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| Type: | Resource | |
| Storage: | The size of this resource is 1.2 MB | |
| Created: | Jan 31, 2023 at 2 a.m. | |
| Last updated: | Jun 22, 2023 at 3:49 p.m. (Metadata update) | |
| Published date: | Jun 22, 2023 at 3:49 p.m. | |
| DOI: | 10.4211/hs.45624efce89d42beb6db58adc83309ba | |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
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| Views: | 1134 |
| Downloads: | 47 |
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Abstract
Flooding is a natural hazard that touches nearly all facets of the globe and is expected to become more frequent and intensified due to climate and land-use change. However, flooding does not impact all individuals equally. Therefore, understanding how flooding impacts distribute across populations of different socioeconomic and demographic backgrounds is vital. One approach to reducing flood risk on people is using indicators, such as social vulnerability indices and flood exposure metrics, to inform decision-making for flood risk management. However, such indicators can face the scale and zonal effect produced by the Modifiable Areal Unit Problem (MAUP). This study investigates how the U.S. Census block group, tract, and county scale selection impacts social vulnerability and flood exposure outcomes within coastal Virginia, USA. Here we show how (1) scale selection can obstruct our understanding of drivers of vulnerability, (2) increasingly aggregated scales significantly undercount highly vulnerable populations, and (3) hotspot clusters of social vulnerability and flood exposure can identify variable priority areas for current and future flood risk reduction. Study results present considerations about using such indicators, given the real-life consequences that can occur due to the MAUP. The results of this work warrant understanding the implications of scale selection on research methodological approaches and what this means for practitioners and policymakers that utilize such information to help guide flood mitigation strategies.
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Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
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| National Science Foundation (NSF) | #1941657 |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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