4.6 Article

Emergency Evacuation Behavior in Small Island Developing States: Hurricane Irma in Sint Maarten

Journal

WATER
Volume 15, Issue 11, Pages -

Publisher

MDPI
DOI: 10.3390/w15112117

Keywords

evacuation; predictors of evacuation; hurricanes Irma; binomial logistic regression; risk management

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Disasters caused by natural hazards are occurring more frequently and with greater intensity, resulting in infrastructure damage and loss of life. Evacuating hazardous areas is a way to reduce disaster risk, but there is still disagreement on which variables can predict individual or household decision to evacuate.
Disasters triggered by natural hazards are becoming more frequent and more intense, causing damage to infrastructure and causing loss of life. One way to reduce disaster risk is by evacuating the hazardous area. However, despite the amount of literature that exists on evacuation behavior, there is still a lack of agreement on which variables can be used as predictors for individuals (or households) to actually evacuate. This lack of agreement can be related to the many variables that can affect the evacuation decision, from demographics, geographic, the hazard itself, and also local or cultural differences that may influence evacuation. Hence, it is essential to analyze and understand these variables based on the specifics of a case study. This study aims to find the most significant variables to be used as predictors of evacuation on the island of Sint Maarten, using data collected after the disaster caused by Hurricane Irma in September 2017. The results suggest that the variables gender, homeownership, percentage of property damage, quality of information, number of storeys of the house, and the vulnerability index are the most significant variables influencing evacuation decisions on the island. We believe the results of this paper offer a clear view to risk managers on the island as to which variables are most important in order to increase evacuation rates on Sint Maarten and to plan more efficiently for future evacuations. In addition, the variables found in this study have the potential to be the base information to set up, validate, and calibrate evacuation models.

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