《大数据与分析学策略和组织影响》（第二章）翻译项目报告.pdf

'■巧．＇＇．‘？．■？．＇－—ＶＶ．．？爲八妹感旅太讀巧译专业学位硕击论文《夫Ａ巧矣令新？？笨？和化物带成》Ｉ？婦巧《報告严浩指專巧巧ｔ王明巧教技专业名称：《译硕击研巧方向：巧语笔巧论文巧交时间：２０１６年４月论文答辩肘间ｔ２０化年５月Ａ文编奇Ｉ棒巧７ SichuanInternationalStudiesUniversityATranslationProjectReportofBigDataandAnalytics:StrategicandOrganizationalImpacts(Chapter2)byYanHaoAthesissubmittedtotheGraduateSchoolinpartialfulfillmentoftherequirementsforthedegreeofMasterofTranslationandInterpretingUnderthesupervisionofProfessorWangMingshuChongqing,P.R.ChinaMay2016 《大数据与分析学：策略和组织影响》（第二章）i 翻译项目报告摘要本文是一篇翻译报告，翻译原文是《大数据与分析学：策略和组织影响》的第二章（BigDataandAnalyticsforGovernmentInnovation），该书的作者是意大利博科尼大学，管理技术学院的副教授莫拉比托(VincenzoMorabito)。第二章主要讲述的是，在“政府信息公开”的倡议下，大数据和公众参与，改变了公共服务供给的模式。由于该原文是信息型文本，所以译者采用了奈达的“功能对等”作为翻译原则。在奈达看来，功能对等是需要让译文与读者之间的距离等同于原文读者和原文信息之间的距离（奈达,1964）。需要根据读者的语言需求和文化期望，适当调正译文信息。因此，在翻译的过程中，译文会变得更为自然流畅，读者也能更好地理解原文传递的信息。最后，希望该报告能给信息技术类的翻译提供一定的借鉴意义。关键词：翻译报告；功能对等；大数据；电子政府ii ATranslationProjectReportofBigDataandAnalytics:StrategicandOrganizationalImpacts(Chapter2)AbstractThisisatranslationprojectreportofBigDataandAnalytics:StrategicandOrganizationalImpacts(Chapter2“BigDataandAnalyticsforGovernmentInnovation”)byVincenzoMorabitowhoisanassociateprofessorattheManagement&TechnologyDepartment,BocconiUniversity,Milan,Italy.Chapter2discussesthetransformationofthepublicserviceprovisionmodelduetobigdata,andinparticularduetopublicengagementinthecontextofopengovernmentinitiatives.Thetexttypeofthesourcetextcanbeseenasinformativetext.Therefore,thetranslatortakestheNida’sfunctionalequivalenceastheguidingtheory.FromtheperspectiveofNida,therelationshipbetweenreceptorandmessageshouldbesubstantiallythesameasthatwhichexistedbetweentheoriginalreceptorsandthemessage(Nida1964).Thetranslatorneedstotailorthemessagetomeetthereceptor’slinguisticneedsandculturalexpectation.Thustocompletenaturalnessofexpressionsandtrytomakethetargetreadersunderstandeasilyaretheobjectives.ThetranslatorhopesthatthisreportwillprovideareferenceforthestudyandpracticeofITtexttranslation.Keywords:translationproject;functionalequivalence;bigdata；e-governmentiii AcknowledgementsItseemsthatIhavebeenaccustomedtothelifeinSISU,whichmakesmereluctanttoleaveawayfromthiscampuswhereIhavestayedforalmosttwoyearsandmyteachers,classmateswhomIhavegotalongwithforalmosttwoyears.HereIwanttoextendmyheart-feltthankstomysupervisor,ProfessorWangMingshu.Hisprofoundknowledge,patience,andlogicalwayofthinkingwillhavebeenveryvaluabletome.Also,Ifeelverythankfultotheadvicehegivesmeinthesetwoyears.Besides,Iamgratefultoalltheteacherswhohavetaughtmeduringthetwoyears.Withtheirhelp,Ilearnedalot.Still,Iwanttoextendmythankstomyclassmates.ItiswiththemthatIhavespentthesetwoyearshappily.Finally,Iwouldliketoexpressmysinceregratitudetomyparents,whoalwayssupportmeunconditionally.iv CONTENTS摘要….………………………………………………………………………...….iiAbstract……………………………………………………………………..……iiiAcknowledgements…………………………………………………….…….…..ivChapter1Introduction…………………………………………………….….…...11.1DescriptionoftheTranslationProject………………………………...…..11.1.1PreparationsfortheTranslation...…………………………………...11.1.2TranslationTools……………………..………………………….......11.1.3StagesofTranslation...........................................................................21.2ObjectivesoftheReport……………………………………………….…31.3SignificanceoftheProject….……………………………….………….....31.4StructureoftheReport………………………………………………..…..4Chapter2AnIntroductiontotheSourceText…………………….…….…….….52.1AnIntroductiontotheAuthor……………………………………………..52.2PublishingFactsoftheSourceText………………………………...….....52.3MainContentoftheSourceText……………………………………......…62.4AnalysisoftheSourceText…………………………………….…......…..6Chapter3TheoreticalBasis,TranslationDifficultiesandTheirSolutions……....83.1TheoreticalBasis…………………………………………………...……..83.2TranslationDifficulties……………………………………….……….......93.2.1Terms………………………………………………………..…...….93.2.2LongandComplicatedSentences…………………………..……...103.3MethodsEmployedintheProcessofTranslation……………….....……123.3.1Conversion………………………………………………..……..…123.3.2Restructuring……………………………………………...….….…133.3.3SequentialTranslation…………………………….……...…..……143.3.4Amplification…………………………………………….……..….16v Chapter4Summary………………………………………………………..…....184.1LessonsLearnedfromtheTranslationPractice……………………….…184.2ProblemstoBeSolved…………………………………….……………..19References…………………………………………………………….………….20AppendixⅠSourceText…………………………………….…………...….....21AppendixⅡ中文译文………………………………………………………..48vi Chapter1IntroductionThefirstpartofthistranslationreportistogivesomeintroductionssuchasthedescriptionofthetranslationproject,objectivesandsignificanceandsoon.1.1DescriptionoftheTranslationProjectInthispart,thetranslatorwillgivemoredetailedinformationaboutthetranslationprojectlikethepreparationsforthetranslation,translationtoolsandstagesoftranslation.1.1.1PreparationsfortheTranslationWrittenbyanacademic,BigDataandAnalytics:StrategicandOrganizationalImpactshasnonethelessthemaingoaltoprovideatoolboxsuitabletobeusefultoknow-how.Definitely,thissourcetextisaninformativetypeandthusthetranslatorissupposedtotranslateitsmoother,simplerandclearer.AstheChinesesayinggoes,todoagoodjob,onemustsharpenone’stools.Firstandforemost,beforetranslatingthis,thetranslatorreferstoalotofprofessionalbookslikeBigData,searchesontheInternetandturnstosomeinformationtechnology(IT)expertsforhelpsoastotranslatethemainideaoftheauthor,makethetargettextreadableandachievethefaithfulnessregardinglanguageandliterarystyleofthesourcetext.ReadingrelevantbooksandbothinEnglishandChinesethetranslatoraccumulatesanumberoftermsandphrasesinthisfieldandhasabetterunderstandingonbigdata.What’smore,thetranslatorreadsBigdataandAnalyticsthoroughlyanddeliberatelyandthusageneralcomprehensionofthisbookremainsinthetranslator’smind.1.1.2TranslationTools1 Duetothefactthatthissourcetextisinformative,sometermsandjargonsinthisbookhavetheirspecificandfixedcounterpartsinChinese.Therefore,sometranslationtoolsshouldbeadopted.Somesearchenginesareused,forexampleGoogleonlinetranslation,Youdaoonlinedictionaryandetc.Additionally,thetranslatorlooksupanunfamiliarwordinsomedictionariessuchasOxfordAdvancedLearners’English-ChineseDictionary.1.1.3StagesofTranslationBigDataandAnalytic:StrategicandOrganizationalImpactsconcernsscienceandtechnology.Thistranslationreportfocusesonthechapter2thattalksaboutthetransformationofthepublicserviceprovisionmodelduetobigdata,underthecontextofopengovernmentinitiatives.Thischapteralsoillustratesthechangingroleofgovernmentsinsocieties,andthetechnologicalenablementtowardsdirectonlinedemocracy,activecitizenengagement,andtheutilizationofbigdata.Finally,thischapterdiscussestheutilizationofnewsourcesofdata,suchasCrowdsourcing,andinstitutionalizesprivate–publicpartnerships.Italsoofferstwocasestudiestoprovedifferentaspectsofthisdiscussion:BarcelonaSmartCityandHaiti’semergencysupportduringthe2010earthquakedisaster.Communicativetranslationattemptstoproduceonitsreadersaneffectascloseaspossibletothatobtainedonthereadersoftheoriginal.Andthistypeoftranslationissuitablefornon-literarywritinglikeinformativetextsandshouldbesmoother,simpler,clearer,moredirectandmoreconventional.Aftertranslation,thetranslatorputsthistargettextasideforseveraldays.Andthen,thetranslatorreadsthesentencesinthetargettextonebyone,tryingtomakethetargettextconformtothehabitofChinesereaders.What’smore,sometranslationskillsareadoptedbythetranslatorduringtheperiodofrevising.2 1.2ObjectivesoftheReportThisisatranslationprojectreportonBigDataandAnalytics:StrategicandOrganizationalImpacts(Chapter2“BigDataandAnalyticsforGovernmentInnovation”).Duringtranslating,theauthortriestofulfillthefollowingobjectives:First,obviously,thissourcetextisakindofinformativetextsothatthetranslatoroverallneedstotranslatetheoriginalmeaningmaximally.Consequently,morepeoplecanhaveabetterunderstandingonthedatausage.Furthermore,thistranslationreportwillsetanewexampletotheITtranslation.Finally,thesourcetextisrelatedtohowgovernmentsfunctioninthefuture.ThistargettextiscertaintoenlightentheChinesegovernment.1.3SignificanceoftheProjectThedevelopmentofcloudcomputing,mobilephoneandInternetofThingsbringusintoInformationAgewherethedatawerebornfromourdigitaldevices,camerasandwirelesssensors,etc.ItiswellknownthatthedataofInternetaretoobig,movestoofast;hence,BigDataaredatathatexceedtheprocessingcapacityofconventionaldatabasesystemsandthatimperceptiblyandgreatlychangesourdailylifeandotherfieldssuchasbusiness,education,healthcareandgovernments,etc.Sothereisagreatdemandtotackletheproblemofhowtotakeadvantageofthebigdata.ThetranslatorplanstotranslatethechaptertwoofBigDataandAnalytics:StrategicandOrganizationalImpactsincludingdetailedconsiderationaboutthestrategicimpactofBigDataandanalyticsoninnovationinsuchdomainasgovernment.Writtenbyanacademic,thebookhasnonethelessthemaingoaltoprovideatoolboxsuitabletobeusefultoknow-how.Thesecondchapterwillgivemuchenlightenmenttoourgovernmentworkingmoreefficiently.Thetranslatorneedstotranslateitsmoothlyinordertoprovideagood3 referencetoITtexttranslationandhelpourgovernmentworkefficientlyandavoidcertainchallenges.1.4StructureoftheReportThisreportincludesfourchapters.Chapter1istheintroduction.Itintroducesthedescriptionofthetranslationproject,objectivesofthereport,structureofthereport,andsignificanceoftheproject.Chapter2isanintroductiontothesourcetextincludingtheintroductiontotheauthor,publishingfactsofthesourcetext,maincontentofthesourcetextandtheanalysisofthesourcetext.Chapter3istheanalysisoftranslationproblemsandtranslationstrategiessuchasthetheoreticalbasis,translationdifficultiesandthemethodsandstrategiestotackletheproblemsintheprocessoftranslation.Chapter4istheconclusionpart.Itdrawsconclusiontothefindingsofthereportandtheproblemsthatneedtobepaidattentiontoandexpectationstothefollowingstudiesarealsopointedout.4 Chapter2AnIntroductiontotheSourceText2.1AnIntroductiontotheAuthorVincenzoMorabito,PhD,isAssociateProfessorattheManagement&TechnologyDepartment,UniversitàCommercialeLuigiBocconi(BocconiUniversity),Milan,Italy.HegainedhisdoctoratefromtheUniversitàCommercialeLuigiBocconiandwasaResearchScholaratboththeCenterforInformationSystemResearch,MITSloanSchoolofManagementandtheDecisionandInformationScienceDepartment,UniversityofFlorida.VincenzoMorabitoisinchargeofthecourseonBusinessOrganization,ManagementofInformationSystems,andInformationManagementforthevariousdegreeprogramsofBocconiUniversity.Hehasparticipatedinavarietyofresearchprojects,financedbytheItalianMinistryofUniversityandScientificResearch.HislatestpapersareontheCommunicationsoftheACM,EuropeanJournalofInformationSystems,InternationalJournalofTechnologyManagementandJournalofInformationTechnology.HehaspreviouslybeenavisitingresearchscholaratMITandtheUniversityofFlorida.2.2PublishingFactsoftheSourceTextBigDataandAnalytics:StrategicandOrganizationalImpacts,totaling90,000words,waspublishedbySpringerInternationalPublishingSwitzerlandonMarch,2015.Thechaptertwoofthisbookisthesourcetextofthistranslationproject,approximately8,000words.Thetargettextcontains15,000Chinese.Aftersearchingonline,thetranslatordoesnotfindanytranslationversionofthesourcetext.5 2.3MainContentoftheSourceTextBigDataandAnalytic:StrategicandOrganizationalImpactsisaninformativetextaboutscienceandtechnology,writtenbyVincenzoMorabito.Thistranslationreportcentersonthechapter2,whichcontainsabout8,000words.Itdiscussesthetransformationofthepublicserviceprovisionmodelduetobigdata,andinparticularduetopublicengagementinthecontextofopengovernmentinitiatives.Thischapteralsoexpoundsthechangingroleofgovernmentsinsocieties,andthetechnologicalenablementtowardsdirectonlinedemocracyandactivecitizenengagement,aswellastheutilizationofbigdataenabledgovernanceasacompetitiveadvantageforattractingresourcesandtalenttomaintainaglobalsmartmegacitystatus.Finally,thischapterdiscussestheutilizationof(a)newsourcesofdata,suchasCrowdsourcing,InternetofThings,(b)engagepublictalent,(c)institutionalizeprivate–publicpartnershipsand(d)seeksfornewmodelsofvalue-for-moneypublicprovision,butalsothechallengesthatbigdatapresentuswithrespecttodataownership,dataquality,privacy,civilliberties,andequality,aswellaspublicsector’sabilitytoattractbigdataanalysttalent.Italsodemonstratesdifferentaspectsofthisdiscussionthroughtwocasestudies:BarcelonaSmartCityandHaiti’semergencysupportduringthe2010earthquakedisaster.2.4AnalysisoftheSourceTextBigDataandAnalytic:StrategicandOrganizationalImpactsisaninformativetextaboutscienceandtechnology.Thesourcetextfocusesontheplaincommunicationoffactssuchasinformation,knowledge,opinions,etc.Mostofthewordsareinclinedtobeformal.Sincethesourcetexttalksaboutthelatesttechnologywhichisusedinthegovernment,thecontentsandwayofsentenceuseareratherasstrictasdocumentwriting.6 