Comparing hierarchical and inductive methods reveals fundamental differences in social vulnerability rankings
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Updated: Jan 16, 2026

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Lotte Savelberg1, Ylenia Casali2,3, Marc van den Homberg1,4
1The Netherlands Red Cross' data and digital unit 510, The Hague, The Netherlands.
Abstract:
Social vulnerability assessments play a crucial role in guiding the allocation of budgets and resources for effective disaster preparedness and humanitarian response. Climate change, escalating conflicts, and the climate finance and humanitarian funding gap make social vulnerability assessments essential. Despite advances in data collection, availability, and analysis, there remains a lack of consensus regarding the most suitable method to assess social vulnerability. This study sheds light on the consequences of methodological choices on social vulnerability assessments by comparing two commonly used methods in space and over time: the inductive principal component approach and the hierarchical INFORM approach. Our analysis focuses on a case study of the 351 communes in Burkina Faso from 2015 to 2022, a period marked by conflicts and extreme weather events. By comparing the two methods, we find important differences in the rankings of the communes' social vulnerability. By investigating the spatial and temporal results, we offer insights into the potential consequences of using different methodological choices. Our findings underscore the need for contextualized approaches.
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