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城市增长模式源于人类流动行为。

Emergence of urban growth patterns from human mobility behavior.

作者信息

Xu Fengli, Li Yong, Jin Depeng, Lu Jianhua, Song Chaoming

机构信息

Beijing National Research Center for Information Science and Technology (BNRist), Department of Electronic Engineering, Tsinghua University, Beijing, China.

Department of Physics, University of Miami, Coral Gables, FL, USA.

出版信息

Nat Comput Sci. 2021 Dec;1(12):791-800. doi: 10.1038/s43588-021-00160-6. Epub 2021 Dec 9.

Abstract

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.

摘要

城市以自下而上的方式发展,形成了具有分形特征的城市形态,并遵循标度律。相关渗流模型通过引入强空间相关性成功地对城市几何形状进行了建模;然而,空间相关城市增长背后潜在机制的起源在很大程度上仍然未知。得益于大规模人类移动性数据的日益丰富,我们对人类移动的理解最近发生了革命性变化。本文介绍了一种计算城市增长模型,该模型以人类移动行为的微观基础来捕捉空间相关的城市增长。我们将所提出的模型与三个实证数据集进行比较,发现人类移动中的强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理,同时也是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移动行为的实证发现联系了起来。

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