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arXiv 1705.03213physics.soc-ph
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多维城市隔离-走向神经网络测量

Multidimensional Urban Segregation - Toward A Neural Network Measure

Madalina Olteanu, Aurélien Hazan, Marie Cottrell, Julien Randon-Furling

摘要:

我们引入了一个多维的,神经网络的方法来揭示和测量城市隔离现象,基于自组织映射算法(SOM)。SOM的多维性允许人们同时理解大量的变量,定义在人口普查或其他类型的统计块上,并沿着它们进行聚类。然后,通过神经网络上的距离与实际地理地图上的距离之间的相关性来测量隔离程度。此外,SOM的随机性使人们能够量化人口普查区块的异质性水平。我们说明了这种新方法的数据为巴黎市。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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