ContentslistsavailableatInternationalJournalofDisasterRiskReductionjou - PDF document

1 ContentslistsavailableatInternationalJou
ContentslistsavailableatInternationalJournalofDisasterRiskReductionjournalhomepage:www.elsevier.com/locate/ijdrr ReviewArticleOperationalizingaconcept:ThesystematicreviewofcompositeindicatorbuildingformeasuringcommunitydisasterresilienceA.Asadzadeh,T.Kötter,P.Salehi,J.Birkmann Correspondingauthor.E-mailaddress:(A.Asadzadeh). metricsaredenedandformulatedbasedontheintroducedCIBpro-cedure.InSection4,thequalityofselectedmeasurementframeworksinmeasuringcommunitydisasterresilienceareanalyzedandassessedbasedonthedevelopedmeta-levelframework,andtheresultsofthequalityassessmentaredepictedandillustrated.Section5concludesthe1.1.Resilience:backgroundandevolutionWhileitisoftenarguedthatthetermresiliencewasrstformulizedintheeldofecologybyHollingHolling,ithasbeenusedsincethe16th16th.However,aftermorethanfourdecadesofvaluablesci-cworkonthetopicofresilience,thedebateonitsvariouscon-ceptualframeworksandtheoriessincersttheorizationandprogressinecologyandsocio-ecologicalsystemssystems–15]untilsubsequentde-velopmentsinotherdisciplinessuchassustainabilitysustainability–19],mitiga-tionandadaptationadaptation–22],andmorerecently,disasterriskreductionreduction–25],iscontroversiallyongoing.Resilienceisnowconsideredtobeahottopicininternationalacademicandpolicycirclescircles,andcarriessimilarinuencewithinenvironmentalchangestotheweightcarriedbyterminenvironmentalplanningduring1980sand1990s.Resilience,speciallytheconceptofcommunityresilience,en-compassesthewayinwhichcommunitiesfacetheincreasingcom-plexityandgrowingchangesinglobaldynamicsinordertobetterperceive,manage,andgoverncomplexsocio-ecologicalsystems,whilealsoincreasingtheirinherentcapacitytocopewith,adaptto,andshapeshape.Overthedecade20052015,theconcepthasbeenenrichedbyscholarsfromvariouselds,includingengineersengineers,socio-economists[31,32],geographersgeographersandmostrecently,urbanandregionalplannersplanners–36].Communityresilienceisbestnedasaconceptthatenhancestheabilityofacommunitytoprepareandplanfor,absorb,recoverfrom,andmoresuccessfullyadapttoactualorpotentialadverseeventsinatimelyandecientmannermanner.Althoughthetheorybehindcommunityresilienceisstillchal-lengingandthetermisconstantlyevolvingevolving,thereisaconsensusamonghazardscholarsthatthesteptowardcommunitydisasterresilienceshouldbefocusedonunderstandinghowitcanbemeasuredandoperationalizedoperationalized.1.2.Communitydisasterresiliencemeasurement:necessityandchallengesAlthoughincreasedattentionisbeingpaidtothemeasurementofcommunitydisasterresilience,theseendeavorsareintheirinfancyandthereremainslittleempirically-basedresearchoncommunitydisasterresiliencemeasurementmeasurement.Resilienceisanabstractconceptanditisculttoquantifytheconceptinabsolutetermsterms.Hence,understandingthecharacteristicsthatcontributetoresilienceisamajormilestonetowardenhancingresilienceandpredisposesdecision-ma-kers,stakeholders,andotherend-userstoprioritizethoseactionsthatareneededtobuildandsustainresilienceresilience.Conceptualframeworksofdisasterresilienceareabundantandincludeanumberofapproachesthathavebeendevelopedtooperationalizeresilienceofcommunities,regions,andsystems.Theserangefromthosethatcon-siderresilienceasasetofengineeringfunctionalityfunctionality,com-munitycapitalscapitals,communitycapacityindexindex,orplace-basedplace-basedmeasurements(seeTable2).Despitetheseendeavors,debateoncharacteristicsthatcontributetoresilienceandtransitionfrommerelytheoreticalframeworkstoempiricalassessmentsofcommunityresilienceisongoingongoing.Whileconstructingcompositeindicatorshasoftenbeenemployedtooperationalizetheconceptofcommunitydisasterresilienceinexistingndingastandardprocedurefordevelopingcompositein-dicatorsischallenging.Thisispartiallybecausetherearesignidiscrepanciesintheconceptualorientationsofdierentmeasurementapproachesthatviewresilienceasaprocess-orientedphenomenon(dynamicconcept)oraresult-oriented(staticpremise)concept.Thesetheoreticalperspectivesonresilienceimpactthedecisionofwhatshouldbemeasured,usingwhatindicators(resilienceofwhat),forwhatpurposeorwhy(asadynamicconceptorastaticpremise),when(long-termprocessandcapacitybuildingorshort-termpersistenceandresult),andresilienceforwhom(resilienceofindividuals,specigroups,orcommunities).Anotherchallengeisthateachframeworkapp

2 liesdierentprocedureforCIBincludingdatat
liesdierentprocedureforCIBincludingdatatransformationtransformation,multivariateassessmentforcategorizationandfactorretentionretention,weightingweighting,aggregationaggregation,visualizationvisualization,andand.Asaresult,thereisnouniversalprocedureforop-erationalizingtheconceptofcommunitydisasterresilience.Further-more,thereiscurrentlynoqualityassessmentofcompositeindicatorswhichhasbeencitedassucientlycomprehensivebymultiplescholarswithintheliterature.Ineortsrelevanttotheareaofdisasterresilience,qualityassessmentshavemainlybeenlimitedtobroadreviewofresi-liencemeasurementframeworks(seeIrajifaretal.al.,andWinderlWinderl)asopposedtooperationalizingtheconceptofdisasterresiliencethroughtheprocedureofCIBanddeningmetricsbasedonsaidpro-cedure,andthenassessingtheexistingresiliencemeasurementframe-worksrespectively.Forinstance,ShariShariassessed36generalresi-lienceframeworksbasedonsixcriteria,includingdimensionrange,cross-scalerelationships,temporaldynamism,uncertainties,typeofmethodology,andoperationalization.Heconcludedthatenvironmentaldimensionhasbeenneglectedinmostofthereviewedframeworksandthereexistsnocomprehensivemodelthatcoversallthesecriteriato-gether.CutterCutteralsoevaluated27disasterresiliencetools/indiceswithregardtofourmetrics,namelyfocusoftheory,spatialorientation,typeofmethodology,anddomainarea.Sheconcludedthatthereisnodominantframeworkacrosstheseattributesbecausethefactorsthatcontributetoresilienceareplacespecicandmulti-scalar,andappearwithinorbetweennatural,social,andbuilt-environmentalsystems.Inotherwords,dierentspatialcharacteristicsofthetermresiliencene-cessitatemultiple,contextually-specicplace-basedmodels.Therefore,thisstudydealswithanongoingchallengeinthemea-surementofcommunitydisasterresilience.ItcompilesandintroducesasynthesizedprocedureforCIBtoservebothasaguidelineinoper-ationalizingcommunitydisasterresilienceandasabasisfordevelopingameta-levelframeworkforqualityassessmentofexistingmeasurementframeworks.Thereviewaimedtounderstandthequalityofcurrentresiliencemeasuresthatcanbeusedtoidentifyweaknessesandlim-itationsofcurrentdisasterresiliencemeasuresandtoimprovethemwhereneeded,inordertomeettheriskpreparationandplanningneedsofstakeholders,decisionmakers,andurbanplanners.2.SurveymethodologyFrommethodologicalperspective,thestudyrstintendstoexploretheexistingliteratureonCIBinordertocompileanoverarchingpro-cedureforCIBtowarddisasterresiliencemeasurement.Itthen()providesalistofselectedmeasurementframeworksthroughasys-tematicsurvey.Next,(Section3),basedontheproposedprocedure,ameta-levelframework(includingitsqualitydimensionsandmetrics)isdevelopedinordertoconductasystematicreviewofselecteddisasterresiliencemeasures.Finally,thedevelopedmeta-levelframeworkis