Modeling Urban Complex Spatiotemporal Systems
With increasingly developed urban data and technology, scientific knowledge about the current cities is now becoming far more frequent and complex than ever before. Understanding such cities as dynamic behavioural geometries is an essential step for sensing, modelling, visualising, and designing the urban future. To achieve this, the development of new theories, methods, and laws that the newly emerging data, technology, and science enable is a necessity as well as a challenge. This lecture will introduce our recent investigations in behavioural geometry computation and relevant laws mining to address various urban complexity, and it will also deliver the approaches to inventing future cities with underlying laws with augmented effectiveness, precision and scientificalness.
Yao SHEN
CAUP, Tongji University
Centre for Urban Science and Planning
Dr Yao Shen is an Associate Professor in Urban Analytics, College of Architecture and Urban Planning, Tongji University, Director in the Sino-UK Joint Lab for Urban Science, Director in the Centre for Urban Science and Planning (CUSP), Tongji University, Honorary Fellow, at the Centre for Advanced Spatial Analysis (CASA), University College London, and Associate Editor for the journal - Transactions in Urban Data, Science, and Technology . He obtained his PhD from The Bartlett, UCL and then worked as a Research Associate at CASA, UCL, before he joined Tongji. His research interests cover spatial analytics, urban modelling, complex network, data science, urban geometry, visualisation, and urban planning. He was elected to the Shanghai Rising-Star Program (2021) and Pujiang Program (2018). Until now, he is now supervising 5 Chinese national projects and published more than 40 articles in international journals. He has been serving as a reviewer for more than 30 international journals and as guest editors for 6 international journals.
May 21 19:00-21:25(UTC-+8)
D3 Lecture Hall, 5th Floor, Building D, CAUP, Tongji University
Zoom ID: 846 8435 7062
Password: 125964