Sara Hamideh is an Assistant Professor of Coastal Resilience at Stony Brook University in the School of Marine and Atmospheric Sciences. Her specializations are disaster recovery and community resilience. She conducts interdisciplinary research on housing recovery, dynamics of social vulnerability in recovery, and recovery policy to provide actionable knowledge for reducing disparities in recovery. Her teaching specializations include disaster resilience, environmental planning and policy, planning analytical methods, and urban sustainability. Hamideh is an Investigator on several federally funded research projects about community resilience, including the Center of Excellence for Risk-Based Community Resilience Planning at Colorado State University funded by National Institute of Standards and Technology.

Projects

Community Resilience-Focused Technical Investigation of the 2016 Lumberton, North Carolina Flood: An Interdisciplinary Approach

Goal: Lumberton field study consists of multiple waves of damage assessment, household dislocation, housing recovery, and business recovery since Hurricane Matthew in 2016 and more recently Hurricane Florence in 2018 which has enabled not only collecting panel datasets of recovery at household and business level, but also understanding the role of policy and resource allocations in building community resilience.

National Institute of Standards and Technology has funded Center of Excellence for Risk-Based Community Resilience Planning headquartered at Colorado State University. The NIST COE is a 12-university collaboration and seeks to develop the computation environment needed to enable quantification of community resiliency to natural hazards.

RAPID: Impacts, disruption, and displacement after low attention disasters: experiences of non-owner and immigrant households

Goal: This research examined: (1) what strategies and resources households use in the absence of substantial federal funds to address housing needs. (2) assistance for immigrant households and consequences of living in unrepaired homes and doubling up with extended family for their well-being and prospects for recovery. (3) whether and how non-homeowner households face different challenges in recovery such as accessing insurance and control over repair decisions.

RAPID – Data collection on Wildfire Urban Interface (WUI) for schools and hospitals following the 2018 California Camp Fire

Goal: We are studying the impacts of the 2018 camp fire on hospitals and schools in Paradise and surrounding areas. this interdisciplinary case study has a qualitative component during which we conduct visits and interviews with local hospitals and schools staff to understand their experiences, challenges, and perspectives about this fire disaster.

CoPe EAGER: Coastal Community Resilience Bonds to Enable Coupled Socio-Physical Recovery, National Science Foundation

Goal: The resilience of a community is a dynamic process resulting from complex interactions that are best studied at the nexus of social sciences, economics, engineering, and technology. In this EAGER, we propose to develop the concept of Coastal Community Resilience Bonds (CCRB) which enable equitable recovery of both physical and social services and institutions through staged and comprehensive planning and investment prior to disasters that result from chronic or acute stressors.

Damage, Dislocation and Displacement after Low Attention Disasters: Experiences of Renter and Immigrant Households

This study examines the impacts of low attention disasters with respect to damage, dislocation, and displacement on renter and immigrant households using Marshalltown, Iowa after an EF-3 Tornado as a case study. Housing damage assessment and household surveys were conducted for a representative sample of households and their housing units selected through a two-stage non-proportional cluster sampling strategy.

A Data-Driven Framework for Smart Decision-Making in Small and Shrinking Communities

This study has three specific aims. (1) Demonstrate the feasibility of applying the shrink-smart concept to rural communities. (2) Assess the feasibility of measuring smart shrinkage through data-driven analysis using deep learning techniques. (3) Test visualization methods for data analysis and communication to stakeholders.