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.


Have we had our last good census?

As readers of this blog know, I make extensive use of census data in my research. Also, I am very concerned (some would say obsessed!) with the details of the census and the data I am working with. Given this, I am concerned about the prospects for the 2020 census and am wondering whether 2010 saw our last good census. A combination of the general state of the nation combined with the actions (and inactions) of the current administration is producing this concern.

Obtaining an accurate census counts depends fundamentally on the cooperation of the entire population. And this cooperation is dependent upon people having a basic level of trust in the government. I don’t think I need to elaborate on the general decline in such confidence. I would especially note such things as the the rhetoric against various groups, the travel ban, the cancellation (at least until now) of the program for the dreamers, and many other things. If I were Hispanic, an immigrant, or especially if I were undocumented, I don’t believe I would respond to the census.

Actually, I might respond while taking the risk into account. And I’m sure that this would be the case for others as well. Because of the importance of the census for redistricting and the allocation of funds, perhaps I would choose to respond but not truthfully and with lots of kids to up the count in my area.

The cuts in funding for the census will weaken its preparation and ability to plan and conduct effective outreach. Especially given the shift this census to conducting the bulk of the enumeration online, I am concerned that this could especially affect the count of lower-income, less-educated persons. Those with higher levels of online sophistication will have no problem filling out the forms. Others may fall between the cracks, census follow-up efforts notwithstanding.

Finally, there is the pernicious effort on the part of the current administration to include a question on citizenship on the census. First off, there is no reasonable need for this information from the census. The American Community Survey asks this question and provides all of the detail needed for any conceivable purpose (and in a more timely fashion than the census as well). I can see only two reasons that the administration might be pushing for the inclusion of this question. The more “benign” (!) purpose would be to discourage non-citizens, especially those who are non-documented, from participating in the census. Even more ominous would be an intent to violate the confidentially of the census to use the information for immigration enforcement purposes. I believe that just the proposal to include the question, even if it does not become part of the census, will further erode trust and participation. Inclusion of the question in the census would be a disaster.

Measuring urban patterns

For my research looking at 59 large urban areas from 1950 to 2010, I developed 4 measures of the urban pattern: housing unit density, the index of dissiilarity for variation in density across census tracts, the centralization ratio, and Moran’s I for clustering. (See this earlier post for more details.) These were used as measures of the urban pattern for each census year.

Over the 60-year period from 1950 to 2010, the mean values of the first 3 measures–density, variation, and centralization–dropped steadily. But as important was what was not revealed by the averages: the urban areas changed in very different ways, with some seeing large gains in the measures while others experienced major losses.

The average values for the measures varied by region of the country. The urban areas in the Northeast mostly had the highest means for both 1950 and 2010, while areas in the South were consistently lowest. But a big shift occurred with the mean densities for the urban areas in the West. In 1950, the average density was just above the South, which was the lowest. But by 2010, the density in the urban areas in the West was the highest among all regions, averaging 40 percent higher than the densities in the Northeast and the Midwest.

I used cluster analysis to develop a typology of the urban areas for 2010 based on their values for these measures, dividing the areas into 6 groups. One group consisted of the very largest urban areas, with the highest densities and the highest mean values on the other three measures as well. These were obviously the most complex urban areas. At the other extreme were the smaller urban areas, mainly in the South, with the lowest densities and among the lowest values for variation, centralization, and clustering.

More information on findings using these urban pattern measures can be found in my paper, “Measures of Urban Patterns in Large Urban Areas in the U.S., 1950–2010” which can be downloaded here.

Always compromises–never black and white

I live in Upland, California, a suburb about 35 miles east of downtown Los Angeles. A short block from our house is Euclid Avenue, a broad, busy boulevard running from south of the city north to the foothills of the San Gabriel Mountains.

Euclid Avenue is the signature feature of Upland, created when the city was originally laid out in the latter 1800s. The street is primarily lined by residences, becoming larger and more impressive as one approaches the mountains. Its 65-foot wide median is lined with large old pepper trees and has a spacious path in the middle constantly used by walkers and runners, the occasional horse, and I once even saw a llama. (The median was originally used for a streetcar line, first powered by a mule which pulled the car up the slope to the top of Euclid Avenue and then got to ride down on a trailer at the back as the car descended powered by gravity.)

Euclid Avenue, Upland, California

Euclid Avenue, Upland, California

Philip Langdon, in his book A Better Place to Live, excoriates contemporary suburbs for their boring and placeless design and their obstacles to mobility given their street patterns. Yet he devotes 8 pages of the book to describing and praising Euclid Avenue. (This is worth reading; his account is accurate and comprehensive.) He praises the frequent cross streets, 28 in 7 miles, as providing pedestrian access and not causing problems with the heavy traffic on Euclid Avenue.

Here’s my problem: Langdon gives the count of the number of cross streets, but he does not make the obvious point that these come each quarter mile. The blocks, in the north-south direction on Euclid, are a quarter-mile long. This separation of the cross streets is not unreasonable for a heavily traveled arterial. But these are awfully long blocks from the perspective of Langdon’s desire for a grid of connected streets and maximum pedestrian opportunities. A compromise reasonably required by the presence of the busy boulevard.

And Langdon fails to address the rest of the story. Upland is not this jewel in the midst of the automobile-oriented faceless suburbia that he rails against. Not only are the blocks a rather long quarter mile in the north-south direction, many extend a full half mile in an east-west direction. Some of these huge blocks are cut through by more-or-less straight streets creating smaller blocks. But many of these large blocks are pierced only by cul du sacs and meandering streets that are the antithesis of the grid that Landon espouses.

