Suchergebnisse

AbstractThere are critical and preventable inequalities in disaster impacts and postdisaster recovery. To formulate solutions for minimizing or preventing these unequal impacts, there is a great need for interdisciplinary methodologies that use social factors to set project scopes and drive engineering analyses and designs. At present time, however, limited guidance exists on how to develop and execute interdisciplinary methodologies, especially related to the study of community disaster resilience. This article offers an approach for developing and assessing interdisciplinary research methodologies. The framework incorporates insights from social science into structural engineering for integrated research focused on community disaster resilience. The two examples offered in the article assess the interdisciplinarity of two loss estimation methodologies. The goal of this perspectives article is to facilitate future interdisciplinary community disaster resilience research given its potential for transformative outcomes in terms of encouraging decision making that is driven by the needs of those who are often overlooked in disaster mitigation and recovery policies.

AbstractHousing recovery is an unequal and complex process presumed to occur in four stages: emergency shelter, temporary shelter, temporary housing, and permanent housing. This work questions the four‐stage typology and examines how different types of shelter align with multiple housing recovery stages given different levels of social vulnerability. This article also presents a Markov chain model of the postdisaster housing recovery process that focuses on the experience of the household. The model predicts the sequence and timing of a household going through housing recovery, capturing households that end in either permanent housing or a fifth possible stage of failure. The probability of a household transitioning through the stages is computed using a transition probability matrix (TPM). The TPM is assembled using proposed transition probability models that vary with the social vulnerability of the household. Monte Carlo techniques are applied to demonstrate the range of sequences and timing that households experience going through the housing recovery process. A set of computational rules are established for sending a household to the fifth stage, representing a household languishing in unstable housing. This predictive model is exemplified on a virtual community, Centerville, where following a severe earthquake scenario, differences in housing recovery times exceed four years. The Centerville analysis results in nearly 5% of households languishing in unstable housing, thereby failing to reach housing recovery. These findings highlight the disparate trajectories experienced by households with different levels of social vulnerability. Recommendations are provided at the end for more equitable postdisaster recovery policies.