Geospatial modelling of post-cyclone Shaheen recovery using nighttime light data and MGWR

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.

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Identifiers

ORCID for Stephen Darby: orcid.org/0000-0001-8778-4394

ORCID for Julian Leyland: orcid.org/0000-0002-3419-9949

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