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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Olteanu, M., Hazan, A., Cottrell, M. et al. Multidimensional urban segregation: toward a neural network measure. Neural Comput & Applic (2019). https://doi.org/10.1007/s00521-019-04199-5
- Machine learning
- Neural networks
- Self-organizing maps