# Multidimensional Urban Segregation - Toward A Neural Network Measure

## TL;DR

This paper presents a novel neural network-based method using Self-Organizing Maps to measure and analyze multidimensional urban segregation, capturing complex patterns and heterogeneity across city census data.

## Contribution

It introduces a new multidimensional neural network approach for urban segregation analysis, enabling simultaneous variable analysis and heterogeneity quantification.

## Key findings

- Effective in capturing complex segregation patterns
- Quantifies heterogeneity across census blocks
- Validated on Paris city data

## Abstract

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.