vulnerability, risk, natural hazards, population density, remote sensing
The focus of the study is on populations threatened by natural disasters. It aims to develop the theoretical base and applicatory methods to support information generation before and during crisis situations. The work emphasises the deployment of modern technology, namely GIS and remote sensing, for an improvement in timely delivery and spatial coverage of relevant data in crisis management.
The work reviews definitions of crucial terms in the realm of disaster management and elaborates on the concept of vulnerability. It proposes a clear allocation of the expressions susceptibility, coping capacity, recovery, resilience and vulnerability to specific phases of a crisis, taking into account the difference in temporal development of slow onset and sudden hazard impacts. The concept of social levels is introduced and a list of potential hazard dependent and hazard independent indicators is provided, paving the way for estimating vulnerability of populations to natural hazards at various spatial scales.
A methodology is proposed in order to identify hot spots worldwide regarding people at risk of natural hazards at sub-national scale. This method is implemented and the results are mapped for the specific case of earthquakes. In this context the development of a composite indicator for the assessment of peoples vulnerability at country level is suggested, which is based on theoretical findings elaborated earlier. The selection of relevant sub-indicators is carried out and supported by statistical analysis, namely the Factor Analysis.
Since population data is a crucial information layer within disaster management in general and for the estimation of populations risk in particular, a methodology for the estimation of population densities at sub-national level is introduced. This method is tested for a rural area of central Zimbabwe. It allows the spatial disaggregation of district census data by applying a surface model based on a number of data layers describing the infrastructural, topographical and land use characteristics of the area. It was found that this approach has the potential to improve the spatial resolution and accuracy of existing population data layers and that it can be transferred to other developing countries when respecting the requirement of some local field knowledge for the modelling process.
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