arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2011.09403physics.soc-phcond-mat.dis-nncond-mat.stat-mech
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城市增长方程

The growth equation of cities

Vincent Verbavatz, Marc Barthelemy

摘要:

城市科学试图理解和解释在世界主要城市系统中观察到的不确定性。对城市人口演变进行建模是这门科学和所有城市研究的核心。量化,最根本的问题是理解城市的等级组织和特大城市的统计发生,首先被认为是由一个普遍的法律由于齐普夫描述,但其有效性已受到最近的实证研究的挑战。一个理论模型还必须能够解释城市和文明相对频繁的兴衰,尽管有许多尝试,这些基本问题仍然没有得到令人满意的回答。在这里,我们通过引入一种新的随机方程来模拟城市人口增长来填补这一空白,我们从最近的数据集(加拿大,法国,英国和美国)的实证分析中构建了这一模型,揭示了罕见但巨大的城市间迁移冲击如何主导城市增长。这个方程预测了城市分布的复杂形状,并表明齐普夫定律由于有限时间效应而通常不成立,这意味着城市的组织更加复杂。它还预测了存在多个时间变化的城市层次结构,与观测结果一致。我们的研究结果强调了罕见事件在复杂系统演化中的重要性,并在城市规划中具有更实际的意义。

英文摘要:

The science of cities seeks to understand and explain regularities observed in the world's major urban systems. Modelling the population evolution of cities is at the core of this science and of all urban studies. Quantitatively, the most fundamental problem is to understand the hierarchical organization of cities and the statistical occurrence of megacities, first thought to be described by a universal law due to Zipf, but whose validity has been challenged by recent empirical studies. A theoretical model must also be able to explain the relatively frequent rises and falls of cities and civilizations, and despite many attempts these fundamental questions have not been satisfactorily answered yet. Here we fill this gap by introducing a new kind of stochastic equation for modelling population growth in cities, which we construct from an empirical analysis of recent datasets (for Canada, France, UK and USA) that reveals how rare but large interurban migratory shocks dominate city growth. This equation predicts a complex shape for the city distribution and shows that Zipf's law does not hold in general due to finite-time effects, implying a more complex organization of cities. It also predicts the existence of multiple temporal variations in the city hierarchy, in agreement with observations. Our result underlines the importance of rare events in the evolution of complex systems and at a more practical level in urban planning.

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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