There is perhaps no more vexing issue for urban policy makers than sprawl. And yet, there’s little consensus on how best to accurately measure it. It’s one thing to impugn the phenomenon for contributing to everything from long commutes, congested highways and worsening air pollution to growing segregation, poverty, obesity and mounting health problems. But it’s another to actually gauge the connection between sprawl and that daunting list of social and economic ills.
A new study by Thomas Laidley, a sociology doctoral student at NYU (where I also hold a research appointment), uses satellite images to develop a new and improved “Sprawl Index,” which he links to a wide range of outcome measures. Laidley uses these aerial images (see above) to estimate sprawl at the Census block level, the smallest level available, estimating the share of metro population in those blocks below three key thresholds: 3,500, 8,500, and 20,000 persons per square mile. His index is based on the average of these three values, with higher scores reflecting higher levels of sprawl.
The map above, from Laidley’s paper, charts his Sprawl Index across America’s 150 largest metros based on data from 2010. The table below lists the top and bottom ranked metros on his Sprawl Index.
Perhaps the biggest surprise is that L.A. ranks as the least sprawling metro in the country, ahead of New York and San Francisco. As Laidley writes: “Although Los Angeles is often popularly associated with sprawl because of its pollution and traffic, its sheer lack of very low-density development places it atop all U.S. metro areas.” In fact, six of the top 10 least sprawling metros in the country are in California: L.A., San Francisco, San Jose, Salinas, Santa Barbara and San Diego. Seven of ten are on the West Coast. Outside of that, Chicago ranks seventh and, also surprisingly, auto-oriented Miami is tenth. The East Coast metros of Philadelphia, Boston and D.C. all fail to make the top 10 list.
Less surprisingly, the metros with the highest levels of sprawl are mainly smaller ones in the Sunbelt. Columbia, South Carolina, is number one, followed by Hickory, North Carolina, Kingsport, Tennessee, Asheville, North Carolina, and Ocala, Florida.
The next map (also from Laidley's study) charts the change in sprawl between 2000 and 2010. The nation as a whole became more sprawling over that period according to the Sprawl Index measures, with the overall measurements for the country growing from 57.9 in 2000 to 59.4 in 2010.
As the table below shows, sprawl grew the fastest in New Orleans, Detroit and Flint, Michigan; and the slowest in Honolulu; Salem, Oregon; and Santa Barbara, California.
Laidley points out that the metros that saw the least sprawl—those that actually grew denser—are ones that have their outward growth limited by so-called “growth control” policies. Oregon, one of the first states to introduce metropolitan growth boundaries, has two metros in the top 10: Salem and Portland. Honolulu, Santa Barbara and Seattle also have their outward growth limited by growth control policies.
Many of the metros that saw the most sprawl are older Rustbelt communities that have suffered from deindustrialization, job loss and population decline, such as Detroit, Flint, Cleveland, Toledo and, perhaps surprisingly, Chicago. These metros are locked in a troubling syndrome of outward expansion without economic or population growth. As Laidley notes:
The plight of declining metropolitan regions—which sprawled the most from 2000 to 2010—highlights the difficulty in preserving compact communities in places suffering from significant losses in population and employment. As controversial as the imposition of growth controls has been, targeted decline raises even more vexing questions as to how to preserve relatively healthy areas amid widespread deprivation.
But what is the connection between sprawl and economic and social outcomes? To get at this, Laidley conducts a series of statistical analyses (including bivariate correlations, multivariate regression analysis and more sophisticated fixed-effects models) to better gauge the connection between sprawl and phenomena like hazardous pollution, carbon emissions and housing affordability. Using regression analyses, he finds that:
For every 10 percent increase in sprawl, there is an approximately 5.7 percent increase in per capita carbon emissions, a 9.6 percent increase in per capita hazardous pollution, and a 4.1 percent and 2.9 percent reduction in the owner and renter housing affordability index, respectively.
On the other hand, he finds that housing prices are higher in denser (less sprawling) metros. This is in line with a good deal of other research that documents the connection between density, walkability and housing prices.
This new Sprawl Index provides additional evidence not just of the extent and costs of sprawl but of the degree to which the United States continues to sort itself into two nations: one denser, more expensive, more educated and healthier, the other more spread out and more affordable (if less wealthy), but more polluted and less healthy.