“The closer to the train station, the worse the kebab” – a “study”

I would second this. In Paris, but I find it to be true in most European city, the area surrounding train station is usually not very nice. It is usually surrounded by cheap hotel for 1-night traveler, overpriced fast food (hence the initial theory) and social housing / cheap building (a lot of people don't want to live next to a train station due to the nuisance, so poor people often get stuck there).

On the other hand, metro station are pretty much anywhere, and they tend to have the opposite effect. Having a metro station close by is very practical and doesn't generate as much nuisance since it is underground and pretty well hidden.

I am almost sure the data will look very different if you only look for train station.

I thought to include metro stations mostly because of the nature of the original French subreddit (https://www.reddit.com/r/MetroFrance/comments/1hcifh1/pas_vu...). It perhaps doesn't make much sense in hindsight, but I also filtered out metro stations later on in the post and it didn't change much.

I'm starting work on a follow-up which will sample more cities, though I still plan to use walking distances w network analysis for now.

Seconded. Train stations are important attractors for some kinds of people and some kinds of eating place, metro stations (particularly relatively small and uniformly distributed ones, as is the case in Paris) are only able to give a restaurant an easily reachable position, only for metro travellers, compared to others in the neighbourhood. Different effects, good for a different study.

> if you remove the "bad/bad" restaurant there is a correlation that appears, but it is due to the collider bias.

This sounds like the correlation appears because of you throwing away some data, but the way I see it, that correlation is real - you're not removing the bad/bad restaurants, the market is.

I've been reading up on collider bias on Wiki and pondering the examples[0] - restaurants, dating, celebrities - and the way I see it, the biased statistics is still true for whoever is doing the classification (person visiting fast-food restaurants, or looking for a date), and if their selection (taste) generalizes, it might also carry over to the general population.

I feel the restaurant example from Wiki, with its associated image below, is worth discussing:

https://en.wikipedia.org/wiki/Berkson%27s_paradox#/media/Fil...

"An illustration of Berkson's Paradox. The top graph represents the actual distribution, in which a positive correlation between quality of burgers and fries is observed. However, an individual who does not eat at any location where both are bad observes only the distribution on the bottom graph, which appears to show a negative correlation."

This feels wrong to me. Why is the regression line nearly horizontal when, eyeballing the graph, a nearly vertical one would fit better and capture an even stronger positive correlation between qualities of hamburgers and fries? In fact, I'm tempted to even throw away the leftmost and rightmost points on the lower panel as outliers.

Anyway, this example assumes the bad/bad restaurants are not visited by the subject - however, if we take your scenario where bad/bad restaurants quickly go out of business, then it's the market that creates the correlation between those two hypothetically independent qualities, so as long as we're talking real world and not some imaginary spherical restaurants in frictionless vacuum, it would be fair to say the correlation exists (and that the causal mechanism behind it is market selection).

--

[0] - https://en.wikipedia.org/wiki/Berkson%27s_paradox

https://github.com/georust/rstar

Banger library. Not sure how it compares to, say, kd-tree in terms of performance but it allows for plenty of primitives.

> with a google review lower than 3 stars

At least here, the majority of 1-2 star reviews are actually complaining about third-party delivery services like Foodora[1].

Of course the fries will be soggy and the burger luke warm when you got a guy who had to pedal a bike for half an hour to deliver it for you. Like what did you expect?

[1]: https://en.wikipedia.org/wiki/Foodora

> 2. All those "bad kebabs" actually located within 500m from the nearest station. No kebab located in further than >500m is bad.

Right, but this is selection bias. There will always exist a distance D from which all bad kebabs are located.

Unless D is provenly chosen _before_ looking at the data, this has no meaning.

One also has to take the kebab density into account.

Bad kebab shops may survive if located near a train station.

I am definitely guilty of sometimes clicking "reply" and then reading the linked article to check that I'm not about to essentially tell you what you'd have read or worse, tell you something the article actually debunks.

I only read articles with headlines that describe informative content, not with headlines that sound funny or thought-provoking.

Hi there, inventor of the kebab plugin for traindeck here. I'm afraid I was the one who introduced the concept of kebab case, way back in the early 1990s. Back then, trains didn't have enough processing power to handle full cuts of meat, so I thought I'd introduce kebabs as a hack, and it ended up taking off! Didn't expect anyone to still be using it. It's always fun to share stories on HN - you never know who you'll meat here.

"study" is already in scare quotes

The point is generally to recover cents on the dollar from money you can't otherwise use. Imagine you have a network of people moving illegal drugs. They collect money from their customers, and they need to pay it to you, the supplier. Arrange for them to eat a lot of food at your restaurant. Maybe they shout their friends. The money goes into the restaurant, and is mingled with other money. That money is used to pay costs for the business, but some of it is still profit. That profit, while less than the illegal input money, is _clean_ money, which is much more valuable because you can spend it without fear generally.

It's really just about making it harder for law enforcement folks to draw associations by looking directly at the bank accounts of several people they suspect are in cahoots. Or, in some cases, about making it harder to trace specific cash serial numbers or other instruments like cheques.

I recall reading of a crime outfit in Australia that would have the elders in their family take their illicit cash and use it to place an endless stream of bets on poker machines. By law they're required to return some high percentage, say, 75%, of the money put into them (the house keeps the rest). If you gamble tens of thousands in those machines, and you don't care about the losses, you get a reasonable expectation value back out as laundered winnings. In that case you don't even have to own the pub with the pokies, that's just how they work.

