From cultural quarters to creative clusters – creative spaces in the new city economy

Copyright @ 2009 Institute of Urban HistoryInstitute of Urban History, Stockhol

The Rise of Innovation Districts: A New Geography of Innovation in America

As the United States slowly emerges from the great recession, a remarkable shify is occurring in the spatial geogrpahy of innovation. For the past 50 years, the landscape of innovation has been dominated by places like Silicon Valley - suburban corridors of spatially isolated corporate campuses, accessible only by car, with little emphasis on the quality of life or on integrating work, housing, and recreation. A new complementary urban model is now emerging, giving rise to what we and others are calling "innovation districts." These districts, by our definition, are geographic areas where leading-edge anchor institutions and companies cluster and connect with start-ups, business incubators, and accelerators. They are also physically compact, transit-accessible, and technicall

Quantification Model of Smart City Development Dynamics Using Structural Equation Modeling

학위논문(석사)--서울대학교 대학원 :공과대학 건설환경공학부,2019. 8. 지석호.In recent years, smart city projects have drawn significant attention as initiatives for enhancing urban development and regeneration. Many studies have incorporated technical and non-technical enablers to better control the design, planning, and progress management of smart cities. However, despite considerable efforts and achievements, the direct and indirect effects of smart city enablers on urban performances have not been quantified comprehensively. Thus, due to this lack of in-depth quantification and understanding, urban leaders encounter difficulties in establishing proper strategies and policies for the successful development of smart cities. To address this issue, the present study has used Structural Equation Modeling (SEM) to identify the critical enablers of smart cities and to quantify their dynamic effects (i.e., direct and indirect effects) on the performances of such cities. More specifically, the authors applied SEM to test and estimate the relationships between four enabler clusters (i.e., technological infrastructure, open governance, intelligent community, and innovative economy) and four performance objectives (i.e., efficiency, sustainability, livability, and competitiveness) using the actual data of 50 smart cities. The statistical results demonstrated that non-technical enabler clusters (i.e., open governance, intelligent community, and innovative economy), as well as the technical drivers (i.e., technological infrastructure), have significant impacts on the performances of smart cities with their highly interrelated, synergetic dynamics. The high percentage of variance explained for performance objectives, which varied from about 71% to 91%, was indicative of good explanatory power. Based on those mathematical findings, urban leaders can enhance strategic planning for smart city transitions through proper policy management.Chapter 1 Introduction 1 1.1 Research Background 1 1.2 Problem Statement 4 1.3 Research Objective 6 1.4 Research Scope 7 1.5 Research Process 8 Chapter 2 Literature Review 9 2.1 Identification of Smart City Enablers 9 2.2 Quantification of Enablers Direct Effects 11 2.3 Limitations of Quantification Strategies 13 Chapter 3 Quantification Model Development 15 3.1 Research Overview 15 3.2 Latent Variables Specification 17 3.3 Hypothetical Model Establishment 22 3.4 Structural Equation Modeling (SEM) 25 Chapter 4 Model Testing and Results 31 4.1 Data Collection and Preparation 31 4.2 SEM Analysis 36 4.3 Results and Discussions 43 Chapter 5 Model Applications 52 5.1 Smart City Maturity Assessment 52 5.2 Smart City Macro Trends Analysis 55 Chapter 6 Conclusion 58 6.1 Summary and Contributions 58 6.2 Limitations and Future Study 60 Bibliography 62 Appendix A 69 Appendix B 70 Abstract (Korean) 71Maste