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

Lü, Yi; Sarkar, Chinmoy; Xiao, Yang

doi:10.1016/j.socscimed.2018.05.022

Cited by 260 publications

(160 citation statements)

References 47 publications

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“…Detailed descriptions of HKTCS are available in Reference [40]. The HKTCS was commissioned by The Transportation Department to identify the general travel behaviors of all Hong Kong population, and thus has a large sample size.…”

Section: Methodsmentioning

confidence: 99%

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

Lü

2018

IJERPH

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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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Section: Methodsmentioning

confidence: 99%

“…Several empirical studies have exploited GSV images to assess different features of urban environment [36,37,38,39]. Some previous studies primarily used color in images to identify vegetation from GSV images [27,40]. Yet, the color technique often falsely identifies man-made green objects, e.g., trucks, windows, or walls, as vegetation.…”

Section: Introductionmentioning

confidence: 99%

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

Lü

2018

IJERPH

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“…Barthelemy Marc's opinion on the conditional probability distribution of the shape factor in a given area defines the constituent factors of urban fingerprints, which will be the basis for classifying cities based on street patterns [40]. Additionally, in Lu's study, it emphasizes the importance of walking behavior in Hong Kong city, and the decision of walking behavior and the length of time are related to the greenery which people visually perceive in the street space, and the validity and reliability of evaluation through Google Street View is proposed, too [41]. The above analysis shows that ʺopen space comfort,ʺ ʺthe impact of different types of green space on living choicesʺ and ʺstreet networksʺ are the main evolutionary contexts of street space and residentsʹ health.…”

Section: Analysis Of Street Space and Residents' Health Evolution Basmentioning

confidence: 99%

Knowledge Atlas on the Relationship between Urban Street Space and Residents’ Health—A Bibliometric Analysis Based on VOSviewer and CiteSpace

et al. 2020

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In order to draw and analyze the current status of the research on the relationship between international urban street space (public space, public open space) and residents' health, we explore the hotspots and frontiers of this research field and the overall evolution path from 1999 to 2019. This study uses 4552 related research articles included in the core database of Web of Science as the basis for data analysis, and uses the advantages of VOSviewer and CiteSpace's bibliometric visualization software to study countries, institutions, literature keywords, and literature co-citation networks, combined with the Alluvial Generator analysis of landmark literature. In this research area, the research content is constantly enriching, including urban street networks, health systems, green spaces, road traffic, thermal comfort, and vulnerable people. The research methods are also constantly enriching. By analyzing and sorting out the current research status, research hotspots and possible future trends in this field of research, the paper hopes to provide more reference for research implementation.

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“…Studies of greenery seen from a human perspective using photography are used to learn about human preferences and behaviours, e.g., with regards to sense of security [29], comfort and stress relief, well-being [30][31][32][33][34][35], or the frequency of use of a given space [36], aesthetic appreciation of landscape [37,38], or in combination with the aspect of the ratio of height to width of the street to assess the acoustic, visual, and audio-visual comfort levels [39]. Landscape analyses from a human perspective are also an important part of visual research in landscape architecture, e.g., as part of the assessment of new investments, protection of valuable views and landscapes particularly "vulnerable" to changes [40].…”

Section: Introductionmentioning

confidence: 99%

Sectoral Analysis of Landscape Interiors (SALI) as One of the Tools for Monitoring Changes in Green Infrastructure Systems

et al. 2020

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The aim of this article was to present Sectoral Analysis of Landscape Interiors (SALI). This method uses the idea of a landscape interior understood as a fragment of a landscape perceived from the level of a person standing at a given point. The analyses were conducted in two stages: stage I-the quantitative stage, and stage II-the qualitative stage. The first part of the research was the analysis of the percentage share of particular elements of landscape interiors in the images perpetuated in photographs, taking into account their level of transparency. The second part was the assessment of their quality based on expert knowledge. The use of the SALI method in the context of the analysis of greenery changes over time was illustrated on the example of the landscape interior of the main street in the village of Psary in Poland. The research was carried out at a time interval of 10 years-for the years 2009 and 2019. The results of the study indicate very large changes and loss of greenery (especially trees) and the associated deterioration of the landscape. The findings confirm the suitability of the method in landscape research at a human scale.The aim of the SALI investigations is to complement with 3D visual analyses the studies and 146 landscape analyses carried out on two-dimensional maps. They consist of recording and evaluating147 landscape interiors seen by a person moving around a built-up area. Therefore, the research 148 conducted so far has been carried out in the landscape interiors of streets. The analyses were carried149 out along them by taking a sequence of panoramic photographs (360°) at distances of 100-150 m 150 (step I/1). The selection of the point where the photographs were taken was determined by the area 151 conditions and the change in landscape physiognomy perceived by the observer. Photographs in the 152 SALI method can be taken during the non-foliation and foliation period, which additionally makes it 153 possible to show the variability of the landscape over time, resulting from the passing of seasons. 154After taking a sequence of panoramic photographs, a matrix should be prepared for further research.

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

Cited by 260 publications

References 47 publications

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

Knowledge Atlas on the Relationship between Urban Street Space and Residents’ Health—A Bibliometric Analysis Based on VOSviewer and CiteSpace

Sectoral Analysis of Landscape Interiors (SALI) as One of the Tools for Monitoring Changes in Green Infrastructure Systems

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