Inaddition,anumberofcomplexsentencesareusedinthesourcetext.Therefore,thetranslatortakesalotoftranslationskillsintopracticesuchassequentialtranslation,amplificationandrestructuringandsoon.7 Chapter3TheoreticalBasis,TranslationDifficultiesandTheirSolutions3.1TheoreticalBasisInordertotranslatewell,sometranslationtheoriesshouldbeappliedintothistranslationreport.Beforereporting,thetranslatormakesmanysearchesandpreparationsinthetranslationtheoriesandeventuallytakestheNida’sfunctionalequivalenceasthetheoreticalbasis.ThetermfunctionalequivalenceisputforwardbyNida.Functionalequivalenceistorepresenttheinformationofferedbythesourcelanguageinthereceptorlanguagewiththeclosestnaturalequivalence(Nida,1964,p57).Therearethreekeywordsinfunctionalequivalence:closest,naturalandequivalence.Functionalequivalenceemphasizestheequivalenceofextralinguisticcommunicativeeffectinsteadofthethenprevailingformalequivalence.Nida(Nida＆Taber,1969,p47-51)makesanexplanationofthefunctionalequivalenceasfollows:Atextshouldbetranslatedmainlyinlinewithfunctionalequivalenceandreflectitsdeepstructure,insteadofformalcorrespondencewhichreflectsitssurfacestructure.Giventhatagreatamountoflinguisticandculturaldifferenceexistbetweenthesourcelanguageandthetargetlanguage,thereisnodoubtthattheformoftheoriginalshouldberestructuredtokeepthecontentofthemessage.Therefore,whenitcomestotheformandcontent,theoriginal’smeaningandspiritshouldbeprioritizedtotheessenceoftranslation;however,thelinguisticstructureshouldbeplacedlessimportant.Itisknowntoallthatwhiledifferentlanguagesareexpressedindifferentways,theysomehowholdconcertedexpressiveeffectandthesameorrelativelysimilarlanguagefunctions.Forexample,expressive,cognitive,interpersonal,informative,vocative,ideational,8 appellative,directive,representational,phatic,andaestheticfunctions.Tothetranslator’sknowledge,thefunctionalequivalenceisensuredtoobtainduringtranslatingiftheresponsetotheseabove-mentionedfunctionsiscommon.Consequently,Nida(Nida＆Taber,1969,p47-51)says,“ifalllanguagesdifferinform,thenquitenaturallytheformsmustbechanged,”andhestresses,“ifalllanguagesdifferinform,thequitenaturallytheformsmustbealteredifoneistopreservethecontent,”and“theextenttowhichtheformsmustbechangedinordertopreservethemeaningwilldependuponthelinguisticandculturaldistancebetweenlanguages.”SubsequentlyNidacomesupwithwhatasuccessfultranslationis:Whatclientsneedandgenerallydemandisfirstandforemostaccuracy.Thus,whentranslatingthetranslatormusttakeanoverallanalysisoftheworksfromtheperspectiveoftheauthor,thecontent,readership,backgroundandwritingstyleoftheoriginalworks,andthenconvertittoatranslatedtextwhichsharesthesamecommunicativefunctionastheoriginal(谭载喜，1999,p71).Inordertoachievetheseobjectives,thetranslatorissupposedtotakeawidely-usedlanguage.Forexample,nomatterwhatthereadersareatahighereducationorlowereducation,thetranslationshouldbeeasilyunderstoodandwidelyacceptedbymostofthereaders.3.2TranslationDifficultiesThesourcetextisrelatedtoITfield.Afterreadingit,thetranslatorfindsthatthereexistmanylanguagedifficultpointstounderstandandtranslatethemintonaturalChinese.Amongthem,thetranslatorhaseventuallysummarizedtheseobstaclesintothefollowingtwocategories:3.2.1Terms9 Moreoftenthannot,wearefacedwithsomeliteraturesratherthansomeprofessionalbooksinourdailyexercises,nottomentionITbooksorpolitics.SomanyITtermsaretoocomplextounderstand,andeventranslatethemintothewaytheordinarypeopleareeasytograsp.Forexample,“OpenGovernment”inthesourcetextcanbetranslateditinto“公开政府”fromtheperspectiveofsurfacemeaning.Nevertheless,itisensuredthatmostreaderscannottotallyunderstanditiftheyareunfamiliarwithpoliticsorgovernmentpolicies.ItshouldbetranslatedintoatransparentChinesemeaning“政府信息公开”.Inadditiontothat,someITtermsalsosetthetranslationobstaclestothetranslator.“Crowdsourcing”isoneofthem.“Crowdsourcing”hasacounterpartinChinese“众包”.Itiscertainthatmostreaderscannotunderstandit.Soitisnecessaryforthetranslatortogivesomenotesorexplanationstoitinordertomakethetargettextunderstoodeasily.Andtherearesomeothertermssuchas“e-government”,“clouddata”andsoon.3.2.2LongandComplicatedSentencesLongandcomplicatedsentenceisanotherchallengeinthetranslationprocess.Example1ST:Whilethereareover100projectswithasmartcityangle,13arehighlightedasstrategicforthesmartfutureofBarcelona,tacklingthenecessaryinfrastructuretosupportsmartcityapplications,kitoutcityassetswithintelligentsensors,anddefinesmartcitypublicservices.TT：关于智能城市的项目过百，其中有13个项目是专为巴塞罗那智能未来设计，此举是为支持申请智能城市，为城市资产配备智能感应器，以及规定智能城市的公共服务，配备必要的基础设施。Ifthetranslatordoesnotdwellonthissentencecomprehensively,thissentencewillbetranslatedas“关于智能城市的项目过百，其中有13个项目是专10 为巴塞罗那智能未来设计，配备必要的基础设施，支持申请智能城市，为城市资产配备智能感应器，以及规定智能城市的公共服务。”Itisbecausethetranslatorfailstofindtheinnerlogicrelationofthissentence.AccordingtoChinese,itiscertainthatmostreadersdonotfindtherelationshipamong“tacklingthenecessaryinfrastructuretosupportsmartcityapplications”,“kitoutcityassetswithintelligentsensors”,and“definesmartcitypublicservices”.Asweknow,ChinesefocusesmoreonparataxisandEnglishhypotaxis.AndaccordingtotheEnglishgrammar,v-ingstandsforsomeintention.Thatis,thereexistsalogicrelationinthissentence.ItisnecessaryforthetranslatortotranslatethislogicrelationapparentlyinordertomakethisChinesemorecompactandreasonable.Inadditiontosomegrammaticalandlogicalobstacles,thetranslatorwillbeimperceptiblystuckinsometranslationese.Example2ST:TransforminggovernmentservicesusingICTshasbeenacomplexandcostlytask,oftenassociatedwiththeautomationofpublicservicesandbusinesssystemsintegration.Thefirsttranslationversionis“政府采用信息通讯技术办公，是一项有难度昂贵的任务，而且常与公共服务的自动化和商业系统的一体化有关”.ThetranslatorfindsthatthistranslationfollowstheEnglishsentencestructure.Weoftencallitiskindoftranslationeseeventhoughthissentenceisnotlongandcomplicated.Itisknowntoallthatthefour-characterorthree-characteridiomsnotonlypreciselyandbrieflyexpressestheinformation,butalsoaddtheliterarinessofthetranslatedtextinordertoarousethereaders’interest.Thenthefinaltranslationis“政府采用信息通讯技术办公，难度大、费用高，而且涉及公共服务的自动化和商业系统的一体化”.11 3.3MethodsEmployedintheProcessofTranslationFunctionalequivalenceisthereforetobedefinedintermsofthedegreetowhichthereceptorsofthemessageinthereceptorlanguagerespondtoitinsubstantiallythesamemannerasthereceptorsinthesourcelanguage.Thisresponsecanneverbeenidentical,fortheculturalandhistoricalsettingaretoodifferent,butthereshouldbeahighdegreeofequivalenceofresponse,orthetranslationwillhavefailedtoaccomplishitspurpose.Example3ST:Cloudcomputingpermitscentralgovernmentstouniformlycoverthewholecountrywithe-governmentsolutions,independentlyofdivergenceoflocaladministrativeunitsthatmaybebetterorworsepreparedtoprovidee-services.TT:在云计算的帮助下，中央政府用电子政府统一管理整个国家，而地方行政部门，则单独提供电子服务,与中央政府互不干涉。Thecorrespondenceinsemanticstructuremusthavepriorityovercorrespondenceinthesyntacticoneiftheequivalenteffectcanberealized.Sothetranslatorabandonsthecorrespondenceoftheforminthesourcetext.Nevertheless,inordertominimizetheforeignnessofthesourcetext,thetranslatorrestructuresandselectsthedeepmeaningofthesourcetext.Thetargetreaderscanalsototallyunderstandtheoriginalmeaning.Butifallthewordsaretranslated,thissentencewillfailtoachievedynamicequivalence,bestuckintranslationeseandbecomeredundant.3.3.1ConversionSometimes,whentranslating,weuseconversiontomakethetargettextnaturalandfluent.ItisbecausetheChineseandEnglisharetwoentirelydifferentlanguagesandtheyholdfarcryinvocabularyandgrammar.Conversionthatisagoodwayintranslationisadoptedtomakeitsurethatthetargettextissmoothandnatural.Thismethodisinagreatdemandowingtomanydifferencesinsyntaxandusagebetweentwolanguages.Conversionmeansthetargettextneeds12 tochangethesentencestructure,partofthespeechandvoiceinthesourcetext.Example4ST:TheutilizationofICTtoimprovepublicsectorserviceshasstartedwiththewholee-governmentdiscussion.TT：采用信息通讯技术来提高公共部门的服务得先从电子政府谈起。ThissentenceiseasytounderstandbutdifficulttotranslateitintoanaturalChineseway.Thetranslatorusestheconversionofpartofspeechtotranslatethesourcetext.Inthesourcetext,“utilization”isanounbutinthetargettext,obviously,thiswordhaschangeditspartofspeechintoaverb.ItisbecausethismethodmakesthetargettextclosetotheChinesereader’sunderstanding.Example5ST:Aftertheissueisreported,itistrackedonlinethesamewaylogisticscompaniestrackthedeliveryofpackagestotheirdestination,onlythatinformationispublishedviaTwitterandFacebooktoinformthepublic.TT:事件上传之后，在线跟踪就开始了，就像物流公司会记录包裹送至目的地的情况，只有这类信息会通过推特网或脸谱网告知公众。Obviously,thissentencecontainsthreepassivevoices.Ifthetranslatorfollowstheoriginalsentencestructureorusessequentialtranslation,thetargettextwillbeponderousandstuckintranslationese.ItiswidelyacceptedbytheChinesereadersthattheChinesesentencesshouldbeinactivevoice.Thusitiswisethatthetranslatorchangesthepassivevoiceintotheactiveoneinordertomakethetargettextmuchmorenatural.3.3.2RestructuringThesentencethatisnaturalinorderinonelanguageprobablyisunnaturalinarrangementinotherlanguages;differentlanguageshavedifferentfeaturesinthewordorderorlogicalarrangementofexpression.InEnglishthenaturaland13 smoothordermayseemawkwardinChinese,andviceversa.Thusitisnecessaryforthetranslatortodorestructuringduringtranslation.Restructuringwhichisoneofthetranslationstrategiesmeansthenecessarychangesofwordorderinasentenceintermsoftheusageofthetargetlanguage.Example6ST:Since2012,bothEUandtheUSareseekingways,thoughlegislativeandpolicychanges,toremoveobstaclesintheuseofbigdatawhichpromisegreatereffectivenesswithlowercostsinthepublicsector.TT:因为大数据在公共部门中使用成本低、效率高，所以自2012年以来，尽管欧盟和美国的政策法律有所改变，但是他们一直在寻找使用大数据便捷的方法。Theabovesourcetextisacomplicatedsentence.Ifthetranslatoradoptsthesequentialtranslation,thetargettextiscertaintolackthenaturalnessandsmoothnessforthereaders.Example7ST:AresourcemuchunderutilizedparticularlyinEurope,despitethedepthandbreadthofskillsrelevanttothepublicsector(Campos2008).TT:在欧洲，这类人才虽精通公共领域的相关技能，但是却没有得到雇佣(Campos2008)。Giventhatthetranslatorfollowsthewordorderofthesourcetext,itissurethatthetargettextwillbecomeawkwardandlackthereadabilityfortheChinesereaders.Sotherestructuringthatisusedbythetranslatorintheexamplemakesthetargettextnaturalandexpressive.Bythisway,thetargettextbecomesmorereadableandcompact.3.3.3SequentialTranslationSequentialtranslationmeansthetranslationfromEnglishintoChineseonthebasisoftheoriginalorderofthesourcetext.Generallyspeaking,ifthecontentof14 anEnglishlongsentenceconformstotheChinese,thetranslatorwilladoptthesequentialtranslationmethod.Example8ST:Whilethereisnotacommonlyagreeddefinition,theInternetofThings(IoTs)referstothenetworkofintelligentdeviceswhichincludesensorstomeasuretheenvironmentaroundthem,actuatorswhichphysicallyactbackintotheirenvironmentsuchasopeningadoor,processorstohandleandstorethevastdatagenerated,nodestorelaytheinformationandcoordinatorstohelpmanagesetsofthesecomponents.TT:虽然物联网没有一个统一的定义，但是它大致是指网络的智能设备，包括测量周围环境的感应器，反馈回周围环境的执行器，如开门，处理和储存数据的处理器，传递信息的节点，以及管理这一系列组件的协调器。