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A Semantic Analysis Method of Public Public Built Environment and Its Landscape Based on Big Data Technology: Kimbell Art Museum as Example

Author

Listed:
  • Zhongzhong Zeng

    (School of Architecture and Design, Beijing Jiaotong University, Beijing 100080, China)

  • Meizhu Wang

    (School of Architecture and Design, Beijing Jiaotong University, Beijing 100080, China)

  • Dingyi Liu

    (School of Architecture and Design, Beijing Jiaotong University, Beijing 100080, China)

  • Xuan Yu

    (School of Architecture and Design, Beijing Jiaotong University, Beijing 100080, China)

  • Bo Zhang

    (Horticulture and Landscape Architecture Department, Oklahoma State University, Stillwater, OK 74078-1015, USA)

Abstract

Based on big data, a new public space evaluation method is proposed. Using programming technology to collect visitor reviews from the travel website TripAdvisor to build a database, based on the data of 99,240 words in 1573 visitor reviews in 10 years, the connection between data and reality is established through systematic data classification and visualization. Following an assessment of the Kimbell Art Museum’s functionality, architectural design, and landscape design, along with visitor feedback, a new evaluation methodology was formulated for application to public buildings with landscapes. By utilizing the unique advantages of big data, it provides convenient and efficient analysis methods for public spaces with similar data foundations and opens the way for the optimization of the built environment in the information age.

Suggested Citation

  • Zhongzhong Zeng & Meizhu Wang & Dingyi Liu & Xuan Yu & Bo Zhang, 2024. "A Semantic Analysis Method of Public Public Built Environment and Its Landscape Based on Big Data Technology: Kimbell Art Museum as Example," Land, MDPI, vol. 13(5), pages 1-16, May.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:5:p:655-:d:1392278
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    References listed on IDEAS

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    1. Alireza Alaei & Ying Wang & Vinh Bui & Bela Stantic, 2023. "Target-Oriented Data Annotation for Emotion and Sentiment Analysis in Tourism Related Social Media Data," Future Internet, MDPI, vol. 15(4), pages 1-21, April.
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