How Do European Small Cities Deal With Design Review for New Developments
Introduction
Rapid urbanization of urban center regions creates complex pressures on infrastructure, systems and services, as well as citizens and the environment, triggering the need for innovative, sustainable solutions to urban development challenges (Caragliu, Del Bo, and Nijkamp 2011). Urban design and planning have an ongoing office in addressing urban challenges through improving '…connections between people and places, move and the urban course, nature and the built fabric' (Radford 2010, 380). Smart approaches, divers by the British Standards Institution (BSI) every bit 'the application of democratic or semi-autonomous engineering science systems' (BSI 2014a, 12), tin can contribute to urban solutions, specially through the institution of digital connections between networks (telecommunications, satellite communications and the Cloud), sensors (embedded sensors and actuators, proximal and remote sensing) and interconnected Information and Communication Technology (ICT) devices, known equally the Internet of Things. However, smart city evolution is substantially a multi-disciplinary endeavour rather than simply offering a technological fix for urban challenges. It requires the 'constructive integration of physical, digital and man systems in the built environment to evangelize a sustainable, prosperous and inclusive future for its citizens' (BSI 2014a, 12).
Despite widespread smart city developments beyond Europe and other continents, strikingly little inquiry has been conducted on the evaluation of smart metropolis interventions and the measurement of outcomes of embedded smart technologies for cities and citizens (Bis 2013; EU Directorate-General 2014). Evaluation practice has been express by a lack of clarity concerning definitions and the best approaches to mensurate the contribution of smart solutions to city performance (BSI 2014b). The European Innovation Partnership on Smart Cities and Communities (EIP-SCC) observed that there are currently no standardized smart city indicator frameworks, widely-accepted by cities to measure urban center performance and evaluate progress against urban strategies (EIP-SCC 2013). A big challenge therefore is to determine the value of smart urban developments and to evidence the impacts on city outcomes. This newspaper builds on findings from the SmartDframe inquiry linked to the MK:Smart programme (mksmart.org), which examined urban center approaches to evaluation and reporting of smart city developments and their impacts on metropolis outcomes, through 5 Great britain city case studies (Caird, Hudson, and Kortuem 2016). Get-go, key conceptual, measurement and evaluation issues in smart city evaluation are examined through reviews of the academic and corporate literature. Second, example studies of city approaches to evaluation of smart city programmes and projects in Birmingham, Bristol, Manchester, Milton Keynes and Peterborough are described with reference to evaluation practices, challenges and urban center authorities' recommendations. The paper concludes with consideration of how to draw on existing measurement and evaluation work to back up the blueprint of appropriate, constructive and credible evaluation of smart urban developments, and to identify their outcomes for cities and citizens.
The smart urban center concept
It has been asserted that the smart urban center is the most popular, worldwide vision for a successful future city, and is even more than popular than, for example, visions of liveable cities, inclusive cities, innovative cities, digital cities, sustainable cities, dark-green cities and and then on (Moir, Moonen, and Clark 2014). While there are many definitions of smart cities bachelor (Albino, Berardi, and Dangelico 2015), there are several key characteristics.
| (i) | The smart city has integrated ICT infrastructure and technologies (BSI 2014c) for improving urban center functioning (Hollands 2008) and achieving the digital transformation of urban systems. | ||||
| (2) | One characteristic focuses on the evolution of human being capital (Hollands 2008; Caragliu, Del Bo, and Nijkamp 2011) through ICT-enhanced governance to support sustainable urban development driven past the knowledge, creativity, innovation and entrepreneurship of urban center actors (Hollands 2008). | ||||
| (3) | Central to the smart city concept is large information, which describes information/data assets characterized by loftier 'volume', 'velocity', 'variety', 'variability' and 'value' for unlike stakeholders that requires high-capacity cloud-processing services (Fujitsu 2012). The data assets include existent-fourth dimension and near real-time data-streams from city infrastructures and sensor networks, and contemporary space platform technologies and web services. Information technology includes volunteered and crowd-sourced citizen information from social media, mobile apps and citizen participation platforms, and also traditional static historic/legacy information sources nerveless through surveys. Corresponding predictive analytics, car learning and stream statistics are required to mine and reveal patterns in voluminous, fast-changing, diverse, structured and unstructured data sources to develop data intelligence. In this fashion, big data enables the development of data-driven urban systems and services (Bis 2013). | ||||
