Global pandemics, such as the Coronavirus Disease 2019 (COVID-19), have serious harmful effects on people′s physical health and mental well-being. It is imperative therefore that we seek to understand community resilience and identify ways to enhance this, especially within our cities and communities. Therefore, great emphasis is now placed on how cities prepare for and recover from such disasters, and community resilience has emerged as a key consideration. Drawing upon research on the theory of resilience, this study seeks to identify the factors that influence community resilience and to analyze their causation toward helping to manage the risks associated with the COVID-19 pandemic. Seventeen factors from the five dimensions of social capital, economic capital, physical environment, demographic characteristics, and institutional factors are used to construct an index system. This is used to establish the structural level and importance of each factor. Data were collected using a questionnaire survey involving 12,000 members of key community groups in the city of Wuhan. An interpretative structural model (ISM) combining the analytic hierarchy process (AHP) method was then used to obtain the multi-level hierarchical structure composed of direct factors, indirect factors, and fundamental factors. The results show that the income level, vulnerability of the population, and the built environment are the main factors that affect the resilience of communities affected by COVID-19. These findings provide useful guidance toward the effective planning and design of urban construction and infrastructure. The results are expected to be useful to inform future decision-making and toward the long term, sustainable management of the risks posed by COVID-19.
While various measures of mitigation and adaptation to climate change have been taken in recent years, many have gradually reached a consensus that building community resilience is of great significance when responding to climate change, especially urban flooding. There has been a dearth of research on community resilience to urban floods, especially among transient communities, and therefore there is a need to conduct further empirical studies to improve our understanding, and to identify appropriate interventions. Thus, this work combines two existing resilience assessment frameworks to address these issues in three different types of transient community, namely an urban village, commercial housing, and apartments, all located in Wuhan, China. An analytic hierarchy process–back propagation neural network (AHP-BP) model was developed to estimate the community resilience within these three transient communities. The effects of changes in the prioritization of key resilience indicators under different environmental, economic, and social factors was analyzed across the three communities. The results demonstrate that the ranking of the indicators reflects the connection between disaster resilience and the evaluation units of diverse transient communities. These aspects show the differences in the disaster resilience of different types of transient communities. The proposed method can help decision makers in identifying the areas that are lagging behind, and those that need to be prioritized when allocating limited and/or stretched resources.
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