Things are seldom black-and-white, good or bad. Before moving to Upland, I lived in Zionsville, Indiana, a suburb of Indianapolis. At its core was the original town settled in the mid-nineteen century with a street grid with small blocks, old houses on small lots, and a small, quaint downtown on a brick street. This part of Zionsville was affectionately referred to as the “village” and was the major element of the town’s attraction. And the village was surrounded by newer suburban development of exactly the sort Langdon decries.

Next door to Upland is the city of Claremont, home, of course, to the Claremont Colleges. It too has an older, somewhat denser core and an extensive, lively downtown area (attributable at least in part to the presence of the colleges). Claremont likewise refers to this portion of the city as the “village.” And most of the remainder of the city consists of typical suburban development.

Can urban environments be too planned?

I worked as a summer intern at HUD in 1970. They were really great, offering multiple educational opportunities for the group of interns. One of these was a tour of the planned new community of Columbia, Maryland.

At the time, the planned communities of Columbia and Reston, Virginia, were considered by many urban planners to be the outstanding examples of good planning in the United States. It’s worth noting as an aside here that many aspects of Columbia’s design would not be considered positively by many contemporary urbanists: For example, the density was too low and the street design included curving streets and cul du sacs. (See my earlier blog posts on connected streets here and here.)

We had the opportunity to walk around some areas and rode our bus to view other parts of Columbia. I was impressed. It was an environment I could see myself living in. {I did end up living in the planned city of Irvine, California, a few years later.)

But as we rode around, I started feeling a sense of unease. I didn’t know why, but I was not completely comfortable with the environment. Then I saw some graffiti spray-painted on the back of a street sign, and I realized my problem: This was the first thing I had seen on the trip that the Rouse Corporation, the developer of Columbia, had not planned.

Columbia was extremely well-planned. But it lacked the element of spontaneity and surprise that is a critical aspect of the urban environment. This is a part of what makes a city a city.

I think something similar was going on with the producers of the movie The Truman Show selecting the new urbanist community of Seaside as the setting for an artificial environment (see this post).

Making policy is hard–affordable housing

During the latter 1970s, the city of Irvine, California, imposed a requirement that new developments include a limited percentage of “affordable” housing units. The first development subject to this requirement included 900–1,000-square-foot townhouses that were to be sold for around $50,000. This was significantly less than market rate for such housing. I had put “affrordable” in quotes because at this time, houses selling for that price were hardly low- or even moderate-income housing.

The units were to be sold to people having an income less than a specified maximum but high enough to qualify for financing. The maximum was high enough that some assistant professors at the University of California-Irvine were eligible. Given the high and escalating housing prices in Orange County, demand was extremely high. Buyers were to be selected using a lottery.

I was teaching a course on land use policy. Students were required to complete a final paper on a topic of their choosing. One student came in, told me she had read about the affordable housing requirement and the lottery and asked whether that would be a suitable topic for the paper. Of course it was.

A week or so later, the student came in again. After starting to research the policy, she discovered that she and her husband were eligible for the housing, and they had entered the lottery. After the lottery had taken place, she came to see me once more, very excited. She and her husband had won and would be purchasing one of the units, which were to be completed soon.

The student kept in touch after the term ended. She came to see me shortly after moving into their new house with an update. The affordable housing program placed no restrictions on resale. The day they moved in and ever since, she and her neighbors had real estate agents knocking on their doors, offering to sell or even buy the houses for at least $20,000 more than they had paid. Most were not accepting the offer, because if that had, they could do no better in the market-rate housing market in Orange County.

This first iteration of the “affordable” housing policy did result in the addition of a limited number of smaller, less expensive (around $70,000 at market prices) houses in Irvine. It also allowed a small number of households with incomes that could qualify to purchase a $50,000 house to buy these townhouses. I’ve always found it interesting to contemplate how similar a policy would have been that required the developer to build these smaller houses, sell them at market rates, presumably around $70,000, and then hold a lottery in which the winners would each get a check from the developer for $20,000. Especially if they were given priority in purchasing the houses, if they chose to do so.

Developing urban pattern measures

No single measure such as density can capture the complexity of urban patterns, including the distribution of housing units. For my research looking at 59 large urban areas from 1950 to 2010, I wanted to develop multiple measures of urban patterns to better characterize these areas.

Since around the turn of the century a significant amount of work has been undertaken to identify many variables to quantify urban patterns, mostly to assess levels of urban sprawl. I have raised questions about these multiple dimensions of sprawl efforts before on this blog (here and here).  I am not claiming now to be measuring sprawl, but these efforts provided many possible measures to consider. Too many, as some studies included literally dozens of variables with meanings and differences difficult to discern.

My objective was to identify a small set concepts that captured the most important aspects of urban patterns. I then selected a single variable for each concept that I felt was both among the best measures and, to the extent possible, was easy to understand and interpret.

The overall density of housing units in the urban area is the first, obvious measure.

The extent to which densities varied across census tracts came next. This is measured using the index of dissimilarity. This is a measure of the proportion of housing units that would have to be moved to other census tracts to produce a uniform distribution with equal densities in all tracts.

The centralization of housing units in the urban area, the extent to which more housing units were closer to the center, is an important aspect of the urban pattern. For this, I developed a measure I am calling the centralization ratio, which looks at the mean distance housing units are located from the center and is the proportional reduction of distance compared with the mean distance to the center if housing units were uniformly distributed.

Finally, while centralization is one form of clustering, multiple clusters of higher density housing units can exist at various locations in an urban area. This clustering is measured using Moran’s I, a measure of spatial autocorrelation. This is essentially a correlation coefficient between tract densities and the densities of the adjacent tracts.

More information on these measures, their rationale, and an empirical assessment can be found in my paper, “Developing Multiple Measures of Urban Patterns,” which can be downloaded here.