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 (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.

Keywords

China / Community resilience / Geographic information system / Geographically weighted regression / Wenchuan Earthquake

References

[1] Adger WN, Hughes TP, Folke C, Carpenter SR, Rockstrom J. Social–ecological resilience to coastal disasters. Science, 2005, 309(5737): 1036-1039

[2] Akaike H. A new look at the statistical model identification. IEEE Transactions on Automatic Control, 1974, AC-19(6): 716-723

[3] Bevington, J.S., A.A. Hill, R.A. Davidson, S.E. Chang, A. Vicini, B.J. Adams, and R.T. Eguchi. 2011. Measuring, monitoring, and evaluating post-disaster recovery: A key element in understanding community resilience. Proceedings of ASCE Structures Congress 2011, American Society of Civil Engineers, 2033–2043.

[4] Brown DD, Kulig JC. The concepts of resiliency: Theoretical lessons from community research. Health and Canadian Society, 1996, 4(1): 30-50.

[5] Bruneau M, Chang SE, Eguchi RT, Lee GC, O’Rourke TD, Reinhorn AM, Shinozuka M, Tierney K, Wallace WA, von Winterfeldt D. A framework to quantitatively assess and enhance the seismic resilience of communities. Earthquake Spectra, 2003, 19(4): 733-752

[6] Brunsdon C, Fotheringham AS, Charlton ME. Geographically weighted regression-modelling spatial non-stationarity. Journal of the Royal Statistical Society: Series D (The Statistician), 1998, 47(3): 431-443.

[7] Chang SE. Urban disaster recovery: A measurement framework and its application to the 1995 Kobe Earthquake. Disasters, 2010, 34(2): 303-327

[8] Chatfield C, Collins AJ. Introduction to multivariate analysis, 1980, London: Chapman and Hall

[9] Cloke P, Philo C, Sadler D. Approaching Human Geography, 1991, London: Chapman

[10] Congdon PD. Modelling spatially varying impacts of socioeconomic predictors on mortality outcomes. Journal of Geographical Systems, 2003, 5(2): 161-184

[11] Cutter SL, Boruff BJ, Shirley WL. Social vulnerability to environmental hazards. Social Science Quarterly, 2003, 84(2): 242-261

[12] Cutter SL, Barnes L, Berry M, Burton C, Evans E, Tate E, Webb J. A place-based model for understanding community resilience to natural disasters. Global Environmental Change, 2008, 18(4): 598-606

[13] Cutter, S.L., C.G. Burton, and C.T. Emrich. 2010. Disaster resilience indicators for benchmarking baseline conditions. Journal of Homeland Security and Emergency Management 7(1). Article no. 51.

[14] Finch C, Emrich CT, Cutter SL. Disaster disparities and differential recovery in New Orleans. Population and Environment, 2010, 31(4): 179-202

[15] Fotheringham AS, Densham PJ, Curtis A. The zone definition problem in location-allocation modeling. Geographical Analysis, 1995, 27(1): 60-77

[16] Fotheringham AS, Charlton ME, Brunsdon C. Geographically weighted regression: A natural evolution of the expansion method for spatial data analysis. Environment and Planning A, 1998, 30(11): 1905-1927

[17] Fotheringham A, Bunsden C, Charlton M. Quantitative geography, 2000, London: Sage

[18] Fotheringham AS, Brunsdon C, Charlton ME. Geographically weighted regression: The analysis of spatially varying relationships, 2003, London: Wiley

[19] Ghosh D, Manson SM. Robust principal component analysis and geographically weighted regression: Urbanization in the twin cities metropolitan area of Minnesota. Journal of the Urban and Regional Information Systems Association, 2008, 20(1): 15-25.

[20] Guo Y. Urban resilience in post-disaster reconstruction: Towards a resilient development in Sichuan, China. International Journal of Disaster Risk Science, 2012, 3(1): 45-55

[21] Hair JF Jr Anderson RE, Tatham RL, Black WC. Multivariate data analysis, 1998 5 Upper Saddle River, New Jersey: Prentice Hall

[22] Harman HH. Modern factor analysis, 1976, Chicago: University of Chicago Press

[23] Hartigan JA, Wong MA. A K-means clustering algorithm. Journal of the Royal Statistical Society, Series C (Applied Statistics), 1979, 28(1): 100-108.

[24] Hastie TJ, Tibshirani RJ. Generalized additive models, 1990, New York: Chapman and Hall

[25] Holling CS. Resilience and stability of ecological systems. Annual Review of Ecology and Systematics, 1973, 4: 1-23

[26] Holling CS. Schulze PC. Engineering resilience versus ecological resilience. Engineering within Ecological Constraints, 1996, Washington, DC: National Academy Press 31-44.

[27] Jolliffe IT. Principal component analysis, 2010 2 New York: Springer

[28] Lam NS, Reams M, Li K, Li C, Mata LP. Measuring community resilience to coastal hazards along the Northern Gulf of Mexico. Natural Hazards Review, 2015, 17(1): 04015013

[29] LeSage JP. Anselin L, Florax R, Rey SJ. A family of geographically weighted regression models. Advances in spatial econometrics: Methodology, tools and applications, 2004, Berlin: Springer 241-264

[30] Li W, Airriess CA, Chen AC-C, Leong KJ, Keith V. Katrina and migration: Evacuation and return by African Americans and Vietnamese Americans in an Eastern New Orleans suburb. The Professional Geographer, 2010, 62(1): 103-118

[31] Li X, Lam N, Qiang Y, Li K, Yin L, Liu S, Zheng W. Measuring county resilience after the 2008 Wenchuan Earthquake. Natural Hazards and Earth System Sciences Discussions, 2015, 3: 81-122

[32] Li, X., W. Zheng, D. Wang, L. Yin, and Y. Wang. 2015b. Predicting seismicity trend in southwest of China based on wavelet analysis. International Journal of Wavelets, Multiresolution and Information Processing 13(2). Article no. 1550011: 1–9.

