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Resilience Assessment: Recovery of Brisbane Neighbourhoods after 2011 Flood

Conference Paper

Resilience Assessment: Recovery of Brisbane Neighbourhoods after 2011 Flood

Abstract

The purpose of this study is to evaluate the link between resilience attributes and conditions of each neighbourhood on recovery of 26 neighbourhoods of Brisbane after the 2011 flood. A systematic review of literature on urban disaster resilience and recovery was conducted to extract the indicators used to measure and monitor recovery. The required data were obtained through governmental and private sector sources including Queensland Reconstruction Authority, Australian Census, Australian Community Capacity Survey (Wave four) and previously studies in the Brisbane area. A cross-case comparative analysis was performed using fs/QCA software to assess the necessity and sufficiency of pre-disaster and post disaster conditions and also to evaluate the combination of these conditions that led to recovery. The results show that there were several pathways combining pre-disaster situation and post disaster conditions which led to recovery in Brisbane neighbourhoods, as measured by the housing reconstruction after the flood. For instance, post-disaster financial assistance played a critical role in recovery of neighbourhoods like Riverview and Graceville; on the other hand, a combination of low Social Vulnerability (SoVI) and high economic stability also led to successful housing recovery. This study showed which pre-disaster and post-disaster causal and mediating factors were associated with housing recovery in Brisbane neighbourhoods after the 2011 flood. These results could be used in the development of resilient community recovery guidelines.
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ResilienceAssessment:RecoveryofBrisbaneNeighbourhoodsafter2011Flood
LeilaIrajifar,PhDcandidate,UrbanResearchProgramme,GriffithUniversity
Email:Leila.irajifar@griffithuni.edu.au
TooranAlizadeh,Lecturer,UrbanResearchProgramme,GriffithUniversity
Email:t.alizadeh@griffith.edu.au
NeilSipe,Programdirector,PlanningandEnvironmentalManagement,UniversityofQueensland
Email:n.sipe@uq.edu.au
Abstract
Thepurposeofthisstudyistoevaluatethelinkbetweenresilienceattributesandconditionsof
eachneighbourhoodonrecoveryof26neighbourhoodsofBrisbaneafterthe2011flood.A
systematicreviewofliteratureonurbandisasterresilienceandrecoverywasconductedto
extracttheindicatorsusedtomeasureandmonitorrecovery.Therequireddatawereobtained
throughgovernmentalandprivatesectorsourcesincludingQueenslandReconstruction
Authority,AustralianCensus,AustralianCommunityCapacitySurvey(Wavefour)andpreviously
studiesintheBrisbanearea.Acrosscasecomparativeanalysiswasperformedusingfs/QCA
softwaretoassessthenecessityandsufficiencyofpredisasterandpostdisasterconditionsand
alsotoevaluatethecombinationoftheseconditionsthatledtorecovery.
Theresultsshowthattherewereseveralpathwayscombiningpredisastersituationandpost
disasterconditionswhichledtorecoveryinBrisbaneneighbourhoods,asmeasuredbythe
housingreconstructionaftertheflood.Forinstance,postdisasterfinancialassistanceplayeda
criticalroleinrecoveryofneighbourhoodslikeRiverviewandGraceville;ontheotherhand,a
combinationoflowSocialVulnerability(SoVI)andhigheconomicstabilityalsoledtosuccessful
housingrecovery.Thisstudyshowedwhichpredisasterandpostdisastercausalandmediating
factorswereassociatedwithhousingrecoveryinBrisbaneneighbourhoodsafterthe2011flood.
Theseresultscouldbeusedinthedevelopmentofresilientcommunityrecoveryguidelines.
Keywords:‐HousingRecovery‐QCA‐Flood‐Brisbane‐Australia
AbstractReferenceNumber:26
Introduction
Disasterrecoveryisoneoftheleastunderstoodaspectsofdisastermanagementphases(Chang&
Shinozuka,2004;Smith&Wenger,2007).Consideringitsdynamicandcomplicatednature,
monitoringandevaluatingrecoveryprogressisconsideredasamultidimensionalconceptwhich
hasbeenapproachedasasocial,economic,design,management,financeorplanningproblem.
