Literature DB >> 30239024

Workforce/Population, Economy, Infrastructure, Geography, Hierarchy, and Time (WEIGHT): Reflections on the Plural Dimensions of Disaster Resilience.

Joost Santos1, Christian Yip1, Shital Thekdi2, Sheree Pagsuyoin3.   

Abstract

The concept of resilience and its relevance to disaster risk management has increasingly gained attention in recent years. It is common for risk and resilience studies to model system recovery by analyzing a single or aggregated measure of performance, such as economic output or system functionality. However, the history of past disasters and recent risk literature suggest that a single-dimension view of relevant systems is not only insufficient, but can compromise the ability to manage risk for these systems. In this article, we explore how multiple dimensions influence the ability for complex systems to function and effectively recover after a disaster. In particular, we compile evidence from the many competing resilience perspectives to identify the most critical resilience dimensions across several academic disciplines, applications, and disaster events. The findings demonstrate the need for a conceptual framework that decomposes resilience into six primary dimensions: workforce/population, economy, infrastructure, geography, hierarchy, and time (WEIGHT). These dimensions are not typically addressed holistically in the literature; often they are either modeled independently or in piecemeal combinations. The current research is the first to provide a comprehensive discussion of each resilience dimension and discuss how these dimensions can be integrated into a cohesive framework, suggesting that no single dimension is sufficient for a holistic analysis of a disaster risk management. Through this article, we also aim to spark discussions among researchers and policymakers to develop a multicriteria decision framework for evaluating the efficacy of resilience strategies. Furthermore, the WEIGHT dimensions may also be used to motivate the generation of new approaches for data analytics of resilience-related knowledge bases.
© 2018 Society for Risk Analysis.

Entities:  

Keywords:  Disaster risk assessment; disaster risk management; engineering analytics; resilience

Year:  2018        PMID: 30239024     DOI: 10.1111/risa.13186

Source DB:  PubMed          Journal:  Risk Anal        ISSN: 0272-4332            Impact factor:   4.000


  2 in total

1.  Modelling vicious networks with P-graph causality maps.

Authors:  Raymond R Tan; Kathleen B Aviso; Angelyn R Lao; Michael Angelo B Promentilla
Journal:  Clean Technol Environ Policy       Date:  2021-05-11       Impact factor: 4.700

2.  Analysis of Influencing Factors of Urban Community Function Loss in China under Flood Disaster Based on Social Network Analysis Model.

Authors:  Lianlong Ma; Dong Huang; Xinyu Jiang; Xiaozhou Huang
Journal:  Int J Environ Res Public Health       Date:  2022-09-05       Impact factor: 4.614

  2 in total

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