Abstract

Tropical cyclones are a highly destructive natural hazard that can cause extensive damage to assets and loss of life. This is especially true for the many coastal cities and communities that lie in their paths. Despite their significance globally, research on post-cyclone recovery rates has generally been qualitative and, crucially, has lacked spatial definition. Here, we used freely available satellite nighttime light data to model spatially the rate of post-cyclone recovery and selected several spatial covariates (socioeconomic, environmental and topographical factors) to explain the rate of recovery. We fitted three types of regression model to characterize the relationship between rate of recovery and the selected covariates; one global model (linear regression) and two local models (geographically weighted regression, GWR, and multiscale geographically weighted regression, MGWR). Despite the rate of recovery being a challenging variable to predict, the two local models explained 42% (GWR) and 51% (MGWR) of the variation, compared to the global linear model which explained only 13% of the variation. Importantly, the local models revealed which covariates were explanatory at which places; information that could be crucial to policy-makers and local decision-makers in relation to disaster preparedness and recovery planning.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.