Use of GIS and dasymetric mapping for estimating tsunami-affected population to facilitate humanitarian relief logistics: a case study from Phuket, Thailand

Kiatkulchai Jitt-Aer (), Graham Wall (), Dylan Jones () and Richard Teeuw ()
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Kiatkulchai Jitt-Aer: Navaminda Kasatriyadhiraj Royal Air Force Academy
Graham Wall: University of Portsmouth
Dylan Jones: University of Portsmouth
Richard Teeuw: University of Portsmouth

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 113, issue 1, No 8, 185-211

Abstract: Abstract The 2004 Indian Ocean tsunami led to improvements in Thailand’s early warning systems and evacuation procedures. However, there was no consideration of better aid delivery, which critically depends on estimates of the affected population. With the widespread use of geographical information systems (GIS), there has been renewed interest in spatial population estimation. This study has developed an application to determine the number of disaster-impacted people in a given district, by integrating GIS and population estimation algorithms, to facilitate humanitarian relief logistics. A multi-stage spatial interpolation is used for estimating the affected populations using ArcGIS software. We present a dasymetric mapping approach using a population-weighted technique coupled with remote sensing data. The results in each target area show the coordinates of each shelter location for evacuees, with the minimum and maximum numbers of people affected by the tsunami inundation. This innovative tool produces not only numerical solutions for decision makers, but also a variety of maps that improve visualisation of disaster severity across neighbourhoods. A case study in Patong, a town of Phuket, illustrates the application of this GIS-based approach. The outcomes can be used as key decision-making factors in planning and managing humanitarian relief logistics in the preparedness and response phases to improve performance with future tsunami occurrences, or with other types of flood disaster.

Keywords: Tsunami inundation; Geographical information system (GIS); Population estimation; Areal interpolation; Dasymetric mapping; Humanitarian logistics (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11069-022-05295-x

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