Classifying cities and other geographical units is a classical task in
urban geography, typically carried out through manual analysis
of specific characteristics of the area. The primary objective of
this paper is to contribute to this process through the definition
of a wide set of city indicators that capture different aspects
of the city, mainly based on human mobility and automatically
computed from a set of data sources, including mobility traces
and road networks. The secondary objective is to prove that such
set of characteristics is indeed rich enough to support a simple
task of geographical transfer learning, namely identifying which
groups of geographical areas can share with each other a basic
traffic prediction model. The experiments show that similarity in
terms of our city indicators also means better transferability of
predictive models, opening the way to the development of more
sophisticated solutions that leverage city indicators
