The two most important things about speed cameras are that they save lives and that they are annoying. People think life-saving is good. They also think getting tickets is bad. These two beliefs are dissonant. Social psychology tells us that people will naturally seek to reconcile dissonant beliefs.
There are lots of ways to do this, some easier than others. For speed cameras, it typically means constructing a rationale for why cameras don’t really save lives or why life-saving initiatives aren’t admirable. A common approach is to claim that municipalities are motivated by ticket revenue, not safety, when implementing automated traffic enforcement (ATE). This implies that cameras’ safety benefits might be overstated, and that ATE proponents are behaving selfishly. Most people understand that this is transparently self-serving bullshit. It’s not really interesting enough to write about.
But there’s another dissonance-resolving strategy that popped into my feed recently that merits a response: what if speed cameras are racist?
This strategy doesn’t attempt to dismiss the safety rationale. Instead, it subordinates it. Sure, this intervention might save lives, the thinking goes, but it is immoral and other (unspecified, unimplemented) approaches to life-saving ought to be preferred.
I appreciate the work that the DC Policy Center does. Full disclosure: I’ve even cited this study approvingly in the past (albeit on a limited basis). But this tweet makes me worry that their work is transmuting into a factoid that is used to delegitimize ATE. I think that would be unfortunate.
So let’s look at this more closely. We can understand the study and its limitations. And, because DC publishes very detailed traffic citation data, we can examine the question of camera placement and citation issuance for ourselves–including from an equity perspective–and come to an understanding of what’s actually going on.
What does the DCPC study SHOW?
The most important result from the study is shown below:
The study reaches this conclusion by binning citation data into Census tracts, then binning those tracts into five buckets by their Black population percentage, and looking at the totals.
Descriptively, the claim is correct. The Blackest parts of DC appear to be getting outsize fines. But the “60-80% white” column is also a clear outlier, and there’s no theory offered for why racism–which is not explicitly suggested by the study, but which is being inferred by its audience–would result in that pattern.
To the study’s credit, it acknowledges that the overall effect is driven by a small number of outlier Census tracts. Here’s how they discuss it at the study’s main link:
Further inspection reveals five outlier tracts which warrant closer inspection. Four of these outliers were found in 80-100 percent black tracts while one was found in a 60-80 percent white tract. Of course, by removing these extreme values, the remaining numbers in each racial category do fall much closer to the average. But notably, the number of citations and total fines per resident within black-segregated tracts remains 29 percent and 19 percent higher than the citywide average, even after removing the outlier locations. Meanwhile, the considerably lower numbers of citations and fines within 80-100 percent white census tracts remain considerably lower than average. (For a more in-depth discussion of the results and the effect of these outliers, please see the accompanying methods post on the D.C. Policy Center’s Data Blog.)
But if you click through to that “methods post” you’ll find this table, which has been calculated without those outlier tracts. The language quoted above isn’t inaccurate. But it’s also clearly trying to conceal the truth that, with those outliers removed, the study’s impressive effect disappears.
What do we know about DC’s ATE cameras?
Let’s take a step back and look at this less reactively. What do we know about DC speed cameras?
The most useful source of data on the topic is DC’s moving violation citation data. It’s published on a monthly basis. You can find a typical month, including a description of the included data fields, here. I had previously loaded data spanning from January 2019 to April 2023 into a PostGIS instance when working on this post, so that’s the period upon which the following analysis is based.
The first important signal we have to work with is the issuing agency. When we bin citations in this way, we see two huge outliers:
ROC North and Special Ops/Traffic are enormous outliers by volume. We can be sure that these represent speed cameras by looking at violation_process_desc for these agencies’ citations: they’re all for violations related to speeding, incomplete stops, and running red lights. The stuff that ATE cameras in DC detect, in other words.
