Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 1,2 , and W. Zheng 1 , S. Liu 3 82 81 , L. Yin 2 , K. Li 2 ected area. The RIM model uses cluster analysis to classify ff ected area. This study applied a new framework, the Resilience ff , Y. Qiang 2 , N. Lam 1,2 This discussion paper is/has beenSystem under Sciences review (NHESS). for Please the refer journal to Natural the Hazards corresponding and final Earth paper in NHESS if available. School of Automation, University of Electronic Science and Technology ofDepartment China, of Chengdu, Environmental Sciences, Louisiana State University, Baton Rouge, Louisiana Geographical & Sustainability Sciences Department, the University of Iowa, Iowa City, IA Abstract The catastrophic earthquake in 2008 hasand caused serious the damage surrounding to Wenchuan area County the in resilience China. of the In a recentInference Measurement years, (RIM) model, great to attention quantifyof and has 105 validate been the counties community paid in resilience to the a cent to the epicenteraway had from the the highest resilience epicenterincluding capacities. returned Counties sex to that ratio, normal were perwere resiliency. farther capita The identified GDP, socioeconomic percent as variables, ofThis the ethnic study provides most minority, useful and influential information tosupport medical socio-economic improve decision-making facilities, county for characteristics resilience sustainable to on development. earthquakes and resilience. 1 Introduction Wenchuan County in Sichuangion Province, prone China to frequent and and its destructivedisasters surrounding earthquakes and counties (Chen their are accompanying et secondary aknown al., re- for 2007). its Thecaused huge Wenchuan more than earthquake destruction 69 227 deaths that and2012). and occurred property Due high damage to in of mortality. over thefrastructure, 2008 845.1 The mountainous billion Wenchuan is RMB landscape, magnitude County (Guo, low and 7.9to economic its earthquakes earthquake development, surrounding and and regions secondary poor arethough disasters in- extremely these such counties vulnerable as have landslides similar and characteristics barrier in lake many flood. aspects, Al- it is observed that counties into four resilienceconditions, levels and according then applies to discriminant the analysisnomic to characteristics exposure, quantify on damage, the the influence and county oflocated recovery resilience. socioeco- right The at analysis the results epicenter show that had counties the lowest resilience, but counties immediately adja- Measuring county resilience after the 2008 Wenchuan earthquake X. Li Nat. Hazards Earth Syst. Sci.www.nat-hazards-earth-syst-sci-discuss.net/3/81/2015/ Discuss., 3, 81–122, 2015 doi:10.5194/nhessd-3-81-2015 © Author(s) 2015. CC Attribution 3.0 License. 1 Sichuan 610054, China 2 70803, USA 3 52242, USA Received: 15 November 2014 – Accepted: 12Correspondence December to: 2014 W. – Zheng Published: ([email protected]) 5 January 2015 Published by Copernicus Publications on behalf of the European Geosciences Union. 5 15 20 25 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | er- ff cult to select in- ffi erent areas (Lam et al., 2014). ff et al., 2005; Cutter et al., 2003, ff culties in assessing resilience. This ffi 84 83 erent understandings of resilience cause various viewpoints on resilience ff cult to generalize and apply to other contexts. Third, some study results, which have The di The RIM model overcomes several major di The ability to survive and recover through disasters is referred to as resilience. There ffi ent natural and socioeconomic environments,dicators which for resilience makes measurement. it Brooks very anda di others (Brooks set et of al., 2005) national-level presented ability indicators using of a vulnerability novel andthat empirical capacity had analysis. to the Based adapt highest on correlations to statistical with climate mortality correlations, vari- were 11 selected variables from a pool of 46 vulner- counties around the epicenter thatWenchuan had earthquake the were most selected serious for this economic study. loss caused by the 2 Related work The term resilience ischology, involved environment, in sociology multiple toresilience disciplines geography, from and the ranging Merriam-Webster beyond. from Dictionary The is engineering,or “the original ability successful psy- to definition again become strong, after of healthy, somethingits bad original happens; shape after the it abilitydefined has of been resilience something pulled, to in stretched, return pressed, tworeturning to bent, forms: to etc.” Holling its engineering (1996) original resilience,sustain successfully state, which its and refers original ecological state tosilience after resilience, the includes disturbance. which Adger ability two indicates et of elements:and al. the elaborated the adapt ability that ability (Adger to re- to etalization self-organize al., of and 2010). seismic the Bruneau resilienceconsequences capacity et from as to failures, al. “the and learn (2003) time abilityboth expressed physical to of and a recovery”. social a They broad systems further to unitresourcefulness, conceptu- consist defined to and of resilience four reduce rapidity for properties: failure robustness, (Bruneauof redundancy, probabilities, resilience and is Reinhorn, oftenadaptability, 2006). mixed and Recently, with sustainability, the other making concept cated. closely the related measurement concepts of such resilience as more vulnerability, compli- measurement in many studies. Also, the concept varies when disaster occurs in di 2010; Reams et al.,measured 2012) resilience of quantitatively county resilience. andcommunity However, with resilience few validation. convincing to approaches The disastersistics challenges are of of many. disaster, First, measuring naturalsignificant due and controversy to social on the how processes,tive to diversity and factors identify and on definitions