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Sara Hamideh

Stony Brook University

Breakout Room 4.


equity; longitudinal; disaggregate; interdisciplinary; community-driven

How can the human dimensions of disaster impacts be more accurately captured and represented in the analysis, modeling and simulation of disasters?

If we approach disaster research as convergent; mixed-methods; longitudinal; and community-driven; and apply disaggregate units of analysis; we can better capture human dimensions of disaster impacts. Human dimensions of disaster impacts and resilience should be addressed not just in social science disaster research but through interdisciplinary and convergent analyses; simulation; and modeling. The interdisciplinary or convergent approaches centered around human experiences; perceptions; decisions; and learning are key for making a difference in reducing human suffering and enhancing the resilience of our communities. Nevertheless; human dimensions of disasters are very complex; change and unfold over time; and often involve substantial inequalities rooted in long established socioeconomic patterns in societies. To better capture and represent human dimensions of resilience and disaster impacts; we need to both ask new research and modeling questions and apply a human-centered lens in the data collection and analyses. New questions would explore; for example; the compounding effects of historic development that shape the current unequal distribution of disaster risks with ongoing disaster risk reduction processes. As more communities face increasing disaster impacts and consider long-term solutions such as relocation; qualitative methods are key to examine the interdependent and often contentious dimensions of how communities perceive risks and viable options and decide about their long term future. Importantly; we must involve people who are affected by disasters and disaster research in the design and execution of our analyses or modeling in order to provide a realistic representation of the human dimensions of disasters in at-risk communities.

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?

Answering human centered disaster research questions calls for collecting data in new ways. First; interdisciplinary collaborations in data collection in the field; Second; longitudinal data collection that involve returning and tracking change and progress in affected communities for years after the initial impact; Third; disaggregate units in survey data collections as well as simulation and modeling such as household and neighborhood level; Fourth; capturing factors such as income; age; health conditions; employment; housing costs and affordability that are key to understanding human dimensions and distribution of impacts. Capturing human dimensions of disaster accurately and realistically present significant challenges in analyses; modeling; and simulation. For example; understanding the effectiveness and equity of assistance in disaster response and recovery is a prime case of representing human dimensions of disasters. To achieve this goal we need governmental and non-governmental assistance data. However; there are significant barriers against accessing this data unfortunately because sometimes they include personally identifiable information. Collecting longitudinal data on experiences; perceptions; decisions; and learning of people after disasters requires new research infrastructure and administrative rules in academic institutions as well as funding agencies that encourage; facilitate; and reward (for example in faculty reviews) interdisciplinary collaborations; long-term involvement in the field; data and instrument sharing; and community engagement in research. Examples include establishment of interdisciplinary graduate programs and centers that bring together faculty and graduate students from different departments; organizing multidisciplinary conferences centered on topics of human dimensions of disasters; expanding definitions of peer-reviewed publications to include reports of community driven research.

In what ways can US-Japan collaborations advance these questions in new and important ways?

My answer is focused on housing recovery. Capturing human dimensions of disaster accurately requires innovation in using mixed-method data and analysis to better examine the intersectional issues of distribution and equity in disaster impact and resilience. In the United States housing recovery is often viewed as a market-driven process and the role of government assistance is limited to supporting unmet needs of low-income households through a very slow and inadequate assistance process. This policy has led to many disparities and slow pace of recovery and subsequently human suffering in many communities. Conversely; in Japan the government plays a more significant role in funding and supporting housing recovery; both temporary and permanent. Given the significant human experiences; perceptions; and decisions that manifest in the process of housing recovery; analysis to compare influential factors and differences between the two countries in terms of housing recovery policy would be beneficial in uncovering potential lessons from each country and topics to be studied by researchers. Due to the widespread attention in some fields of study to inequalities in disaster impacts and recovery in the United States; especially since Hurricane Katrina; there are many studies that focus on these inequalities and look at disaggregated impacts of and recovery from disasters in the US. This aspect of human experiences of disasters has not been addressed as widely by Japanese scholars. Hence; collaboration between researchers from the two countries can help inspire new questions; data and analysis tools to apply to case studies in both US and Japan.

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