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As cities evolve at unprecedented speed and scale, traditional digital twins virtual models of physical urban systems fall short in capturing the dynamic and complex nature of urban life. This book bridges the gap between digital urban representations and the dynamic processes that shape our cities.
Moving beyond conventional data and technological infrastructure, this work formulates a comprehensive framework for embedding urban dynamics and complexity into digital twins. It reimagines them as living systems that adapt, learn and evolve in real time, near real time and over the long horizon. Through integrated feedback loops between data, physical infrastructure, high-dimensional models and reduced-order approximations, digital twins are transformed into powerful tools for the modeling, simulation, prediction and proactive management of urban development.
This book presents cutting-edge methods to learn, simplify and encode urban dynamics and complexity into digital twins whether or not the underlying mechanisms are fully understood. It also addresses critical challenges such as scalability, uncertainty propagation, network sensing and data quality, and demonstrates how dynamic digital twins can be continually refined through new information and emerging insights.
At the intersection of urban theory, artificial intelligence, machine learning and big spatiotemporal data, this book charts a new course for the modeling and governance of cities. It is a vital resource for researchers, practitioners and decision-makers across disciplines inviting collaboration between academia, industry, government and professionals working on the frontlines of our ever-changing urban environments.
Explore a bold vision for cities that can think, adapt and respond one where digital twins become not just mirrors, but engines of transformation.
List of contents
Chapter 1. Introduction.- Chapter 2. Dynamics and Complexity of Cities.- Chapter 3. Building Digital Twins of Cities via Governing Equations.- Chapter 4. Data-Driven Approach to the Construction of Digital Twins of Cities.- Chapter 5. Building Digital Twins of Cities via Urban Theory-Informed Neural Networks.- Chapter 6. Building Digital Twins of Cities as Complex Networks of Interdependence.- Chapter 7. Multiagent Approach to Building Digital Twins of Cities.- Chapter 8. Incorporation of Multiscale Dynamics and Complexity into Digital Twins of Cities.- Chapter 9. Interoperability between Models and Data for the Incorporation of Dynamics and Complexity into Digital Twins of Cities.- Chapter 10. Sensor Network and Data Quality Assurance for Digital Twins of Cities.- Chapter 11. Summary and Conclusion.
About the author
Yee Leung is Emeritus Professor of the Department of Geography and Resource Management and Honorary Senior Research Fellow of the Institute of Space and Earth Information Science at the Chinese University of Hong Kong, Hong Kong SAR, P. R. China. He is Pioneer of the theory and applications of fuzzy sets approach to geographical research and has done novel research on uncertainty analysis in geographical modeling and spatial information involving probability, statistics, fuzzy set, rough set, and granular computing. His research also covers artificial intelligence in general and machine learning including deep learning, neural network, evolutionary computation, and optimization in particular. He has published six monographs and more than 200 articles in reputed international journals and book chapters mainly in geography, geographical information science, artificial intelligence, computer science, and information science. His research is holistically reflected in the landmark research monographs.