inaccurate the character- weights of assigned main the todi factors. variables terms, make Second, the the there measurement model many is explored subjec- seismic resilience of2003; counties, Chang have and seldom Shinozuka, 2004). beenresearchers To developed validated address the (Bruneau some Resilience of Inference et Measurement thesethe al., (RIM) issues, community model Lam resilience to and quantify (Lam other cessfully et applied al., in 2014; the Li,hazards Gulf 2011). (Li, of The 2011). Mexico The RIM region RIM modeland model to has can is measure be theoretically been county easily sound, suc- resilience enables extended empirical to to validation, various coastal disasters and di study applies the RIM modelWenchuan to earthquake. analyze quantitatively We seismic focus resiliencespecifically on after the the the hardest-hit 2008 quake-prone counties region of2008 in Sichuan, Wenchuan Southwestern Gansu, earthquake. China, and Due Shaanxi to provinces by the the limitation on data availability, a total of 105 is an extensive literature2003; on Cutter definitions et (Holling, al., 1996), 2003) frameworks and (Bruneau case et studies al., (Boru some counties had less damage duringwards. earthquakes According and to recovered more these quicklycounties observations, after- more two resilient questions to are earthquakesacteristics put than make forward: others; a (1) and county are (2)help what some more improve socioeconomic resilient? the char- The resiliencenomic answers of characteristics. to counties these by two promoting questions or could controlling certain socioeco- 5 5 25 10 15 20 10 15 20 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | cult problems discussed above. ffi 86 85 cient in the assessment framework of seismic resilience ffi ected region using the RIM model will fill this void. ected region of Wenchuan earthquake. Our study on resilience as- ff ff The heavy mortalities and property loss caused by the Wenchuan earthquake have In the context of seismic disaster, recent studies that focus on resilience or vulnerabil- et al., 2013), gravelly2011), soil and liquefied stress evolution (Cao (Nalbant etmenshan and McCloskey, fault al., 2011; zone 2011), Shan (Li et surface et al.,earthquake. al., deformation 2013) 2013; Y. (Fu of Ran Guo Long- et et (2012) al., al.,work 2013) proposed focusing triggered on a by governmental the post-disaster reconstruction.reconstruction 2008 Comparing urban with Wenchuan scenarios key resilience aspects of of design governmental urbanof frame- resilience, reconstruction Guo explored and alternative indicated scenarios urban that context of it the is manypacities critical urban so components, to that elements, consider networks, they the dynamics,disaster can and specific (Guo, make ca- post-disaster radical 2012). social However,to and Guo’s evaluate spatial study urban changes did resilience. toresilience not cope of There the provide with is a future a urgent quantitative need measure to evaluate and quantify seismic captured extensive public attention.ducted However, concerning relatively the fewment. earthquake, studies Most especially have studies been on werestudies con- the devoted on to community debris-flow the resilience (Tang2011; physical Guo et assess- and aspect al., Hamada, of 2013; 2012), Tang the et landslide al., earthquake, 2011; (Dai Xu such et et as al., al., 2012), gas 2011; emission (Zheng Gorum et al., sessment of the a above, and building on theseismic RIM resilience model by (Lam choosingrecovery et the al., capability population 2014; of growth Li, counties rateGansu, 2011), in and as we Shaanxi the the aim provinces area to key by the assess where indicator 2008 of counties Wenchuan the were earthquake. hit hard in Sichuan, 3 The Resilience Inference Measurement (RIM)In model the RIM model, community resilience(Lam is defined et by al., three dimensions 2014; andto Li, two hazards abilities 2011). (such The as three the dimensions number of resilience of are times (1) a the community exposure is hit by earthquake), (2) the ability variables. These variables werea then focus assigned group a of weightedan experts. score aggregated By from index averaging 1 was the toclimate obtained weighted 5 variability. to by scores Brooks’ represent approach of vulnerability used alllacked and expert quantitative selected capacity validation knowledge variables, to of as adapt the aSocial derived to form Vulnerability index. of Index Cutter validation; et (SoVI) it ards al. to (2003) using assess constructed county-level social the socioeconomicThe vulnerability method and to produced 11 demographic environmental factors haz- data fromon 42 factor in variables to analysis, the explain and 76.4 UnitedCutter % created of States. et SoVI variance based al. scoresBaseline using (2010) Resilience the subsequently Index factor introduced scores forBRIC another Communities for approaches set each (BRIC). lacked county. of However, empirical bothThis indicators validation the shortcoming of to SoVI exists the derive in and variablegave many the the selection us studies an and on approach weighting. to resilience overcome measurement. some The of RIM the model di ity assessment are generally basedmated on from loss estimation, physical particularly damage to economic(2003) loss infrastructures developed esti- (Cho a et framework al., tothe 2001). assess Bruneau seismic speed resilience and of from others recovery economicand in losses economic) and four of resilience five dimensions systemsand (“global”, (technical, recovery electric systems). organizational, Chang power, social, water, andof hospital, measures Shinozuka based and (2004) on response outlined the framework ait developed in more by a Bruneau succinct probabilistic et series context. al. However, (2003) thoseserved and factors or in reframed measured the framework and often haveleads cannot to be to be ob- quantified bias accordingsocioeconomic in to status the human is experience, insu measurement which of seismic resilience. Moreover, measurement of which is usually based on lossdisasters estimation. As draws influence increasing on life attention qualitythe caused from by recovery people, seismic and there adaptive becomes aspect a need of to a consider community after the disaster. For the reason 5 5 15 20 10 25 10 20 25 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | k (2) obser- n stands for } k S , ... , 1 S { ected by the earthquake. are the independent vari- ff = } n S x , ... , 1 ering from high damage under low x cients which maximize the distance ff { ffi = x 88 87 . i S is a constant. c c + n x . (1) n 2 b are the discriminant coe