GiventhatthelogicoftheoriginalsentencesresemblesthoseofChinese,theycanbetranslatedinlinewiththeoriginalsentencesequence.Theoriginalsentenceisratherlong,soapplyingthesequentialtranslationhelpsthetranslatoreasilytranslatetheclosestmeaning.Example9ST:Tothisend,thetelecommunicationsnetworkisrevampedtointegratefiberopticnetworks,andWi-Finetworking,publicandacentralizedmanagementsystemenablingtheinteroperabilityandprioritizationofmobility,publictransportandurbaninfrastructure,applyingconceptssuchaspriorityandintermodalitytomakemoreefficientandsustainablemobilityincities.TT:为了该目的，远程通信网络融合光纤网、无线网、有互通性和移动性优化的公众和中央管理系统、公共交通和城市基础设施等，将这些理念如优先性和多式运联运用其中，让城市效率更高，移动性更持续。Inthistranslationreport,manysentencesaretranslatedbymeansofsequentialtranslation.Theauthordoesnotchangetheorderoftheoriginalsentenceandmaximallyexpressestheoriginalmeaning.Inthisway,mostreadermusttotallyandpreciselyunderstandthedefinitionoftheInternetofThings.Andthetranslatorhascompliedwiththeprincipleoffidelityandproducesthe15 equivalenteffectcomparedwiththesourcetext.3.3.4AmplificationAmplification,accordingtothecontext,logicalrelationshipandexpressionsofthesourcetext,meanssupplyingnecessarywordsinourtranslationonthebasisofaccuratecomprehensionoftheoriginal.Atranslatorshouldaddsomewordsorexpressionsinthetargettextinordertomakethetargettextmorecomprehensibleforthetargetreaders.Thisisbecauseeachlanguagehasitsownhistoricalandculturalbackground.Besides,manynotions,idiomaticexpressionsandabbreviations,etc.thatarewellgraspedbythenativescannotmakesensetoforeigners.Therefore,thetranslatorisrequiredtoaddmoredetailstothesourcetextsoastonotonlyfacilitatethecomprehensionbutalsoarousethereaders’interest.Example10ST:Since2012,bothEUandtheUSareseekingways,thoughlegislativeandpolicychanges,toremoveobstaclesintheuseofbigdatawhichpromisegreatereffectivenesswithlowercostsinthepublicsector.TT:因为大数据在公共部门中使用成本低、效率高，所以自2012年以来，尽管欧盟和美国的政策法律有所改变，但是他们一直在寻找使用大数据便捷的方法。Thisexampleadoptsnotonlytherestructuringmethodbuttheamplification.Inthetargettext,thetranslatorusestheamplificationmethodaddingtheseChinesewordslike“因为”“所以”.ItisobviousthattherevisedversionismoreexpressiveandidiomaticwithaclearerlogicinChinese.Example11ST:“Nothingnew”onemightsay,reportingissueslikethiscouldbedoneinthepastusingothermeans,likecallingthecouncilorwritingaletter.16 TT:有人可能会说，“毫无新意”。因为，以往举报这些事情，可以通过其它方式，比如给委员会打电话或写信。Thisexamplealsousesamplification.Accordingtotheoriginalsentence,thereexistsacauseeffectalthoughthesourcetextcontainsnoanysuchwordsas“because”,or“so”.Functionalequivalenceseekstheclosestnaturalequivalenttothesource-languagemessage.Therefore,thetranslatorisobligedtomakethisinnerrelationshiptransparent.17 Chapter4Summary4.1LessonsLearnedfromtheTranslationPracticeDuringtheperiodoftranslating,thetranslatorhasgraduallyperceivedsomecruciallessonsthatareofmuchuseandsignificanceforthetranslator’sfutureimprovement,enhancementandpracticeintranslation.Firstandforemost,mostpeopleholdaviewthattranslationpracticeiscontradictorywithtranslationtheories.However,astheoldsayinggoes,factsspeaklouderthanwords.Afterdoingthistranslation,thetranslatorfindsthatweshouldnotseparatethepracticefromthetheories.Itisbecausethoughsometranslationtheoriesfailtodirectlyproduceareasonablewordtothetranslator,theycancomprehensivelyhelpthetranslatormasterthetranslationandtranslatemorelogically.Itisknowntoallthatmostofthetheoriesaretoillustrateandpredictthetranslation.Andthen,duringthewholeprocessoftranslating,thetranslatoroverallrealizesthatinordertobettertranslatethemeaningofthesourcetext,thetranslatorshouldsparemoretimeandenergyinthetranslation,masterthelanguagefeaturesofthesourcetext,refertosomerelatedreferencesduringthetranslatingperiod.Thatistosay,thereisnodoubtthatthetranslator’sbilingualabilityandknowledgewillhaveagreateffectonthequalityofthetargettext.Lastbutnottheleast,thetranslatorlearnsthatproofreadingplaysanimportantroleinthetranslationactivity.Asweknow,thetranslatorcannotimmediatelyfindoutthemistakesthathappeninthetargettext.Thetranslatorshouldputitasideforseveraldays,andthentakeontheproofreadingstepinordertomakeabetterproofreading.Inthisway,itwillbeeasierforthetranslatortofindtheobvioustranslationerrors.Suchproofreadingmethodisquitecriticalforqualitycontrolandguaranteesabettercomprehensionandexpressionoflongandcomplicatedsentences.Majorityofthepragmatictextsareinformativeor18 expressivetexts.Inadditiontothat,theyareequippedwiththeinformativefunction.Accordingtosomewell-knowntranslators,differenttextswithdifferentfunctionswillneedrespectivestrategiesandapproachestotranslate.Thus,thestrategiesandapproachesappliedinpragmatictranslationarenotthesameastothoseliterarytranslations.Obviously,thesourcetextisaninformativetext.SothetranslatoradoptstheNida’sfunctionalequivalencetotranslate.Thefunctionoftheinformativetextdecidesthattheinformativetranslationisaimedatminimizingobstaclessoastocommunicateandachievethefunctionofthetranslatedtextefficiently.4.2ProblemstoBeSolvedAsthetranslatormentionedbefore,differentlanguageshavedifferentcultureandlanguagebackground.Therefore,itisnecessaryforthetranslatortostudythelanguagefunctionsandtoimprovethebilingualcompetence.Thenpragmatictextsowninformativefeaturespromotingtranslatorstodwellmoreonhowtoconveytoreadersinaneffectivewayandobtaintheexpectedfunctionoftranslatedtexts.Someadjustmentandmodificationshouldbemadeintheformandcontentoforiginaltextstothedemandoftranslatedtexts,whichisusuallyseenintheinformativetranslation.Inthisrespect,moreresponsibilityliesinthetranslatorsforthetranslation.Agoodinformativetranslationmeansthetranslator’sfantasticcommandoflanguageskillsandsophisticatedprofessionalknowledge.Moreover,becauseofalackofprofessionalknowledgeofthesourcetext,somewordingsmightbealittlebitinappropriateeventhoughthetranslatorhasthoughtsomeofthemformanytimes.Someanalysesofthelongandcomplicatedsentencesstillneedtobefurtherexploredandrevised.Forexample,somesentencesarenoteasytounderstand,whichresultsintranslationeseinsomepartsofthetranslation.Allofwhatthetranslatormentionedaretheproblemstobesolved.19 ReferencesJohn,Murray.(2013).BigData:ARevolutionThatWillTransformHowWeLive,WorkandThink.Boston:HoughtonMifflinHarcourtPublishingCompany.Munday,Jeremy.(2001).IntroducingTranslatingStudies:TheoriesandApplications.London:Routledge.Newmark,Peter.(1988).ATextbookofTranslation.UnitedKingdom:PrenticeHallInternational(UK)Ltd,.Newmark,P.(2001).ApproachestoTranslation.Shanghai:ShanghaiForeignLanguageEducationPress.Newmark,P.(2006).AboutTranslation.Beijing:ForeignLanguageTeachingandResearchPress.Nida,Eugene.(1964).TowardaScienceofTranslating.Leiden:E.J.Brill.Nida,E.A＆Taber,C.R.(1969).TheTheoryandPracticeofTranslation.Leiden:E.J.Brill.Nida,E.(2007).TowardaScienceofTranslating.Shanghai:ShanghaiForeignLanguageEducationPress,VincenzoMorabito.(2015).BigDataandAnalytics:StrategicandOrganizationalImpacts.Switzerland:SpringerInternationalPublishing.范武邱.(2011).科技翻译能力拓展研究.北京:国防工业出版社.方梦之.(2003).实用文本汉译英.青岛：青岛出版社.何敏.(2013).论科技翻译的美学性.北京:北京外国语大学.连淑能.(2010).英汉对比研究.北京：高等教育出版社.思果.(2001).翻译研究.北京：中国对外翻译出版公司.谭载喜.(1999).新编奈达论翻译.北京：中国对外翻译出版公司.20 AppendixⅠSourceText2BigDataandAnalyticsforGovernmentInnovationAbstractThischapterdiscussesthetransformationofthepublicserviceprovisionmodelduetobigdata,andinparticularduetopublicengagementinthecontextofopengovernmentinitiatives.Weoutlinethechangingroleofgovernmentsinsocieties,andthetechnologicalenablementtowardsdirectonlinedemocracyanactivecitizenengagement,aswellastheutilizationofbigdataenabledgovernanceasacompetitiveadvantageforattractingresourcesandtalenttomaintainaglobalsmartmegacitystatus.Tothisend,thischapterdiscussestheutilizationof(a)newsourcesofdata,suchasCrowdsourcing,InternetofThings,(b)engagepublictalent,(c)institutionalizeprivate–publicpartnershipsand(d)seeksfornewmodelsofvalue-for-moneypublicprovision,butalsothechallengesthatbigdatapresentuswithrespecttodataownership,dataquality,privacy,civilliberties,andequality,aswellaspublicsector’sabilitytoattractbigdataanalysttalent.Wedemonstratedifferentaspectsofthisdiscussionthroughtwocasestudies:BarcelonaSmartCityandHaiti’semergencysupportduringthe2010earthquakedisaster.2.1IntroductionThischapterdiscussestheimpactofbigdatainthepublicsphereonpublicserviceprovisionandnewopportunitiesforpublicserviceorganizationandstructurethatmaytransformtheroleofgovernmentsinsocieties.TheutilizationofICTtoimprovepublicsectorserviceshasstartedwiththewholee-governmentdiscussion.TransforminggovernmentservicesusingICTshasbeenacomplexandcostlytask,oftenassociatedwiththeautomationofpublicservicesandbusinesssystemsintegration.Whilee-governmentprojectsfocusedonoperationalefficiency,initiativessuchasOpenGovernmenteffortssoughttofosterpublicservicetransparency,civicparticipation,andinter-departmentalcollaboration.Thiscouldbeachievedbysharingpublicsectorinfrastructure,seamlessinformation21 sharingwithotheragencies,bundlingcorecompetenciestoimproveservicedeliveryandengagingexternalentities,suchasuniversitiesandbusinesses(ExecutiveOfficeofthePresidentofUSA2014).Whilethesechangesdefinitelyseektoeffectefficiencies,theyarealsoqualitativeinnature,changingfundamentallythenatureoftherelationshipbetweengovernmentsandcitizens.Bigdatainitiativescometounderpintheirprogress.Since2012,bothEUandtheUSareseekingways,thoughlegislativeandpolicychanges,toremoveobstaclesintheuseofbigdatawhichpromisegreatereffectivenesswithlowercostsinthepublicsector(Nagy-Rothengass2013).Civicparticipationviasocialmedia,forexample,canalsoreducethecostofpublicservicedelivery.Crowdsourcinginformationonpotholes,forexample,cancutdownoninspectioncosts.Bigdataalsopromiseclockworkprovisionofpublicservice.Intelligentassets,suchasintelligenttrafficlightscannotifyacentralassetmanagementsystemabouttheirstateofmaintenanceaheadoftimeandlarkingissuesintheirworkingcondition,sorepairworkcanbestreamlinedwithoutdisruptionofservice(Thomas2013).Beforewegoontoelaborateontheroleofbigdataincivillife,itisimportanttounderstandsomeunderlyingshiftsintheroleofGovernmentsanditsrelationshipcitizens.2.1.1NewNotionsofPublicService:TowardsaProsumerEra?Everythingaboutcivilservice,evenitsverynaming“service”,hasemphasizedthetransactionalrelationshipbetweencitizensandgovernment.Therelationshipissimple.Civilianspaytaxesandinexchangetheyareservedinvariousfields,health,education,roadmaintenanceandthelike.Recently,however,anewunderstandingofpublicprovisionputcitizensintheroleofpartners.Thekeyideaisthatthepursuitofpublicendsistheresponsibilityofeverybody—privateandnonprofitentities,thepublic,andgovernment.Partnershipwithcitizensandcommunityinvolvementwasabigpartofthe2010ManifestooftheConservativepartyinUKundertheauspicesofthe“BigSociety”program(ConservativeParty2010).TheUSOpenGovernment22 initiative,announcedonlyayearbeforein2009alsoseeksfostercivicparticipation(McDermott2010).Botharepremisedontheideaofenablingpeopletotakecareofthemselvesandofeachother.Socialmediaandsmartphonescanfacilitatetheinteractionbetweencitizensandgovernmentsonthego.Theycanalsoamplifythecommunicationandengagementofpublicthoughcommunitiesofinterest.Tocombatcrime,forexample,citizensneedtocoalescewithpoliceinmonitoringandreportingsuspiciousactivities.Recently,thisisalsohappeninginotherareastooofcivicresponsibility.Applicationdevelopers,suchasCitysourced.comhavedevelopedapplicationsenablingcitizensandresidentstoreportandprovideinformationtolocalgovernmentaboutallsortsofcivicissues,frompotholestograffiti,flytipping,brokenpavementsorstreetlights.Peoplecandosoanonymouslyornot,theycanuploadphotos,andpinthemonastreetmap.Thereportissenttocouncilsandthereisatrackingofprogressontheissueonline(CitySourcedInc.2014).Thisisatypicalexampleofhowtechnologyhasfacilitatedcitizenstoplaytheroleofcouncilinspectorsandthisisafreeservicetothecommunityandtothegovernmenttoo,asitminimizesinspectioncosts.Charitiesandinterestgroupsworktogethertoamplifythemessage.Cyclistcommunities,forexample,haveabiginterestinpotholesasitisabignuisanceforthem,sopotholereportingispromotedbycyclingcharitiesandassociations(TheNationalCyclingCharity2014).2.1.2OnlineDirectDemocracyAndevenmorefundamentalchangemighttakeplaceduetosocialchangesandtechnologyadvancements;onethataspirestogivecitizensdecisionmakingpoweronsocialissues,muchlikethetypeofdirectdemocracyofancientGreece.PartyX(Nelsonetal.2015),issuchaninitiative.Theyseektotakeadvantageofdevelopmentsinonlinecollectivedecisionmaking,toinvolveeverystakeholderinpoliticaldecisionmaking.Asthisiscurrentlyonbetaversionandusedonlyatlocallevelforlocalissues,itdoesnotfallunderthebigdataagendaasyet.Shouldthiskindoftechnologyhowevergointoadoptionphaseandusedtodebateglobal23 