urban indicators and the smart city agenda...jung-hoon lee, yonsei university, seoul, korea 2012 one...

ThisprojecthasreceivedfundingfromtheEuropeanUnion’sSeventhFrameworkProgrammeforresearch,technologicaldevelopmentanddemonstrationundergrantagreementno.613286.

POCACITOPolicyBriefNo.5,December2016

URBANINDICATORSANDTHESMARTCITYAGENDA

CatarinaSelada,CarlaSilvaandAnaLuísaAlmeidaINTELI–InteligênciaemInovação,CentrodeInovação

POLICYBRIEFNo.5|December2016

TABLEOFCONTENTSSUMMARY 2

1 INTRODUCTION 2

2 IMPORTANCEOFURBANINDICATORS 2

3 SUSTAINABLEDEVELOPMENTINDICATORS 2

4 SMARTCITYINDEXESANDINDICATORS 3

5 EVOLUTIONOFKPIINITIATIVES 4

6 THEPOCACITOAPPROACH 5

7 LIMITATIONSOFKPIINITIATIVES 6

8 POLICYRECOMMENDATIONS 7

REFERENCES 8

LISTOFFIGURESANDTABLESFigure1.Dimensionsandsub-dimensionsofthePOCACITOapproach 6

Table1.Examplesofsustainabledevelopmentindicators 3Table2.Examplesofsmartcityindexesandindicators 3

Table3.TraditionalandrecentKPIinitiatives 4

LISTOFABBREVIATIONS

ICT Informationandcommunicationtechnologies

KPI Keyperformanceindicator

WCCD WorldCouncilonCityData


2URBANINDICATORSANDTHESMARTCITYAGENDA

SUMMARYThe use of urban indicators and benchmarkingexercises by local governments has proliferated inthe last few years, mainly owing to thesustainabilityagenda.Thesekindsofindicatorsaimat measuring, tracking, assessing and comparingcities’ performance, with a view to guiding policyformulation and implementation. Diverseframeworksforsustainabledevelopmentindicatorshave emerged,mostly produced by European andinternational organisations. Recently, initiativesinvolving key performance indicators (KPIs) havebeen influencedby the smart city agendaand thedigital revolution, in terms of content, datacollection, analysis and dissemination processes.ICT, big data, open data, real-time information,data analytics, dashboards and operation centresare some of the main components of thismovement. Taking into account the different KPIinitiatives and their evolution, the POCACITOproject defined a set of urban indicators orientedto assessing cities’ performance and to analysingtheir transition towards a post-carbon future,comprising economic, environmental and socialdimensions. However, severalmethodological andpolicy limitations can be observed when urbanindicatorsareused,notablyinthedevelopmentofbenchmarking exercises among cities of differentcountries.Finally, thisPolicyBrief looksatwaystosupporttrendstowardsstandardisation,openness,interoperability, innovation and collaboration,which can inform data-driven policy-making atlocal/regional,nationalandEuropeanlevels.

1 INTRODUCTIONWithin the framework of the POCACITO project,keyperformance indicators (KPIs)weredefined toassess and compare cities’ performance alongeconomic, social and environmental dimensions,and to analyse their transition process towards apost-carbonfuture.

The aim of this Policy Brief is to assess KPIinitiatives, comprising urban indicators andbenchmarking exercises, in the context of thesmart city approach. The objectives are tounderstand theevolutionof these initiatives in anera of digital revolution, and to identify theirlimitations and fragilitieswith a view to informinglocal,nationalandEuropeanpolicies.

2 IMPORTANCEOFURBANINDICATORS

According to Godin (2003), “indicators arerecurrentquantifiedmeasures thatcanbetrackedover time to provide information about stasis andchangewithrespecttoaparticularphenomenon”.

KPIs seek to measure, track and assessperformance, so as to inform and guide policyformulation and implementation. This is usuallycalled evidence-based or knowledge-based policy-making. Moreover, indicators intend to explainpresentpatternsandtopredictandsimulatefuturesituations. They steer operational practices withrespect to specified targets, providing evidence ofthe success or failure of measures and policies(Kitchinetal.,2015).

