Use of Deep Learning to Examine the Association of the Built Environment With Prevalence of Neighborhood Adult Obesity

Maharana, Adyasha; Nsoesie, Elaine O.

doi:10.1001/jamanetworkopen.2018.1535

Cited by 84 publications

(78 citation statements)

References 61 publications

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“…After that point, the surrogate (that relies on detected cars) was not able to discriminate between different unemployment rates. This hints that an improved model could perhaps be produced if additional objects, besides cars, or even image features (similarly to [32]) are used for surrogate computation. This is one of our directions for future work in this area.…”

Section: Discussionmentioning

confidence: 99%

“…In [30] the authors use GSV images to determine the number of pedestrians present in street segments in order to estimate pedestrian volume, while in [31] the authors automatically extract three measures of visual enclosure which are shown to be correlated with walkability. Moving even further, [32] uses features of the built environment, extracted through CNN and builds regression models that associate these features with adult obesity prevalence.…”

Section: Related Workmentioning

confidence: 99%

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Image-Based Surrogates of Socio-Economic Status in Urban Neighborhoods Using Deep Multiple Instance Learning

Diou¹,

Lelekas²,

Delopoulos³

2018

Preprint

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1) Background: Evidence-based policymaking requires data about the local population's socioeconomic status (SES) at detailed geographical level, however such information is often not available, or is too expensive to acquire. Researchers have proposed solutions to estimate SES indicators by analyzing Google Street View images, however these methods are also resource-intensive, since they require large volumes of manually labeled training data. 2) Methods: We propose a methodology for automatically computing surrogate variables of SES indicators using street images of parked cars and deep multiple-instance learning. Our approach does not require any manually created labels, apart from data already available by statistical authorities, while the entire pipeline for image acquisition, parked car detection, car classification and surrogate variable computation is fully automated. The proposed surrogate variables are then used in linear regression models to estimate the target SES indicators. 3) Results: We implement and evaluate a model based on the proposed surrogate variable at 30 municipalities of varying SES in Greece. Our model has $R^2=0.76$ and correlation coefficient 0.874 with the true unemployment rate, while it achieves mean absolute percentage error 0.089 and mean absolute error 1.87 on a held-out test set. 4) Conclusions: The proposed methodology can be used to estimate socioeconomic status indicators such as unemployment rate at the local level automatically, using images of parked cars detected via Google Street View, without the need for any manual labeling effort.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Image-Based Surrogates of Socio-Economic Status in Urban Neighborhoods Using Deep Multiple Instance Learning

Diou¹,

Lelekas²,

Delopoulos³

2018

Preprint

View full text Add to dashboard Cite

show abstract

“…After that point, the surrogate (that relies on detected cars) was not able to discriminate between different unemployment rates. This hints that an improved model could perhaps be produced if additional objects, besides cars, or even image features (similarly to Reference [32]) are used for surrogate computation. This is one of our directions for future work in this area.…”

Section: Discussionmentioning

confidence: 97%

“…In Reference [30] the authors use GSV images to determine the number of pedestrians present in street segments in order to estimate pedestrian volume, while in Reference [31] the authors automatically extract three measures of visual enclosure which are shown to be correlated with walkability. Moving even further, Reference [32] uses features of the built environment, extracted through CNN and builds regression models that associate these features with adult obesity prevalence.…”

Section: Related Workmentioning

confidence: 99%

Image-Based Surrogates of Socio-Economic Status in Urban Neighborhoods Using Deep Multiple Instance Learning

2018

View full text Add to dashboard Cite

(1) Background: Evidence-based policymaking requires data about the local population’s socioeconomic status (SES) at detailed geographical level, however, such information is often not available, or is too expensive to acquire. Researchers have proposed solutions to estimate SES indicators by analyzing Google Street View images, however, these methods are also resource-intensive, since they require large volumes of manually labeled training data. (2) Methods: We propose a methodology for automatically computing surrogate variables of SES indicators using street images of parked cars and deep multiple instance learning. Our approach does not require any manually created labels, apart from data already available by statistical authorities, while the entire pipeline for image acquisition, parked car detection, car classification, and surrogate variable computation is fully automated. The proposed surrogate variables are then used in linear regression models to estimate the target SES indicators. (3) Results: We implement and evaluate a model based on the proposed surrogate variable at 30 municipalities of varying SES in Greece. Our model has R 2 = 0 . 76 and a correlation coefficient of 0 . 874 with the true unemployment rate, while it achieves a mean absolute percentage error of 0 . 089 and mean absolute error of 1 . 87 on a held-out test set. Similar results are also obtained for other socioeconomic indicators, related to education level and occupational prestige. (4) Conclusions: The proposed methodology can be used to estimate SES indicators at the local level automatically, using images of parked cars detected via Google Street View, without the need for any manual labeling effort.

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“…This increase in obesity prevalence is due to a complex interplay of biological, structural and individual factors (Hill and Peters, 1998;Nelson et al, 2006;Papas et al, 2007;Ogden et al, 2010). Factors such as public safety, socioeconomic status, and the neighborhood built environment may impact access to recreational facilities, and fresh, healthy foods (Freedman et al, 2002;Giles-Corti et al, 2003;Hill et al, 2003;Ellaway et al 2005;Gordon-Larsen et al, 2006;Lopez-Zetina et al, 2006;Mobley et al, 2006;Bennett et al, 2007;Papas et al, 2007;Casagrande et al, 2009;Maharana and Nsoesie, 2018). An individual's social environment can also influence health behaviors (such as, poor diet and physical inactivity) that are considered risk factors for obesity (Christakis and Fowler, 2007;McFerran et al, 2009;Yakusheva et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Use of social media, search queries, and demographic data to assess obesity prevalence in the United States

et al. 2019

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Obesity is a global epidemic affecting millions. Implementation of interventions to curb obesity rates requires timely surveillance. In this study, we estimated sex-specific obesity prevalence using social media, search queries, demographics and built environment variables. We collected 3,817,125 and 1,382,284 geolocated tweets on food and exercise respectively, from Twitter's streaming API from April 2015 to March 2016. We also obtained searches related to physical activity and diet from Google Search Trends for the same time period. Next, we inferred the gender of Twitter users using machine learning methods and applied mixed-effects state-level linear regression models to estimate obesity prevalence. We observed differences in discussions of physical activity and foods, with males reporting higher intensity physical activities and lower caloric foods across 40 and 48 states, respectively. In addition, counties with the highest percentage of exercise and food tweets had lower male and female obesity prevalence. Lastly, our models separately captured overall male and female spatial trends in obesity prevalence. The average correlation between actual and estimated obesity prevalence was 0.797(95% CI, 0.796, 0.798) and 0.830 (95% CI, 0.830, 0.831) for males and females, respectively. Social media can provide timely community-level data on health information seeking and changes in behaviors, sentiments and norms. Social media data can also be combined with other data types such as, demographics, built environment variables, diet and physical activity indicators from other digital sources (e.g., mobile applications and wearables) to monitor health behaviors at different geographic scales, and to supplement delayed estimates from traditional surveillance systems.

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Use of Deep Learning to Examine the Association of the Built Environment With Prevalence of Neighborhood Adult Obesity

Cited by 84 publications

References 61 publications

Image-Based Surrogates of Socio-Economic Status in Urban Neighborhoods Using Deep Multiple Instance Learning

Image-Based Surrogates of Socio-Economic Status in Urban Neighborhoods Using Deep Multiple Instance Learning

Image-Based Surrogates of Socio-Economic Status in Urban Neighborhoods Using Deep Multiple Instance Learning

Use of social media, search queries, and demographic data to assess obesity prevalence in the United States

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