I am primarily interested in ATE’s effect on safety. The relationship between speeding and safety is very well established. The relationship between safety, red light running and stop sign violations is less well-studied. So I confined my analysis to the most clear-cut and voluminous citation codes, which account for 86% of the citations in the dataset:
violation_code | violation_process_desc
----------------+------------------------------------------
T118 | SPEED UP TO TEN MPH OVER THE SPEED LIMIT
T119 | SPEED 11-15 MPH OVER THE SPEED LIMIT
T120 | SPEED 16-20 MPH OVER THE SPEED LIMIT
T121 | SPEED 21-25 MPH OVER THE SPEED LIMIT
T122 | SPEED 26-30 MPH OVER THE SPEED LIMIT
I’m not going to focus on human speed enforcement, but it is interesting to examine its breakdown by agency:
DC publishes the location of its ATE cameras, but it’s easier to get this information from the citation data than from a PDF. Each citation record includes a latitude and longitude, but it’s only specified to three decimal places. This results in each citation’s location being “snapped” to a finite set of points within DC. It looks like this:
When an ATE camera is deployed in a particular location, every citation it issues gets the same latitude/longitude pair. This lets us examine not only the number of camera locations, but the number of days that a camera was in a particular location.
One last puzzle piece before we get started in earnest: DC’s wards. The city is divided into eight of them. And while you’d be a fool to call anything having to do with race in DC “simple”, the wards do make some kinds of equity analysis straightforward, both because they have approximately equal populations:
And because wards 7 and 8–east of the Anacostia River–are the parts of the city with the highest percentage of Black people. They’re also the city’s poorest wards.
With these facts in hand, we can start looking at the distribution and impact of the city’s ATE cameras.
Are ATE cameras being placed equitably?
Are ATE cameras issuing citations equitably?
A high camera location:camera days ratio suggests deployment of fewer fixed cameras and more mobile cameras. A high citation:camera day ratio suggests cameras are being deployed in locations that generate more citations, on average.
We can look at this last question in more detail, calculating a citations per camera day metric for each location and mapping it. Here’s the result:
Some of those overlapping circles should probably be combined (and made even larger!): they represent cameras with very slightly different locations that are examining traffic traveling in both directions; or stretches where mobile cameras have been moved up and down the road by small increments. Still, this is enough to be interesting.
Say, where were those DCPC study “outlier tracts” again?
Area residents will probably have already mentally categorized the largest pink circles above: they’re highways. Along the Potomac, they’re the spots where traffic from 395 and 66 enter the city. Along the Anacostia, they trace 295. In ward 5, they trace New York Avenue’s route out of the city and toward Route 50, I-95, and the BW Parkway. Other notable spots include an area near RFK Stadium where the roads are wide and empty; the often grade-separated corridor along North Capitol Street; and various locations along the 395 tunnel.
We can look at this analytically using OpenStreetMap data. Speed limit data would be nice, but it’s famously spotty in OSM. The next best thing is road class, which is defined by OSM data’s “highway” tag. This is the value that determines whether a line in the database gets drawn as a skinny gray alley or a thick red interstate. It’s not perfect–it reflects human judgments about how something should be visually represented, not an objective measurement of some underlying quality–but it’s not a bad place to start. You can find a complete explanation of the possible values for this tag here. I used these six, which are listed from the largest kind of road to the smallest:
motorway
trunk
primary
secondary
tertiary
residential
I stopped at “residential” for a reason. As described above, camera locations are snapped to a grid. That snapping means that when we ask PostGIS for the class of the nearest road for each camera location, we’ll get back some erroneous data. If you go below the “residential” class you start including alleys, and the misattribution problem becomes overwhelming.
But “residential” captures what we’re interested in. When we assign each camera location to a road class, we get the following:
How does this compare to human-issued speed citation locations? I’m glad you asked:
The delta between these tells the tale:
ATE is disproportionately deployed on big, fast roads. And although OSM speed limit coverage isn’t great, the data we do have further validates this, showing that ATE citation locations have an average maxspeed of 33.2 mph versus 27.9 for human citations.
Keep in mind that this is for citation locations. When we look at citations per location it becomes even more obvious that road class is overwhelmingly important.
ATE is disproportionately deployed on big, fast roads. And ATE cameras deployed on big, fast roads generate disproportionately large numbers of citations.
But also: big, fast roads disproportionately carry non-local traffic. This brings into question the entire idea of analyzing ATE equity impact by examining camera-adjacent populations.
Stuff that didn’t work
None of this is how I began my analysis. My initial plan was considerably fancier. I created a sample of human speed enforcement locations and ATE enforcement locations and constructed some independent variables to accompany each: the nearby Black population percentage; the number of crashes (of varying severity) in that location in the preceding six months; the distance to one of DC’s officially-designated injury corridors. The idea was to build a logit classifier, then look at the coefficients associated with each IV to determine their relative importance in predicting whether a location was an example of human or ATE speed enforcement.
