  Challenges due to the rapid urbanization of the world -- especially in
emerging countries -- range from an increasing dependence on energy, to air
pollution, socio-spatial inequalities, environmental and sustainability issues.
Modelling the structure and evolution of cities is therefore critical because
policy makers need robust theories and new paradigms for mitigating these
problems. Fortunately, the increased data available about urban systems opens
the possibility of constructing a quantitative 'science of cities', with the
aim of identifying and modelling essential phenomena. Statistical physics plays
a major role in this effort by bringing tools and concepts able to bridge
theory and empirical results. This article illustrates this point by focusing
on fundamental objects in cities: the distribution of the urban population;
segregation phenomena and spin-like models; the polycentric transition of the
activity organization; energy considerations about mobility and models inspired
by gravity and radiation concepts; CO2 emitted by transport; and finally,
scaling that describes how various socio-economical and infrastructures evolve
when cities grow.

Physics and Society;Disordered Systems and Neural Networks