Multidimensional urban segregation: toward a neural network measure


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.

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    These are social benefits paid to prevent people from falling into extreme poverty. They vary from 300 euros to about 800 euros per month.

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    Note that such a simple, direct measure of the correlation between geographical distance and Kohonen distance is well suited to intricate patterns of segregation, as observed in real cities. However, if one considers artificial patterns with much regularity, this correlation measure works well on checker-board patterns (provided the mesh is not too small), but obviously not as well on concentric patterns. More work is needed to circumvent this difficulty.


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Correspondence to Julien Randon-Furling.

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Olteanu, M., Hazan, A., Cottrell, M. et al. Multidimensional urban segregation: toward a neural network measure. Neural Comput & Applic (2019).

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  • Segregation
  • Machine learning
  • Neural networks
  • Self-organizing maps