The social vulnerability from the point of view of social and environmental focuses on identifying groups that are disadvantaged or vulnerable and the terms and conditions of the environments in which it lives, trying to determine the factors that explain the difficulty to cope with situations of social disadvantage.
Due to its complexity, linked to the multi-dimensionality, it is not always easy to identify the social strata and urban areas affected. The following models are combined with Geographic Information Systems make it viable prediction of Social Vulnerability.
It will be evaluated the connection between certain dimensions of social vulnerability and its residential context, building a System of Decision Support useful for the planning of social and urban performing a holistic approach to the available census and demographic data building a knowledge-based model of Artificial Neural Networks of the Map Self-organized map type that identifies and characterizes the demographic residential variables through conditional inference trees.