BACKGROUND:
It has been hypothesized that low access to healthy and nutritious
food increases
health disparities. Low-accessibility areas, called
food deserts, are particularly commonplace in lower-
income neighborhoods. The
metrics for measuring the
food environment's
health, called
food desert indices, are primarily based on decadal
census data, limiting their frequency and geographical resolution to that of the
census. We aimed to create a
food desert index with finer geographic resolution than
census data and better responsiveness to environmental changes. MATERIALS AND
METHODS:
We augmented decadal
census data with real-
time data from platforms such as Yelp and Google Maps and crowd-sourced answers to
questionnaires by the
Amazon Mechanical Turks to create a real-
time, context-aware, and geographically refined
food desert index. Finally, we used this refined index in a concept application that suggests alternative routes with
similar ETAs between a source and destination in the Atlanta metropolitan area as an intervention to expose a traveler to better
food environments.
RESULTS:
We made 139,000 pull requests to Yelp, analyzing 15,000 unique
food retailers in the metro Atlanta area. In addition, we performed 248,000
walking and driving route analyses on these retailers using Google Maps'
API. As a result, we discovered that the metro Atlanta
food environment creates a strong
bias towards
eating out rather than preparing a
meal at home when access to vehicles is limited. Contrary to the
food desert index that we started with, which changed values only at
neighborhood boundaries, the
food desert index that we built on top of it captured the changing exposure of a subject as they walked or drove through the city. This model was also sensitive to the changes in the
environment that occurred after the
census data was collected.
CONCLUSIONS:
Research on the environmental components of
health disparities is flourishing. New
machine learning models have the potential to augment various
information sources and create fine-tuned models of the
environment. This opens the way to better
understanding the
environment and its effects on
health and suggesting better interventions.