is the data matrix of observations, i } + ) µ n n − b x E) caused more than 69 227 deaths and 845.1 billion RMB in prop- ... , , j 0 x + ......

24 2 i , , ◦ sets in order to minimize the within-cluster sum of squares (Hartigan and x S 1 1 is the mean of points in 2 ∈ x b k j X ( i b { x µ 1 = + = k = 1 i N, 103 X b X 0 x 1 S 00 b ◦ Based on the derived discriminant functions, classification functions can be com- Then, discriminant analysis was used to characterize the a priori resilient groups via Applying the RIM model involves two statistical procedures. Firstly, K-means clus- = An overwhelming majority of mortality and damage was caused in the area around the 4.1 Study area and data On 12 May(31 2008, a magnitude 7.9 devastating earthquake in Wenchuan County based on theThis observation’s posterior independent classification from variables discriminantori analysis (e.g., classification can socioeconomic be from compared K-means indicators). withto analysis, the indicate and a pri- the how classification goodgroups. accuracy If the can the set classification be accuracy of used inant is independent high analysis and variables are the are met, statisticalresilience assumptions in group the membership of distinguishing set for discrim- observations the of inresilience other four classification assessment regions. has functions Thus two the can main RIMand advantages: be model inferential validation for potential used by by using to employing the predict inferential damage statistics the data (Lam et al., 2014; Li, 2011). 4 Measuring the resilience puted. The procedure will then re-classify the observations into one of the four groups erty loss. Six provinces and 15 million people were directly a ables for each observation; and between the means of dependent variables; groups (Klecka, 1980). Also itpredicted. can Given be the used independent to variables evaluatevariable (socioeconomic whether (K-means indicators) cases groups) and are for dependent classified each observation, as inant discriminant functions analysis as derives a discrim- linear combination of independentL variables (Klecka, 1980): where argmin vations into Wong, 1979): structing a function to distinguish a set of observations according to previously defined where a number of socioeconomic indicators. Discriminant analysis is commonly used in con- damage from exposure to(such hazards as (such population as return).the property The damage), relationships two and “abilities”, from (3) vulnerabilityCommunities exposure the and with to adaptability, recovery high indicate damage vulnerability and are those from su damage to recovery (Fig. 1). ter analysis was conductedties to (susceptible, recovering, derive resistant, the anda a usurper). multidimensional priori Each real observation resilient vector, is and rankings regarded K-means for as cluster the analysis 105 segregates coun- the sets, and exposure. Similarly, communities with highage. adaptability recover They quickly are from represented highand as dam- between the damage slopes and of recoveryRIM the in framework. lines Fig. From between 2. high exposure Theretant, to and are recovering, low, damage four the and resilience framework susceptible. rankingsresilience includes Resilience in rankings four of the according states: to community the usurper, is twoeral, resis- classified abilities susceptible (vulnerability as communities and have one adaptability). high of Incommunities vulnerability gen- the have and average low vulnerability adaptability. andlow Recovering adaptability. vulnerability Resistant and average communities adaptability, have whileability usurper and communities high have adaptability low (Lam vulner- et al., 2014; Li, 2011). 5 5 25 15 20 10 20 25 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | E, which is 0 24 ◦ ecting range of the ff N, 103 0 E), which is about 190 km 54 ◦ 0 06 ◦ N, 104 0 36 cial publication of mortality data for the ◦ ffi 89 90 cial yearbooks). Third, the county did not have more than 10 % ad- ffi As described in the previous section, resilience consists of three dimensions: ex- The intensity of the 2008 Wenchuan earthquake ranged from 9.14 to 3.0 MMI (Mod- Due to the data availability of reported property damage and a ministrative boundary changes between 2000 andwas 2011. collected Since the during socioeconomic this data For period, example, significant about 15 boundary counties changes inian, Sichuan may Province, bias and such as the others, Songpan, analysis. Beichuan, hadThey Anx- significant were not administrative included boundary infrom changes the the during study earthquake. 