But there are other urbanist and market books on the market. What makes this one unique? First, it focuses on Southern California, rather than taking a nationwide or worldwide perspective (though Stephens does have a few essays about other cities). Second, the book’s short-essay format means that one does not have to read a huge amount of text to understand his arguments.

Because the book is a group of short essays, it doesn’t have one long argument. However, a few of the more interesting essays address:

1. The negative side effects of liquor license regulation. Stephens writes that the Los Angeles zoning process gives homeowners effective veto power over new bars. As a result, the neighborhood near UCLA has no bars, which in turn causes UCLA students go to other neighborhoods to drink, elevating the risk to the public from drunk driving.
2. The Brooklyn Dodgers’ move to Los Angeles; Los Angeles facilitated the transfer by giving land to the Dodgers- but only after a referendum passed with support from African-American and Latino neighborhoods. On the other hand, the construction of Dodger Stadium displaced a Latino community. To me, this story illustrates that arguments about “equity” can be simplistic. Los Angeles Latinos were both more likely than suburban whites to support Dodger Stadium, yet were more likely to be displaced by that stadium. So was having a stadium more equitable or less equitable than having no stadium? (On the other hand, a stadium that displaced no one might have been more equitable than either outcome).
3. Why developers are so often vilified. Stephens suggests that this may be because their products are visible on the streets to people who don’t use them, who can condemn those products as they walk or drive past them. By contrast, if we don’t buy a consumer product we might never know what it looks like.

More broadly, Stephens points out the gap between what urban planners want and what actually happens. Urban planners are often blamed for overregulation; but Stephens suggests that most urban planners share his vision for Los Angeles, but are frustrated by neighborhood activists’ veto power over new development.

The post Review: The Urban Mystique, by Josh Stephens appeared first on Market Urbanism.

For those of who you don’t feel like downloading the full paper, here’s a summary:

1. Jeffrey Harris of MIT (whose article seeded this controversy) wrote that COVID-19 infections rose most rapidly before subway ridership began to decline; this alone, of course, is not a strong argument because as subway ridership declined, many other crowded places (such as restaurants) were also shutting down. Harris also notes that infections rose more slowly in Manhattan, where ridership declined most rapidly. However, a majority of the city’s jobs are in Manhattan. Thus, Manhattan’s lower subway ridership may have been a reflection not of changed behavior by Manhattan residents, but of the citywide loss of jobs as non-Manhattanites stopped riding the subway to Manhattan jobs. Furthermore, Alon Levy writes that ridership did not decline as rapidly in residential parts of Manhattan (which nevertheless have low infection rates).

Levy also asserts that Harris’s reliance on data from subway entrances is misleading in one technical but important respect.  If a Manhattan stops riding the subway to a Manhattan job, this means there are two fewer subway entries for that person.  On the other hand, if a Queens resident stops riding the subway to a Manhattan job, this means there is one fewer Queens entry and one fewer Manhattan entry.[  Why does this matter?  Suppose that on March 1, there were 100 Manhattan-to-Manhattan commuters and 100 Queens-to-Manhattan commuters, and a week later 30 of each group stop riding the subway.  Because there were 90 fewer entries at Manhattan stations (60 from the first group and 30 from the second group), one might think Manhattan subway ridership declined by 90 percent, when in fact it declined by only 30 percent.

2. Harris also relies on the pattern of infections by zip code- and in particular, infections in zip codes along subway lines, because any given rider of a subway line can be infected not only by residents of their own neighborhood, but also by riders who enter at other subway stations on the rider’s route (which perhaps explains why neighborhoods at the end of subway lines tend to have high infection levels). He finds that some subway lines had more drastic declines in ridership than other subway lines- and that the subway lines with more dramatic declines in March ridership also had lower infection rates as of early April. I’m not sure whether the other commentators fully address this point, but maybe I’m missing something.

3. Harris relies on unusually high infection rates among subway workers. Levy responds that subway workers and subway riders do not experience the same risks- subway workers had risks that subway riders did not experience (such as picking up possibly-contaminated rubbish without masks) while conversely, not all subway workers ride packed rush-hour trains.

Any comments?

The post More on Subways and COVID-19 appeared first on Market Urbanism.

