The Meaning and Measure of Sprawl

Planners do not like sprawl one little bit and who can blame them? I once heard urban designer Doug Kelbaugh sneeringly describe sprawl as “a smear on the landscape.” Who likes a smear?

Definitions
Let’s concede that the discussion of what sprawl is and is not covers an awful lot of ground, even if we save space by limiting ourselves to underlying characteristics rather than their consequences. It is not a blog-friendly topic. USA Today says you get close enough just sticking to population density and its rate of change. Smart Growth America (Ewing, Pendall and Chen, 2003) add in land use mixing, highway access and degree of “centeredness.” But if you’ve read Downs’ summaries or Ewing’s 1997 JAPA article, “Is L.A.-style sprawl desirable?” (one of my all-time favorite paper titles because you don’t need to go to the trouble of reading it to guess at the answer) then you already have a start on how the definition can, and should, expand: More detailed density & design elements, the continuity of land development, land use mix, car dependence, circulation patterns, and so on.

This has received terrific attention in textbooks lately, with one or more chapters on definitions in Gillham’s The Limitless City, Bruegmann’s Sprawl: A Compact History, and the explicitly visual (and perhaps visceral) approach in Hayden’s A Field Guide to Sprawl. (Gillham performs the additional service of listing the definitions from a multitude of official constituencies.)

For the sake of argument and economy, one could group these into 3 kinds of smears: Unplanned growth (functional chaos and ugliness), badly planned growth (functional chaos and ugliness), or merely suburban growth in its myriad forms (e.g., low densities, car dependence, enforced homogeneity in form and culture, discontiguous development, esthetic banality,….). Each of these labels has the merit of also suggesting its cause and thus treatment. First, how can we pin it down empirically?

Measures
Planning scholars making signficant advances in the empirical measurement of sprawl lately include Landis, Pendall, and Galster and their colleagues. The real motivation for this post is to also make you aware of a forthcoming 2006 QJE article, “Causes of Sprawl: A Portrait from Space,” by Birchfield, Overman, Puga and Turner. It moves things forward a couple of ways.

Part 1 is descriptive, with an enormous data processing effort using aerial photos and satellite imagery from two years, 1976 and 1992, for the 8.7 billion 30×30 meter parcels of land of the entire continental U.S. They overlay these two years to measure changes in the amount of developed land, by land use type, resulting in this very cool poster. You can be frightened by the urbanization rate of 2.5% per year over the period (totalling 48% over 16 years) or relieved that by 1992 this still only amounted to 1.9% of the land surface. (That is 2/3rds what Fulton, Pendall, Nguyen and Harrison estimate because, according to Birchfield et al., the more aggregate nature of the Fulton et al. National Resource Inventory data misclassifies large amounts of undeveloped land as developed.)

These averages obscure large differences by place. Coastal areas are more developed, Florida is sprawling faster, and California is sprawling more slowly. Both Atlanta and Miami sprawled quickly but Atlanta mainly via scattered greenfield development at the periphery while Miami sprawled more contiguously or via infill.

Wait, what do they mean by sprawl? Not density. And they aren’t distinguishing between good and bad growth or any design or social capital feature. This is just developed land.

To measure the extent of sprawl … we calculate the percentage of open space in the immediate square kilometer [of each 30m cell]. We then average across all residential development in each metropolitan area to compute … a very intuitive index of sprawl: the percentage of undeveloped land in the square kilometer surrounding an average residential development. (p. 10/11)

Think of each 30m cell as a house. They calculate the % of the square kilometer around that house that is undeveloped, average this over the metropolitan area, and then compare 1976 with 1992. So it is more of a pure land coverage measure than density measure.

The result: In 1976, 42% of the land in the square kilometer surrounding residential develoment was open space. By 1992, this had grown to only 43%. By this measure the country is not tending toward sprawl substantially more than in the past. There is more land covered, to be sure, but much is infill or contiguous. Again, this varies by place.

Part 2 tries to explain these patterns and the rest of the paper is a nice summary of the sprawl hypotheses of modern urban economic theory, physical geography, and political economy; namely, that cities will sprawl more if (p.20):

they specialize in sectors where employment is not typically located close to the city center,
they were built around the car rather than around public transport,
they have experienced slow population growth,
there is greater uncertainty regarding their future population growth,
aquifers underlie a greater fraction of their urban fringe,
they are not surrounded by high mountains,
terrain in their urban fringe is rugged,
their climate is temperate,
they begin with substantial unincorporated areas on the urban fringe,
local tax payers pay a smaller share of local government expenses.

Most of these are some kind of expectation regarding the relative cost of expanding rather than filling in the urban footprint. Their data seem to support most but not all.??They are particularly surprised by 3 results: The pattern of sprawl is substantially affected by the location of groundwater (future growth tends to stay over the aquifer, as in the past, rather than sprawl) and by the ability to leapfrog out of municipal authority into county authority, while the density of roads does not matter. They have no good explanation for the last result, other than to guess that more road density may stimulate both sprawl and infill, which then wash out in the analysis.??

Right tools for the job?
If you have a nail in your tire, as my wife happens to have today, that means pretty soon you are without a ride. A mechanic could fix it, even a novice.