2002–2012. even though some of them had serious damages earthquake, 105 counties nearas the epicenter the of study Wenchuan(Fig. earthquake area, 3). were which selected The liethe study across county counties three was were provinces, evaluated(2008b). selected as Second, Sichuan, according economic the Shaanxi loss to worst-hit data and(in for the area this the Gansu by case, following county the is China criteria. o available Earthquake from First, a Administration credible source from the epicenter ofrounding Wenchuan region earthquake. of Themagnitude Wenchuan communities and and County huge cities destructiveness. are Although into highly this earthquakes the region exposed due is sur- to generally to very theand earthquakes vulnerable weak poor economic with social foundation, high less resources, diverserecovered some industrial more counties structure, quickly) had during betterthat and performance made after (e.g. the counties the lost perform disasters. less better Therefore, and is finding the the key to factors promote resilience. posure, damage and recovery.sity In of this the study,tify 2008 (1) the Wenchuan the destructiveness earthquake exposure ofthe indicator (Fig. seismic Wenchuan is 4), disaster earthquake the which (Eiby,(2008a). inten- was 1966). is (2) The obtained commonly Since intensity from used distribution there the to of is quan- U.S. very Geological limited Survey (USGS) o to represent its exposure.all The in top Sichuan five province,City including counties (8.21 MMI), Mianzhu that Dujiangyan City City had (8.15 MMI), (averageCounty the Pengzhou intensity: (7.49 City highest MMII). (8.07 8.46 MMI), MMI) exposure and Shifang Wenchuan were ified Mercalli Intensity scale) inpolygon the form study in area. The intervalspolated original of data into 0.2 were raster in intensity of MMI10. units. contour- 6.72 Then km The we pixels MMI utilized (Fig. original the 4) data “Zonal” by was tools the first to “Topo inter- to obtain Raster” the tools average intensity in in ArcGIS each county about 100 km away from thepeople, epicenter and of the Wenchuan earthquake, 7.2-magnitude and earthquakeand killed in about Pingwu the 9000 County boundary on between 16 Songpan County August 1976 (32 Wenchuan Earthquake atby the the county earthquake was level, selectedfrom as direct Sichuan the economic Yearbook damage (2009), indicator Shaanxi loss (Fig. Yearbook (2009),published 5), per and by which Gansu the capita were Yearbook Provincial (2009) collected People’s caused Governmentis (2009a, b, estimated c). by (3) the Thefrom recovery population the status provincial growth statistical rates yearbooks published from(2003a, by 2002 the b, Provincial to c, Bureau 2012a, 2011, of Statistics following b, which disturbances c). were have not It obtained reached is consensusery noted on the (Bevington; that best et researchers way to examining al.,represented measure sources 2011). recov- by of Although other recovery aspects, the suchgrowth recovery as as status GDP the of and recovery a income indicatorthe growth, because county summative we the could outcome chose variable of also population has variousMoreover, be often aspects population been of data seen recovery to is (Chang, indicate ization relatively 2010; across easy Li space to et and find, al., time making 2010). possible. comparison and general- epicenter which lies inrecords, within Sichuan, a Gansu 200 km and radiusequal of Shaanxi to the provinces or Wenchuan earthquake (Fig. abovethe epicenter, magnitude 3). an other earthquake 7 In two destructive takes historical earthquakes placeearthquake also about took in every place Maoxian in 40 years. the County In region: on the the 7.5-magnitude 25 last century, August 1933 at 31 5 5 25 25 10 15 20 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | er- ff 9.82%) − 12.39%), − 14.16%), Wenx- − 12.81%), Nanzheng County ( − 92 91 (CNY 538 695.65), Mianzhu City (CNY 276 848.25), 1 11.8%). Population growth rate in Wenchuan County ( − culty in recovery after the earthquake. 14.06%), Yangxian County ( ffi − Without special note, Lixian County is the one which located in Sichuan Province. 1 The four a priori groups derived by K-means cluster analysis and the 15 predic- The most severe economic loss occurred in Wenchuan County and Lixian County, In light of previous vulnerability and resilience research and considering the di As for the damage indicator, the top five counties that had the greatest economic We chose population growth rate from 2002 (pre-event status) to 2011 (post-event 4.2 Clustering resilience groups Next, K-means clusteraccording analysis to was the exposure, conducted damage, andconverted to recovery into indicators. derive All z-score the the before rawfrom four data the the has resilience cluster K-means been analysis analysis. groups andFigure Figure Table 8 1 7 maps shows plots the the group the number membership of groups of counties derived the in 105 eachwhich counties. cluster. made up theeconomic susceptible group. loss The per line capitadespite graphs in (Fig. that these 7) they show two didthese that counties not the two was have average counties much the higher showed highest than the average other lowest intensity groups, population of growth the after earthquake. the Also, earthquake. Both and Lixian County ( was lower than most ofCounty the had counties di in the study area, which reflected that Wenchuan tor variables were enteredgrouping into variable discriminant and analysis independentcriminant in variables. functions SPSS Using linearly discriminant statistical combined package analysis, predictor as three variables dis- were obtained. Two of the ence in data definition and availability15 in China socioeconomic (Cutter variables et al., thatselected 2003; describe Nelson for et the pre-event al., 2009), discriminant conditionsshould analysis of be (Table the used, 2). counties Thesocioeconomic instead were capacity pre-event of can condition the withstand (Year disasters.mographic, post-event 2000) These social, condition, variables economic, represented to health, the indicate andstudy de- social how area. welfare the The capitals of underlying majoritysus each published of county by variables in the the were(2001). National The collected national Bureau population from of census was thethe Statistics conducted closest every of 2000 10 year years the population before and the People’s Year 2000 cen- from Republic Wenchuan is provincial Earthquake. yearbooks of near The China Year 2000 other to variablescluding be were the consistent collected Provincial with the Statistical census Yearbooks 2002 variables,Statistics in- published (2003a, by b, c). the Before Provincial performing Bureauvariables discriminant