Keywords

Local Social Change, Social Vulnerability, Eco-social Index, Data Mining, Dynamic Systems.

Location

Session H5: Systems Modeling and Climate Change: A Systematic Methodology for Disentangling Elements of Vulnerability, Adaptation and Adaptive Capacity

Start Date

16-6-2014 10:40 AM

End Date

16-6-2014 12:00 PM

Description

The objective of this research is to develop and to model an indicator of social vulnerability as part of a multidimensional, multivariable and non linear process. Social vulnerability can be considered as a complex system, in which many relationships in society and environment can be described by considering individual and structural factors. Numerous studies provide tools to analyze and revise social systems but these tools were developed generalizing and overviewing the relationship among many intermediate realities, for another type of systems, more formalized and conceptualized. Social systems do not have this classical formalization. The development of the SocIaL Vulnerability Index, SILVIO, is an interesting new approach to the research on environmental systems. SILVIO integrates particularly the social (SVI) and ecological (CCL) aspects of a spatially defined region, using as data basis not only statistical information, but as well the perspective and experience of its citizens in their local reality. In this paper, we used the concept of social vulnerability to develop the social part of an index, SVI, a local reality considering the man-environment relationship in a more structured approach. SILVIO displays in a comprehensive way the local situation generated by actors in the system. Furthermore, SilVIo represents the work with local communities and development of actions that include the citizens’ perspective, a very important item in the quest to find fields for action which give answers regarding the basic needs in the area of research. Finally, our scientific target is to develop a social vulnerability index for the local reality which uses complex systems tools, dynamics systems and data mining to represent the citizens’ local perception.

 
Jun 16th, 10:40 AM Jun 16th, 12:00 PM

SilVIo, Modelling Social Vulnerability under a Local Perspective

Session H5: Systems Modeling and Climate Change: A Systematic Methodology for Disentangling Elements of Vulnerability, Adaptation and Adaptive Capacity

The objective of this research is to develop and to model an indicator of social vulnerability as part of a multidimensional, multivariable and non linear process. Social vulnerability can be considered as a complex system, in which many relationships in society and environment can be described by considering individual and structural factors. Numerous studies provide tools to analyze and revise social systems but these tools were developed generalizing and overviewing the relationship among many intermediate realities, for another type of systems, more formalized and conceptualized. Social systems do not have this classical formalization. The development of the SocIaL Vulnerability Index, SILVIO, is an interesting new approach to the research on environmental systems. SILVIO integrates particularly the social (SVI) and ecological (CCL) aspects of a spatially defined region, using as data basis not only statistical information, but as well the perspective and experience of its citizens in their local reality. In this paper, we used the concept of social vulnerability to develop the social part of an index, SVI, a local reality considering the man-environment relationship in a more structured approach. SILVIO displays in a comprehensive way the local situation generated by actors in the system. Furthermore, SilVIo represents the work with local communities and development of actions that include the citizens’ perspective, a very important item in the quest to find fields for action which give answers regarding the basic needs in the area of research. Finally, our scientific target is to develop a social vulnerability index for the local reality which uses complex systems tools, dynamics systems and data mining to represent the citizens’ local perception.