Urban well-being and health tracking has taken another step forward, as researchers from the University of Washington have created an artificial intelligence algorithm that estimates obesity levels by analyzing a city's infrastructure. Published in JAMA Network Open, the researchers' report explains how the algorithm uncovers urban relationships by using satellite and Street View images from Google. As Quartz reports, the project correlated areas with more green spaces and areas between buildings with lower obesity rates.
Trained using more than 150,000 satellite images across six cities, the algorithm uses deep learning to understand city planning and its affect on obesity. The study looked to answer how convolutional neural networks can assist in the study of the association between the built environment and obesity prevalence. Aiming to analyze and improve a city's health, the project hopes to shape new construction. 96 categories of points of interest were included in the work, accounting for the effect urban amenities can have on the the activity of a neighborhood.
Researchers have explicitly stated their understanding that the algorithm can be skewed by income and wealth. Recognizing this condition, the project can also draw correlations between wealthier neighborhoods and resident obesity. By conducting a series of validation tests, researchers found that the algorithm does link green space and the number of buildings to obesity, not just wealth. As the paper states, "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." Researchers hope their work can show how convolutional neural networks (CNN) can allow for consistent quantification of a built environment's features.
While the study was based on US data, researchers hope the algorithm can be adapted to analyze cities around the world. However, the project does begin to provide evidence of the efficacy of CNNs at associating obesity prevalence with significant physical environment features.