| (4) | One feature addresses the development phases of smart cities (Zygiaris 2013) and their smart evolution maturity (IDC 2013). The smart city characterization applies both to newbuild cities, where Greenfield city developments are well-nigh commonly recognized as smart cities, for example, Masdar (UEA) and Songdo (S. Korea), and too to cities with a multifariousness of retrofit smart city developments, for example, Rio de Janeiro and Barcelona (Batty et al. 2012; Shelton, Zook, and Wiig 2015). | ||||
| (five) | A various range of smart city projects underpin smart metropolis development, implemented at dissimilar scales across buildings, neighbourhoods, cities and regions (Bosch et al. 2017). These projects address a broad range of city challenges through urban regeneration (e.grand. carbon-neutral smart neighbourhood infrastructures), urban development (east.g. smart energy, h2o and waste resource management systems and smart grids) and urban innovation (due east.one thousand. testbed micro-infrastructures for networks, intelligent traffic systems, open-data and citizen co-creation platforms) (Batty et al. 2012; EU Directorate-General 2014). | ||||
The smart city vision is typically presented as beneficial in both ambition and outcomes. The BSI smart city vision is citizen-centred, collaborative, digital and characterized by open up data (BSI 2014c). This is associated with extensive social, economical and ecology benefits that maintain or improve quality of life for citizens (BSI 2014c). However, the merits of the techno-utopian smart city image have been also contested from dystopian perspectives on smart cities (Hollands 2008; Townsend 2013; Kitchin 2014; Vanolo 2014). For cities, there are concerns about 'new geometries of power' (Vanolo 2014, 884), including: the political and corporate utilise of big data; technocratic governance; corporate authorization of urban center systems; technological lock-ins; the security of hackable, attackable city systems (Kitchin 2014); panoptic surveillance and command of citizens (Townsend 2013; Kitchin 2014); and public-sector marginalization through public-private city partnerships (Vanolo 2014). For citizens, there are additional bug of information privacy and command (Kitchin 2014), and concerns nigh social inequalities and marginalization concerning access to the benefits of smart cities (Hollands 2008). With little research conducted on the evaluation of smart city developments (Bis 2013), in that location is a need for more evidence of the benefits and disbenefits for cities and citizens.
Standardization and smart urban metrics
Considerable international and national work is shortly ongoing through the International Standards Organization (ISO), European Committee for Standardization (CEN) and BSI to establish expert standards in smart urban development and performance metrics (SSCC-CG 2015). The ISO has already agreed standards for 'Smart Community Infrastructures' performance metrics (for case, ISO/TR 37150:2014 and ISO/TS 37151:2015) (see iso.org). BSI, working with ISO, have provided a significant body of work on smart city standards and urban performance metrics (BSI 2014a, 2014b, 2014c), including the Smart City Framework, Publicly Available Specification PAS181 (BSI 2014c). Moreover, a key European Commission (EC) EUROCITIES initiative called CITYkeys (citykeys-project.eu) aims to develop valid city performance measurement frameworks, Cardinal Performance Indicators (KPIs) and standardized data drove procedures to speed up the improvidence of smart metropolis solutions across cities through supporting comparable, scalable and replicable smart metropolis solutions (Bosch et al. 2017).
Standardized smart urban indicators and metrics are not widely accepted by cities while the evolution of standards is at early stages. Recommendations by the European Innovation Partnership on Smart Cities and Communities (EIP-SCC) are that standardized smart city measurement indicators should:
| • | Align with city strategies and operational levels of urban center evolution; | ||||
| • | Constitute measurement over-time, evidenced against baseline measures and strategic targets, using mainly existent-time information; | ||||
| • | Develop through a stakeholder procedure with skilful and community groups; | ||||
| • | Be open to improvement and future innovation; | ||||
| • | Build on existing urban evolution and operation indicators aligned with typologies of European cities; | ||||
| • | Support open reporting and cities' evaluation of progress towards becoming smart through comparative urban center benchmarking (EIP-SCC 2013). | ||||
Smart city indicator frameworks and city indexes
Extensive reviews of metropolis indexes (such every bit conducted by Moonen and Clark 2013; Albino, Berardi, and Dangelico 2015; Joss et al. 2015) accept identified surprisingly few published smart urban center indicator frameworks designed to measure smart city development and performance outcomes. This selective review identifies ii models providing insight into the phases and indicators of smart city development (Tabular array 1), and a further v smart city indicator frameworks and city performance indexes (Table 2).
