Loss of agricultural land in an uncertain world

One of the negative consequences attributed to urban sprawl is the loss of farmland that is converted to urban use.

In their paper questioning the desirability of compact cities and the problems associated with urban sprawl, Peter Gordon and Harry Richardson begin by presenting arguments that sufficient agricultural land is available in both the United States and the rest of the world such that any losses should not be a reason for concern. (See “Are Compact Cities a Desirable Planning Goal?” Journal of the American Planning Association, 1997). In his response, Reid Ewing accepts some of the evidence Gordon and Richardson presented but raises numbers of countervailing arguments. (See “Is Los Angeles-Style Sprawl Desirable” in the same issue.)

Obviously different conclusions can be drawn from these conflicting presentations. But one issue was not raised in discussing the significance of losses of agricultural land–climate change and global warming. (Ewing did mention greenhouse gas emissions and climate change when discussing energy use but not in relation to the loss of agricultural land.) Higher temperatures and related climate change could have significant effects on agricultural productivity. Both the climatic effects and their impacts on food production are highly uncertain. And this uncertainty should heighten concern over the potential consequences of the loss of agricultural land.


Brookings map shows MSAs mapped the right way

The previous post discussed the problems with maps of Metropolitan Statistical Areas that showed the extent of the counties included. It included this map from the Census giving the misleading impression that the area of the greatest population increase was in the southwestern United States, the large dark purple area:

Percentage Change in Metropolitan and Micropolitan Statistical Area Population: 2000 to 2010. Source: Metropolitan and Micropolitan Statistical Area Population: 2000 to 2010. Source: U.S. Bureau of the Census. 2011. Population Distribution and Change: 2000 to 2010. 2010 Census Briefs.

Source: U.S. Bureau of the Census. 2011. Population Distribution and Change: 2000 to 2010. 2010 Census Briefs.

In that post I also presented an alternative map of MSAs that avoids this problem and referred to a note with a more extensive discussion of these issues. That note ended with my doubts about whether the way MSAs were mapped would change. I was wrong.

A recent post by William Frey of the Brookings Institution looked at recent population changes from a new census report (US population disperses to suburbs, exurbs, rural areas, and “middle of the country” metros). It includes a map illustrating population changes from 2016 to 2017 for the 100 largest metropolitan areas:

Source: William H. Frey analysis of U.S. Census Population Estimates, released March 22, 2018

Source: William H. Frey analysis of U.S. Census Population Estimates, released March 22, 2018

Hooray! Point symbols are used to map the MSAs and their population changes. The map does not give the misleading impression of the Census map shown above.

One very minor suggestions to Brookings for further improvement: The points appear to be located at the centers of the MSAs. It would be even better if they were located in the vicinity of where most of the populations of the MSAs were located.

The problem with maps of Metropolitan Statistical Areas

Metropolitan Statistical Areas (MSAs) are one of the most important statistical units for the reporting of data by federal agencies. It is common to see maps of showing those areas, such as this map from the census illustrating changes in population from 2000 to 2010:

Percentage Change in Metropolitan and Micropolitan Statistical Area Population: 2000 to 2010. Source: Metropolitan and Micropolitan Statistical Area Population: 2000 to 2010. Source: U.S. Bureau of the Census. 2011. Population Distribution and Change: 2000 to 2010. 2010 Census Briefs.

Source: U.S. Bureau of the Census. 2011. Population Distribution and Change: 2000 to 2010. 2010 Census Briefs.

The MSAs are colored with various shades of purple, darker for greater percentage population change. (The green areas are the Micropolitan Statistical Areas, the smaller analogues of the MSAs.)

A large area of MSAs can be seen in the southwestern United States, extending from eastern Arizona to the Pacific (and up through California). But much of this area is hardly metropolitan in any meaningful sense. It is sparsely settled mountains and desert land. The only reason this is within MSAs is that MSAs are composed of entire counties. And many counties in the West are very large, resulting in such empty areas being included along with the portions of the counties that are truly metropolitan. (See my earlier post on not calculating densities for MSAs for further discussion.)

This not only affects perceptions of the extent of metropolitan areas, it can also be quite misleading when MSAs are used to map data. Consider the map shown above. The darkest purple areas are the MSAs that had the greatest percentage population increase over the decade. One immediately observes the very large dark purple area extending across southern California, Arizona, and even into Nevada and Utah. So is this where large population gains were most dominant? Looking more closely at the map, you might notice that numbers of the MSAs in Texas are also dark purple. In comparison, these areas look small and scattered. But they include all of the largest MSAs in Texas and many smaller ones. And the Texas MSAs in this highest growth category had a total population in 2010 that was over twice that of the highest-growth MSAs in the southwest. Sizes of MSAs can be very misleading.