If you have unplanned growth, that means — unless you are lucky and the natural course of things establishes its own order (as Suburban Nation contends for places like Alexandria, Virginia) — there will be disorder. A planner should be able to fix that too, even an uncreative or uncritical one.

Badly planned growth is accidental disorder. Suburban Nation suggests that an enlightened urban designer (or maybe planners trained by one) is your best bet to fix that.??

Simple suburban growth — such as measured by Birchfield, et al. — reflects, among other things, the pressures of rising incomes and falling transportation costs, terrain/geography, plus governance issues associated with exclusionary zoning (e.g., Zoned Out) and jurisdictional competition/fragmentation. For their part, Birchfield, et al. only suggest the possible value of groundwater regulation and municipal-county land use harmonization as policy initiatives.??

The theoretical appeal of direct controls, such as urban growth boundaries is that they literally draw a line on the map to cage in growth. In practice, however, they may be neither binding (as with the rolling boundary in Portland) nor particularly responsive to the underlying problem — whether that be good vs bad planning, or more fundamental determinants of urban growth.??

Another approach would be to address associated externalities, such as traffic congestion/pollution and the undervaluation of open space. This is always a good idea but is unlikely to be satisfactory to those hoping to make a substantial dent in sprawl. Again, that tack mainly deals with consequences, not causes. It wouldn’t eliminate sprawl, for example, only its excesses.??

Perhaps I need to think about this more.

Published:: Monday, February 27th, 2006

Author:: randall Crane

Topics:: environment, metrics, sprawl, suburbs

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Anonymous

February 27th, 2006
20:04 CST

Randall,

Marina Alberti at UW does landcover change modeling, some of it for NSF, but for Puget Sound region. Check out some of her work. Her older work is with the 30m LandSat pixel resolution, but she also has higher res. for other work as well. She has encountered the same issue wrt developed/grass pixel analysis & seems to have started to overcome it & has developed indicators of development and urban ecosystem degradation.

Best,

Dan Staley

Rolf Pendall

March 3rd, 2006
18:16 CST

Randy —

This article uses data sources that aren’t longitudinally compatible. I asked a colleague — Greg Taff, remote sensing and GIS maven at the UNC Chapel Hill Geography Department — to comment on the sources. Here’s what he had to say:

“The 2 datasets that the ‘Causes of sprawl’ article uses are not very compatible for the purpose of locating new exurban areas. The 1976 NLCD data requires 20% urban landcover to be classified as urban, and the 1992 NLCD data requires 30% urban landcover to be classified as urban. From experience in my lab at UNC, this 10% difference in definition can result in quite large classification differences. Thus, the 1992 NLCD classification procedure would classify far fewer areas as urban than would the 1976 NLCD classification procedure, if they were based on the same time point, since the 1992 procedure requires 10% higher urban cover to be classified as such. This may be why they are seeing less (particularly exurban) land development than you have seen with the National Resources Inventory [NRI] data.”

The NRI, by contrast, is a panel sample of the same data points over a series of years (1982, 1987, 1992, 1997 at the sub-national level; up to 2002 in national reporting). It avoids the problem of incompatibility and is, intrinsically, therefore a better source for measuring gross land-use change for multi-county areas than the combination of 1976 and 1992 data that the authors use. (By the way, the “1976″ data actually represent 12 years of data from 1971 to 1982, with 1976 being just the median year.)

I think the authors’ main concern is probably the “10 acre” rule, which classifies as “urban” any undeveloped land of 10 acres or less that is surrounded by urbanized land. In regions where development is very scattered (southern New England, for example, where wetlands and their regulation tend to force scattering), this classification process would tend to increase the total amount of land read as “urbanized,” but I don’t know how that would make a difference for the calculation of change over time. It would seem to be just as important at the beginning as at the end of the time period.

The authors also contend that the NRI is not appropriate for analysis at the sub-state level because it’s a sample. According to what I know, however, the broad land use categories are ground-truthed to a greater extent than the other data in the NRI, which is otherwise based mainly on aerial photography. (And anyway, why should we believe the data on Maricopa or San Diego County less than the data on Rhode Island? It’s a matter of the number of sample points.)

At the national level, the USDA NRCS does publish confidence intervals for the estimates of developed land. In 1982, 72.8 million acres were developed in the contiguous (+/- .4M @ 95% c.i.). By 2002, that figure jumped to 107.3 million acres (+/- .7M). Granted, that’s still “only” 5.5% of the contiguous U.S. land area, but it’s up from 3.8% in 1982. And a 47% increase in developed land is something we should pay attention to. (See http://www.nrcs.usda.gov/technical/land/nri02/landuse.pdf for the data.)

Also, evidence from the 2002 NRI suggests strongly that the pace of land development increased sharply between 1992 and 2002 compared with the previous decade. Since the paper stops in 1992, the “no problem” subtext is, I think, premature. 1976-1992 had two periods of substantial economic downturn, making it a less active period of land conversion than 1992-2002. The 1992-2002 period accounted for about 60% of the urbanized land for the two decades.

