Somewhere in the sea of colored dots on this map is a single dot representing your job. Or, at least, your job in 2010, the year of the most recent census data. Zoom back, and you can see the patterns of jobs across your neighborhood and the country as a whole.
Inspired by a similarly detailed map of race in America, the map plots out each job in four simplified categories. Factory and trade jobs are red, professional jobs are blue, health care, education, and government jobs are green, and service jobs like retail are yellow.
“Dot maps like this one and the racial dot map are particularly useful for visualizing density, especially of people,” says Robert Manduca, the Harvard PhD student who created the map. “There’s a certain simplicity to them–one dot equals one job or one person–that makes them powerful and relatively straightforward to interpret.”
Across the country, there are clear differences by city. Las Vegas, based on its tourism industry, has a sea of yellow dots. While San Francisco has few factory jobs, nearby San Jose has blocks of red. Detroit and Cleveland still have swaths of red as well, though it’s also apparent how few jobs are left. In New York City, Manhattan is solidly blue and green, unlike the other boroughs.
While jobs in Houston sprawl into the suburbs, Manduca points out that jobs in most cities are surprisingly concentrated. “People sometimes talk about the suburbanization of jobs,” he says. “You can see some of that on the maps, but downtown office districts still have heavy concentrations of jobs in most cities. And even where employment has moved to the suburbs, it appears to have clustered in certain districts or along certain transportation corridors.”
Despite the complexity, the map was relatively straightforward to make, thanks to evolving mapping tools and the fact that the creator of the Racial Dot Map shared his code online. “Even five or ten years ago a project like this would have required me to write much more original code,” Manduca says. “Now it’s more a matter of selecting the right packages and tools to use and stringing them all together. That means that you can go from a dataset and an idea to a finished visualization in a matter of days.”