of were analysis, normalized the by 15 convertingpercentage. socioeconomic into densities per square mile, per capita, or counties are located atsilience (Fig. the 8). epicenter, However, it andcounties is are immediately observed surrounding thus that these expected resilienceaway two rose to from counties. to the have The the epicenter the remaining highest showed counties average lowest level resilience. in farther re- the 4.3 Discriminant analysis After the clusterresilience analysis, level discriminant of a analysis countycriminant was can analysis be carried was predicted also out by used its to to socioeconomic validate test characteristics. the Dis- whether accuracy of the the a priori groups. ian County ( counties that had the lowest population growth were Xixiang County ( loss per capita caused(CNY 618 by 269.23), Lixian the County 2008 Wenchuan earthquake were Wenchuan County status) as the recovery indicator forties each with county the in this highest study population (Fig.cluding growth 6). rates Yuexi The were County top all (31.2 five %), from coun- Sichuan(25 Chengdu %), province Metropolitan as Abaxian Area well, County in- (30 %), (22.95 Hongyuan %), County and Wenjiang District (22.33 %). The bottom five Shifang City (CNY 205 311.78), andlocated Maoxian in County (CNY Sichuan 203 province. 669.72); all of them are 5 5 25 10 15 20 20 25 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | is j e , and j on function i is the number of significant dis- n , i 94 93 , (3) j e . j j e is the discriminant loading of variable ij l Sum of all × is the potency index of variable 2 ij i l 1 n = j X = i Table 5 ranks the 15 variables by their potency indices. It shows that sex ratio had Figure 10 shows the distribution of the misclassified counties. Nine of the 15 mis- The discriminant functions provide probabilities of group membership for each the greatest influence on resilience,facilities. followed Low by sex per ratio capita means GDP,with ethnicity, high and high-resilient female medical proportion, counties. which By isresilience comparing found group, the to we average be find value associated that of counties each in variable Group in Usurper each and Resistant had (1) higher the eigenvalue of function criminant functions, nant analysis. 5.2 Potency index The Potency index of eachindicator variable variables can using be all significant used discriminantthere to functions, evaluate are which the more is discriminant often than used power1979). two when of The significant potency discriminant index is functions calculated derived as: (Perreault Jr et al., Potency classified counties were inRecovering (Wenxian Group County, Usurper, Wudu inshan District, which County, Chaotian and six Santai District, County) were JiangeResistant and downgraded County, (Dayi the Ming- to remaining County, three Group Mianyang were Metropolitan downgraded Area, to Group and Qionglai City) by discrimi- ering. In general, countiesarea, in whereas Group counties Susceptible immediatelyhigh were neighboring resilience concentrated rankings the in belonging threeexceptions, the in the susceptible rest epicenter either counties of Usurper had theGroup or counties Recovering Resistant farther by both away groups. K-means from With and the a discriminant epicenter analysis. few were classified as where Potency 5 Results 5.1 Spatial distribution of resilience The predicted group memberships by thewhich discriminant functions shows are that illustrated in 20were Fig. 9, of in the Group 105in Resistant, counties Group followed were Susceptible. by classified The 72spatially as spatial in contiguous Group distribution pattern. Group Usurper, of Counties Recoveringian and county in and County, 10 resilience and the the shows Maoxian epicenter remaining athe County) area generally 3 study had (Wenchuan the counties. County, lowest Lix- analysis Maoxian resilience but County to was was earthquake classifiedcioeconomic not among as variables. all classified Susceptible Counties as by eastMianyang Susceptible discriminant of Metropolitan by analysis the Area, K-means Pingwu based epicenterulation County, area on and growth (such its Shifang despite as so- City) maintained PengzhouPrefecture the generally high City, earthquake had pop- higher disaster. resilienceties, Counties to such earthquake in disaster as Chengdu than Chengdu anyAll City Metropolitan other of Area, coun- and the Longquanyi Aba District, counties and in Wenjiang Gansu District. and Shaanxi province were classified as Group Recov- county. According to the probabilityinto of one group of membership, the each fourmatched groups. county 85.7 % The was of group classified the membership athe predicted priori 15 groups by selected derived discriminant socio-economic from analysis variables K-meanresilience can analysis, level. which discriminate Only means 85.7 15 % that counties ofand were the misclassified misclassified. 105 counties The counties’ are counties’ shown resilience in rankings Table 4. discriminant functions, which explainedthe 70.1 total and variance, 24.8 % respectivelyfunction (Table (accumulating described 3), the to remaining were 94.9 %) 5.1 statistically %. of significant, while the third 5 5 25 20 15 10 15 20 25 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 96 95 Third, Group Susceptible (red area in Fig. 8) had two counties classified by K-means, Second, Group Recovering (yellow areas in Fig. 8) was highly positively correlated First, the discriminative analysis results indicate that the resiliency of the top two To further interpret the association between indicator variables and resilience groups, Wenchuan County and Lixian County, whichcounties are had located in been the epicenter severelyproperty area. damages impacted The than two by others. So theceptible, they Wenchuan by were classified K-means earthquake, as analysis. the and lowest Moreover, had level, Figs. Group more 11 Sus- and 12 show that the Susceptible of social welfare home beds perof 10 