> If nobody eats there, how do they launder money?

The laundered money is reported to the tax office as cash payments from customers. Add some fake invoices from meat suppliers to make your books look legit, and you have converted dirty money into clean, even taxable income.

This only works to a certain degree, though. If the tax office becomes suspicious, they will definitely use undercover people to count customers and meat deliveries over a few weeks. But if for example only 10% of your sales volume is fake, it is quite hard for the tax office to prove this. For smaller laundry volumes, you don't even need fake supplier bills, you can always just claim that you put a bit less meat on the kebap, etc. The general idea is to take advantage of the fact that even the tax office acknowledges that it is impossible to keep books accurate to the last penny in such businesses.

When I was a student I often ate pizza in a really nice Italian restaurant which later turned out to be a money laundering front for the Mafia. But the pizza was great!

You have money you can't explain to the IRS how you got it, that's dirty money. Now you use some clean money to buy a kebab shop. Every day you give your dirty money to the kebab shop, the kebab shop prints fake receipts. Now the IRS can be convinced the money came from cash in hand customers.

They already have the money, they just need a business as a facade to claim the money was earned by running the business. But you are right, eventually it would be suspicious if they only sold food but never bought the matching raw ingredients. That's probably why they now moved to barber shops - nobody knows or can prove you didn't cut the hair of the hundreds of people that were never there.

They pretend like people _do_ eat there, that's how the dirty cash ends up in the register.

As an example of something similar happening, my family and I once went to a little village in the middle of Bulgaria to visit a family fried, and booked a hotel room for the week. This was a huge hotel, next door to another huge hotel, serving a village with a population of a few hundred people.

At one point our friend told us it was well known that both were owned by the local mafia. If you were to check the books you'd find the place was fully booked every day of the year and doing a roaring trade, while if you actually went and visited you'd find 3 slightly bemused looking English people and maybe a wedding party.

Although no one eats there, he declares 50 kebab sales, the purchase of a frozen kebab skewer and bread rolls, etc. The raw materials will either be thrown away or sold on to another real restaurant. But to pass the checks, the purchases of raw materials must be consistent with the sales. The 50 sales are made with cash from drug trafficking. The simpler the activity, the fewer raw materials and the bigger the margins, the better. Amsterdam has its Nutella waffle stands, Berlin its kebabs, Paris its nail bars and massage parlours.

I think there is some exaggeration

> obviously aimed at the money laundering business

Places also provide facade to some social networks. They can keep relatives having a job, can act like a store front for doing whatever deal.

I accept they don’t survive by just serving food, but some of it basically just bad business or some guys to have a legit place for other activities.

> but you can clearly see that the bottom-right quadrant of the plot is basically empty, which is an important insight.

I don't think so? It's mostly a result of the fact that (obviously) the best place to sell food is where there are people, which is also the best place to put a metro station. So on average the kebabs are pretty good and on average they're near a station. In Figure 9 one of the worst reviewed restaurants is over 3km from a metro.

You're likely seeing a pattern where there isn't one, which is normal for humans.

There's one obvious place to go around here for a good kebab, it's a few minutes walk to the station, but the way you can tell it's the best place for a kebab is how late it's open every night. Long after other kebab places are dark they're still doing enough business to justify remaining open.

The best place for pizza in my city is very close to a train station but that's a total accident, they park (it's a van, no really, best pizza in the city but they hated owning a restaurant so they put their oven in a van instead) in the car park of a railway station's pub about five minutes walk from me.

> "You can sell good kebabs anywhere, but you can only sell bad kebabs near a train station."

Insightful!

97.38% of bad studies measure the wrong variable.

Do drunk french people buy kebabs? In my city one central late night kebab place has great kebabs. Anecdotally I remember one great kebab cart serving at least one drunken customer in Nice (France) - not near a station and a long way from the Paris metro!

I think there's some population selection flaws. Drunk people don't leave reviews. In foreign countries it is difficult to know the correct search term.

I suggest an alternative study: how much lager does it need to make a train station kebab taste great?

Wonder whether that's an instance of Berkson's paradox.

Basically nobody bothering to report about Kebab shops that are neither good nor conveniently located. Probably not, but "one quadrant empty" is a handy red flag that you might encounter Berkson's.

https://en.wikipedia.org/wiki/Berkson%27s_paradox

The original data includes "train and metro stations", but figure 9 filtered the data to only include train stations and arrived at the same conclusion.

At the end of article it's shown that only considering train stations didn't really change the result.

That saying in France is usually understood to be for cities outside of Paris and only referring to "Gares" (that word is used for train stations, not for subway stations). Anecdotally, I'd say it holds true in general in most cities I've visited (with Paris being an exception)

The last office I worked in not only had terrible coffee, but the machine had a touch screen and required network connectivity and regularly crashed, prohibiting all dispensation of coffee. It also reportedly came with a 5 figure monthly operating cost.

The coffee in the lobby was only slightly better, but at least the baristas didn’t crash during their OTA updates.

A 10 minute walk away and you’d find the best coffee for at least a couple miles around.

Visit us. You'll be surprised. :D

OP failed to reject the null hypothesis of no correlation at the given power level.

> Hence the "pepper" in the name.

Pepperomi doesn't quite have the same ring to it

we must invade italy and show them the true enlightenment that is pineapple on pizza