Table1Toplistpublisherswiththeirrespectivedatabases/libraries.PublisherNumberofRespectivescienticjournalsdatabases/librariesElsevier2571ScienceDirectSpringer-Verlag2209SpringerLinkTaylorandFrancis1803Taylor&FrancisOnlineJohnWileyand1604WielyOnlineLibraryA.Asadzadehetal. appliedinordertoperformaqualityassessmentoftheselectedresi-liencemeasures(Section4Fig.1givesanoverviewoftheresearchowandtasksinvolved.2.1.AssortedstepsofcompositeindicatorbuildingprocedureConstructingcompositeindicatorshasoftenbeenappliedformea-suringdisasterresiliencelevelinexistingliteratureliterature–53].Althoughliteraturereviewoncompositeindicatorsformeasuringdis-asterresilienceisplentifulandencompassingdierenttheoreticalperspectives,andgeographicalcontextsandscalesscales,thereisnowidelyagreedanddominantprocedureorsoundsetofstepstobetakenforCIBinmeasuringcommunitydisasterresilienceresilience.Throughexploringandanalysisoftherelevantliterature,thisstudycompilesandintroducesasynthesizedproceduretoservebothasabasisfordevelopingthemeta-levelframeworkforqualityassessmentofexistingframeworkandalsoasaguidelinetofollowbyotherusersinoperationalizingcommunitydisasterresilienceinvariouscontexts.Fig.2illustratesthesynthesizedprocedureforCIBanditseightsteps.TheprocedureproposesaexibleandtransparentprocessofCIBwhichcomprisesthefollowingsteps:1.Developingorapplicationofatheoreticalframeworkasabasisfor Table2Selectedassessmentframeworks,theirtype,origin,andhazard/disastercontext.Framework/authorTypeOriginContextContextToolSocio-ecologicalResilienceHurri

3 caneCDRI/Shaw&IEDMIEDMIndexSocio-ecologi
caneCDRI/Shaw&IEDMIEDMIndexSocio-ecologicalResilienceTsunamiCRI/Sherriebetal.al.IndexSocio-ecologicalResilienceHurricaneBRIC/Cutteretal.al.IndexSocio-ecologicalResilienceMultiplePEOPLES/Renschleretal.al.ToolEngineeringResilienceEarthquakeCDRI/DRLA&UEHUEHToolSocio-ecologicalResilienceEarthquakeResilUS/Miles&ChangeChangeToolEngineeringResilienceEarthquakeCRI/Tilioetal.al.ToolEngineeringResilienceEarthquakeRCI/Fosteretal.al.IndexSocio-ecologicalResilienceMultipleSRI/Verruccietal.al.ToolEngineeringResilienceEarthquakeEarthquakeIndexSocio-ecologicalResilienceHurricaneCDRI/Orenio&FujiiFujiiIndexEngineeringResilienceOXFAM/Hughes&BushellBushellIndexSocio-ecologicalResilienceResilienceToolEngineeringResilienceYoon&KangKangIndexSocio-ecologicalResilienceClimatechangeBRIC/Cutteretal.al.IndexSocio-ecologicalResilienceCDRI/Parsonsetal.al.ToolSocio-ecologicalResilienceCommunityDisasterResilienceIndex,CommunityResilienceIndex,BaselineResilienceIndicatorsforCommunities,IResilienceCapacityIndex,ResilienceIndex,CommunityBasedResilienceAssessment,PEOPLSFramework,ResilienceUnitedStatesandOxfamaMultidimensionalApproachforMeasuringResilience. Fig.1.owchartofthestudy. Fig.2.Overarchingprocedureforcompositeindicatorsbuildingtowardmeasuringdis-asterresilience.A.Asadzadehetal. indicatorbuilding2.Identifyingandselectingindicatorsthataresound,robustandre-3.Datatransformationandovercomingincommensurability4.Datareductionandfactorretentionthroughmultivariateassessment5.Weightingandallocatingimportanceacrossindividualindicators6.Aggregationofindicatorsorgroupofthem7.Mappingobtainedresults8.Validityandreliabilityofresults2.2.Strategyforselectingmeasurementframeworks;asystematicsurveyrststepforasystematicassessmentisidenticationofrelevantitemsforreviewandfurtherprocessprocess.Tothisend,wechosesixcdatabases,searchenginesanddigitallibraries;thosepro-vidingthewidercoverageofscienticpublicationsandpublishmorethan1000scienticjournals(Table1Additionaltothedatabasesoftheselectedpublishers,wealsoin-cludedWebofScienceCoreCollectionandGoogleScholartomaketheresultofoursystematicsurveymoreinclusive.Sincesearchstrategiesinasystematicsurveyareusuallyiterativeiterative,toavoidbiasandensurefairlycomprehensivecoverageofallrelevantpapersinourdatacol-lection,thesestrategieswererunbytwomembersoftheresearchteam(AsadzadehandSalehi)separately.Duetodiofse-lectedsearchengines,someofthemuseslightlydierentsearchop-erators.Therefore,itwasnotpossibletocollectthemostrelevantstu-dieswhileapplyingsamerigidsearchstringstoallthesesixvarioussearchengines.Inordertodealwiththischallenge,wehadtouseerentsearchstringsforeachsearchengine,butallthesestringswerecomposedofandlimitedtosameindividualkeywordsasfollowing:Measuring,Analyzing,Assessing,Quantifying.Resilience,CommunityResilience,DisasterResilience.Model,Framework,Tool,Index,Guideline,Baseline.Moreover,inorderforkeepingthescopeofthestudyinmanageableproportion,wedecidedtoextractalimitednumberof50articlesfromeachsearchengines.Forthispurpose,werstcustomizedthetime-frameandlimitedthesearchresultstothosepublicationsbetweentheyears20062016.Then,weutilizedexpertinterventionforselectingrelevantpapers.Accordingly,thereviewers,basedontheapplicablesearchoperatorsofeachsearchengine,useddierentsearchstringscomposedofdenedkeywordsandthentheresearchteamdecidedwhetheranarticleshouldbeincludedintorespectivelistsof50articlesforeachsearchengineornot.Thearticlesweregenerallyincludedorexcludedthroughreadingthetitles,nevertheless,skimmingofarticleabstractswasconductedincaseofneedformoreprecisedecisionmaking.Therefore,thereviewersshortlisted50articleswithrelativelyhighrelevancetoourresearchobjectivesfromeachsearchengine.InitialsearcheswereperformedinearlySeptember2016.Fig.3thestepstowardthenalnumberofarticlesretrievedthroughthesystematicsurvey.Whenall300articles(6searchenginesand50articlespereach)wereextractedandshortlisted,theywereimportedtoMendeleytoremoveduplicates.Duplicateswereduetosameresultsfromdisearchengines.Theteamfoundoutthat47paperswereduplicates,andassuch,theywereeliminatedfromfurtherprocessing.Inthenextstep,253articleabstractswerereviewedtoincludeorexcludethembasedonfourinclusionmetrics.Thesecriteriaweredenedbasedonthere-searchobjectivesandaimedtoreducethenumberofstudiestothosewithfollowingcha

4 racteristics;1.Communitylevel2.Multi-fac
