IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0239390.html
   My bibliography  Save this article

Quantifying the usage of small public spaces using deep convolutional neural network

Author

Listed:
  • Jingxuan Hou
  • Long Chen
  • Enjia Zhang
  • Haifeng Jia
  • Ying Long

Abstract

Small public spaces are the key built environment elements that provide venues for various of activities. However, existing measurements or approaches could not efficiently and effectively quantify how small public spaces are being used. In this paper, we utilized a deep convolutional neural network to quantify the usage of small public spaces through recorded videos as a reliable and robust method to bridge the literature gap. To start with, we deployed photographic devices to record videos that cover the minimum enclosing square of a small public space for a certain period of time, then utilized a deep convolutional neural network to detect people in these videos and converted their location from image-based position to real-world projected coordinates. To validate the accuracy and robustness of the method, we experimented our approach in a residential community in Beijing, and our results confirmed that the usage of small public spaces could be measured and quantified effectively and efficiently.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pone00:0239390
    DOI: 10.1371/journal.pone.0239390
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0239390
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0239390&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0239390?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xinyu Hu & Yifan Ren & Ying Tan & Yi Shi, 2023. "Research on the Spatial and Temporal Dynamics of Crowd Activities in Commercial Streets and Their Relationship with Formats—A Case Study of Lao Men Dong Commercial Street in Nanjing," Sustainability, MDPI, vol. 15(24), pages 1-23, December.
    2. Zichen Zhao & Zhiqiang Wu & Shiqi Zhou & Wen Dong & Wei Gan & Yixuan Zou & Mo Wang, 2023. "Resident Effect Perception in Urban Spaces to Inform Urban Design Strategies," Land, MDPI, vol. 12(10), pages 1-24, October.
    3. Siyu Chen & Ying Chang & Jack S. Benton & Bing Chen & Hongchen Hu & Jing Lu, 2024. "Impacts of the COVID-19 Pandemic on Health-Related Behaviours in Community Gardens in China: An Evaluation of a Natural Experiment," Land, MDPI, vol. 13(7), pages 1-19, July.
    4. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0239390. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.