But this argument has led to a counterargument: that all the Manhattan statistics are useless because most Manhattanites are rich people who fled the city, so of course there are few records of Manhattan infections.

This argument contains a grain of truth. In fact, more people did leave Manhattan than the outer boroughs: according to a New York Times story based oha few estimates based on monitoring smartphones, between 13 and 19 percent.

But the gap between Manhattan and the outer boroughs is far greater. Currently, Manhattan’s COVID-19 death rate is 11.7 per 10,000 residents. By contrast, the Bronx’s death rate is 21.3 per 100,000- 82 percent higher. The Queens death rate is 20.6 per 100,000- 76 percent higher. Brooklyn’s death rate is 17.9- 53 percent higher.

It could be argued that even if borough-wide data is still useful, neighborhood COVID-19 data is not, because some Manhattan neighborhoods lost far more than 20 percent of their population. For example, the neighborhood that has lost the most population is the student-oriented East Village,* where population has gone down by half. I haven’t found any data on COVID-19 deaths by zip code, but I have found data on infections.** The East Village corresponds roughly with zip code 10003, which has 757 diagnosed cases per 100,000 residents. By contrast, the worst off outer-borough neighborhoods have over 4000 cases per 100,000 residents. In other words, even if we double the East Village infection rate to account for people who left (to 1500 per 100,000), its infection rate is still less than half that of the hardest-hit outer borough neighborhoods. The East Village is hardly atypical; as of May 12, only one zip code south of Columbia University had over 1500 cases per 100,000, and many had under 1000.

*One interesting area for further research is: who is (disproportionately) leaving town? Is it students with parents who have a spare bedroom? Seniors with country homes? Or families with children? The East Village is younger than the average city neighborhood, but this is less true of other Manhattan neighborhoods where many people have left, so we don’t really know. It could be argued that everyone who left is an old person with a house in Suffolk County (the most common location for vacation homes in metro New York). But according to Census data, there are just over 53,000 housing units used for “seasonal or recreational use” in Suffolk County. If you assume that each unit is used by two Manhattan residents (which may not be the case) that’s just over 100,000 people, still only 7 percent of Manhattan’s population. This is of course a silly assumption since presumably some of these units are owned by non-Manhattanites; on the other hand, some Manhattanites might have country homes in other places.

**Which is less reliable, in my opinion, because there are far more people who suffer from COVID-19 than there are who have been tested for it.

The post The “everybody left Manhattan” argument (updated 5-15 to reflect recent data) appeared first on Market Urbanism.

New York City is an epicenter of the global novel coronavirus pandemic. Through April 16, there were 1,458 confirmed cases per 100,000 residents in New York City. Always in the media eye, and larger than any other American city, New York City has become the symbol of the crisis, even as suburban counties nearby suffer higher rates of infection.

In a paper dated April 13, 2020, Jeffrey E. Harris of M.I.T. claims that “New York City’s multitentacled subway system was a major disseminator – if not the principal transmission vehicle – of coronavirus infection during the initial takeoff of the massive epidemic.” Oddly, he does not go on to offer evidence in support of this claim in his paper.

Conversely, as I will show, data show that local infections were negatively correlated with subway use, even when controlling for demographic data. Although this correlation study does not establish causation, it more reliably characterizes the spread of the virus than the intuitions and visual inspections that Harris relies on. 

Data

In an ongoing crisis with a shortage of tests, all infection and mortality data come with a major asterisk: we do not fully know the extent of the data. Only when all-cause mortality data and more-extensive testing data are available can any conclusions be confirmed. This study, like Harris’ and others, is subject to potentially massive measurement error.

Data from the American Community Survey (2018 5-year averages) show that commuting modes vary extensively across New York City. New York is broken into Community Districts (CDs), which generally correspond (on either a one-to-one or two-to-one basis) with Census Public Use Microdata Areas (PUMAs). These 55 areas contain between 110,000 and 241,000 people each. The most car-dependent PUMA (Staten Island CD3) has a car-commute share of 75%; the least car-dependent PUMA is Manhattan CD 1 & 2 with just 4% commuting by car. Generally, subway and automobile commuting are mirror images – the correlation is -.88 – even though a substantial share of New Yorkers use non-subway transit, walking, or biking to get to work. The subway commute share varies from 2.5% (Staten Island CD3 again) to 72% (Manhattan CD10 – Central Harlem). Other transit, mostly buses, is positively correlated with automobile share; both reflect the absence of subway access. However, other transit use never exceeds 29%.

