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Descriptive and Network Post-Occupancy Evaluation of the Urban Public Space through Social Media: A Case Study of Bryant Park, NY

Author

Listed:
  • Bo Zhang

    (Department of Landscape Architecture, Oklahoma State University, 107 Whitehurst, Stillwater, OK 74078, USA)

  • Yang Song

    (Department of Landscape Architecture, Texas A&M University, 400 Bizzell St, College Station, TX 77843, USA)

  • Dingyi Liu

    (School of Architecture, Beijing Jiao Tong University, Haidian District, Beijing 100044, China)

  • Zhongzhong Zeng

    (School of Architecture, Beijing Jiao Tong University, Haidian District, Beijing 100044, China)

  • Shuying Guo

    (Landscape Architecture and Spatial Planning Group, Wageningen University and Research, Droevendaalsesteeg 4, 6708 PB Wageningen, The Netherlands)

  • Qiuyi Yang

    (School for Environment and Suitability, University of Michigan, Ann Arbor, MI 48109, USA)

  • Yuhan Wen

    (School of Architecture, Tianjin University, Nankai District, Tianjin 300072, China)

  • Wenji Wang

    (Department of Landscape Architecture, Shanghai Academy of Landscape Architecture Science and Planning, Xuhui District, Shanghai 200232, China)

  • Xiwei Shen

    (Department of Landscape Architecture, University of Nevada, Las Vegas, 4505 S Maryland Pkwy, Las Vegas, NV 89154, USA)

Abstract

In modern cities, urban public spaces, such as parks, gardens, plazas, and streets, play a big role in people’s social activities, physical activities, mental health, and overall well-being. However, the traditional post-occupancy evaluation (POE) process for public spaces such as large urban parks is extremely difficult, especially for long-term user experiences through observations, surveys, and interviews. On the other hand, social media has emerged as a major media outlet recording millions of user experiences to the public, which provides opportunities to inform how public space is used and perceived by users. Furthermore, unlike previous research that primarily presented descriptive characters of park programs, our study employs a network model to elucidate the interactive relationships and intensities among reported park elements, human activities, and experiences. This approach enables us to track the sources within the space that impact people’s perceptions, such as weather conditions, food options, and notable landmarks. The utilization of this network model opens avenues for future research to comprehensively investigate the factors shaping people’s perceptions in public open spaces. This study uses Bryant Park as an example and presents a new analytical framework, POSE (post-occupancy social media evaluation), to support long-term POE studies for large public spaces. Methods such as data automation, descriptive statistics, and social network analysis were used. The identification and quantification of meaningful park activities, scenes, and sentiments as well as their relationships will help optimize the design and management of park programs.

Suggested Citation

  • Bo Zhang & Yang Song & Dingyi Liu & Zhongzhong Zeng & Shuying Guo & Qiuyi Yang & Yuhan Wen & Wenji Wang & Xiwei Shen, 2023. "Descriptive and Network Post-Occupancy Evaluation of the Urban Public Space through Social Media: A Case Study of Bryant Park, NY," Land, MDPI, vol. 12(7), pages 1-17, July.
  • Handle: RePEc:gam:jlands:v:12:y:2023:i:7:p:1403-:d:1192934
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    References listed on IDEAS

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    1. Anna Kovacs-Gyori & Alina Ristea & Clemens Havas & Bernd Resch & Pablo Cabrera-Barona, 2018. "#London2012: Towards Citizen-Contributed Urban Planning Through Sentiment Analysis of Twitter Data," Urban Planning, Cogitatio Press, vol. 3(1), pages 75-99.
    2. Saleh Kalantari & Mardelle Shepley, 2021. "Psychological and social impacts of high-rise buildings: a review of the post-occupancy evaluation literature," Housing Studies, Taylor & Francis Journals, vol. 36(8), pages 1147-1176, October.
    3. Claudia Fongar & Geir Aamodt & Thomas B. Randrup & Ingjerd Solfjeld, 2019. "Does Perceived Green Space Quality Matter? Linking Norwegian Adult Perspectives on Perceived Quality to Motivation and Frequency of Visits," IJERPH, MDPI, vol. 16(13), pages 1-16, July.
    4. Zhengsong Lin & Yuting Wang & Yang Song & Tao Huang & Feng Gan & Xinyue Ye, 2022. "Research on Ecological Landscape Design and Healing Effect Based on 3D Roaming Technology," IJERPH, MDPI, vol. 19(18), pages 1-15, September.
    5. Anik, Md Asif Hasan & Sadeek, Soumik Nafis & Hossain, Moinul & Kabir, Shafquat, 2020. "A framework for involving the young generation in transportation planning using social media and crowd sourcing," Transport Policy, Elsevier, vol. 97(C), pages 1-18.
    6. Yang Song & Bo Zhang, 2020. "Using social media data in understanding site-scale landscape architecture design: taking Seattle Freeway Park as an example," Landscape Research, Taylor & Francis Journals, vol. 45(5), pages 627-648, July.
    7. Kaplan, Andreas M. & Haenlein, Michael, 2010. "Users of the world, unite! The challenges and opportunities of Social Media," Business Horizons, Elsevier, vol. 53(1), pages 59-68, January.
    8. Ling Wang & Mengting Ge & Naiguang Chen & Jiahui Ding & Xiwei Shen, 2022. "An Evaluation Model of Riparian Landscape: A Case in Rural Qingxi Area, Shanghai," Land, MDPI, vol. 11(9), pages 1-19, September.
    9. Gal-Tzur, Ayelet & Grant-Muller, Susan M. & Kuflik, Tsvi & Minkov, Einat & Nocera, Silvio & Shoor, Itay, 2014. "The potential of social media in delivering transport policy goals," Transport Policy, Elsevier, vol. 32(C), pages 115-123.
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