Indicatorsarealsousedtocompareandrankcities’performance, through benchmarking exercises.According to Kitchin et al. (2015), “citybenchmarking consists of comparing urbanindicatorswithinandacrosscitiestoestablishhowwell an area/city is performing vis-à-vis otherlocalesorbestpractices”.

3 SUSTAINABLEDEVELOPMENTINDICATORS

Overthepastdecades,theuseofurbanindicatorsand benchmarking exercises by local governmentshasproliferated.Themainreasonbehindthistrendhas been the sustainability agenda, which has ledto the expansion of sustainable developmentindicators. In this context, “sustainability dependson social, economic, environmental andgovernancefactors”(EuropeanCommission,2015).Different frameworks of indicators are being usedbypublicauthorities,withvariedobjectives,scales,conceptualmodelsandmethodologies(Table1).

The selection of indicators is always a key issue,mainly driven by the criteria of relevance, clearmessages,dataavailabilityanddataquality(Silvaetal.,2014).Sometimesthisprocesstakesplacewiththesupportofstakeholders.Inaddition,theuseofcomposite indicators and indexes, combiningseveral measures and systems of weights, iscommoninurbanbenchmarkingexercises.



Table 1. Examples of sustainable developmentindicators

Indicatorframework;study/report Organisation Year

ChinaUrbanSustainabilityIndex UrbanChinaInitiative 2014EuropeanGreenCapitalAward EuropeanCommission 2010

EuropeanGreenCityIndex EconomistIntelligenceUnit;Siemens

2009

GlobalCityIndicatorsProgram GlobalCityIndicatorsFacility

2007

IndicatorsforSustainability SustainableCitiesInternational

2012

ReferenceFrameworkforSustainableCities(RFSC)

RFSC 2012

CitiesStatistics-UrbanAudit Eurostat 2004UrbanSustainabilityIndicators EuropeanFoundationfor

theImprovementofLivingandWorkingConditions

1998

STARCommunityRatingSystem SustainabilityToolsforAccessingandRatingCommunities(STAR)

2011

UrbanEcosystemEurope InternationalCouncilforLocalEnvironmentalInitiatives(ICLEI);AmbienteItalia

2007

UrbanMetabolismFramework EuropeanEnvironmentalAgency

2010

ATKearneyGlobalCitiesIndex ATKearney 2008

The traditional data underpinning indicators aremainlyofficial statisticsordataprovidedbypublicor private entities. These analyses are oftencomplemented with “small data studies” (Kitchin,2014), suchas case studies, questionnaires, auditsandfocusgroups.

These data “often rely on samples, are generatedonanon-continuousbasis,thenumberofvariablesare quite small, are aggregated to a relativelycoarse spatial scale, and are often of limitedaccess”(Kitchinetal.,2015).

Thetrendtowardsstandardisationofcitydatahasemerged recently. The first international standardwas published in May 2014 (ISO 37120 –Sustainable Development of Communities:Indicators for City Services and Quality of Life),whichincludes100indicatorsthatmeasureacity’ssocial, economic and environmental performance.ItwasdevelopedusingtheframeworkoftheGlobalCity Indicators Facility, which has been tested bymorethan250citiesworldwide.TheWorldCouncilon City Data (WCCD) provides a certification tocities basedon the number of indicators reportedandverifiedaccordingtoISO37120.

According to the WCCD, cities need standardisedindicatorstomanageandmakeinformeddecisionsthrough data analysis, benchmark and target,leveragefundingwithsenior levelsofgovernment,

planandestablishnewframeworksforsustainableurban development, and evaluate the impact ofinfrastructureprojectson theoverall performanceofacity.

