Category Archives: Urban sprawl

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.


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.

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.

Nearly everything involves tradeoffs

Those who advocate for urban futures typically describe an ideal urban environment that will be superior to current urban areas. I think they are missing two important facts in doing so. One is that people are different and have different preferences. An urban environment that best meets the needs of one person will fail to be ideal for someone else. The second thing that is seldom addressed is that in making choices about the urban environment (and many other things), nearly every choice involves making tradeoffs among competing objectives. I’ll give a few examples of the latter.

The most commonly considered tradeoff, at least among urban economists, is that between accessibility to the center and space that is the foundation of the standard monocentric model. People can reduce transportation costs by choosing a residence closer to the center or they can have more space for the residence by living farther away. But the tradeoff with space involves not only costs of commuting to the center. There is a tradeoff between having a walkable neighborhood with multiple destinations within walking distance and space for the residence as well. This must be the case because higher residential densities can support higher densities of commercial and other activities. In lower-density areas, commercial activities will be more widely spaced to achive sufficient markets.

In the design of street patterns for residential areas, a tradeoff exists between connectivity and restricting through traffic. High street connectivity supports walkability (though there are other ways of achieving this as well) while restricting through traffic with cul du sacs and curving streets may increase safety, especially for smaller children. I discussed this in an earlier blog post.

Another transportation tradeoff involves the use of streets. Space for lanes used for motor vehicle traffic can be converted for dedicated transit use or bicycle lanes. This achieves very laudable objectives. But it also can slow automobile travel and increase congestion. Political conflict associated with making such tradeoffs can be very real. Los Angeles had reduced the number of traffic lanes in one section of the city to increase safety, including by the addition of bike lanes. This produced an outcry among both motorists and businesses in the area, led to lawsuits, and ultimately to a restoration of the traffic lanes.

Limiting the physical expansion of an urban area to reduce sprawl can achieve worthwhile objectives. But given the laws of supply and demand, reducing the supply of developable land may lead to higher housing prices. This might be ameliorated by other regulatory policies, but those too will involve yet more tradeoffs.

This is not your father’s (or mother’s) suburb

I currently am living in Southern California, about 35 miles east of downtown Los Angeles. I frequently drive further east into the adjoining city of Rancho Cucamonga for shopping or other activities (recently, jury duty!). A normal person would take the freeway or the main east-west commercial artery, the old Route 66. But being interested in urban patterns, I occassionally will drive on other through streets that run mainly through residential areas. I had certainly seen numbers of apartment complexes, but on one of these explorations I was especially struck by the continuous line of apartments. Not exactly stereotypical suburbia.

First, a little background on Rancho Cucamonga. This is a very new city, largely developed since the 1970s. When I lived in Southern California in the mid–1970s, when driving through this area on the freeway, one passed through miles of vineyards. This was the last undeveloped gap between Los Angeles and San Bernardino to be filled in, as you can see on these maps for 1970 and 2010 of census tracts which I classified as urban for my research:

Urban census tracts in the Los Angeles area in 1970 (Rancho Cucamonga outlined in red).

Urban census tracts in the Los Angeles area in 1970 (Rancho Cucamonga outlined in red).

Urban census tracts in the Los Angeles area in 2010 (Rancho Cucamonga outlined in red).

Urban census tracts in the Los Angeles area in 2010 (Rancho Cucamonga outlined in red).

The popuation of the city of Rancho Cucamonga was 55,000 at the 1980 census and 177,000 according to the 2016 census estimate. No earlier populations are reported as the city was only incorporated in 1977!

Suburban residential development is generally associated with large expanses of single-family housing units on medium- to large-size lots. Some suburbs do have some apartments, but the general impression is that single-family housing dominates. In Rancho Cucamonga in 2015, only 62 percent of the housing units are detached single-family houses. So nearly 40 percent of the housing does not fit the suburban stereotype.

The overall population density for the city in 2016 was 4,430 persons per square mile, certainly more than many suburbs. But even this is misleadingly low. The city still has significant amounts of vacant land and has large areas developed with industrial uses, mainly for warehouse and distribution activities. (The broader area has developed as a huge distribution center, handling many of the millions of containers that come through the ports of Los Angeles and Long Beach.)

