Constructing a social vulnerability index to earthquake hazards using a hybrid factor analysis and analytic network process (F’ANP) model

Iran is a seismic prone country and has been host to a long series of devastating earthquakes which have resulted in heavy casualties and damages. In order to assess social vulnerability (SV) to earthquake hazards, this paper presents the development of a hybrid factor analysis and analytic network process model for aggregating vulnerability indicators into a composite index of SV to earthquake hazards. The proposed model is then applied in Iran as a case study. The proposed model uses factor analysis (FA) to extract the underlying dimensions of SV. The identified dimensions of SV and their primary variables are then entered into a network model in Analytic Network Process (ANP). The ANP is used to calculate the relative importance of different SV variables, taking into consideration the results obtained from FA and the possible interdependence between variables of the individual dimensions of SV. These weights are then used to compute the factor scores for the individual dimensions of SV and also the composite social vulnerability index (SOVI). The application of the proposed model to a real world case study and its validation show that it is a robust approach for constructing a composite SOVI. Its application to counties in Iran indicates that there exist severe regional differences in terms of SV to earthquake hazards. The pronounced regional variations in SV warrant special attention by both local authorities and the national government to reconsider current natural disaster management strategies.