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

Bin Tian, Bin Meng, Juan Wang, Guoqing Zhi, Zhenyu Qi, Siyu Chen and Jian Liu
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Bin Tian: College of Applied Arts and Sciences, Beijing Union University, Beijing 100191, China
Bin Meng: College of Applied Arts and Sciences, Beijing Union University, Beijing 100191, China
Juan Wang: College of Applied Arts and Sciences, Beijing Union University, Beijing 100191, China
Guoqing Zhi: College of Applied Arts and Sciences, Beijing Union University, Beijing 100191, China
Zhenyu Qi: College of Applied Arts and Sciences, Beijing Union University, Beijing 100191, China
Siyu Chen: College of Applied Arts and Sciences, Beijing Union University, Beijing 100191, China
Jian Liu: College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China

Sustainability, 2022, vol. 14, issue 7, 1-18

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.

Keywords: social media; fitness behavior; LDA model; geodetector; Beijing spatio-temporal patterns; Weibo (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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