Geographical Analysis of Community Resilience to Seismic Hazard in Southwest China

Xiaolu Li; Lei Wang; Shan Liu

doi:10.1007/s13753-016-0091-8

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Article Navigation > International Journal of Disaster Risk Science > 2016 > 7(3): 257-276

Xiaolu Li, Lei Wang, Shan Liu. Geographical Analysis of Community Resilience to Seismic Hazard in Southwest China[J]. International Journal of Disaster Risk Science, 2016, 7(3): 257-276. doi: 10.1007/s13753-016-0091-8

Citation:

Xiaolu Li, Lei Wang, Shan Liu. Geographical Analysis of Community Resilience to Seismic Hazard in Southwest China[J]. International Journal of Disaster Risk Science, 2016, 7(3): 257-276. doi: 10.1007/s13753-016-0091-8

Citation:

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Geographical Analysis of Community Resilience to Seismic Hazard in Southwest China

doi: 10.1007/s13753-016-0091-8

Xiaolu Li¹,
Lei Wang^{2
,
,},
Shan Liu¹

1 School of Automation, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China;
2 Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA 70810, USA

Funds:

This material is partially based upon Project funded by the China Postdoctoral Science Foundation (Award No. 2016M592647). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies.

Available Online: 2021-04-26

Abstract

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 (Δdp) instead of population growth rate (dp) when using socioeconomic data from a fast-growing country such as China. Using Δdp 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.
- China,
- Community resilience,
- Geographic information system,
- Geographically weighted regression,
- Wenchuan Earthquake

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References(61)

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