  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 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.