| (1) | The Smart Urban center Maturity (SCM) Model recognizes that cities are at different maturity phases of becoming smart. This offers a high-level, urban center benchmarking, comparative tool to identify the maturity phases of a metropolis'southward smart city evolution based on smart metropolis strategies and the calibration of evolution. The maturity phases range from the least mature advertising hoc project-planning phase to the nearly mature smart city 'Optimized' stage characterized by a city-wide urban center of systems (IDC 2013). | ||||
| (2) | The Smart City Reference (SCR) Model was designed to identify innovation policies and processes needed to support planning for smart, sustainable urban development. This offers an in-depth conceptualization of the range of piece of work needed for smart city development through vii city layers/stages, including integrated ICT infrastructure and technologies to support capabilities for city intelligence and innovation services (Zygiaris 2013). This has similarities to IBM's Smarter Urban center Assessment Tool for identifying cities' capabilities for smart urban evolution through interconnection, instrumentation and intelligence (IBM plc 2009). Each metropolis layer is associated with different types of evolution, and dissimilar Cardinal Functioning Indicators (KPI) linked to the performance, economy, efficiency and effectiveness of new city infrastructure, systems and services (Zygiaris 2013). | ||||
| (3) | The European Smart Cities Ranking (ESCR) Model (smart-cities.european union) aims to measure medium-sized European cities against smart city indicators to enable city ranking, benchmarking and inter-city comparisons (Giffinger et al. 2007). The ESCR Model offers a comprehensive smart metropolis indicator framework defined across six city characteristics/dimensions, namely Smart Governance, Economy, People, Living, Environment and Mobility, and includes both development and performance indicators, building on data nerveless at local, regional and national spatial levels across European countries (Giffinger et al. 2007). | ||||
| (4) | The Smart City Index Master Indicators (SCIMI) framework is a Smart Cities Council initiative to enable ranking cities in terms of liveability, workability and sustainability indicators (smartcitiescouncil.com/resources/smart-city-index-primary-indicators-survey). SCIMI measures similar smart city dimensions to the ESCR Model, namely Smart Government, Economy, People, Living, Environment and Mobility dimensions, although it presents an alternative conceptualization, drawing on a wide range of international data sources applicable to cities and buildings (Cohen 2014). | ||||
| (5) | The CITYkeys Indicator Framework measures smart city project-level success outcomes linked to smart city-level indicators beyond People, Planet, Prosperity, Governance and Propagation themes aligned with Eu policies. Building mainly on existing urban frameworks, the People, Planet and Prosperity indicators stand for to the Triple Lesser Line of social, environmental and economic sustainability. The Governance indicators pertain to leadership in smart city developments, while the Propagation indicators identify the potential for upscaling and replication of smart city solutions in other contexts. | ||||
| (6) | The Ericsson Networked Social club City (ENSC) Index, developed by Ericsson Ltd with Sweco Ltd, measures the ICT maturity of networked cities confronting indicators of ICT Infrastructure, Readiness and Usage indicators, which correspond with the evolution, diffusion and adoption of ICT infrastructure and technologies. This adopts a systems approach to examine relationships between city ICT maturity across Economic, Social and Environmental Touch city dimensions (the sustainability Triple Bottom Line) (Ericsson 2014). | ||||
| (7) | The Cities of Opportunity (CoO) Index of Leading Cities measures Smart, Quality of Life and Economic Indicators, drawing on extensive city indexes and open data sources (PricewaterhouseCoopers/Partnership for New York City (PwC/PNYC) 2014). This is i of the few full general city indexes that measures 'Smart' indicators equally an embedded dimension of cities using measures of Intellectual Upper-case letter and Innovation, Technological Readiness and City Gateway indicators (Table 2). | ||||
Tabular array 1. Smart City (SC) development models.
Table two. Smart City (SC) models, measurement frameworks and indexes.
Tables i and 2 place a broad range of smart urban indicators focused on urban development (Table 1) and urban performance outcomes (Table ii), applicable beyond dimensions of governance, society, economy and environment. A comparison of Tables 1 and two city models and frameworks reveals dissimilar conceptualizations of the key smart metropolis dimensions and indicators, raising challenges apropos the validity, measurability, complexity, credibility and utility of smart city indicators representing complex urban systems.
Validity issues are illustrated through a comparing of the ESCR Model (Giffinger et al. 2007) and the SCIMI framework (Cohen 2014), which both measure like city dimensions, although based on dissimilar factors, indicators and metrics. The ESCR Model Environs indicators focus on the natural environment, whereas the SCIMI framework focuses more than strongly on urban planning and the built environs. Moreover, although both have Smart Mobility indicators, the ESCR Model offers only i ICT-related factor, namely the 'Availability of ICT- Infrastructure', whereas the SCIMI framework offers a large number of ICT indicators. Raising farther conceptual bug, the SCIMI's indicators focus on the integration of ICT infrastructure, systems and services across metropolis dimensions, whereas the ENSC Index is focused on indicators of evolution, diffusion and adoption of ICT infrastructure and technologies in networked cities.
Measurability challenges are illustrated by the ESCR Model'south difficulties in providing measures for the Smart Governance Participation factor 'Political strategies and perspectives' (Giffinger et al. 2007). In addition to difficult-to-measure indicators, more than consideration needs to be given to intangible and tangible indicators (Carmona et al. 2001; Holman 2009). Related to intangibles, few frameworks (Tables one and 2) include indicators of citizen outcomes. Exceptions include the ESCR Model and CITYkeys Indicator Framework, which include citizen satisfaction indicators, for instance, the ESCR Model measures satisfaction with governance, living, the environment and mobility in the city (Table 2).
Holman (2009) noted that the bulldoze towards comprehensiveness has created large quantities of urban indicators, although the potential for comprehensive measurement is an illusion. For example, the ESCR Model includes 74 full indicators and numerous metrics, merely two of which were ICT-related indicators and hence non comprehensive (Tabular array 2). A challenge for urban indicator frameworks is to correspond: the complexity of dynamic, evolving, open and unbounded urban systems (Arnold 2004); the interrelationships betwixt deadening-changing urban forms, including buildings, physical infrastructure and planned infinite, and relatively fast-irresolute flows, including capital, people, communications, free energy and pollutants (Williams 2014); and the interacting social, economic, political, technological and ecology factors in urban systems shaped by virtuous and vicious system feedbacks, cumulative causation and historical path-dependencies (Arnold 2004). Systems approaches are illustrated in both the ENSC Index and CITYkeys Indicator Frameworks, although both approaches simplify cause-consequence relationships in smart urban systems, focusing less on organization dynamics and more than on impacts (Table 2).
