In this thesis the design and generation of indicators of vulnerability to extreme natural phenomena is proposed. To this end, a series of publications have been carried out that develop these indicators based on the use of two types of techniques, such as the standardization of variables and the reduction of variable dimensions [Principal Components Analysis (PCA)]. In turn, these techniques have been implemented in two geographical areas with very different levels of development, namely Purba Medinipur district (West Bengal), in India, and the province of Malaga (Andalusia), in Spain. The units of analysis in each case are equivalent from the point of view of their administrative level, being blocks, in the case of Purba Medinipur, and municipalities, in the case of Malaga.
The thesis presents the main groups of contents shared by the developed works. Basically, these contents include:
Firstly, an introduction which contextualizes the issue of vulnerability within the broader framework of studies on natural hazards and disasters.
Secondly, the objectives of the work are specified.
Thirdly, a chapter on methodology explains the strategies that have been followed in the different studies for the application of the aforementioned techniques (standardization and PCA).
Fourthly, a chapter with two fundamental parts is presented. On the one hand, the main results derived from the published works are presented. On the other hand, the opportunity to compile all these works and their results gives rise to an extensive discussion section in which the application strategies of the techniques used and the results obtained are critically analyzed, leading to a series of alternative proposals that attempt to improve the results obtained in the first instance.
Fifthly, a series of general conclusions and brief proposals for future work in this line are presented.
The final part of the thesis contains the original publications that make up the compendium presented, as well as a selection of other activities and productions derived from the doctoral training period.
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