racteristics;1.Communitylevel2.Multi-facetedapproach3.Quantitativemethodology4.OperationalizedmodelAttheendofthisstep,only26articlescouldmeetalltheinclusioncriteria.Forthefourthstep,thereferencesandbibliographiesofallthese26articleswereinvestigatedforretrievinganyimportantandwidelycitedarticlesthatwerenotfoundthroughinitialsearches.Indeed,twonewarticleswerefoundatthisstepandwereaddedtothose26articlesforfulltextreview.Inthelaststep,thefulltextsofall28articleswerecarefullystudiedtonalizethelistofstudyforthefurtheranalysis.Basedonfourdenedmetricsoftheresearch,17ar-ticleswereselectedandlistedforfurtheranalysis.2.3.Selectedassessmentframeworksforsystematicreviewnalizedlistofassessmentframeworksforthissystematicre-viewispresentedinTable2.AccordingtoCutterCutter,disasterresilienceassessmentframeworksaretypicallyclassiedintoindices,scorecards,andtoolstools.Theprevalentcategoriesinthislistareindicesandtools.Whileindicesbasedassessmentsdemonstrateinherentcharacteristicsofresiliencewithinaspecicgeographicalcontext,toolsbasedonesalsoproposeatheoreticalbackgroundforquantitativeindicatorsindicators.Asthetableindicates,mostoftheselectedframeworksbelongtothesocio-ecologicalresiliencesincethefocusofthisstudyisoncommunitydisasterresilienceapproaches.Theframeworkscanalsobecategorizedbasedontheirhazardordisastercontexts.Themostprominentarethosethathavebeendevelopedforearthquakesandmultiplehazards.Basedontheresearchstructure,thenextstepistocarryoutaqualityassessmentoftheabove-mentionedselectedframeworksthroughdeningandestablishingqualitymetricsandtheirrespective3.Developingmeta-levelframework:deningdimensionsandmetricsforqualityassessmentInordertoassessthecomprehensivenessofselectedframeworksinapplicationofCIBproceduretowardmeasuringdisasterresilience,wasnecessarytodenedimensionsandsub-dimensions(metrics)forqualityassessment.Throughreadingdisasterresilienceliteratureingeneralandcompositeindicatorsbuildinginparticular,19dimensionsand36metricswereextracted,dened,andformulatedtodevelopourqualityassessmentframework,dubbedhereasmeta-levelframework. Fig.3.Processofsystematicsurveyforselectingassessmentfra-A.Asadzadehetal. Thenthedimensionsandmetricswereregroupedbasedontheeightstepsoftheproposedprocedure(Fig.2)toenableustoscrutinizeandreviewtheselectedresiliencemeasurementframeworks.Thefollowingsubsectionsareaboutdeningtherelevantdimensionsandmetricsforeachoftheeightstepsofthesynthesizedprocedure.DimensionsandmetricsundereachstepshelpusunderstandtowhatextendaresiliencemeasurementframeworkhasconsideredthatparticularstepinCIB.3.1.TheoreticalcomprehensivenessTheprimarystepofCIBisstartedbydoingasystematicliteraturereviewtoprovideacomprehensivelistoftheoreticalframeworksaswellasconceptualizingandformulizationofthetermresilienceresilience.Avalidtheoreticalframeworkpredisposesresearcherstoenhancetheirperceptionofthesubjecttobemeasuredandalsoaggregatesunderlyingsubindicatorsintoasignicantcompositein-in-.Inordertounderstandhowtheselectedframeworksviewresilience,andtoanswerthefundamentalquestionsofwhyresilience,resilienceforwhen,andresilienceofwhat,wehavedenedthreedi-mensionsincludingsemanticcompletenessmeasurementfocus,anderationalizeddomain.Table3displaysmetricsforthesedimensionsandprovidesreferencestotheoriginalliterature.3.1.1.SemanticcompletenessSemanticcompletenessofdisasterresilienceframeworksischar-acterizedbythedistinctionbetweentheirattitudestowardthetermresilience.Whilesomeframeworksdeneresilienceasaprocessofcapacitybuilding,othersdescribeitasanoutcomeoutcome.Semanticcompletenessinthisstudyisaddressedbyfollowingaspects:1),referstothosemodelsthatviewthetermresilienceasbouncingbacktothesameconditionbeforeanadverseeventevent,and,denotesadaptiveresiliencesuchasre-spondto,recoverfrom,andadapttonewconditionsconditions.3.1.2.MeasurementfocusThemeasurementfocusofresilienceframeworkscanbeclassiinto:1)measuringandstabilityofcommunitiesbyfocusingonreturntimeandeciencyofcharacteristics(engineeringresilience),2)levelofcommunitiesthroughfocusingonbuf-feringcapacity,withstandingshocks,andmaintainingfunctions(eco-logicalresilience),and3)measuringadaptivecapacity,aswellaslearning,andtransformability(socio-ecologicalresilience)whichen-ablecommunitiestorespondsuccessfullyto,recoverfrom,andadapttonewconditionscond

5 itions.3.1.3.OperationalizeddomainDisast
itions.3.1.3.OperationalizeddomainDisasterresiliencemeasurementframeworksendeavortooper-eitherthecharacteristicsofthesystemsorcommunity,orthecapacitieswithinthemthem.Whilethecapacitybasedassessmentsfocusonevaluatingofsystemsorcommunityelementsthatareimportantforlevelofperformance,characteristicbasedas-sessmentsmeasureuniquequantitiessomeattributesincommunitieswithoutanyevaluationofqualitythatmakethemdierentfromothers.3.2.IndicatorsappropriatenessThesecondcrucialsteptowarddevelopingcompositeindicatorsiscationofrelevant,measurable,androbustindicatorsindicators.Thisstepisoftendependedonthetheoreticalfoundationofameasureandhowthetermresilienceisdenedorviewedviewed.Tounderstandtheappropriatenessofdevelopedindicatorsintheselectedframeworks,wedenedthreedimensionsincludinglevelofmea-,anddataqualityTable4displaysmetricsforthesedimensionsandprovidesreferencestotheoriginalliterature.3.2.1.RepresentativenessThetermrepresentativenessreferstotheabilitytoportraythefundamentalcharacteristicsofaresearchsubjectwhileconsideringknowndiversityofthesubjectsubject.Tounderstandwhetheranin-dicatorsetrepresentstheunderlyingcharacteristicsofdisasterresi-lience,wedenedtwometrics:1),referstotheanalyticalsoundnessofindicatorconcon,and2),denotesthedegreeofindicatorsetinclusivenessinclusiveness.3.2.2.LevelofmeasurementMitchelletal.al.classi
edfourlevelsofmeasurementthatvar-iousindicatorsintendtomeasure.1),orspecicactionindicatorswhicharemostlyresultedfromgovernmentalandinstitutionalinputsaswellasinvestmentsindisasterresilienceresilience.2)dicatorsthatrefertoreductionofexposuretodisasters,resilienceca-pacitiesandactionsactions.3)indicatorsthatmeasuretheactualoutcomescausedbydisasterssuchaseconomiclossesorfailureof Table3Qualitymetricsrelatedtotheoreticalcomprehensiveness(typeSreferstoasubjectivemetricandOtoanobjectiveone).DimensionMetricDescriptionSemantic-completenessStaticDeningresilienceasbouncingbacktothesamestateorcondition,andconcentratingonendandresultresult.ODynamicFocusingonadaptiveresilienceandlearningtorespond,recover,andadapttonewconditionsconditions.MeasurementfocusRecovery/ConstancyMeasuringresiliencethroughhighlightingeciencyandreturntimetime.OPersistence/RobustnessQuantifyingtheabilityofhumancommunitiestowithstandexternalshocksandmaintainfunctionsfunctions.Adaptive/TransformabilityQuantifyingtheabilitytoabsorbperturbation,degreeofself-organization,degreeoflearnability,andcapacityforr.OperationalizeddomainCapacityMeasuringthequalityperformanceorabilityofsystemsorelementsofcommunitiescommunities.OCharacteristicMeasuringuniquequantitiesofsomeattributesincommunitiesthatmakethemdierentfromothers Table4Qualitymetricsrelatedtoindicatorappropriateness.DimensionMetricDescriptionRepresentativenessRobustnessTheextentthatanindicatorsetisanalyticalsoundandbestrepresentationofthesubjectunderanalyzinganalyzing.SCoverageTheextentthatasetofindicatorstakesthedisasterresiliencedimensionintoaccountaccount.LevelofmeasurementInputWhenindicatorsmeasureinputs,orspecicactionsrelatedtodierentsectorsofcommunitiescommunities.OOutputWhenindicatorsmeasureoutputsofactionsthataimtoincreasecapacityandresiliencelevellevel.OutcomeWhenindicatorscaptureoutcomesresultedfromhazardsanddisastersdisasters.ImpactWhenindicatorscatchimpactsandadverseee.DataqualityTimelinessHowup-to-datedaretheappliedinformation(10years)forthemeasurementmeasurement.OConsistencyWhetherameasureappliedconsistencycheckandtestedinternalconsistencyofdataornotnot.A.Asadzadehetal. criticalinfrastructuresinfrastructures,and4)indicatorswhichrepresenttheextenttowhichthatsystemelementscansustainwell-beingdespiteanadverseeventevent.3.2.3.DataqualityDataqualitydenotestheadequacyofaparticulardatumordatasets,andischaracterizedbytwometrics.1),measuringhowup-to-datedatais(10years),relativetothesubjectsubject,and2),understandingtheinternalconsistencyofdataandwhethertheyarepresentedinthesameneededformatornotnot.3.3.DatatransformationSinceindicatorsareexpressedinavarietyofstatisticalunits,rangesorscales,thenextstepistotransformthemintoacommonscaleormeasurementunitbymeansofdatanormalizationordatastandardi-zationtechniquestechniques.Althoughthetypeofnormalizationmethoddependsonthemodelthatthedataisfedto,transformationofdataintoastandardmeasurementunitisgenerallyackn