A better picture of the level of residential densities can be seen by looking at densities in individual census tracts, some of which will naturally be more residential and more completely developed than others. My research (using 2000 tract boundaries) has 9 census tracts within or predominantly within the City of Rancho Cucamonga. For several reasons, I have chosen to focus on housing units and housing unit densities rather than population densities for my research. The two census tracts with the highest housing unit densities in 2010 have densities of 3,330 and 2,835 units per square mile. Since population densities are more commonly used and are thus more familiar, these can be estimated by using the national average of 2.34 persons per housing unit. This gives estimated population densities for these tracts of 7,791 and 6,634 persons per square mile. The denser of the two tracts has a population density far greater than the overall density of the densest Urbanized Area in the U.S., Los Angeles. Both are denser than the New York Urbanized Area.

There is, of course, no standard criterion specifying what are “suburban” densities as opposed to “urban” densities. I believe that for a variety of reasons, no urbanists want to go there, as there would be no consensus. But an interesting article on the FiveThirtyEight website tried to get at this, reporting on work by the real estate website Trulia that surveyed people asking whether where they lived was urban, suburban, or rural. They also asked for ZIP codes and compared the responses to characteristics of the ZIP codes. The study found, perhaps not surprisingly, that the best predictor of how people described where they lived was the population density of the ZIP code. And the dividing line between suburban and urban was 2,213 households per square mile (or by my estimation of population density, 5,178 persons per square mile). Using that criterion, the two densest tracts in Rancho Cucamonga could easily be considered urban as opposed to suburban, and two more, with over 1,900 units per square mile, come close. (I need to acknowledge that their use of ZIP codes could have produced a lower value than if tracts could have been used.)

The bottom line is that this very recent development in Rancho Cucamonga does not look like the traditional idea of suburbia with single-family homes on spacious lots.

On the sharing of data from research

The National Academies recently released a report addressing integrity in scientific research, including the social and behavioral sciences. One of the recommendations is that after publication researchers share with others the data on which an article is based. This supports research transparency and should lead to greater reproducability of research. I think sharing data is generally a good thing, and I have done so. But I feel that the authors of the report have failed to address some important issues related to such data sharing. This is obviously a topic much broader that the subject of this blog. But at two points in my comments I will give examples that relate directly to things discussed here.

In discussing the recommendation on data sharing, the report points favorably to the policies of some journals requiring that authors make the data for an article availble to others on request. But further discussion in the report strongly implies that data should be made available in a repository from which anyone can download it. The difference is significant because in making the data available online, the person(s) who created the data then lose all control over how it might be used.

But first, a simple, practical issue. Making data available for others to use entails a significant amount of work relating to formatting, documentation, and so forth. I am very careful about documenting my data as I do research, but that original documentation is completely meaningful only to me. Just sharing data with co-authors requires some additional effort. Sharing it publicly would require more. I suspect that the majority of datasets from articles would never be used by others. So it is inefficient to put the work in for every dataset to get it into a form in which it can be shared. It makes much more sense to put in this effort when someone makes the request to use the data. At that point, I am happy to do so.

The authors of the report (many from the natural sciences) seem to most often view datasets as the products of experiments, to be reported in an paper, which then is the end of the story. Indeed, they actually see as a problem “the temptation to publish multiple papers on just one experiment or dataset.” (p. 17) They fail to realize that for certain types of research, datasets are developed, often with a great deal of effort, to support the investigation of multiple research questions. Those creating the data have a reasonable expectation of being able to carry out their research without having it preempted by others using their data.

My urban patterns dataset with data on housing units by census tract for 59 large urban areas from 1950 to 2010 is an example of this. I spent at least a year-and-a-half building the dataset. I have a long list of research questions I intend to address using this dataset. The papers currently on the Research page represent just a start. I feel that it is reasonable that I shold be able to be the first to use this data to address these questions. I certainly would not have put in the effort I did in creating the dataset only for one or two papers. This does not mean that I would be unwilling to share the data with others before I have completed this program. I’m finished with all of the questions I have intended to address relating to the negative exponential model. If someone wants to do more, I could be willing to share the data. Or if someone wants to combine my data with some other dataset, sharing could be appropriate. But that’s why I believe I need to have control over the sharing.

