Author Archives: John Ottensmann

Measuring urban sprawl: some results

I measure sprawl by looking at the pattern of development in the suburban portions of 59 large urban areas in the United States (see my earlier post).  I have delineated the urban areas for the census years from 1950 to 2010 using census tract data for housing units. Urban areas are defined as the contiguous tracts meeting a minimum density threshold in much the same way that the Census defines Urbanized Areas. The suburban areas are then defined as consisting of those tracts added to the urban areas after 1950.

Multiple measures of density in the suburban areas are used for the creation of the sprawl index. These include density, housing-unit-weighted density, and the percentages of housing units in census tracts with densities less than the first density quartile and the median density for all of the suburban tracts. Standardized scores are used to combine these measures into a single sprawl index for 2010.

First looking at which areas sprawl the most: Greenville-Spartanburg, South Carolina tops the list. But sprawling areas are not limited to the South, as Albany-Schenectady-Troy comes in second. And they are also not limited to the smaller urban areas in the dataset, as Boston-Providence is the eighth most sprawling of the 59 urban areas, which may be a surprise to many. But remember we are not talking about the very urban environments of Boston or Cambridge, we are talking about the levels of sprawl in the suburban areas developed after 1950.

The least spawling areas will be equally surprising. Las Vegas has the lowest level of sprawl, followed by Miami-Fort Lauderdale-West Palm Beach. And Los Angeles and Phoenix, areas seen by some as exemplars of sprawl, are the areas with the fourth and fifth lowest levels of sprawl.

Looking at the means of the sprawl index for the areas in the 4 census regions, average levels of sprawl are by far the highest for the areas in the Northeast. And they are equally low for the areas in the West. Areas in the Midwest and the South have mean levels of sprawl closer to the overall mean for all areas.

One factor that may be contributing to lower levels of sprawl in some areas is the presence of physical barriers to the expansion of the urban area. Urban areas up against mountains and wetlands had much lower levels of sprawl than other urban areas.

For those questioning this approach to measuring sprawl because it places Boston as high sprawl and Los Angeles low, consider this: The suburban areas around Boston had a density of 584 housing units per square mile. For Los Angeles it was 1,335. And 46 percent of all suburban housing units in Boston were located in tracts with densities less than the median for all of the urban areas, 611 units per square miles compared with only 9 percent for Los Angeles.

More on the measurement of sprawl and these results can be found in my paper, “An Alternative Approach to the Measurement of Urban Sprawl” which can be downloaded here.


Measuring urban sprawl: what is sprawl?

The previous post described my intentions to measure urban sprawl by considering the patterns of development in the suburban portions of urban areas. This necessarily requires specifying what aspect of that pattern is associated with degrees of urban sprawl. In other words, I need to define sprawl–a topic on which there has been little agreement.

Some have simply said of sprawl that, like pornography, “I know it when I see it.” Obviously this doesn’t help for measurement. Nor does citing areas as exemplars of sprawl or describing sprawl using aesthetic standards, e.g., ugly development.

Some have defined sprawl as unplanned development. But nearly all jurisdictions in the United States have planning and zoning, yet sprawl certainly is common. So is sprawl development that results from “bad” planning? How would you define this? It better not be planning that leads to sprawl.

Another approach has been to define and measure sprawl based on its causes or consequences. For example, automobile dependency might be seen as an indicator of sprawl. But then how would one account for other consequences of sprawl, say the loss of agricultural land or negative health effects? By saying that automobile dependency is associated with certain patterns of development? But then the patterns of development would seem to take precedence and be the actual indication of sprawl.

I come to the conclusion that sprawl must be defined by some aspects of the pattern of development. And low density and scattered or leapfrog development are most often cited as characteristics of sprawl. Virtually all studies that use a single measure for sprawl have used measures of either density or fragmentation.

And I can take one further step in simplifying things. Scattered development is separated by vacant land. Considering the pattern of development over somewhat larger areas, this vacant land results in low densities. So it is reasonable to take some measures of density for the measurement of urban sprawl.

A full description of my use of multiple measures of housing-unit density for the measurement of sprawl can be found in my paper, “An Alternative Approach to the Measurement of Urban Sprawl” which can be downloaded here.

Measuring urban sprawl: where is sprawl?

As I began my urban patterns research, I had intended to stay away from the characterization and measurement of urban sprawl. This is, after all, such a value-laden topic with much of the writing shedding more heat than light. But as I worked with my data, some ideas about the measurement of sprawl emerged that I thought might be a unique and useful contribution. So I undertook the research, wrote a paper, and am posting about it on this blog. I am doing  a series of 3 posts. This first one addresses what is the most distinctive contribution of my approach, asking what areas should be considered when measuring sprawl. The next discusses the definition of sprawl and its measurement in those areas. The third gives some results for sprawl for large urban areas in 2010.

