“This is a light way to look at a heavy problem,” said the artist in an interview about this innovative geographic work. Ioffe’s work makes broad use of different techniques of public health data collection.
The fast food locations are sourced from the automated web-crawler, fastfoodmaps.com, developed by Ian Spiro. Obesity data come from the CDC Behavioral Research Factor Surveillance Study (BRFSS), an annual survey of close to 400,000 households about risk factor data. Finally, the artist and a team from Arcadia collected nutrition facts from each of the restaurant chains described to support the meal comparisons.
Taken together, these sources combine automated information retrieval, national public health collection, and some often delicious fieldwork.
The figure is actually composed of several visual elements, including a shaded choropleth of the BRFSS data overlaid by a scatter geolocation plot of the fast-food restaurants. The lower half describes each restaurant chain and the food products and consumer markets they serve.
Notably, this graphic is not intended to imply causality between obesity, restaurant density, and income. Rather, it shows the promise of marrying disparate health data sets. Using shading and scatter plotting, a viewer can track the changes in America’s food and restaurant industry and maybe decide on a place for lunch.
Explore a deeper analysis of this data visualization.
SQL, Excel, D3.js SVG & NVD3, with Illustrator
Data sourced from fastfoodmaps.com w/ CDC Behavioral Risk Factor Surveillance System, US Census