Previousstudiesondisasterrecoverymostlyusedqualitativeandsubjectiveinformation,obtained
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bysocialaudittechniquesandparticipatorymethods(e.g.focusgroupmeetings,household
surveysandkeyinformantinterviews).However,recentlyaseriesofquantitative,systemicand
objectiverecoverystudiesareconductedusingdirectobservationandnonparticipatorymethods
(e.g.remotesensing,repeatphotographyandadvancedfieldsurveytechniques)thatallow
detailedgeocodedobservations.Thesetoolseachhavetheirownstrengthsandweaknessesand
havebeenusedinpreviousrecoverystudiestocollectdifferentformsofdata
(subjective/objective,quantitative/qualitative,crosssectional/longitudinal,primary/secondary
etc.).Urbandisasterrecoveryismostlyassessedbyusingrecoveryindicators.Forexample,Brown
etal.(2010)intheRecoveryProjectconductedbytheCentreforRiskinBuiltEnvironmentat
CambridgeUniversityidentified24recoveryindicatorsinSixmajorcategoriesofVulnerability,
Livelihoods,Housing(includingdrinkingwateraccess),Services,Environment(includingvegetation
andremovaloffloodwatersandanddebris)andInfrastructures(includingroadaccessand
reconstruction).However,themostfrequentlyusedrecoveryindicatorsarereconstructionof
houses,criticalfacilitiesandlifelines,noncriticalfacilitiesandlifelines,transportationsystems,
numberofbuildingpermitsandpopulationreturn.Thefocusofthisresearchisonhousing
recoveryatneighbourhoodlevel.
Thenumberofindividualpostdisasterrecoverycasestudieshasincreasedinrecentyearswhich
advanceunderstandingofthecomplexities,politicsandprocessesofurbandisasterrecovery.But
therehavebeenverylimitedcomparativestudiesindisasterrecoveryassessment.TheBrisbane
floodin2011anditssubsequentrecovery,providesausefulcasestudyforacomparativerecovery
study.Differentaspectsof2011Brisbanefloodhasbeeninvestigatedfromcommunitycapacity
assessment;buildingsdamageassessments,criticalinfrastructuresandparticipatorysocialaspects
ofrecovery.ButtherehavenotbeenabroadcomparativestudyofBrisbaneneighbourhoods’
recoveryaftertheflood.Hence,inthisresearchweanalysedthecombinationsofresilience
attributesandconditionsbeforeandafterthedisastertoexaminethecausesofdifferentrecovery
ratesinneighbourhoods.
ResearchMethod
AswedesiretocomparativelyanalyserecoveryinBrisbaneneighbourhoodsaffectedinthe2011
flood,acrosscasequalitativecomparativeanalysis(QCA)seemedappropriateforthispurpose.
ThefirststepinQCAinvolvesidentifyingaparticularoutcomeofinterest,besidestheconditions
thataretheorizedtohaveanimpactonoutcome.Thenitwillanalyseallthepossiblecomplex
combinationofconditionsthatcouldresultintargetedoutcome.
Aseriesofresilienceindicatorsthataretheorizedtoaffectcommunityrecoverywasextractedby
reviewingtheresilienceassessmentliterature.However,consideringtheunitofanalysis,disaster
typeanddataavailabilityinthiscasestudy,weusedasubsetoftheseidentifiedindicatorswhich
includes:Socialvulnerability(calculatedbasedonCutteretal’sSoVI(2003)),Economicstability
(Incomelevel,employment,femaleemployment),SocioEconomicstatusofthearea(SEIFAas
calculatedbyAustralianbureauofmeteorology),urbanform(%notsinglefamilydetachedhouses,
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dwellingdensity),Recoveryfunds(Federal/State/Localgovernmentfinancialassistance)and
damageloss.
Thedatausedforthisanalysiswereobtainedfromadifferentrangeofsources.Thedatarelated
tocalculatingtheSoVI,SEIFAandeconomicstabilityandurbanformwereobtainedfrompublicly
availabledatasourcesincludingAustralianCensusandQueenslandGovernmentonlinedatabase
andNEXIS(NationalExposureInformationSystem).Therecoveryfunddatawereobtainedfrom
Australiancommunitycapacitysurvey(ACCS),alongitudinalsurveythatitsfourthwavewas
conductedjustbeforeandafterthe2011Brisbanefloodin148suburbsacrossthegreatBrisbane
area.DARMsysdata(DamageAssessmentandReconstructionMonitoringsystem)wereusedfor
calculationoftherecoveryoutcomes.ThisdatasetwasobtainedfromtheReconstruction
AuthorityofQueenslandthatmonitoreddamageandreconstructionstatusbytravellingstreetby
streetandauditingalltheaffectedpropertiesinthestudyarea.
Asmentionedintheintroductionsection,therecoverycanbemeasuredbyavarietyofindicators.