The term “urban sprawl” is generally used to refer to patterns of suburban developemnt. Sprawl is seen as a characteristic of suburban areas built since about the middle of the last century. Indeed, numbers of people have used the term “suburban sprawl” to mean exactly the same thing.

So I have a modest proposal: If sprawl is a characteristic of suburban development, then the measurement of the extent of urban sprawl should consider the patterns of development in suburban areas. I think this seems perfectly logical and reasonable. However it contradicts virtually all efforts to measure and compare sprawl across urban areas, which have considered the patterns of development in entire urban areas.

If two urban areas have identical suburbs with the same amount of sprawl, why should it matter if the older, denser, nonsprawling portion of one area is denser than the other? Why should that urban area be considered to have a lower level of sprawl? But that is exactly what results from looking at the pattern of the entire urban area.

Measuring sprawl by looking at the pattern of the entire urban area can actually create a paradoxical situation. An urban area with a very large, very dense older core surrounded by a very sprawling suburban area could actually be seen as less sprawling than another area with a smaller and less dense core surrounded by suburbs with low levels of sprawl.

Further arguments for focusing on suburban areas when measuring sprawl and the use of this approach for such measurement can be found in my paper, “An Alternative Approach to the Measurement of Urban Sprawl” which can be downloaded here.

Pros and cons of spatial targeting

An earlier post described how some residents of inner-city Baltimore in the 1960s were excluded from the benefits of certain programs because they lived outside the areas in which these programs were targeted. The programs had identified areas with the highest proportions of those in need and limited their services to those areas and their residents. Such spatial targeting has both benefits and costs, which I will consider here.

First, a definition: By spatial targeting, I mean a program for the delivery of services to some group of people, typically disadvantaged, that begins by identifying an area having the highest concentration of those people in need. The services of the program are then provided by facilities that are located in the target area. The targeting of those services can be more or less exclusive. More restrictive targeting limits eligibility for the service to those residing within the target area. Less restrictive policies make the service available to all who would otherwise be eligible, though obviously obtaining the service will be less convenient for those living farther from the target area.

Targeting has advantages. It is efficient in the sense of placing the service locations closest to the greatest numbers of intended beneficiaries. The more restrictive form of targeting can eliminate the need for what might be considered to be obtrusive means tests by restricting eligibility to the residents of the target area. (This is essentially equating “need” with target-area residence.)

A more subtle potential benefit of spatial targeting could come from the effect of the service in improving the overall levels of well-being within the area. The concentration of disadvantge in an area can have negative effects for all area residents (what economists would call a negative externality). By improving conditions for some residents of the area, these adverse neighborhood effects may be reduced, yielding further benefits that accrue to all residents.

Of course these benefits do not come without costs. A targeted program effectively discriminates against those living outside the target area. Those with equal need not living in the area may not be able to receive the services, either because of a policy restricting benefits to target-area residents or because distance to the target area makes the services inaccessible, even without a residence requirement.

With strict targeting substituting a residence requirement for a means test, some service can be provided to those with lesser need. Arguably this could be seen as being less efficient in that not as much service is being provided to those with the greatest need.

Finally, spatial targeting can limit political support for the provision of the service. To be sure, if the service is aimed at the disadvantaged, they will not constitute a majority of the electorate in any event. But by restricting the service to a limited areas, those in areas outside, both disadvantaged and not, may be less inclined to support an activity that does not assist those in need outside the target area. And spatial targeting by its very nature emphasizes the fact that the service is being provided for a relatively small segment of the population.

Images of Infinite Suburbia

Several years ago, I did a series of posts discussing how articles and books critical of urban sprawl were inevitably illustrated with overhead views of fairly dense single-family development (first post), while work promoting new urbanism and smart growth more frequently showed those environments from the ground-level perspective of their residents (second post). And indeed, some of those new urbanist developments looked surprisingly similar to the sprawling areas when vieweed from above (third post).

The new book Infinite Suburbia (edited by Alan Berger and Joel Kotkin) contains scores of articles looking at suburban development from a wide range of perspectives. (I will post some additional comments on this book when I have finished reading it. (The book is huge–over 700 pages, about 6 pounds!) Many different points of view are expressed in the various pieces. The book is certainly not an unambiguous critique of urban sprawl.

