United States Federal Emergency Management Agency regional clustering by disaster exposure: a new paradigm for disaster response

Colton Margus (), Attila Hertelendy (), Yudong Tao (), Erik Coltey (), Shu-Ching Chen (), Steven Luis (), Mei-Ling Shyu () and Gregory R. Ciottone ()
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Colton Margus: Beth Israel Deaconess Medical Center
Attila Hertelendy: Beth Israel Deaconess Medical Center
Yudong Tao: University of Miami
Erik Coltey: University of Missouri-Kansas City
Shu-Ching Chen: University of Missouri-Kansas City
Steven Luis: University of Missouri-Kansas City
Mei-Ling Shyu: University of Miami
Gregory R. Ciottone: Beth Israel Deaconess Medical Center

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2023, vol. 116, issue 3, No 26, 3427-3445

Abstract: Abstract The Federal Emergency Management Agency (FEMA) divides the United States (US) into ten standard regions to promote local partnerships and priorities. These divisions, while longstanding, do not adequately address known hazard risk as reflected in past federal disaster declarations. From FEMA’s inception in 1979 until 2020, the OpenFEMA dataset reports 4127 natural disaster incidents declared by 53 distinct state-level jurisdictions, listed by disaster location, type, and year. An unsupervised spectral clustering (SC) algorithm was applied to group these jurisdictions into regions based on affinity scores assigned to each pair of jurisdictions accounting for both geographic proximity and historical disaster exposures. Reassigning jurisdictions to ten regions using the proposed SC algorithm resulted in an adjusted Rand index (ARI) of 0.43 when compared with the existing FEMA regional structure, indicating little similarity between the current FEMA regions and the clustering results. Reassigning instead into six regions substantially improved cluster quality with a maximized silhouette score of 0.42, compared to a score of 0.34 for ten regions. In clustering US jurisdictions not only by geographic proximity but also by the myriad hazards faced in relation to one another, this study demonstrates a novel method for FEMA regional allocation and design that may ultimately improve FEMA disaster specialization and response.

Keywords: Federal Emergency Management Agency; Spectral clustering; Emergency management; Disaster exposure; Disaster management; Data analytics (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11069-023-05817-1

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