However,hereweusehousingreconstructionin10,13and17monthsafterthefloodasrecovery
outcomes.Housingreconstructioniscalculatedbasedonthelongitudinalfieldsurveydamagedata
providedbytheReconstructionAuthorityofQueensland.Thepercentageofhousingstock
reconstructedineachtimepointforeachneighbourhoodwascalculatedandthendirectly
calibratedusingminimummaximumscalingmethod.Fourneighbourhoods(Paddington,
Greenslopes,KholoandSinnamonPark)werefullyrecoveredwithin10monthsafterthefloodand
theywereratedas1inthefuzzyset.Whilelessthan50%oftheaffectedpropertiesintwo
neighbourhoods(GoodnaandYeerongpilly)werereconstructedover10monthsandwererated
as0.Thesamemethodswereusedforcalibrationofhousingrecoveryin13and17monthsafter
theflood.Astherewasawiderangeofdatavaluesforconditionsandoutcomes,weusedfuzzy
setQCAinwhicheachoftheconditionsandoutcomesareassignedavaluefrom0(completelyout
oftheset)to1(completelyintheset).Thereforeweaggregatedtheindicatorsofeachcondition
andusedaminimummaximumscalingmethodtodirectlycalibratethedatatoafuzzy‐setscore.
Thesecalibrateddatawereimportedandanalysedinfs/QCAsoftware.Thetruthtablewasbuilt
withinthesoftwarewhichsummarizestheconfigurationsofneighbourhoods’conditions.To
generateanintermediatesolution,someassumptionswasmadeaboutthepresenceorabsence
oftheconditionbasedonauthorsknowledge.Moreover,twoimportantfactorswereconsidered
inpathwaysanalysis:Consistency(whichshowstheextenttowhichtheneighbourhoods
representedbyaparticularconfigurationhavethesamerecoveryoutcome),Coverage(which
showshowmanyoftheneighbourhoodsareexplainedbyaparticularconfiguration).
ResearchResults
Theanalysisoftruthtableresultedinthreemainpathwaysthataresufficientforsuccessful
housingrecoveryaftertheflood.Table1.1showsthesepathwaysandalsoindicatesthespecific
neighbourhoodsrelatedtoeachofthesepathways.Theconsistencycutoff,usedforthisanalysis
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is0.85astherewasabreakintheconsistencyscoresatthispoint.Theoverallcombined
consistencyscoreofthesesolutionis0.88andtheoverallcoveragescoreis0.68.
SEIFAseemstobeanimportantfactorinhousingrecoveryastwoofthethreepathwayshasthis
conditionincommon.InadditiontoSEIFA,successfullyrecoveredneighbourhoodshaveeitherlow
socialvulnerabilityorlowlevelofdamagesinmorecompactneighbourhoods.Thethirdpathway
torecoveryinBrisbaneneighbourhoodswasthroughfederal/localgovernmentfinancialassistance
andinsurancelikewhathappenedinRiverviewandGraceville.
Itshouldbenotedthatthecoveragescoreof0.69showsthattherearesomeneighbourhoods
whicharenotrepresentedbythesethreepathways.Forinstance,GailesandRedbankdespitetheir
highsocialvulnerabilityandlowSEIFAlevelareinthesetofsuccessfulrecovery.Both
neighbourhoodsreceivedhighamountsofrecoveryfunds,whicharehypothesizedtoleadto
successfulrecovery.Itisalsopossiblethatrecoveryoftheneighbourhoodswhichdonotappearin
thepathwaysintable1.1couldbeexplainedbyothervariables,whichwerenotapartofthis
analysis.
Table1.1.HousingrecoverypathwaysresultedfromqualitativecomparativeanalysisofBrisbane
neighbourhoodsconditions
PathwaysCoverageConsistencyCases
SEIFA*~SoVI0.540.90Tennyson,MountOmmaney,Anstead,
Chelmer,Kholo,SinnamonPark,
Barellan,Karalee,Jindalee
SEIFA*~Damage*UrbanForm0.470.92Paddington,Yeronga,Woolloongabba,
Moorooka,Sherwood,Yeerongpilly,
Fairfield,Greenslopes
FederalAssistance*Local
Assistance*Insurance
0.280.93Riverview,Graceville
SolutionCoverage:0.69
SolutionConsistency:0.88
Thenecessityandsufficiencyofeachconditionwasassesseddirectlyusingthefs/QCAsoftwareto
furtherunderstandtherecoverypathways.Accordingtopreviousstudies,thecutoffscorefor
necessityandsufficiencyisconsideredas0.80and0.85respectively.Necessityanalysis(Table1.2)
showedhighincomelevelwasnotnecessaryforrecoveryinBrisbaneneighbourhoods,yetitwas
nearlysufficient.Inotherwords,notallofthesuccessfullyrecoveredneighbourhoodsnecessarily
hadhighlevelsofeconomicstabilitybutiftheyhadhigheconomicstability,inmostcases
(coverage=0.78)theyrecoveredsuccessfully.Thissoundsreasonableaspeoplewithfinancial
resourcescansurvivewithoutjobsandrebuildwithoutwaitingforrecoveryfunds.Nonetheless,
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thepathwaysshowthatevenalowincome,sociallyvulnerableneighbourhoodsuchasRiverview
andRedbankrecoveredwhenotherconditionslikerecoveryfundsandinsurancewerepresent.
