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Quantifying and Characterizing Urban Leisure Activities by Merging Multiple Sensing Big Data: A Case Study of Nanjing, China

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  • Shaojun Liu

    (Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China
    State Key Laboratory Cultivation Base of Geographical Environment Evolution, Nanjing 210023, China
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China)

  • Yao Long

    (Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China
    State Key Laboratory Cultivation Base of Geographical Environment Evolution, Nanjing 210023, China
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China)

  • Ling Zhang

    (Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China
    State Key Laboratory Cultivation Base of Geographical Environment Evolution, Nanjing 210023, China
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China)

  • Hao Liu

    (Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China
    State Key Laboratory Cultivation Base of Geographical Environment Evolution, Nanjing 210023, China
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China)

Abstract

Studying the spatiotemporal pattern of urban leisure activities helps us to understand the development and utilization of urban public space, people’s quality of life, and the happiness index. It has outstanding value for improving rational resource allocation, stimulating urban vitality, and promoting sustainable urban development. This study aims at discovering the spatiotemporal distribution patterns and people’s behavioral preferences of urban leisure activities using quantitative technology merging ubiquitous sensing big data. On the basis of modeling individual activity traces using mobile signaling data (MSD), we developed a space-time constrained dasymetric interpolation method to refine the urban leisure activity spatiotemporal distribution. We conducted an empirical study in Nanjing, China. The results indicate that leisure plays an essential role in daily human life, both on weekdays and weekends. Significant differences exist in spatiotemporal and type selection in urban leisure. The weekend afternoon is the breakout period of leisure, and entertainment is the most popular leisure activity. Furthermore, the correlation between leisure resource allocation and leisure activity participation was argued. Our findings confirm that data-driven approaches would be a promising method for analyzing human behavior patterns; therefore, assisting in land planning decisions and promoting social justice and sustainability.

Suggested Citation

  • Shaojun Liu & Yao Long & Ling Zhang & Hao Liu, 2021. "Quantifying and Characterizing Urban Leisure Activities by Merging Multiple Sensing Big Data: A Case Study of Nanjing, China," Land, MDPI, vol. 10(11), pages 1-20, November.
    • Handle: RePEc:gam:jlands:v:10:y:2021:i:11:p:1214-:d:675045
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    References listed on IDEAS

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    1. Yaolin Liu & Ying Jing & Enxiang Cai & Jiaxing Cui & Yang Zhang & Yiyun Chen, 2017. "How Leisure Venues Are and Why? A Geospatial Perspective in Wuhan, Central China," Sustainability, MDPI, vol. 9(10), pages 1-21, October.
    2. Scott, Darren M. & Horner, Mark W., 2008. "Examining The Role of Urban Form In Shaping People’s Accessibility to Opportunities: An Exploratory Spatial Data Analysis," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 1(2), pages 89-119.
    3. David Newman & Louis Tay & Ed Diener, 2014. "Leisure and Subjective Well-Being: A Model of Psychological Mechanisms as Mediating Factors," Journal of Happiness Studies, Springer, vol. 15(3), pages 555-578, June.
    4. Lingbo Liu & Zhenghong Peng & Hao Wu & Hongzan Jiao & Yang Yu, 2018. "Exploring Urban Spatial Feature with Dasymetric Mapping Based on Mobile Phone Data and LUR-2SFCAe Method," Sustainability, MDPI, vol. 10(7), pages 1-15, July.
    5. Bo Huang & Yulun Zhou & Zhigang Li & Yimeng Song & Jixuan Cai & Wei Tu, 2020. "Evaluating and characterizing urban vibrancy using spatial big data: Shanghai as a case study," Environment and Planning B, , vol. 47(9), pages 1543-1559, November.
    6. Jia, Zhijie & Lin, Boqiang, 2021. "How to achieve the first step of the carbon-neutrality 2060 target in China: The coal substitution perspective," Energy, Elsevier, vol. 233(C).
    7. De Cantis, Stefano & Ferrante, Mauro & Kahani, Alon & Shoval, Noam, 2016. "Cruise passengers' behavior at the destination: Investigation using GPS technology," Tourism Management, Elsevier, vol. 52(C), pages 133-150.
    8. Shaojun Liu & Ling Zhang & Yi Long, 2019. "Urban Vitality Area Identification and Pattern Analysis from the Perspective of Time and Space Fusion," Sustainability, MDPI, vol. 11(15), pages 1-27, July.
    9. Ma, Xiaolei & Liu, Congcong & Wen, Huimin & Wang, Yunpeng & Wu, Yao-Jan, 2017. "Understanding commuting patterns using transit smart card data," Journal of Transport Geography, Elsevier, vol. 58(C), pages 135-145.
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    Cited by:

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    4. Jing Huang & Xiao Hu & Jieqiong Wang & Andong Lu, 2023. "How Diversity and Accessibility Affect Street Vitality in Historic Districts?," Land, MDPI, vol. 12(1), pages 1-23, January.

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