A Schelling Extended Model in Networks-Characterization of Ghettos in Washington D.C.

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.

Automated summary

Segregation affects urban dwellers and results in the creation of ghettos. Traditional segregation models are defined over regular lattices, but recent research has shifted to using GIS or networks to define different environments. Our work bridges the gap between these methods and analyzes spatially segregated areas using machine learning. We achieved an 80% accuracy for the case study of Washington D.C.