An Internal Validation Assessment of Scale Across Social Vulnerability Index Model Structures
Authors: | |
---|---|
Owners: | Selena Ann Hinojos |
Type: | Resource |
Storage: | The size of this resource is 4.4 MB |
Created: | Jan 14, 2025 at 8:46 p.m. |
Last updated: | Jan 17, 2025 at 2:36 p.m. |
Published date: | Jan 17, 2025 at 2:36 p.m. |
DOI: | 10.4211/hs.63069c38b2184b9197da1d93ae9910ac |
Citation: | See how to cite this resource |
Sharing Status: | Published |
---|---|
Views: | 94 |
Downloads: | 4 |
+1 Votes: | Be the first one to this. |
Comments: | No comments (yet) |
Abstract
Proactive and equitable 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 tools 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 Center for Disease Control SVI (CDC SVI) and the University of South Carolina Hazards Vulnerability and Resilience Institute SVI (HVRI SoVI). 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 emphasize the critical role of scale selection in shaping index outcomes and underscore the need for critical evaluation in SVI creation to advance equitable hazard management.
Subject Keywords
Coverage
Spatial
![](https://b.tile.openstreetmap.org/11/568/825.png)
![](https://c.tile.openstreetmap.org/11/569/825.png)
![](https://c.tile.openstreetmap.org/11/568/826.png)
![](https://a.tile.openstreetmap.org/11/569/826.png)
![](https://a.tile.openstreetmap.org/11/567/825.png)
![](https://a.tile.openstreetmap.org/11/570/825.png)
![](https://b.tile.openstreetmap.org/11/567/826.png)
![](https://b.tile.openstreetmap.org/11/570/826.png)
![](https://c.tile.openstreetmap.org/11/566/825.png)
![](https://b.tile.openstreetmap.org/11/571/825.png)
![](https://a.tile.openstreetmap.org/11/566/826.png)
![](https://c.tile.openstreetmap.org/11/571/826.png)
Content
Credits
Funding Agencies
This resource was created using funding from the following sources:
Agency Name | Award Title | Award Number |
---|---|---|
U.S. National Science Foundation | #2244715 |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/![CC-BY](https://storage.googleapis.com/hydroshare-beta-static-media/static/img/cc-badges/CC-BY.04d377d96855.png)
Comments
There are currently no comments
New Comment