6 owledgedasanecessarystepbeforeproceeding
owledgedasanecessarystepbeforeproceedingtofurtheranalysis.Forassessingthisstepintheselectedframeworks,weutilizedtwodimensionsof,andThesedimensionsaredisplayedalongwiththeirmetricsTable53.3.1.StandardizationStandardizationisaprocessthatalldierentindicatorsarecon-vertedtothesamescalesothattheycanbecomparedcompared.Thismethodoroftenusesz-scoretechniquethatproducesin-dicatorswithameanof0andastandarddeviationof11.3.3.2.NormalizationNormalizationiscommonlyalineartransformationtoaunirangeorrate.istransformationoforiginalrangeofdatathatisusuallyperformedthroughemployingMin-Maxscalingtechnique.Min-Maxdecomposeseachindicatorsvalueintoasamerangebetween0and1andprovideseasilyunderstoodcomparisonsamongplacesataparticularpointintimetime.Theotherformofnormalizationistotransformthedataintoauniedrate()bymeanofordinalorpercentilemethods.3.4.Multivariateassessment(datareductionandfactorization)ThefourthsteptowardCIBisdatareductionandfactorizationofthembydoingmultivariateassessment.Thisstepaimstosimplifyandreducealargenumberofcandidateindicatorsandtosorttheminasetofunderlyingcomponents(factors)ofasubjectundermeasuringmeasuring.Althoughtherenowexistawiderangeofdierentmethodstoperformthisstepintherelevantliterature,wedenedthreedimensionstounderstandtheappliedmechanismsintheselectedframeworksin-datareductionfactorretention,andTable6proposedmetricsforthesedimensionsandprovidesreferencestotheoriginalliterature.3.4.1.DatareductionDatareductionisformofanalysisthatsharpens,sorts,focuses,discards,andorganizesdatainsuchawaythatnalconclusionscanbedrawnandveriveri.Avarietyofstatisticalmethodsareavailabletoperformthistaskinliterature,however,weextractedtwomostpopulartechniquesfromdisasterresilienceliterature.1),denotesthoseanalysesthatexaminenon-linearrelationshipswithinthedataanddescribetheoverallstructureofthedatabymeansofmethodssuchasmultidimensionalscalinganalysisanalysis,and2)Parametricanalysis,referstotheanalysesthatexaminelinearrelation-shipsamongdierentparametersbymeansofmethodssuchasprin-cipalcomponentanalysis(PCA),andPearsonCorrelationCoeCoe.3.4.2.Factorretention(componentsofanalysis)Factorretentionaimstoidentifytheunderlyingdrivingfactors(components)thatcontributetoresilienceandinteractionsthatareneededtomaintainandenhanceitit.Factorretentionisdecom-posingofindividualindicatorsintosub-indicatorsinwhichtheyrethenatureofindividualindicatorsbelongtothem.Thereexisttwopredominantapproachesintherelevantliteratureincluding:1),thosetheoriesthatuseddeductiveandhierarchical(e.g.expertsurvey)methodsmethods,and2),thoseframeworksthatap-pliedaninductivemethodology(e.g.PCA)forperformingthisstepstep.3.4.3.TechniqueAsstated,thefourthstepofCIBisasignicantstepthaten-compassesavarietyofstatisticaltechniquesandstakeholder'sengage-ments.Twogeneraltypesoftechniquesintheliteratureareare.None-Participatorytechniquesdenotethoseframeworksthatapplymethodology.Top-downassess-mentsareoftenbasedonnationalorlocaldatasetsandmostlyutilizequantitativemethodsinthedevelopmentofcompositeindicatorsindicators.Bottom-uporparticipatoryassessmenttechniquesaretypicallyquali-tativebasedandappliedbythecommunityinquestionquestion.3.5.WeightingWeightingofindicatorsorgroupofthemisthefthstepofCIB,andmostlyreferencedasaseriousproblemindisasterresiliencemeasure-measure-.Althoughthereexistanumberofmethodsforweightingintheliterature,theycanbeclassiedintotwocategoriesof1),and2)unequalweightingTable7displaysmetricsforthesedimensionsandprovidesreferencestotheoriginalliterature.3.5.1.EqualweightingEqualweightingorallocatingequalimportanceacrossdiindicatorsisoftenperformedwheninvestigatorshavenosigniabouttheexistinginteractionsamongthemandthetrade-sisnotfullyperceivedperceived.Therationalebehindanequalweightingistoavoidlargeconcentrationoffewindicatorsandmakingindicatorsstraight-forwardandeasytocommunicatecommunicate.3.5.2.UnequalweightingUnequalweightingorallocatingdierentimportanceacrossvariousindicatorsisemployedwhenthereisconsiderableknowledgeabouttherelativeimportanceofindicatorsorofthetrade-osamongthemthem.Assigningvariousweightingorunequalweightingcanbeperformedbythreeapproachesincluding:1)Knowledgedriven(oftenreferredtoasnormative),whichisbasedonasubjectivemethodforassigningdif-ferentweighting(unequalimportance)acrossdierentindicators.Thist

7 echniqueinvolvesparticipatorymethodssuch
echniqueinvolvesparticipatorymethodssuchasexpertarguments,stakeholderdecisions,andpublicopinionsurveyssurveys–85],2),whichoftenreferstoanobjectiveapproachandismostlybasedonmathematicalrelationshipsamongthesetofindicators.This Table5Qualitymetricsrelatedtothedatacomparabilitystep.DimensionMetricDescriptionTypeStandardizationRescalingThosemodelsthatusedz-scorefordatatransformationorcalculatinghowmanystandarddeviationsfromitsmeanmean.ONormalizationRangingThosemodelsthatusedmin-maxfordatatransformationandmakingvaluestobenormalizedfrom0to11.ORatingThosemodelsthatusedothertechniquessuchascategorical,ordinal,interval,andand.OA.Asadzadehetal. techniqueaimstondoutasmallnumberofshapefunctions,orasmallnumberofeigenvectorsthatresolvethespatialandtemporalpropertiesofthedatadata.Mostofstatisticalmethodsincludingprincipalcom-ponentanalysis(PCA)anddataenvelopmentanalysis(DEA)belongtothiscategoryofweightingthatextractdierentrelativeimportanceforvarioustypesofindicators,andnally3),whichincludesbothdata-drivenandknowledgedriven(normative)methods.3.6.AggregationAggregationisthenextstepinCIBprocedurethataccumulatesunderlyingsubindicatorsintoasignicantcompositeindicator.Althoughresearchersapplydierenttypesofaggregationmethods,thedebateonwhichaggregationtechniqueshouldbeappliediscon-troversialandongoingongoing.However,themostappliedtechniquesintheliteratureofcompositeindicatorsareTable8displaysmetricsforthementioneddi-mensionsandpresentsreferencesoftheoriginalliterature.3.6.1.CompensatoryaggregationCompensabilityorcompensatoryaggregationreferstotheexistenceoftrade-osamongdimensionsofasubjectunderanalysisanalysis.Therefore,apoorresultinonedimensioncanbecounterbalancedbyanaboveaverageresultinanotherdimensiondimension.Commonlyappliedcompensatoryaggregationtechniquesare;1),referstothosemodelsusingalinearadditiveaggregationtechniquetechnique,and2),denotesthoseframeworksapplyingapplying.3.6.2.Non-compensatoryaggregationToavoidconcernsrelatedtointeractionandcompensabilityamongdimensionsofasubject,researchershaveproposedanon-compensatorymulti-criteriaapproachsuchasParetorank,dataenvelopmentanalysis(DEA),andCondorcetmethodsmethods.Thesemethodsacknowl-edgethattheremightexistconictsamongdierentindicatorsordi-mensions,andendeavortoaddressthemthem.3.7.VisualizationAfterconstructingcompositeresilienceindicatorsandcomputingthescoreforeachofthem,thenextstepistovisualizetheobtainedresults.Thereareafewwaystovisualizeandpresentcompositein-dicatorssuchassimpletabularorusingcomplicatedmulti-dimensionalgraphicalsoftware.However,inselectingtheformofpresentation,themainconcernshouldbehowavisualizationmethodaectstheinter-pretationofresultsandeaseofunderstanding.aretwodeneddimensionsforthisstepthataredisplayedalongwiththeirmetricsinTable93.7.1.Concise-representationConcise-representationdenotestheextenttowhichcomplexin-cancompactlyberepresentedrepresented.Tounderstandwhetherameasurehasaconciserepresentationregardingtheconceptofdisasterresilience,wedenedtwoqualitymetricsofibility.Understandabilityreferstotheextentthatthemessageofcom-positeindicatorsisclearwithoutambiguityandeasilycomprehendedcomprehended.Legibilitydealswithreadabilityofpicturedinformationandresultsingeneral,andindicateshowwellthepresentedindicatorsen-hanceconceptualclarityofthetermdisasterresilienceinparticularparticular.3.7.2.CommunicationCompositeindicatorsshouldbeviewedastransparentexpressivetoolsthathavehighpotentialtocommunicatewithdecision-makersandotherend-usersend-users.Therefore,visualizedcompositeindicatorsmustbeabletoprovideanexplicitpicturethatiseasilyinterpretableinterpretable.Interpretabilitydenotestheextentthatrepresentedresultsareinappropriateformatsandtheoperationalizationcanbetranslatedforpolicy-makers,stockholders,andend-users.3.8.ValidationThelaststeptowardconstructingasoundsetofcompositein-dicatorsisvalidationandvericationofthemodel.Validationisasetof Table6Qualitymetricsrelatedtothemultivariateassessmentstep(datareductionandfactorretention).DimensionMetricDescriptionDatareductionNon-parametricExaminingnon-linearrelationshipswithinthedatabymeansofmethodssuchasmultidimensionalscaling(MDS)methodd.OParametricDescribinglinearrelationshipbetweendierentparametersbymeansofmethodssuchasprincipalcomponentanalysis(PCA)andPearsonCorrel

8 ationCoeCoe.OFactorretentionDeductiveTho
ationCoeCoe.OFactorretentionDeductiveThoseframeworksthatweredevelopeddeductivelyandsimilartohierarchicalhierarchical.OInductiveThoseframeworksthatweredevelopedinductivelyinductively.OTechniqueParticipatoryThoseapproachesthatapplybottom-uporidiographicmeasuresmeasures.ONon-participatoryThoseframeworksthatapplytop-downornomotheticmeasures Table7Qualitymetricsrelatedtotheweightingstep.DimensionMetricDescriptionTypeEqualweightingIndependentThosemethodologiesthatallocatedequalimportanceacrossdidi.OUnequalweightingKnowledgedrivenThosemethodologiesthatusednormative(e.g.MCDM)techniquesforweightingweighting.ODatadrivenThoseapproachesthatapplieddatadriven(e.g.PCA)methodsmethods.OHybridThosemeasurementsthatappliedaconnective(e.g.PCAandANP) Table8Qualitymetricsrelatedtoaggregationstep.DimensionMetricDescriptionCompensatorySummationThosemethodsthatappliedadditiveaggregationorsimplysums-uptechniquesforaggregatingindicatorsorgroupofthemthem.OMultiplicationThosemethodsthatappliedgeometricaggregationtechniqueforaggregationaggregation.ONon-compensatoryMulti-criteriaThosemethodsthatappliedmulticriteriaanalysistechniqueforaggregationaggregation.OA.Asadzadehetal. methodsforjudgingamodel'saccuracyinmakingrelevantpredictions.Modelvalidationisusuallyappliedinordertoexaminethereliabilityoftheoriesandunderlyingassumptionsofaconceptualframeworkframework.Sincethereexistseveraltoolsandtechniquesformodelvali-dationintheliteratureincludingfacevalidation,internalvalidity,crossvalidity,andexternalvalidityvaliditytonamebutafew;thisstudyhasconsideredtwodimensionsof,andtoin-vestigatewhethertheselectedassessmentframeworkshavebeenvali-datedbythedevelopersorotherscholars.Table10displaysthein-cludedmetricsforthisdimensionandprovidesreferencestotheoriginalliterature.3.8.1.Vericationorvalidationisaprocessofdeterminingthatwhetherameasurementmodelaccuratelyrepresentstheconceptualdescriptionofamodelandisanliteralrepresentationoftherealworldworld.Va-lidationofaframeworkcanbeexploredbythemetricsof,whichreferstoexaminethereliabilityoftheoriesandun-derlyingassumptionsofaconceptualframeworkframework.3.8.2.CredibilityCredibilityofamodeldenotesthejudgementsmadebyend-userstodeterminethetrustabilityorpopularityofameasuremeasure.Inthisresearch,reputationofaframeworkisconsideredascreditabilityofit.ofascienticassessmentframeworkcanbedeterminedinerentqualitativeorquantitativeways.Inthisstudyinordertoavoidbias,credibilityofameasurewasevaluatedbythenumberofcitationsofthatmodelintheliterature.Inthisregard,theaveragenumberofcitationsperyearofaframeworkincurrentliterature,eitherinapuretheoreticeortorintheapplicationoftheframeworkinacasestudy,canbeperceivedasalegitimatecriterionforthelevelsoftrustabilityorpopularityofameasureamongotherscholarsandusers.4.ResultsanddiscussionEachoftheselectedresiliencemeasurementindices/toolsfollowsanumberoralloftheeightstepsofCIBprocedureincludingtheoreticalfoundationforprimaryindicatorselection,multi-criteriaassessmentforanalyzingdata,weightingandaggregationofindividualindicatorsorgroupofthem,visualizationofresults,andvalidationofunderlyingassumptions.ThesestepsalongwithproposeddimensionsandmetricsforqualityassessmentarelistedinTable11.Whiletheobjective(O)metricswereliterallyextractedfromanalyzedframeworks,subjective(S)metricswereassessedbasedontheknowledgeofauthorsthroughdeployingSaaty'spairwisecomparisonmethod(19scale).UtilizingSaaty'spairwisecomparisonmethodwasaimedatminimizingthebiasesinjudgementsaswellasachievingconsistentverdictsasthemethodcheckstheconsistencyofjudgementsjudgements.Therefore,foreachofthesixsubjectivemetrics,onepairwisecomparisontablewasde-signedwherealltheselectedmeasurementframeworkscouldbejudgedcomparatively.Allfourauthorsstatedtheiropinionsandtheresultswereaveragedinordertoachievethenalresultofeachframeworkundereachmetric.Then,inordertoprovidemorereadableandlegibleresultsforthesemetrics,thenalaveragedscoresweretranslatedintothreecategoriesofpoor,medium,andhighthroughapplyingZ-score.Akeyfeatureofzisthatbytransforminganymeasurefromadistribu-tionintoaz-score,itispossibletosayhowlikelyitisthataparticularz-scorewillliebetweencertainlimitsorhowfarfromthemeananob-servationislocatedlocated.Asaresult,theframeworkswiththescoreofmorethan1.0werelabelledas1.0to1.0asquality,andlessthan

9 1.0wereconsideredasqualityintheareaoftha