4 SMARTCITYINDEXESANDINDICATORS

Given the growing economic importance of cities,the process of urbanisation and the challengesimposedbyclimatechange,thesmartcityconcepthasarisenasanewurban-developmentparadigm,which in turn has led to the emergence of smartcity indicatorsand indexes(Table2).Theobjectiveis to evaluate urban intelligence and to comparetheperformanceofcities,demonstratinghowcitiesbest use ICT to improve quality of life, fostersustainability, and boost competitiveness andinnovation.

Table 2. Examples of smart city indexes andindicators

Indicatorframework;study/report Organisation/author YearSmartCities:RankingofEuropeanMedium-sizedCities

ViennaUniversityofTechnologyetal.

2007

TheTopSmartestCitiesinNorthAmerica

FastCompany,BoydCohen

2012

TheTopSmartestEuropeanCities FastCompany,BoydCohen

2012

TheTopSmartestCitiesinAsia/Pacific

FastCompany,BoydCohen

2013

SmartCityRankinginChile BoydCohen,UniversidaddelDesarrollo

2014

UKSmartCitiesIndex EricWoodsetal.,NavigantResearch

2016

SmartCityRankingBrazil UrbanSystems 2015SmartCityIndexPortugal INTELI 2012

IcityRate–ClassificationofItalianSmartCities

ForumPA 2012

ItaliaSmart–SmartCityIndex2016 DigitalAgency-Italy 2016

WhitePaper“SmartCitiesAnalysisinSpain”

IDC 2011

TowardaframeworkforSmartCities:AComparisonofSeoul,SanFrancisco&Amsterdam

Jung-HoonLee,YonseiUniversity,Seoul,Korea

2012

One of the first approaches was outlined in thestudy on Smart Cities: Ranking of EuropeanMedium-sized Cities (Vienna University ofTechnologyetal.,2007),inwhichsixcharacteristicsof smart cities were presented: smart economy,smart people, smart governance, smart mobility,smart environment and smart living. The smarteconomy comprises factors associated witheconomic competitiveness, such as innovation,entrepreneurship and internationalisation. Insequence, smart people includes the level of



qualificationsof theresidents, thequalityofsocialinteractions and openness. Political participation,the functioning of the administration and publicservicesmakeupthesmartgovernancedimension.Smart mobility refers to local and internationalaccessibility, the availability of ICT and transportsystems.Smartenvironment includessuchaspectsas natural conditions, pollution, resourcemanagement and environmental protection.Finally,smartlivingincludesqualityoflife(culture,safety,housing,tourism,etc.).Thestudyintegrates74indicatorsand70cities.

According to the “Strategic Implementation Plan”(European Commission, 2013) of the EuropeanInnovation Partnership on Smart Cities andCommunities, smart city performance indicatorsand metrics are very important to measure andcompare cities’ progress. Yet, there is still nointegrated indicator system that supports reliableprogressmonitoring in all fields relevant to smartcities, both within a city over time and amongcities. Furthermore, the “OperationalImplementation Plan” (European Commission,2014)explicitlyreferstotheneed“todevelopandpilotanEUwidesmartcityindicatorframeworkasa collaborative exercise; adapting [sic] existingmeasurement assets; and establish a means toachievewideadoption”.

Withthisobjective,aprojectcalled“CITYkeys”wasapproved in 2015 under Horizon 2020. 1 BeingcoordinatedbytheresearchinstituteVTT(Finland),theaim is todevelopandvalidate,with theaidofcities, key performance indicators and datacollectionproceduresforcommonandtransparentmonitoring as well as the comparability of smartcitysolutionsacrossEuropeancities.

5 EVOLUTIONOFKPIINITIATIVES

Inlinewiththesmartcityagenda,KPIinitiativesarebeinginfluencedbythepossibilitiesofferedbythedigital revolution (Table 3). Fixed and mobileinternet, ubiquitous computing, social media andWeb2.0applications,databasedesignandsystemsofinformationmanagement,distributedstorageofdata and new forms of data analytics are keyelementsofthisdigitalrevolution(Kitchin,2014).