But it didn’t work! My confusion matrix was badly befuddled; my ROC curve AUC was a dismal 0.57 (0.5 means your classifier is as good as a coin flip). I couldn’t find evidence that those variables are what determine ATE placement.
The truth is boring
Traffic cameras get put on big, fast roads where they generate a ton of citations. Score one for the braindead ATE revenue truthers, I guess?
It istrue that those big, fast roads are disproportionately in the city’s Black neighborhoods. It’s perfectly legitimate to point out the ways that highway placement and settlement patterns reflect past and present racial inequities–DC is a historically significant exemplar of it, in fact. But ATE placement is occurring in the context of that legacy, not causing it.
Besides, it’s not even clear that the drivers on those highways are themselves disproportionately Black. That’s a question worth asking, but neither I nor the DCPC study have the data necessary to answer it.
The Uncanny Efficacy of Equity Arguments
Before we leave this topic behind entirely, I want to briefly return to the idea of cognitive dissonance and its role in producing studies and narratives like the one I’ve just spent so many words and graphs trying to talk you out of.
The amazing thing about actually, that thing is racist content is that it attracts both people who dislike thatthingand want to resolve dissonance by having their antipathy validated; AND people who like the thing. Arguably, it’s more effective on that second group, because it introduces dissonance that they will be unable to resolve unless they engage with the argument. It’s such a powerful effect that I knew it was happening to me the entire time I was writing this! And yet I kept typing!
I think it’s rare for this strategy to be pursued cynically, or even deliberately. But it is an evolutionarily successful tactic for competing in an ever-more-intense attention economy. And the 2018 DCPC study debuted just as it was achieving takeoff in scholarly contexts:
None of this is to say that racism isn’t real or important. Of course it is! That’s why the tactic works. But that fact is relatively disconnected from the efficacy of the rhetorical tactic, which can often be used to pump around attention (and small amounts of money) by applying and removing dissonance regardless of whether or not there’s an underlying inequity–and without doing anything to resolve the inequity when it’s truly present.
Speed cameras are good, stop worrying about it
Speeding kills and maims people.
Speed cameras discourage speeding.
Getting tickets sucks, nobody’s a perfect driver, but ATE cameras in DC don’t cite you unless you’re going 10 mph over the limit. It’s truly not asking that much.
Please drive safely. And please don’t waste your energy feeling guilty about insisting that our neighbors drive safely, too.
This essay was originally published in Fairer Disputations on June 30, 2023.
In 2016, a then-puzzling study was published that showed that approximately one in five sexually active, openly lesbian-identified teen girls had recently had a male sex partner.
This type of disparity has become less surprising in the seven years that have followed. While lesbian and bisexual women’s sexuality has historically been the subject of scrutiny—and the reasons why a lesbian-identified woman may engage in sex acts with a man are varied—I’m not speaking here of skepticism or compulsory heterosexuality.
On the contrary, as acceptance of minority sexual orientations and gender identity have grown, these categories have become much more nebulous. Rather than being guided by physical experience, one’s sexuality and gender identity are now determined by something much harder to define: feelings. The YouTuber Contrapoints may have put it best in a now-deleted tweet: “Gen Z people are hard to figure out. They’re like, ‘I’m an asexual slut that loves sex! You don’t have to be trans to be trans. Casual reminder that your heterosexuality doesn’t make your gayness any less valid!”
To Zoomers, the tweet read like a mean-spirited joke—even, to some, as bigotry. Still, it accurately characterized Gen Z’s relationship to self-identification. Zoomers, who identify as LGBTQIA+ at higher rates than any previous generation, are not only liberal with how they apply sexual classification, they’re downright creative with it. Formerly well-defined boundaries, like “lesbian” or, more fundamentally, “woman” are being completely deconstructed.
In a recent article for Compact, Geoff Shullenberger described this shift away from sexual orientation as being a label to describe a set of behaviors or, at a minimum, a specific type of embodied attraction. Writing about the strange preponderance of “sexually active asexuals,” he says:
The decoupling of sexual identity from sex is evident beyond asexuality itself. … The regime Foucault describes, which emerged from 19th-century science, transferred the primary locus of sex from body to soul and recast the relevant sex acts and outward manifestations as external expressions of an inner essence.