1.0wereconsideredasqualityintheareaofthatsubjectivemetric.4.1.TheoreticalfoundationSincecommunitydisasterresilienceencompassesmanyfactors,asoundtheoreticalfoundationenablesscholarstoidentifyindicatorsthatareutilizedasproxiesforresilienceandtransitionfromconceptualfoundationtoempiricalassessmentassessment.Thistransition,however,dependsonthetheoreticalperspective(semanticcompleteness)ofaframeworkanddistinguishingwhetherresilienceisseenasastaticoutcomeoraprocessofcapacitybuilding.Thisisamajorconcernaboutcommunityresilienceanddemonstratesresilienceisbeingmea-suredorenhancedenhanced.Thereviewrevealedthatengineeringbasedframeworkstendtoviewresilienceasbouncingbacktothesamecondition(result-oriented)andachieveacertainoutcome,whereasmostofsocio-ecologicalbasedmeasuresfocusonadaptivecapacitybuildingorenhancementinorderforeectiveresponse,adaptationtonewconditions,andlearningfrompreviousevents(process-oriented).Althoughmostoftheselectedapproachesviewresilienceaseitheranoutcomeorprocess,afewnumberofthemconsiderresilienceasbothofresult-orientedandprocessorientedsimultaneously(CDRI2009,CoBRA2013,andBRIC2014).Disasterresilienceassessmentscanbeclassiedintomeasuringpersistence(robustness),recovery(constancy),andadaptivecapacity(transformative).Theseclassicationsindicatethetimeframeofameasure(shorttermorlongterm)orresilienceforfor–101].Whilethefocusofmeasurementinresult-orientedapproachesisonquantifyingshort-termpersistencelevelsofcommunities,theprocess-orientedframeworksconcentrateonmeasuringlong-termadaptivecapacitiesofcasestudyareas.Whentheprimarygoalistobuildresi-liencetoshort-termdisturbances,thefocusisonthepre-eventcondi-tionsofcommunitiessuchaspersistenceandrecoverylevels.Whereastheobjectiveinlong-termanalysisispost-eventconditionsandasses-singthecapacityofcommunitieselementstolearnfromandrespondtoupcomingchangeschanges.Oneofthefundamentalpointofoperationalizingresilienceisthequestionofresiliencetoandresilienceofof.Theformerquestionmatterstothedevelopmentofunderlyingassumptions Table9Qualitymetricsrelatedtovisualizationstep.DimensionMetricDescriptionConciserepresentationUnderstandabilityTowhatextentdovisualizedcompositeindicatorsprovideeasilycomprehendedresultsfordecision-makersandend-usersend-users.SLegibilityTowhatextenddopresentedresultsenhanceconceptualclarityofthetermdisasterresilienceresilience.CommunicationInterpretabilityTowhatextendarerepresentedresultsinappropriateformats,symbols,andoperationalizationisinterpretabletoend-users Table10Qualitymetricsrelatedtothevalidationstep.DimensionMetricDescriptionTypecationAcknowledgmentThosemeasuresthatappliedavalidationtoolforexaminingthereliabilityoftheoriesandtheunderlyingassumptionsoftheirconceptualconceptual.OCredibilityReputationThenumberoftotalcitationofeachmeasureperyearyear.OA.Asadzadehetal. Table11Qualityassessmentofthecompositeindicatorbuildingprocedureineachofselectedframeworks.CIProcedureTools/IndicesSemanticcompletenessStatic×MeasurementfocusCharacteristic×××××RobustnessHMHHMPPHMHHMMHHHHMMMMPMMMPHMMHMHMLevelofmeasurementOutputOutcome×××××××××××××××××××××××DataqualityDatatransformation×××××××××××××××××××××××××MultivariiateanalysisDatareduction×××××××MultivariiateanalysisFactorretention×××××××××××××Participatory×××××××××××Equalweighting×××UnequalweightingKnowledgedriven××××××××Datadriven×××××××××××××××××××××××××××××××××Summation×××××××××××v××××××ConciserepresentationUnderstandabilityMMPHMMMPHPHMMMMHM(continuedonnextpage)A.Asadzadehetal. andtheoriesofresilienceframeworks.Thereviewshowedthatmostofanalyzedframeworkshavebeendevelopedfo

10 rearthquakesandmul-tiplehazards(seeTable
rearthquakesandmul-tiplehazards(seeTable2).Thelatter,canbebestcategorizedbasedonwhethermeasuringasetofattributesorassetsorevaluatingasetofcapacitiesandprocesseswithincommunitiesorbothboth.Whiledy-namictools/indicesmeasurethecapacityorpotentialperformanceofcommunities,thestatictools/indicesfocusonevaluatingtheuniquequantitiesorattributeswithincommunitiestobouncebackrapidlyfromanadverseeect.Nevertheless,insomeinstances,combinationofbothcapacitiesandcharacteristicshavebeenconsidered(CoBRA2013,andCDRI2016).Disasterresiliencemeasuresalsovarybasedonthequestionofre-siliencefor.Whoseresilienceisprivilegedandwhobenetsorlosesinthisprocessprocess.Thisisoftendependedonthetargetlayerofmeasuresandwhetherthemeasuresintendtoenhancetheresilienceofindividuals,specicgroups,orcommunitiescommunities.Asstatedbefore,thisreviewhasfocusedoncommunity-basedframeworksthathavebeendevelopedtoassesscommunityaslocation-basedentitythatcanbesmallasaneighbourhoodorasacounty(asadynamicphenomenonsuchasurbanareasinsteadofastaticentity).Fig.4illustratestheprevalenttheoreticalattributesineachofselectedapproaches4.2.IndicatorappropriatenessSelectingprimaryindividualindicatorsisstillachallengingprocessandcurrentendeavorsareintheirinfancyinfancy.Sincedisasterresilienceisacomplexandmultidimensionalconcept,constructingcandidatesetofindicatorsinameasureexplicitlydeneswhatorwhichaspectsofresiliencearegoingtobemeasuredmeasured.However,theintentionofindicatorsselectionistoconvincethattheselectedindicatorsarere-presentative(robust),andreectthemindeset(coverage)ofdevelopersdevelopers.Thiscanbeinvestigatedbythenumberofmainmeasuredcon-ceptsineachoftheincludedindices/tools(Fig.5AccordingtoFig.5,threeoftheanalyzedapproaches(PEOPLES2010,BRIC2012,BRIC2014)includemoreaspectsofcommunitydisasterresilienceintotheirmodels,comparedtotheotherones,andassuch,theypossessmoreattributesofdisasterresilience.Indicatorscanalsobedistinguishedbasedonthelevelofmea-surementthattheytendtocapture.Itwasalreadymentionedthatdisasterresilienceindicatorsareappliedtomeasureinputs(speciactions)thatareplannedfordisasterriskreduction,outputs(capacitiesandcharacteristics)ofcommunities,outcomes(resultscausedbyha-zardsandlosses),andimpacts(functionalityorperformanceofacommunity)afteranadverseeventevent.Thereviewrevealedthatmostofresiliencemeasuresfocusonmeasuringinputsandoutputs(Fig.6sincetheyusuallyconsiderproactiveactionsforreducingexposuretorisksandcharacteristicsforenhancingadaptivecapacities.Qualityofdataisanotherimportantfactorthatleadstorealisticresults.Qualityisoftendenedasexcellence,value,conformitytocations,meetingend-usersexpectationsexpectationsormorespecically,astnessforuseuse.Timelinessisoneofthemainvitalmetricforassessingqualityofdataandcanbeanalyzedbycorrectnessandupdatenessofavailabledataset.Besidesthat,consistencycheckingal-lowstotestwhetherthedatasetcapturethemainconceptsofcom-munitydisasterresilienceresiliencebyanalyzinguniformityorinternalconsistencyofdata.Thereviewshowsthatwhileallselectedresiliencemeasureshaveusedupdateddata,afewofthemhaveconsideredconsistencycheck.4.3.DatatransformationAfterindicatorselection,integrationoftheselectedindicatorsintosub-indicatorsnecessitatesdatatransformationusingdatanormal-izationordatastandardizationmethodsmethods.Whiledatanormalizationisdonetohavethesamecomparablereferencepointsforallkindofof,datastandardizationrescalesdatatohaveameanof0and Table11CIProcedureTools/IndicesLegibilityPPMHMMPMHMHPPPHHMInterpretabilityMMPHMPMPHPHPMMMHM××××××××ReputationMMPHMPPPMPMPMHMHP×:notaddressedornotenoughinformation.H:high.M:medium.P:poor.A.Asadzadehetal. standarddeviationof1(unitvariance).Dataratingisalsoatechniquefordatatransformationthatisperformedthrougharatertoassignavaluebymethodssuchascategorical,ordinal,interval,orpercentile.Fig.7indicatesthatmostofthedisasterresiliencemeasureshaveap-pliedratingmethodfordatatransformation.Theresultsalsoindicatethatthemeasurementframeworkshavemainlyutilizednormalizationratherthanstandardization.Whereasassessmentindiceshavemostlyappliedmin-max(linearscaling)rangingmethodtotransformvaluestoaminimum-maximum(between0and1)scale;assessmenttoolshaveoftenemployedratingmethodforthisstep.4.4.MultivariateassessmentResearchersoftendealwithalargenumberofcandidateind

