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A Dynamic Prediction Framework for Urban Public Space Vitality: From Hypothesis to Algorithm and Verification

Author

Listed:
  • Yue Liu

    (Faculty of Sciences, Engineering and Technology, School of Architecture and Civil Engineering, The University of Adelaide, Adelaide, SA 5005, Australia)

  • Xiangmin Guo

    (School of Architecture, Harbin Institute of Technology, Shenzhen 518055, China)

Abstract

Predicting and assessing the vitality of public urban spaces is crucial for effective urban design, aiming to prevent issues such as “ghost streets” and minimize resource wastage. However, existing assessment methods often lack temporal dynamics or heavily rely on historical big data, limiting their ability to accurately predict outcomes for unbuilt projects. To address these challenges, this study integrates previous methodologies with observations of crowd characteristics in public spaces. It introduces the crowd-frequency hypothesis and develops an algorithm to establish a time-dimensional urban vitality dynamic prediction model. Through a case study of the Rundle Mall neighborhood in Adelaide, Australia, the effectiveness of the prediction model was validated using on-site observation sampling and comparative verification. The prediction model framework allows for the determination of urban vitality within specific time ranges by directly inputting basic information, providing valuable support to urban planners and government officials during the design and decision-making processes. It offers a cost-effective approach to achieve sustainable urban vitality construction. Furthermore, machine learning techniques, specifically the decision tree model, were applied to case data to develop a set of preliminary algorithm tools, which enable output of reference urban vitality levels (high-medium-low).

Suggested Citation

  • Yue Liu & Xiangmin Guo, 2024. "A Dynamic Prediction Framework for Urban Public Space Vitality: From Hypothesis to Algorithm and Verification," Sustainability, MDPI, vol. 16(7), pages 1-19, March.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:7:p:2846-:d:1366169
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    References listed on IDEAS

    as
    1. Jinyao Lin & Yaye Zhuang & Yang Zhao & Hua Li & Xiaoyu He & Siyan Lu, 2022. "Measuring the Non-Linear Relationship between Three-Dimensional Built Environment and Urban Vitality Based on a Random Forest Model," IJERPH, MDPI, vol. 20(1), pages 1-18, December.
    2. Jingxuan Hou & Long Chen & Enjia Zhang & Haifeng Jia & Ying Long, 2020. "Quantifying the usage of small public spaces using deep convolutional neural network," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-14, October.
    Full references (including those not matched with items on IDEAS)

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