In addition to commuting data, I report some ACS demographic data by PUMA.

New York City began publishing Zip code-level data on coronavirus tests and infections on April 1. These data reflect positive tests that result from virus exposures that took place up to two weeks prior. Ideally, one would prefer to use data from mid- to late-March to identify geographic patterns underlying the early spread. Citywide, 18,035 cases are reported from tests administered March 1 – March 20; we might think of these as the wave of cases contracted mainly before the city substantially shut down over the weekend of March 14. Tests administered March 21 to 30 added another 40,230 cases; these cases may have been contracted during the shutdown. Thus, it is possible that a majority of even the earliest available detailed-geography data are from post-shutdown infections.

As of April 1, the city could identify a clear coronavirus hotspot centered on Corona, Queens (because apparently the Grim Reaper has a cruel sense of humor). But by then the virus was everywhere.

I use a geographic correspondence file to ascribe Zip code level infection data to PUMAs. The borders are not generally coterminous, but many Zip codes are contained entirely in a single PUMA.

Correlations

Table 1 shows that the April 1 case rate was positively correlated with automobile and, to a lesser degree, non-subway transit commute shares. Measures of affluence and access to healthcare are negatively correlated with the case rate. Asian share is uncorrelated with case rate.

The correlations strengthen over time: affluence is a very strong, negative predictor of (log) case growth during April, partially because affluent people have fled the city in large numbers.

Of course, many of these variables are correlated among themselves; income and bachelors share are almost perfectly aligned, while subway and automobile shares are photo negatives. Thus, I use ordinary least squares to measure the controlled correlations (dropping some variables to avoid collinearity).

For reference, the average case rate per thousand is 4.6; the range is 2.9 to 8.2. A coefficient X can be easily interpreted: a ten percentage point difference in the independent variable is associated with an X/10 increase in the dependent variable. Thus, a PUMA with a 10 percentage point higher automobile commuting share is expected to have 0.32 more cases per thousand.

Put another way, a standard deviation increase in automobile commuting share (17 percentage points) accounts for almost half a standard deviation of the case rate.

The relationship between automobile share and COVID-19 case rate is the only significant one. It persists despite the outliers, not because of them, as Figure 2 shows.

Finally, in Figure 3, we see a very strong association between car commuting and the growth in case count after April 1. The three Staten Island PUMAs (in orange) occupy the upper right-hand corner of the graph. Although their infection rates were only a bit above average on April 1, their case counts grew fast. Regression 2 confirms the effect, and shows much more explanatory power from the same controls.

Robustness

To check the robustness of these controlled correlations, I ran Regression 1 five times, each time dropping one borough. When the Bronx or Queens is omitted does the coefficient for automobile commute share become insignificant at the 10 percent level, although the coefficient remains similar. The results do not, as one might reasonably suspect, rely on the uniqueness of Manhattan.

In Regression 3, below, I include transit shares of commuting instead of automobile shares. Both subway and other transit commute share are negatively associated with Apr. 1 case rate. (If I include both automobile share and subway share in the same regression, one of them becomes insignificant and small due to the collinearity between them).

Reviewing Harris (2020)

This study has used very different methods than Harris (2020) to analyze the same phenomenon, but come to the opposite conclusion. This section reviews Harris’ methods.

Harris first introduces a figure showing that subway use declined precipitously beginning around Wednesday, March 11th. New reported cases finally leveled off around March 16^th, as subway use was cratering. As Harris notes, however, this is likely endogenous. Figure 3 below is a reproduction of Harris’ Figure 3, except instead of subway entries, the blue bars show meals eaten in restaurants (relative to a year prior) as measured by OpenTable. All sorts of activities declined in unison as the city became aware of the spreading disease.

Harris’ second piece of evidence is that subway ridership declined differentially during the crisis: least in Staten Island and the Bronx; most in Manhattan. Manhattan also slowed its COVID-19 growth rate most drastically. Harris claims that this is consistent with (though not proof of) subways as the primary vector of transmission.

