GIS-SmartCity 2017

Michael Batty

Michael Batty

Centre for Advanced Spatial Analysis (CASA) University College London

Professor Michael Batty

Michael Batty is Bartlett Professor of Planning at University College London where he is Chair of the Centre for Advanced Spatial Analysis (CASA). He has worked on computer models of cities and their visualisation since the 1970s and has published several books, such as Cities and Complexity (MIT Press, 2005) which won the Alonso Prize of the Regional Science Association in 2011, and most recently The New Science of Cities (MIT Press, 2013). His blogs cover the science underpinning the technology of cities and his posts and lectures on big data and smart cities are at . His research group is working on simulating long term structural change and dynamics in cities as well as their visualisation. Prior to his current position, he was Professor of City Planning and Dean at the University of Wales at Cardiff and then Director of the National Center for Geographic Information and Analysis at the State University of New York at Buffalo. He is a Fellow of the British Academy (FBA) and the Royal Society (FRS), was awarded the CBE in the Queen’s Birthday Honours in 2004 and the 2013 recipient of the Lauréat Prix International de Géographie Vautrin Lud. In 2015 he received the Gold Medal of the Royal Geographical Society for his work on the science of cities. In 2016, he received the Senior Scholar Award of the Complex Systems Society and the Gold Medal of the Royal Town Planning Institute. He has Honorary Doctorates from the State University of New York and from Leicester University. His recent research is summarised in the following articles in 2016: (with Marshall, S.) Thinking Organic, Acting Civic: The Paradox of Planning for Cities in Evolution, Landscape and Urban Planning, In press online; (with Zhong C., Manley E., and Wang J.) Variability in Regularity: A Comparative Study of Urban Mobility Patterns in London, Singapore and Beijing Using Smart-Card Data, PLoS ONE. doi: 10.1371/journal.pone.0149222; (with Edwards, R.) Featured Graphic: City size: Spatial dynamics as temporal flows, Environment and Planning A, 48(6), 1001–1003; Evolving a Plan: Design and Planning with Complexity, in J. Portugali and E. Stolk (Editors) Complexity, Cognition, Urban Planning and Design,  Springer International Publishing Switzerland, DOI 10.1007/978-3-319-32653-5_2, 21-42; Alexander’s Challenge: Beyond Hierarchy In City Systems and Systems of Cities, in M. W. Mehaffy (Editor) A City is Not a Tree, 50th Anniversary Edition, Sustasis Press, Portland, OR, 37-45.

Big Data and Geo-Spatial Analytics:

New Tools for Understanding and Planning the Smart City


The most recent wave of computation in contemporary societies involves the application of new digital technologies to the public domain where the idea of automating the city and its functions has now become central to urban planning. The so-called smart city which is the label used for these collective technologies is based on the embedding of a multitude of passive and active sensors into the built environment which is producing vast quantities of data in space and time through real time streaming. This data is ‘big’ in the volumetric sense (Batty, 2016) and it is also complete in that is rarely related to any sampling of these data volumes. Moreover, it is changing our conception of the city from thinking about urban changes in the medium or long term to the short term and the routine – to the hourly and daily cycle of activities – which in turn is changing the very things that we seek to plan and optimize (Batty, 2016). To an extent this also represents a change in scale for where time scale are very short, spatial scales are also shorter and this the focus is on much more local spatial environments. In this presentation, I will begin with a conceptual model of the smart city, talk about real time streaming and the kinds of sensors that are now being employed to generate new data. I will review the problems and potential of this data and the kinds of model that are need to look at movement and change in the 24-hour city and how this transitions into urban change in the long term. I will present examples from our work on transit systems where there is much transit data dealing with real time streaming showing examples in Singapore, London and Beijing, and web-based simulation models representing long term change in UK cities. I will conclude with ideas about how the real time city can contribute to our understanding of longer time periods and urban change. Reference: Batty, M. (2016) Big Data and the City, Built Environment, 42, 3, 321-337.


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