Urban Modelling with Big Data and Machine Learning
Machine Learning and Big Data together offer a universal way of looking at the world phenomena, which is radically different than the classical expert based disciplinary research. This new approach of computational modelling has inverted the classical notion of expertise from “having the answers to the known questions” to “learning to ask good questions”, where the answers can always be found with an appropriate level of modelling skills. In this regard, I will show the results of some of our ongoing data driven projects such as urban morphology, real estate market, urban air pollution and urban water flow modelling.
Previously trained and practiced as systems engineer, currently Vahid Moosavi is a senior researcher at the chair for Computer Aided Architectural Design (CAAD), ETH Zurich. In his PhD he was focused on theories of computational urban modelling and issues of “representation” and “idealisation”. Parallel to research and teaching “Data Driven Modelling” to graduate architectural design students at ETH, he has been conducting several applied machine-learning projects such as urban traffic dynamics, urban design, air pollution modelling, networked economy and systemic risk, real estate analysis and recently on scalable emulations of urban water flow.