  Segregation affects millions of urban dwellers. The main expression of this
reality is the creation of ghettos which are city parts characterized by a
combination of features: low income, poor cultural level... Segregation models
have been usually defined over regular lattices. However, in recent years, the
focus has shifted from these unrealistic frameworks to other environments
defined via geographic information systems (GIS) or networks. Nevertheless,
each one of them has its drawbacks: GIS demands high-resolution data, that are
not always available, and networks tend to have limited real-world
applications. Our work tries to fill the gap between them. First, we use some
basic GIS information to define the network, and then, run an extended
Schelling model on it. As a result, we obtain the location of ghettos. After
that, we analyze which parts of the city are segregated, via spatial analysis
and machine learning and compare our results. For the case study of Washington
D.C., we obtain an $80\%$ accuracy.
