A Local View of Informal Urban Environments: a Mobile Phone-Based Neighborhood Audit of Street-Level Factors in a Brazilian Informal Community

Remigio, Richard V.; Zulaika, Garazi; Rabello, Renata S.; Bryan, John H. D.; Sheehan, Daniel M.; Galea, Sandro; Carvalho, Marília Sá; Rundle, Andrew; Lovasi, Gina S.

doi:10.1007/s11524-019-00351-7

Cited by 15 publications

(27 citation statements)

References 36 publications

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“…19 A study in Brazil that examined utility wires indicated they may also present the risk of electrocution/electrical fire. 51 Studies show an association between visible utility wires and negative health outcomes. 19 Computer vision models were unable to accurately identify other indicators of physical disorder, including litter (too small to be seen), graffiti (rare outside of some urban settings), and poor building condition (large variation in appearance).…”

Section: Methodsmentioning

confidence: 99%

Leveraging 31 Million Google Street View Images to Characterize Built Environments and Examine County Health Outcomes

et al. 2020

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Objectives Built environments can affect health, but data in many geographic areas are limited. We used a big data source to create national indicators of neighborhood quality and assess their associations with health. Methods We leveraged computer vision and Google Street View images accessed from December 15, 2017, through July 17, 2018, to detect features of the built environment (presence of a crosswalk, non–single-family home, single-lane roads, and visible utility wires) for 2916 US counties. We used multivariate linear regression models to determine associations between features of the built environment and county-level health outcomes (prevalence of adult obesity, prevalence of diabetes, physical inactivity, frequent physical and mental distress, poor or fair self-rated health, and premature death [in years of potential life lost]). Results Compared with counties with the least number of crosswalks, counties with the most crosswalks were associated with decreases of 1.3%, 2.7%, and 1.3% of adult obesity, physical inactivity, and fair or poor self-rated health, respectively, and 477 fewer years of potential life lost before age 75 (per 100 000 population). The presence of non–single-family homes was associated with lower levels of all health outcomes except for premature death. The presence of single-lane roads was associated with an increase in physical inactivity, frequent physical distress, and fair or poor self-rated health. Visible utility wires were associated with increases in adult obesity, diabetes, physical and mental distress, and fair or poor self-rated health. Conclusions The use of computer vision and big data image sources makes possible national studies of the built environment’s effects on health, producing data and results that may inform national and local decision-making.

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Section: Methodsmentioning

confidence: 99%

Leveraging 31 Million Google Street View Images to Characterize Built Environments and Examine County Health Outcomes

et al. 2020

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“…From images, we also extracted the presence of visible wires. The literature on visible wires is nascent and more of this work has been done abroad, for instance in Rio de Janeiro, where the wires represent both an unsightly presence and a possible electrocution/electrical fire risk [28]. In the United States, visible wires have mainly a visual impact on the landscape.…”

Section: Data Sources Google Street View Data For Built Environment Imentioning

confidence: 99%

Health and the built environment in United States cities: measuring associations using Google Street View-derived indicators of the built environment

et al. 2020

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Background: The built environment is a structural determinant of health and has been shown to influence health expenditures, behaviors, and outcomes. Traditional methods of assessing built environment characteristics are timeconsuming and difficult to combine or compare. Google Street View (GSV) images represent a large, publicly available data source that can be used to create indicators of characteristics of the physical environment with machine learning techniques. The aim of this study is to use GSV images to measure the association of built environment features with health-related behaviors and outcomes at the census tract level. Methods: We used computer vision techniques to derive built environment indicators from approximately 31 million GSV images at 7.8 million intersections. Associations between derived indicators and health-related behaviors and outcomes on the census-tract level were assessed using multivariate regression models, controlling for demographic factors and socioeconomic position. Statistical significance was assessed at the α = 0.05 level. Results: Single lane roads were associated with increased diabetes and obesity, while non-single-family home buildings were associated with decreased obesity, diabetes and inactivity. Street greenness was associated with decreased prevalence of physical and mental distress, as well as decreased binge drinking, but with increased obesity. Socioeconomic disadvantage was negatively associated with binge drinking prevalence and positively associated with all other health-related behaviors and outcomes. Conclusions: Structural determinants of health such as the built environment can influence population health. Our study suggests that higher levels of urban development have mixed effects on health and adds further evidence that socioeconomic distress has adverse impacts on multiple physical and mental health outcomes.

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“…Visible utility wires hanging overhead are visually striking and may impact residents’ aesthetic sense of their environment, altering perceptions of safety or pleasurability and influencing both mental health (by affecting stress levels) and physical health (by disincentivizing walking). Other studies that have examined this indicator have been done outside the U.S., where they may also represent an unsightly presence and electrocution/electrical fire risk [ 27 ]. Computer vision models have struggled with small objects, precluding us from labeling other indicators of physical disorder such as litter or trash [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases

Nguyen

Huang

Kumar

et al. 2020

IJERPH

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The spread of COVID-19 is not evenly distributed. Neighborhood environments may structure risks and resources that produce COVID-19 disparities. Neighborhood built environments that allow greater flow of people into an area or impede social distancing practices may increase residents’ risk for contracting the virus. We leveraged Google Street View (GSV) images and computer vision to detect built environment features (presence of a crosswalk, non-single family home, single-lane roads, dilapidated building and visible wires). We utilized Poisson regression models to determine associations of built environment characteristics with COVID-19 cases. Indicators of mixed land use (non-single family home), walkability (sidewalks), and physical disorder (dilapidated buildings and visible wires) were connected with higher COVID-19 cases. Indicators of lower urban development (single lane roads and green streets) were connected with fewer COVID-19 cases. Percent black and percent with less than a high school education were associated with more COVID-19 cases. Our findings suggest that built environment characteristics can help characterize community-level COVID-19 risk. Sociodemographic disparities also highlight differential COVID-19 risk across groups of people. Computer vision and big data image sources make national studies of built environment effects on COVID-19 risk possible, to inform local area decision-making.

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A Local View of Informal Urban Environments: a Mobile Phone-Based Neighborhood Audit of Street-Level Factors in a Brazilian Informal Community

Cited by 15 publications

References 36 publications

Leveraging 31 Million Google Street View Images to Characterize Built Environments and Examine County Health Outcomes

Leveraging 31 Million Google Street View Images to Characterize Built Environments and Examine County Health Outcomes

Health and the built environment in United States cities: measuring associations using Google Street View-derived indicators of the built environment

Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases

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