11 icatorswhilemeasuringcommunitydisasterre
icatorswhilemeasuringcommunitydisasterresilience.Therefore,multivariateassessmentisneededtounderstandtheassociatedcomplexitiesamongindicatorsincludingdependencies,andinterconnectedness.Thestepisdatareductionandexcludingthosevaluesthathavelowre-levancetothetopictopic.Resilienceindiceshaveusedsimplecorre-lationtechniquefordatareduction,whereasassessmenttoolsappliedmultivariatescalingmethods.Figurationofsubcomponentsorfactor Fig.4.Theoreticalattributesofselecteddisasterresilienceas- Fig.5.Mainmeasureddimensionsofcommunitydisasterresi-lienceineachoftheselectedindices/tools. Fig.6.FrequencyofmeasurementlevelineachoftheselectedA.Asadzadehetal. retentionisanotherdistinguisheddierencebetweentheselectedmeasurements.Thereviewshowsthatmostofresiliencemeasuresapplydeductiveandsimilartohierarchicalmethodtowardconstructingcompositeindicators.Similarly,avastnumberofmeasurementsarebasedonnon-participatory(top-down)methods(Fig.84.5.WeightingandaggregationWeightingofindicatorsorgroupofthem(components)reectstherelativeimportanceofeachindicatorregardingasubjectunderana-ana-.Thereviewdemonstratesthatthemajorityofdeductiveassessmenttools/indicesallocateequalweightingtotheindicatorsFig.9).Oncontrary,unequalweightinghasbeenutilizedmainlybyinductiveresilienceassessments.Althoughunequalweightingisusuallybasedonthreemethods(seeTable7),knowledgedrivenisthemostappliedtypeamongselectedassessmentsandwasperformedusingparticipatorymethodssuchasexpertarguments(judgement),stake-holderdecisions,andpublicopinionsurveys.Thepredominanttypeofaggregationinourobservatorybelongstothecompensatoryaggrega-tion.Whilemostofmeasurementtoolshaveappliedmultiplication(geometricaggregation)technique,resilienceindiceshaveusedsum-mation(linearadditive)aggregation.Nonecompensatoryaggregationwasnotusedinanyofreviewedresiliencetools/indices.4.6.VisualizationandvalidationAfterconstructingasoundsetofcompositeindicators,thenextstepistovisualizetheresultsforacomparativeassessmentofcommunitydisasterresilience.Sinceresilienceisarelativeconceptconcept,thegen-eralexpectationaboutdisasterresiliencelevelisstillmissing.However,thegoalofthisstepistofacilitatebetterunderstandingofdisasterre-silience,andtoprovideanaccurateillustrationtodecision-makersand Fig.7.Mostappliedtechniquesfortransformationofdatainresilienceassessmenttools/indices. Fig.8.Dominantmethodologyofcompositeindicatorbuildingineachoftheselectedassessments. Fig.9.Prevailingtypeofweightingandaggregationineachoftheselectedmeasurementframeworks.A.Asadzadehetal. otherend-users.Resilienceassessmentshaveutilizedafewtechniquestovisualizeandpresentcompositeindicatorssuchassimpletabularormultidimensionalgraphicsoftware.Whilerepresentationofresultsintablesisthesimpleandstraightforward,graphicrepresentationtech-niquesprovideanunderstandableandlegiblepicturewherethemes-sagetakenfromcompositeindicatorsiswellunderstoodandeasilyinterpreted.Thereviewshowsthatdisasterresilienceindices(BRIC2010,RCI2012,andBRIC2014)representmoreunderstandable,le-gible,andinterpretableresults(Fig.10Thelaststeptowardconstructingcompositeindicatorsisvalidationcheckedthroughvericationoftheresultsorcreditability(reputationofthemeasure).Vericationisthesetofmethodsforjudgingamodel'saccuracyinmakingpredicationsandunderstandingtheextentamodelisanaccuraterepresentationoftherealworldworld.Amongthese17resilienceassessments,onlysixtools/indiceshavevalidatedtheirre-sultsusingeitheranexternalvalidation(CDRI2009,CRI2010,BRIC2012,CDRI2013,andBRIC2014)oraninternalvalidation(CoBRAReputationwastheothermetricforassessingqualityofvalidationthatdenotesthelevelofcreditabilityofamodelamongotherscholarsofthisknowledgearea.Tothispurpose,theresilienceassessmentswereconsideredbasedontheiraveragenumberofcitationsperyearintheliterature.Numberofcitationshelptobetterunderstandtheextenttowhichtheassessmentshavecontributedtotheliteratureofcommunitydisasterresilience.CDRI2009,andBRIC2010,aboveall,arerespec-tivelythemostcitedassessmentsamongallselectedframeworks.Itshouldbenotedthatthesubjectivemetricsweredisplayedontherangeof13asproxiesforpoor,medium,andhighqualityscoresrespec-5.SummaryandconclusionAlthoughanumberofattemptshavebeenmadetomeasuredisasterresiliencebydevelopingcompositeindicators,theseendeavorsremainintheirinfancyandthedebateonwhat

12 mightconstituteastandardmechanismtowardm
mightconstituteastandardmechanismtowardmeasuringdisasterresilienceisongoing.Furthermore,acomprehensiveanalysisandreviewofexistingmea-surementframeworksislackingwithinthecurrentdisasterresilienceliterature.Addressingthesegapsintheliteraturehasbeenthemainpurposeofthispaper.Toperformthistask,rstlywecompiledandintroducedasynthesizedprocedureforCIB.Thisprocedureincludedeight-steps:theoreticalcomprehensiveness,indicatorappropriateness,datatransformation,multivariateassessment,weighting,aggregation,visualization,andvalidation.Thesefundamentalstepswereextractedanddepictedthroughexplorationandinvestigationofdisasterresi-lienceframeworksingeneral,andCIBprocedureinparticular.