Smart Cities and standards – The approach of the Horizon2020-project ESPRESSO

A Smart City integrates physical, digital and human systems to deliver a sustainable, prosperous and inclusive future for its citizens. Many of these innovative solutions will be based on sophisticated information and communication technologies. However, technological complexity, as well as the complexity of the various sectoral services involved within a Smart City, require a system approach to standardisation. Such an approach must promote the greatest possible reuse of existing open standards to accelerate the Smart City deployment. In an effort to leverage the promise of a system approach, the Horizon 2020-project ESPRESSO (systEmic standardisation apPRoach to Empower Smart citieS and cOmmunities) will focus on the development of a conceptual Smart City Information Framework based on open standards. A further goal of ESPRESSO will be to envisage the impact of those technologies for urban planning and also in societal terms. The partner cities will be engaged to analyse how their services can be streamlined and improved through large-scale use of standards. This will be done by analysing the downstream changes from the new scenarios enabled by large-scale interoperability and what this could bring for a future Smart City. Based on a detailed requirements-engineering campaign executed in close cooperation with cities, standardisation organizations, administrative bodies, and private industry, the project will identify open standards matching the elicited requirements and will establish a baseline for interoperability between the various sectoral data sources and the Smart City enterprise application platform. In a comprehensive set of coordination, support and networking activities, the project will engage a very large number of stakeholders, such as Smart Cities (both existing and those with aspirations), European Standardisation Organizations (ESOs), National Standardisation Bodies (NSBs), Standards Development Organizations (SDOs), public administrations, industries, SMEs, and other institutions. ESPRESSO’s approach emphasises cost reduction and will foster an open market for many actors, avoiding lock-in to proprietary solutions

Value Creation through Urban Data Platforms: A Conceptual Framework

In the context of smart cities, data-driven innovation and digital transformation have received increasing attention from practitioners and academics. The data-centric nature of smart city transformations highlights the essential role of urban data platform (UDP) to manage large and heterogeneous urban data sets and to facilitate interaction among data providers and users in a city ecosystem. To realize value creation through UDP, a comprehensive understanding of the key UDP dimensions and how they influence UDP adoption, use, and value creation are required. For this purpose, we first identify key UDP dimensions through a literature review. Second, by exploring and discussing their relationships with an expert panel, we develop a framework for understanding value creation through UDPs. By identifying key dimensions of UDP and their effects on value creation through UDP, the proposed framework provides a systematic and comprehensive approach for understanding UDP adoption, use, and value creation. Thereby, this study helps city policymakers and business developers in realizing value from UDPs in city ecosystem

Citizen Science and Smart Cities

The report summarizes the presentations, discussions, and conclusions of the Citizen Science and Smart Cities Summit organised by the European Commission Joint Research Centre on 5-7th February 2014. In the context of the Summit, the label Citizen Science was used to include both citizen science projects, and others that are about user-generated content, not necessarily addressing a scientific process or issues. The evidence presented by 27 different projects shows the vitality and diversity of the field but also a number of critical points: • Citizen science project are more than collecting data: they are about raising awareness, building capacity, and strengthening communities. • Likewise, smart cities are not only about ICT, energy and transport infrastructures: Smart cities are about smart citizens, who participate in their city’s daily governance, are concerned about increasing the quality of life of their fellow-citizens, and about protecting their environment. Technology may facilitate, but is no solution per se. • Unfortunately to date there seems to be little synergy between citizen science and smart cities initiatives, and there is little interoperability and reusability of the data, apps, and services developed in each project. • It is difficult to compare the results among citizen science, and smart cities projects or translate from one context to another. • The ephemeral nature of much of the data, which disappear short after the end of the projects, means lack of reproducibility of results and longitudinal analysis of time series challenging, if not impossible. • There are also new challenges with respect to the analytical methods needed to integrate quantitative and qualitative data from heterogeneous sources that need further research. • Building and maintaining trust are key points of any citizen science or smart city project. There is a need to work with the community and not just for, or on, the community. It is critical not just to take (data, information, knowledge) but to give back something that is valued by the community itself. The development of citizen science associations in Europe and the US are important developments that may address some of the points above. There are also actions through which the European Commission Joint Research Centre can make an important contribution: • Map citizen science and smart cities projects, and generate a semantic network of concepts between the projects to facilitate search of related activities, and community building. • Provide a repository for citizen science and smart cities data (anonymised and aggregated), software, services, and applications so that they are maintained beyond the life of the projects they originate from, and made shareable and reusable. • Develop regional test beds for the analysis and integration of social and environmental data from heterogeneous sources, with a focus on quality of life and well-being. • Undertake comparative studies, and analyse issues related to scaling up to the European dimension. • Support citizen science and smart cities projects with the JRC knowledge on semantic interoperability, data models, and interoperability arrangements. • Partner with the European Citizen Science Association, and contribute to its interoperability activities. • Work towards making the JRC, and the European Commission, a champion of citizen participation in European science.JRC.H.6-Digital Earth and Reference Dat

