Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1016/j.landurbplan.2018.08.028 |
Measuring daily accessed street greenery: A human-scale approach for informing better urban planning practices | |
Ye, Yu1; Richards, Daniel2; Lu, Yi3; Song, Xiaoping2; Zhuang, Yu1; Zeng, Wei4; Zhong, Teng5 | |
2019-11-01 | |
发表期刊 | LANDSCAPE AND URBAN PLANNING
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ISSN | 0169-2046 |
EISSN | 1872-6062 |
出版年 | 2019 |
卷号 | 191 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China; Singapore |
英文摘要 | 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. |
英文关键词 | Visible greenery Google Street View Space syntax Human-scale Accessible greenery Machine learning |
领域 | 资源环境 |
收录类别 | SCI-E ; SSCI |
WOS记录号 | WOS:000491614300005 |
WOS关键词 | TREE COVER ; NEIGHBORHOOD ; VIEW ; ACCESSIBILITY ; PERCEPTION ; NETWORK ; IMAGERY ; SYNTAX |
WOS类目 | Ecology ; Environmental Studies ; Geography ; Geography, Physical ; Regional & Urban Planning ; Urban Studies |
WOS研究方向 | Environmental Sciences & Ecology ; Geography ; Physical Geography ; Public Administration ; Urban Studies |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/188158 |
专题 | 资源环境科学 |
作者单位 | 1.Tongji Univ, Coll Architecture & Urban Planning, Dept Architecture, Shanghai, Peoples R China; 2.Swiss Fed Inst Technol, Future Cities Lab, Singapore ETH Ctr, Singapore, Singapore; 3.City Univ Hong Kong, Dept Architecture & Civil Engn, Hong Kong, Peoples R China; 4.Shenzhen Inst Adv Technol, Shenzhen VisuCA Key Lab, Shenzhen, Peoples R China; 5.Univ Hong Kong, Dept Urban Planning & Design, Hong Kong, Peoples R China |
推荐引用方式 GB/T 7714 | Ye, Yu,Richards, Daniel,Lu, Yi,et al. Measuring daily accessed street greenery: A human-scale approach for informing better urban planning practices[J]. LANDSCAPE AND URBAN PLANNING,2019,191. |
APA | Ye, Yu.,Richards, Daniel.,Lu, Yi.,Song, Xiaoping.,Zhuang, Yu.,...&Zhong, Teng.(2019).Measuring daily accessed street greenery: A human-scale approach for informing better urban planning practices.LANDSCAPE AND URBAN PLANNING,191. |
MLA | Ye, Yu,et al."Measuring daily accessed street greenery: A human-scale approach for informing better urban planning practices".LANDSCAPE AND URBAN PLANNING 191(2019). |
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