Table1.2.Necessityandsufficiencyofconditionsforeachrecoveryoutcome
ConditionRecoveryafter10month Recoveryafter13month Recoveryafter17month
NecessitySufficiencyNecessitySufficiencyNecessitySufficiency
SEIFA0.820.730.790.820.790.81
SoVI0.69 0.810.65 0.78 0.630.82
DamageLoss0.330.750.330.760.310.80
FederalAssist0.540.800.550.830.500.84
LocalAssist0.37 0.880.37 0.91 0.360.98
Insurance0.510.840.520.860.470.88
UrbanForm0.530.860.480.810.490.93
Income0.70 0.820.69 0.81 0.660.86
Conclusion
Thisresearchexaminedthelinkbetweenresilienceattributesof26floodaffectedneighborhoods
inBrisbaneandtheirhousingrecoverylevelovertime.Fuzzysetqualitativecomparativeanalysis
wasutilizedtoanalyzetheconditionsandrecoveryoutcomesineachneighborhood.Theresilience
andrecoveryassessmentmethodswerereviewedtofindthesuitableindicatorsofrecovery
outcomeandconditions.Therecoveryoutcomewascalculatedusingtimeseriesdamageand
reconstructionsstatusdatacollectedbyReconstructionAuthorityofQueenslandduring17months
aftertheflood.Thepredisasterattributesusedinthismodelwaseconomicstability,urbanform,
SoVI(SocialVulnerabilityIndex),SEIFA(SocioEconomicIndexforAreas),andfloodexposure.On
theotherhand,postdisasterconditionsconsideredinthemodelweredamageloss,recoveryfunds
available(Federal/State/Localgovernmentfinancialassistance)andInsurance.Thenecessityand
sufficiencyofeachconditionforhousingrecoverywereevaluatedandthreedifferentpathways
forhousingrecoverywereidentified.
Thecombinationofconditionsidentifiedintheanalysisrevealedthattherearedifferentareasthat
communitiescaninvestontobuildamoreresilientneighborhoodthatsuccessfullyrecoversfrom
futurefloods.Differentcommunitiescouldhavedifferentpathwaysofrecovery.Forinstance,
communityresiliencecanbeimprovedbyplanningforsocialvulnerabilityreductionandincreasing
theeconomicstability.Strengtheningsocialcapitalandnetworksincommunitieswithhighlevels
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ofsocialvulnerabilitycancontributetotheresilienceoftheneighborhood.Whentheseconditions
arenotavailable,resiliencecouldbeimprovedthroughinterventionsinissuessuchasaccessto
resources,landusemanagementandetc.Theresultsalsoshowedtheimportanceofurbanform
inthiscontext.Neighborhoodswithamorecompactdesignandlesssinglefamilydetachedhouses
wererecoveredmorequickly.Althoughitmaycreateadensityconundrumbetweenhighexposure
andcapabilitytorecoverquickly,butitemphasizesthefactthatincitieslikeBrisbanethathas
beenbuiltonafloodplainandfloodcannotbecompletelyavoided,attentiontothebuildingtypes
anddevelopmentpatternscouldimprovetheresiliency.
AlthoughSEIFAandlackofsocialvulnerabilityappearedintwopathwaysintable1.1,noneofthem
areentirelynecessaryacrossallneighbourhoodswhichcouldbepromisingwherecommunity
resilienceneedstobeimprovedthroughdifferentpathways.Whilesocialvulnerabilityand
economiccapacityaredifficulttomodify,resilienceplannerscangetpreparedtoprovide
additionalsupporttoareasofconcentratedsocialvulnerabilityduringhousingrecovery.
References
Brown,D.,Platt,S.,&Bevington,J.(2010).DisasterRecoveryIndicators:guidelinesformonitoring
andevaluation.CURBE,CambridgeUniversityCentreforRiskintheBuiltEnvironment.