He goes on: “Identity categories no longer cast a particular subset of behavior as the effect of a particular identity, but rather function as an end in themselves, potentially coexisting with any conceivable acts or expressions, or none at all.”
In other words, sexual orientation no longer has anything to do with who you actually have sex with.
Progressive Zoomers would say this decoupling of sexual activity from identity befits the complexity of these categories: they’re as fluid as an individual’s feelings, and their meaning evolves with one’s environment and internal state. But what, ultimately, is so “complex” about sex that it defies language? Culture war skeptics cynically suggest skyrocketing rates of bisexuality are a result of some combination of attention-seeking, social contagion, or mental illness, but this underestimates the fundamental shift in how we conceptualize gender identity and sexuality.
Another theory is that some identities have been subsumed into online affinity networks— participatory digital communities driven by a specific interest. This shift has been fueled by changes in the lifestyles of the upper middle and middle classes, which have become increasingly untethered from geography. In Affinity Online, Rachel Cody Pfister and Crystle Martin described how “online access is tied to a growing and flexible palette of choices for affiliation and a resulting shift away from affinities grounded in local places and organizations.” Online affinity networks sometimes also intersect with fandoms—for example, a Harry Potter-themed doll-making community on Facebook—but aren’t fandoms per se, which require an element of consumerism. Instead, the digital era has seen the transformation of these traditional physical spaces into platforms where people can connect over shared interests, making online affinity networks an inclusive space where geographical location is no barrier to participation.
The existing academic literature primarily focuses on the digitization of hobbies and niche interests (like professional wrestling, the creation of anime music videos, or knitting). However, one can easily imagine how this logic might extend to other aspects of one’s identity. If your hobbies are no longer bound by what’s in your immediate vicinity, then perhaps nothing else needs to be either, especially as other forms of physical community vanish. As our lifestyles become more internet-based and therefore disembodied, we see a distinct shift in people choosing identities that interest them or, as Shullenberger describes, reflect their “internal essence.” This starkly contrasts with identity labels—like “lesbian”—that once represented a lived experience, inherited trait, or behavioral pattern.
This shift isn’t limited to sexuality. The term “Latina,” for example, no longer necessarily describes the experience of being from Latin America or even being an active member in a Latin-American diaspora. Over the last decade or so, unspoken freedom has emerged in how one identifies their heritage that will rarely be questioned in social settings. In more extreme examples, people will be revealed as complete charlatans, like Hilaria Baldwin or Rachel Dolezal. But less extreme examples—people who lean into one heritage or another, despite being several generations removed and typically completely unembedded in the culture or the diaspora—are so ubiquitous as to go completely unnoticed.
It’s not that these individuals are lying, exactly. Rather, it’s that the purpose labels serve has evolved over time. Many identity categories do not function to describe one’s lived experience. Thus the label “lesbian” no longer necessarily describes the experience or behavioral patterns of same-sex attraction.
Gay and lesbian identities were uniquely situated to undergo this shift from “experience-based” to “affinity-based” identification. One reason for that is, as Alexander Cho writes in Networked Affect, homosexuals often have had to exist “between the lines.” By nature, “queer culture,” if such a thing exists, is “ephemeral, unofficial, evasive”—in Cho’s words, “an archive of feelings.” In a climate where one is already trafficking in winks and dog whistles and artifacts that are “guilty-by-association,” it’s relatively easy to blend in with the right signifiers. This is even more true in the disembodied environment of cyberspace, where there is no opportunity to discount or discredit one’s group membership through behavior (or the lack thereof).
Online, even more than in the physical world, homosexuality is, according to Cho:
the experience of alterity … the dark optimism of a hovering possibility for community, the release of self-expression in the midst of a system that you perceive to be tilted against you, and the potential for kinship and intimacy outside of heteronormative family and relationship structures.
Cho notes that this is true even “regardless of any one person’s overt claim to static sexual orientation.” Cho describes how taken aback he was by the micro-blogging platform Tumblr because of how well it distilled “queer affect” without the use of text. He says, “All there was was the gist.” By contrast, on the blogs of Cho’s youth, people communicated through text-based “literal testimonials” of their sexual experiences. In other words, Tumblr was the first place where Cho encountered the “vibes”—another word for sensibility or affect—that are now an animating force on the Internet.