But MSAs do include entire counties, so what is the alternative for mapping these areas? One possibility is to use point symbols in place of the county areas for showing the presence and location of MSAs. And if one wants to show some characteristic of the MSAs, the sizes of those symbols can be varied. This is an alternative map of MSAs with the symbols graduated to show the population sizes of the areas:

Metropolitan Statistical Areas by Population 2010.

Metropolitan Statistical Areas by Population 2010.

A more extensive discussion is in the note, “The Problems with Maps of Metropolitan Statistical Areas,” which can be downloaded here.

What you can’t say about variation within metropolitan areas

The New York Times recently published an article titled (on the web) “Why Outer Suburbs in the East and Midwest Have Stopped Booming.” It’s a curious piece, based on data by county across the U.S., showing those counties in which deaths have exceeded births. Indeed, the article includes an animated map of the entire country showing such counties from 1991 to 2016 and notes that over 1,200 counties had more deaths than births in 2016. This map of the counties, by the way, provides absolutely no way of identifying the counties in the outer suburbs of metropolitan areas or, for that matter, metropolitan area counties in general.

But the primary focus of the article, from the first paragraph to the last, is on what the title refers to as “outlying suburbs.” The article states that “about one in four outer-ring suburbs were experiencing more deaths than births, including 18 of 30 such counties in New York, New Jersey and Pennsylvania,” equating the outer-ring suburbs with counties. I won’t quibble about the lack of any definition of how a county is considered to be an “outer-ring suburb.” This is, after all, a piece of journalism, not a scholarly article.

The problem with drawing the conclusion about regional variation in births and deaths in outlying counties (claimed to be an issue in the Northeast and Midwest) is that this requires the existence of such “outer-ring suburb” counties. And the reporter’s view does not go that far west of the Hudson River, as is so often the case with the New York Times. Note the reference to the counties in New York, New Jersey, and Pennsylvania.

In order for a metropolitan area to have outer suburban counties, the metropolitan area must have a sufficient number of counties so that these can be distinguished from the inner suburban counties and from the urban area counties. For this to be the case, the metropolitan area must be fairly large and the counties must be quite small. And this is the problem. The Northeast and Midwest have numerous metropolitan areas that meet these criteria, for which one can draw such conclusions about “outer-ring suburbs.” Some metropolitan areas in the South also meet these criteria–the Atlanta metropolitan area would be a prime example, consisting of many counties.

But metropolitan areas in the West, where counties tend to be larger–often much larger–have few, if any counties that could be considered to be “outer-ring.” The San Diego Metropolitan Statistical Area (MSA) consists of one county. The Los Angeles MSA has 2 counties. San Francisco-Oakland MSA has 4 counties, but none could reasonably be considered to be “outer-ring suburbs.” If one took a more expansive view of the metropolitan areas than the MSA definition (which I would advocate), it might be plausible to identify a few counties as being outlying suburban counties. But even then, these would be few and isolated. Nowhere near the number of counties for which you could draw conclusions like the 18 of 30 counties in New York, New Jersey, and Pennsylvania.

Bottom line: It may be the case that “outer-ring” suburban counties in the Northeast and Midwest are now experiencing more deaths than births (though this is necessarily a weak conclusion given the lack of a definition of those counties). But stating this conclusion implies that this is not the case in the South and West. And it is impossible to draw any conclusions about “outer-ring” suburbs in most of the West based on county-level data.

You cannot say anything meaningful about variation within metropolitan areas across the U.S. using county level data. Differences in the sizes of counties and the very large counties in the West make this impossible.

The shopping center paradox

I had a simple (or not so simple) planning problem I liked to pose to students. You’re planning for a currently undeveloped area on the edge of a large city. The area is expected to see significant urban development over the next few decades. The extent of the development is such that you can anticipate a demand for a major retail center to serve the area. This could be a traditional mall, as in the past, or a new life-style center, or some other type of major retail development. The problem is to select the location for this development.

Now for the details. The area is the flat, featureless plain of location theory, so nothing in the topography would favor any location. The area is served by two major transportation arteries, which could be major arterials, freeways, or rail transport. One extends out from the city through the area that will see future development (say north and south). The other transport artery runs perpendicular to the first through the middle of the area where growth is expected to occur (east and west).