1Seehttp://citykeys-project.eu/.

At present, more data are being produced everytwodaysthaninallhistorypriorto2003.AccordingtoRial(2013),1.7millionbytesofdataperminuteare being generated globally. This is the so-called‘bigdata’phenomenon,whichconsistsof“massive,dynamic, varied, detailed, interrelated, low costdatasets that can be connected and utilized indiverseways, thus offering the possibly of studiesshifting from: data-scarce to data rich; staticsnapshots to dynamic unfoldings; coarseaggregation to high resolution; relatively simplehypothesis and models to more complex,sophisticated simulations and theories” (Kitchin,2014). In the same vein, Kleinman (2016) definesbig datasets as “those available at massive scale;accessible in real time or close to it; have highdimensionality; and aremuch less structured thanconventionaldatasets”.

Thepossibilityofaccessingreal-timedata,whichisbeing captured through sensors, cameras, socialmedia andotherdevices, is a characteristic of thesmart city vision. Kitchin (2014) describes thisphenomenonasthe“real-timecity”.

Table3.TraditionalandrecentKPIinitiatives

Variable Traditionalurbanindicators Innovativeurbanindicators

Focus Focusonsustainabledevelopmentandonenvironmental,economicandsocialdimensions

FocusonsmartdevelopmentandonthecontributionofICTtoeconomicdevelopment,environmentalprotection,andsocialcohesion

Datacollection

Officialstatisticsordataprovidedbypublicorprivateentities,censusesGeneratedonanon-continuousbasisDataarescarceIsolatedandsimpledataCentredonsamplesSmallnumberofvariablesHigh-costdatasets

Real-timedataandinformation,collectedthroughsensors,cameras,smartphones,etc.GeneratedonacontinuousbasisDatarich(bigdata)InterrelatedandvarieddataTendtobecentredontheentirepopulationsorsystemsHighnumberofvariablesLow-costdatasets

Dataanalysis

TraditionalstatisticsSimplehypothesisandmodelsCoarseaggregation

UrbanscienceComplex,sophisticatedsimulationsandtheoriesHighresolutionHigh-poweredcomputationDataanalyticscentres

Datadissemi-nation

MainlycloseddataStaticdata;reports

OpendataDynamicdataandonlinedashboards(visualisation)

Policy-makingprocesses

Limitedevidence-baseddecision-andpolicy-makingprocesses

Moreaccurateevidence-baseddecision-andpolicy-makingprocesses

Mainactors

Institutionallysupportedmeasurement(cities,internationalorganisations)

Corporate-supportedmeasurements(cities,consultants,multinationals)



In this respect, “governments are oftenoverwhelmed by data, and express considerableinterest in better, faster, and cheaper ways ofextracting value from data” (Kleinman, 2016).Traditionally,thishasbeenthedomainofstatisticalanalysis, but conventional statistics are nowbeingcomplemented or even challenged by the newparadigmofdatascience.“Bycombiningelementsof statistics, computer science, and artificialintelligence, data science may offer bettertechniques and methods for how to learn fromdata”(Kleinman,2016).

This new urban science is replacing traditionalurban studies. Urban science is defined byTownsend (2015) “as an emerging domain ofresearchat the intersectionof scienceanddesign,drawing on new disciplines in the natural andinformational sciences, that seeks to exploit thegrowingabundanceofcomputationanddata”.

In this context, citiesareopeningupdata, sharingthem with residents, businesses and universities.Larger cities, such as London, Amsterdam,Barcelona, Berlin, Copenhagen, Paris, Stockholmand Vienna, are launching open data initiatives(EuropeanCommission,2016).The releaseofdataon transport, mobility, the environment and soforth enables the development of applications bycompanies and developers, enhancing innovation.Publicandprivateentitiescansupportthisprocessthrough the organisation of app contests andhackathons.

