Literature DB >> 29758477

The effect of street-level greenery on walking behavior: Evidence from Hong Kong.

Yi Lu1, Chinmoy Sarkar2, Yang Xiao3.   

Abstract

Accumulating evidence shows that urban greenspaces have great health benefits, but establishing a causal relationship is difficult. It is often hypothesized that walking and physical activity are mediators in the relationship between urban greenspaces and health outcomes. Furthermore, most urban greenspace-physical activity studies have focused on parks rather than on landscaped streets, even though the latter are the most popular places for physical activity. The lack of research attention for landscaped streets is largely due to the fact that street greenery is difficult to measure, especially at eye level. Using readily available Google Street View images, we developed methods and tools to assess the availability of eye-level street greenery. A two-layered study was developed that 1) examined the association between urban greenspaces and the odds of walking (versus not walking) for 90,445 participants in the Hong Kong Travel Characteristics Survey and 2) carried out sensitivity analysis of the association between urban greenspaces and total walking time for a subset of 6770 participants. Multilevel regression models were developed to reveal the associations between street greenery and walking behaviors while controlling for sociodemographic characteristics and other activity-influencing built environment factors, taking into account the inherent clustering within the data. The results showed that both street greenery and the number of parks were associated with higher odds of walking; street greenery but not parks was associated with total walking time. Our results suggest that walking behavior is at least as strongly affected by eye-level street greenery as by parks. They also implicitly support the health benefits of urban greenspaces via walking and physical activity. With the large sample size, our findings pertain to the entire population of Hong Kong. Furthermore, the use of Google Street View is a sound and effective way to assess eye-level greenery, which may benefit further health studies.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Physical activity; Street greenery; Urban design; Urban planning; Walkability; Walking

Mesh:

Year:  2018        PMID: 29758477     DOI: 10.1016/j.socscimed.2018.05.022

Source DB:  PubMed          Journal:  Soc Sci Med        ISSN: 0277-9536            Impact factor:   4.634


  22 in total

1.  Using machine learning to examine street green space types at a high spatial resolution: Application in Los Angeles County on socioeconomic disparities in exposure.

Authors:  Yi Sun; Xingzhi Wang; Jiayin Zhu; Liangjian Chen; Yuhang Jia; Jean M Lawrence; Luo-Hua Jiang; Xiaohui Xie; Jun Wu
Journal:  Sci Total Environ       Date:  2021-05-08       Impact factor: 10.753

2.  The Premium of Public Perceived Greenery: A Framework Using Multiscale GWR and Deep Learning.

Authors:  Yonglin Zhang; Xiao Fu; Chencan Lv; Shanlin Li
Journal:  Int J Environ Res Public Health       Date:  2021-06-24       Impact factor: 3.390

3.  The association between spatial access to physical activity facilities within home and workplace neighborhoods and time spent on physical activities: evidence from Guangzhou, China.

Authors:  Ye Liu; Xiaoge Wang; Suhong Zhou; Wenjie Wu
Journal:  Int J Health Geogr       Date:  2020-06-20       Impact factor: 3.918

4.  A Systematic Measurement of Street Quality through Multi-Sourced Urban Data: A Human-Oriented Analysis.

Authors:  Lingzhu Zhang; Yu Ye; Wenxin Zeng; Alain Chiaradia
Journal:  Int J Environ Res Public Health       Date:  2019-05-20       Impact factor: 3.390

5.  Do street-level scene perceptions affect housing prices in Chinese megacities? An analysis using open access datasets and deep learning.

Authors:  Xiao Fu; Tianxia Jia; Xueqi Zhang; Shanlin Li; Yonglin Zhang
Journal:  PLoS One       Date:  2019-05-30       Impact factor: 3.240

6.  "Biophilic Cities": Quantifying the Impact of Google Street View-Derived Greenspace Exposures on Socioeconomic Factors and Self-Reported Health.

Authors:  Anna C O'Regan; Ruth F Hunter; Marguerite M Nyhan
Journal:  Environ Sci Technol       Date:  2021-06-23       Impact factor: 9.028

7.  Urban Environment and Health: A Cross-Sectional Study of the Influence of Environmental Quality and Physical Activity on Blood Pressure.

Authors:  Regina Grazuleviciene; Sandra Andrusaityte; Audrius Dėdelė; Tomas Grazulevicius; Leonas Valius; Aurimas Rapalavicius; Violeta Kapustinskiene; Inga Bendokiene
Journal:  Int J Environ Res Public Health       Date:  2021-06-06       Impact factor: 3.390

8.  The Association of Urban Greenness and Walking Behavior: Using Google Street View and Deep Learning Techniques to Estimate Residents' Exposure to Urban Greenness.

Authors:  Yi Lu
Journal:  Int J Environ Res Public Health       Date:  2018-07-25       Impact factor: 3.390

9.  The Association of Built Environment and Physical Activity in Older Adults: Using a Citywide Public Housing Scheme to Reduce Residential Self-Selection Bias.

Authors:  Yi Lu; Long Chen; Yiyang Yang; Zhonghua Gou
Journal:  Int J Environ Res Public Health       Date:  2018-09-10       Impact factor: 3.390

10.  Neighbourhood physical environment, intrinsic capacity, and 4-year late-life functional ability trajectories of low-income Chinese older population: A longitudinal study with the parallel process of latent growth curve modelling.

Authors:  Shiyu Lu; Yuqi Liu; Yingqi Guo; Hung Chak Ho; Yimeng Song; Wei Cheng; Cheryl Hiu Kwan Chui; On Fung Chan; Chris Webster; Rebecca Lai Har Chiu; Terry Yat Sang Lum
Journal:  EClinicalMedicine       Date:  2021-06-16
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