issues,thenwecanstartseeingbigdatamakinginroadsintothepoliticalandlegislativesphere.Real-time,bigvolume,unstructuredinformationaside,globalpoliticaldebatingwilladdanotherdimensionofinterestinourdiscussionofbigdata;thatof‘multilingualism’.DealingwithmultilingualismisadimensionalreadyhighinEUagenda(Nagy-Rothengass2013).2.1.3Megacities’GlobalCompetitionSince2011,morepeopleliveincitiesthaninruralareasforthefirsttimeinhumanhistory.Megacities,i.e.citieslargerthan10millionpeople,areanemergingphenomenon.AccordingtotheUN,thenumberofmegacitieswillhavegrownfromfivein1975to26,with24ofthemlocatedinthedevelopingworld(UnitedNationsDepartmentofEconomicandSocialAffairs2006).Megacitiesarenotalocalornationalissue.Theywillaffectthefutureprosperityandstabilityoftheentireworldastheywillshapethebalanceofpowerofnationaleconomiesinaglobalworld,affectpopulationmobilityandconfigurationoftalent,andwillinfluencethesocialandpoliticaldynamicsoftheworld(UnitedNationsDepartmentofEconomicandSocialAffairs2006).Megacitieshaveafunctionalandasymbolicrole.WhatwouldUKbewithoutLondon?AndwhatwouldTheEmiratesbewithoutDubai?Megacitiesarenotjustkeyinstrumentsofsocialandeconomicdevelopmentatallfrontsbutalsoharborsofsocialinnovationfortheprivateandpublicsectorduetotheiruniquedynamics.Megacitiesareanattractivepropositionforthoseseekingabetterqualityoflifeintermsofahigherstandardofliving,betterjobs,fewerhardships,andbettereducation.InagloballycompetitiveenvironmentMegacitiescompeteforcapitalresourcesincludingglobaltalent.Theytypicallyfacea5%populationgrowthrate,whichchallengesthequalityoflivingindices(suchassecurity,costofliving,mobility,employment,environmental)thatputpressureonurbaninfrastructureandpublicpolicy.Toraisetheirattractiveness,megacitiesneedtoimproveonthoseindices(UnitedNationsDepartmentofEconomicandSocialAffairs2006;Mostasharietal.2011).Hence,MegacityMayorsfaceuniquedilemmas,primarilyonhowtoraisestandardsoflivingacross24 anumberofwellbeingindicesinthefaceofhighpopulationgrowthrates,whilecompeteintheglobalenvironment.Smartcityinfrastructure,thebundleofInternetofThingssolutionsforcityinfrastructuremanagementandintelligentinfrastructure-citizeninterfacesareconsideredtoprovideawayforward.Thequantityofdataproducedandthecriticalityofinfrastructuremanagementwillraisethebarforbigdataanalyticsandmanagement.Wewilldiscussthisopportunityaspartofthecontentinthenextsection.2.2PublicServiceAdvantagesandOpportunities2.2.1NewSourcesofInformation:CrowdsourcingCrowdsourcingisbecominganincreasinglycommontermandopensnewavenuesforcreatingfreepublicvalue,civicengagement,andtransparency.Itcantakemanyforms.‘Crowdreporting’,forexample,isacommonformofcrowdsourcinginthepublicsphere,atthemoment,andinlinewiththenewconceptionofcitizenasapartner.The“SeeClickFix.com”isatypicalexample(SeeClickFix2014).Itisanonlineservicedesignedtohelpcitizensreportnon-emergencyissuesintheirneighborhood,viaawebinterface,Facebookorsmartphoneapps.Theissuehandlingprocessistrackedonline.Aftertheissueisreported,itistrackedonlinethesamewaylogisticscompaniestrackthedeliveryofpackagestotheirdestination,onlythatinformationispublishedviaTwitterandFacebooktoinformthepublic(SeeClickFix2014).“Nothingnew”onemightsay,reportingissueslikethiscouldbedoneinthepastusingothermeans,likecallingthecouncilorwritingaletter.Whatissodifferentafterall?Iguesstheanswershouldbeimmediacyandtransparency,andperhapsnon-evasiveness.Thepublicdoesnotneedtogooutoftheirwaytoreportsuchissuesanymore.ThereisanappforthatortheycanjustlogintoFacebook.Thereisnowaitonthetelephonetoreachanoperator,thereisnotimeconsumingwritingofmemo.Theprocessisblendedintooureverydaysociallife.Noweverybodywithasmartphonecangoaroundandreportcivicissues.Inaddition,directfeedbackandtraceabilitycangivepeoplesatisfactionandasenseofachievementthattheyhavecontributed25 tocommongood.Inthepast,reportedinformationwasnotacknowledgedandfollow-upinformationwasnon-existent.So,directinteractionbetweenpublicandgovernmentagenciesinthiscivicreportingencapsulatedthreegovernmentalgoals:(i)toengagethepublicintociviclife,ascitizensactivelyengageintheprocess;(ii)todecreasethecostofcivilservice,ascitizenengagementisvoluntaryandfreeofcharge;and(iii)toimprovetransparencyofpublicserviceprocesses,astheissuehandlingprocessisnowtraceableonlineatparwithprivateorganizations(ViciniandSanna2012).Thecreationofpublicgoodsviacrowdreportingisnot,however,thesoleprivilegeofgovernmentagencies.Weatherunderground,forexample,combinescrowd-sourcedhumanobservationwithweatherstationdatatoestablishanewlevelofaccuracywithinweatherreporting.Weatherdataisassimilatedfrom2,000weatherstationsmaintainedbytheFederalAviationadministration,26,000stationspartoftheMeteorologicalAssimilationDataIngestSystem(MADIS)anda16,000ofpersonalweatherstationsadheringtoqualitycontrolsandstandards.Coupledwithcrowdobservationsandmeaningscientificanalysisfrommeteorologistsprovidevaluableinsightsforthecointothesciencebehindthedataandtherelationshipbetweenweatherandclimatechange(TheWeatherChannelInc.2014).Thisblendofhumaninsightwithprivateandpublicsensorsystems,givesrisetotheideaoftheInternetofEverything(Danova2014),themergeofstructuredandunstructureddatainavarietyofforms,fromtextualtopictures,videosandaudiomaterial.Crowdreportingcanalsotaketheformoffeedback.Forexample,the“DidYouFeelit”serviceintheUSsurveyspeopleonanumberofearthquakeparametersregardingtheirexperienceofparticularearthquakeincidents.Enrichingnumericaldescriptorswithempiricaldatacanenrichknowledgeofqualitativedescriptorssuchasintensity(USGS2014).UsingcrowdfeedbacktotrainintelligentInternetofThings(IoTs)technologyutilizesthewisdomofthecrowdsinartificiallyintelligentpublicsystems.Wediscussthisideaofpublicorganizationinthesubsequentsection,whenweintroducetheconceptofCognitivecityasa26 potentialdomainforbigdataanalyticsapplication.2.2.2NewSourcesofInformation:InternetofThings(IoTs)Whilethereisnotacommonlyagreeddefinition,theInternetofThings(IoTs)referstothenetworkofintelligentdeviceswhichincludesensorstomeasuretheenvironmentaroundthem,actuatorswhichphysicallyactbackintotheirenvironmentsuchasopeningadoor,processorstohandleandstorethevastdatagenerated,nodestorelaytheinformationandcoordinatorstohelpmanagesetsofthesecomponents(ZhangandMitton2011).IoTshavemadewayintoutilities,smarthomes,healthcareandwellbeingapplications,andtheyareexpectedtoproliferateintootherareas,suchascommutingandtransport(asshowninFig.2.1).IoTswillpushourdatastorage,connectivityandarchitecturelimitstoanewhigh.Thesocio-economicimplicationsforhowwillliveourlivesmightbehuge.Forexample,McKinseyGlobalInstitute(Manyikaetal.2011)reportsa300%Fig.2.1TheinternetofeverythingadaptedfromDanova(2014).Thenumberofdevicesinusegloballyareshownonthey-axisincreaseinconnectedmachine-to-machinedevicesoverpast5yearsandasheer80–90%declineinmicroelectronicspricingwhichisanticipatedtoleadin127 trillionmore‘things’connectingtotheInternetacrossindustriessuchasmanufacturing,healthcare,andminingwithapotential$36trillioncostsavinginoperatingcosts(Manyikaetal.2011).Whilebigemphasisisgivencurrentlyintothedevelopmentofintelligentassetsbyequippingthemwithsensorsthatbeaminformationabouttheirpropertiesandconditions,whatreallymakesuptheinternetofthingsistheirdistributed,purposefulcollaboration,andthisrequiresarchitecture,i.e.organizationandstructure,inlinewiththeethicsoftransparency(Bentleyetal.2014).Hencemostofsuchtechnologiesareclusteredaroundcommonpurposeforexamplesmartcitytechnologies,orsmartcartechnologiestodenotewhatdrivesthelogicoftheirarchitecture.Oneofthetrendiestsuchclustersis,ofcourse,SmartCity,whichsuggestsanadvancedconnectivitythroughahighlynetworkedcityinfrastructureviaintelligentassets.Thepromiseofcourseisafertilefoundationandovertimecapabletoattractnewbusinessesandinvestmentsandfurtherurbangrowthandsocio-economicdevelopment(URBACT2012).WhiletheInternetofthingscanprovideefficientuseofresources,itneverthelesslacksinresponsivenessandagility.Toinstillresponsivenessintocitymanagement,wemayberequiredtotakeastepfurtherintodevelopingcognitivecitysystems.Decisionsaboutcitiesneedtobebasedonprinciples,values,andtherebyqualitativemetricsastohowpeoplewanttolivetheirlives,andwhatwell-beingmeanstothem.Hence,asmartcityisasdifferenttoacognitivecity,asaroboticarmistosocialanthropoid.Aplatformthatcancombinecitizens’activeengagementintodecisionmakingaboutlocalgovernment’sdecisionsandcommunitywell-being.GiventhatarchitectureiscentraltoournotionoftheInternetofthings,wecanviewcognitivecitiesasprovidingtheprinciplesforhumanizingtheirdesign.Notonlysenseandperceivebutalsolearn,memorize,recallrelevantexperiencesinordertoadapttheirresponsesaccordingly.Almostlikebiofeedback,peoplecanprovidereal-timefeedbackteachingthesystemhowtobehave.Afterall,citiesseektoimprovetheirqualityoflifeoftheircitizens;andqualityisintheeyeofthe28 beholder.Peoplecantrainmachinestoincludefeatureextraction,classificationandclustering,allofwhichtheycanthenbeperfectedthroughsuccessiveoptimizationoftheiralgorithm(Warner2011).Smartcitysensorscanthereforebetrainedbypeopletolearn,memorize,recallrelevantexperiences,whoteachesthemandwhomakesthefinalvaluejudgmentonthings?Thisisanareaofdebatestillinitsinfancy,butonethatbreedsbiglegal,political,andethicalquestionsaboutthefutureofsociallife.EUforexample,supportsinitiativesthatfostersocialinnovationandinclusivenesstogetherwitheconomicinnovationandenvironmentalsustainabilitytoengagecitizensinpublicserviceco-creationandlocalauthoritysupport.Mostcriticaltocrowdsourcingsuccessisthefeelingbyparticipantsthattheireffortswereconsideredandthatresultscamefromtheinitiative(Evans-Cowley2011).Thisrequiresmoderatorswhoarecompetentsocialnetworkers,abletousedcrowdsourcingtoolsthatlinkandprovidefeedbackeasily,aswellasachangeinprocessesusedtodevelopgovernmentalservices(Brabham2009).2.2.3PublicTalentinUseConsultationisnothingnewinpubliclife.Indevelopednationsitisactuallyinstitutionalized.Cityandregionalplannersarealwayslookingforwaystoengagethepublicintheprocessofplanning.AsaconsequenceCrowdsourcingofideasandsolutionshasemergedasaweb-basedmodeltohelpinsolvingdifficultchallenges.ThisSectionexplorestheuseofcrowdsourcingtosupportproblemsolvinginplanning.Acasestudyofdesigningaplanningcurriculumusingcrowdsourcingishighlighted.Inthiscase,crowdsourcingwasasuccessfulmodelforgeneratingcreativeideastosupportcurriculumrevision(Evans-Cowley2011).ThestudydiscussedinEvans-Cowley(2011)thatpeoplechosetoparticipateforaltruisticreasons,suchasanopportunitytocontributetothecommunity,tocontributetheirknowledge,andthattheywantedtobepartofaconversationonthetopic.Thus,publicinvolvementisacentralconcernforurbanplanners.Consideringthedifficultiesinherentinthetypicalpublicinvolvementprocess,thechallengeforplannersishowbesttoimplementsuchprograms.TheWebcanbe29 