As COVID-19 swept across the city of Detroit, it brought with it a wave of food insecurity, particularly among low-income residents and seniors who rely on public transportation and can only afford to buy small amounts of food at a time. Now, a U-M research team has stepped in to help identify solutions.
Funded by a National Science Foundation RAPID grant, they aim to help the city identify the most seriously affected areas and provide policy and technical recommendations. These recommendations might include redesigning bus routes and schedules, using scooter or bike share services to improve food access, repurposing existing city assets like shuttle vehicles and improving school lunch delivery programs.
The project includes a data analysis technique that makes it possible to combine sets of geographical data that are organized in different ways—for example, comparing school lunch data that’s organized by school district with transit data that’s organized by ZIP code.
The two data sets would ordinarily by difficult or impossible to compare, but will use a system called GeoAlign that uses what’s called a “crosswalk algorithm” to find other variables in the dataset that correlate with the ones being studied and are available on a finer geographic level. It then uses those additional data points to infer the geographic distribution of the data that’s being studied.
