Harvard Medical School, Harvard University, Boston, MA, USA.
Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA.
Nat Hum Behav. 2020 Aug;4(8):800-810. doi: 10.1038/s41562-020-0875-0. Epub 2020 May 18.
The geographic variation of human movement is largely unknown, mainly due to a lack of accurate and scalable data. Here we describe global human mobility patterns, aggregated from over 300 million smartphone users. The data cover nearly all countries and 65% of Earth's populated surface, including cross-border movements and international migration. This scale and coverage enable us to develop a globally comprehensive human movement typology. We quantify how human movement patterns vary across sociodemographic and environmental contexts and present international movement patterns across national borders. Fitting statistical models, we validate our data and find that human movement laws apply at 10 times shorter distances and movement declines 40% more rapidly in low-income settings. These results and data are made available to further understanding of the role of human movement in response to rapid demographic, economic and environmental changes.
人类活动的地域差异在很大程度上是未知的,主要是因为缺乏准确和可扩展的数据。在这里,我们描述了来自超过 3 亿智能手机用户的全球人类流动模式。这些数据几乎涵盖了所有国家和地球 65%的人口居住地区,包括跨境流动和国际移民。这种规模和覆盖范围使我们能够开发出一种全球综合性的人类活动分类法。我们量化了人类活动模式在社会人口和环境背景下的变化,并展示了跨越国界的国际流动模式。通过拟合统计模型,我们验证了我们的数据,并发现人类活动规律在距离缩短 10 倍的情况下仍然适用,而在低收入环境中,活动减少的速度则快 40%。这些结果和数据可供进一步了解人类活动在应对快速的人口、经济和环境变化方面的作用。