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- W4301434370 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." @default.
- W4301434370 created "2022-10-05" @default.
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- W4301434370 date "2017-05-09" @default.
- W4301434370 modified "2023-09-23" @default.
- W4301434370 title "Multidimensional Urban Segregation - Toward A Neural Network Measure" @default.
- W4301434370 doi "https://doi.org/10.48550/arxiv.1705.03213" @default.
- W4301434370 hasPublicationYear "2017" @default.
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