Using Virtual Street Audits to Understand the Walkability of Older Adults’ Route Choices by Gender and Age

Brookfield, Katherine; Tilley, Sara

doi:10.3390/ijerph13111061

Cited by 36 publications

(12 citation statements)

References 45 publications

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“…Our study contributes to the nascent body of literature utilizing GSV to implement virtual neighborhood audits for neighborhood features such as walkability,(Brookfield and Tilley, 2016) physical disorder,(Mooney et al, 2014) retail alcohol stores,(Less et al, 2015) and urban greenery,(Li et al, 2015) Researchers have also implemented computerized approaches to label images for pedestrian count(Yin et al, 2015) and visual enclosure (i.e., proportion sky visible from a point on the street)(Yin and Wang, 2016)—measures that are connected with walkability. A previous study found Google's Computer Vision API to be effective at characterizing the naturalness of urban areas with GSV images from the city of Edinburgh.…”

Section: Discussionmentioning

confidence: 95%

Using Google Street View to examine associations between built environment characteristics and U.S. health outcomes

Nguyen

Khanna

Dwivedi

et al. 2019

Preventive Medicine Reports

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Neighborhood attributes have been shown to influence health, but advances in neighborhood research has been constrained by the lack of neighborhood data for many geographical areas and few neighborhood studies examine features of nonmetropolitan locations. We leveraged a massive source of Google Street View (GSV) images and computer vision to automatically characterize national neighborhood built environments. Using road network data and Google Street View API, from December 15, 2017-May 14, 2018 we retrieved over 16 million GSV images of street intersections across the United States. Computer vision was applied to label each image. We implemented regression models to estimate associations between built environments and county health outcomes, controlling for county-level demographics, economics, and population density. At the county level, greater presence of highways was related to lower chronic diseases and premature mortality. Areas characterized by street view images as ‘rural’ (having limited infrastructure) had higher obesity, diabetes, fair/poor self-rated health, premature mortality, physical distress, physical inactivity and teen birth rates but lower rates of excessive drinking. Analyses at the census tract level for 500 cities revealed similar adverse associations as was seen at the county level for neighborhood indicators of less urban development. Possible mechanisms include the greater abundance of services and facilities found in more developed areas with roads, enabling access to places and resources for promoting health. GSV images represents an underutilized resource for building national data on neighborhoods and examining the influence of built environments on community health outcomes across the United States.

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Section: Discussionmentioning

confidence: 95%

Using Google Street View to examine associations between built environment characteristics and U.S. health outcomes

Nguyen

Khanna

Dwivedi

et al. 2019

Preventive Medicine Reports

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show abstract

“…Previous studies have used Google Street View to perform virtual audits to assess walkability, 27 physical disorder, 28 retail alcohol stores 29 and urban greenery 30 among other neighbourhood characteristics. Researchers have also developed a Computer Assisted Neighborhood Visual Assessment System to allow users to conduct virtual neighbourhood audits of Google Street View images.…”

Section: Discussionmentioning

confidence: 99%

Neighbourhood looking glass: 360º automated characterisation of the built environment for neighbourhood effects research

Nguyen

Sajjadi

McCullough

et al. 2018

J Epidemiol Community Health

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BackgroundNeighbourhood quality has been connected with an array of health issues, but neighbourhood research has been limited by the lack of methods to characterise large geographical areas. This study uses innovative computer vision methods and a new big data source of street view images to automatically characterise neighbourhood built environments.MethodsA total of 430 000 images were obtained using Google’s Street View Image API for Salt Lake City, Chicago and Charleston. Convolutional neural networks were used to create indicators of street greenness, crosswalks and building type. We implemented log Poisson regression models to estimate associations between built environment features and individual prevalence of obesity and diabetes in Salt Lake City, controlling for individual-level and zip code-level predisposing characteristics.ResultsComputer vision models had an accuracy of 86%–93% compared with manual annotations. Charleston had the highest percentage of green streets (79%), while Chicago had the highest percentage of crosswalks (23%) and commercial buildings/apartments (59%). Built environment characteristics were categorised into tertiles, with the highest tertile serving as the referent group. Individuals living in zip codes with the most green streets, crosswalks and commercial buildings/apartments had relative obesity prevalences that were 25%–28% lower and relative diabetes prevalences that were 12%–18% lower than individuals living in zip codes with the least abundance of these neighbourhood features.ConclusionNeighbourhood conditions may influence chronic disease outcomes. Google Street View images represent an underused data resource for the construction of built environment features.

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“…Accounting for available programming and activities fostering social support and interaction, such as clubs and groups, would similarly be useful (Diaz Moore 2014). OWI combines an audit with a survey, and it is also possible to combine with geospatial data such as Google Street View (Brookfield and Tilley 2016). This kind of integration is promising.…”

Section: Whatmentioning

confidence: 99%

Measuring the Built Environment for Aging in Place: A Review of Neighborhood Audit Tools

Kan

Forsyth

Molinsky

2020

Journal of Planning Literature

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What audit tools are available to assess neighborhood suitability for aging in place? Drawing on theories from environmental gerontology, the article outlines physical and social environmental characteristics influencing aging. Eight research audit tools and three community/practitioner tools were identified using a Boolean search strategy. They emphasize walkability and the physical attributes of places. Areas for further development include addressing specific features related to aging, a wider range of environments, additional physical and social dimensions, and conducting audits using technologies and participatory processes to better incorporate aging experiences. For planners, these audit tools raise awareness of how environments can better support aging populations.

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Using Virtual Street Audits to Understand the Walkability of Older Adults’ Route Choices by Gender and Age

Cited by 36 publications

References 45 publications

Using Google Street View to examine associations between built environment characteristics and U.S. health outcomes

Using Google Street View to examine associations between built environment characteristics and U.S. health outcomes

Neighbourhood looking glass: 360º automated characterisation of the built environment for neighbourhood effects research

Measuring the Built Environment for Aging in Place: A Review of Neighborhood Audit Tools

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