Understanding evacuation behavioral patterns using anonymized device data and high resolution parcel level data: application to hurricanes Irma and Matthew

Sudipta Dey Tirtha (), Dewan Ashraful Parvez (), Jason Lemp (), Pragun Vinayak (), Krishnan Viswanathan (), Michalis Xyntarakis (), Andrew Sussman (), Elizabeth Payne (), Roberto Miquel () and Naveen Eluru ()
Additional contact information
Sudipta Dey Tirtha: Whitman, Requardt & Associates, LLP
Dewan Ashraful Parvez: City of Charlotte
Jason Lemp: Cambridge Systematics, Inc
Pragun Vinayak: LOCUS, Inc.
Krishnan Viswanathan: Whitman, Requardt & Associates, LLP
Michalis Xyntarakis: Cambridge Systematics, Inc
Andrew Sussman: Florida Division of Emergency Management
Elizabeth Payne: Northeast Florida Regional Council
Roberto Miquel: Whitman, Requardt & Associates, LLP
Naveen Eluru: University of Central Florida

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 14, No 27, 16857-16880

Abstract: Abstract Hurricanes can have devastating impacts on coastal populations in the US and across the world. To mitigate the potential impacts of hurricanes, emergency personnel need to prepare detailed evacuation plans to ensure the population has adequate time to evacuate in the event of a hurricane. The traditional approach to collecting evacuation behavior information involved conducting surveys, which are time-consuming, costly, and increasingly suffer from lowering response rates. In this study, we document a novel approach that relies on mobile location data and high-resolution parcel data to analyze evacuation decision processes such as decision to evacuate (Yes/No), and evacuation destination type (family/friends, hotel/motel, public shelter and out-of-state). The mobile location data were used to analyze the evacuation travel patterns across two major hurricanes in Florida in 2016 and 2017. The location data was augmented with high-resolution land use data sourced from Florida Department of Revenue to determine the land-use patterns to understand the home and destination characteristics of the devices. Using processed device data, we estimate evacuation participation rate and evacuation destination type (or refuge) rates by multiple variables including evacuation zone type, residential land use type, median income, and rural/urban location. Further, we employ a linear regression model system to model participation rate (per thousand) and a multinomial logit fractional split model to understand evacuation destination choice behavior. The results from the estimated models indicate that evacuation zone type, hurricane characteristics, zonal level demographic characteristics, and land use characteristics are key determinants of county-zone level participation rate and refuge rate.

Keywords: Hurricane evacuation; Device data; Land use; Participation rate; Refuge rate; Linear regression (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11069-025-07453-3 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:121:y:2025:i:14:d:10.1007_s11069-025-07453-3

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

DOI: 10.1007/s11069-025-07453-3

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 ().