Map of Oakland with existing, pending, and proposed Slow Streets highlighted. Four streets are marked as "Completed April 11", four more are "Pending Installation April 17", and approximately 20 more scatters around the city are "Other Streets to be Evaluated"

As I was saying…

I’d been working on my post about disproportionate impact since before COVID-19 hit the Bay Area, but it happened to land in the middle of a broad conversation about the disproportionate impact of the pandemic on poor and working-class BIPOC, and the opening of Oakland’s opportunistic Slow Streets program, which streets advocates across the country are now demanding their cities emulate.

Last night Oakland held its weekly COVID-19 town hall; the segment led by Warren Logan, Libby Schaaf’s Director of Mobility and Interagency Relations, really made the point about how we’re equity-washing streets programs.

Map of Oakland showing median income. All of East Oakland is shaded in red (less than $50K) or orange ($50-$100K). The hills to the north are in blue (>$200K) , while the areas further east and south trend to green and yellow ($100-$200K)

Disproportionality

I keep encountering a trope about how poor people of color are disproportionately impacted by the societal harms related to the topic the author is writing on. Whether it be pollution, housing insecurity, food insecurity, or health issues like asthma, diabetes, and now COVID-19, low-income BIPOC have it worse than anyone else. The trope goes further, to employ the fact of disproportionate disadvantage as a justification for the author’s favored project: a bike lane, or a housing development, or a park or an urban farm or whatever it is the writer thinks is important.

I will note that it’s generally true. BIPOC have been disadvantaged in America since Columbus first showed up, and the effects are still seen throughout out society.

But the problem with disproportionate impact—besides its existence and its effects on BIPOC—is that its very ubiquity limits its usefulness as a tool for policy analysis.

East Oakland and resilience

On Tuesday I did my Tour of Oakland bike ride, and one of the impressions I came away with is that East Oakland, especially Deep East, has been less affected by the shelter-in-place order than the rest of the city. Much of the economy of East Oakland is informal, so shutting down the formal economy doesn’t hit the neighborhood as hard. I saw a few of the Scraper Bike Team kids out, too.

Displacement and density

After doing some crunching this week on data about rapidly-gentrifying Valencia Street in San Francisco, and finding that residential density is actually dropping in the neighborhood despite new housing construction, I wondered whether the same phenomenon could be found elsewhere in the country. I didn’t have to wait long for another case study, as Lynda Lopez and a number of other peeps I follow from Chicago posted about Mayor Lori Lightfoot’s ill-considered statement about “vibrancy” in Pilsen, a gentrifying neighborhood west of the Loop. It turns out that the demographic dynamics in Pilsen are very similar to Valencia Street, and they challenge the claim that infill development is a climate solution.

Two maps of Valencia Street in San Francisco, showing a large increase in median household income from 2000-2018, and a sizable decrease in Hispanic population.

Bike infrastructure and displacement

I have been really, really trying to avoid getting into the housing debate. But transportation and housing are inextricably linked, and my investigation of bike infrastructure’s relationship to gentrification is part of the Bike Lab’s work. A Twitter thread sparked by Phil Matier’s terrible SF Chronicle article led me to do some data analysis of Valencia Street’s transformation. Some of the results are pretty staggering.

Map of San Francisco showing Golden Gate Park and a selected census tract (67000) in the Outer Sunset

Error bar

Phil Matier is a terrible journalist. One of the ways in which he’s terrible is that he doesn’t understand statistics at all, or at least, the way he uses statistics in his writing is uninformed (at best) or intentionally misleading (at worst). I’ll be charitable and assume that the fallacies in his work come from a place of ignorance rather than deception.

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