Schläpfer Markus, Dong Lei, O'Keeffe Kevin, Santi Paolo, Szell Michael, Salat Hadrien, Anklesaria Samuel, Vazifeh Mohammad, Ratti Carlo, West Geoffrey B
Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
Santa Fe Institute, Santa Fe, NM, USA.
Nature. 2021 May;593(7860):522-527. doi: 10.1038/s41586-021-03480-9. Epub 2021 May 26.
Human mobility impacts many aspects of a city, from its spatial structure to its response to an epidemic. It is also ultimately key to social interactions, innovation and productivity. However, our quantitative understanding of the aggregate movements of individuals remains incomplete. Existing models-such as the gravity law or the radiation model-concentrate on the purely spatial dependence of mobility flows and do not capture the varying frequencies of recurrent visits to the same locations. Here we reveal a simple and robust scaling law that captures the temporal and spatial spectrum of population movement on the basis of large-scale mobility data from diverse cities around the globe. According to this law, the number of visitors to any location decreases as the inverse square of the product of their visiting frequency and travel distance. We further show that the spatio-temporal flows to different locations give rise to prominent spatial clusters with an area distribution that follows Zipf's law. Finally, we build an individual mobility model based on exploration and preferential return to provide a mechanistic explanation for the discovered scaling law and the emerging spatial structure. Our findings corroborate long-standing conjectures in human geography (such as central place theory and Weber's theory of emergent optimality) and allow for predictions of recurrent flows, providing a basis for applications in urban planning, traffic engineering and the mitigation of epidemic diseases.
人类流动影响着城市的诸多方面,从其空间结构到对疫情的应对。它也是社会互动、创新和生产力的最终关键所在。然而,我们对个体总体流动的定量理解仍不完整。现有的模型,如引力定律或辐射模型,专注于流动的纯粹空间依赖性,并未捕捉到对同一地点重复访问的不同频率。在此,我们基于来自全球不同城市的大规模流动数据,揭示了一个简单且稳健的标度律,该标度律捕捉了人口流动的时空谱。根据这一定律,任何地点的访客数量随着其访问频率和旅行距离乘积的平方反比而减少。我们进一步表明,流向不同地点的时空流产生了具有遵循齐普夫定律的面积分布的显著空间集群。最后,我们构建了一个基于探索和优先返回的个体流动模型,为发现的标度律和新兴的空间结构提供了一个机理解释。我们的研究结果证实了人文地理学中长期存在的猜想(如中心地理论和韦伯的涌现最优性理论),并允许对重复流动进行预测,为城市规划、交通工程和疫情缓解方面的应用提供了基础。