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

TL;DR

This paper extends the Schelling segregation model to network-based environments using GIS data, successfully identifying and analyzing ghettos in Washington D.C. with high accuracy, bridging the gap between traditional lattice models and real-world applications.

Contribution

It introduces a novel approach combining GIS-based network modeling with an extended Schelling model to detect urban ghettos, enhancing realism and applicability.

Findings

Achieved 80% accuracy in identifying segregated areas in Washington D.C.

Demonstrated the effectiveness of combining GIS data with network models for segregation analysis.

Provided insights into the spatial distribution of ghettos using machine learning and spatial analysis.

Abstract

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.