usedtoexploitcollectiveintellectamongapopulationinwaysone-to-onemeetings(Brabham2009).Consequently,atthestateoftheart,forexample,(Brabham2009)arguesthatthecrowdsourcingmodelisappropriateforenablingcitizenparticipationinpublicplanningprojects.Startingwithanexplorationofthechallengesfacedbypublicparticipationinurbanplanningprojects,(Brabham2009)supportstheargumentoftheWebasanappropriatetechnologyforharnessingfar-flunggenius.Then,itconcludeswithanexplorationofcrowdsourcinginahypotheticalneighborhoodplanningexample,togetherwithadescriptionofthechallengesofimplementingcrowdsourcing.Also,governmentsgraduallyusetheInternettoaidintransparency,accountability,andpublicparticipationactivities,andthereisgrowinginterestininnovativeonlineproblem-solvingtoservethepublicgood.Thecrowdsourcingmodelinfluencesthecollectiveintelligenceofonlinecommunitiesforparticularpurposes.Todevelopbettertoolsthatengagethepublic,itisimportanttounderstandhowandwhypeopleparticipateinthesekindsofactivities.In2009,forexample,theFederalTransitAdministrationinUSusedcrowdsourcingforpublicparticipationintransitplanning(TheNextStopDesignproject).Basedoninterviewswith23participants,theyanalyzedthemotivationsofthoseparticipantstoengagetheproject(OGP2014).Takingtheaboveissuesintoaccount,itisworthnotingthatwhiledataistherawmaterialofknowledge,itisreallytheinterpretationofsuchdatathatcanbeacteduponbyprovidinginsights,foresight,knowledgeorskill,etc.Interpretationofinformationrequiresaschema,awayoflookingatdataanddrawingconclusions.Inmodernsocietiesthiswastraditionallythejobofexperts.Inthedigitalera,thisischanging!Printingempowersindividualownershipofideas,thedigitaleraempowersco-ownership.Wikipediaisaprimeexampleofhowdataandinformationisorganizedandinterpretedinacollaborativewayonlinetoprovideanunderstandingoneachsubjectmatterthroughaconstanteverevolvingdebate.TheideaispremisedonMarshallMcLuhanmottothat“themediumisthe30 message”,inthatacertainmediumfacilitatescertaininterpretationsthatothermediadonot(Bentleyetal.2014).Tothisend,onlinecommunitieshavebecomeanimportantsourceforknowledgeandnewideas.Making“bigdata”availabletoalargenumberofanalystsmeansthatmoreideascanconvergeonhowtominesuchdata.AplatformfordoingsoisKaggle(2014),aworld’sleadinganonlineplatform,whichoperatesasaknowledgebrokerbetweencompaniesaimingtooutsourcepredictivemodelingcompetitionsandanetworkofover100,000datascientists(KaggleInc.2014).Currently,clientsincludecompaniessuchasGeneralElectric(GE),Ford,FacebookandMicrosoft,andhealthserviceorganizations,suchasHeritageProviderNetwork,inCaliforniawhoseekstodevelopapredictivealgorithmthatcanidentifypatientswhowillbeadmittedtoahospitalwithinthenextyear.SuchpredictiveanalyticsintheserviceofhealthcareprovisioncaninformthedevelopmentofmoreaccuratePublichealthbudgetsandcostcuttingplans(PinsentMasonsLLP2013).2.2.4Private–PublicPartnershipsPrivate–Publicpartnershipscanbeseentoemergeinalmosteveryaspectofpublicservice.Withtheprivatizationofmostutilitiesandthetrendtowardsoutsourcing,muchofthepublicsectorinadvancedsocietiesisrunbytheprivateorganizations.CountriesintheperipheryofEurope,suchasGreecearegoingthroughtheteethingpainsofmakingthistransitionnow.Withglobalcompetitiononpublicservicemanagement,weareenteringofcourseanewphaseofrelationsbetweenpublicandprivateorganizations.Thatofpartnership;thepursuitofalongstandingmutuallybeneficialandloyalrelationthatwillensurelong-termplanningandprosperity(Miller2013).BigDatamanagementhasacoreroletoplayinsupportingdecisionsonallofthesepartnerships,whichiswhyprogressivegovernmentsareaimingtomobilizeandsupportinthefield.TheeventorganizedbytheWorldResourceInstituteon“Public–PrivatePartnershipsforOpenGovernment”inLondon2013ischaracteristic.Itsoughttosharethetaskofmobilizingcitizenengagement,31 developingmoretransparentandaccountablegovernments(StefanandKisker2011).Thishasimplicationsfordataownershipanddatamanagementatallpublicservicefrontsfromhealthcareandnaturalresourcestopublicizingdataoncontracting,governmentexpenditureinfrastructure,andgovernmentaidtothirdparties.Oneaspectthatdeemsthesepartnershipsfundamentalisthelackofexpertisewithinthepublicsectorandtheabilitytovetandsupportthem.Another,ofcourse,istheupfrontinvestmentininfrastructure,bigdatacloudsforexample,thatlargeorganizationscanaffordwiththeviewtorecoupthemthoughlong-termgovernmentcontracts.Yetanotherreasonistheabilityandurgeofprivatecompaniestoexperimentwithnewtechnologiesinlesssensitivecontextsandprovidegovernmentswith‘safe’technologyoptionsthatarenotliketoraisepubliccriticism.Duetothepowertoprofilepeopleandtriangulateinformationaboutindividuals,bigdataanalyticsoffersdealswithsomefundamentalconcernsofpublicservices.Identitymanagement,forexample,isabigissueformostpublicservices,fromtaxcollectiontohealthprovisiontoparkingticketcollection.Bigdataanalytictechnologiesmaymakeitpossibleforpublicserviceorganizationstocombatfraud,improvedatabreachmonitoringandauthentication(ZhangandChen2010).Ontheotherhand,gettingitwrongmayhavesomeseriousrepercussions.WediscussthisinmoredetailsinSect.2.3.2.2.5GovernmentCloudDataGovernmentshaveslowlyembarkedonCloudcomputingtotacklethemuchtobedesiredtransparency,participation,andcollaborationamongstitsagenciesbutalsowiththepublic.Theidea,theconcept,andtheterm,thatiscloudcomputing,havepassedintocommoncurrencyinanambiguousmanner.Theveryconceptcharacterizedbythreemainentities—Software,HardwareandNetworktodescribethefusionofVirtualization,Gridcomputing,UtilitycomputingandWebtechnologiesthatresultinnewmodelsofITservicedelivery(Clarketal.2013).Cloudcomputingpermitscentralgovernmentstouniformlycoverthewholecountrywithe-governmentsolutions,independentlyofdivergenceoflocal32 administrativeunitsthatmaybebetterorworsepreparedtoprovidee-services.Service-orientedarchitecturefacilitatesprovisionofcompoundservicescoveringwholecustomerprocesses,whereacustomermaybeacitizenoranenterprise.Therolloutofsuchsystemscanhappensimultaneouslyandcostefficiently,aslicensingandsupportcanbenegotiatedonthewhole.Bynowconsumers,corporations,andgovernmentsareusedtostoretheirdatato“thecloud”sotheycanbeaccessedfromanydevice,anywhereanytime.AccordingtoLerman(2013)over69%ofAmericansnowusewebmailservices,storedataonline,andutilizeonlineapplications.Thistrendisonlygoingtocontinue,withindustryanalystspredictingcloudstobe$40and$160billionoverthenextfewyears,notaccountingfortheinternetofthingsapplications(Lerman2013).Withthewideradoptionofcloudcomputing,thetermC-Governmentwascoinedtoconnoteagilityduetoitsvirtualization,scalabilityduetogridcomputingandthesimplicityofWeb2.0.Cloudsneedtobeinterconnectedtomakeiteasierforuserstoswitchbetweencloudserviceprovidersaswellastheproviderstosupplyinfiniteresources(Yiu2012).Whiletherelevanceofcloudsforgovernmentsandtheirpotential,e.g.,forvotinginformationsystemshasbeengreatlywelcomed,enthusiasmhasbeencurbedbyidentifyvulnerabilitiesinvolvedinthedigitalizationofgovernmenttransactionsandtheelectoralprocess,thussurfacingissueswithtrustandtransparency(Manyikaetal.2011;Al-khouri2012).Theissueswillbedealtwithinlatersections.2.2.6ValueforMoneyinPublicServiceDeliveryThewholemovefrompaper-fillingtoe-governmentserviceswastofacilitatecostcuttingpartiallythoughintegrationacrosspublicservices.BigDataoffersGovernmentsthepossibilitytodosowithoutgoingthroughthepainsofBusinesssystemsintegration.TheUKalone,accordingtotheHeadofDigitalGovernmentUnit,ChrisYu,estimatesthatsome16–33billionperyearcanbesavedbytakingadvantageofbigdatainthepublicsector,whichcanleadto£250–£500percapitagains.Thisis33 calculatedbasedonanumberofinitiativeshavingtodowithperformancemanagementtoimprovetheoverallefficiencyofgovernmentoperations,reducefraudanderror,andtaxcollectionalone(Laney2014).McKinseyGlobalInstituteestimatesthepotentialsavingsataEuropeanleveltomountupto€150–€300billionayear(Yiu2012).Anumberofanalyticaltoolscanalsominedataregardingcitizensentimenttowardspublicservicestoprovidefeedbackandhighlightopportunitiestocustomizeservicedeliverybyhelpingemployeesbetterunderstandtheneedsofeachcitizen.Thisisnodifferenttohowcommercialorganizationsuseittocustomizetheirservicetopayingcustomers.Providingqualityservicetocitizenshasbeengovernmentagendaformostdevelopednationsandinparticularthosewhowanttoattractinternationaltalent.Forexample,predictiveanalyticsintheareaofhealthinformatics,particularlyepidemiology,canalsobeahugehelpforgovernmentswhoneedtogearupforcrisismanagement.Themoreaccuratelypeoplecanpredictthespreadofdiseasethemorecosteffectivepreventionandtreatmentisexpectedtobe,thelessthedisruptiontopubliclife.Suchbenefitsaccruewithoutfurtherchangesinthecurrentpublicservicestructures,yetevenmorebenefitsmayaccrueifweconsiderthepotentialofadoptingpredictiveanalyticstoadvancecrimepreventionandreducepolicingresourcesorintroducingsmartgridtechnologytoimprovetheefficientuseofutilityresources,(gas,electricityandwater).2.3GovernmentalChallenges2.3.1DataOwnershipWithdataownershipcomegreatresponsibilityforitsmanagement,storage,useandmisuse.Butintheageofopen(public)datawhoreallybearssuchresponsibility?Whoisthelegalguardianofitandwhatiswrittenintheimplicitcontractbetweenciviliansandgovernmentregardingitsuseandprotection?Inonesenseallpublicdataisprivatedatainthatitiseithercivilianspersonalorlifestyleinformationorrelatestothefunctioningofthepublicservicewhichisneverthelessaccountabletothepublic,atleastindemocraticsettings.Inthatsense,34 governmentsandpublicorganizationsarecustodiansofourdata,grantedpermissiontouseitinexchangeofprovidinguswithpublicservicesandpromotepublicgood(Gudipatietal.2013).TaketheUnitedStatesPatentandTrademarkOfficeonlinedatabase,forexample,whichcontainsover8millionpatentsand16millionfilingsdatingbacktoSamuelHopkins’s1790andreceives200,000patentapplicationsand100,000trademarkapplicationseachyear(HoffmanandPodgurski2013).Whodoesthisinformationbelongtoo?Obviously,thepatentholdersandsubmitters.Yet,suchinformationwouldbeuselessforboththepersonandthecountryunlesstheUSgovernmentsafeguardsitsexistenceandintegrity.Indeed,personaldatacanbecreatedbyavarietyofsourcesfrompeople,machines,devices,andtherightfulownerofsuchdataistheauthoritywhichcanverifyitsveracityasbeingthe‘TrueOwner’ofthedata(Al-khouri2012).Thiswillbearinterestingquestionswhendeviceswillmonitorourbehaviorandconditionbeaminginformationaboutourwhereaboutsandstate.Whiledevicesaresusceptibletoerror,peoplearesusceptibletodeceptionandself-deception.Governmentsandpublicagencieswillhavetomakerulestodecidehowtohandletheinconsistencies.2.3.2DataQualityBigDatacanamplifytherepercussionsandimplicationsofpoordataquality,andisaparticularlyimportantissueforgovernmentsandciviliansalike.Recordeddatacanbeflawed(erroneous,miscoded,fragmented,orincomplete)duetoworkloadpressuresanduserinterfaceworkarounds(JunquédeFortunyetal.2013).Datashouldbecheckedforcompleteness,conformity,consistency,accuracy,duplication,andintegrity,andgoodpracticesarounddataqualitydoexist.Inprivateorganizations,non-qualitydatacanbeignoredfromconsideration,withoutcompromisingtheintegrityofanalysisoraffectingtheuser.Forexample,ifaretailerdoestheiranalysisonlyonthe‘clean’dataoftheircustomerstoprofileandpredictfuturesales,thecustomerisnotgreatlyaffected(Crawford2013).Thesamecannotbetrueforthehealthservice,forexample,particularlyif‘unclean’dataarecharacteristicofalocality(e.g.alocalhospital).Poordataqualitycan35 resultfromintegratingdatasources.Dataissuescanalsoemergefromtheintegration,federationorconglomerationofdata,andgiventhevarietyandvolumeofbigdata,testingthisdatacanbeabigtask.Variousbigdatatestingprocedureshavestartedtoemerge,usinggridprocessingtechnologies,suchasHadoop,tosupportatimelyprocessing(Crawford2013).While,datastewardshipanddatatestingprocedurescandealwiththe“Garbagein”problem,anotherissueislurkingintheseamsofpublicsectordecision-making;andthisisallformsbias[selectionbias,confoundingbias,andmeasurementbias(KerrandEarle2013)].Confoundingoccurswhenwecorrelatetwophenomena,whichhasbeenstudiedindependently(Howarth2014;IQAnalytics2014).Forexample,pullingtogethersmokinghabitsfromFacebookprofilesandassociatingthiswithyouthdiabetesstatistics,mighterroneousspeakofcausalrelationshipthatcouldbemediatedbyanotherfactor,forexampleunemploymentorheredityorthefamily’seconomicstatus.Selectionbiascancomefromallpossiblesourcesandmostimportantlyduetodifferentialdeviseuseamongstdifferentcountries,agesandsocioeconomicgroups.KateCrawfordprincipleresearchinMicrosoftandVisitingprofessorofMIT,givesbrilliantaccountsonthehiddenbiasesinbigdatainHarvardBusinessReviewblog(Crawford2013).Giventhesensitivityofpublicservicesthatcanbearlifethreateningandlegalimplications,treatinginformationwithutmostrigorisfundamental.Thus,weneedtooutlineandprogresstheconversationsonregulatoryandotherinterventionstoaddressdataanalysisdifficultiesthatcouldresultininvalidconclusionsandunsoundpublichealthpolicies(Microsoft2014).2.3.3Privacy,CivilLibertiesandEqualityPrivacyandcivillibertiesarethetwosidesofthesamecoin.Userprofilingistheprocessofcollectinginformationaboutauserinordertoconstructtheirprofile.Theinformationinauserprofilemayincludevariousattributesofausersuchasgeographicallocation,academicandprofessionalbackground,membershipingroups,interests,preferences,opinions,etc.More,ominousapplicationsincludecell-phonetrackingandtheproposedcreationofanationalbiometricdatabase.36 