Spatially modeled high detail population and climate prospects for a European transect: an outlook to future patterns of vulnerability

Abstract

Population exposure mapping forms an important part and is usually considered the starting point for the assessment of the social dimension of vulnerability. Integration of social structure would then further differentiate situation-specific vulnerability patterns on a local scale. Census data available in inhomogeneous spatial reference units are still considered the standard information input for assessing potentially affected people, e.g. in case of an emergency. There is a strong demand on population data that are independent from administrative areas. Raster representations meet this demand but are not yet available for all European countries. In this paper we will present an approach of spatial disaggregation of population data for a European transect referring to current population statistics and anticipated future prospects. A recently published new data set providing the degree of soil sealing for the EU27+ countries is applied as basic proxy for population density in the spatial disaggregation model. In order to assess future patterns of climate change related vulnerability, results of a European regional climate model are considered for projecting the situation in the year 2030. Various variables such as 'heat wave frequency' and 'number of hot nights' will be accounted for in that regard featuring conditions regarded especially strenuous for elderly or physically weak persons. Integrated analysis of the population and climate prospects data enables identification of hot spots in the examined European transect, i.e. regions of high population density and particularly demanding projected climatic patterns. In the context of climate impact mitigation as well as for the assessment and management of future risks and emergencies, integrated and consistent spatial analyses on European scale are of utmost importance for decision making processes as well as for risk communication and future safety and security considerations.

Application and prospect of a high-resolution remote sensing and geo-information system in estimating earthquake casualties

“…N is a dependent variable, and changes with time and other factors (X) such as place and weather. This variable (N) is complex, and many studies have been conducted regarding the distribution and change in the number of people (Aubrecht et al, 2009(Aubrecht et al, , 2010a. v is the dependent variable of the function, and is affected by the features of the structure, such as the number of stairs, corridor width, stair width, pedestrians in the corridor, and the strength of the stairs.…”