摘要
The catastrophic earthquake that struck Sichuan Province,China,in 2008 caused serious damage to Wenchuan County and surrounding areas in southwestern China.In recent years,great attention has been paid to the resilience of the affected area.This study applied the resilience inference measurement(RIM) model to quantify and validate the community resilience of 105 counties in the impacted area.The RIM model uses cluster analysis to classify counties into four resilience levels according to the exposure,damage,and recovery conditions.The model then applies discriminant analysis to quantify the influence of socioeconomic characteristics on the county's resilience.Analysis results show that counties located at the epicenter had the lowest resilience,but counties immediately adjacent to the epicenter had the highest resilience capacities.Counties that were farther away from the epicenter returned to normal resiliency quickly.Socioeconomic variables—including sex ratio,per capita GDP,percent of ethnic minority,and medical facilities—were identified as the most influential characteristics influencing resilience.This study provides useful information to improve county resilience to earthquakes and support decision making for sustainable development.
The catastrophic earthquake that struck Sichuan Province, China, in 2008 caused serious damage to Wenchuan County and surrounding areas in southwestern China. In recent years, great attention has been paid to the resilience of the affected area. This study applied the resilience inference measurement (RIM) model to quantify and validate the community resilience of 105 counties in the impacted area. The RIM model uses cluster analysis to classify counties into four resilience levels according to the exposure, damage, and recovery conditions. The model then applies discriminant analysis to quantify the influence of socioeconomic characteristics on the county's resilience. Analysis results show that counties located at the epicenter had the lowest resilience, but counties immediately adjacent to the epicenter had the highest resilience capacities. Counties that were farther away from the epicenter returned to normal resiliency quickly. Socioeconomic variables-including sex ratio, per capita GDP, percent of ethnic minority, and medical facilities-were identified as the most influential characteristics influencing resilience. This study provides useful information to improve county resilience to earthquakes and support decision making for sustainable development.
基金
supported by the US National Science Foundation(Award number 1212112)
the Louisiana Sea Grant program,the China Postdoctoral Science Foundation(No.2016M592647)
the National Natural Science Foundation of China(Grant No.61305022)
the Opening Fund of State Key Laboratory of Virtual Reality Technology and Systems (Beihang University)(Grant No.BUAA-VR-16KF-11)
