Measuring daily accessed street greenery: A human-scale approach for informing better urban planning practices

• Published in: Landscape and urban planning Vol. 191; p. 103434
• Main Authors: Ye, Yu, Richards, Daniel, Lu, Yi, Song, Xiaoping, Zhuang, Yu, Zeng, Wei, Zhong, Teng
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2019
• Subjects: Accessible greenery; algorithms; analytical methods; artificial intelligence; data collection; decision support systems; Google Street View; Human-scale; Machine learning; prioritization; remote sensing; satellites; Singapore; social benefit; Space syntax; urban planning; vegetation; Visible greenery; Singapore; Google Street View; Human-scale; Visible greenery; Accessible greenery; Space syntax; Machine learning
• DOI: 10.1016/j.landurbplan.2018.08.028
• ISSN: 0169-2046

•Daily accessed, visible street greenery is quantitatively measured at city scale.•An exploratory tool to map priority streets for potential urban greening efforts.•Google Street View (GSV) images and machine learning algorithms are used.•It might be biased if we use urban green cover as the only dominant criterion. The public benefits of visible street greenery have been well recognised in a growing literature. Nevertheless, this issue was rare to be included into urban greenery and planning practices. As a response to this situation, we proposed an actionable approach for quantifying the daily exposure of urban residents to eye-level street greenery by integrating high resolution measurements on both greenery and accessibility. Google Street View (GSV) images in Singapore were collected and extracted through machine learning algorithms to achieve an accurate measurement on visible greenery. Street networks collected from Open Street Map (OSM) were analysed through spatial design network analysis (sDNA) to quantify the accessibility value of each street. The integration of street greenery and accessibility helps to measure greenery from a human-centred perspective, and it provides a decision-support tool for urban planners to highlight areas with prioritisation for planning interventions. Moreover, the performance between GSV-based street greenery and the urban green cover mapped by remote sensing was compared to justify the contribution of this new measurement. It suggested there was a mismatch between these two measurements, i.e., existing top-down viewpoint through satellites might not be equivalent to the benefits enjoyed by city residents. In short, this analytical approach contributes to a growing trend in integrating large, freely-available datasets with machine learning to inform planners, and it makes a step forward for urban planning practices through focusing on the human-scale measurement of accessed street greenery.