Category Archives: Urban sprawl

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.

The controversy over “Growing Cities Sustainably”

Writing the last post addressing sprawl research issues got me thinking back to a controversy that occurred a number of years ago over the publication of a piece of research relating to urban sprawl. In 2012, the Journal of the American Planning Association published the article “Growing Cities Sustainably: Does Urban Form Really Matter?” by Echenique, Hargreaves, Mitchell, and Namdeo. They used simulation models for 3 urban regions in Great Britain to forecast future development under several policy scenarios including compact development and dispersal. The results were then evaluated with respect to multiple indicators of sustainability, including travel, energy use, and greenhouse gas emissions. Their conclusion was that the form of development has very limited effect on sustainability.

The publication of the article spurred an outcry among planning academics, many of whom are opponents of urban sprawl and supporters of compact development. Some dismissed the research because it involved cities in Great Britain. Others found simulation to be a flawed tool.

One writer took the reviewers for the Journal to task for supporting the publication of the article. This was something of a cheap shot, since reviewers are anonymous and cannot defend their work. But it avoids the personal conflict that would come from critizing the editor, who selects the reviewers and is always ultimately responsible for the decision to publish.

It was suggested that the article should not have been published without a simultaneous rebuttal. This echoed the famous pair of articles debating compact development with Gordan and Richardson versus Ewing in the Journal in 1997. I would argue that the two cases are different. The Gordon and Richardson article was fundamentally an opinion piece. It was not unreasonable to solicit and publish an opposing opinion at the same time. But I do not feel that it is appropriate to expect concurrent critiques of any research article which reaches conclusions that many do not like.

But most troubling to me was the statement that planning journals should not publish anything that does not support compact development. I consider this an astounding proposition. This is a call for nothing less than censorship. Consider the effect of a polcy not to publish anything at variance with broadly accepted ideas about the correct way to plan. There would have been a time when an article questioning the strict segregation of land uses would have been unpublishable.

The free and open exchange of ideas is the key to greater understanding, in planning as in other domains. I trust (hope?) that journal editors will not be cowed into refusing to publish articles that do not support mainstream opinion. But I fear that some of the notions expressed in this controversy cannot help but having a continuing negative effect going forward. An editor may not refuse to publish pieces reaching certain conclusions, but after this, will he or she think twice? Will researchers wonder whether they should publish results reaching unpopular conclusions? (This might be especially true for younger, untenured faculty, who might reasonably be concerned about the effect on their career prospects.)

The worst impact of this controversy, however, is the effect, especially on those outside the planning profession, considering research in planning journals that addresses the costs of sprawl and the benefits of compact development. If there is prevailing sentiment that any research that comes to different conclusions should not be published, then how much credence can and should be given to that which does see publication?

Some research showing the negative effects of sprawl may be wrong

I am certain that some will react negatively to the title of this post, feeling that it is needlessly provacative. My response is that I really have tried to moderate it. One important inspiration for this post is an article by John Ioannadies, of Stanford, which he titled “Why Most Published Research Findings Are False.” (PLos Medicine 2(8):e124) I’m discussing three of the sources of the problem: publication bias, p-hacking, and researcher bias.

While there are some notable and important exceptions, journals tend to publish articles reporting positive results. Researchers obtaining negative results will most likely forego attempts at publication and move on to the next research question. Consider a possible research question relating to walkability: Does the smoothness of concrete sidewalk surfaces affect the amount people walk in a residential neighborhood? If a researcher studies this and finds no effect, I seriously doubt whether any journal would publish the findings. On the other hand, the finding of a statistically significant relationship might well lead to publication.

The importance of statistical significance for publication—typically a p-value less than 0.05—raises a related problem. Researchers examine alternative measures and even hypotheses until they find a statistically significant relationship, a procedure sometimes referred to as “p-hacking.” Continuing with the sidewalk smoothness example, the amount people walk in a neighborhood might be measured in a variety of ways, as could the smoothness of sidewalk surfaces. Or if none of these work, does the sidewalk surface affect accidents to walkers? Or perhaps sidewalk width or distance from the street affects amount of use or accidents. Examine enough combinations and the likelihood of finding a statistically significant relationship that occurred purely by chance becomes very high.

The last point I am making is the effect of researcher bias. I’m not just referring cases where a researcher deliberately makes choices in the research to prove a hypothesis consistent with his or her beliefs (though I am certainly aware of instances where exactly that has happened). Rather, bias can and does affect researcher behavior in instances in which the researcher is striving to be fair and impartial. This has been shown over and over to occur in many fields, even with activities such as reading and recording quantitative values from a measuring device. That is why the best design for clinical trials is a double-blind approach where neither the patients nor the physicians evaluating the patients know who is getting the treatment and who is getting the placebo. With research on the effects of sprawl, many of the researchers have strong, publicly declared beliefs in the undesirability of urban sprawl. So bias is a part of the research enterprise.

