From hidden universalities to predictive models
Today’s rapid urbanisation and the increasing complexity of our cities worldwide call for new, science-based approaches in urban planning and design. This presentation will show how complexity science and its combination with big data analyses help reveal ‘universal’ patterns in the dynamics of cities and, derive simple models that predict these patterns.
To that end, the speaker will focus on two exemplary applications: the first example uses anonymised mobile phone records from millions of individuals to derive a new scaling law for the prediction of urban mobility patterns. The second example explores the relation between the size of a city and the heights and shapes of its building. The value of these new insights for the planning and design of more efficient urban infrastructures will be discussed.
Markus Schläpfer leads the Urban Complexity research within the FCL at the Singapore-ETH Centre. He is visiting researcher at the Santa Fe Institute and a Research Affiliate at MIT’s Senseable City Lab. He received his PhD in 2010 from ETH Zurich and conducted postdoctoral fellowships at both the Santa Fe Institute and MIT. His work has been featured worldwide, including the New York Times, Nature, The Atlantic, Quartz, MIT Technology Review, and Spiegel Online.
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