000 male persons. exists We can infor interpret that economically male high undeveloped babies percentage region, stilllower where dominates. resilience to the These earthquake traditional underdeveloped disaster.productivity counties preference Higher is were proportion often found of characterized primary tocould by industry make have low the with counties low income more andservices susceptible low to would hazard. density indirectly Furthermore, of poorsocial decrease buildings, social welfare which the welfare service resiliency aregenerally of to more fragile serve the and the counties. need disadvantaged additional The support group objectives in of the of society, recovery and period. they are with sex ratio and proportion of primary industry, but negatively correlated with number age of population withemployment. education Moreover, counties of belonging senior to secondarymostly the school associated Usurper and and with Resistant above, economicallythose groups and developed were in ratio and Chengdu of highly City,per populated Mianyang capita areas City, and and such gross Deyangper as output City. square value We kilometer of can areeconomic farming, interpret health forestry, indicators that animal might of GDP have husbandry the helpedhealth and in care economic fishery longer-term might state recovery expedite ofcentage after immediate earthquakes. a of relief Better from urban county. The disasters. populationresilient, Counties state as and with more of per wealth higher capita can per- centages help saving of them population deposit reduce with balances and educationpromote recover might of the from senior resilience be damage. secondary of Higher more a school per- terize county. and More a above younger resilient can and county. also employed residents also charac- of hospital beds per 10 000of people, farming, percentage forestry, of animal urban husbandry population,population and gross between fishery output age value per 15–64, square kilometer, resident percentage saving of deposit balances per capita, percent- most resilient groups, Groups Usurperwas highly and positively Resistant correlated (blue with the and following green eight areas factors: GDP in per Fig. capita, 8), number the loadings of the indicatortions variables (Fig. were 12). plotted From onto the the two first plots two (Figs. discriminant 11 func- and 12), we can observe the following: counties in Group Usurpernomic characteristics and of Resistant the werewas counties often the were reason adjacent similar that areas. about ingraded The (6 from both Group out socioeco- groups Usurper of (Fig. to 15) 11). Groups 40 % Resistant That of and also misclassification Recovering. counties were down- 5.3 Discriminant score and variable loading The discriminant scores of thethe counties first two in functions the in four Fig.covering resilience 11. and groups The Susceptible plot were clearly – plotted shows onto wereResistant the well three mainly groups separated overlapped – using Usurper, with the Re- two Group functions, Usurper. while In Group the spatial distribution (Fig. 8), proportion of female, agedbanPop); (2) 15–64 higher (RtoPopAge15–64) GDP andkilometer per (GOVFFAF); urban (3) capita higher and population ratio of gross (RtoUr- school population output and with value above education (RtoEduSecSch); of of and senior agriculture (4)per secondary per more capita savings square deposit (PCSvgsDpstB). balance Themary of residents recovering industry group and hadondary the school the lowest and highest proportion above (RtoEduSecSch). proportion ofextreme The susceptible population values of group of with pri- also included education variables,portion some including of of high senior ethnic minorities’ proportion sec- industry population of (PSecIndus), (RtoEthMinPop), and male high very (SexRatio), proportion low population high of density secondary pro- (PopDensity). 5 5 25 20 15 10 25 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent from the variable of ff 98 97 ect of governmental policy on post-disaster re- ff ected by the Wenchuan earthquake in 2008 using a quantitative measurement ap- Finally, if the data is available, the e Secondly, the potency index has shown that sex ratio had the greatest influence First of all, the results indicate that the two counties (Wenchuan, Lixian) located in the ff covery should be consideredseveral for funds future had research. been AfterChinese sanctioned for extreme government post-disaster disasters invested reconstruction. took 654.5post-Wenchuan For place, billion earthquake example, the yuan recovery in andarea the and reconstruction, surrounding stricken especially counties communities in (2010).mote for the It the the high is epicenter resilience possible of that counties these surrounding funds the could epicenter partly area. pro- 7 Conclusion In this study, we appliedties the in RIM southwestern model China after tocommunity the assess resilience 2008 the based Wenchuan county on earthquake. resiliencecriminant We the for power then socioeconomic 105 of interpreted characteristics; coun- every evaluated indicator,tors and the and demonstrated dis- resilience the ranks associationright across between at the indica- the impacted epicenter area region. (Wenchunimmediately We and surrounding Lixian) found had the that the epicenter counties lowestwere resilience, area located farther but away had counties from the the epicenter highestdiscriminant returned analysis, resilience. to we the Counties found normal that that level the ofto 15 resilience. predict selected Through socioeconomic the variables resilience were group able (85.7 %). membership with The a variables reasonably thatper high showed degree capita great of GDP, accuracy influence percent onThis of study the produced ethnic resilience the minorities, were: firstthe sex and quantitative study ratio, analysis average area. of The number seismic findings of resilience should hospital assessment provide in useful beds. benchmark