Emergence of urban growth patterns from human mobility behavior

Xu, Fengli; Li, Yong; Jin, Depeng; Lu, Jianhua; Ci, Song

doi:10.1038/s43588-021-00160-6

Cited by 43 publications

(27 citation statements)

References 49 publications

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“…3 reveals the heterogeneity among cities in terms of polycentric organization, but with a primary, central urban county 47 , 48 . Recent multi-scale modeling analyses of urban mobility and growth 49 , 50 ; 51 , 52 are noteworthy in combining diverse data sources and theoretical approaches, but there is a need for empirical data analysis at a more disaggregated level 53 , 54 .…”

Section: Discussionmentioning

confidence: 99%

Spatial structure of city population growth

et al. 2022

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We show here that population growth, resolved at the county level, is spatially heterogeneous both among and within the U.S. metropolitan statistical areas. Our analysis of data for over 3,100 U.S. counties reveals that annual population flows, resulting from domestic migration during the 2015–2019 period, are much larger than natural demographic growth, and are primarily responsible for this heterogeneous growth. More precisely, we show that intra-city flows are generally along a negative population density gradient, while inter-city flows are concentrated in high-density core areas. Intra-city flows are anisotropic and generally directed towards external counties of cities, driving asymmetrical urban sprawl. Such domestic migration dynamics are also responsible for tempering local population shocks by redistributing inflows within a given city. This spill-over effect leads to a smoother population dynamics at the county level, in contrast to that observed at the city level. Understanding the spatial structure of domestic migration flows is a key ingredient for analyzing their drivers and consequences, thus representing a crucial knowledge for urban policy makers and planners.

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Section: Discussionmentioning

confidence: 99%

Spatial structure of city population growth

et al. 2022

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“…Existing work addresses this problem in two main ways. On the one hand, studies represented by statistical physics [34] or complexity science (such as cellular automata [24]) usually model city growth from a bottom-up perspective [34]. These models can capture key growth mechanisms of cities, but the generated form is often very different from the real situation because of ignoring many complex high-dimensional features of urban morphology [24].…”

Section: Introductionmentioning

confidence: 99%

MetroGAN: Simulating Urban Morphology with Generative Adversarial Network

Zhang

Zhu

et al. 2022

Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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Simulating urban morphology with location attributes is a challenging task in urban science. Recent studies have shown that Generative Adversarial Networks (GANs) have the potential to shed light on this task. However, existing GAN-based models are limited by the sparsity of urban data and instability in model training, hampering their applications. Here, we propose a GAN framework with geographical knowledge, namely Metropolitan GAN (MetroGAN), for urban morphology simulation. We incorporate a progressive growing structure to learn hierarchical features and design a geographical loss to impose the constraints of water areas. Besides, we propose a comprehensive evaluation framework for the complex structure of urban systems. Results show that MetroGAN outperforms the state-of-the-art urban simulation methods by over 20% in all metrics. Inspiringly, using physical geography features singly, MetroGAN can still generate shapes of the cities. These results demonstrate that MetroGAN solves the instability problem of previous urban simulation GANs and is generalizable to deal with various urban attributes. CCS CONCEPTS• Applied computing → Sociology; Arts and humanities; • Computing methodologies → Computer vision.

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“…Human mobility modeling aims to explore the regularities and patterns of human behavior [1,9] and plays a significant role in numerous applications, such as urban planning [35], travel demand management [5,36], health risk assessment [37], epidemic spreading modeling and control [13], and so on. In the big data era, the accessibility to GPS, mobile phone records, and location-based social networks (LBSNs) provides an unprecedented chance to understand and model human mobility [1,20].…”

Section: Introductionmentioning

confidence: 99%

Human Mobility Prediction with Causal and Spatial-constrained Multi-task Network

Huang¹,

Xu²,

Wang³

et al. 2022

Preprint

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Modeling human mobility helps to understand how people are accessing resources and physically contacting with each other in cities, and thus contributes to various applications such as urban planning, epidemic control, and location-based advertisement. Next location prediction is one decisive task in individual human mobility modeling and is usually viewed as sequence modeling, solved with Markov or RNN-based methods. However, the existing models paid little attention to the logic of individual travel decisions and the reproducibility of the collective behavior of population. To this end, we propose a Causal and Spatial-constrained Long and Shortterm Learner (CSLSL) for next location prediction. CSLSL utilizes a causal structure based on multi-task learning to explicitly model the "when→what→where", a.k.a. "time→activity→location" decision logic. We next propose a spatial-constrained loss function as an auxiliary task, to ensure the consistency between the predicted and actual spatial distribution of travelers' destinations. Moreover, CSLSL adopts modules named Long and Short-term Capturer (LSC) to learn the transition regularities across different time spans. Extensive experiments on three real-world datasets show a 33.4% performance improvement of CSLSL over baselines and confirm the effectiveness of introducing the causality and consistency constraints. The implementation is available at https://github.com/urbanmobility/CSLSL.

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Emergence of urban growth patterns from human mobility behavior

Cited by 43 publications

References 49 publications

Spatial structure of city population growth

Spatial structure of city population growth

MetroGAN: Simulating Urban Morphology with Generative Adversarial Network

Human Mobility Prediction with Causal and Spatial-constrained Multi-task Network

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