London was one of the first European cities thatstarted an open data initiative in 2010. In March2016, the “Data for London: City Data Strategy”was published with the following ambition: “wewant London to have the most dynamic andproductiveCityDataMarket in theworld”.Lisbon,one of POCACITO’s case study cities, has recentlylaunched its open data portal within the “LisboaAberta”(SmartOpenLisbon)programme.Itintendsto facilitate Lisbon’s city life by challenging start-upstosolveurbanproblemswithopendata.

In some cities, real-time data are beingcommunicated to residents through ‘citydashboards’,whichdisplay anumberof indicatorsand provide visualisations (maps, graphics, etc.)that help interpretation and analysis. The London

City Dashboard 2 and Dublin Dashboard – CityIntelligence3aresomewell-knownexamples.

Furthermore, dynamic dashboards are often ondisplay on computer monitors in modern controlrooms(singledataanalyticscentres)(Kitchin,2014;Kitchin et al., 2015). One recognised case studylooks at Rio de Janeiro’s Operations Centre,supportedbyIBM.Itintegrates30cityoperatorsinthe same room and collects real-time data andinformation with a view to supporting decision-making processes mainly related to naturaldisastersandaccidents.

6 THEPOCACITOAPPROACH

TakingintoaccountthedifferentKPIinitiativesandtheirevolution,thePOCACITOprojectdefinedasetof urban indicators oriented to assessing andcomparing cities’ performance, and to analysingtheir transition process towards a post-carbonfuture. It is still a traditional approach,due to thecurrent data limitations, but it integrates some oftheinsightsofthenewdatacollectionandanalysistrends.

The POCACITO team categorised the KPIs undersocial,environmentalandeconomicdimensions.Alltherelationships, interconnections,feedbackloopsandredundancies fromonedimensiontoanother,includingtheirsub-dimensionsandindicators,wereanalysed to gain an overview of the transitionprocess without compromising the evaluationmechanism.

Thesocialdimensionisconcernedwithequity,bothwithin the current generation and between thegenerations during the transition process to post-carbon cities, which is expected to be smooth forallresidents.Thebenefitsforinhabitantsthatcomeout of living in a reduced-carbon city arehighlighted, showing that these cities are placeswhereitispleasanttoliveandthevaluesofequityand social inclusion are present. Special attentionhas been given to standards of living related tosuchessentialaspectsaseducationandhealth(forexample, life expectancy and well-being).Unemployment rates and poverty are also issues

2Seehttp://citydashboard.org/london/=.3Seehttp://www.dublindashboard.ie/pages/index.



consideredinthecontextofpost-carboncities.Thepublicservicesandinfrastructurethatareavailablefor residents are analysed, as well as aspects ofgovernance and civic society promoting thepositivesenseofcultureandcommunity.

The environmental dimension investigates thesustainable profile of the cities and assesses notonly the current impacts on the environment, butalsothoseduringthetransitionprocess,evaluatingthe environmental resilience of the cities. It isimportant to continually adapt the strategies inorder to mitigate the negative impacts on theenvironment during the transition process. Theenvironmentaldimensioncoverstheenergysectorin general, taking into account final energyefficiency as well as the resource depletionassociated with energy consumption. Post-carboncities pay special attention to greenhouse gasemissionsandtheircontributiontoclimatechange.Some energy-intensive sectors are emphasised,such as transportation/mobility and the buildingstock. Biodiversity and air quality are criticalthemes that also belong to this dimension.Concerns regarding waste and water are alsoevaluated.

The economic dimension covers sustainableeconomicgrowthbasedonthewealthofthecitiesandtheirinhabitants.Itrecognisesthatinvestmentis crucial to promoting post-carbon cities, inparticular investment related to sustainablefacilities.Thelabourmarketandthebusinesscycleare considered to demonstrate the dynamics of apost-carbon economy in a green economyparadigm. Public finances are also analysed,because those cities with a lower level ofindebtedness are more prepared to face thechallengesduring the transitionprocess towardsapost-carbon city. This dimension also includesexpenditure on research and development, as nocity can become a post-carbon one withoutinnovation.

