Dasymetric mapping of socio-economic exposure for flood risk assessment in Italy

Detailed spatial representation of socio-economic exposure and the related vulnerability to natural hazards has the potential to improve the quality and reliability of risk assessment outputs. We apply a spatially-weighted dasymetric approach based on multiple ancillary data to downscale important socio-economic variables and produce a grid dataset for Italy that contains multilayered information about physical exposure, population, GDP and social vulnerability. We test the performances of our dasymetric approach compared to other spatial interpolation methods. Next, we combine the grid dataset with flood hazard estimates to exemplify an application for the purpose of risk assessmen