[33] Milligan GW, Cooper MC. An examination of procedures for determining the number of clusters in a data set. Psychometrika, 1985, 50(2): 159-179

[34] National Bureau of Statistics of the People’s Republic of China Tabulation of the 2000 population census of the People’s Republic of China, 2001, Beijing: China Statistics Press

[35] Nelson E, Mendoza G, Regetz J, Polasky S, Tallis H, Cameron D, Chan KM, Daily GC Modeling multiple ecosystem services, biodiversity conservation, commodity production, and tradeoffs at landscape scales. Frontiers in Ecology and the Environment, 2009, 7: 4-11

[36] Norris FH, Stevens SP, Pfefferbaum B, Wyche KF, Pfefferbaum RL. Community resilience as a metaphor, theory, set of capacities, and strategy for disaster readiness. American Journal of Community Psychology, 2008, 41(1–2): 127-150

[37] NRC (National Research Council) Disaster resilience: A national imperative, 2012, Washington, DC: The National Academies Press

[38] Panik MJ. Regression modeling: Methods, theory, and computation with SAS, 2009, New York: Taylor and Francis Group

[39] Paton D, Johnston DM. Disasters and communities: Vulnerability, resilience and preparedness. Disaster Prevention and Management: An International Journal, 2001, 10(4): 270-277

[40] Paton D, Johnston DM. Disaster resilience: An integrated approach, 2006, Springfield: Charles C. Thomas Publisher

[41] Paton D, Smith L, Violanti J. Disaster response: Risk, vulnerability and resilience. Disaster Prevention and Management, 2000, 9(3): 173-180

[42] Peet R. Modern geographical thought, 1998, Oxford: Blackwell

[43] Pelling M. The vulnerability of cities: Natural disasters and social resilience, 2003, London and New York: Earthscan

[44] Shan W. Comparing ethnic minorities and Han Chinese in China: Life satisfaction, economic well being and political attitudes. East Asian Policy, 2010, 2: 13-22.

[45] Sichuan Provincial Bureau of Statistics Sichuan statistical yearbook, 2003, Beijing: China Statistics Press

[46] Sichuan Provincial Bureau of Statistics Sichuan statistical yearbook, 2006, Beijing: China Statistics Press

[47] Sichuan Provincial Bureau of Statistics Sichuan statistical yearbook, 2008, Beijing: China Statistics Press

[48] Sichuan Provincial Bureau of Statistics Sichuan statistical yearbook, 2009, Beijing: China Statistics Press

[49] Sichuan Provincial Bureau of Statistics Sichuan statistical yearbook, 2011, Beijing: China Statistics Press

[50] Smith M. Social science in question, 1998, London: Sage

[51] The H. John Heinz III Center for Science, Economics, and the Environment The Hidden costs of coastal hazards: Implications for risk assessment and mitigation, 2000, Washington, DC: Island Press

[52] Turner BL, Kasperson RE, Matson PA, McCarthy JJ, Corell RW, Christensen L, Eckley N, Kasperson JX, Luers A, Martello ML, Polsky C, Pulsipher A, Schiller A. A framework for vulnerability analysis in sustainability science. Proceedings of the National Academy of Sciences, 2003, 100(14): 8074-8079

[53] UNISDR (United Nations International Strategy for Disaster Reduction) Living with risk: A global review of disaster reduction initiatives, 2004, Geneva: UNISDR

[54] Unwin DJ. GIS, spatial analysis and spatial statistics. Progress in Human Geography, 1996, 20(4): 540-551

[55] USGS (United States Geological Survey). 2008. Magnitude 7.9: Eastern Sichuan, China. U.S. Geological Survey Earthquake Hazards Program. http://earthquake.usgs.gov/earthquakes/eqinthenews/2008/us2008ryan/. Accessed 27 May 2016.

[56] Wheeler DC, Calder CA. An assessment of coefficient accuracy in linear regression models with spatially varying coefficients. Journal of Geographical Systems, 2007, 9(2): 145-166

[57] Wheeler DC, Tiefelsdorf M. Multicollinearity and correlation among local regression coefficients in geographically weighted regression. Journal of Geographical Systems, 2005, 7(2): 161-187

[58] Yu D, Peterson NA, Reid RJ. Exploring the impact of non-normality on spatial non-stationarity in geographically weighted regression analyses: Tobacco outlet density in New Jersey. GIScience and Remote Sensing, 2009, 46(3): 329-346

[59] Zheng W, Li X, Lam N, Wang X, Liu S, Yu X, Sun Z, Yao J. Applications of integrated geophysical method in archaeological surveys of the ancient Shu ruins. Journal of Archaeological Science, 2013, 40(1): 166-175

[60] Zheng W, Li X, Xie J, Yin L, Wang Y. Impact of human activities on haze in Beijing based on grey relational analysis. Rendiconti Lincei, 2015, 26(2): 187-192

[61] Zheng W, Li X, Yin L, Wang Y. Spatiotemporal heterogeneity of urban air pollution in China based on spatial analysis. Rendiconti Lincei, 2016, 27(2): 351-356