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How Green Are the Streets Within the Sixth Ring Road of Beijing? An Analysis Based on Tencent Street View Pictures and the Green View Index

Author

Listed:
  • Rencai Dong

    (State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China)

  • Yonglin Zhang

    (State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Jingzhu Zhao

    (State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China)

Abstract

Street greenery, an important urban landscape component, is closely related to people’s physical and mental health. This study employs the green view index (GVI) as a quantitative indicator to evaluate visual greenery from a pedestrian’s perspective and uses an image segmentation method to calculate the quantity of visual greenery from Tencent street view pictures. This article aims to quantify street greenery in the area within the sixth ring road in Beijing, analyse the relations between road parameters and the GVI, and compare the visual greenery of different road types. The authors find that (1) the average GVI value in the study area is low, with low-value clusters inside the third ring road and high-value clusters outside; (2) wider minor roads tend to have higher GVI values than motorways, major roads and provincial roads; and (3) longer roads, except expressways, tend to have higher GVI values. This case study demonstrates that the GVI can effectively represent the quantity of visual greenery along roads. The authors’ methods can be employed to compare street-level visual greenery among different areas or road types and to support urban green space planning and management.

Suggested Citation

  • Rencai Dong & Yonglin Zhang & Jingzhu Zhao, 2018. "How Green Are the Streets Within the Sixth Ring Road of Beijing? An Analysis Based on Tencent Street View Pictures and the Green View Index," IJERPH, MDPI, vol. 15(7), pages 1-22, June.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:7:p:1367-:d:155206
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    Citations

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    Cited by:

    1. Yiwei Bai & Yihang Bai & Ruoyu Wang & Tianren Yang & Xinyao Song & Bo Bai, 2023. "Exploring Associations between the Built Environment and Cycling Behaviour around Urban Greenways from a Human-Scale Perspective," Land, MDPI, vol. 12(3), pages 1-19, March.
    2. Ziqian Bao & Yihang Bai & Tao Geng, 2023. "Examining Spatial Inequalities in Public Green Space Accessibility: A Focus on Disadvantaged Groups in England," Sustainability, MDPI, vol. 15(18), pages 1-22, September.
    3. Jin Zhu & Yao Gong & Changchang Liu & Jinglong Du & Ci Song & Jie Chen & Tao Pei, 2023. "Assessing the Effects of Subjective and Objective Measures on Housing Prices with Street View Imagery: A Case Study of Suzhou," Land, MDPI, vol. 12(12), pages 1-25, November.
    4. Jingjing Luo & Shiyan Zhai & Genxin Song & Xinxin He & Hongquan Song & Jing Chen & Huan Liu & Yuke Feng, 2022. "Assessing Inequity in Green Space Exposure toward a “15-Minute City” in Zhengzhou, China: Using Deep Learning and Urban Big Data," IJERPH, MDPI, vol. 19(10), pages 1-17, May.
    5. Yixing Chen & Qilin Zhang & Zhang Deng & Xinran Fan & Zimu Xu & Xudong Kang & Kailing Pan & Zihao Guo, 2022. "Research on Green View Index of Urban Roads Based on Street View Image Recognition: A Case Study of Changsha Downtown Areas," Sustainability, MDPI, vol. 14(23), pages 1-17, December.
    6. Wang, Ruoyu & Cao, Mengqiu & Yao, Yao & Wu, Wenjie, 2022. "The inequalities of different dimensions of visible street urban green space provision: A machine learning approach," Land Use Policy, Elsevier, vol. 123(C).
    7. Caigang, Zhuang & Shaoying, Li & Zhangzhi, Tan & Feng, Gao & Zhifeng, Wu, 2022. "Nonlinear and threshold effects of traffic condition and built environment on dockless bike sharing at street level," Journal of Transport Geography, Elsevier, vol. 102(C).
    8. Yonglin Zhang & Xiao Fu & Chencan Lv & Shanlin Li, 2021. "The Premium of Public Perceived Greenery: A Framework Using Multiscale GWR and Deep Learning," IJERPH, MDPI, vol. 18(13), pages 1-16, June.
    9. Yanyan Zhang & Meng Wang & Junyi Li & Jianxia Chang & Huan Lu, 2022. "Do Greener Urban Streets Provide Better Emotional Experiences? An Experimental Study on Chinese Tourists," IJERPH, MDPI, vol. 19(24), pages 1-21, December.
    10. Marco Helbich, 2019. "Spatiotemporal Contextual Uncertainties in Green Space Exposure Measures: Exploring a Time Series of the Normalized Difference Vegetation Indices," IJERPH, MDPI, vol. 16(5), pages 1-13, March.
    11. Wang, Ruoyu & Cao, Mengqiu & Yao, Yao & Wu, Wenjie, 2022. "The inequalities of different dimensions of visible street urban green space provision: a machine learning approach," LSE Research Online Documents on Economics 117694, London School of Economics and Political Science, LSE Library.
    12. Gabriele Stancato, 2024. "The Visual Greenery Field: Representing the Urban Green Visual Continuum with Street View Image Analysis," Sustainability, MDPI, vol. 16(21), pages 1-29, October.

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