Approaches to developing urban center frameworks are intrinsically value-laden and therefore ideological, which is evidenced past the selection of indicators and measures, and decisions on data normalization, weighting systems and assemblage methods. Constructivist perspectives describe attention to the unlike meanings that value holds for unlike stakeholders (Carmona, De Magalhães, and Edwards 2002), which have many roles in shaping, implementing and engaging with smart city programmes (encounter BSI 2014c, 19). To exist credible to skillful and customs stakeholders, different values may demand to be reflected, both in the evolution and subsequent application of urban indicator frameworks.
The chief utility for smart city models and frameworks (Table i and 2) is to support planning, city benchmarking and intercity comparisons. However, Vanolo (2014) criticized city frameworks as a simplistic, reductionist performance applied science used to benchmark cities based on their city rankings. The claiming is to develop appropriate, valid, credible and useful approaches to metropolis measurement. Holman (2009) argues that indicators should be policy instruments designed to accept clear links to policy changes and innovative local governance.
Evaluation of smart city developments
Without evaluation to make up one's mind the appropriateness, effectiveness and value of programmes and projects and their impacts in specific contexts, information technology is hard to guess success (Arnold 2004). In their report on international smart city case studies, Bis (2013) criticize existing approaches to smart city evaluation as inadequate and not-standard, and more focused on implementation processes and investment metrics than on metropolis outcomes, although there were some exceptions, for example, examining denizen value in Rio de Janeiro and city benefits in Boston (Bis 2013). The major European 'Mapping Smart Cities' study has likewise conducted evaluations based on types of projects, their goals, scale, scalability, targeted stakeholders, level of citizen-engagement and success outcomes (Eu Directorate-Full general 2014). Yet, despite the proliferation of smart metropolis developments, there have been few evaluation studies.
A growing body of studies has explored evaluation and value in urban design (Carmona et al. 2001; Carmona, De Magalhães, and Edwards 2002; Chiaradia, Sieh, and Plimmer 2017), urban planning (Oliveira and Pinho 2010) urban regeneration (Tyler et al. 2013) and innovation and engineering policy studies (Arnold 2004; Edler et al. 2012 ). The UK Government'due south Green Volume provides guidance on project and programme evaluation, including techniques to appraise the economic, fiscal, social and environmental benefits and costs of urban design, development and innovation, with attending to unlike stakeholder interests (HM Treasury 2013).
The evaluation procedure requires setting objectives, identifying target groups, articulating primal questions, clarifying touch dimensions, setting upwards the evaluation logistics and timing. The procedure requires choosing methods for data collection, assessment and analysis and judging the quality, usefulness and consequences of the evaluation (Edler et al. 2012). Consideration of urban frameworks and city indexes may offer useful measurable indicators and metrics. Assessments may draw on quantitative data-driven methods (e.g. price-benefit, price-effectiveness assay, before-afterwards assessments, audits and technical monitoring) and qualitative methods (e.g. citizen surveys, expert and community stakeholder workshops, Design Quality Indicators, Goals-Achievement Matrix, Multi-criteria analysis) (HM Treasury 2013). It is recognized that qualitative methods can capture intangibles and externalities (Carmona et al. 2001) and the different meaning of value for dissimilar stakeholder groups (Carmona, De Magalhães, and Edwards 2002).
Articulating a theory of modify is a pop way to begin urban evaluations. This describes the way policy program objectives should deliver the anticipated outcomes through a logical framework of linked activities, outputs, outcomes and impacts, with recognition of direct/indirect effects and contextual factors (Tyler et al. 2013). This logical framework may be informed by critical strategic and operational success factors for smart urban center development programmes, which include the establishment of a 'do good realization' strategy to map, rail and deliver success outcomes (BSI 2014c). Evaluation design and practices need to examination the underlying programme theories (Pawson and Tilley 2004), which in smart metropolis studies is the theory that the expected urban center benefits will exist delivered through smart urban center developments.
Overview of city case studies
Emerging from this review are questions concerning how cities approach the evaluation of smart metropolis projects and programmes. This is examined through case studies in U.k. cities, including Birmingham, Bristol, Manchester, Milton Keynes and Peterborough (Caird, Hudson, and Kortuem 2016; Caird 2018). Case studies were developed during 2015, following interviews with representative urban center authorities, and reviews of their strategic hereafter metropolis and smart city programmes. Cities were selected to represent dissimilar-sized conurbations, relevant to their population and geographical area. Each selected urban center was actively developing equally a smarter city, having several funded projects within their strategic future and smart urban center programmes, and an involvement in Great britain and European smart urban center networks, including the EIP-SCC, Small Giants and UK Core Cities and the EUROCITIES network of over 140 major European Cities (Figure 1).
