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Comparing Human Activity Density and Green Space Supply Using the Baidu Heat Map in Zhengzhou, China

Author

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  • Shumei Zhang

    (College of Forestry, Henan Agricultural University, Zhengzhou 450002, China)

  • Wenshi Zhang

    (College of Information and Management Sciences, Henan Agricultural University, Zhengzhou 450002, China)

  • Ying Wang

    (College of Forestry, Henan Agricultural University, Zhengzhou 450002, China)

  • Xiaoyu Zhao

    (College of Forestry, Henan Agricultural University, Zhengzhou 450002, China)

  • Peihao Song

    (College of Forestry, Henan Agricultural University, Zhengzhou 450002, China)

  • Guohang Tian

    (College of Forestry, Henan Agricultural University, Zhengzhou 450002, China)

  • Audrey L. Mayer

    (School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI 49931, USA)

Abstract

Rapidly growing cities often struggle with insufficient green space, although information on when and where more green space is needed can be difficult to collect. Big data on the density of individuals in cities collected from mobile phones can estimate the usage intensity of urban green space. Taking Zhengzhou’s central city as an example, we combine the real-time human movement data provided by the Baidu Heat Map, which indicates the density of mobile phones, with vector overlays of different kinds of green space. We used the geographically weighted regression (GWR) method to estimate differentials in green space usage between weekdays and weekends, utilizing the location and the density of the aggregation of people with powered-up mobile phones. Compared with weekends, the aggregation of people in urban green spaces on workdays tends to vary more in time and be more concentrated in space, while the highest usage is more stable on weekends. More importantly, the percentage of weekday green space utilization is higher in small parks and green strips in the city, with the density increasing in those small areas, while the green space at a greater distance to the city center is underutilized. This study validates the potential of applying Baidu Heat Map data to provide a dynamic perspective of green space use, and highlights the need for more green space in city centers.

Suggested Citation

  • Shumei Zhang & Wenshi Zhang & Ying Wang & Xiaoyu Zhao & Peihao Song & Guohang Tian & Audrey L. Mayer, 2020. "Comparing Human Activity Density and Green Space Supply Using the Baidu Heat Map in Zhengzhou, China," Sustainability, MDPI, vol. 12(17), pages 1-14, August.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:17:p:7075-:d:406233
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    References listed on IDEAS

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    1. Xiao-Yong Yan & Wen-Xu Wang & Zi-You Gao & Ying-Cheng Lai, 2017. "Universal model of individual and population mobility on diverse spatial scales," Nature Communications, Nature, vol. 8(1), pages 1-9, December.
    2. Zuo Zhang & Yangxiong Xiao & Xiang Luo & Min Zhou, 2020. "Urban human activity density spatiotemporal variations and the relationship with geographical factors: An exploratory Baidu heatmaps‐based analysis of Wuhan, China," Growth and Change, Wiley Blackwell, vol. 51(1), pages 505-529, March.
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    Cited by:

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    3. Zhenrao Cai & Dan Gao & Xin Xiao & Linguo Zhou & Chaoyang Fang, 2023. "The Flow of Green Exercise, Its Characteristics, Mechanism, and Pattern in Urban Green Space Networks: A Case Study of Nangchang, China," Land, MDPI, vol. 12(3), pages 1-19, March.
    4. Yilei Tao & Ying Wang & Xinyu Wang & Guohang Tian & Shumei Zhang, 2022. "Measuring the Correlation between Human Activity Density and Streetscape Perceptions: An Analysis Based on Baidu Street View Images in Zhengzhou, China," Land, MDPI, vol. 11(3), pages 1-19, March.
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    6. Xiaojia Liu & Xi Chen & Yan Huang & Weihong Wang & Mingkan Zhang & Yang Jin, 2023. "Landscape Aesthetic Value of Waterfront Green Space Based on Space–Psychology–Behavior Dimension: A Case Study along Qiantang River (Hangzhou Section)," IJERPH, MDPI, vol. 20(4), pages 1-22, February.
    7. Qidi Dong & Jun Cai & Shuo Chen & Pengman He & Xuli Chen, 2022. "Spatiotemporal Analysis of Urban Green Spatial Vitality and the Corresponding Influencing Factors: A Case Study of Chengdu, China," Land, MDPI, vol. 11(10), pages 1-17, October.
    8. Huaibin Wei & Liyuan Zhang & Jing Liu, 2022. "Hydrodynamic Modelling and Flood Risk Analysis of Urban Catchments under Multiple Scenarios: A Case Study of Dongfeng Canal District, Zhengzhou," IJERPH, MDPI, vol. 19(22), pages 1-18, November.
    9. Fan Liu & Danmei Sun & Yanqin Zhang & Shaoping Hong & Minhua Wang & Jianwen Dong & Chen Yan & Qin Yang, 2022. "Tourist Landscape Preferences in a Historic Block Based on Spatiotemporal Big Data—A Case Study of Fuzhou, China," IJERPH, MDPI, vol. 20(1), pages 1-20, December.
    10. Hongxu Guo & Zhuoqiao Luo & Mengtian Li & Shumin Kong & Haiyan Jiang, 2022. "A Literature Review of Big Data-Based Urban Park Research in Visitor Dimension," Land, MDPI, vol. 11(6), pages 1-17, June.

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