Abstract Previous research has reported that greenery is an important factor in walking activities, with greenery existing in various forms, including trees, gardens, green walls, and other examples. However, traditional methods of measuring urban greenery involve limitations in coverage of various forms of greenery and do not reflect the actual degree of exposure to pedestrians. Accordingly, this study examined the street Green View Index (GVI) and its associations with walking activities by different income groups using survey data on walking behaviors in 2350 residents in Seoul, Korea. This study utilized Google Street View (GSV) and deep learning to calculate the GVI by semantic segmentation, referring to greenness from the visual perspective of pedestrians. Correlation analyses between traditional greenery variables and GVI were conducted to examine differences, and multiple regression models were applied to identify the relationships between walking time and greenery variables. The results of this study show differences between conventional greenery variables and GVI in terms of specific greenery forms and perspectives. As hypothesized, GVI was more closely associated with walking time than the traditional greenery variables. Also, this study found that the low-income residents generally lived in low GVI neighborhood, but walking time is more sensitive to GVI. These results were because GVI represents the actual greenery exposure to pedestrians, and there was a difference between income groups in the degree of vehicle usage in daily life. The results of this study indicate that, when analyzing the relationship between urban greenness and walking behavior, it is necessary to examine the relationship from multiple angles and to investigate the importance of eye-level street greenery. Our findings provide useful insights for public policies to promote pedestrian walking environments.

中文翻译：

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使用谷歌街景和深度学习分析邻里街道绿景指数对步行时间的影响

摘要 先前的研究表明，绿化是步行活动的重要因素，绿化以各种形式存在，包括树木、花园、绿墙等。然而，传统的城市绿化测量方法在各种形式的绿化覆盖范围内存在局限性，并不能反映行人的实际暴露程度。因此，本研究使用韩国首尔 2350 名居民的步行行为调查数据，研究了街道绿色景观指数 (GVI) 及其与不同收入群体步行活动的关联。本研究利用谷歌街景 (GSV) 和深度学习，通过语义分割计算 GVI，从行人的视觉角度参考绿色。进行了传统绿化变量与 GVI 之间的相关分析以检查差异，并应用多元回归模型来识别步行时间与绿化变量之间的关系。本研究的结果显示了传统绿化变量与 GVI 在特定绿化形式和视角方面的差异。正如假设的那样，与传统的绿化变量相比，GVI 与步行时间的相关性更密切。此外，本研究发现低收入居民普遍居住在低 GVI 社区，但步行时间对 GVI 更为敏感。这些结果是因为 GVI 代表了行人实际的绿化暴露程度，并且收入群体之间在日常生活中使用车辆的程度存在差异。本研究结果表明，在分析城市绿化与步行行为之间的关系时，有必要从多个角度审视这种关系，并探讨视线水平的街道绿化的重要性。我们的研究结果为促进行人步行环境的公共政策提供了有用的见解。