information on commu- education for female, while the traditionalin preference the for male undeveloped babies counties. ispercent more of This dominant female-headed households sex-ratio often variableis used is in often the di United associated States,2009, with where 2012). the poverty, latter low-income status, and low resilience (Lam et al., characteristics, and in China it may meandeveloped, and that counties the with more high developed resilience counties are have the better more work opportunities and equity on resilience assessment, followed byLow per sex capita ratio means GDP, ethnicity, high and femalelower medical proportion, the facilities. and ratio, the the higher assessment the resultwe county’s shows can resilience. that While interpret the there that is no the published sex evidence, ratio can be treated as a broad indicator of community unexpected, as counties closerHowever, to the the findings epicenter can areseismic expected also hazards, to be such recover interpreted as morehazards that slowly. those and people living hence they near that may2004). the are be epicenter, better often may prepared exposed and be to adapted more (Chang aware and of Shinozuka, the epicenter region had the lowest resiliencerounding level. However, these the two counties counties immediately sur- had thefrom highest the resilience. epicenter Then return the counties to farther the away lower resilience level again. This finding is somewhat group is highly positivelyHigh correlated percentage of with ethnic percentage minorities’earthquake of population disaster. ethnic would High make minorities’ percentage aciated population. of county with ethnic less the minorities’ resilient societies population to cation of is in cultural mostly China. barriers, asso- Andto poor the respond economy, and local to less-developed government disasters(Shan, edu- because usually 2010). of takes the a lack more of conservative technical policy support in the6 minority area Discussion This study aimed to measurea 105 communities’ resilience inproach, the the area Resilience that Inference Measurement was (RIM) greatly model. 5 5 10 15 20 25 25 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 99 100 This material is partially based upon work supported by the US National 8.0, Soil Dyn. Earthq. Eng., 31, 1132–1143, 2011. , B. J., and Shirley, W. L.: Social vulnerability to environmental hazards, Soc. = ff erent from other countries. The assessment results will also provide more ff , B. J., Emrich, C., and Cutter, S. L.: Erosion hazard vulnerability of US coastal counties, ff 1995 Kobe earthquake, Disasters, 34, 303–327, 2010. munities, Earthq. Spectra, 20, 739–755, 2004. of activity segmentation ofGeology, Longmenshan 29, fault 657–673, zone 2007. since late-quaternary, Seismology and ing transportation network andurban earthquake, regional J. economic Regional models Sci., 41, to 39–65, estimate 2001. the costs of a large assess and enhance the2003. seismic resilience of communities, Earthq. Spectra, 19,earthquake, 733–752, Ms Sci. Quart., 84, 242–261, 2003. baseline conditions, J. Homel. Secur. Emerg., 7, 22, 2010. triggered by the 2008895, Ms 2011. 8.0 Wenchuan earthquake, China, J. AsianNew Earth Zealand J. 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K.: Comparison of di 5 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | YB YB YB YB YB YB 2 2 − − Unit/ 10 000 person Unit/ 10 000 person Unit Source km Yuan/ person %% YB 10 000 YB yuan km %Yuan/ YB person Medical Capacity Social Welfare Meaning Population Person Education %Employment CS %Commercial CS & Industrial Development GenderEthnicity % %Urban YB Age CS % % CS CS Commercial & Industrial Development Commercial & Industrial Development Agricultural Activity Agricultural Activity Residential Property 106 105 Number of hospital beds per 10 000 persons Number of social welfare homes beds per 10 000 persons kilometer Percent of the population with diploma of senior secondary school and technical secondary school and above that is employed Gross domestic product per capita The ratio of males to females Percent non-Han Chinese population Percent of population that live in urban areas Percent of population between the ages of 15 and 64 years old Proportion of the primary industry to GDP Proportion of the secondary industry to GDP Gross value of agricultural output (farming, forestry, animal husbandary and fishery) per square kilometer Percent of cultivated land to total area Savings deposit balances per capita = per 10 000 Persons (2002) Homes Beds per 10 000 Persons (2002) with Education of Senior Secondary School and Technical Secondary School and above (2000) (at current prices) 100) Industry (2002) Minorities Population (2000) Population (2000) Age 15–64 (2000) Industry (2002) Farming, Forestry, Animal Husbandary and Fishery per Square Kilometer (at current prices) (2002) Land Area (year-end) (2002) Deposit Balances of Residents (2002) Health NoHospBed Number of HospitalSocial Beds welfare NoSWBed Number of Social Welfare Social RtoEduSecSch Percentage of Population Economic GDPperCapita GDP per Capita (2002) LabelDemographic PopDensity Socioeconomic Variable Population Density (2002) Population per square Variable RtoEmpPop Employment Ratio (2000) Percent of the workforce SexRatio Sex Ratio (2002) (female PPriIndus Proportion of Primary RtoEthMinPop Percentage of Ethnic RtoUrbanPopRtoPopAge15–64 Percentage of Urban Percentage of Population PSecIndusGOVFFAF Proportion of Secondary Gross Output Value of PCLA Proportion of Cultivated PCSvgsDpstB Per Capita Savings Note: The 2000 population censussocioeconomic was data obtained was from from the the National Provincial Bureau Statistical of Yearbooks 2003 Statistics (YB). of the People’s Republic of China (CS), and the 2002 Variance explained by discriminant functions. Socioeconomic variables for discriminant analysis. Function Eigenvalue1 % of Variance2 Cumulative3 % Canonical Correlation 1.379 0.487 0.100 70.1 24.8 5.1 70.1 94.9 100.0 0.761 0.572 0.302 Table 3. Table 2. