Mario Marin Herrera

TL;DR: In this paper, the authors show how single and multi-hazard damage to energy, transport, industrial, and social critical infrastructures in Europe are likely to develop until the year 2100 under the influence of climate change.

TL;DR: In this paper, the authors presented a novel, complete and consistent dataset describing tourist density at high spatial resolution with monthly breakdown for the whole of the European Union, which is achieved thanks to the integration of data from conventional statistical sources with big data from emerging sources, namely two major online booking services containing the precise location and capacity of tourism accommodation establishments.

TL;DR: Southern and south-eastern European countries will likely be most affected, and economic losses could be highest for the industry, transport and energy sectors, according to projections of multiple climate risks to critical infrastructures.

TL;DR: This study processed and combined diverse LULC data to create a harmonised, ready-to-use map covering 41 countries and decomposed the class ‘Industrial and commercial units’ into ‘Production facilities’, ‘Commercial/service facilities�’ and ‘Public facilities” using machine learning to exploit a large database of points of interest.

TL;DR: This paper discusses the integration of both data sets in order to produce a single geo- database covering an extended time series spanning from 1950 to 2006, and demonstrates the usefulness of the newly integrated geo-database.