AsKerrandEarle(2013)argueprofilingindividualsonthebasisoftheirhealth,location,electricityuse,andonlineactivityraiserisksofdiscrimination,exclusionandlossofcontrol.Whentheseinvolveaccesstopublicservices,repercussionsareexacerbated.Thepromiseofbigdataisbasedonpredictionwiththeviewtopreemptpossiblethreats.If,forexample,onecanpredictincreasedburglariesinanarea,localgovernmentcanincreasepolicingofthisareatopreemptsuchincidents(BarcelonaCityCouncil2014).Preemptiveactionisbasedonpredictionandpredictiononpredictivealgorithmbasedonsocialinformationandthiscurtailscivillibertiesreplacingproofwithriskestimates.Withincreasedpredictivecapabilitycomesincreasedresponsibilitytoavoidsuchthreatsthatcanmakegovernmentsmoreconservativeinhowtheyapproachsocialrisks.Towhatextentcangovernmentsaffordtoletunemployedyouthroamthestreetsfreely,oncewehaveestablishedthatitishighlyprobabletocommitpettytheftordrugdealing?Ontheotherhand,shallwedetainorcurtailthefreedomofyouthtomeetandsocializeandleadthemtoisolationanddepression?Towhatextentpolicemenshouldbeinformedaboutsuchcorrelationsandwouldthatleadtodiscriminationofeveryunemployedyouth,whotreatedwithsuspicionmayeventurntocrimeasself-fulfillingprophesytheorywouldpredict?Also,ProfessorLerman(2013),forexample,raisesanotherissue;theissueofequalityregardingpublictreatmentofpeopleandgroupswhodonotfullyparticipateintheinformationsociety,becausetheydon’thavethemeans,timeorappetitefor.Statisticsaboutthedigitaldivideshowgreatvariationindigitalengagementfromcountrytocountry,agegroupparticipation,socio-economicclass,urbanorrurallivingand,ofcourse,betweencountries(Lerman2013).Someinteresting2014statisticsabouttheglobalinternetandsocialmediausecanbefoundatthesocialmediacommunitybloghub(Kemp2014).Theriskhereisthatgovernmentsmaycometorelysomuchonbigdatathattheyforgettoensurethattheyengagepeopletounderstandtheirneedsandconsideredtheseduringdecision-making.37 2.3.4TalentRecruitmentIssuesGiventhescarcityofdataanalyststalentinthemarket,thepublicsectorwillhaveahardtimeattractingsuchtalentaspermanentstuff.IntheUK,forexample,publicsectororganizationsareobligedtorecruitbelowmarketgoingratestojustifyHumanResource(HR)expenses(PageGroup2014).Inaddition,theonceuponthetimejobsecurityandfringebenefitsofworkingforthepublicsectorincreasinglydisappear,makingpublicsectororganizationsevenlessattractive.Withlargeorganizationssuchasbanks,insurances,largeonlineretailersandconsultanciescompetingforsuchresources,governmentswillhaveahardtimeattractingandkeepingsuchtalent.Ontheotherhand,governmentscoulduseUniversitytalent.AresourcemuchunderutilizedparticularlyinEurope,despitethedepthandbreadthofskillsrelevanttothepublicsector(Campos2008).Ontheotherhand,thereisalwaystrainingasanoptiontoupskillingpublicsectorstaff.InFebruary2014,forexample,theUKgovernmentannouncementthat£150,000ofgovernmentfundingwouldbededicatedtoOpendatatrainingofmorethan150PublicSectoremployees(Gangadharan2013).2.4CaseStudiesBarcelonaembarkedonthesmartcityjourney10yearsagoinaninformalfashionresultinginmanysmartcityprojectsnowdispersedinvariousdepartmentsacrossthecity,currentlybeingcollatedunderasingleprogram.The22@Barcelonaregion,onceinneedofredevelopment,hasbeentransformedintoalivingtestsiteforpilotingnewtechnologies(DepartmentforBusinessInnovationandSkills2013).XaviesTrias,mayorofBarcelonasince2011,hasrecognizedtheimportanceofdigitaltechnologiesforthefutureprosperityofthecity.InhiswordsinoutlininghiscommitmentstatesthatBarcelona“…shouldnotwastetheopportunitywehavetoapplythesenewtechnologiestoimprovingpeople’squalityoflife,bygeneratinganew“economyofurbaninnovation”basedaroundsmartcities.Thisisanotherofourfuturecommitments”(DepartmentforBusinessInnovationandSkills2013).38 ToprogressthisagendaheformedUrbanHabitat,agovernmentwidemanagementstructuretopromotecollaborationacrosswater,energy,humanservicesandenvironmentagencies.Whereas,housingandurbanplanningwerealsogroupedtogether.Tofurthercementcrossagencycollaboration,theSmartCityPersonalManagementOffice,oversawallprojectswithasmartcityaspect.Whilethereareover100projectswithasmartcityangle,13arehighlightedasstrategicforthesmartfutureofBarcelona,tacklingthenecessaryinfrastructuretosupportsmartcityapplications,kitoutcityassetswithintelligentsensors,anddefinesmartcitypublicservices.Tothisend,thetelecommunicationsnetworkisrevampedtointegratefiberopticnetworks,andWi-Finetworking,publicandacentralizedmanagementsystemenablingtheinteroperabilityandprioritizationofmobility,publictransportandurbaninfrastructure,applyingconceptssuchaspriorityandintermodalitytomakemoreefficientandsustainablemobilityincities.Thisisunderpinnedwithintelligentdataprojectcollatinginformationfromsmartassetsandpublicserviceorganizationswiththeviewtoopeningtheseuptothepublic.NewpublicservicesareprogressedsuchasenergyprojectsrelatingtotheurbanlightingofBarcelona,creatingmicrogridstocreatelocalgenerationandconsumptionofgreenenergy,telemanagementofirrigationurbangreenspacesandelectriccarmobilityoptions,aswellas,smartparkingoptionstoenablespeedyparkingavoidingunnecessarycitytraffic.Citizenswillhavecontactlessandmobileappstousecityservices.Someprojectsfocusmoregenerallyonamentalitychangearoundsmartcityagendas.TheO-Governmentproject,forexample,seekstogainsupportforOpenGovernment,strategyandroadmapsandimprovetransparency,opendataandcivicparticipation.The“Citizencompromisetosustainability2012–2022”seekstogaindefinitionandtractionforacityroadmapthatcanprovideamoreequitable,prosperousandself-sufficientenvironmenttoitspeople(DepartmentforBusinessInnovationandSkills2013).Theregenerationofthe22@Barcelonaregionwasapublic-privatepartnershipwerecompanies,universities,research,andcommunitiesworkincloseproximitywithmunicipalleaderstoexchangeknowledgeandstreamline39 innovation,butalsoensureinvitingandengagingurbanplanningbysubsidizinghousinganddevelopinggreenspaces.Localandinternational,privateandpublicfundingwasusedforinfrastructuredevelopmentandthetestingofnewpublicservices.ThegovernmentfacilitatedaccesstopublicfundsbyinstitutionalizingInnoActiva,aconsultancyagencywhichtosupportsprivatecompaniestomaketheircasetopublicauthoritiesandinstitutions.FollowingtheSiliconValleyclustermodel,itissettingupclustersinareasthattheycandevelopacompetitiveadvantage.Hence,22@isorientedtoattractingtalentandexpertiseinMedia,InformationandCommunicationTechnologies,medicaltechnologies,energyanddesign.Astobigdataandanalyticsissues,opendataisactuallyacorepartofBarcelona’ssmartcityevent.Publicandbusinessaccesstoinformationsuchaselectionresults,population,publicfacilities,oreconomysitsinapublicrepositorycalledOpenDataBCN.Microsoftforexampleutilizeddatarelatingtoatownfestivalcalled“LaMerce”asapilottoprovidingimprovedcrowdmanagementsolutions.Tothisend,datafeedsfromsocialmedia,creditcardtransactions,websitevisits,customerserviceinquiries,GPSdata,trafficstatus,weatherdata,andparkingwascollectedandanalyzed.Thesedatasoughttogaininsightsaboutpeople’sperceptionsofthefestival’sentertainmentandfoodvenues,citizeninterests,peoplemobilitypatterns,andmedicalandcrimeincidentsthatcanhelptheplanningandmanagementofthenextevent(ViennaUniversityofTechnology2014).Thecityalsopilotstheprovisionofservicesbasedonmobileidentificationtechnologies.Throughasmartphoneapp,citizenscanaccessinformationaboutparkingticketsandcartowingdestinations,requestpublicsubsidiesfornonprofitactivities,andthelike,providingaproofofconceptandoftechnologyandgettingthenecessarypublicengagementtomovetothenextlevel(DavenportandPrusak1997).Consequently,publictransportsmartappsareaheadoftheirgenerationduetopopulardemand.InBarcelonatransportinformationisbasedonahyper-realityapp.Anyone40 canobtaininformationonbusstoplocations,location,linesandevenbedirectedtoitbysimplypointingasmartphonecamerainanydirection,workingwondersforcitizensnewtoanarea,touristsandevenblindpeoplewhocanbeorientedtowardstheirtargetdestinationusingvoicedirectionsorMicrosoft(2014).Publicengagementalsomanifestindevelopmentalworkforthesmartcity.Sentilo,forexample,isanopensourcesensorandactuatorplatformsponsoredbytheBarcelonaCityCouncil,anddesignedbyanopencommunity.PointofAttention:Barcelona’sopen-source,smartcityplatform,engageslocaltalentinsmartcitydevelopment,ensurestechnologyandproviderindependenceanddatastewardshipandremainswiththepublic,underitsstewardshipandsafeguardscivilliberties.Withtheviewtoestablishthecity’sreputationasasmartcity,BarcelonaalsodrivestheSmartCityProtocolinitiativewhichseekstoconnectglobalcitiesinpilotprojectstoaddresscommonchallenges(BainandSentilo2014).Barcelonaisasmallcosmopolitancitywiththevisiontogrowandanexemplarforsmartcitydevelopmentthatremainsopen,transparent,anddemocraticthroughanexchangeofallcapitalresourcesfromcapitalandinfrastructuretoknowledgeandtalent.Whileformost,smartcityapplicationsarestillconsideredanice-to-havefeatureinourcitylife,crisismanagementistheacidtestforanysmartapplication.Allemergencyservicesshareacommonrequirement,whenitcomestoinformationmanagement.Theyneedtoaccuratelyanalyzelifecritical,real-timeinformationfromdiversesources,inordertodeployandmanageemergencyserviceworkflows(Gangadharan2013).DuringtheHaitiearthquakein2010,emergencyservicesneededtobedispatchedtotheareatosupportthegovernmentcopewiththecircumstances.InStedd,acompanyspecializingintechnologydesignforemergencyservicessuchasnaturaldisastersanddiseases,offeredsupporttotheemergencyservicesandpeople.Within48hthecompanyhassetupthetelecomsinfrastructureandgain41 buyinforsettingupanemergencyresponsenumber.Thecompanyofferedamessage-integratedcommunicationsfromtwomobilenetworkcompanies;incomingaidrequestswerereceivedinHaitianCreole.ThesewereroutedtoRiff/EISforanalysis(Alehegn2010).Riffhasthecapabilitytoautomaticallyextractfeatures,classifydataandtagdataandtheirmetadata(e.g.sourceandtargetgeo-location,time,routeoftransmission)andbeforeitcanprocessitviaalgorithms.Theanalyticsmodulecandetectrelationshipsbetweentheseextractedfeatureswithinacollaborativespaceoracrossdifferentcollaborativespaces.RiffcanalsocombineinformationfromGeoChat,acollaborationtoolgeolocatinghumancomments,observationsandreportstomakeinformationricherandrelevant.RiffthensharedinformationwithCrowdfloweranotherworkflowprovider,handlingthedistributionoftaskstoabilingualvolunteerworkforcefortranslation,tagging,andgeocoding.InformationwasthenforwardedtoUshahidi,awebsiteinitiallydevelopedtomapreportsofviolenceinKenyanowturnedaglobalcrowdsourcingplatformwithhumanitariangoals.