It has been found that human mobility exhibits random patterns following the Levy flight, where human movement contains many short flights and some long flights, and these flights follow a power-law distribution. In this paper, we study the social-economical development trajectories of urban cities. We observe that social-economical movement of cities also exhibit the Levy flight characteristics. We collect the social and economical data such as the population, the number of students, GDP and personal income, etc. from several cities. Then we map these urban data into the social and economical factors through a deep-learning embedding method Auto-Encoder. We find that the social-economical factors of these cities can be fitted approximately as a movement pattern of a power-law distribution. We use the Stochastic Multiplicative Processes (SMP) to explain such movement, where in the presence of a boundary constraint, the SMP leads to a power law distribution. It means that the social-economical trajectories of cities also follow a Levy flight pattern, where some years have large changes in terms of social-economical development, and many years have little changes.
翻译:人们发现,人类流动在利维飞行之后出现了随机模式,人类流动包含许多短飞行和一些长飞行,这些飞行遵循的是权力法的分配。我们在本文件中研究了城市的社会经济发展轨迹。我们观察到城市的社会经济流动也体现了利维飞行的特点。我们从几个城市收集社会和经济数据,例如人口、学生人数、国内生产总值和个人收入等数据。然后我们通过深造嵌入自动计算机的方法将这些城市数据映入社会和经济因素中。我们发现,这些城市的社会经济因素可以大致地作为权力法分配的一种运动模式。我们使用巧妙的多复制过程(SMP)来解释这种运动,在存在边界限制的情况下,SMP导致权力法的分配。这意味着城市的社会-经济轨迹也遵循了利维飞行模式,在这种模式中,一些年来社会-经济发展发生了重大变化,许多年来变化很小。