However, if subways (or ferries) are the primary vector, why is Staten Island, with a 67 percent automobile commute share, just as susceptible to COVID-19 case growth as the rest of the city? The change in transit usage is plausibly consistent with Harris’ hypothesis; the level of transit usage is inconsistent with it.

Next, Harris shows us a map which suggests– visually – that the Q46 bus, which terminates at Long Island Jewish Medical Center, has spread coronavirus along Union Boulevard in Queens. Harris, to his credit, does not mention this: in a city so dense with bus routes and subway tracks, almost any spatially-correlated pattern will match some transit corridor. Harris does, however, insinuate that the Flushing Local might be a culprit, but only makes the suggestion via a narrative. He never comes out and says it.

Harris argues, perhaps reasonably, that subway lines (not stops) are the correct unit of analysis. But he does not use this analytical tool.

As the culmination of his argument, Harris presents a map of New York, with some of its subways lines shown, which suggests an obvious and immediate visual conclusion: COVID-19 infection rates, as of April 12, are highest in the least-dense, most automobile-dependent, peripheral parts of New York City. I reproduce his powerful image below.

Refuting Harris is quite difficult, since he makes few clear claims and develops no argument, either verbal or quantitative. Instead, each piece of data is caveated:

• “Simple comparison of the two trends in Figure 1 cannot by itself answer questions of causation.” (p. 4)
• “[It] would be inappropriate to draw firm conclusions from what would amount to a Manhattan-versus-the-rest study.” (p. 7)
• “[We’re] already at a juncture where some readers may react with extreme skepticism.” (p. 12)
• “An overall assessment of these research efforts would surely lead a scientific reviewer to conclude that cause-and-effect is difficult to prove.” (p. 16)

In fact, the only clear claim in Harris’ paper is the title: “The Subways Seeded the Massive Coronavirus Epidemic in New York City.” The data analysis presented in this study provides far more evidence against that title than Harris musters in its favor.

View of the World from 9th Avenue

Looking outside the boundaries of the five boroughs, New York’s experience does not appear to be anomalous.  The five large suburban counties in New York State all report higher case rates than New York City (as of April 16), although their COVID-19 death rates are lower. Suburban counties in New Jersey report comparable case rates to New York City.

Globally, transit-dependent cities have not been hit particularly hard. Asian cities with extremely high rates of transit use, such as Hong Kong and Seoul, are among the safest places in the world at the moment. European transit hubs like London and Paris have fared less well, though they are nowhere near as hard-hit as New York. Alon Levy has shown that in Germany, transit-dependent cities do not appear to have systematically higher infection rates.

Policies, and perhaps culture, appear to have a large impact on infection rate. To the small extent that transportation options matter, automobiles appear to be more dangerous disease vectors than subways.

Discussion

One thorny issue remains: how could automobiles spread a virus? They carry at most a few passengers, who are often members of the same household anyway. Strangers’ hands don’t touch your steering wheel as they touch the straps and bars in a subway car. Like many people, I have avoided public transit since early March, but driven regularly.

There are two reasonable explanations for the likely fact that coronavirus spreads more along roads than rails. First, subway-dependent people may have cut their travel more than car-dependent people. Since travel brings us in contact with others at our destinations (stores, jobs, restaurants), the excess drop in travel may have made subway people safer precisely because the subway seems so dangerous.

Second, and less obviously, subway-dependent people likely have more geographically-determined circles of contact. Car owners can move freely well beyond their immediate neighborhood. In the language of networks, non-car owners are more likely to approximate “neighbor flooding”; car owners to approximate “uniform gossip” (hat tip to Wesley Chow for this conceptual framework). That is, if a grocery store in a low-car-ownership neighborhood becomes an infectious spot, it is likely to infect a bunch of people who will all “reinfect” each other at the drug store and the park. In a car-oriented context, by contrast, infected grocery customers would drive off to different pharmacies and parks and infect other people.

Taken together, the global trends, suburb versus city infection rates, and neighborhood trends within New York suggest that transit-dependent cities are easier to protect from viral infections even when the transit system remains open. How to re-open the city safely remains a vital question, and strong, sensible safety measures, such as mask requirements and constant station cleaning, should be the default.

