Geographical Analysis of Community Resilience to Seismic Hazard in Southwest China

Abstract

This article presents an explorative analysis of community resilience to seismic hazard in the 2008 Wenchuan Earthquake area of Southwest China. We used a regression model to analyze the impact of 13 key socioeconomic and demographic variables on community resilience in 105 counties, based on data derived from population census and provincial statistical yearbooks of China. In this research, we argue that community resilience should be measured by the change of population growth rate (Ddp) instead of population growth rate (dp) when using socioeconomic data from a fast-growing country such as China. Using Ddp as the dependent variable resulted in a better regression model. To avoid the common multicollinearity problems among the independent variables, a principal component-based factor analysis was used to consolidate the socioeconomic variables into four comprehensive factors. The geographically weighted regression coefficient maps revealed the spatial pattern of the association of the variables with resilience. We also used the K-means cluster method to segment the study area into four subregions that exhibit localized characteristics defined by the regression coefficients. In this way, we could infer location-sensitive disaster management policies that help to enhance social resilience to seismic hazards.

Measuring County Resilience After the 2008 Wenchuan Earthquake

“…This approach is similar to some studies in the literature that use actual damage data as the dependent variable to regress with a set of indicators to determine variable selection and weighting (Peduzzi et al 2009;Lam et al 2014). As in any statistical/quantitative analysis including factor analysis and regression (Cutter et al 2003;Li et al 2016), the cons of the RIM approach are that all the variables used in the RIM framework are subject to different interpretations and definitions, time periods, and spatial scales. Moreover, the results may be relative only to the cases included in the study.…”

“…Wenchuan County in Sichuan Province, China, and its surrounding counties are part of a region prone to frequent and destructive earthquakes and their accompanying secondary disasters (Chen et al 2007;Li et al 2016). The Wenchuan Earthquake, which occurred on 12 May 2008 is known for its huge destruction and high mortality.…”

Cross-Cultural Adaptation and Validation of the 10-Item Conjoint Community Resiliency Assessment Measurement in a Community-Based Sample in Southwest China

“…In the meantime, conceptual research about disaster vulnerabilities and disaster risks from a resilience perceptive has also witnessed significant growth in China (Lei et al 2014;Xue et al 2018). However, research about resilience at the community level is just emerging, with relevant studies still predominantly focusing on the identification of the essential factors associated with community resilience in China (Li et al 2016;Qin et al 2017). In practice, along with recent global and national policy advocacies, disaster risk reduction programs implemented to enhance community disaster resilience are not uncommon, especially in southwest China (ODI 2015).…”

Influence of Social-economic Activities on Air Pollutants in Beijing, China

“…In additional, because of the linear hypothesis, there is bias on the influence factor analysis of air pollution using some method, such as the Principal Component Analysis and the Multivariate Statistical Methods [9], while the influence of factors on air pollution is nonlinear. For this reason, it is complex to explore the impact of human activities on the formation of urban haze components in Beijing [7].…”