Multidimensional urban segregation: toward a neural network measure

  • Madalina Olteanu
  • Aurélien Hazan
  • Marie Cottrell
  • Julien Randon-FurlingEmail author
WSOM 2017


We introduce a multidimensional, neural network approach to reveal and measure urban segregation phenomena, based on the self-organizing map algorithm (SOM). The multidimensionality of SOM allows one to apprehend a large number of variables simultaneously, defined on census blocks or other types of statistical blocks, and to perform clustering along them. Levels of segregation are then measured through correlations between distances on the neural network and distances on the actual geographical map. Further, the stochasticity of SOM enables one to quantify levels of heterogeneity across census blocks. We illustrate this new method on data available for the city of Paris.


Segregation Machine learning Neural networks Self-organizing maps 


Compliance with ethical standards

Conflict of Interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.SAMM (EA4543)Université Paris 1 Panthéon-SorbonneParisFrance
  2. 2.LISSI (EA 3956), Senart-FB Institute of TechnologyUniversité Paris-EstLieusaintFrance
  3. 3.MaIAGE, INRA, Université Paris-SaclayJouy-en-JosasFrance

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