New York versus Los Angeles — multi-family housing

When thinking about housing in New York, the first thing that comes to mind is apartments, especially in large structures. For Los Angeles, on the other hand, it is the large expanses of single-family homes. So let’s look at the percentages of housing units in multi-family structures. For this purpose, I am considering units in structures with 3 or more units, since duplexes or other 2-unit structures seem to me more like single-family housing. All of the data are from the American Community Survey for 2015, 1-year data.

We’ll start by comparing New York City with the City of Los Angeles:

  • New York City, 71 percent multi-family
  • City of Los Angeles, 52 percent multi-family

New York, as expected, has a larger share of housing units in multi-family structures. But Los Angeles has a surprisingly high share, just over half. So the City of Los Angeles is far from being primarily single-family housing.

Of course the cities are just a part of the larger urban areas, and the suburban portions outside the large cities tend to have more single-family housing. So let’s consider multi-family housing in the census Urbanized Areas (as defined in 2010):

  • New York Urbanized Area, 48 percent multi-family
  • Los Angeles Urbanized Area, 40 percent multi-family

The proportions are lower, as expected, but now the difference between New York and Los Angeles is considerably less.

The final comparison looks at multi-family housing in the suburban portions of the Urbanized Areas, the areas outside of the cities of New York and Los Angeles:

  • New York suburbs (Urbanized Area outside city), 28 percent multi-family
  • Los Angeles suburbs (Urbanized Area outside city), 34 percent multi-family

The Los Angeles suburbs actually have a higher proportion of multifamily housing than the New York suburbs. This would be one factor contributing to the Los Angeles suburbs being twice as dense as New York’s (as mentioned in this earlier post).

Why is Los Angeles an examplar of sprawl?

The Los Angeles area has long been viewed as a prime example of urban sprawl. The title “Is Los Angeles-style sprawl desirable?” was chosen by Reid Ewing for his response to Gordon and Richardson in the famous debate over compact development and sprawl (Journal of the American Institute of Planners, 1997). A great deal of development in the Los Angeles area can be described as sprawl. But if sprawl is especially characterized by low-density development, why should Los Angeles be used as the exemplar when the Urbanized Area has a higher density than every other area in the country?

Los Angeles is very big. There is a tendency to associate sprawl with development extending over large areas, despite the fact that areas with greater populations will always tend to cover larger areas whatever the pattern of urban development.

Los Angeles is one of the first and certainly the largest urban areas to develop primarily during the twentieth century. It lacks the large CBD and the dense core of nineteenth century cities, so it is not like New York or Chicago.

Los Angeles is highly dependent on the automobile, lacking (at least until recently) the rail transit infrastructure common to older urban areas.

I am sure all of these factors play a role. But I believe a significant contributor to perceptions of Los Angeles and sprawl stem from the topography of the area, the location of the airport, and images that are used to illustrate sprawl. This may sound strange, but stay with me.

As I argued in the post on images of sprawl, sprawl is nearly always illustrated by the view from above. The standard sprawl image shows at least moderately dense single-family residential development, usually unobscured by a dense tree canopy, emphasizing the sea of rooftops. This is what much of the Los Angeles area looks like from above–fairly dense housing with limited tree cover (and palm trees do not produce a dense canopy).

Now for the first influence of topography. The presence of mountains limits the directions in which the Los Angeles urban area can expand. This has produced long stretches of urban development. The longest extends west from downtown to the Pacific Ocean and east, south of the San Gabriel and San Bernardino Mountains, out to San Bernardino and Redlands. This is an 80-mile stretch that is now virtually completely developed.

Los Angeles International Airport (LAX) is at the western end of this expanse of urban development, virtually on the Pacific. Most flights from the east coast or midwest come in towards the area from the northeast. They have to cross over the line of mountains that limits the northward expansion of the Los Angeles area. But these are high mountains, with some peaks exceeding 10,000 feet. So the planes have to maintain a fairly high altitude while crossing the mountains and will have to do a major descent into LAX. So the flights typically cross over the mountains as far east as they can, around San Bernardino. (Other mountains block paths farther east.) So the flights come over the developed portion of the Los Angeles urban area about 80 miles east of LAX and head west, descending over this continuously developed area. This is by far the longest stretch of the stereotypical view of urban sprawl that exists anywhere.

Flying into most airports in large urban areas in the United States, you will see some areas that have the appearance of sprawl as the continuous sea of rooftops. But much of the suburban areas will be much lower-density development, greatly obscured by trees, that do not produce the same visceral “sprawl” response (even though the much lower density is more problemmatic in many ways). The image of Rye, New York, in the images of sprawl post is an example of what I mean. And given the locations of the airports and the flight paths, the extent of urban development flown over, with whatever characteristics, will nearly always be much less.