How can the human dimensions of disaster impacts be more accurately captured and represented in the analysis, modeling and simulation of disasters?
Many of the existing research on preventive measures for the built environment have a primary focus on strengthening the resilience towards singular events. Interventions informed by such measures do not adequately account for sources of resilience that emanate from non-engineering disciplines. Existing frameworks that most engineering researchers studying sustainability in low income housing frequently use; do not adequately account for sources of resilience that emanate from non-engineering disciplines. Because of the disproportionate focus on the physical aspects of resilience; the outcomes of these efforts overlook the significance of diversity of needs across different communities. We need to address these concerns through systematically and holistically assessing sources and determinants of resilience in low income housing through mixed methods approaches that allow us to learn from the lived experiences of both the frontline communities and humanitarian sector organizations. Many of the existing research on preventive measures for the built environment have a primary focus on strengthening the resilience towards singular events. This perpetuates silo thinking; which is dangerous in both public health and natural disaster resilience strategies; yet still exists in disaster risk reduction efforts. We have learned from the current pandemic that addressing the existing complex challenges requires an enhanced understanding of how we can cope with interconnected; compounding risks and consecutive risks.
What type of data and supporting research infrastructure would be necessary to enable novel, transdisciplinary approaches to answering these and other human-centered disaster questions?
The following challenges need to be solved to enhance/reduce disaster risks and enhance resilience. First; difficulty in obtaining accurate localized predictions for use in preparation and mitigation. Secondly; the inability to obtain accurate localized predictions; which makes decision making uncertain and misinformed. Thirdly; the inability to more elaborately link populations; climate change; disaster risk; policy actions and interventions with impacts on disaster risk reduction and resilience mean that the impacts of the actions of policy makers cannot be quantified. We propose to use the combination of big data and machine learning; specifically deep learning; to produce models that perform localized predictions. Socio-economic modeling could link climate change; disaster risk; policy actions and impacts on disaster resilience. Socio-economic models should be integrated into visual analytics tools as part of a decision support system that can support policymaking. The idea is to generate actionable insights that can encourage decision makers to consider various policy choices while simultaneously evaluating the associated impacts to disaster risk reduction and increased resilience. The application of social-econometric strategies to design; appraise and monitor the implementation of the structures proposed especially for the economic and social impacts on both social-cultural as well as social-economic contexts is highly desirable. Engagements with multi-disciplinary teams can provide the various data sets required as well as coordinating with them to provide forecasting and projection possibilities via econometric modeling. Doing this successfully will require a comprehensive analysis of various cost-related variables.
In what ways can US-Japan collaborations advance these questions in new and important ways?
Systemic change toward resilient science and technological pathways in a warming climate is globally challenging and necessitates understanding the actors and institutions involved in the design and development of built assets (including housing); disaster risk reduction strategies; and the theories of change behind these. Identifying what works and what does not work in these systems through a US-Japan comparative case studies will allow the researchers to fast track the speed at which we can abstract learnings from lived experiences with individual disasters to distill and identify larger; more macro-scale processes that hamper uptake of viable interventions that can be applied and democratically governed at scale for intersecting and compounding disasters. The proposed bilateral collaboration will help the scientific community explore the following question more rigorously: (1) For each context; what is the “theory of change” for disaster risk reduction for intersecting and/ or compounding disasters? Where do these goals come from? How do they map onto existing definitions of disaster resilience and human resilience? (2) What are the governance structures in each context? That is; what public; private; and NGO actors and institutions; at different scales; influence the (lack of) adoption of possible solutions? (3) What are the social conditions in which efforts to enhance disaster resilience and human resilience unfold in each case study? How do social identities such as ethnicity; class; gender; age; and/or disability intersect with local understandings of disaster risk reduction; as well as who is made responsible for implementing them; and who benefits from them?