An Internal Validation Assessment of Scale Across Composite Index Model Structures

Planning for natural hazards is vital, as these events can cause mass destruction and severely impact livelihoods. To aid hazard preparedness decision-making, organizations can utilize composite indices like a Social Vulnerability Index (SVI), developed to identify vulnerable populations to ensure that those with inherent social inequities are considered in planning. However, SVI construction involves various approaches that introduce epistemic uncertainty, potentially affecting resulting decisions. While progress has been made in understanding how construction processes affect index results, the spatial elements of SVI models are underexplored, with conflicting views on the influence of scale selection. This study addresses this gap by evaluating how changes in the selection of scalar properties (areal units and geographic boundaries) and indicator selection impact SVI ranks for two indices, the Centers for Disease Control SVI and the Hazards Vulnerability and Resilience Institute SVI. We examine these changes across three model structures: hierarchical with z-score standardization, hierarchical with percentile ranking normalization, and inductive with z-score standardization, employing an uncertainty and sensitivity analysis. When altering scalar and indicator properties, we found the inductive model less robust than hierarchical models. We also observed indicator selection as the primary driver of variability in SVI ranks across all model structures. However, we found significant yet mixed effects of scale selection and interaction effects on variability in SVI ranks. Our findings highlight the role of scale selection in shaping index outcomes and underscore the need for critical evaluation in SVI creation to advance equitable hazard management.