Articles | Volume 18, issue 6
Research article
13 Jun 2018
Research article |  | 13 Jun 2018

A statistical model to estimate the local vulnerability to severe weather

Tobias Pardowitz

Abstract. We present a spatial analysis of weather-related fire brigade operations in Berlin. By comparing operation occurrences to insured losses for a set of severe weather events we demonstrate the representativeness and usefulness of such data in the analysis of weather impacts on local scales. We investigate factors influencing the local rate of operation occurrence. While depending on multiple factors – which are often not available – we focus on publicly available quantities. These include topographic features, land use information based on satellite data and information on urban structure based on data from the OpenStreetMap project. After identifying suitable predictors such as housing coverage or local density of the road network we set up a statistical model to be able to predict the average occurrence frequency of local fire brigade operations. Such model can be used to determine potential hotspots for weather impacts even in areas or cities where no systematic records are available and can thus serve as a basis for a broad range of tools or applications in emergency management and planning.

Short summary
The paper presents a statistical analysis of socioeconomic factors influencing vulnerability and exposure to severe weather. By means of statistical modelling, the risks of weather impacts can be predicted at very high spatial resolutions. Such models can serve as a basis for a broad range of tools or applications in emergency management and planning and thus might help to enhance resilience to severe weather.
Final-revised paper