Towards evaluation design for smart urban center development
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Figure i. Overview of Uk smart city instance studies.
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Case study: Birmingham
Smart metropolis approach: Birmingham Quango established a Smart City Committee, which includes leading figures from business, universities and the public sector supported by Digital Birmingham, the city's digital partnership. The Committee's Smart City Vision and Roadmap is focused on thematic areas to address economic growth and city challenges.
Projects: The 'Technology and Place' theme addresses issues of connectivity, digital infrastructure, open data and information markets, with projects focused on the provision of high speed broadband connectivity, costless Wi-Fi in public buildings and open up data platform, linked to Birmingham's Big Data Corridor and Information Factory Portal (Effigy ii). The 'People' theme addresses digital inclusion, citizens' skills, employment and digital innovation with digital inclusion programmes for citizens and communities and the Smart Metropolis weblog for community ideas-sharing. The 'Economic system' theme addresses a range of issues including health and wellbeing, ICT, effective mobility, energy efficiency and carbon reduction. Several projects with the National Health Service (NHS) address telehealth services providing remote health monitoring, consultation and diagnosis. Additional 'economy' projects include smart traffic command and parking, intelligent energy saving, smart street lighting and an SME digital university program.
Towards evaluation design for smart city development
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Effigy 2. View of Birmingham City Town Hall, 1 of some 200 metropolis public buildings having gratuitous Wi-Fi provided as a means of encouraging citizens to appoint with urban center data feeds and smart city solutions.
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Approach to evaluation: The Smart Metropolis Commission decided not to measure specific smart KPIs at the Roadmap level, but instead build an evaluation framework supported by PAS181 (BSI 2014a) to place how smart urban center developments deliver desirable urban center outcomes. The EC has also been a significant influence on their evaluation approach, requiring smart city projects to provide clear measurement and input information to other EC-funded projects. Although Digital Birmingham regime were considering ways to evaluate progress with their Roadmap actions, their main focus has been on operationalizing projects. Their current approach was to measure progress at the project-level and work with partners to measure out project KPIs. Their program is to develop their evaluation work with partners, once they achieve greater maturity with the Roadmap deliverables.
Example study: Bristol
Smart city arroyo: Smart City Bristol was established by Bristol Council as a collaborative plan betwixt different sectors, the customs and citizens. It is led by Bristol Futures, a Quango Advisers, whose vision is to ensure that Bristol becomes a resilient, sustainable, prosperous, inclusive and liveable identify. The work is delivered through Connecting Bristol, the city'south digital partnership.
Projects: Projects were initially developed around themes of Smart Information, Transport and Energy, although the focus has expanded into new areas, including telehealth projects to address connected wellness data and provide digital home health help. Bristol's two flagship projects include the Bristol Hereafter City Demonstrator, which supports digital infrastructure development and the Bristol Living Lab. A second flagship project, Bristol is Open up, is a joint venture with University of Bristol to provide an open digital infrastructure to develop the Net of Things, brand city performance data available through an open data portal, and test solutions relevant to city challenges, for example, traffic congestion (Figure three). The city's many smart transport and energy projects include driverless car trials (the Venturer projection www.venturer-cars.com), intelligent energy saving in public buildings and social housing, and 1 of the first Council-endemic smart energy companies (bristol-energy.co.great britain). Bristol too works with international partners through the EU-China Smart Cities Projection to engage citizens with a co-creation process to design and ameliorate city services.
Towards evaluation design for smart city development
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Effigy 3. View across Bristol, centred on the 'We The Curious' collaborative science museum (previously 'At-Bristol') (www.wethecurious.org), with its planetarium and Open up Data Dome in the foreground. The Dome can portray a variety of city datascapes aided past local metropolis companies such every bit Zubr (zubr.co) and enables interactive public participation.
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Approach to evaluation: Bristol's smart urban center approach began with work commissioned by Bristol City Council to benchmark Bristol'due south activities against international cities, leading to the Smart City Benchmark and Smart City Reports. Their approach to evaluation has been influenced past methodologies developed by the EC, and their own work to develop KPIs particularly for smart energy projects. Their evaluation work is typically project-focused, led past their project partners and driven by their funders' requirements for evaluation of projection impacts linked to KPIs. Going frontward, they recognize that their smart city work needs to contribute to city strategies and challenges, although null specific has been established to evaluate the overall city impacts of smart city development.
Example study: Manchester
Smart city approach: The Council-led Smarter City Programme explores ways to utilize new technologies to optimize urban center systems and outcomes for citizens, focusing on themes of how and where we live, work, play, motility, learn and organize in urban environments (manchester.gov.united kingdom of great britain and northern ireland/smartercity/). This urban transformation programme was established within the Community Strategy framework, which is delivered through the Manchester Partnership of public, private and third sector organizations.