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 108 107 Usurper Usurper No Usurper Resistant Yes Recovering Resistant Yes ∗ ∗ ∗ HuiliJiajiangJiangeJiangyang Recovering RecoveringJiangyou Recovering Recovering Recovering UsurperJinchuan Recovering No ResistantJingyan No No RecoveringJinkouhe Recovering ResistantJintang Usurper Recovering Yes RecoveringJinyang Recovering No Jiuzhaigou Recovering Usurper Yes No Junlian Usurper No UsurperKaijiang UsurperLezhi Usurper Recovering UsurperLixian Recovering RecoveringLongquanyi Recovering No UsurperLuding No No Recovering No No Lushan Susceptible RecoveringLuxian Susceptible Usurper No RecoveringMaerkang No Recovering RecoveringMaoxian Usurper Recovering Recovering No Mianyang No RecoveringMianzhu No Resistant UsurperMingshan No SusceptibleMiyi Resistant UsurperMuchuan Yes No ResistantNanjiang RecoveringNaxi Recovering Recovering RecoveringPanzhihua Recovering No Yes RecoveringPengan Recovering No Pengxi No No RecoveringPengzhou Recovering RecoveringPingchang Recovering Resistant RecoveringPingwu No Recovering Recovering No Pixian Resistant RecoveringPujiang No Resistant No Qianwei No Qingshen Usurper Resistant UsurperQionglai Recovering RecoveringQuxian Usurper Recovering No Recovering Usurper Usurper No No Recovering No No Resistant Recovering No Yes ChongzhouCuipingDanba UsurperDayiDazhu Usurper RecoveringDongxing Recovering RecoveringDujiangyan Recovering No Ebian No Usurper RecoveringGanluo Recovering Resistant No Recovering RecoveringGaoxian Usurper ResistantGongxian No No Guanganqu Recovering RecoveringGulin Yes Recovering Recovering Yes Recovering RecoveringHanyuan Recovering Recovering No RecoveringHeishui Recovering No No Hejiang No No Hongyuan Recovering Recovering Recovering Recovering Usurper No Recovering Usurper No Recovering Usurper Usurper No No No HuixianKangxianLiangdangLixian (Gansu) Recovering RecoveringTanchang Recovering Recovering Recovering RecoveringWenxian Recovering RecoveringWudu No No No Recovering No Xihe Recovering UsurperFoping No Hantai Usurper RecoveringLueyang Recovering Yes Mianxian Recovering Recovering RecoveringNanzheng Recovering Yes Xixiang Recovering No Recovering RecoveringYangxian Recovering Recovering No RecoveringZhenba Recovering No No Recovering No Recovering Recovering No Anyue Recovering RecoveringBaoxing RecoveringCangxi No No RecoveringChaotian No Chengdu Recovering Usurper Recovering Recovering Usurper No Usurper Recovering No Recovering No Yes ProvinceSichuan County Huaying K-means Recovering Discriminant Recovering Misclassification No Shaanxi Chenggu Recovering Recovering No Sichuan Abaxian Usurper Usurper No ProvinceGansu County Chengxian K-means Recovering Discriminant Recovering Misclassification No Continued. County resilience rankings and misclassification. Table 4. Table 4. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 110 109 Recovering Recovering No ∗ ShawanShehongShifang RecoveringShimian Recovering RecoveringSuining Recovering Resistant No Tongjiang Recovering No Wangcang Resistant Recovering RecoveringWenchuan Recovering Recovering No Wenjiang Susceptible Recovering No Wutongqiao No Susceptible No Usurper RecoveringXiaojin No Xichong RecoveringXuyong Usurper No UsurperYanbian RecoveringYanjiang Recovering Recovering No Yanting Usurper Recovering Recovering No Yingjing Recovering ResistantYucheng No Recovering Recovering No Yuechi Recovering Yes No UsurperYuexi Recovering RecoveringZitong Recovering Recovering No Zizhong Yes No Recovering Usurper Usurper No Recovering Recovering Usurper Usurper No No No Usurper Resistant Recovering Susceptible index Metropolitan Area. ProvinceSichuan County Santai K-means Discriminant Usurper Misclassification Recovering Yes ∗ “No” in the Misclassification column“Yes” in stands the for Misclassification accurate column classification. stands for misclassification. Potency index and mean value of each variable in each group derived from discrimi- Continued. VariablesSexRatioGDPperCapitaRtoEthMinPopNoHospBed 8772.65GOVFFAF 11173.22 104.49RtoPopAge15–64 30.65PSecIndus 3840.21 106.13 70.75 35.20PPriIndus 5.87RtoUrbanPop 116.35 Mean Value 6991.78PCSvgsDpstB 109.54 73.15 32.64RtoEduSecSch 32.48 94.67 0.129 NoSWBed 5334.66 31.76 4.38 25.58 112.38RtoEmpPop 68.71 20.81 5656.38 12.30 47.51PCLA 59.76 38.47 0.131 PopDensity 17.82 76.55 2857.18 12.09 68.78 37.72 35.22 61.55 4.26 Potency 0.098 19.92 3238.90 3.05 32.15 0.046 0.062 660.60 63.91 8.41 9.81 0.030 0.055 54.21 18.13 371.46 18.09 17.56 0.043 62.40 17.01 11.79 0.033 342.19 0.042 5.26 0.026 60.92 16.48 21.46 0.38 0.021 0.019 0.023 1.08 0.019 Table 5. nant analysis. Table 4. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | axis shows the deviations of y 112 111 The conceptual framework of the Resilience Inference Measurement (RIM) model Four states of resilience in the RIM framework. The exposure, damage and recovery from their means (Lam et al., 2014; K. Li, 2011). Figure 2. (Lam et al., 2014; K. Li, 2011). Figure 1. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 114 113 The intensity of 2008 Wenchuan earthquake. Counties examined in this study. Figure 4. Figure 3. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 116 115 The population growth rate by county from 2002 to 2011. The economic loss per capita by county caused by the 2008 Wenchuan earthquake. Figure 6. Figure 5. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 118 117 Map of the four resilience groups derived by K-means cluster analysis. Plot of the four K-means clusters on the three dimensions. Figure 8. Figure 7. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 120 119 Map of the misclassified counties. Map of the four resilience rankings derived by discriminant analysis. Figure 10. Figure 9. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 122 121 Plot of the 15 variables on the first two discriminant functions. Plot of the four resilience groups on the first two discriminant functions. Figure 12. Figure 11.