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Artificial intelligence can estimate an area’s obesity levels by analyzing its buildings

Published on September 5, 2018

A couple of shops on the corner of Station Road in the Fenland town of March. Image Credit: CC BY-SA 2.0: geograph: Richard Humphrey

Two researchers from the University of Washington Institute for Health Metrics and Evaluation have found a way to estimate a US city’s obesity level without having to look at its inhabitants.

The duo trained an artificial intelligence algorithm to find the relationship between a city’s infrastructure and obesity levels using satellite and Street View images from Google. By understanding how city planning influences obesity, health campaigns and new construction can be coordinated to improve a city’s health, the researchers wrote in a paper published in JAMA Network Open.

The algorithm was trained using more than 150,000 satellite images of six cities, as well as 96 categories of points of interest like grocery stores and pet shops. It was then correlated with obesity rates reported from each city. The researchers included the points of interest because they could have an effect on the activity of a neighborhood. An area with pet shops could have more people taking dogs for a walk, for instance.


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Originally posted on Quartz by Dave Gershgorn
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