Projects: The Council have over thirty–twoscore smart city projects, funded through European, national and local investment. Many more smart city projects be, although not all are led by the Council. A major EC-funded infrastructure projection, Triangulum, aims to transform the Manchester Corridor into a depression carbon smart urban center commune through establishing an electric vehicle transport infrastructure, renovating historical buildings and developing an autonomous energy grid to supply district heating and electricity (triangulum-projection.eu) (Effigy iv). Another central UK-funded smart city project, CityVerve, demonstrates the potential of Cyberspace of Things technologies to engage citizens and communities in areas of healthcare, energy and environs, send and culture (cityverve.org.uk). Manchester Quango also works with international partners to improve public service efficiencies through the EU-People's republic of china Smart Cities Project.
Towards evaluation blueprint for smart city development
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Figure four. View across Manchester of Oxford Road, a focal bespeak for the Corridor Manchester, Innovation District identified for urban transformation through the Triangulum project.
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Approach to evaluation: Manchester's evaluation work was at early stages with the chief pressures for evaluation arising from their project funders' requirements. The authorities did not believe that any city could claim to accept a fully co-ordinated evaluation programme. The EC has been the master influence on Manchester's evaluation work, particularly through the Quango's involvement with the EUROCITIES Smart City Forum and the CITYkeys initiative to compare Manchester's smart urban center solutions with other European cities.
The Council plan to develop an Impact Cess Framework for the Triangulum project, with Academy of Manchester taking a leading part. The aim is to monitor how well smart metropolis developments work, supported by the development of a digital map of key city transport, energy and utilities infrastructures. Once established, this Framework has the potential to be expanded geographically to city-calibration and linked thematically to metropolis strategies and functioning measures. Even so, there was concern that a pinnacle-downwardly evaluation programme is non always appropriate and adept ideas could be terminated past premature evaluation of urban center innovation projects at early development maturity stages.
Case study: Milton Keynes
Smart city arroyo: Milton Keynes smart city work is conducted through the Quango-led Futurity City Programme, which is designed through collaborations between business concern, universities and government partners, including four national Catapult innovation centres. The programme aims to back up economic growth, address infrastructure challenges, meliorate citizens' lives and develop Milton Keynes' reputation as a new city.
Projects: MK:Smart is one of the city's flagship programmes, led by The Open Academy with partners from local authorities, manufacture and universities. This aims to develop innovative solutions in smart ship, energy, water, enterprise, citizen engagement and pedagogy. Key to the programme is the MK Data Hub, which draws together data relevant to urban center functions, including data from key urban center infrastructures, sensor networks, satellite data and social media. The Future Metropolis Program includes the MK Cyberspace of Things network demonstrator and several smart mobility projects, including the Electrical Bus Trial, LUTZ Pathfinder (ori.ox.ac.uk/projects/lutz-self-driving-pods) and UK Autodrive (ukautodrive.com) for trialling autonomous vehicles (Effigy v).
Towards evaluation design for smart city development
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Figure 5. View of Milton Keynes, n-east from the railway station towards the metropolis heart. Plans with LUTZ Pathfinder are to use this route for an autonomous passenger ship service, connecting the station with the concern district.
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Approach to evaluation: Most of Milton Keynes' smart city work has been externally-funded, therefore each project has reporting requirements set by respective funding bodies. The Council has non notwithstanding established an overall city-level framework for tracking the progress of the Future City Programme, although some projects accept established KPIs for measuring outcomes, and some early stage projects are evaluated/judged through demonstration of an innovation concept. Since most of their smart city projects are not implemented at the city scale, it is not possible to measure impacts at the city-level. Nonetheless, the Council regime recognize difficulties in identifying the causal relationships between projects and city outcomes, and considered that evaluation may work best at the project-level where outputs are more immediately demonstrable.
Instance study: Peterborough
Smart city approach: Peterborough's Smart City work has adult through Peterborough DNA, a Time to come Cities Demonstrator programme, funded by Introduce UK. This is led and delivered past the Urban center Quango and Opportunity Peterborough, the city'due south economic evolution company.
Projects: Peterborough DNA aims to accost city challenges through citizen-centred projects in thematic areas focused on 'Skills for our future', 'Innovation' through citizens and entrepreneurial activeness, 'Open data' bachelor through a living data portal, and 'Smart business' with a digital platform developed to encourage business organisation engagement with the sharing economy at the SME business area, known equally Smart Fengate (Figure 6). As part of Peterborough DNA, the Council likewise organized a Smart Metropolis Leadership event to engage public and private sector organizations across urban center areas.
Towards evaluation design for smart city evolution
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Figure half dozen. View across Peterborough, past the Cathedral, towards the Smart Fengate Business Cluster (www.futurepeterborough.com/project/smart-fengate), an industrial area of the city, where a sharing economy digital platform is being trialled to provide solutions for greater resource efficiency and reduced waste product.
