The Association of Urban Greenness and Walking Behavior: Using Google Street View and Deep Learning Techniques to Estimate Residents' Exposure to Urban Greenness.
The Association of Urban Greenness and Walking Behavior: Using Google Street View and Deep Learning Techniques to Estimate Residents' Exposure to Urban Greenness.
复制标题
城市绿化与步行行为的关联：利用谷歌街景和深度学习技术来估计居民对城市绿化的接触程度
DOI：
10.3390/ijerph15081576
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发表时间：
2018-07-25
影响因子：
--
通讯作者：
Lu Y
中科院分区：
文献类型：
--
作者：
Lu Y
许多研究已经证实，城市绿化与更好的健康结果相关。然而，大多数研究都是通过俯视测量来评估城市绿化程度，例如公园面积或树木数量，这通常与人在地面上平视时所感知到的绿化程度不同。此外，这些研究经常因居住地自我选择偏差的局限性而受到批评。在本研究中，结合深度学习技术，从谷歌街景（GSV）街景图像的剖面图中提取和评估城市绿化。我们还探讨了香港全市住宅重新分配计划中出现的独特研究机会，以减少住宅自我选择偏差。我们进行了两项多水平回归分析，以检验城市绿化与（1）香港 24,773 名公共住房居民步行的几率，（2）1994 名居民的总步行时间之间的关系，同时控制了潜在的混杂因素。结果表明，在 400 m 和 800 m 缓冲区中，眼睛水平的绿色度与较高的步行几率和较长的步行时间显着相关。距最近的地铁站的距离也与步行的几率较高有关。商店数量与 800 m 缓冲区中步行的较高几率相关，但与 400 m 缓冲区中的步行概率无关。通过 GSV 图像和深度学习技术评估眼平绿度，可以有效估计居民日常接触城市绿化的情况，而这又与他们的步行行为相关。由于样本量很大，我们的研究结果适用于香港的所有公共住房居民。
Many studies have established that urban greenness is associated with better health outcomes. Yet most studies assess urban greenness with overhead-view measures, such as park area or tree count, which often differs from the amount of greenness perceived by a person at eye-level on the ground. Furthermore, those studies are often criticized for the limitation of residential self-selection bias. In this study, urban greenness was extracted and assessed from profile view of streetscape images by Google Street View (GSV), in conjunction with deep learning techniques. We also explored a unique research opportunity arising in a citywide residential reallocation scheme of Hong Kong to reduce residential self-selection bias. Two multilevel regression analyses were conducted to examine the relationships between urban greenness and (1) the odds of walking for 24,773 public housing residents in Hong Kong, (2) total walking time of 1994 residents, while controlling for potential confounders. The results suggested that eye-level greenness was significantly related to higher odds of walking and longer walking time in both 400 m and 800 m buffers. Distance to the closest Mass Transit Rail (MTR) station was also associated with higher odds of walking. Number of shops was related to higher odds of walking in the 800 m buffer, but not in 400 m. Eye-level greenness, assessed by GSV images and deep learning techniques, can effectively estimate residents’ daily exposure to urban greenness, which is in turn associated with their walking behavior. Our findings apply to the entire public housing residents in Hong Kong, because of the large sample size.
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影响因子：
3.4
作者：
Boone-Heinonen, Janne;Gordon-Larsen, Penny;Guilkey, David K.;Jacobs, David R., Jr.;Popkin, Barry M.
通讯作者：
Popkin, Barry M.
影响因子：
5.1
作者：
Chin, Gary K. W.;Van Niel, Kimberly P.;Knuiman, Mathew
通讯作者：
Knuiman, Mathew
DOI：
10.1016/j.socscimed.2009.11.020
发表时间：
2010-03
期刊：
Social science & medicine (1982)
影响因子：
--
作者：
Coombes E;Jones AP;Hillsdon M
通讯作者：
Hillsdon M
DOI：
10.1016/s0277-9536(03)00414-3
发表时间：
2004-05-01
期刊：
Social science & medicine (1982)
影响因子：
--
作者：
Diez Roux, Ana V
通讯作者：
Diez Roux, Ana V
影响因子：
2.1
作者：
Adkins, Arlie;Dill, Jennifer;Neal, Margaret
通讯作者：
Neal, Margaret