Vibes were easy to curate on Tumblr because the website favored curation over creation. He describes the difference between a platform like LiveJournal, which relied on text, with Tumblr:
The experience of Tumblr is less like reading a LiveJournal blog and more like walking through a million different constantly shifting galleries—both may contain serious emotional heft and personal investment, but the latter relies much more on aesthetics, intimation, sensibility, and movement—in short, affect. … We don’t really know the stories behind these vintage posts. Without any sort of caption or credit viewers are simply left to fill in the blanks with their own assumptions of who these people are.
This empty space creates fluidity in what a label like “lesbian” can mean to the user, while also allowing people to engage in online behaviors that had a “gay vibe.” These behaviors might include sharing, creating, and consuming content in a community setting. The user is in some sense “participating” in a homosexually coded community, but their behavior is untethered from sex. It is no longer necessary to be a homosexual to be gay; only to have an affinity for and a talent for projecting the homosexual affect.
Again, sexual orientation isn’t the only place where this shift is seen. One—rather extreme—example is “wannarexia,” a slang term describing people who engage in online spaces for anorexia without themselves suffering from the disorder. “Wannarexia” and pro-anorexia culture more generally emerged for a complex web of reasons. The Internet allowed it to proliferate, evolve, and become consumable, making it accessible to new groups of people.
If anorexia, for example, is a moodboard and discourse, and not a set of harmful behaviors that require clinical treatment, then it is, in theory, open to anyone who feels an affinity towards it. Anorexia evolves from a diagnosis to a music playlist, a style of imagery, and an aesthetic, all in service to a “vibe.” The behaviors associated with it shift from restrictive dieting to cultivating a sensibility—expressing thinness without needing to be thin. If your day-to-day life is more predicated on digital behaviors than embodied ones, one can easily imagine how these identities feel real to the user, even though they are not lived in the traditional sense. They become real enough to say, “I’m a lesbian,” without ever engaging in the behaviors that would conventionally label a person as such.
Sexual orientation was especially well-suited to this shift, because the affect that connotes homosexuality was already well-established before the Internet. There was already a robust canon of film, literature, color palettes, and imagery that evoked homosexuality–ripe for sharing on the Internet.
Truly, homosexuality has become “queer.” To paraphrase an obscure text from the nineties, “I don’t know what [queer] is, but it’s not gay sex.”
Here’s what it is: a sensibility that can resonate with anyone, regardless of their physical-world behavior.
Whatever happens in Arizona in November we got a preview of the difficulty Republican candidates will have in states where high stakes ballot initiatives literally put abortion on the ballot. Shortly after Arizona’s high court ruled that the state must go back to the 1864 abortion law which forbids virtually every abortion, Kari Lake, probable GOP senate nominee (and governor over the water) released a remarkable statement. She first denounced the 1864 law, which she said she supported as recently as last fall. She said she opposed today’s ruling. She then demanded Gov. Katie Hobbs and the Republican state legislature “come up with an immediate common sense solution that Arizonans can support.” She then said that the decision will be up to voters in the ballot initiative that will be on the November ballot, that is, the initiative she actually opposes.
So let’s review. Lake opposes the law she had consistently said she supported. She denounces the court decision which ruled that the old law is in effect. She thinks the decision should be left to the states. She also opposes abortion. Also, Arizona is a state.
She also wants Governor Hobbs to solve the situation. Which would presumably mean making abortion legal. Even though Lake opposes abortion.
She then comes very close to saying that abortion is a very personal issue that should be left to a state and her doctor. No really. I’m not kidding. Here’s the exact quote: “this is a very personal issue that should be determined by each individual state and her people.”
Don't trust Kari Lake. But when a party starts to free itself from a single-issue orthodoxy, this is how is starts, with stuff like this. These cracks in the orthodoxy can eventually add up to a big change, if we keep pushing.
A photo of a cashier Zooming into work at a fried chicken shop in New York City from the Philippines went viral on X this weekend. The cashier sits on a computer monitor on top of an empty cashier desk and a virtual background of the store’s logo. Brett Goldstein, who posted the photo, wrote that this was “insane” and said it “only takes a short hop to imagine the near future where this is an AI avatar.”