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Approach to evaluation: When Peterborough embarked on the Peterborough DNA plan, their funders were more interested in city innovation demonstrations that could exist scaled upwardly to work in bigger cities than evaluation of the programme impacts. While BSI's smart city standards has been a stiff influence on their smart city leadership, work on evaluation was still at initial stages of development. However, the city was starting time to consider a more formulated impact assessment and they have conducted an initial evaluation to amend the DNA programme, reduce project complexity and address the potential scalability of projects. Going frontwards, Peterborough plans to focus on city challenges and map the key metrics and data sources available to use in smart city assessments and evaluation.
Evaluation practices, challenges and recommendations
The cities' smart city work is embedded in their future and smart city programmes led by Councils and Directorates. This covers a wide range of smart city projects designed to address city challenges relevant to key metropolis infrastructures and dimensions of governance. This work is a product of collaborative partnerships with public, individual and third sector organizations and denizen groups. All five cities describe their time to come city vision every bit smart and/or continued, although Birmingham is the only city with an established Smart Urban center Commission, Vision and Roadmap.
The cities' approach to smart city evaluation is currently focused at a project level and primarily driven by their funders' requirements. Several cities, including Birmingham, Bristol and Milton Keynes, take established project KPIs with their partners, which potentially cover a range of technical, social, economic and environmental operation measures. The urban center government considered establishing baseline measures and strategic targets and KPIs to exist a good approach for monitoring operation and measuring progress over-time (as recommended EIP-SCC 2013; BSI 2014c). This would demonstrate the validity of innovation concepts and assistance identify projects with the biggest urban center impacts and replication potential.
Some of the city projects were already delivering meaning information outputs aligned with city strategies, such as on energy, climate change, transport, waste, economic development and liveability. This was supporting city interests in developing information intelligence through establishing new mechanisms for generating, collecting and sharing data. All the cities operate web-based, open data portals linked to their data hubs, providing public admission to information. Such data sources may be aggregated, as for example by the ESRI Inc 'Urban Observatory' (www.urbanobservatory.org), which permits side-past-side comparison of key data metrics from multiple cities, then visualizing the circuitous, urban themes of international cities beyond piece of work, movement, people, public services and systems (Figure 7).
Towards evaluation blueprint for smart city development
Published online:
23 May 2018
Figure 7. The ESRI Inc. 'Urban Observatory' showing metropolis housing density data for Peterborough, Manchester and Bristol (www.urbanobservatory.org).
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The cities were aware of work ongoing with standardization initiatives, and several were actively engaged with BSI'due south standards and the CITYkeys initiatives. However, city authorities were less familiar with the smart city indicator frameworks reviewed in Tables 1 and two, although some were concerned that these were likewise general when a perceived better arroyo would be to focus measurement on specific areas. For example, Birmingham has trialled a smart metropolis framework specifically focused on energy, in partnership with Arup.
The case studies showed that the cities were at the early stages of developing plans to evaluate the metropolis-level impacts of smart metropolis developments and were working in partnerships, mainly with local universities, to address evaluation challenges. Although most were not advanced with evaluation plans, Birmingham had made progress in developing a city-level evaluation framework, aligned with their smart city strategy and Roadmap. Manchester was developing an Impact Cess Framework for their Triangulum project and a plan to assess city-level impacts. Other cities, including Milton Keynes and Peterborough, had developed many measures through their city programmes to contribute to a smart urban center evaluation framework, although this piece of work was at an early on phase. However, the cities' evaluation practices were not embedded in city management structures and performance reporting processes. Moreover, in that location is currently no statutory obligation for UK cities to report their smart city work through urban center performance and political reporting processes, and therefore the smart city work was only beginning to influence metropolis decisions, particularly around development and investment decisions.
Some cities were unconvinced of the demand for an overarching, standardized smart metropolis framework, which might not be sufficiently relevant to their unique city challenges, strategies, circumstances and projects. Moreover, cities already accept statutory obligations to measure and report numerous KPIs against city strategies and actions. For case, Bristol authorities mentioned that there are approximately 150 KPIs that the Quango report on annually, which they considered burdensome. Rather than developing new smart city KPIs, some metropolis authorities would prefer to measure the contribution of smart metropolis projects and programmes confronting existing city KPIs in establishing urban center-level impacts.
The main evaluation challenges identified past cities centred on choosing suitable methodologies to mensurate the causal impacts of their smart metropolis work on urban center outcomes, and how to prove the value for cities and citizens. A synthesis of the Council authority recommendations suggests that the design of smart city evaluation should be appropriate to the projection, plan or metropolis level, and to the innovation development maturity and scale of urban center projects. Evaluation approaches should reflect strategic city objectives and be open up to improvement and evolution (every bit recommended past EIP-SCC 2013). Evaluation frameworks should be flexible, relevant and adaptable to different metropolis challenges and circumstances. Some city authorities also considered that evaluation should have a diagnostic utility, helping cities identify both gaps in their smart city development and emergent innovation opportunities. Rather than focusing on capricious or easily-measured indicators, the choice of measures should include quantitative and qualitative, meaningful and comprehensive indicators that reflect the multi-faceted nature of smart cities and the complexity of urban systems. Overall, evaluation design should build on city information intelligence to support development of future urban center visions and strategies, which some authorities noted should be based more than on a vision of liveable cities with embedded smart technologies rather than simply a digital city vision.
