Scale implications and evolution of a social vulnerability index in Atlanta, Georgia, USA

Joseph Karanja and Lawrence M. Kiage ()
Additional contact information
Joseph Karanja: Georgia State University
Lawrence M. Kiage: Georgia State University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 113, issue 1, No 33, 789-812

Abstract: Abstract The implications of hazards on populations are accentuated or alleviated by the nature of social systems, yet the multi-scalar variations of socioeconomic and demographic variables are partially understood across space and time. Targeted response strategies to a hazard rely upon accurate and complete data. However, social vulnerability studies could benefit from more robust explorations regarding critical data variables, geographic scale, data weighting mechanics, data transformations, broader timeframe, and visualization models. Our study addresses each of these topics for our study area of Atlanta, Georgia (USA) over a 20-year time frame. The study compares equal and variance-based weightings and their influences on local and global measures of spatial autocorrelation for both gridded and census tract scales. Our results establish the critical drivers of vulnerability as race, language, poverty, gender, living alone, and age. We found variance-based weighting to have more clustering and a higher magnitude of vulnerability than equal weighting. A uniform 30-m gridded scale revealed more data nuances than the traditional census tract scale. Besides, local and global measures of spatial autocorrelation returned inconsistent results, confirming variations in outputs attributable to scale choices. A 20-year historical view provides a context for assessing changes over time, crucial for understanding the evolution of critical drivers. Combining multiple Social Vunerability Index (SVI) derivation techniques altered the magnitude and intensity of the level of vulnerability, thereby justifying the need for further research.

Keywords: Principal component analysis; Moran’s I; Hazard; Social vulnerability; Weighting mechanics; Spatial autocorrelation (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s11069-022-05324-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:113:y:2022:i:1:d:10.1007_s11069-022-05324-9

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-022-05324-9

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().