Scholarlink

<p>The Green View Index (GVI) is utilized to evaluate urban street value and ecosystem services and to gauge public perceptions of street greening. This study investigates the spatial heterogeneity of the GVI and its influencing factors in Yuzhong District, Chongqing, a mountainous city in China. Deep learning algorithms were employed to calculate the green visibility of street view images, and Geographic Weighted Regression (GWR) and the Optimal Parameter-Based Geodetector (OPGD) were utilized to analyze the relationships between GVI and factors such as road physical attributes, the Normalized Difference Vegetation Index (NDVI), and topographic features. The results indicate that: (1) In Yuzhong District, 58.9% of streets have a GVI within a low to moderate range, suggesting room for improvement. Higher GVI levels are generally associated with elevated Digital Elevation Models (DEM), while slope, aspect, and terrain undulation have relatively minor overall impacts on GVI. (2) The GVI is highest in the western regions and lowest in the eastern regions, with streets along the riversides exhibiting lower GVI levels. (3) GWR analysis reveals that road type and NDVI significantly influence the GVI. Higher DEM values promote increased GVI, whereas high road density suppresses it. (4) The interaction between influencing factors drives the differentiated distribution of GVI within the study area. The interaction effects between Road type, NDVI, and DEM are particularly notable among these.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Spatial pattern and heterogeneity of green view index in mountainous cities: a case study of Yuzhong district, Chongqing, China

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月12日

查看全文
  • Aiping Gou,
  • Xuyuan Wang,
  • Jiangbo Wang,
  • Chenjie Wang,
  • Guanzheng Tan

摘要

The Green View Index (GVI) is utilized to evaluate urban street value and ecosystem services and to gauge public perceptions of street greening. This study investigates the spatial heterogeneity of the GVI and its influencing factors in Yuzhong District, Chongqing, a mountainous city in China. Deep learning algorithms were employed to calculate the green visibility of street view images, and Geographic Weighted Regression (GWR) and the Optimal Parameter-Based Geodetector (OPGD) were utilized to analyze the relationships between GVI and factors such as road physical attributes, the Normalized Difference Vegetation Index (NDVI), and topographic features. The results indicate that: (1) In Yuzhong District, 58.9% of streets have a GVI within a low to moderate range, suggesting room for improvement. Higher GVI levels are generally associated with elevated Digital Elevation Models (DEM), while slope, aspect, and terrain undulation have relatively minor overall impacts on GVI. (2) The GVI is highest in the western regions and lowest in the eastern regions, with streets along the riversides exhibiting lower GVI levels. (3) GWR analysis reveals that road type and NDVI significantly influence the GVI. Higher DEM values promote increased GVI, whereas high road density suppresses it. (4) The interaction between influencing factors drives the differentiated distribution of GVI within the study area. The interaction effects between Road type, NDVI, and DEM are particularly notable among these.