Alteryx, data visualisation, Maps, Tableau

Alaska Fried Chicken: the UK’s curious approach to naming chicken shops.

I went a little bit viral a couple of weeks ago when I tweeted about chicken shops in the UK which are named after American states which aren’t Kentucky. If I’d thought about it, I’d have written this blog up first, created a Tableau Public viz, and had all kinds of other shit ready to plug once I started getting some serious #numbers… but I didn’t. So, to make up for that, this blog will go through that thread in more detail and answer a few questions I received along the way.

It all started when I walked past Tennessee Fried Chicken in Camberwell, pretty close to where I live. It’s clearly a knock-off KFC, and I wanted to know how many other chicken shops had the same name format: [American state] Fried Chicken.

The first thing to do is to get a list of all the restaurants in the UK. I spent a while wondering how to get this data, but then I remembered that my colleague Luke Stoughton once built a Tableau Public dashboard about food hygiene ratings in the UK. All UK chicken shops – hopefully! – are inspected by the Food Standards Agency. So, Luke kindly showed me his Alteryx workflow for scraping the data from the FSA API, and I adjusted it to look for chicken shops.

My first line of inquiry is pretty stringent: how many chicken shops in the UK are called “X Fried Chicken” where X is an American state which isn’t Kentucky?

Turns out it’s 34. “Tennessee Fried Chicken” – including variants such as Tenessee and Tennesse – is the most popular with 13 chicken shops. The next highest is Kansas with six, which I’m assuming is so the owners can refer to their shops as KFC, although maybe the owner/s just really like tornadoes, wheat, and/or the Wizard of Oz. Then there’s four Californias, a couple of Floridas, and one each of Arizona, Georgia, Michigan, Mississippi, Montana, Ohio, Texas, and Virginia.

1 state fried chicken map

[tangent: I’m aware that a lot of these states aren’t exactly famed for their fried chicken, but as a Brit, all I have to go on for most of them are my stereotypes from American media. But hey, maybe it’s still accurate, and Ohio Fried Chicken tastes of opiates and post-industrial decline, Arizona Fried Chicken comes pre-pulped for the senior clientele who can’t chew so well these days, and Florida Fried Chicken is actually just alligator. Michigan Fried Chicken is, I dunno, fried in car oil rather than vegetable oil, and Alaska Fried Chicken is their sneaky way of dealing with the bald eagle problem up there? I’m running out of crude state stereotypes now, I’m afraid. Out of all these states, I’ve only actually been to California.]

There’s also a “DC Fried Chicken”, which is close but not quite close enough for me, and a “South Harrow Tennessee Fried Chicken”, which I’m not counting because either.

Here is where these American State Fried Chicken shops are in the UK:

2 map uk

Interestingly, this isn’t a case of a map simply showing population distributions. The shops cluster around the London and Manchester regions, but with almost none in any other major urban centre.

Let’s have a look at the clusters separately. Here’s the chicken shops around the Manchester area:

2.1 map greater nw

None of them are in the proper centre of Manchester itself, but they’re in the towns around. One town in particular stands out: Oldham. Let’s have a look at the centre of Oldham:

2.2 oldham only

Oldham, you’re fantastic. There are six separate “X Fried Chicken” shops in Oldham, and four of them – Georgia, Michigan, Montana, and Virginia – are the only ones by that name in the whole country.

For comparison, here’s the London area:

2.3 greater london area only

This is where all the Tennessees are, as well as the one Texas and Mississippi.

It looks like there’s a lot more variety in the north of England compared to the south, and sure enough, a split emerges:

3 latitude scatterplot

[chicken icon from https://www.flaticon.com/packs/animals-33%5D

Chicken shops in the south of England (and that one Tennessee place in Wales) tend to name their shops after states in the geographical south of the USA, while chicken shops in the north of England name their shops after any states they like.

This is where my initial Twitter thread ended, and I woke up the next day to a lot of comments like “Y IS THEIR NO MARYLAND THEIR IS MARYLAND CHICKEN IN LEICESTER”. Well, yeah, but it’s not Maryland Fried Chicken, is it?

So I re-ran the data to look at chicken shops with an American state in the name. This is the point at which it’s hard to tell if there’s any data drop out; the FSA data categorises places to inspect as restaurants, takeaways, etc., but not as specifically as chicken shops. All I’ve got to go on is the name, so I’ve taken all shops with an American state and the word “chicken” in the name. This would exclude (sadly fictional) places like “South Dakota Spicy Wings” and “The Organic Vermont Quail Emporium”, but it’d also include a lot of false positives; for example, you’d think that taking all takeaway places with “wings” in the name would be safe, but when I manually checked a few on Google Street View (because I’m dedicated to my research), about half of them are Chinese and refer to the owner’s surname, not the delicacy available.

