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Understanding How People Perceive and Interact with Public Space through Social Media Big Data: A Case Study of Xiamen, China

Author

Listed:
  • Shuran Li

    (College of Design and Innovation, Tongji University, No. 281 Fuxin Road, Yangpu District, Shanghai 200093, China)

  • Chengwei Wang

    (Future City (Shanghai) Design Consulting Co., Ltd., No. 568 Dalian Road, Yangpu District, Shanghai 200082, China)

  • Liying Rong

    (Future City (Shanghai) Design Consulting Co., Ltd., No. 568 Dalian Road, Yangpu District, Shanghai 200082, China)

  • Shiqi Zhou

    (College of Design and Innovation, Tongji University, No. 281 Fuxin Road, Yangpu District, Shanghai 200093, China)

  • Zhiqiang Wu

    (College of Architecture and Urban Planning, Tongji University, Shanghai 200093, China)

Abstract

Public space is a crucial forum for public interaction and diverse activities among urban residents. Understanding how people interact with and perceive these spaces is essential for public placemaking. With billions of users engaging in social media expression and generating millions of data points every second, Social Media Big Data (SMBD) offers an invaluable lens for evaluating public spaces over time, surpassing traditional methods like surveys and questionnaires. This research introduces a comprehensive analytical framework that integrates SMBD with placemaking practices, specifically applied to the city of Xiamen, China. The result shows the social sentiment, vibrancy heatmaps, leisure activities, visitor behaviors, and preferred visual elements of Xiamen, offering urban designers valuable insights into the dynamic nature of citizen experiences. The findings underscore the potential of SMBD to inform and enhance public space design, providing a holistic approach to creating more inclusive, vibrant, and functional urban environments.

Suggested Citation

  • Shuran Li & Chengwei Wang & Liying Rong & Shiqi Zhou & Zhiqiang Wu, 2024. "Understanding How People Perceive and Interact with Public Space through Social Media Big Data: A Case Study of Xiamen, China," Land, MDPI, vol. 13(9), pages 1-27, September.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:9:p:1500-:d:1478915
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    References listed on IDEAS

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    1. Guo, Yue & Barnes, Stuart J. & Jia, Qiong, 2017. "Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation," Tourism Management, Elsevier, vol. 59(C), pages 467-483.
    2. Setha Low & Kurt Iveson, 2016. "Propositions for more just urban public spaces," City, Taylor & Francis Journals, vol. 20(1), pages 10-31, February.
    3. Matthew Carmona, 2015. "Re-theorising contemporary public space: a new narrative and a new normative," Journal of Urbanism: International Research on Placemaking and Urban Sustainability, Taylor & Francis Journals, vol. 8(4), pages 373-405, December.
    4. Qunying Huang & David W. S. Wong, 2015. "Modeling and Visualizing Regular Human Mobility Patterns with Uncertainty: An Example Using Twitter Data," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 105(6), pages 1179-1197, November.
    5. Barbara Martini, 2016. "The Data Revolution. Big Data, Open Data, Data Infrastructures and Their Consequences," Regional Studies, Taylor & Francis Journals, vol. 50(3), pages 553-554, March.
    6. Sutian Duan & Zhiyong Shen & Xiao Luo, 2022. "Exploring the Relationship between Urban Youth Sentiment and the Built Environment Using Machine Learning and Weibo Comments," IJERPH, MDPI, vol. 19(8), pages 1-20, April.
    7. Seldjan Timur & Donald Getz, 2009. "Sustainable tourism development: how do destination stakeholders perceive sustainable urban tourism?," Sustainable Development, John Wiley & Sons, Ltd., vol. 17(4), pages 220-232.
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