Laura Bliss is CityLab’s west coast bureau chief. She also authors MapLab, a biweekly newsletter about maps (subscribe here). Her work has appeared in the New York Times, The Atlantic, Los Angeles magazine, and beyond.
New maps use math to define the amorphous term.
Cities have “limits,” drawn to mark populations and bound services, but they are porous as scrim. Cars, cash, freight, and natural resources flow readily between neighboring jurisdictions, following the market more than any lines on a map.
Multiple metros glued together in this way—think of the Northeast Corridor or “Char-lanta”—are sometimes called a “megaregion.” Far from a euphemism for unbridled sprawl, megaregions have become an important framework for developing projects that serve boundary-crossing populations, like high-speed rail or key environmental protections.
But defining the borders of a megaregion can be as tricky and subjective as city limits. You might look on a map and assume that Los Angeles, San Diego, and San Bernardino should be clustered together, since they all have large, dense populations, share freeways and railroads, and have some ineffable cultural Southern California-ness in common (a heady brew of smog, sunscreen, and tacos?). The Regional Plan Association counts 11 megaregions across the U.S., based on density and population growth in adjacent Census tracts. But this doesn’t account for the in-and-out movements that make the concept so relevant.
Research published in PLOS One offers a quantitative approach. Using a combination of math and maps, Garrett Dash Nelson, a postdoctoral student in geography at Dartmouth College, and Alasdair Rae, an urban data analyst at the University of Sheffield, solidify the concept of the “megaregion” as an interlocking, yet self-contained, economic zone. They use millions of point-to-point daily commutes—perhaps the best proxy for economic geography there is—to outline at least 35 urban cluster-oids around the U.S. What gets revealed, according to the paper, are a “set of overlapping, interconnected cogs which, working together, constitute the functional economy of the nation.”
Rae and Nelson could have simply taken commute data from the 2006-2010 American Community Survey—roughly four million tract-to-tract flows representing more than 130 million travelers—and slapped it into GIS software. Actually, they did. That was their first step; you can see, in the map above, what the Minneapolis-St. Paul region looks like. In yellow, are the shorter, higher-volume commutes; in red are the longer-haul, less-traveled routes. It’s obvious that Minneapolis and St. Paul are prominent at the center of a network of shleps.
But this map doesn’t reveal much about the shape of the region. Which of these links are statistically significant? Do these starbursts constitute a single functional “megaregion,” or are some of these smaller communities more connected to another city, by virtue of geography, economy, or infrastructure? To answer those questions, Nelson and Rae turned away from GIS, and used a software that portioned out the “strongest” commute links, as measured by the volume of flows between Census tracts. Common sense would suggest that closer-together communities would be more bonded; the algorithm confirmed that, but also showed that some routes were outliers. The researchers then hybridized their two analyses, translating the algorithmic findings back into the map.
Through this lens, the Twin Cities megaregion turns out to be a fairly self-contained, “with relatively few commutes stretching to or from neighboring” zones, the paper states. That’s certainly true compared to the density of interconnections between Los Angeles and San Diego—according to Nelson and Rae’s analysis, the two metros are really at the center of their own commute-universes, even though there are lots of interconnections between them.
These maps and methods have some limitations. For one, they rely on the latest, most complete ACS commuter dataset, which comes from 2006 to 2010. (The fresh ACS numbers come out tomorrow.) That overlaps with the height of the Great Recession; regions may have merged or divided as a result of changing economic patterns since the recovery. And Rae admits there is a risk that thinking in “megaregion” terms for every planning decision—many or even most such decisions are best made with the characteristics and needs of local communities in mind.
But for policymakers thinking about how to pay for large-scale transportation and infrastructure projects, a “region” might be better measured by its market-led transfers of jobs and cash than by its geographic or cultural ties. Voting districts also require an “evaluation of what territorial space is appropriate to treat as a ‘single’ region,” the authors write. “We hoped to start a conversation about how the country functions from an economic, spatial point of view,” says Rae.
This is a very timely provocation—not only because of all the post-election hand-wringing about urban-rural estrangement, but because of the attention being paid to President-elect Trump’s talk of building out the nation’s infrastructure. Where would those efforts make the most economic sense, and who should bear the biggest share of the costs? These maps might help point us in the right direction.