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

Abstract

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.

Neighborhood Walkability and Housing Prices: A Correlation Study

“…This study includes the level of greenness measured by the (normalized difference vegetation index (NVDI) as a variable to measure the neighborhood's environmental quality. Although the walkability score includes access to parks, it is considered that the greenness factor should be examined further because of its impact on the walkability of the neighborhood environment [61][62][63]. NDVI is the most common index quantifying vegetation using remote sensing [64][65][66][67].…”

Daily Accessed Street Greenery and Housing Price: Measuring Economic Performance of Human-Scale Streetscapes via New Urban Data

“…Moreover, studies assisted by machine-learning algorithms are going further in measuring that which was considered "unmeasurable" in the past. Many intangible, perceptual qualities on streets, such as visual quality [39], façade maintenance, and the continuity of street walls [40], walkability affected by visible greenery [41,42] have been achieved.…”

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

“…In order to perform tests using the SALI method, it is necessary to take field photos. This makes it more labor-intensive than methods which are based on ready-made photos, e.g., GSV [34,39,41]. We must remember, however, the use of ready-made GSV images has its limitations due to the lack of information about the exact time of taking the photo.…”

“…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].…”