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Spatio-Temporal Patterns of Fitness Behavior in Beijing Based on Social Media Data

Author

Listed:
  • Bin Tian

    (College of Applied Arts and Sciences, Beijing Union University, Beijing 100191, China
    Laboratory of Urban Cultural Sensing & Computing, Beijing Union University, Beijing 100191, China)

  • Bin Meng

    (College of Applied Arts and Sciences, Beijing Union University, Beijing 100191, China
    Laboratory of Urban Cultural Sensing & Computing, Beijing Union University, Beijing 100191, China)

  • Juan Wang

    (College of Applied Arts and Sciences, Beijing Union University, Beijing 100191, China
    Laboratory of Urban Cultural Sensing & Computing, Beijing Union University, Beijing 100191, China)

  • Guoqing Zhi

    (College of Applied Arts and Sciences, Beijing Union University, Beijing 100191, China
    Laboratory of Urban Cultural Sensing & Computing, Beijing Union University, Beijing 100191, China)

  • Zhenyu Qi

    (College of Applied Arts and Sciences, Beijing Union University, Beijing 100191, China
    Laboratory of Urban Cultural Sensing & Computing, Beijing Union University, Beijing 100191, China)

  • Siyu Chen

    (College of Applied Arts and Sciences, Beijing Union University, Beijing 100191, China
    Laboratory of Urban Cultural Sensing & Computing, Beijing Union University, Beijing 100191, China)

  • Jian Liu

    (College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China)

Abstract

Fitness is an important way to ensure the health of the population, and it is important to actively understand fitness behavior. Although social media Weibo data (the Chinese Tweeter) can provide multidimensional information in terms of objectivity and generalizability, there is still more latent potential to tap. Based on Sina Weibo social media data in the year 2017, this study was conducted to explore the spatial and temporal patterns of urban residents’ different fitness behaviors and related influencing factors within the Fifth Ring Road of Beijing. FastAI, LDA, geodetector technology, and GIS spatial analysis methods were employed in this study. It was found that fitness behaviors in the study area could be categorized into four types. Residents can obtain better fitness experiences in sports venues. Different fitness types have different polycentric spatial distribution patterns. The residents’ fitness frequency shows an obvious periodic distribution (weekly and 24 h). The spatial distribution of the fitness behavior of residents is mainly affected by factors, such as catering services, education and culture, companies, and public facilities. This research could help to promote the development of urban residents’ fitness in Beijing.

Suggested Citation

  • Bin Tian & Bin Meng & Juan Wang & Guoqing Zhi & Zhenyu Qi & Siyu Chen & Jian Liu, 2022. "Spatio-Temporal Patterns of Fitness Behavior in Beijing Based on Social Media Data," Sustainability, MDPI, vol. 14(7), pages 1-18, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:4106-:d:783337
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    References listed on IDEAS

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    Cited by:

    1. Jinghu Pan & Xiuwei Zhu & Xin Zhang, 2022. "Urban Vitality Measurement and Influence Mechanism Detection in China," IJERPH, MDPI, vol. 20(1), pages 1-24, December.

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