  More than one-third of the adult population in the United States is obese.
Obesity has been linked to factors such as, genetics, diet, physical activity
and the environment. However, evidence indicating associations between the
built environment and obesity has varied across studies and geographical
contexts. Here, we used deep learning and approximately 150,000 high resolution
satellite images to extract features of the built environment. We then
developed linear regression models to consistently quantify the association
between the extracted features and obesity prevalence at the census tract level
for six cities in the United States. The extracted features of the built
environment explained 72% to 90% of the variation in obesity prevalence across
cities. Outof-sample predictions were considerably high with correlations
greater than 80% between predicted and true obesity prevalence across all
census tracts. This study supports a strong association between the built
environment and obesity prevalence. Additionally, it also illustrates that
features of the built environment extracted from satellite images can be useful
for studying health indicators, such as obesity. Understanding the association
between specific features of the built environment and obesity prevalence can
lead to structural changes that could encourage physical activity and decreases
in obesity prevalence.
