Emergence of urban growth patterns from human mobility behavior
Emergence of urban growth patterns from human mobility behavior
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DOI：
10.1038/s43588-021-00160-6
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发表时间：
2021-12-01
期刊：
影响因子：
--
通讯作者：
Song, Chaoming
中科院分区：
文献类型：
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作者：
Xu, Fengli;Li, Yong;Song, Chaoming
城市以自下而上的方式发展，导致以尺度规律为特征的分形城市形态。相关渗流模型通过施加强的空间相关性成功地模拟了城市几何形状；然而，空间相关性城市增长背后的潜在机制的起源在很大程度上仍然未知。由于大规模人类活动数据的日益可用性，我们对人类运动的理解最近发生了革命性的变化。本文介绍了一个计算城市增长模型，该模型以人类流动行为的微观基础为基础，捕捉了空间上相关的城市增长。我们将提出的模型与三个经验数据集进行比较，发现人类运动中的强烈社会互动和长期记忆效应是导致分形城市形态的两个基本原则，以及城市增长的三个重要规律。我们的模型将城市增长模式和人类流动行为的实证结果联系起来。
Cities grow in a bottom-up manner, leading to fractal-like urban morphologies characterized by scaling laws. The correlated percolation model has succeeded in modeling urban geometries by imposing strong spatial correlations; however, the origin of the underlying mechanisms behind spatially correlated urban growth remains largely unknown. Our understanding of human movements has recently been revolutionized thanks to the increasing availability of large-scale human mobility data. This paper introduces a computational urban growth model that captures spatially correlated urban growth with a micro-foundation in human mobility behavior. We compare the proposed model with three empirical datasets, discovering that strong social interactions and long-term memory effects in human movements are two fundamental principles responsible for fractal-like urban morphology, along with the three important laws of urban growth. Our model connects the empirical findings in urban growth patterns and human mobility behavior.