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Article Dans Une Revue Neural Computing and Applications Année : 2019

Multidimensional urban segregation: toward a neural network measure

Résumé

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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Dates et versions

hal-02918596 , version 1 (20-08-2020)

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Madalina Olteanu, Aurélien Hazan, Marie Cottrell, Julien Randon-Furling. Multidimensional urban segregation: toward a neural network measure. Neural Computing and Applications, 2019, 32 (24), pp.18179-18191. ⟨10.1007/s00521-019-04199-5⟩. ⟨hal-02918596⟩
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