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Evaluating Perceived Cultural Ecosystem Services in Urban Green Spaces Using Big Data and Machine Learning: Insights from Fragrance Hill Park in Beijing, China

Author

Listed:
  • Lingbo Fu

    (School of Cultural Industries Management, Communication University of China, Beijing 100024, China)

  • Hongpeng Fu

    (Khoury College of Computer Science, Northeastern University, Seattle, WA 98122, USA)

  • Chengyu Xiong

    (School of Cultural Industries Management, Communication University of China, Beijing 100024, China)

Abstract

Cultural ecosystem services (CESs) are essential for the sustainable development and management of urban green spaces. However, there remains a gap in leveraging big data and unsupervised machine learning to comprehensively evaluate perceived CESs. This study introduces a hybrid research methodology integrating latent dirichlet allocation (LDA) and importance–performance analysis (IPA) to analyze 20,087 user-generated reviews of Fragrance Hill Park in Beijing from Meituan. The key findings are the following: (1) ten types of CESs were identified, including five related to personal well-being, four to public well-being, and one bridging both categories; (2) the most significant dimensions were “recreational activities”, “aesthetic appreciation”, “physical well-being”, and “mental well-being”; (3) users expressed positive sentiments toward “history and culture”, “mental well-being”, and “religious engagement”, while “social relations” received the most negative feedback; (4) IPA results highlight “recreational activities” and “aesthetic appreciation” as priority areas for improvement. This study provides a scalable, data-driven framework for evaluating CESs in urban green spaces. The insights gained can inform urban green space management and policy decisions to enhance user experiences and promote sustainable urban development.

Suggested Citation

  • Lingbo Fu & Hongpeng Fu & Chengyu Xiong, 2025. "Evaluating Perceived Cultural Ecosystem Services in Urban Green Spaces Using Big Data and Machine Learning: Insights from Fragrance Hill Park in Beijing, China," Sustainability, MDPI, vol. 17(4), pages 1-22, February.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:4:p:1725-:d:1594473
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    References listed on IDEAS

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