Citizens Adoption and Intellectual Capital Approach

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 협동과정 기술경영·경제·정책전공, 2019. 2. Hwang, Junseok .The emergence of knowledge intensive industries gave rise to the issue of intellectual capital management which is used as an instrument to identify and measure the hidden sources of value creation at the firm, regional and national level. Knowledge-intensive companies are rated much higher than their book value suggests, and thus need to identify the intangible valuables of the company for the improvement and sustainability of their learning and capitalization system. Intellectual capital components are the key resources that can be leveraged for smart city development which intends to use information and communication technologies in order to bring efficiency and sustainability to the urban functions. The role of intellectual capital components in smart city implementation needs to be studied due to the fact that attributes of intellectual capital components would have a distinguished impact on value creation and the increase in productivity and performance. Despite the existence of a significant number of literatures on intellectual capital, the role of its components in the success of smart city implementation has not been examined. This research aims to investigate the role of intellectual capital components towards smart city success using an analysis of experts preferences for human capital and structural capital. The research also includes the demand-side perspective towards smart city information services characteristics that influences the adoption decision. The analysis is performed using two methodologies: Analytics Hierarchy Process (AHP) for human capital and structural capital and discrete choice analysis using a mixed logit model for the adoption of smart city information services. The first study employs a multidimensional approach to the development of a model for human capital using individual-level characteristics and the collective behavior. The identification of the sources of value in human capital is critical to the success of smart city implementations as these capabilities can be leveraged and upgraded to improve productivity and performance. Human capital components have been categorized into personal qualifications, personal traits, culture and social factors. The findings reveal that the most important category is personal qualifications followed by culture. Moreover, the overall priority weights estimation shows that the existence of domain-specific tacit knowledge gained through experience, the multi-disciplinary scope of education and the density of R&D personnel are the top-three ranked attributes of human capital towards smart city success. The study on the structural capital examined 24 smart city cases across the globe to identify the structural capital elements valuable in the smart city development process. The different orchestration of these structural capital elements can influence the outcome of the development process and its impact on the efficiency of the urban systems. The identified structural capital elements have been categorized into process, relational and infrastructural dimensions. The findings reveal that the infrastructural dimension comprising communication and information system is most critical towards the smart city success, followed by the process category with the most dominant component of policy. The overall ranking of these elements suggest that the decision makers need to focus on city-level policies and the development and enforcement of procedures for innovation generation. Finally, the citizens preferences analysis was performed for the case of Islamabad city in Pakistan which is at the early stage of smart city development and can benefit from a better understanding of the demand-side perspective. The characteristics of smart city information services considered in the study comprise language, access mode, service ownership, interoperability and security. Willingness-to-pay was used to observe the price sensitivity of the end users choices. The findings reveal that citizens in Islamabad have a higher utility towards the use of the English language, a mobile access mode and a high level of security. In conclusion, the study provides guidelines for policy makers who are concerned with the early stage of smart city development. The demand-side study of Islamabad city provides valuable insights in to existing trends that affect the rapid adoption of smart city services.