Given that brief overview on the concept of smart city, it is clear that the area of my research into a merge between the technologic and human factors. The area that I want to cover is not related to the infrastructure of the technology, nor the participation of individuals per si. However, my goal is to make sense of the data generated to improve the understanding of cities.
Urban Science
Due to the amount of data gathered and its increasing complexity, a new science field is emerging: Urban Science \cite{Barthelemy_2015}. It aims to understand and model phenomena happening in a city. Topics such urban morphology, city growth, residence location choice, evolution of urban networks can now be quantitatively understood.
What is the place of math in this context?
Physical Statistics View
It aims understand the object 'city' and to identify the dominant forces that shape its formation and evolution
- Universal generalities of cities, similar to the standard physics approach to the understanding of reality.
- More theoretical.
Research Examples:
- Stochastic out-of-equilibrium model of city growth that predicted: the scaling exponent of the total distance commuted daily, the total length of the road network, the total delay due to congestion, the quantity of CO2 emitted, and the surface area with the population size of cities. Also, the sublinear increase of the number of centers with population size \cite{louf2015}.
- Cities characterization
Key Authors:
- Marc Barthelemy
- Rémi Louf
Key Institutions:
Data Science View
Research Examples
Key Institutions: