Hidden patterns of urban mixing across five global cities

IF 15.3
Zhuangyuan Fan, Becky P. Y. Loo, Fabio Duarte, Carlo Ratti, Esteban Moro
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Abstract

Here we leverage large-scale travel surveys covering over 200,000 residents across Boston, Chicago, Hong Kong, London and São Paulo. With rich individual-level data, we reveal patterns in social mixing, which cannot be identified from high-resolution mobility data alone. Inferring socioeconomic status from residential neighborhoods yields social mixing levels 16% lower than using survey data. Individuals over the age of 66 experience greater social mixing than those in late working life (aged 55–65), lending data-driven support to the ‘second youth’ hypothesis. Teenagers and women with caregiving responsibilities exhibit lower social mixing levels. Living near major transit stations reduces the influence of socioeconomic status on social mixing. Finally, inputs of home-space, activity-space and demographic attributes are embedded and fed into a supervised autoencoder, revealing that mobility shapes experienced social mixing more than sociodemographic characteristics, home environment and transit proximity. Yet, activity spaces remain stratified by income, resulting in structurally different social mixing experiences. Using travel surveys from five global cities, this study explores how daily mobility shapes social mixing. Results show that activity spaces explain most place exposure variations, outweighing demographics and transit proximity.
五个全球城市混合的隐藏模式
在这里,我们利用大规模的旅行调查,覆盖了波士顿、芝加哥、香港、伦敦和圣保罗的20多万居民。通过丰富的个人层面数据,我们揭示了社会混合的模式,这些模式无法单独从高分辨率的流动性数据中识别出来。从居民区推断社会经济地位产生的社会混合水平比使用调查数据低16%。66岁以上的人比工作年龄较晚的人(55-65岁)经历了更多的社会混合,这为“第二青年”假说提供了数据支持。承担照顾责任的青少年和妇女表现出较低的社会混合水平。居住在主要交通站点附近,可以减少社会经济地位对社会混合的影响。最后,将家庭空间、活动空间和人口属性的输入嵌入并馈送到一个有监督的自编码器中,揭示出流动性形态比社会人口特征、家庭环境和交通距离更能经历社会混合。然而,活动空间仍然根据收入分层,导致结构上不同的社会混合体验。通过对全球五个城市的旅行调查,本研究探讨了日常流动性如何影响社会混合。结果表明,活动空间解释了大多数地方暴露变化,超过了人口统计和交通距离。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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