globalchange  > 过去全球变化的重建
DOI: 10.1371/journal.pone.0171110
论文题名:
How green are the streets? An analysis for central areas of Chinese cities using Tencent Street View
作者: Ying Long; Liu Liu
刊名: PLOS ONE
ISSN: 1932-6203
出版年: 2017
发表日期: 2017-2-14
卷: 12, 期:2
语种: 英语
英文关键词: Urban environments ; China ; Crawling ; Research validity ; Urban geography ; Scientists ; Urban areas ; Urban ecology
英文摘要: Extensive evidence has revealed that street greenery, as a quality-of-life component, is important for oxygen production, pollutant absorption, and urban heat island effect mitigation. Determining how green our streets are has always been difficult given the time and money consumed using conventional methods. This study proposes an automatic method using an emerging online street-view service to address this issue. This method was used to analyze street greenery in the central areas (28.3 km2 each) of 245 major Chinese cities; this differs from previous studies, which have investigated small areas in a given city. Such a city-system-level study enabled us to detect potential universal laws governing street greenery as well as the impact factors. We collected over one million Tencent Street View pictures and calculated the green view index for each picture. We found the following rules: (1) longer streets in more economically developed and highly administrated cities tended to be greener; (2) cities in western China tend to have greener streets; and (3) the aggregated green view indices at the municipal level match with the approved National Garden Cities of China. These findings can prove useful for drafting more appropriate policies regarding planning and engineering practices for street greenery.
URL: http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0171110&type=printable
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/25654
Appears in Collections:过去全球变化的重建
影响、适应和脆弱性
科学计划与规划
气候变化与战略
全球变化的国际研究计划
气候减缓与适应
气候变化事实与影响

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作者单位: School of Architecture and Hang Lung Center for Real Estate, Tsinghua University, Beijing, China;China Academy of Urban Planning and Design, Shanghai, China

Recommended Citation:
Ying Long,Liu Liu. How green are the streets? An analysis for central areas of Chinese cities using Tencent Street View[J]. PLOS ONE,2017-01-01,12(2)
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