‘Ushahidians’,asacommunityofinteresthelpedtomap,accuratelygeotaginformationtoprovideaccuratecoordinatestothesearchandrescueteamontheground(Meier2012).PointofAttention:Smartappsandopencollaborationplatformcanbecomethecriticalinfrastructureplatformfortheapplicationofbigdataandanalyticstodisasterrecovery.Thevalueofswarmintelligence-basedapproachesforworkflow-basedemergencymanagementsystemshasbeenoutlinedasfarbackas2007(Bentleyetal.2014).Thiswasanexampleparexcellenceforabundleofcrowdsourcingservicescombinedtoprovideanemergencyresponseinformationarchitectureworkingwiththeaddedcomplicationofbilingualism.Dataqualityisofparamountimportancetoprioritizecallsandminimizeerroneousdispatchingofscarcerescueresources.Timelyandaccurateinformationprocessingwaslifecritical.42 Internationalcrowdsofvolunteerswereutilizedandimportantsafetycriticaldecisionshadtobetakenontheflybythegovernmentandparticipatingcompaniesalike.Theventure’ssuccesswasbasedoncompanies’technicalcapabilityandsocialresponsibility,andopennesstocollaborationwithothercompaniesandvolunteersforthesamecause.2.5RecommendationsforOrganizationsGovernmentswillhavetofindtheirfeetandstriketherightbalancebetweenprogressandthechallengesofthebigdataeraandredefineitsrelationshiptothepublicandtoprivatecapitalinaworldofglobalcompetition.Smartcityhasbecomeperhapsapillarofcompetitiveadvantageforthosewhocangrasptheopportunity,whileotherswilllagbehind.Astotheseissues,inwhatfollowswepointoutsomekeyfactorsforaneffectiveapplicationandexploitationofbigdataandanalyticsinpublicsectordigitalization,particularly,forsmartcitiesandserviceorientedinitiatives.2.5.1SmartCityReadinessEachcountrywillhavetoaccessthereadinessofitscitiestobecomeanditspositioninginasmartcitiesgloballandscape.TheEuropeanSmartCitiesinitiatives,auditscitiesonthebasisofthesixfactorsshowninTable2.1(ViennaUniversityofTechnology2014).Smartcitystrategyatanationallevelislikelytobefacedwithbudgetarytensionsbetweenruralandurbandevelopmentandanationshouldhaveavisionandaviewofhowtoengageitspeopleandprivateinvestorsintheconversation.Moreover,bothlocalandcentralgovernmentswillhavea‘goodcop,badcop’roletoplaybetweenrolemodelingtheopeningupinformationandeffectingtransparencyandensuringthatinformationissafeguardedfromabusebyinvolvedparties.Inaddition,smartcitieswilldivulgeresponsibilityforcityservicestomachines,partnershipswithprivatecompaniesandthepublicandthisrequiresnotonlyeducatedcitizensbutalsoachangeinmentalityaboutcivicresponsibilityfromalltheseparties.43 2.5.2LearntoCollaborateLikemostsociotechnicalchanges,torealizethebenefitsofbigdataandsmartcityinitiativesweneedtochangethewaywedothings.Inparticular,thesetechnologiesrequirethediversestakeholderscollaborate.Governmentagenciesanddepartmentshavebeentraditionallyseparatedbyinternalrivalriesandfinancialcompetitiondevelopingintoanembeddedsilomentalityandculture.Informationhasbeenseenaspowerandithasbeenhoardedtomakepeopleindispensableinthefaceofdownsizing,costcutting,andothermodernizationattempts.DavenportandPrusakhighlightedsuchissueasfarbackas1997(DavenportandPrusak1997).Focusonefficiencyandyearsofrecruitingpeopleandmanagersfocusingoncostcuttingexerciseshavestrippedthepublicsectorfrominnovativehumanresourcesandknow-howandpractices(Parker2014).Publicsectorpracticesintermsofrenewingtheirstaffandpoliciesaboutpayingtheirstaffatthelowendofmarketpricesmakesitdifficultforthemtoattracthumantalent,orindeedtomanageexternalassociateswhorecruitsuchtalent.Thepublicsectorwillneedtorethinkitsinternalrecruitmentprocessestoemploysmartpeoplewhowillfocusoncreatingsuccessfulpartnershipswiththeprivatesectorandthepublic.Ifgovernmentsaretoshowthewayofdeveloping44 smartercities,theyshouldorientthemselvestoattractingpeoplewithhighqualifications,affinitytolife-longlearning,socialandethnicplurality,creativity,flexibility,cosmopolitanismandopen-mindedness.Inaddition,managingsuccessfulcollaborationswillrequirenewmanagerialskills.Highpartneringskillsinvolve:1.creatingrapportviaopennessandself‐disclosureandfeedback,2.trustbuildingthroughactionsandwords,3.creativeconflictresolutionandproblemsolving,4.appetiteforchange,and5.welcominginterdependence(Dent2006).Furthermore,partneringinthesphereofemergingtechnologieswillalsorequirereviewingpublicsectorprocurementpoliciestoallowwiderparticipationinthesupplierpoolandperhapsevenparticipationofnewlyestablishedtechnologyventuresthatmightbeconsideredrisky(Uyarraetal.2014).Finally,forGovernmentstocontinuetoberelevantinabigdataworld,withlimitedresources,theyneedtobecomesmarterandthismeansfosteringpublicparticipationindecisionmaking,publicandsocialservices,andmakinggovernance,politicalstrategiesandperspectivestransparentandlean.2.5.3CivicEducationandOnlineDemocracyThekeyaspirationsofopengovernmentaretheengagementofthepublicinthepoliticalprocessesandtheirinvolvementinself-servicepublicservices.Thiswillrequireheightenedlevelsofinterest,knowledge,andmaturityfromthepublic,aswellasnewmodesofparticipationbygovernments.Onemeanstoachievetheformeriseducation.AUnitedNationsreviewofsuchprogramintheUS,showedthatsuchprogramhadchangedbothpeople’sengagementlevelsandfeelingofadequacytoengageinthepoliticalprocess,butnotpeople’srespectfordifferentpoliticalviewpoints,socialcohesionandtrust(UnitedNationsPublication2010).Onlineparticipationindemocraticprocessescanprovideanaffordablewaystoconsultgovernmentsandtakepartindecisionmakinginwaysthatitwasnotpossiblebefore.Relevantinitiativesspringupslowlyindifferentcountries.In45 January2014,California,forexample,institutionalizedtheCaliforniaReportCard,mobile-friendlyweb-basedplatformthatencouragescitizenstoengageinthedeliberativeprocessviachatroomswheretheywouldentertheirownsuggestionsbutalsorateothers’suggestions(NewsomandGoldberg2014).2.5.4LegalFrameworkDevelopmentLegalframeworkslagbehindtechnologicaldevelopmentsatallfrontsofvirtuallyenabledliving.ThepersistenceandriseofCyberbullingisatestamenttothat.Bigdataprofilingraisesmanyissuesregardingprivacy,civilliberties,andequality,astheyweredescribedabove.Withondemandpublicservicesviasmartapplicationsenteringthemix,suchissues,particularlythoseofinclusionandexclusionfromthisvirtualworld,canachieveanotherlevelofinequality.Thus,Governmentsneedtodefinenewlegalframeworkstoregulatelifeandperhapstheyevenneedtodosoatagloballevel,asinternetengagementisaglobalphenomenon.Bigdataanalyticscanbeusedasthetooltohelpinternationalgovernmentbodiestoanalyzepeople’ssentimentsbutalsointegratebestpracticesonsuchmatters,butalsotomakelawmoreunderstandablebyitslawenforcementgroupsandthepublic(Morabito2014).2.6SummaryThischapterdiscussedtheimpactofbigdatainthecontextofpublicserviceprovisionandnewopportunitiesforpublicserviceorganizationandstructurethatmaytransformtheroleofgovernmentsinsocieties.Westartedouranalysisbydiscussingdevelopmentsinpublicserviceprovision,whichtreatscitizensasprosumers(proactiveconsumers)ofpublicservicedelivery,movestowardsdirectonlinedemocracy,andfinally,toactiveengagementandaglobalsmartmegacitiescompetitionforresourcesandtalent.Inthiscontext,governmentsseektogainanadvantagebyutilizinga)newsourcesofdata,suchasCrowdsourcing,InternetofThings,b)engagepublictalent,c)institutionalizeprivate–publicpartnershipsandd)seeksfornewmodelsofvaluefor-moneypublicprovision.Despiteitspotential,theadoptionofbigdataandanalyticsarenotwithoutchallenges,particularlyforcentralgovernments.Of46 particularinterestarethechallengesregardingdataownership,dataquality,privacy,civilliberties,andequality,aswellaspublicsector’sabilitytoattractbigdataanalysttalent.Weshowcasedtwocasestudiesdemonstratinghownewformsofpublicserviceprovision.BarcelonaSmartCityprovidesanexampleparexcellenceofcollaborationbetweentheprivateandpublicsectorforregionalredevelopment.Haiti’semergencysupportduringthe2010earthquakedisasterdemonstrateshowbigdatainthehandsofpassionatevolunteerscanorganizeandsupportwithlife-criticalemergencyservices,providingalifeexampleastowhatcanbeachievedthroughtheblendofhumanintuitionandavailablebigdataintegrationandadvancedanalytics.Likemostsociotechnicalchanges,challengesresideinthesocialsphereoftechnologyacceptanceanduse,aswellaswiththeregulationofsuchtechnology,henceourrecommendationsaredirectedtowardsauditingreadinessforSmartCitydevelopment,reskillingpublicservantswithpartnershipmanagementskills,developingpublic’smentalityofcivicparticipationandupdatinglegalframeworkstocopewithdevelopmentsinthebigdataarea.47 AppendixⅡ中文译文2大数据与分析学在政府服务创新中的作用摘要本章探讨了，公共服务供给模式之所以改变，是因为大数据的产生，以及开放式的政府举措。本章概述了政府在社会角色上的转变，技术让网络民主成为可能，同时大数据的使用让政府在引进资源和人才时有竞争优势，拥有国际化的智能城市的地位。本章最后，探讨1）新资源的数据使用，比如众包技术、物联网；2）公共人才的参与；3）私立和公立组织的合作伙伴关系的制度化；4）为公共供给寻求一新的物有所值的模式，以及大数据带来的挑战，如数据所有权、数据质量、隐私、公民自由平等以及公共部门吸引大数据分析人才的能力。我们通过两个案例分析了以上问题的不同方面：巴塞罗纳智能城市和2010年海地地震应急支援服务。2.1引言本章主要讨论公共领域中的大数据对公共服务供给的影响，以及大数据给公共服务组织结构带来的新机遇，而这一切有可能转变政府的社会角色。采用信息通讯技术来提高公共部门的服务得先从电子政府谈起。政府采用信息通讯技术办公，难度大、费用高，而且涉及公共服务的自动化和商业系统的一体化。电子政府计划，是为了提高办公效率，但是“政府信息公开”的倡议旨在使公共服务透明化、鼓励公民参与和加强部门合作。这可以通过以下方式实现：共用公共部门的基础设施、与其他机构信息无缝分享、增强核心竞争力以提高服务交付及与外部实体合作，如大学和商业部门等。(美国总统行政办公室2014)。虽然这些改变是为了实实在在提高办事效率，但实际上，它从根本上改变了政府和市民之间关系的性质。大数据的产生有利于它们的发展。因为大数据在公共部门中使用成本低、效率高，所以自2012年以来，尽管欧盟和美国的政策法律有所改变，但是他们一直在寻找使用大数据便捷的方法(Nagy-Rothengass2013)。比如，公民通过社会媒体进行参与，可以减少公共服务交付成本。此外，对地面凹坑信息众包，也能减少检测成本。大数据也48 能保证按时提供公共服务。一些智能资产，如智能交通灯能够对中央资产管理系统的维护状态提前报告，提醒在它们使用状态中容易忽略的问题。因此，维修工作效率会更高，且不会中断(Thomas2013)。在继续阐述大数据给人们生活所带来的影响前，我们有必要了解政府和公民关系角色在根本上的变化。2.1.1公共服务的新观念：是否进入专业消费者的时代？公民服务，既称为“服务”，当然是强调公民与政府之间再简单不过的交易的关系。公民纳税，作为交换，他们就要享受如医疗、教育、道路维护等服务。然而，最近把公民视为合作伙伴，是对公共供应新的理解。其核心概念是，追求公共目的是每个人的责任，私营或实体机构，公民或政府皆是如此。与公民和社区成为合作伙伴，是2010年英国保守党宣言里的重要内容，英国“大社会”计划对此也很支持(保守党2010)。恰好在2009年，美国宣布“政府信息公开”的倡议，该倡议鼓励公民积极参与(McDermott2010)。这两个国家都是基于公民能自助和助人的观点而做出决策。社会媒体和智能手机让公民和政府之间的沟通更为便捷。通过共同利益，他们可以扩大公众交流和参与。比如，为了打击犯罪，公民需要和警察一起监督和举报犯罪活动。最近，公民在其他领域也需要承担责任。比如，Citysourced.com的应用程序开发员开发了一个程序，让人们向当地政府提供各种市内信息，如地面凹坑、乱涂、乱倒、人行道或路灯的毁坏等。公民以匿名或实名的方式，上传相关照片，并附在街道地图上。举报信息会直接呈送给委员会，网上也会继续跟进该事件(CitySourcedInc.2014)。这是科技如何让市民承担委员会监管责任的典型例子。同时，市民免费提供信息，为社区和政府减少了监管成本。慈善机构和利益相关的组织会一起报道该事件。比如，自行车社团，对地面凹坑深恶痛绝。因此在自行车慈善机构和协会的支持下，他们积极提供地面凹坑的信息(国家自行车慈善协会2014)。2.1.2线上直接民主由于社会变革和技术进步，深层面的东西在改变；针对某些社会问题，将决策权下放给公民，此举与古希腊的直接民主相似。X党(Nelsonetal.2015)就采取了该方式。他们利用线上集体决策，让每个有利益关系的人都参与到49 政治决策。因为这尚在测试阶段，并只解决本地问题，所以还没有受到大数据的影响。然而，一旦使用该技术，用来解决国际问题，我们就会发现大数据进入了政治法律的领域。时效性、大容量、非结构化信息和国际政治争论使大数据更复杂；多种语言交织就是如此。欧盟很重视处理多种语言交织问题(Nagy-Rothengass2013)。2.1.3超大城市的国际竞争自2011年来，人类历史上第一次出现城市人口超过农村。超大城市指的是城市人口多于1000万，这类城市是一个新兴现象。联合国统计数据表明，超大城市的数量从1975年的5个，增长到现在的26个，而且其中的24个都是在发展中国家(美国经济和社会部门，2006)。超大城市不再是地方和单个国家的问题。由于它们会影响各国家经济力量的平衡、人口流动、人才配置和世界社会政治动态，最后影响整个世界的繁荣和稳定(美国经济和社会部门，2006)。超大城市的作用举足轻重。假如英国失去伦敦会如何？阿联酋长国失去迪拜会如何？超大城市在各个方面都是社会和经济发展的重要部分，而且由于它们独特的作用，为私人或公共领域的社会创新提供了场所。超大城市极具吸引力，因为它为寻求优质生活的人提供了良好的居住环境、工作和教育的机会。在国际竞争的环境下，各超大城市竞争资本资源和国际人才。它们还面临独有的5%人口增长率，这为人们生活质量指数带来了挑战（例如，安全、生活费、人口流动、就业和环境等），也给城市基础设施和公共政策带来了压力。超大城市为了更有吸引力，改善了上述问题(美国经济和社会部门2006;Mostasharietal.2011)。因此，超大城市的市长处于两难境地，一方面，在高人口增长率的压力下，他们得通过一系列的福利措施提升生活水平，另一方面还得在国际环境下竞争。智能城市的基础设施、物联网为城市基础设施管理提供解决措施和智能的人与设施交互等促进了发展。大量数据的产生和基础设施管理的重要性对大数据分析管理要求更高。我们会在以下探讨这些机遇。2.2公共服务的优势和机遇2.2.1信息的新来源：众包。50 众包逐渐成为一个常用术语，为免费的公共价值、公民参与和信息公开等提供了新途径。它以多种形式存在。比如，“众报告”是“众包”的一种在公共范畴里的常见形式，这与公民成为合作伙伴的新概念相一致。SeeClickFix.com就是个典型例子(SeeClickFix，2014)。这是一种在线服务，帮助市民通过网络界面、脸谱网或智能手机的应用软件举报周围的一些非紧急事件。线上继续跟踪事件进度。事件上传之后，在线跟踪就开始了，就像物流公司会记录包裹送至目的地的情况，只有这类信息会通过推特网或脸谱网告知公众(SeeClickFix2014)。有人可能会说，这“毫无新意”。因为，以往举报这些事情，可以通过其它方式，比如，给委员会打电话或写信。那究竟这两者有什么区别呢？我认为在于，两者间不同的时效性、透明性以及当事人所要承担的责任。公民举报此类信息，直接使用专门的软件或者登录脸谱网，再不用出门。也不用打电话，等待接线员接听，不用花时间在备忘录上。这一过程与我们每天的社会生活息息相关。如今，人手一部智能手机，市民可以随时随地上传市区问题。而且，即时的反馈和对事件的追踪，提升了公众的满意度，同时，也让他们能为公共利益做出贡献，而富有成就感。以前，信息举报，不会有嘉奖，后续信息也不会有。因此，政府与公民就公众问题直接沟通有三个目的：1）让公众参与到市区生活中，同样的公民在该过程中也能积极参与；2）市民自愿参与，有利于减少公共服务的成本；3）线上追踪事件进度，与私人机构的调查相一致，有利于公共服务过程的透明性(ViciniandSanna，2012)。然而，通过众报告创造出来的公共物品并不是只属于政府的特权。比如，地下气象台将众包的个人观察和气象台的数据结合，使天气预报更精准。天气数据由联邦航空局下的2千个气象台，气象同化数据引入系统下的2万6千个气象台，以及1万6千个严格把关质量和水平的私人气象台所提供的。众观察和气象学家合理的科学分析相结合，为数据的科学性以及天气和气候变化提供了有价值的见解(气象频道.2014)。将人的见解和私人的、公共的传感系统相结合，万物网的想法就产生了(Danova2014)，同时伴随以多种形态存在的结构性和非结构性的数据，如：文本、图片和影音等。51 众报告也是反馈信息的一种方式。比如，美国调查关于人们在地震中的感受，用“你感受到了吗？”的服务，得到了许多地震参数。用经验数据详尽数字描述，可以丰富如强度等定性描述(美国地质勘察局2014)。用众反馈支撑智能物联网技术是依靠在人工智能公共系统中大众智慧。后面会介绍认知城市是大数据分析应用中的潜在领域，并讲述公共组织。2.2.2信息的新来源：物联网物联网没有一个统一的定义，物联网大致指的是智能设备的网络系统，包括测量周围环境的感应器，然后通过执行器反馈回周围环境，比如开门，处理器处理和储存产生的数据，节点负责传递信息，协调器管理这一系列的元件(ZhangandMitton2011)。物联网为公用事业、智能家居、医疗保健和福利等的适用提供了便捷，并且，这一切还在往其他方面发展，如交通(如2.1表格所示)。物联网使数据储存的空间更大、连通性更快和结构更优化。社会经济影响大到波及我们的生活。比如，麦肯锡全球研究所（Manyikaetal.2011）报道，5年来连接机器与机器的设备增长了300%，微电子产品价格降低了80%-90%，有望让1万亿的“物”通过以下行业连接到网络：制造业、医疗卫生和可以节约大概36万亿生产成本的采矿业。图2.1Danova(2014)所制万物网；Y轴表示全世界电子设备的数量。52 虽然当下对智能资产的发展高度重视，并为其配备了可以报告性能状态的感应器，但是物联网却是由它们分散的、具有目的性的协作组成的，这需要一定的架构，如：组织和结构，并要遵循透明原则(Bentleyetal.2014)。因此大多这类技术都有着共同目的，比如智能城市技术，以及展示是什么带动逻辑架构的智能汽车技术。