The Zoom cashier is real. She works at Sansan Chicken, a small Japanese fried chicken shop nestled next to New York University’s campus in the East Village. The store has a touchscreen ordering kiosk similar to those that are common at McDonald’s. But it also has a separate setup with the Zoom worker, who also takes orders from customers.
The iPad that would normally serve as a payment terminal reads “WELCOME TO MY STORE” in Comic Sans. When the cashier leaves to take a break, she switches her background to say “The cashier will be back :).” On a piece of paper, taped to the cash register and facing customers, is the handwritten Zoom meeting ID and password, presumably so a human employee at the restaurant can dial in the remote worker.
The cashier desk. Image: Jules Roscoe
“An everyday experience you’d think would have to be in person—to have that mediated through a screen is pretty weird,” Alex Tey, an NYU student who visited the shop a couple months ago told 404 Media. “It’s a lot to ask of your customers, I think. It’s even a little alienating.”
Tey said that when she had been last, there was both an in-person and a Zoom cashier. After she ordered, Tey went to speak to the physical cashier, who directed her to speak to the virtual one instead.
“They were both human, but the person who rang up my owner was on a screen, so it didn’t feel like I was talking to a person,” Tey said. “The choppy part was talking to somebody with lag. Checking out in person with lag is not something that we’re used to.”
Inside Sansan Chicken East Village. Image: Jules Roscoe
When I walked into the store Monday, the cashier (who was a different woman than in Goldstein’s photo) greeted me by saying, “Hi, welcome to Sansan Chicken!” Her voice was slightly laggy and emanated from an Insignia speaker sitting on the desk, next to two fake plants. She greeted everybody who walked through the door like this, though nobody seemed to be paying much attention to her. When one man was taken aback by being greeted by a computer screen, she offered to take his order.
In the red and white wall behind the ordering kiosk, there is a 2-foot-wide rectangular window, through which the two or three in-person staff members pass orders from the kitchen. I asked a few questions through this window.
The ordering kiosk and kitchen window. Image: Jules Roscoe
“When you do the business, you want to control the cost,” a manager who did not give her name told me through the window. “In New York City, the regular income is very high, so you don’t want to pay attention to think about this kind of question,” she continued, gesturing toward the cashier as if to indicate that she did not want to have to worry about another employee. “As a business owner, you’ve got to think about this. It’s very useful.”
She said that the cashiers work through a different company based in the Philippines, which has a 12-hour time difference to New York, and usually change shifts at around 3 p.m. The cashier confirmed that she was in the Philippines, where she said that it was 1:11 a.m.
When I asked what company she worked for, she said that she was sorry, but could not answer any more questions. Goldstein wrote in his X thread that the company in question was called “Happy Cashier” and that they had no website, though they were operating in five Asian food stores throughout the city. 404 Media was unable to verify which company the remote worker actually worked for, and could find no job listings or information for “Happy Cashier” on the internet. The setup at Sansan Chicken appears to be more or less a DIY remote work solution, because they are literally using Zoom, a webcam, and a separate pair of speakers for the job.
Sansan Chicken also has a location in Long Island City, though it is unclear whether this store has a Zoom cashier.
“It totally makes sense to me if this is some dumb start-up outsourcing cashier labor, which is already underpaid, to the Global South for cheap,” Tey said. “It’s something that’s usually invisible to Western consumers, but this time it’s visible. They’re not even claiming it’s an AI or algorithm before outsourcing it to the Philippines.”
Outsourcing work to the Global South has been a recent topic of discussion after Amazon shuttered its famous Just Walk Out checkout system at Amazon Fresh stores last week, which relied on remote workers in India to train its AI systems and to do manual troubleshooting if anything went wrong.
Sansan did not respond to requests for comment through their website or through an Instagram DM. However, on Monday afternoon the restaurant shared a post by New York City meme page @newyorkers to their Instagram story of Goldstein’s original photo, with the caption “Thanks for the repost.”
Tey told 404 Media that the chicken she ate was “okay.”
This is a mess. Switzerland has some of the best results on decarbonization in the world, and it's a direct democracy. If Switzerland isn't good enough, then the countries outside the ECJ system shouldn't even bother trying.
I have to admit that I have always been a realist, yet, when it comes to technology, I tend to side with the enthusiastic optimists. For many years, I shared the illusion (and perhaps common delusion) that the Internet was a force for good in the world, believing it would […]
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