Temporary disaster debris management site identification using binomial cluster analysis and GIS

Bibliographic Data

ID3153677
AuthorsStanislaw Grzeda (Program Manager at the National Geospatial‐Intelligence Agency Springfield Virginia United States), Thomas A Mazzuchi (0000-0002-4584-4018, Professor of Engineering Management and Systems Engineering as well as Decision Sciences Chair in the Department of Engineering Management and Systems Engineering, School of Engineering and Applied Science George Washington University Washington DC United States), Shahram Sarkani (Professor of Engineering Management and Systems Engineering (EMSE) as well as Faculty Advisor and Academic Director of EMSE Off‐Campus Programs, Department of Engineering Management and Systems Engineering, School of Engineering and Applied Science George Washington University Washington DC United States)
Year2014
Volume38
Issue2
Pages398-419
Publication date2014-04-01
Open AccessYes
TypeARTICLE
VenueDisasters (JOURNAL)
Journal identifiersISSN: 0361-3666 • E-ISSN: 1467-7717
PublisherWiley (PUBLISHER • GB)
DOI10.1111/disa.12040
PMID24601923
OpenAlexW1976215799
LanguageEN
Citations received6
References cited25

An essential component of disaster planning and preparation is the identification and selection of temporary disaster debris management sites (DMS). However, since DMS identification is a complex process involving numerous variable constraints, many regional, county and municipal jurisdictions initiate this process during the post-disaster response and recovery phases, typically a period of severely stressed resources. Hence, a pre-disaster approach in identifying the most likely sites based on the number of locational constraints would significantly contribute to disaster debris management planning. As disasters vary in their nature, location and extent, an effective approach must facilitate scalability, flexibility and adaptability to variable local requirements, while also being generalisable to other regions and geographical extents. This study demonstrates the use of binomial cluster analysis in potential DMS identification in a case study conducted in Hamilton County, Indiana

Cluster (spacecraft · Debris · Forensic engineering · Geography · Identification (biology · Medical emergency · Meteorology · Poison control · Computer Science · Engineering · Environmental Science · Evacuation and Crowd Dynamics · Facility Location and Emergency Management · Flood Risk Assessment and Management · Medicine

Tools

Open DOISci-Hub