Conclusions
With the proliferation of smart city developments aiming to transform the urban context, information technology is of import to identify and develop suitable methodologies to evidence the value, outcomes and impacts of smart urban center projects and programmes in complex urban contexts. The cities examined in the SmartDframe research were mainly project-focused in their evaluation work, and were at the early stages with their plans to evaluate the city-level impacts of smart city developments. This paper therefore aims to inform urban discourse on the evolution of advisable, valid, credible and useful approaches to smart city evaluation by reviewing conceptual, measurement and evaluation bug, and examining the SmartDframe case-report inquiry findings on city evaluation approaches to smart city development. This aims to guide evaluation practice applicative to smart city developments and back up farther research in this expanse.
The review of smart city models, measurement frameworks and indexes, identifies tools available to support loftier-level urban planning, development and benchmarking activities, together with a wide range of measurable indicators available to support evaluation of smart urban developments. Much work on smart city measurement has been directed at the city-level, while the SmartDframe study showed that the cities' evaluation piece of work has been primarily focused on micro-scale innovation projects, where evaluation is needed. This supported a major European study'south findings that most smart metropolis solutions were niche, pilot innovation projects, which were at early stages of implementation and adult at limited geographical scales rather than developed explicitly city-broad (EU Directorate-General 2014). This raises challenges: of the applicability of smart metropolis-level indicators and metrics to measuring the outcomes of smart city projects and programmes; and of the contribution of project measured success outcomes to the measurement of city-level functioning. The CITYkeys initiative has begun to identify respective project and city indicators of smart city success outcomes, although recent findings revealed that about project-level success indicators could not be linked quantitatively with corresponding urban center-level indicators (Bosch et al. 2017). This suggests that the pick of measurement indicators for evaluation purposes should be appropriate to smart city developments at project, programme and city levels, and different geographical scales, with potential correspondences mapped between each level.
A key challenge for evaluation blueprint is in developing standardized smart city development and performance indicators that provide meaningful, city and citizen-centred evaluation. Currently, national and international standardization initiatives play an important function in developing smart city standards and metrics, shaping city approaches to urban development. Moreover, standardized measurement indicators offer value for development policy and the potential for transforming governance (Holman 2009). However, several metropolis regime in the SmartDframe study were unconvinced of the demand for new standardized, specific-smart urban center KPIs and frameworks, unless they were sufficiently relevant and adaptable to their unique city and project circumstances. An alternative preferred past some authorities is to measure the impacts of smart urban center developments confronting existing city KPIs aligned with city strategies. This would serve to leverage the value of embedded smart technologies for urban policies and deportment.
A further key evaluation challenge for cities is to determine the value and causal impacts of smart city projects and programmes. Assumptions of smart technological determinism of outcomes, and the appropriateness of attributing measured city outcomes to smart city developments, are problematic in complex urban contexts. Evolutionary-systems perspectives on complication propose that the impacts of program interventions can exist best adamant by setting a narrow telescopic for evaluation with clearly specified spatial and temporal arrangement boundaries to control inapplicable influences (Arnold 2004). New urban data sources from smart metropolis infrastructures, sensor networks, space platform technologies, web services and social media, together with data-driven analytics, can help address this claiming through informing complication modelling and amend understanding of crusade-effect relationships in cities.
Emerging from the SmartDframe written report are recommendations that evaluation design needs to test theories underlying smart city development programmes and the benefits planned for citizens (run into Pawson and Tilley 2004). Evaluation design should be advisable to smart city project, programme and city levels of intervention and different urban center scales of development, and designed in collaboration with cardinal urban center stakeholders with agreed success factors to support credible evaluation. Consideration is needed of the selection of evaluation methods, urban measurement indicators and data sources capable of determining the impact of smart city projects on citizens' lives, and measuring the tangible/intangible and direct/indirect consequences of smart metropolis developments. A range of qualitative and quantitative evaluation methods are bachelor to identify value for specific purposes and stakeholder audiences (Oliveira and Pinho 2010; Edler et al. 2012; HM Treasury 2013), potentially informed by triangulation methods to link multiple, multilevel, multiscale evaluations to offer holistic urban policy evaluations (Magro and Wilson 2013).
Integrative approaches to evaluation design are needed to appoint expert and community city stakeholders with evaluation processes, accost project, programme and metropolis levels of intervention and scales of smart city evolution, while capitalizing on big data opportunities and new generations of data analytics to inform city evaluation and its contribution to urban development. Embedding evaluation practices in city functioning management processes is essential to decide the value, outcomes and benefits of smart city developments for cities and citizens. In this style, we can discern how finer gimmicky urban challenges are being addressed through a smart vision for futurity cities.
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
The Smart Dframe project is supported by funding from The Open University (OU) Smart Cities Open Challenge Contest. This is linked to the OU-led MK:Smart programme, which received meaning funding from the College Education Funding Council for England.
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Source: https://www.tandfonline.com/doi/full/10.1080/13574809.2018.1469402
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