当中最火热的，当属智能城市，它通过智能资产中强大的网络城市基础设施展示了先进的连通性。这样的期望是好的，也极具吸引力，会有越来越多的商业契机和投资机遇，加快了城市成长和社会经济的发展(欧洲可持续城市发展项目2012)。虽然物联网能提供很多可以有效利用的资源，但是它反应缓慢。我们需要采取更有效的方法改善认知城市系统，以提高城市管理的反应度我们要根据相关原则、价值观甚至一些定性指标，对城市做出决策，比如人们希望怎么生活，幸福对他们意味着什么等等。因此，智能城市跟认知城市有所不同，就像一只机械手臂跟监控社交网络的“社交类人猿”不相同。这个平台，让市民积极参与当地政府和社区幸福的决策中。假如物联网的核心概念是架构，那认知城市则是专为其人性化的设计提供原则。通过感觉、感知、学习、记忆和回忆相关的经历进而相应地调整反馈。比如生物反馈，通过人们提供的实时反馈，系统做出相应运作。城市毕竟要提高市民的生活质量；旁观者很关注生活质量。人们可以设定机器对一些特征进行提取、分类和整合，而这一切可以通过不断地演算进行优化(Warner2011)。根据人们的设置，智能城市感应器可以感应、记忆、回忆相应的经历。但是谁来设定这一切？谁去评判最后的结果？这一领域的探讨还很初步，但是这却会引起关于今后社会生活中法律、政治和道德问题。比如，欧盟就支持如下倡议：有助社会创新和社会包容、经济创新和环境可持续，因为这些方案可以让市民参与公共服务的共同创新中以及对当地政府的支持。众包的成功关键在于让每一位参与者都感到自己的奉献有价值，这些结果都来源于倡议(Evans-Cowley2011)。这就要求监督者是很有能力的社会网络工作者，能够使用众包工具熟练地连接和提供反馈，同时改变发展政府服务的过程53 (Brabham2009)。2.2.3采用公共人才咨询在公共生活中司空见惯。在发达国家，这已经制度化了。城市和区域规划者总在想方设法让公众都参与到规划中来。因此，众包以网络为依，解决各种问题。该部分讲述了众包在解决规划问题中的使用。重点讲述使用众包涉及课程的案例。此案例中，众包为课程修改提供了许多新想法，是一个成功的案子(Evans-Cowley2011)。该案例在Evans-Cowley的书中(2011)有讲述，人们都愿意参与到助人活动中，比如为社区做贡献，出谋划策而且他们也想成为话题交谈的一部分。因此，城市规划者对公众参与极为关心。考虑在典型公众参与过程中的困难，规划者面临的挑战是如何最优化地实施这些计划。网络通过大众一对一的交谈，聚集集体的智慧(Brabham2009)。所以，就目前的科技水平来看，Brabham认为众包模式让公民参与到公众规划项目中是最适合的。Brabham探讨了在城市规划项目中，公众参与所面临的挑战，网络是利用一些身处他地人才，最合适的技术。接着，他总结了众包在假想社区规划中的探索，并讲述了使用众包的挑战。而且，政府开始使用因特网公开政府信息、加强责任制和组织公众参与，对创新线上问题解决，为公益事业服务的关注越来越多。众包模式，为某种特定目的，影响了虚拟社区中的集体智慧。要想改进能让公众参与的工具，首先得弄清楚公民为何、如何参加这些活动。例如，2009年，美国联邦运输管理局采用众包技术让公民参与到运输规划中(下一设计项目)。他们对23位参与者采访，分析了他们参与该项目的动机(政府公开合作关系2014)。考虑到以上几点，值得注意的是，虽然数据是信息的原材料，但是它讲述了这些数据可以提供见解、远见、知识或技能等等。信息说明需要一定的架构，关注数据，做出结论。在现代社会里，这都是专家的工作，但是在数字时代，这一切都变了。印刷术让个人所有制成为可能，数字时代也产生了共同所有权。维基百科就是个好例子，它通过线上合作的方式，解释了数据和信息是如何组织的，不断地完善信息，为各个问题提供解释。这个想法是基于马歇尔·麦克卢汉的座右铭,“媒体即信息”。因为某一媒体会有其他媒体54 没有的信息。最后，虚拟社区成为新知识和新想法来源的重要渠道。让更多的分析家接触到大数据，意味着会有更多的想法去挖掘数据。Kaggle，一个主要的全球性网络平台，在寻求外包预测建模竞争的公司和十几万数据科学家之间，充当起知识经纪人的角色。现有的公司包括通用电气、福特、脸谱网和微软，一些卫生服务组织，如在加利福尼亚的医疗供应网络，主要开发预测计算，能够确定哪些病人在未来的一年会被医院接收。这类的医疗卫生供应的预测分析能够了解更准确的公共卫生预算和成本削减的方案发展(PinsentMasonsLLP2013)。2.2.4公私合作关系在公共服务领域，公私合作无处不在。随着大多公用事业的私有化以及外包的火热，在许多先进社会，私人机构开始管理公共部门。一些在欧洲边缘的国家，如希腊，正在经历这个过渡期。面对在公共服务管理的国际竞争，我们也进入到公立和私立机构的新型关系阶段。该关系是：追求对长期稳定互利忠诚的关系，以确保长期规划和繁荣。大数据管理在该合作关系中对做出各种决定起着重要作用，这也是为何一些进步政府对该领域大力动员和支持的原因。一个典型的事件是，在2013年，世界资源研究所在伦敦组织“公开政府下的公私合作”。这是为了共同动员市民参与、公开政府信息和让政府更负责(StefanandKisker2011)。这对公共服务领域中数据所有权和数据管理，如医疗卫生和自然资源公开了合同、政府基础设施的支出和政府对第三方的帮助等相应数据，产生了很多影响。一方面看重合作关系，但是在公共领域缺少专业知识，欠缺审查和支持的能力。另一方面，当然是在基础设施上的前期投资，比如，大数据云服务，大型组织可以通过长期与政府合作将其收回。另外一个原因是，在合适的环境下，私人公司对新技术的试验的能力和欲望，为政府提供安全技术，且不会因此引起公众批评。大数据分析能够对人进行描述和对每个人提供三方面的信息，所以它能处理公共服务方面最基础的问题。比如，征税、健康服务和违规停车罚单收集里的身份管理对于公共服务是一个重要问题。大数据分析技术帮助公共服55 务组织，反欺诈、防止数据外泄和鉴别其是否真实(ZhangandChen2010)。另一方面，一旦出错，后果严重。在2.3章节，这一方面会有详细叙述。2.2.5政府的云数据政府逐渐开始采用云计算，解决与代理机构和公众之间所需要的透明、参与和合作。云计算，一种新想法、新概念和专业词，广为人知，却不明确。这主要体现在，软件、硬件和网络三个主要的实体对虚拟化、网格计算、效用计算和网络技术的综合使用，这也产生了信息技术服务交付的新模式(Clarketal.2013)。在云计算的帮助下，中央政府用电子政府统一管理整个国家，而地方行政部门，则单独于中央，提供电子服务，它们能更好或者更差地提供电子化服务，服务导向式架构，使得提供覆盖了整个客户过程的复合式服务，更为便捷，这个客户过程有可能是市民或企业。该系统的推出可以同时发生，成本效益也高，因为在整个过程中的许可和支持是可以协商的。到今天，消费者、企业和政府都习惯将数据保存在云盘里，因此，在任何地点、任何时间和任何设备里，都能获取。根据Lerman（2013）所说，超过69%的美国人都在使用网页邮件的服务、线上存储数据和使用在线应用。这种趋势还会继续下去，产业分析师预测这种云功能将会在未来的几年将会有400亿美元和1600亿美元，还不算物联网应用(Lerman2013)。随着云计算的广泛使用，云政府这个新生词的应运而生来源于，云计算虚拟化的灵活，网格计算的可扩展性以及2.0网络的简便。云技术之间的相互联系，让提供云技术服务的人和提供多资源的人，相互转化极为方便(Yiu2012)。虽然云技术跟政府和他们的潜能之间有联系，例如，选举信息系统受到欢迎，但是由于要确定政府交易和选举过程数字化中的危险，这方面的热情遏制了不少，最终此事也逐渐明朗和可信(Manyikaetal.2011;Al-khouri2012)。在后面的章节，这些问题将会得到处理。2.2.6在公共服务交付中的物有所值整个过程是通过公共服务的一体化，从文件填写到电子政府服务都是有益于削减成本。大数据为政府提供了这方面的便利，而且让政府不必遭受商业系统的一体化过程。56 单就英国而言，据数字政府负责人克里斯·余所估计，大数据的使用每年可以节约160亿到330亿英镑不等，这也就相当于人均250到500英镑不等的收入。得出这样的结论，是因为许多倡议与提高政府整体工作效率的绩效管理、减少诈骗和错误以及单纯的税收等有关(Laney2014)。麦肯锡全球研究所估计这还有可能会增长到每年1500亿到3000亿英镑不等(Yiu2012)。许多的分析工具也可以挖掘，市民对公共服务的看法，进而提供相应的反馈，同时通过帮助员工更好了解每个市民的需求，更注重对服务交付的量身设计。毫无差别的是，商业组织采用该技术为顾客提供个性付费服务。大多数发达国家的政府致力于提供优质服务，特别是那些希望能吸纳国际人才的国家。例如，卫生信息学，尤其是传染病学里的预期分析，帮助了希望能为危机管理做准备的政府。人们对疾病的传播预测得越为精准，防治就越价廉高效，对公共生活的影响就越小。不需调整现有的公共服务结构，就可以获得该有利条件。如果我们对以下方面采用预测分析法，如防范犯罪、资源监控以及智能电网技术对提高资源（天然气、电和水）利用效率等，那么我们将会获得更多的好处。2.3政府面临的挑战2.3.1资料所有权伴随资料所有权而来的问题是由谁来承担对资料的管理、存储、使用和滥用的责任？但是在这个公开（公共）数据的年代，谁又该去承担这个责任呢？谁又是它的法定管理人？关于它的使用和保护，市民和政府的隐性合约写的是什么？从某种程度上说，所有的公用数据都是私人数据，因为这有可能是公民自己的资料、生活方式的信息或者是与公共服务功能的信息，但是这些都是对公众，至少对民主，负责。从这方面来说，政府和公共组织都是我们资料的监管者，允许使用它为我们提供公共服务，促进公共事业(Gudipatietal.2013)。例如，美国线上专利商标局数据库，存储了8百万专利，1600万存档日期可以追溯到1790年萨缪尔·霍普斯金，还接收了每年大约20万专利申请和10万商标申请(HoffmanandPodgurski2013)。这些信息是谁的？很明显，是属于专利持有者和提交者。但是只有在美国政府保证它们完好无缺的情况下，这些信息对于个人和国家才会有用。的确，个人信息来源渠道很广57 泛，如个人、机器和设备等，这些数据的合法拥有者应该是这些能辨别该数据真伪的人，他们才是数据的真正拥有者(Al-khouri2012)。同时这也会产生一些问题，这些设备什么时候监控我们的行为和情况，发送我们所在之处和状态的信息。虽然设备很有可能出错，但是人也同样也会受到欺骗和自欺。政府和公共机构得制定相应的规章制度来处理这些问题。2.3.2数据质量大数据能扩大劣质数据带来的影响，对政府和公民都是一个很重要的问题。因为工作负荷和用户界面等问题，记录的数据也有错漏（不准确、错编、不完整等）(JunquédeFortunyetal.2013)。数据检查要关注它的完整、一致、稳定、准确、可复制以及可信度，正确的实践也应该运用到数据质量中。在私人组织中，劣质数据不应采纳，这样就不会损害正确的分析和影响使用者。例如，假若零售商根据顾客的描述得出的纯净数据，进行分析和预测下一步的销售，顾客就不会受到较大的影响(Crawford2013)。但是这与健康服务行业不太相同，比如，本地公司的一个特点就是不纯净数据（如：本地医院）。数据来源的整合产生了劣质数据。数据的整合、结合以及聚集也能产生数据问题，数据多样化、数量大等特点，而检测数据也是份艰巨的任务。类型多样的大数据开始检测程序，采用网格处理技术，比如海杜普，就是一个适时的数据处理软件(Crawford2013)。虽然数据管理和数据测试程序可以处理“无用数据输入”的问题，但是另外一个问题隐藏在公共领域问题决策的衔接里；有各种偏差【如：选择偏差、混杂偏差和计量偏误等(KerrandEarle2013)】。在联系到两个现象（这两个现象已经在分别研究了），数据混杂就会产生(Howarth2014;IQAnalytics2014)。例如，从脸谱网的配置文件中得出的吸烟习惯联系到年轻人的糖尿病数据，就有可能会误判这对因果关系，这也会因另外一个因素得到解决，比如失业、遗传或家庭的经济状况等。选择偏差来源颇多，最主要是在不同国家、年龄段和社会经济群体之间的设计使用差别。凯特·克劳福德是对微软的主要研究者，也是麻省理工大学的客座教授，在哈佛商业评论博客上，对大数据下的偏差进行了很精彩的解释(Crawford2013)。假若公共服务的敏感可以经受生存威胁和法律问题，那信息处理的严谨性就是最重要的。因此,我们58 需要概述和加快在监管上和其他方面干预措施的协商,以解决数据分析的问题,因为这些问题可能导致无效的结论和不健全的公共卫生政策(微软2014)。2.3.3隐私、公民自由和平等隐私和公民自由是钱币的两面。用户配置文件是对一位用户收集信息的过程，目的是为详尽收集他们的信息。用户配置文件的信息会包括用户的各个方面，如地理位置、学习和职业背景、群体成员、兴趣、偏好以及观点等。而且包括一些不好的应用程序，如手机追踪，新提议出的一个关于国家生物计量资料库。克尔和厄尔(2013)对个人信息有意见，是因为他们的健康、位置、电量使用和网络活动等会引起歧视、不受控制等风险。这些问题若进入到公共服务领域，后果将更严重。大数据的好处是由于它们能预测有可能的危机。比如，要是能够预测某一区域的盗窃数量的上升，那当地政府就能增加该区域的警力，以阻止该不法行动(巴塞罗那市议会2014)。先发制人的行动是根据相关预算法则对社会信息的预测，这会限制公民自由，对相关证据取而代之的是风险估计。随着预测能力的加强，规避该类风险责任加重，这就会让政府对于如何处理社会危机更加保守。一旦我们确定有可疑的小偷或毒品交易，政府会在何种程度上允许无业青年在街头自由游荡？另一方面，我们是否应该限制青年人社交自由，让他们孤立、失望？到了何种程度，警察就该知晓这些关系？到了何种程度，会导致对每个无业青年的歧视，因为这些有嫌疑的人，可能会导致真正的犯罪，这样的事会预测得到吗？而且，莱尔曼教授(2013)指出了另外一个问题：由于公众和群体不明方法，没有时间和兴趣，在没有完全参与信息社会的情况下，平等的问题。数字鸿沟的相关统计资料，反映了国家之间的数字联系、同年龄范围内的人群以及社会经济阶级、城市和农村的巨大差别(Lerman2013)。关于全球因特网和社会媒体的使用有一些有趣的统计资料，于2014年出现在社交媒体博客社区中心(Kemp2014)。不过，政府过于依赖大数据也有危险，因为政府在做决策时，忽略公民的真正需求。2.3.4人才雇佣问题59 由于缺少市场数据分析人才，公共领域将很难雇佣相关人才为其长久效力。比如，英国公共部门组织，负责采用低于市场现行利率，以核实人力资源的开支(PageGroup2014)。此外，公共领域中的相关工作保障和福利日渐消失，这使得公共部门更缺少吸引力。同时，以下大型企业机构、竞争资源，如：银行、保险公司、大型网络零售商以及咨询公司等，政府雇佣人才举步维艰。另一方面，政府可以采用大学人才。在欧洲，没有雇佣这类精通公共领域相关技能的人才(Campos2008)。另一方面，针对公共领域的人员也常有专业培训。比如，2014年2月，英国政府花费15万英镑在公开数据中，为150多名公共领域的员工提供了专业培训(Gangadharan2013)。2.4案例研究10年前，巴塞罗纳就着手打造智能城市，现如今许多智能城市项目分散在各个部门中，目前单独由一个项目管理。曾经一度需要重建的22@巴塞罗纳区域，现在已经改造成新技术检测点(业务创新和技术部门2013)。巴塞罗纳的市长特里阿斯，在2011年就意识到数字技术对城市未来的繁荣相当重要。用他的话说，“巴塞罗纳不应该浪费将新技术运用到提高人们生活质量中的机会，在智能城市的基础上，能够有新的城市经济创新，这也是我们以后的责任所在。”(业务创新和技术部门，2013)为了加快该进程，他筹办了城市栖息地计划，该计划指政府进行大规模管理，是为促进在水、能源、公共事业和环境机构的相互合作。但是，房屋和城市规划应归于一类。为了加强跨机构合作，智能城市人事管理办公室负责监督所有与智能城市有关的项目。关于智能城市的项目过百，其中有13个项目是专为巴塞罗那智能未来设计，此举是为支持申请智能城市，为城市资产配备智能感应器，以及规定智能城市的公共服务，配备必要的基础设施。为了该目的，远程通信网络整合成融合光纤网、无线网、有互通性和移动性优化的公众和中央管理系统、公共交通和城市基础设施等，将这些理念如优先性和交叉关系运用其中，让城市效率更高，移动的持续性更长。智能数据计划对此大力支持，并整理了来自于智能资产的信息和公共服务组织对外公开的信息等。新的公共服务因以下项目提升不少，如巴塞罗纳城市照明设备60 能源项目、创建微型电网以创建局部生成和绿色能源消费、远程管理灌溉城市绿地和电动车移动选项以及减少多余的城市交通的同时，又能快速停车的智能选项。市民运用遥控和移动的应用软件使用城市服务。一些计划普遍关注智能城市议程里的观念变化。比如，政府信息公开计划，目的就是为政府信息公开赢得更多的支持，寻求策略和路线，增强透明度、公开数据和鼓励公民参与。“市民平衡2012-2022可持续性”指的是，增加城市路线图定义和指引,这样一来就可以为人民提供一个更加平等、兴盛和自给自足的环境(业务创新和技术部门2013)。22@巴塞罗纳区的重建依靠了公共部门和私立部门的共同合作，包括公司、大学、研究机构、社区等与市政领导相互交流知识和流线创新，而且通过资助住房和发展绿地，吸引参与城市规划。本地、国际、私立和公立的基金都是用来完善基础设施和检测新的公共服务。InnoActiva，一家咨询机构，帮助私人公司提交案件给公共机构，政府通过让该机构制度化的方式，方便使用公共基金。根据硅谷的群集模型，一般会建在有竞争优势的区域。因此，22@面向媒体、信息通讯技术、医疗技术和能源设计招揽人才和专家。对于大数据分析，公开数据实际上是巴塞罗纳智能城市核心。公众和商业所能接触到的信息，如选举结果、人口、公共设施或者在公共知识库经济，都是巴塞罗纳的公开数据。例如，微软公司使用与圣梅尔塞节有关的数据为众包管理提供相关解决方法。为此，相关数据如社会媒体、信用卡交易、网站访问量、顾客服务咨询、全球定位系统数据、交通状况、天气数据和停车等，都已收集和分析。这些数据是为了帮助了解人们对该节日的娱乐和食物场所的看法、市民兴趣所在、人口流通模式，以及掌握医疗和犯罪数据以便计划和管理下次事件(维也纳科技大学2014)。城市根据移动识别技术引导服务供给。通过智能手机的应用软件，市民可以了解到停车罚单和拖车目的地的信息，请求为非盈利活动提供公共补贴等，提供相关概念和技术证明，帮助公众参与到下一个阶段中(DavenportandPrusak1997)。所以，依据大众的需求，公共交通智能应用软件可以抢先更新。巴塞罗纳的交通信息都是来源于信息真实的应用软件。任何人都能获得汽车站位置、方位、路线等信息，甚至智能手机的摄像头，可以引导市民前61 往不熟悉的地方，游客或盲人可以通过语音指导或微软到达他们的目的地(2014)。市民参与的程度也能反映出智能城市发展进程。比如，森提罗是由巴塞罗纳市议会赞助，并由公共社区设计的开放资源和执行器平台。注意事项：巴塞罗纳的公共资源和智能城市平台让本地人才参与到智能城市的发展中，确保技术和供应者的独立和数据监管，在监督之下与公众保持一致，确保公民自由。为了打响城市名声，巴塞罗纳提倡智能城市，该倡议与有试点项目的国际城市取得联系，解决共同危机(BainandSentilo2014)。相对而言，巴塞罗纳是一座比较小的国际大都市，却一直希望能为智能城市树立典范，通过各种资本资源的互通有无，如资本、基础设施、知识和人才等，让智能城市公开、透明和民主。虽然在大多数人看来，智能城市的应用程序在我们生活中是“有则更好”的事，但是对于任何智能应用程序，危机管理都是一个严峻的考验。所有的应急服务对信息管理都有同样的要求。他们需要准确分析来自各种渠道的生命攸关，实时的信息，以更好地部署和管理应急服务(Gangadharan2013)。2010年海地地震中，政府采取应急服务。Instedd，该公司专为自然灾难和疾病的紧急服务提供技术方案，支援紧急服务和人员。48小时内，该公司建立起电信基础设施，也获得购买建立应急回应号码。同时，该公司提供了来自于两家移动网络公司的信息整合通讯系统；海地克里奥尔语接收到的求助。这些会发送到资源互换档案/主管信息系统进行分析(Alehegn2010)。资源互换档案能够自动提取特征、分类数据和标明数据和元数据（如：来源和目标地理定位、时间、传送渠道等）在它之前可以通过算法进行处理。分析组建可以检测这些在协作空间内或者在不同写作空间中提取出的特点。资源互换文件格式还可以合并来自于Geochat的信息，GeoChat是一种合作工具，定位人们的评论、观察和报告，这样一来，信息就会更全面，关联性也越高。资源交换档案与Crowdflower，一家工作流程供应商，信息共享，分配工作给双语志愿工作者进行翻译、标记和地理编码。接着，信息会传送给目击者，该网站最初编制在肯尼亚发生暴力的地图报表，现在变成了一个与人62 道主义目标有关的全球众包平台。“Ushahidians”,该软件专门绘制地图,准确标注地理信息，最后提供准确坐标，方便地面搜索和救援(Meier2012)。注意事项：智能应用软件和公众协作平台，是大数据分析为灾难复原的重要基础设施平台。早在2007年，群体智能为工作流紧急管理系统所起的价值作用已经有所概述。这是应用众包服务的一个典型例子，在复杂的双语工作中，提供信息架构的紧急反应。数据质量是至关重要的，保证通话质量，减少紧缺救援资源分配错误。及时和准确的信息处理是至关重要的。国际志愿者充分发挥了其特长，重要的安全决定需要根据政府和参与的公司而做出。投资企业的成功是基于公司的技术能力和社会责任,与其他公司和志愿者公开合作也是出于同样的原因。2.5对组织机构的建议政府得先立足，在进步、大数据的挑战和在全球竞争之下，对公众和私人资本重新定义其关系等，权衡利弊。对于这些能抓住机遇的人来说，已经成为一个竞争优势。反之，其他人就会落后。对于这些问题，接下来，我们会指出，在公共部门，特别是智能城市的数字化和服务为导向的倡议中，大数据分析有效应用和利用的重要因素。2.5.1智能城市的准备每个国家都将进行城市准备的环节，以及对在全球布局中的智能城市的定位。欧洲智能城市的倡议审查城市是基于在2.1表格中的六大因素(维也纳科技大学2014)。在国家层面上，智能城市战略可能面临农村和城市发展之间，预算上的紧张关系。国家要拥有一种眼界，能帮助他们的人民和私人投资者进行谈话。而且，当地政府和中央政府都会有‘好警察和坏警察’在以下过程中扮演角色，如：信息公开、透明影响和确保信息完整，不会被参与者滥用。而且，智能城市会泄漏城市服务对机器的责任，以及与私人企业和公众的合作关系。这不仅需要高素质的市民，而且还需要改变各个参与者对公民责任的看法。2.1表审查智能城市倡议的因素63 因素描述创新精神和企业家精神、生产率、劳动智能经济力的灵活性、改变的能力和国际接轨本地和国际的访问、信息通信技术基础智能流动设施的利用、创新交通系统具有吸引力的自然条件、污染、环境保智能环境护和可持续的资源管理素质水平、期望终生学习、社会和人种智能人民多样性、创新、灵活、世界主义和开放思维文化设施、健康状况、个人安全、住房智能居住质量、教育设施、旅游吸引力和社会凝聚力参与决策、公共和社会服务、透明政府、智能管理政治策略和观点2.5.2学习合作像大多社会技术改变一样，要想能从大数据和智能城市倡议中获利，我们就得改变我们现在做事的方式。这类技术特别需要不同的利益相关者之间的合作。政府机构和部门传统上因为内部竞争和经济竞争而分化，而且逐渐演变成内在的孤岛心里和文化。信息就是力量。足够的信息量，使得在面临裁员、成本削减和其他现代化尝试的时候，人的地位就显得非常重要。Davenport和Prusak早在1997年就已经对这些问题高度重视(DavenportandPrusak1997)。注重效率和多年来征用的人员大多都以削减成本为主，使得公共部门越发缺少创新人才、专业知识和实践能力(Parker2014)。公共部门录取新人员和更新政策，却付给新录用人员最低的市场价格这样一来，就很难吸引人才，而且也很难管理外部录用该类人才的合作伙伴。公共部门需要反思内部雇佣智能人才的招聘流程，因为这些人才会更注64 重与私人部门和公众合作关系。如果政府要展示智能城市的发展历程，他们应该雇佣高素质人才，致力于终身学习，社会和种族的多元化、创新、灵活性、世界主义和开放思想。而且，成功的合作依赖新的管理方法。好的合作方法包括：1、通过坦诚分享和反馈的方式，建立亲密的联系，2、通过行动和语言创建相互的信任，3、新的矛盾和问题解决方法，4、不断改变5、最后，要相互依赖(Dent2006)。而且，在新兴技术领域的合作也需要审议公共部门的采购政策，让供应商广泛参与进来，而这些囊括了新起的高风险技术投资公司(Uyarraetal.2014)。最后，政府在有限的资源下，处于大数据世界，他们就需要更智能化，这就意味着他们需要培养公众参与决策、公共和社会服务的意识，让管理、政治决策和观点更加透明和精炼。2.5.3公民教育和线上民主政府信息公开主要是希望公众参与到政治过程和公共服务的自助服务里。这需要更多的兴趣、知识、公众高度成熟以及新式参与政府的模式。实现前一者的方式是教育。联合国在美国审查的该项目，表明这些项目已经改变了人们参与水平和人们参与政治过程适度性的感受，并不是因为人们尊重不同的政治立场、社会凝聚力和信任(联合国出版，2010)。线上参与民主过程为政府咨询和决策参与提供了一个成本较低的方式，而在之前，这完全不可能。在不同国家，相关倡议也逐渐出现。例如，2014年1月，加利福利亚让加利福利亚州工作报告和移动的网络平台成为了一种惯例，帮助市民通过聊天室参与到审议过程中，在这里他们可以发表个人观点，点评别人的建议(NewsomandGoldberg2014)。2.5.4法律体制的发展在实际生活各个方面，法律体制落后于技术发展。网络欺凌有力地说明了这点。如上所述，大数据描述引发了许多关于隐私、公民自由和平等的问65 题。通过智能应用程序，各种不同公共服务需求，特别是与虚拟世界相关的事情，会造成另一种程度的不平等。因此，政府需要定义新的法律体制，管理生活。他们得从全球的角度来做这件事情，因为网络参与是一个全球现象。大数据分析可以帮助国际政府机构分析人们的情感，而且在该类事件上整合最佳实践，让执法部门和公众更能理解法律的实施(Morabito2014)。2.6总结本章主要讲述了，在公共服务供给中，大数据带来的影响。同时，在新的机遇之下，公共服务组织结构会转变政府的社会角色。我们先分析了公共服务供给的发展，在公共服务供给中，市民都是积极的消费者，更接近直接的线上民主，积极参与，国际化智能城市对资源人才的竞争。在该环境下，政府通过利用1）新的数据来源，如众包、物联网等，2）公共人才的参与，3）让私人和公共部门的合作关系制度化，4）为公共供给寻求物有所值的新模式。大数据分析虽然有极大的利用潜力，但是也有诸多挑战，特别是中央政府所面临的挑战。这些挑战包括：数据所有权、数据质量、隐私、公民自由、平等以及公共部门吸引大数据分析人才的能力。我们列举了两个关于公共服务供给形式的案例。巴塞罗纳智能城市的案例，极好地展示了私人和公共部门在区域重建问题上共同合作。2010年地震灾难中，海地救援支持说明了，在热情志愿者掌握了大数据之后，能够很好地组织和帮助生命攸关的应急服务，这个普通的例子说明，通过人类的直觉、大数据的整合和先进的分析，这些都是可以做到的。如大多数社会技术的变革，技术接受和使用的社会圈以及管理这类技术里都有挑战，因此我们的建议会有助于审议智能城市发展、培训公务员合作管理的技能、培养公民对公众参与的心态以及更新法律体制以处理大数据的发展。66'