(Fig.2Followingthecreationofthisprocedure,theresearchteamdevelopedameta-levelassessmentframeworkinordertosystematicallyreviewexistingdisasterresiliencemeasures.Thismeta-levelframeworkwasestablishedbasedontheabove-mentionedeight-stepsofCIBprocedureandqualiedthroughtheintroductionof36metricsand19dimen-sions.Itshouldbenotedthatthesequalityassessmentdimensionsandmetricswereextractedoutofthecurrentrespectiveliterature.Inordertoselectmeasurementframeworksforthisreview,asystematicsurveytocollectsuchmeasureswasapplied.Inaddition,thestrategiesofthisstudyweredeployedasinclusioncriteriainthisprocess(Fig.3).Therelevantresultscollectedthroughscienticdatabases,libraries,andsearchengineswerenarroweddownto17disasterresilienceassess-ments(Table2)thatwerecommunity-based,multifaceted,quantita-tive,andoperationalized(inclusionmetrics).Thequalityassessmentresultsdemonstratein-depthinformationregardingthecharacteristicsofeachmeasure(Table11).Inregardtotheoreticalcomprehensiveness,theselectedframeworksreectthetwocentralperspectives:socio-ecologicalorengineering.Theresultsde-monstratethatalthoughsocio-ecological-originatedmeasuresconsiderresilienceasaprocess-orientedphenomenon(dynamicconcept)andoftenfocusonoperationalizingpost-event(adaptivecapacity)andpre-event(inherentrobustness)conditions,engineering-originatedmea-surementsviewresilienceasaresult-orientedconcept(staticpremise)andconcentrateonmeasuringpre-event(inherentrobustness)andduring-event(constancy/recovery)conditions.Intheareaofindicatorappropriateness,aswellasthemultivariateassessmentofsaidin-dicators,thisstudyshowsthatthreeframeworks(PEOPLES,BRIC2012,andBRIC2014)demonstratethemostextensivecoverageofdisasterresiliencedimensions.Includingsocial,economic,institutional,infra-structureandhousing,communitycapital,andenvironmentalcompo-nents.Moreover,thereviewrevealsthatconsiderableoverlapbetweensomeoftheindicatorssetsutilizedincurrentassessmentframeworksFig.4).Thisisbecausethevastmajorityoftheseframeworkshavebeendevelopeddeductivelyandsimilartohierarchicallyratherthaninductively.Becausedeductivemethodsarebasedonteamworkskills,thereissomeinitialcollaborationamongtheexpertsandgroupmembersintermsofthemostrelevantconceptsandindicatorsindicatorsandassuch,thisoverlapisensued.Inaddition,thestudydemonstratesthattheselectedassessmentshavemainlybeendevelopedbasedonnon-participatory(top-down)methods.Regardingtheemploymentofweightingandaggregationtechni-ques,theresearchindicatesthat,althoughtheprevalenttypeofweightingintheselectedmeasuresisequalweighting,thedominantmethodforaggregationislinearadditivetechnique(Fig.9).Althoughequalweightingimpliesthattheweightsofallindicatorsareequivalentequivalent,applyingunequalweightingmayinfactleadtosmallererrors Fig.10.Qualityscoresofvisualizationandvalidationineachoftheselectedtools/indices.A.Asadzadehetal. thanequalweightingweighting.Communityresilienceisamultifacetedconceptandthesignicanceofparticularcriteriamayvarywithinerentcontextsandscales.Insofarasthestepsforthevisualizationandvalidationofthecompositeindicatorsisconcerned,thereviewindicatesthatthedominanttechniqueinthereviewedindicesisgraphicrepresentationusingmultidimensionalsoftwaresuchasArc-GIS;thisisdespitethefactthatthepredominantvisualizationtechniqueinthetoolsanalyzedistabularform.Surprisingly,althoughallthereviewedmeasureshaveperformedthevisualizationstepintheireorts,onlysix(outof17)frameworksconsideredthevalidationstep.Thissigniofthevalidationstepissubsequentlydemonstratedwhenweconsiderthatvalidationteststhereliabilityofassessmenttools/indicesandcanthereforeeecttheircredibility.Itwas

13 discussedthatthetermresilienceisanabstra
discussedthatthetermresilienceisanabstractconceptandconstructingasoundsetofcompositeindicatorscanpredisposetothetransitionfromamerelyconceptualbackgroundtoanempiricalevi-denceofwhatcontributestoresilienceandhowitcanbebuiltandenhanced.Constructingcompositeindicators,however,isassociatedwithmajorchallengesincludingtheoreticalfoundation,multivariateassessment,indicatorweighting,andvalidationthatcanservetoobtainerentresults.Avalidsetofcompositeindicatorsshouldstartwithdevelopingorapplicationofasoundtheoreticalfoundationforoper-ationalizingthetermresilienceaswellasabasisforprimaryindicatorbuilding.Thetheoreticalbackgroundofdisasterresiliencemeasurescanbedistinguishedbasedontheirsemanticcompleteness(whyresi-lience),measurementfocus(resilienceforwhen),operationalizeddo-main(resilienceofwhat),andunitofanalysis(resilienceforwhom).Amajorpointofconcernformeasuringresilienceisthequestionofwhyresilienceisbeingmeasuredandwhattheultimategoalofsuchas-sessmentis.Thedistinctionbetweenprocessoriented(measuringsetofcapacitiesandprocess)andresult-oriented(measuringsetofchar-acteristicsorassets)characterizesthemeasurementliteraturetodate.Resiliencemeasuresarealsocategorizedastowhethertheyaremea-suringpersistence(robustness),recovery(constancy),andadaptivecapacity(transformative)conditionsorthetimeframeofresilience(resilienceforwhen).Theexistingmeasuresmaydierbasedonwhethermeasuringasetofattributesorassetsorevaluatingasetofcapacitiesandprocesseswithincommunitiesorboth(resilienceofwhat).Finally,theconceptualfoundationofaframeworkcanbestructuredaroundthequestionofwhoseresilienceisgoingtobeboosted(resilienceforwhom).Itisarguedthatresilienceisadynamicprocessandmeasurementframeworksneedconceptuallyfocusontransitionfrommerelyapre-eventinherentresilience(robustness)toapost-eventadaptivecapacity(transformation),andconsiderationofthetermasbothstaticresultsanddynamicprocessesprocesses.Thisisamajorchallengeofdisasterresiliencemeasurementsincethedevelopmentofprimaryindicatorsforanalyzingresilienceisheavilydependonas-sumptionsaboutresilienceforwhom,what,andwhenwhen.Thechoiceofmethodologyformultivariateassessments(asafun-damentalstepinbuildingcompositeindicators)isafunctionoftheaimofresiliencemeasurement.Sinceresiliencemeasurementisperformedinthecontextofaparticularplace,itisadvantageoustomovebeyondsubjective(deductive)methodstomoresystematic(inductive)methodstoavoidoverlapwhileidentifyingthemainconceptsaswellasex-tractingthelatentandplace-basedpatternsofdisasterresiliencelevel.Thosemeasuresthatemployaparticipatory(bottom-up)approachformeasuringresiliencecansubsequentlydevelopasimpleassessmentasabenchmarkingtoolforbetterunderstandingofcapacitiesand/orassets,andcanalsoengagestakeholdersintheprocedureforCIB,utilization,andadjustment.IntheweightingstepofCIBprocedure,itisnecessarytoconsiderthetrade-osamongvariouscriteriawhichcontributetoresilienceandasaresult,combinationofbothequalandunequalweightingintheprocessofformingahybridmethodforweighting.Sincevisualizationmethodsmayaecttheinterpretationandunder-standabilityoftheobtainedresults,thegraphicproductsofArc-GISensuresucientlegibilityandclaritytoeectivelycommunicateresultstointerlocutors.Disasterresiliencemeasuresalsosuerfromthelackofvalidationorreliabilityoftheirunderlyingassumptionsorre-sults.Therefore,furthermeasuresneedtotesttheirresultswithex-ternalcriteria,andtoexaminehowreliablyaframeworkhasmeasuredtheconceptofdisasterresilienceinanempiricalapplication.Aboveall,weneedtoconsiderthatweemploydisasterresiliencemeasurestodeepenourperceptionofresilience,aswellastounder-theinterventionsthatareneededtobuildandsustainit.Weknowthatdisasterresiliencemeasurements(tools/indices)cannotsponta-neouslymakeacommunityresilient,buttheycanbeconsideredasthekeysteptowarddisasterriskreduction,planning,andmanagement.However,thequestionofwhetherresiliencemeasuresarebasedontheactualneedsofstakeholders,leaders,planners,andgovernmentsre-mainsontheagenda.Therefore,thenextworkontheareaofdisasterresiliencemeasurementmayfocusonthevariousneedsofstakeholdersinmeasuringcommunityresilience.[1]D.Alexander,Whatcanwedoaboutearthquakes?Towardsasystematicapproachtoseismicriskmitigation,in:ProceedingsoftheNZSEEConference:2012,pp.[2]S.L.Cutter,ThelandscapeofdisasterresilienceindicatorsintheUSA,

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