Indicatorswere selected taking intoaccount i) thedefinitionofa‘post-carboncity’,ii)asetofexistingframeworks of indicators and iii) a set of criteriadefined by POCACITO partners, namely relevance,clear messages, data availability and data quality.Partners participated in the indicator selectionprocess by discussing indicators and developingmind maps. Three mind maps were produced toidentify indicators that are highly correlated, i.e.the indicators that ‘incorporate’more information– the most ‘substantial’ indicators. At the end, a

finallistwasproduced,whichwasusedinthecasestudyanalysis.

Figure 1. Dimensions and sub-dimensions of thePOCACITOapproach

7 LIMITATIONSOFKPIINITIATIVES

Despite the benefits, several limitations of KPIinitiatives have been identified. Some of thesedifficulties were faced by the partners duringPOCACITOdevelopment.

Thefirstgroupoflimitationsisrelatedtotechnicaland methodological issues. During the POCACITOproject,questionsofdataavailability, veracityandqualityemerged.Thedefinitionofthegeographicallimits of a city brings additional problems, namelywhen we want to compare cities located indifferent countries. The majority of data andinformationareonlyavailableatEurostatNUTSI,IIor III levels, as data and information at themunicipality or city levels is almost inexistent.Moreover, it is difficult to collect the data ofdifferent countries for the same years, which canprejudice theanalysis.These limitsareparticularlyrisky as decision- and policy-making processes arebasedonthiskindofinformationandknowledge.

The second groupof limitations is linked to policyissues.Infact,KPIinitiativesareneverneutral;theyare contingent, relational and contextual. Data donot exist independently of the ideas, practices,contexts and systems used to generate, processand analyse them (Kitchin et al., 2015). The



selectionofindicators,parametersandweightingisalso a non-objective process. Additionally, KPIanalysescanbereductionist,sincetheysimplifythecomplexity and multidimensional picture of thecity. Such analysis “decontextualizes a city fromhistory, its political economy, the wider set ofsocial, economic and environmental relations thatframe its development, and its hinterland andwiderinterconnectionsandinterdependenciesthatstretches out over space and time” (Kitchin et al.,2015).Itassumesthatitispossibletocomparethecities using same type of information in astandardised way, despite the variety of contextsandenvironments.

It is considered possible to objectively measureurban life in an ‘instrumented city’, a perspectiveinfluencedbya technocratic governanceapproachdefended by some advocates of the ‘technology-drivensmartcity’conception.Indeed,accordingtoHollands (2008), an “element characterizing self-designatedsmartcitiesistheirunderlyingemphasison business-led urban development (…) there is ageneral world-wide recognition (…) of thedomination of neo-liberal urban spaces, a subtleshift in urban governance in most western citiesfrom managerial to entrepreneurial forms, andcities being shaped increasingly by big businessand/orcorporations”.

Thus, “how cities view indicators, benchmarkingand dashboards, the kinds of indicators andsystems they deploy, and how they employ themfalls into two broad camps, both of which revealthe inherent tension in such initiatives betweenfacilitating empowerment, democracy,accountability and transparency, and enactingregulation, control, efficiency and effectiveness”(Kitchinetal.,2015).

8 POLICYRECOMMENDATIONS

The above analysis suggests the following trendsand recommendations for European, national andlocalgovernments:

Standardisation. Use agreed common standards(definitionsandmetrics)tocontributetoimprovedcollaboration within and between localgovernments.

Interoperability.Useagreedprotocolsandcommondata formats that facilitate interoperability acrosssystems(EuropeanCommission,2014).

Openness. Make data accessible to third parties(companies, entrepreneurs, residents, universities,etc.) inordertopromotetransparency,democracyand the development of innovative applications(opendata).

Innovation.Useinnovativeindicatorsreflectingthetechnological, social and economic changes (thesharing economy, do-it-yourself, etc.), such asindicators related to electricmobility, bike-sharingandcar-sharingsystems.

