Robert Gebeloff

Anybody who’s paying attention to the 2020 election knows that. But there’s a more fundamental question: Just what are the suburbs anyway?

In a statistical sense, they are surprisingly hard to define.

The U.S. Census Bureau, the primary source of demographic data, doesn’t offer a lot of insight; it distinguishes only between urban and rural areas.

So The New York Times decided to develop its own method for defining suburbia. From there, we were able to evaluate the demographics and, by extension, the political implications of demographic change.

The key finding: Suburbia should not be considered a distinct entity, but two separate realms. The difference between inner-ring and outer-ring suburbs goes well beyond geography.

In fact, reexamining the 2016 presidential election through that lens, we found that the fault line in party preference was precisely at the boundary between old and new.

— Quantifying Suburbia

“Much of America looks suburban,” economists Shawn Bucholtz and Jed Kolko wrote last year. But just because we can see and feel “suburban” doesn’t mean we can easily count it.

The absence of an official federal definition of suburban, they said, “makes it hard to measure the reach and impact of federal programs and to produce vital statistics about Americans and their communities.”

The two proposed a system for quantifying suburbs based on how residents describe their neighborhoods in the American Housing Survey, a technique similar to a survey that Kolko, an occasional Upshot contributor, conducted as chief economist at Trulia in 2015.

For our version, we decided to look at both the density of people and the density of building development. Analyzing census-tract data from the American Community Survey and spatial data from satellite imagery, we created a rural-suburban-urban density continuum index for more than 70,000 neighborhoods in the continental United States.

We evaluated how many people lived in each census tract in proportion to the land mass, and measured the development density by analyzing the pixels from satellite imagery.

— A Scale of 1 to 10

The satellite imagery, published by the Multi-Resolution Land Characteristics consortium, portrays the U.S. mainland as millions of tiny dots. Each dot is color-coded to represent a different land use — like forest land, farmland, pavement and buildings. For every census tract on the continent, we counted the dots and measured the percentage representing developed land.

We then converted the percentages to a 1-to-10 scale, and did the same with each tract’s population per square mile. We then combined the two factors into a single index representing the combined density of people and buildings. For neighborhoods with unusual divergences between the two factors — industrial parks that were heavily paved but that had low population density, for example — we always assigned the higher index score.

We categorized the tracts that scored 1 or 2 as rural, and those that scored 9 or 10 as urban.

Everything in between was suburbia, although we eventually divided the suburbs into two groups as well. The reason? When we started running the numbers for demographics and 2016 election results, we realized that the more-dense suburban tracts were, as a group, far different from the less-dense tracts.

We called less-dense suburbs “outer ring,” and denser suburbs “inner ring.”

The comparison totally ignores political boundaries, and intentionally so. In this world, the consideration is the density of neighborhoods, so we observe that 99% of New York and San Francisco residents live in urban density, but only 60% of Phoenix residents do, as well as 17% of Indianpolis residents and 4% of those who live in Jacksonville.

This doesn’t differentiate all aspects of the suburban experience. For example, people might prefer a suburban community because of the school system or because of their job situation.

But our system correlated pretty strongly with demographic trends and even more strongly with voting patterns in the 2016 election.

In past stories, we’ve deployed a county-level method. There are various “typology” schemes that describe counties based on whether they are inside a metropolitan area, and whether they are the metro’s primary county or adjacent to the primary county.

One we’ve used was created by demographers at the Brookings Institution, which places counties on a scale ranging from “Primary City” to “Exurb.”

But how many counties in the United States include neighborhoods that are all of one type? Not many. There are only nine where more than 90% of residents live in urban density, and only 39 where at least half of the residents live in a true city environment.

There are 95 counties where almost everybody lives in a suburban environment, and nearly 1,000 counties that are nearly all rural.

But in every other county, there is a mix of neighborhood types, so single labels just don’t seem to do them justice.

— ‘Political Boundary of the Density Divide’

Then there is the distinction within the suburbs. All of suburbia has grown more diverse, but inner-ring neighborhoods have a much higher share of nonwhite residents than outer-ring neighborhoods do.

And the inner ring is more likely to support Democratic candidates; the outer more likely to vote Republican. Our analysis jibes with what some others have pointed out: There is a relationship between density and political preference.

“Majorities tend to flip from blue to red roughly where commuter suburbs give way to ‘exurban’ sprawl,” wrote Will Wilkinson, a researcher at the libertarian Niskanen Center, in a recent report. “That’s where the political boundary of the density divide is drawn.”

If 2016 is an indication, the battle lines are clear for 2020. Hillary Clinton dominated the inner-ring suburbs, and Donald Trump was dominant in the outer ring.

This is as true within states that are deep red or deep blue as for the nation as a whole. In Alabama, for example, Clinton dominated in densely populated neighborhoods. But only one-quarter of the state’s voters live in such neighborhoods, and Trump won the state easily.

In true battleground states like Michigan, the pattern held, though the voting population was more evenly distributed by neighborhood density. Yet the fact that about one-fifth of Michigan votes came from rural tracts helped Trump pull off his upset victory.

In the end, the most important takeaway from this work is that the suburbs can’t be depicted with a broad brush, and that when we analyze them for demographic or political purposes, we should be aware of the polarization within.

This article originally appeared in

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The Border Between Red and Blue America

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Robert Gebeloff

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The Border Between Red and Blue America