  Characterizing the structure of cities constitutes an important task since
the identification of similar cities can promote sharing of respective
experiences. In the present work, we consider 20 European cities from 5
respective countries and with comparable populations, each of which
characterized in terms of four topological as well as one geometrical feature.
These cities are then mapped into respective networks by considering their
pairwise similarity as gauged by the coincidence methodology, which consists of
combining the Jaccard and interiority indices. The methodology incorporates a
parameter alpha that can control the relative contribution of features with the
same or opposite signs to the overall similarity. Interestingly, the maximum
modularity cities network is obtained for a non-standard parameter
configuration, showing that it could not be obtained were not for the adoption
of the parameter alpha. The network with maximum modularity presents four
communities that can be directly related to four of the five considered
countries, corroborating not only the effectiveness of the adopted features and
similarity methodology, but also indicating a surprising tendency of the cities
from a same country of being similar, while differing from cities from other
countries. The coincidence methodology was then applied in order to investigate
the effect of several features combinations on the respectively obtained
networks, leading to a highly modular features network containing four main
communities that can be understood as the main possible models for the
considered cities.