국문초록 지식집약적 산업의 출현으로 기업, 지역 및 국가 차원에서 가치 창출의 숨겨진 출처를 파악하고 측정하는 도구로 사용되는 지적 자본 관리가 쟁점으로 떠올랐다. 지식집약적 기업은 순자산보다 훨씬 높은 평가를 받고 있기 때문에 그들의 학습과 자본화 시스템의 개선과 지속 가능성을 위해 회사의 무형 가치를 확인할 필요가 있다. 지적 자본요소는 정보통신 기술을 이용해 도시 기능에 효율성과 지속성을 높이는 스마트 시티 개발에 활용될 수 있는 핵심 자원이다. 지적 자본 요소의 속성은 가치 창출과 생산성 및 성능 향상에 가변적인 영향을 미칠 수 있기 때문에 스마트 시티 구현에서의 지적 자본 요소의 역할을 연구할 필요가 있다. 지적 자본에 관한 중요한 연구 문헌들이 있지만 스마트 시티의 성공적인 구현을 위한각 요소들의 역할은 검토되지 않았다. 이 연구는 인적자본과 구조자본에 대한 전문가의 선호도 분석을 사용하여 스마트 시티의 성공을 위한 지적 자본 요소의 역할 조사를 목적으로 한다. 또한 수용 의사 결정에 영향을 미치는 스마트 시티 정보 서비스 특성에 대한 수요 측면의 관점도 조사한다. 분석은 인적 자본 및 구조적 자본을 위한 분석 계층 프로세스(AHP)와 스마트 시티 정보 서비스 채택을 위한 혼합 로짓 모델을 이용한 이산 선택 분석이라는 두 가지 방법을 사용한다. 첫 번째 연구는 다차원적 접근법을 사용해 개인 수준의 특성과 집단 행동을 이용한 인적 자본에 대한 모델을 개발한다. 인적 자본의 가치의 근원을 식별하는 것은 스마트 시티 구현 성공에 매우 중요하다. 이러한 능력들이 활용되고 개선되어 생산성과 성능을 향상시킬 수 있기 때문이다. 인적 자본 요소는 개인의 자격, 성격, 문화, 사회적 요인으로 분류되었다. 그 결과, 첫번째로 중요한 것은 개인의 자격요건이며 두번째는 문화임을 밝혀냈다. 또한, 전체적인 우선순위 가중치 추정은 경험을 통해 얻은 도메인 고유의 암묵적 지식의 존재, 다분야의 교육 범위 및 R&D 인력의 밀도는 스마트 시티 성공을 위한 인적 자본의 상위 3대 속성임을 보여준다. 구조적 자본에 관한 연구는 전 세계 24개 스마트 시티 사례를 조사해 스마트 시티 개발 과정에서 가치 있는 구조적 자본의 요소를 확인했다. 서로 다른 구조적 자본 요소의 조정은 개발 프로세스의 결과와 도시 시스템의 효율성에 영향을 미칠 수 있다. 확인된 구조적 자본 요소는 프로세스, 관계 및 기반 구조 차원으로 분류되었다. 이는 통신과 정보 시스템을 구성하는 기반 구조의 차원이 스마트 시티의 성공에 가장 중요하며 그 다음으로 정책의 가장 우세한 구성 요소를 가진 프로세스 범주가 중요하다는 것을 보여준다. 이들 요소의 전체 순위는 의사결정자들이 혁신 생성을 위한 도시 수준의 정책과 절차 개발과 집행에 초점을 맞출 필요가 있음을 시사한다. 마지막으로, 스마트 시티 개발의 초기 단계에 있으며 수요 측면 관점에서 유용할 수 있는 파키스탄의 이슬라마바드 도시에 대한 시민의 선호 분석이 이루어졌다. 본 연구에서 고려한 스마트 시티 정보 서비스의 특성은 언어, 접근 모드, 서비스 소유권, 상호운용성 및 보안으로 구성된다. 지불 의지는 최종 사용자의 선택에 따른 가격 민감도를 관찰하기 위해 사용되었다. 연구 결과는 이슬라마바드 시민들이 높은 수준의 보안과 함께 영어 사용에 더 높은 효용을 가지고 있다는 것을 보여준다. 결론적으로, 이 연구는 특별히 스마트 시티 개발의 초기 단계에 있는 정책 입안자들을 위한 지침을 제공한다. 이슬라마바드시에 대한 수요 측면 연구는 스마트 시티 서비스의 신속한 채택을 지원하는 기존 추세에 대한 귀중한 통찰력을 제공한다. 주요 단어: 스마트 시티, 지적 자본, 인적 자본, 구조적 자본, 정보 서비스Chapter 1 Introduction 1 1.1 Overview 1 1.2 Purpose of the Research 9 1.3 Contribution of the Research 12 1.4 Research Outline 15 Chapter 2 Literature Review 18 2.1 Smart Cities 18 2.1.1 Smart City Definitions 19 2.1.2 Smart City Components 22 2.1.3 Smart City Systems Architecture 28 2.2 Intellectual Capital 30 2.2.1 Existing Studies on Intellectual Capital 32 2.2.2 Intellectual Capital and Smart Cities 37 2.2.3 Intellectual Capital Components 39 Chapter 3 Study on the Role of Human Capital for Smart City Success 50 3.1 Model 52 3.1.1 Personal Qualifications 54 3.1.2 Personal Traits 57 3.1.3 Culture 58 3.1.4 Social Factors 59 3.2 Methodology 60 3.2.1 Survey for Analytic Hierarchy Process 63 3.3 Estimation of Results 66 Chapter 4 Study on Structural Capital Role for Smart City Success 74 4.1 Model 77 4.1.1 Process Elements 77 4.1.2 Relational Elements 81 4.1.3 Infrastructural Elements 82 4.2 Methodology 85 4.2.1 Survey for Analytic Hierarchy Process 85 4.3 Estimation of Results 87 Chapter 5 Adoption of Smart City Information Services 95 5.1 Citizens Preferences Analysis towards the Adoption of Smart City Information Services 95 5.2 Model 97 5.3 Methodology 101 5.3.1 Random Utility Model 101 5.3.2 Willingness to Pay 104 5.4 Survey Design and Data 105 5.4.1 Survey for Discrete Choice Analysis 105 5.5 Estimation of Results 109 Chapter 6 Discussion and Conclusion 115 6.1 Discussion and Implications 115 6.2 Conclusion 128 6.3 Limitations and Future Work 131 References 134 Appendix A: Description of Attributes for AHP Survey 152 Appendix B: Survey Questionnaire for AHP 155 Appendix C: Conjoint Survey for Citizens Preference Analysis 163 국문초록 166 Acknowledgments 169Docto