I was also a little surprised that the authors didn’t look at foreign immigration and growth in the foreign-born as a driver of population density, and they didn’t use the share of federal land either. These have been found to be significant and important influences on sprawl in other work.

John Carruthers’s work on sprawl, fiscal structure, and municipal fragmentation is still the best in the literature, in my opinion. No references to any of that in the paper, however.

Rolf

March 3rd, 2006
20:57 CST

Rolf:

my tacit comment was that the conclusions in Randall’s post weren’t quite correct.

Thank you for doing this work and making these concerns explicit – I should have brought those out with a little more detail.

BTW – John Carruthers is an old prof of mine. I’ll pass along the compliment. )

Regards,

Matthew Turner

March 9th, 2006
20:33 CST

As Professor Crane notes in his gracious comments about our paper, “Causes of sprawl: a portrait from space” (soon to be published in the Quarterly Journal of Economics), our purpose is to advance our understanding of urban sprawl in two ways. First, to concentrate on and measure a well-defined aspect of sprawl, the scatteredness of development. Second, to investigate what causes differences in this aspect of sprawl across metropolitan areas.

We measure the scatteredness of development as the percentage of land that is not developed in the square kilometer surrounding the average house in each metropolitan area. The data we use are the best available for this purpose.

Despite its many virtues, the National Resource Inventory (NRI) is unsuitable for this measurement. Based on a sample of the United States, the NRI estimates the amount of land that is under different uses in the nation and in individual administrative areas. If all you want to do is estimate what percentage of land is developed in the United States or in individual states, then the NRI is perfectly suitable. It provides a slightly larger estimate of how much U.S. land was built up in 1992 than our data because it counts larger gaps between buildings (up to 500 feet) as developed. However, the NRI also gives a slightly lower estimate of the growth rate of developed land precisely because it cannot detect the infilling of these gaps. That does not make it better or worse, it just uses a different definition of what land is considered developed.

If, instead of state or national level numbers, you want to get an idea of how much land is developed at the level of individual counties, you start running into trouble with the NRI. The U.S. Department of Agriculture is the agency that produces the NRI data. Right in the first section of USDA’s user guide for the data, it says: “the NRI was NOT designed for analyzing issues at the county level” (emphasis in the original). In case this was not clear enough, the user guide goes on to illustrate this with an example of the confidence intervals that Professor Pendall mentions in his post: for the U.S. as a whole the margin of error for the amount of cultivated cropland is only 0.6%, but for Greyson County KY it jumps up to 46.1%. In our discussion of the NRI in “Causes of sprawl” we are just passing along the USDA’s report.

Our main purpose is not to measure how much land is developed, but instead to measure how scattered is development in individual metropolitan areas (and, in follow-up work, in individual neighborhoods). For this you need to be able to see where houses are located and then study land in the square kilometer around each house you see. Here the problem with the NRI is not its unreliability for small administrative areas, it is simply that you cannot reproduce our calculation of how scattered development is with counts of land in different uses at the level of administrative areas.

Turning from the NRI to our data, while there are many similarities between our 1970s and the 1992 data, we agree with Professor Pendall that there are also some subtle differences in the classification schemes that could have implications for the analysis. We have been very careful in dealing with this issue. The key here is to note that, while land is often redeveloped, it is almost never undeveloped. At the national level, according to the NRI, less than 0.8% of developed land was converted from urban to non-urban uses over the 15-year period 1982-1997. With virtually no undevelopment taking place, urban land in 1992 is the union of pre-1970s development and development taking place between the 1970s and 1992. Thus, we do not need to compare the 1970s and 1990s data directly. We only need to use the 1970s data to split 1992 development between pre-1970s and post-1970s development. To this end, we define `old development’ as land that was classified as urban in both the 1990s and 1970s. We define `new development’ as land that was classified as urban in the 1990s, but was not urban in the 1970s. We also use the 1970s data to account for any conversion between residential and commercial uses. Two additional observations reinforce our confidence in this procedure. First, the national growth rate of development that we record is in the same ballpark as what we see in the NRI for a similar 15-year period. Thus, on average, our classification of 1992 developed area into `new’ and `old’ is probably about right. Second, a comparison of maps produced from the data with images for the 1970s and 1990s shows remarkable accuracy. Since the two data sets, in spite of their slight differences, are intended to measure the same thing, we shouldn’t really be too surprised by this.

While we believe our data measures levels and changes in developed area quite accurately, the main purpose of our QJE article, however, is to study what causes differences in sprawl across U.S. metropolitan areas. For the purposes of our statistical analysis, the key is not whether there is some noise in the data but whether any such noise is systematically related to the unexplained component of sprawl. We have no reason to suspect such a correlation. Professor Pendall’s post mentions population growth through foreign immigration and federal lands. We looked at both population growth and proximity to public lands (and the results are mentioned in the text of our paper). We should also make it clear that we do not intend a “no problem” subtext. We do not make any judgment about whether there is too much or to little development, whether it is too scattered or too compact. We measure sprawl and explain its causes.

Diego Puga and Matthew Turner

urban planning research

The Meaning and Measure of Sprawl

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