This study suggests that far more attention should be paid to the dangers of spreading coronavirus by car. In New York City, immediately increasing the tolls on the city’s bridges and tunnels would discourage people from coming in and out of the city, spreading the virus as they go.

In suburban locales fighting severe outbreaks, limited-access highways ought to be closed to most drivers. High travel speeds on empty highways allows drivers to rapidly spread disease to previously unaffected areas. Keeping drivers on low-speed local roads discourages people from indulging their wanderlust and helps geographically contain outbreaks. However, like the subway, roads and driving are an essential aspect of maintaining the crucial infrastructure – health, food, utilities, information – that allows us the luxury of a long-term lockdown. And as the economy reopens, car commuters will need to be return to their usual routes. Drivers need to understand that they pose a risk of rapid geographic spread, and thus need to take extra precautions in interactions outside their own neighborhoods.

The post Automobiles Seeded the Massive Coronavirus Epidemic in New York City appeared first on Market Urbanism.

He begins by asking: why do cities exist? He writes that cities allow people to “widen contact with as many people as possible… The more people one came in contact with the more he increased his chances of finding a suitable mate or potential “business partners” with whom he might exchange goods.” Thus, cities need places where one can come into contract with people that one does not already know.

He adds that “the more public space per person within a study area ­ the lower are the chances that people may enjoy mutual presence in public space. ” In other words, if most of the city is parkland or roads,your chances of actually meeting another person in the park is lower, since most of the parkland will be unoccupied at any given time.

Schocken suggests that his view is supported by data: he studies four cities and the most pedestrian-friendly ones (Nice and a Brazilian favela) have relatively low amounts of public space per person, while Ashdod, Israel (which is more auto-oriented) has more, perhaps because more land is used for roads than in the other towns studied. He also studies Poundbury, a British new urbanist development which he thinks has far too much public space and is thus not as lively as it could be.

The post Do cities have too much public space? appeared first on Market Urbanism.

Recently, I was at the train stop for Central Islip, Long Island, a low-income, heavily Hispanic community 40 miles from Manhattan. There is a Family Dollar almost across the street from the train stop, and guess what is right next to it, in the very same strip mall? You guessed it- a grocery store! *

It seems to me that dollar stores and traditional grocery stores might actually be complementary, rather than competing uses. You can get a lot of non-food items and a few quick snacks at a dollar store, and then get a more varied food selection at the grocery next door. So it seems to me that the widespread villification of dollar stores may not be completely fact-based.

Having said that, I’m not ready to say that my theory is right 100 percent of the time. Perhaps in a very small, isolated town (or its urban equivalent), there might be just enough buying power to support a grocery store or a dollar store, but not both. But I suspect that this is a pretty rare scenario in urban neighborhoods.

*If you want to see what I saw, go on Google Street View to 54 and 58 E. Suffolk Avenue in Central Islip.

The post Are Dollar Stores Wiping Out Grocery Stores? appeared first on Market Urbanism.

But it seems to me that this conclusion would be wrong. Here’s why: new buildings in the East Village are generally more expensive than old buildings.* So I suspect that if yuppies are moving into old buildings like Moss’s, it is probably because they cannot afford newer buildings, or more affluent neighborhoods like Tribeca. It logically follows that if more new buildings were allowed in Moss’s neighborhood, he would have less affluent neighbors, which presumably would make him happier.

*I searched listings at streeteasy.com, and found that of about 170 pre-war one-bedrooms, 77 of them (or 45 percent) rented for less than $3000 per month. By contrast, of the 32 postwar one-bedrooms in the East Village, only 3 rent for under $3000.

The post Even NIMBYs should be YIMBYs appeared first on Market Urbanism.

In fact, Many Dubai neighborhoods aren’t very dense at all.

There is one Dubai neighborhood that is more dense than most urban neighborhoods in North America: Ayal Nasir (which has about 200,000 people per square mile). But it looks far more like Paris than the popular stereotype of Dubai: streets are narrow, and most buildings are five or so stories high. The neighborhood next door, Al Murar, has 130,000 people per square mile and has a similarly human-scale urban fabric.

The post For once I agree with the NIMBYs: please don’t turn my neighborhood into Dubai- because Dubai isn’t dense enough! appeared first on Market Urbanism.