But despite taking a balanced view of suburban development, the book gives in to the idea that such development should be illustrated primarily with views from above. The book includes many 2-page color spreads (nearly 50) of aerial photos of suburban areas from around the world, frequently showing areas discussed in the accompanying articles. The pictures are gorgeous, but they reinforce the notion that suburban patterns should be perceived and understood from above, not from the points of view of their inhabitants.

2-page aerial view from Infinite Suburbia

2-page aerial view from Infinite Suburbia

To be sure, many of the articles include additional images of the suburban environments being discussed. But these are much smaller than the 2-page aerial-view spreads. (Some of the largest of the pictures within the articles were one-sixth the size (in terms of area) of the overhead images.) And many of these were additional overhead images, not views from the perspective of residents of the areas. The message, unfortunately, is unambigous: Suburban development is best viewed from above.

Talk about place-based discrimination

In the previous post I described the way in which people in Baltimore were frustrated that programs were being targeted to specific areas and excluded their neighborhoods and themselves. A New York Times Upshot article describes a truly horrible proposal passed by one house of the Michigan legislature that would establish place-based discrimination in an indefensible manner.

At issue is the establishment of a work requirement as a condition of eligibility for Medicaid. Whatever one thinks about the appropriateness of such a requirement, I think everyone would accept that some Medicaid recipients should be exempted due to an inability to work. The proposal would exempt recipients from the work requirement if they lived in a county with an unemployment rate greater than 8.5 percent. The rationale is that people should not be required to work if they live in areas in which jobs are hard to find.

It is of course entirely reasonable to exempt Medicaid recipients from the work requirement if it is impossible for them to find a job. And the level of unemployment is certainly one indicator of the difficulty. Of course, so also might be a measure of the number of discouraged workers who have given up looking for jobs and who therefore do not contribute to the unemployment rate. And why the unemployment rate in a county as opposed to some other area, such as the city where the recipient lives? (The Times article raises this.) Further, the skill requirements for available jobs versus the qualifications of the Medicaid recipients is clearly relevant to their ability to find work.

It might be argued that the county unemployment rate exemption could be reasonable as one criterion for exemption from a work requirement along with other criteria relating to the inability to find a job. But since any other conditions are necessarily going to be more involved, they will serve as a higher threshold for those Medicaid recipients living in the nonexempt counties.

And as the Times article points out, the counties and Medicaid recipients exempted are predominantly white and rural. It would be difficult not to conclude that this proposal reflects racially biased stereotypes about the deserving versus the undeserving poor.

“We’ve been screwed again”

Fifty years ago (!) I was working as a VISTA Volunteer with the antipoverty program in Baltimore. The Community Action Agency provided services from neighborhood centers in their target areas on the east and west sides of the inner city. The neighborhood center at which I worked was at the southwest corner of the west side target area. In fact, the center was actually one block outside of the target area in an old, unused branch public library building.

This detail on location is important. People came into our center looking for help in getting a job. A variety of programs were available that provided employment assistance and job training. But many of these programs had as a condition of eligibility that the recipient live within the antipoverty program target area. Someone who came in from down the block, living outside the target area, could not participate in those programs.

At least they were not supposed to. To get them help, we would suggest that they use the address of someone they knew a few blocks over, inside the target area. Not exactly proper. But it was a way of getting help for people who needed it.

This obviously seemed more than a little unfair to us and to the residents living near the neighborhood center who were confronted with this residence requirement. But a later event showed that the unfairness was perceived more widely.

A Baltimore city planner was undertaking a subcity district planning effort for an area of southwest Baltimore extending out from the center of the city. He had worked with a local priest to bring together the leaders of numerous community organizations to provide input to the planning process. (This was pretty revolutionary stuff for 1968.)

Baltimore had been approved for participation in the new Model Cities program and was planning the election of citizen advisory boards, the first step in implementation. The Model Cities program was restricted (by statute) to spatially limited areas. Baltimore’s Model Neighborhood encompassed an area on the west side similar to the antipoverty program target area.

The city planner briefed the community representatives he was working with about the Model Cities program. This was seen as a big deal by both city officials and neighborhood residents as it promised to bring major federal resources to the Model Neighborhood. (The Model Cities program failed to meet this promise but that was later.)

But there was one problem. The Model Neighborhood included only the inner portion of the planning area from which the community representatives had come. Those representing neighborhoods inside the Model Neighborhood were excited about the prospects for the program. Then a representative of one of the neighborhoods outside the Model Neighborhood got up to express the sentiments of the others, obviously thinking about the antipoverty program targeting as well: “We’ve been screwed again.”

Category: General
Tags: antipoverty, Model Cities, citizen participation, spatial targeting, VISTA, volunteer