I was surprised that the authors of the report failed to address reputational risks that could be associated with data sharing (and by this, I am not including risks associated with others finding out about problems with the original research). Putting data on an archive for anyone to use can result in uses that can negatively impact the reputation of the data creator.

The first (and least significant) reputational risk comes from someone taking the data and producing and publishing a very crappy piece of work. While most such efforts are justifiably ignored, occasionally they will achieve notoriety for their sheer absence of quality. Assuming the author of the crappy research appropriately cites the creator of the data, the creator will forever be linked with the work. While everyone should understand that the data creator is not responsible, just being associated would not the most pleasant thing.

For certain types of data, the reputational risk can be much greater. For example, suppose researchers post data dealing with a social problem that includes information on race. A white supremacist could obtain the data, improperly manipulate it, and falsely claim that the results supported their racist views. And they might well prominently note that the creators of the data were respected researchers at a major university. Such a nightmare scenario is why researchers have a legitimate interest in controlling the sharing of their data.

For researchers working in a field involving contentious positions with extremely strong partisans on both sides, risks can extend to the use of the data by others in that field. Getting back to the subject of this blog, urban sprawl and its effects represents just such a field. A study is published indicating that sprawl or compact cities does or does not have some effect, and those whose position has not been supported can be vociferous in their attacks and arguments against it. This has happened–in both directions. I have no doubt that if the data from such a study were made freely available for download that that someone whose position had not been supported might reanalyze the data making the assumptions necessary to reach the opposite conclusion in an attempt to discredit the original study and its author.

Scattered, leapfrog development vs. low-density development

Two residential development patterns are most often associated with urban sprawl. Scattered or leapfrog development refers to the building of new residences, either separately or in a subdivision, at some distance from existing built-up areas. Low-density development refers to the construction of individual houses on larger lots. It is possible, of course, for scattered development to also be done on larger lots, though this is not the distinguishing feature of such leapfrog patterns.

In looking at densities in areas larger than the actual residential lots, scattered development will also be low-density because of the vacant land that has been skipped over. But I think most people would agree that scattered, leapfrog development and low-density development are two distinctive types of residential development and sprawl.

But how different are the two? Here is a thought experiment: Imagine an undeveloped area of land one mile square at the edge of an urban area. Now consider two ways in which this land might be developed. The first will be very low-density development. The area is divided into 64 10-acre lots and a house is built on each, completely developing the area. (For simplicity, we will be ignoring the need for land for roads to provide access.) Let’s assume that the owners only landscape small areas surrounding their residences, leaving the remainder of their lots undisturbed.

Now consider the second alternative of extreme scattered development. Sixty-four houses are constructed on 1-acre lots that are fairly evenly distributed across the mile-square area. In this case, only 10 percent of the land has been developed, but the developed area is 10 times as dense as in the previous case. Now suppose that these 64 houses are exactly the same as the very-low density houses and are located in exactly the same places. There would literally be no way to distinguish the scattered development from the very low-density development based on any physical characteristics of the developments. The only way to tell whether the development is very low-density or scattered is by looking at the land records.

Of course that difference in ownership matters–to a degree. The owners of the homes on the scattered 1-acre lots have no control over the undeveloped 90 percent of the land, which could be developed at any time. In the case of the very low-density development, each owner exercises control over 10 acres surrounding the residence by virtue of ownership. One might assume that these very large lots were acquired because the owners wanted the space and the control. (Though it is possible that land use regulations and/or choices made by the prior owner or developer of the area limited options available to the purchasers of these lots.) It is very likely that you are not going to see the purchasers of these large lots soon subdividing their land for higher-density development.

But the operative word here is “soon.” Over time, as demand increases and conditions change, further subdivision and development in the very low-density area becomes an increasing possibility. I currently live in an area that was developed from that late 1960s through the early 1980s with lots around a half acre. There are several lots in the neighborhood where the owners have built a second, substantial house on the rear portion of the lot (more than just an accessory unit or “granny flat.”)

So the very low-density developed area perhaps is not that completely different from the area with the scattered development.