So now, where to locate the retail land use? The immediate, obvious answer is at the intersections of the two transportation arteries (with some of the students rolling their eyes wondering why I had even bothered posing such a trivially obvious problem). Correct!

Except not necessarily complete. The intersection of the two transportation arteries creates four corners or quadrants. In which one should the shopping center be located? Now we’re raising a whole host of new questions: In making a plan, how general or specific can or should one be with respect to specifying such a location? What about when you move to regulating development with a zoning ordinance? Do you choose a corner? What if the owner of land on one of the other corners comes in and says that they want to develop a shopping center? Or do you just allow the shopping center development for whomever asks first?

Each time, the discussion would veer in different directions. But it always provoked thinking about the complexities, uncertainties, and limitations in planning.

More on population-weighted density

Some time ago I did two posts on population-weighted density, which is the mean of the densities of small areas such as census tracts weighted by their populations. In the first post, About population-weighted density, I described this alternative to traditional, conventional density and discussed some of the issues surrounding the use of this measure. The second post was Population-weighted density and urban sprawl, which argued that both conventional density and population-weighted density were appropriate measures of the extent of urban sprawl, relevant to different consequences associated with sprawl.

Doing these posts got me more interested in population-weighted density and led to my writing a full paper exploring this alternative density measure. In addition to expanding on the topics addressed in those two earlier posts, the paper provides much new information. Two of the highlights are the demonstration of the relationship of population-weighted density to conventional density and the comparison of conventional densities and population-weighted (actually housing-unit-weighted) densities across the 59 large urban areas in my urban patterns research.

In the first post I said that to the extent that people are more heavily concentrated in some tracts with higher densities than in others, those higher density tracts will be given more weight in calculating the population-weighted average. This will cause the population-weighted density to be greater than the simple or conventional density. In the paper, I move beyond this qualitative statement to deriving the mathematical relationship between population-weighted density and conventional density. It is actually quite simple: population-weighted density is equal to conventional density plus the variance in density across the subareas divided by conventional density. To the best of my knowledge, this is the first time this relationship has been demonstrated in this manner.

Looking at conventional and housing-unit-weighted densities for the large urban areas, the distribution of the weighted densities is more highly skewed towards higher values, with New York being an extreme outlier on weighted density. The housing-unit-weighted densities were more strongly related to the size of the urban areas, including size in earlier years, suggesting that the presence of areas of concentrated high densities was established in some urban areas decades ago.

Much more information on can be found in my paper, “On Population-Weighted Density” which can be downloaded here.

Density in Houston without zoning

Houston is well-known for being one of the only cities of any size not to have zoning. So an obvious question is how the urban pattern of Houston might differ from that of other large urban areas. Let’s consider the population density.

The density of the Houston Urbanized Area in 2010 was 2,978 persons per square mile. Dallas-Fort Worth seems to be a reasonable area for comparison. It is another large urban area in Texas, so aside from zoning, one might suspect other factors affecting density might be similar. The density of the Dallas-Fort Worth Urbanized Area in 2010: 2,879 persons per square mile, very close to Houston.

What about other large urban areas? Seattle is about as far away from Houston both in terms of distance and many other characteritics as you can get. The density of the Seattle Urbanized Area in 2010: 3,028 persons per square mile, just a tiny amount larger than Houston. Or how about the Philadelphia and Detroit Urbanized Areas, with densities of 2,746 and 2,793 persons per square mile, slightly below Houston.

If Houston doesn’t differ in terms of density, what about the other measures of the urban pattern that I have been using (see this post? Here are the values for 2010 for the Houston and Dallas-Fort Worth areas as defined for my research (note density is now in housing units per square mile):

Urban area Density Dissimilarity (Variation) Centralization Ratio Moran’s I (Clustering)
Houston 1,066 0.31 0.12 0.41
Dallas-Fort Worth 1,066 0.31 0.17 0.51

Density and variation are identical (and their densities were nearly identical in 1950 as well). Houston is somewhat lower than Dallas-Fort Worth with respect to centralization and clustering. Not sure what to make, if anything, of these small differences. And remember that Dallas-Fort Worth is an area resulting from two separate urban areas growing together, so there’s that difference as well.

My major conclusion is that, despite the absence of zoning, the urban pattern in Houston does not look that different from other large urban areas.