Dissemination. Promote the dissemination of dataand information through new visualisationtechniques, such as maps and graphics (onlinedashboards), and make data available in publicspaces.

Collaboration. Enhancecollaborationamongcities,universitiesandindustryindatacollection,analysisand dissemination processes, through urbansciencemethodologies.

Civic participation. Promote the participation ofresidents and communities in data collection andanalysis processes, through do-it-yourself sensorsorsmartphones.

Awards. Launch a ‘European Capital of SmartGrowth’ initiative (similar to the European Capitalof Innovation), with the evaluation based on acommonsetofdimensionsandindicators.

Rescaling. Produce and use data at lowergeographical levels, such as municipality and citylevels, privileging functional territories overadministrativeterritories.

Funding.Fundresearchandinnovationactivitiesinthefieldofopendataandbigdata,enhancingthedevelopmentofinformationproductsandservices.

Legislation.Producedata-friendlylegalframeworksand policies, namely in the area of publicprocurement to bring the results of datatechnologytothemarket.



REFERENCESEuropean Commission (2013), “Strategic

Implementation Plan”, EuropeanInnovation Partnership on Smart CitiesandCommunities,Brussels.

European Commission (2014), “OperationalImplementation Plan”, EuropeanInnovation Partnership on Smart CitiesandCommunities,Brussels.

European Commission (2015), “Indicators forSustainableCities”,Brussels,November.

European Commission (2016), “Open Data inCities”,EUOpenDataPortal,May.

Godin, B. (2003), “The emergence of S&Tindicators: Why did governmentssupplement statistics with indicators?”,ResearchPolicy,32(4):679-691.

Hollands, R.G. (2008), “Will the real smart citypleasestandup?”,City,12(3):303-320.

Kitchin,R.(2014),“Thereal-timecity?Bigdataandsmarturbanism”,Geojournal,79(1):1-14.

Kitchin, R., T.P. Lauriault and G. McArdle(2015), “Knowing and governing citiesthrough urban indicators, citybenchmarking and real-timedashboards”,Regional Studies,RegionalScience,2(1):6-28.

Kleinman, M. (2016), “Cities, data, and digitalinnovation”, IMFG Paper on MunicipalFinance and Governance, No. 24,UniversityofToronto.

Rial, N. (2013), “The power of big data inEurope”,NewEurope,May.

Shadi, R., R. Khoury, D. Karam and J. Rahbani(2015),Smartcities:Agatewaytodigitallife,BoozAllenHamilton,McLean,VA.

Silva, C., C. Selada,G.Mendes and I.Marques(2014), Report on Key PerformanceIndicators,POCACITOprojectdeliverable1.2,EcologicInstitute,Berlin.

Townsend,A.(2015),MakingSenseoftheNewUrbanScience,Data&SocietyResearchInstitute, Rudin Centre forTransportation Policy & Management,NewYorkUniversity.

Vienna University of Technology, University ofLjubljana, TU Delft (2007), Smart Cities:Ranking of European Medium-sizedCities,Finalreport,October.

PROJECTThis Policy Brief was written as part of thePOCACITO project (Post-Carbon Cities ofTomorrow–foresightforsustainablepathwaystowards liveable, affordable and prosperingcities in a world context), coordinated by theEcologicInstitute.

Moreinfo:http://www.pocacito.euTwitter:@EUCities

ACKNOWLEDGEMENT&DISCLAIMERNeither the European Commission nor anyperson acting on behalf of the Commission isresponsible for theuse thatmightbemadeoftheabove information.Theviewsexpressed inthis publication are the sole responsibility ofthe authors and do not necessarily reflect theviewsoftheEuropeanCommission.

Reproduction and translation for non-commercial purposes are authorised, providedthesourceisacknowledgedandthepublisherisgivenpriornoticeandsentacopy.

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urban indicators and the smart city agenda...jung-hoon lee, yonsei university, seoul, korea 2012 one...

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