Resilience is a framework widely used to help address questions in disaster risk reduction (DRR) research, policy and practice. There is increasing demand for understanding this at the community scale. Building on case studies, this paper links inter- and trans-disciplinary methodologies to map stakeholders’ knowledge and combine this with the best available data for disaster resilience planning, involving, for example, understanding baseline resilience and what indicators or might be used for “measuring” community resilience? Some components are particularly critical, such as the functioning of social networks in contexts of disaster planning and response, and require specially designed methods.
Social network analysis has recently become used more widely in resilience research and can be compared with another complementary method, involving qualitative, participatory social network mapping allowing inclusion of stakeholders in understanding their own disaster resilience planning and response.
The authors share insights from this work: discussing drivers, barriers, governance and adaptation using networks. They also discuss how this data can be used for development of scenarios and agent-based models (ABMs) resulting in effect in participatory ABMs (P-ABM). These models can then underpin assessments of future scenarios, providing better understanding of the potential effects of natural hazards in the context of changes in resilience or vulnerability.
The paper discusses recent experience using these participatory methodologies, explores stakeholders’ “competing” ideas of vulnerability and resilience, and shows how the modelling process allow those same stakeholders to “play” with the idea of community resilience and what it would mean to them and their communities.
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