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Daily rhythm of urban space usage: insights from the nexus of urban functions and human mobility

Author

Listed:
  • Fangye Du

    (University of Electronic Science and Technology of China)

  • Jiaoe Wang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Liang Mao

    (Department of Geography, University of Florida)

  • Jian Kang

    (University of Electronic Science and Technology of China)

Abstract

As urban density increases, it becomes increasingly common for multiple functions to coexist within the same space, intensifying the complexity of human activity dynamics. However, traditional urban zoning, which relies on the spatial distribution of urban functions and human activities, focuses on the spatial heterogeneity of urban space and fails to capture the temporal dynamics of urban space usage. This paper aims to investigate the daily rhythm of urban space usage and illustrate how the distribution and combination of urban functions affect the daily usage rhythm. Taking Beijing in China as a case, we first identified the daily rhythm of urban space usage with the k-means algorithm and zoned urban space accordingly. Subsequently, multinomial logistic (MNL) models were employed to elucidate how the distribution and combination of urban functions influenced these daily usage patterns. Furthermore, a validation study in typical zones was conducted. The results revealed the existence of a distinct daily rhythm in urban space usage, resulting in the classification of urban space into seven distinct zones: high equilibrium, low equilibrium, diurnal, nocturnal, morning, evening, and noon-type zones. Also, we found that this daily usage rhythm is closely intertwined with the distribution and coexistence of urban functions. Our findings could provide valuable insights for the enhancement of various intricate aspects within urban decision-making processes, including urban planning, transportation management, and more, at a fine-grained scale.

Suggested Citation

  • Fangye Du & Jiaoe Wang & Liang Mao & Jian Kang, 2024. "Daily rhythm of urban space usage: insights from the nexus of urban functions and human mobility," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.
    • Handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-023-02577-y
    DOI: 10.1057/s41599-023-02577-y
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    References listed on IDEAS

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    1. Mi Diao & Yi Zhu & Joseph Ferreira Jr & Carlo Ratti, 2016. "Inferring individual daily activities from mobile phone traces: A Boston example," Environment and Planning B, , vol. 43(5), pages 920-940, September.
    2. Jie Huang & David Levinson & Jiaoe Wang & Zi-jia Wang, 2018. "Tracking job and housing dynamics with smartcard data," Working Papers 2018-01, University of Minnesota: Nexus Research Group.
    3. Thomas Louail & Maxime Lenormand & Miguel Picornell & Oliva García Cantú & Ricardo Herranz & Enrique Frias-Martinez & José J. Ramasco & Marc Barthelemy, 2015. "Uncovering the spatial structure of mobility networks," Nature Communications, Nature, vol. 6(1), pages 1-8, May.
    4. repec:nas:journl:v:115:y:2018:p:12710-12715 is not listed on IDEAS
    5. Peter Widhalm & Yingxiang Yang & Michael Ulm & Shounak Athavale & Marta González, 2015. "Discovering urban activity patterns in cell phone data," Transportation, Springer, vol. 42(4), pages 597-623, July.
    6. Yang Xu & Shih-Lung Shaw & Ziliang Zhao & Ling Yin & Feng Lu & Jie Chen & Zhixiang Fang & Qingquan Li, 2016. "Another Tale of Two Cities: Understanding Human Activity Space Using Actively Tracked Cellphone Location Data," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 106(2), pages 489-502, March.
    7. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
    8. Zuoxian Gan & Min Yang & Tao Feng & Harry Timmermans, 2020. "Understanding urban mobility patterns from a spatiotemporal perspective: daily ridership profiles of metro stations," Transportation, Springer, vol. 47(1), pages 315-336, February.
    9. Ron Martin & Peter Sunley, 1996. "Paul Krugman's Geographical Economics and Its Implications for Regional Development Theory: A Critical Assessment," Economic Geography, Taylor & Francis Journals, vol. 72(3), pages 259-292, July.
    Full references (including those not matched with items on IDEAS)

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