IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i7p4106-d783337.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/7/4106/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/7/4106/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shibo Gao & Chenggu Li & Yuefang Rong & Qing Yan & Wei Liu & Zuopeng Ma, 2022. "The Places–People Exercise: Understanding Spatial Patterns and the Formation Mechanism for Urban Commercial Fitness Space in Changchun City, China," Sustainability, MDPI, vol. 14(3), pages 1-22, January.
    2. Mahmoud Zaqout & Krishna Vyncke & Luis A. Moreno & Pilar Miguel-Etayo & Fabio Lauria & Denes Molnar & Lauren Lissner & Monica Hunsberger & Toomas Veidebaum & Michael Tornaritis & Lucia A. Reisch & Kar, 2016. "Determinant factors of physical fitness in European children," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 61(5), pages 573-582, June.
    3. Ivory, Vivienne C. & Blakely, Tony & Pearce, Jamie & Witten, Karen & Bagheri, Nasser & Badland, Hannah & Schofield, Grant, 2015. "Could strength of exposure to the residential neighbourhood modify associations between walkability and physical activity?," Social Science & Medicine, Elsevier, vol. 147(C), pages 232-241.
    4. Abdulrahman Jbaily & Xiaodan Zhou & Jie Liu & Ting-Hwan Lee & Leila Kamareddine & Stéphane Verguet & Francesca Dominici, 2022. "Air pollution exposure disparities across US population and income groups," Nature, Nature, vol. 601(7892), pages 228-233, January.
    5. 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.
    6. Robert Schlich & Kay Axhausen, 2003. "Habitual travel behaviour: Evidence from a six-week travel diary," Transportation, Springer, vol. 30(1), pages 13-36, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chris Harding & Ahmadreza Faghih Imani & Siva Srikukenthiran & Eric J. Miller & Khandker Nurul Habib, 2021. "Are we there yet? Assessing smartphone apps as full-fledged tools for activity-travel surveys," Transportation, Springer, vol. 48(5), pages 2433-2460, October.
    2. Heinen, Eva & Chatterjee, Kiron, 2015. "The same mode again? An exploration of mode choice variability in Great Britain using the National Travel Survey," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 266-282.
    3. Robert Stewart & Marie Urban & Samantha Duchscherer & Jason Kaufman & April Morton & Gautam Thakur & Jesse Piburn & Jessica Moehl, 2016. "A Bayesian machine learning model for estimating building occupancy from open source data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(3), pages 1929-1956, April.
    4. Sanjay Gupta & Kushagra Sinha, 2022. "Assessing the Factors Impacting Transport Usage of Mobility App Users in the National Capital Territory of Delhi, India," Sustainability, MDPI, vol. 14(21), pages 1-20, October.
    5. Shahabi, Cyrus & Kim, Seon Ho, 2023. "Evaluating Accessibility of Los Angeles Metropolitan Area Using Data-Driven Time-Dependent Reachability Analysis," Institute of Transportation Studies, Working Paper Series qt7pm429tk, Institute of Transportation Studies, UC Davis.
    6. Camille Salesse, 2022. "Inequality in exposure to air pollution in France: bringing pollutant cocktails into the picture," CEE-M Working Papers hal-03882438, CEE-M, Universtiy of Montpellier, CNRS, INRA, Montpellier SupAgro.
    7. Wang, Jianying & Dong, Lei & Cheng, Ximeng & Yang, Weijun & Liu, Yu, 2019. "An extended exploration and preferential return model for human mobility simulation at individual and collective levels," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    8. Tao, Sui & Rohde, David & Corcoran, Jonathan, 2014. "Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap," Journal of Transport Geography, Elsevier, vol. 41(C), pages 21-36.
    9. Börjesson, Maria & Eliasson, Jonas, 2014. "Experiences from the Swedish Value of Time study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 59(C), pages 144-158.
    10. Arias-Molinares, Daniela & Romanillos, Gustavo & García-Palomares, Juan Carlos & Gutiérrez, Javier, 2021. "Exploring the spatio-temporal dynamics of moped-style scooter sharing services in urban areas," Journal of Transport Geography, Elsevier, vol. 96(C).
    11. Minnen, Joeri & Glorieux, Ignace & van Tienoven, Theun Pieter, 2015. "Transportation habits: Evidence from time diary data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 76(C), pages 25-37.
    12. Perchoux, Camille & Kestens, Yan & Thomas, Frédérique & Hulst, Andraea Van & Thierry, Benoit & Chaix, Basile, 2014. "Assessing patterns of spatial behavior in health studies: Their socio-demographic determinants and associations with transportation modes (the RECORD Cohort Study)," Social Science & Medicine, Elsevier, vol. 119(C), pages 64-73.
    13. Milad Mehdizadeh & Alireza Ermagun, 2020. "“I’ll never stop driving my child to school”: on multimodal and monomodal car users," Transportation, Springer, vol. 47(3), pages 1071-1102, June.
    14. Börjesson, Maria & Fosgerau, Mogens, 2015. "Response time patterns in a stated choice experiment," Journal of choice modelling, Elsevier, vol. 14(C), pages 48-58.
    15. Jesper Bláfoss Ingvardson & Sigal Kaplan & João de Abreu e Silva & Floridea Ciommo & Yoram Shiftan & Otto Anker Nielsen, 2020. "Existence, relatedness and growth needs as mediators between mode choice and travel satisfaction: evidence from Denmark," Transportation, Springer, vol. 47(1), pages 337-358, February.
    16. Charles Raux & Tai-Yu Ma & Eric Cornelis, 2011. "Variability versus stability in daily travel and activity behaviour. The case of a one week travel diary," Working Papers halshs-00612610, HAL.
    17. Abdul Rawoof Pinjari & Chandra R. Bhat, 2011. "Activity-based Travel Demand Analysis," Chapters, in: André de Palma & Robin Lindsey & Emile Quinet & Roger Vickerman (ed.), A Handbook of Transport Economics, chapter 10, Edward Elgar Publishing.
    18. Ron Buliung & Matthew Roorda & Tarmo Remmel, 2008. "Exploring spatial variety in patterns of activity-travel behaviour: initial results from the Toronto Travel-Activity Panel Survey (TTAPS)," Transportation, Springer, vol. 35(6), pages 697-722, November.
    19. John Voorheis & Jonathan Colmer & Kendall Houghton & Eva Lyubich & Mary Munro & Cameron Scalera & Jennifer Withrow, 2023. "Building the Prototype Census Environmental Impacts Frame," Working Papers 23-20, Center for Economic Studies, U.S. Census Bureau.
    20. Cirillo, C. & Axhausen, K.W., 2006. "Evidence on the distribution of values of travel time savings from a six-week diary," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(5), pages 444-457, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:4106-:d:783337. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.