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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Climate change, combined with the rapid and often unplanned urbanisation trends, is associated with a rising trend in the frequency and severity of disasters triggered by natural hazards. Among the weather-related disasters, floods and storms (i.e. hurricanes) account for the costliest and deadliest in the last decades. The situation is of particular importance in Small Islands Developing States (SIDS) because their relative higher vulnerability to the impacts of climate change, due to their location, fragile economies, limited resources, and more vulnerable habitats. Therefore, SIDS must implement adaptation measures to face the impacts of climate change and those of the urbanisation growth; for which is necessary to have an appropriate Disaster Risk Assessment (DRA), which should include the hazard itself, the intrinsic socio-economic vulnerability of the system and the exposure of infrastructure and humans to the hazard. Traditional DRA approaches for disaster risk reduction (DRR) have focused mainly on the natural and technical roots of risk, this is the modelling of the hazard and implementation of physical and structural defences, for which the hazard component is the centre. Traditional DRA methods pay no or little attention to the other dimensions of disaster risk, and do not often investigate the spatial and temporal relationships between the hazard, the vulnerability and the exposure components. A better alternative when dealing with DRA is a holistic risk assessment, which looks at risk as a whole, looking into the components and seeking to understand the interactions, interrelatedness and interdependences between different processes and parts of the whole. @en

Small Island Developing States (SIDS) are vulnerable to sea-level rise and hydro-meteorological hazards. In addition to the efforts to reduce the hazards, a holistic strategy that also addresses the vulnerability and exposure of residents and their assets is essential to mitigate the impacts of such hazards. Evaluating the socioeconomic vulnerability of SIDS can serve the purpose of identification of the root drivers of risk. In this paper, we present a methodology to assess and map socioeconomic vulnerability at a neighbourhood scale using an index-based approach and principal component analysis (PCA). The index-based vulnerability assessment approach has a modular and hierarchical structure with three components: susceptibility, lack of coping capacities and lack of adaptation, which are further composed of factors and variables. To compute the index, we use census data in combination with data coming from a survey we performed in the aftermath of Irma. PCA is used to screen the variables, to identify the most important variables that drive vulnerability and to cluster neighbourhoods based on the common factors. The methods are applied to the case study of Sint Maarten in the context of the disaster caused by Hurricane Irma in 2017. Applying the combined analysis of index-based approach with PCA allows us to identify the critical neighbourhoods on the island and to identify the main variables or drivers of vulnerability. Results show that the lack of coping capacities is the most influential component of vulnerability in Sint Maarten. From this component, the "immediate action" and the "economic coverage" are the most critical factors. Such analysis also enables decision-makers to focus their (often limited) resources more efficiently and have a more significant impact concerning disaster risk reduction.

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