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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 6, issue 2
Nat. Hazards Earth Syst. Sci., 6, 167–178, 2006
https://doi.org/10.5194/nhess-6-167-2006
© Author(s) 2006. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

Special issue: Methods for risk assessment and mapping in Germany

Nat. Hazards Earth Syst. Sci., 6, 167–178, 2006
https://doi.org/10.5194/nhess-6-167-2006
© Author(s) 2006. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

  15 Mar 2006

15 Mar 2006

Regionalisation of asset values for risk analyses

A. H. Thieken1, M. Müller1,4, L. Kleist2,4, I. Seifert1,3, D. Borst3,4, and U. Werner3 A. H. Thieken et al.
  • 1GeoForschungsZentrum Potsdam (GFZ), Section Engineering Hydrology and Data Center, Telegrafenberg, 14 473 Potsdam, Germany
  • 2University of Karlsruhe (TH), Institute for Economic Policy Research, 76 128 Karlsruhe, Germany
  • 3University of Karlsruhe (TH), Institute for Finance, Banking and Insurance, 76 128 Karlsruhe, Germany
  • 4Center for Disaster Management and Risk Reduction Technology (CEDIM), 76 128 Karlsruhe, Germany

Abstract. In risk analysis there is a spatial mismatch of hazard data that are commonly modelled on an explicit raster level and exposure data that are often only available for aggregated units, e.g. communities. Dasymetric mapping techniques that use ancillary information to disaggregate data within a spatial unit help to bridge this gap. This paper presents dasymetric maps showing the population density and a unit value of residential assets for whole Germany. A dasymetric mapping approach, which uses land cover data (CORINE Land Cover) as ancillary variable, was adapted and applied to regionalize aggregated census data that are provided for all communities in Germany. The results were validated by two approaches. First, it was ascertained whether population data disaggregated at the community level can be used to estimate population in postcodes. Secondly, disaggregated population and asset data were used for a loss evaluation of two flood events that occurred in 1999 and 2002, respectively. It must be concluded that the algorithm tends to underestimate the population in urban areas and to overestimate population in other land cover classes. Nevertheless, flood loss evaluations demonstrate that the approach is capable of providing realistic estimates of the number of exposed people and assets. Thus, the maps are sufficient for applications in large-scale risk assessments such as the estimation of population and assets exposed to natural and man-made hazards.

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