Spatial pattern and heterogeneity of green view index in mountainous cities: a case study of Yuzhong district, Chongqing, China
Abstract 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 Chi...
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Nature Portfolio
2025-04-01
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| Online Access: | https://doi.org/10.1038/s41598-025-97946-9 |
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| author | Aiping Gou Xuyuan Wang Jiangbo Wang Chenjie Wang Guanzheng Tan |
| author_facet | Aiping Gou Xuyuan Wang Jiangbo Wang Chenjie Wang Guanzheng Tan |
| author_sort | Aiping Gou |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-42fcac82939940feba98bf2155c74bb1 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-42fcac82939940feba98bf2155c74bb12025-08-20T03:10:06ZengNature PortfolioScientific Reports2045-23222025-04-0115111410.1038/s41598-025-97946-9Spatial pattern and heterogeneity of green view index in mountainous cities: a case study of Yuzhong district, Chongqing, ChinaAiping Gou0Xuyuan Wang1Jiangbo Wang2Chenjie Wang3Guanzheng Tan4School of Ecological Technology and Engineering, Shanghai Institute of TechnologySchool of Ecological Technology and Engineering, Shanghai Institute of TechnologyCollege of Architecture, Nanjing Tech UniversitySchool of Ecological Technology and Engineering, Shanghai Institute of TechnologySchool of Ecological Technology and Engineering, Shanghai Institute of TechnologyAbstract 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.https://doi.org/10.1038/s41598-025-97946-9Mountainous citiesGreen view indexSpatial patternInfluencing factorsOptimal parameters-based geographical detector(OPGD) |
| spellingShingle | Aiping Gou Xuyuan Wang Jiangbo Wang Chenjie Wang Guanzheng Tan Spatial pattern and heterogeneity of green view index in mountainous cities: a case study of Yuzhong district, Chongqing, China Scientific Reports Mountainous cities Green view index Spatial pattern Influencing factors Optimal parameters-based geographical detector(OPGD) |
| title | Spatial pattern and heterogeneity of green view index in mountainous cities: a case study of Yuzhong district, Chongqing, China |
| title_full | Spatial pattern and heterogeneity of green view index in mountainous cities: a case study of Yuzhong district, Chongqing, China |
| title_fullStr | Spatial pattern and heterogeneity of green view index in mountainous cities: a case study of Yuzhong district, Chongqing, China |
| title_full_unstemmed | Spatial pattern and heterogeneity of green view index in mountainous cities: a case study of Yuzhong district, Chongqing, China |
| title_short | Spatial pattern and heterogeneity of green view index in mountainous cities: a case study of Yuzhong district, Chongqing, China |
| title_sort | spatial pattern and heterogeneity of green view index in mountainous cities a case study of yuzhong district chongqing china |
| topic | Mountainous cities Green view index Spatial pattern Influencing factors Optimal parameters-based geographical detector(OPGD) |
| url | https://doi.org/10.1038/s41598-025-97946-9 |
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