secure households are less vulnerable. However, unexpected correlations also appeared: food security was positively associated with flood impact, while disability occurrence showed a negative association. These findings challenge resilience assumptions and raise concerns about data accuracy and aftershock dynamics. Insights highlight the need for DRM strategies that incorporate exposure and socioeconomic indicators, along with improved data collection to ensure vulnerable groups are adequately represented. Despite challenges from data scarcity and spatial granularity, this study demonstrates the potential of Spatial Econometric Models to identify spatially interconnected vulnerabilities. The approach can be transferred to other contexts, but further research is needed to refine spatial resolutions and ensure actionable vulnerability characterizations. Integrating spatial dependencies into risk assessments highlights the need for spatially explicit policy interventions to strengthen resilience and advance risk-layering strategies

Archaeology · Cartography · Environmental planning · Flood myth · Geographic information system · Geography · Layering · Population · Socioeconomic status · Sociology · Water resource management · Agricultural risk and resilience · Climate change impacts on agriculture · Computer Science · Demography · Environmental Science · Flood Risk Assessment and Management

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