Chang,S.E.,&Shinozuka,M.(2004).Measuringimprovementsinthedisasterresilienceof
communities.EarthquakeSpectra,20(3),739755.
Cutter,S.L.,Boruff,B.J.,&Shirley,W.L.(2003).Socialvulnerabilitytoenvironmentalhazards*.
SocialScienceQuarterly,84(2),242261.
Smith,G.P.,&Wenger,D.(2007).Sustainabledisasterrecovery:operationalizinganexisting
agendaHandbookofdisasterresearch(pp.234257):Springer.
Author’sBiography
LeilaIrajifar
LeilaIrajifarisaPhDcandidateatUrbanResearchProgrammeat
GriffithUniversity,Brisbane,Australia.ShehasaB.A.inArchitecture
andM.Sc.inPostdisasterreconstructionfromShahidBeheshti
University,Tehran,Iran.Herresearchinterestsincludeurbandisaster
resiliency,urbanmodellingandsystemstheory.
ResearchGate has not been able to resolve any citations for this publication.
Chapter
Full-text available
Disaster recovery represents the least understood aspect of emergency management, from the standpoint of both the research community and practitioners (Berke, Kartez, & Wenger, 1993; Rubin, 1991). When compared to the other widely recognized phases of emergency management, that is, preparedness, response, and mitigation, scholars have yet to address fundamental questions, while practitioners have failed to establish an integrated policy framework or utilize readily available tools to improve disaster recovery outcomes (Berke et al., 1993; May and Williams, 1986; Mileti, 1999). Since the 1990s the concept of sustainability has been adopted by hazards researchers and applied to mitigation (Berke, 1995a; Burby, 1998; Godschalk, et. al., 1999; Mileti, 1999), recovery (Becker, 1994a; Berke, Kartez, & Wenger, 1993; Eadie et al., 2001; Oliver-Smith, 1990; Smith, 2004; United States Department of Energy, 1998), and to a lesser extent preparedness and response (Tierney, Lindell, & Perry, 2001). While recognized as a meaningful paradigm among scholars and a limited number of practitioners, achieving sustainable recovery following disasters is not a widespread phenomenon in the United States, owing in large part to the current recovery model in practice today. It is therefore the intent of this chapter to describe an improved policy implementation framework focused on achieving sustainable recovery. Emphasis is placed on the analysis of the United States model of recovery and the development of specific recommendations to improve the process. Key issues and research questions are identified in order to advance this agenda, including the need to develop a theory of recovery that emphasizes specific factors that facilitate or hinder this approach. Next, a review of the literature highlights the fact that while past research has addressed several recognized dimensions of sustainable recovery, the research has not been linked to a unifying theory that helps to clarify our understanding of how sustainable recovery can be achieved.
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This paper demonstrates the concept of disaster resilience through the development and application of quantitative measures. As the idea of building disaster-resilient communities gains acceptance, new methods are needed that go beyond estimating monetary losses and that address the complex, multiple dimensions of resilience. These dimensions include technical, organizational, social, and economic facets. This paper first proposes resilience measures that relate expected losses in future disasters to a community's seismic performance objectives. It then demonstrates these measures in a case study of the Memphis, Tennessee, water delivery system. An existing earthquake loss estimation model provides a starting point for quantifying resilience. The analysis compares two seismic retrofit strategies and finds that only one improves community resilience over the status quo. However, it does not raise resilience to an adequate degree. The exercise demonstrates that the resilience framework can be valuable for guiding mitigation and preparedness efforts. However, to fully implement the concept, new research on resilience is needed that goes beyond loss estimation modeling.
Article
Objective. County-level socioeconomic and demographic data were used to construct an index of social vulnerability to environmental hazards, called the Social Vulnerability Index (SoVI) for the United States based on 1990 data. Methods. Using a factor analytic approach, 42 variables were reduced to 11 independent factors that accounted for about 76 percent of the variance. These factors were placed in an additive model to compute a summary score-the Social Vulnerability Index. Results. There are some distinct spatial patterns in the SoVI, with the most vulnerable counties clustered in metropolitan counties in the east, south Texas, and the Mississippi Delta region. Conclusion. Those factors that contribute to the overall score often are different for each county, underscoring the interactive nature of social vulnerability-some components increase vulnerability; others moderate the effects.
Disaster Recovery Indicators: guidelines for monitoring and evaluation
  • D Brown
  • S Platt
  • J Bevington
Brown, D., Platt, S., & Bevington, J. (2010). Disaster Recovery Indicators: guidelines for monitoring and evaluation. CURBE, Cambridge University Centre for Risk in the Built Environment.