This brings in a few more states – Marlyand, New Jersey, and Nevada:

4 state chicken map

Let’s have another look at the UK’s south vs north split. We’ve got a bit of midlands representation now, with the Maryland Chickens in Leicester and Nottingham, the Nevada Chickens in Nottingham and Derby, and a California Chicken & Pizza near Dudley. The latitude naming split between the south/midlands and the north isn’t quite as obvious anymore:

5 latitude with no fried restriction

…but, there is still a noticeable difference. This graph shows each chicken shop with an American state and the word “chicken” in the name, ordered by latitude going south to north:

6 north vs midlands and south

In the south and the midlands, there’s the occasional chicken shop that’s going individual – there’s the Texas Fried Chicken in Edmonton, the two Mississippi places in London which don’t seem to be related (Mississippi Chicken & Pizza in Dagenham, Mississippi Fried Chicken in Islington), the Kansas Chicken & Ribs place in Hornsey is almost definitely a different chain from the six Kansas Fried Chicken shops in and around Manchester, and the California Fried Chicken in Luton is probably independent of the California Fried Chickens on the south coast – but most of them are Tennessee or Maryland chains in the same area. In all, the south and midlands have 17 chicken shops named after 8 American states (excluding Kentucky), or a State-to-Chicken-Shop ratio of 0.47.

In the north, however, there’s a proliferation of independent chicken shops – 15 shops named after 9 different states (excluding Kentucky), or a State-to-Chicken-Shop ratio of 0.6. There’s the chain of six Kansas Fried Chicken places and two Florida Fried Chicken places in Manchester and Oldham, but the rest are completely separate. Good job, The North.

The broader question is: why does the UK do this? There’s obviously the copycat nature of it; chicken shops want to seem plausible, and sounding like a KFC (and looking like one too, since they’re almost always designed in red/white/blue colours) links it in people’s minds. I think there’s more to it, though. Having a really American-sounding word in the name is probably a bit like how Japanese companies scatter English words everywhere to sound international and dynamic (even if they make no sense), or how Americans often perceive British names and accents as fancier and more authoritative (even if to British ears it’s somebody from Birmingham called Jenkins). We’re doing the same, but… for fried chicken.

Finally, since this data is all from the Food Standards Agency’s hygiene ratings, it’d be a shame not to look at the actual hygiene ratings:

7 hygiene

It looks like independently-named chicken shops named after American states in the north are more hygienic. The chains in the south and midlands – Tennessee, Maryland, California, and especially New Jersey – don’t have great hygiene ratings, and the independent shops do pretty badly too. In contrast, the chicken shops in the north score highly for cleanliness. In fact, a quick linear regression of hygiene onto latitude gives me an R2 of 0.74 and a p-value of < 0.0001. Speculations as to why this is on a postcard, please.

Preëmpting your questions/comments:

“I live in […] and my local shop […] isn’t mentioned!”
Maybe you’re talking about a Dallas Chicken place. That’s not a state. Nor is Dixy Chicken, it just sounds a bit American. If it’s definitely a state, then does it have chicken in the name? If not, I won’t have picked it up. I also haven’t picked up shops which have, say, “Vermont Fried Chicken” written on the shop sign if it’s registered in the database as “VFC”. Same with if the state is misspelled, either by the shop or by the data collectors. If it’s all still fine, perhaps the shop is so new that it hasn’t had an inspection… or perhaps the shop is operating illegally and isn’t registered for a hygiene inspection.

“Did you know about Mr. Chicken, the guy who designs the signs?”
I didn’t, but I do now! He’s brilliant.

“How did you do all this?”
I use Alteryx for data scraping/preparation and Tableau for data visualisation.

“I have an idea for something / I want to talk to you about something, can I get in touch?”
Please do! My Twitter handle is @GwilymLockwood, or you can email me on gwilym.lockwood@theinformationlab.co.uk

“Your analysis is amazing, probably the best thing I’ve ever seen with my eyes. Where can I explore more of your stuff?”
Thanks, that’s so kind! There’s a lot of my infographic work on my Tableau Public site here.

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Alteryx, football, Maps, Tableau

Centre of Gravity, Metaphorically: Plotting time-based changes on maps

I haven’t written a blog in far too long. My bad. So, to get back into the swing of things, here’s something I’ve been playing with this week: centre of gravity plots.

It started with an accident. I had some EU member data, and I was simply trying to make a filled map based on the year each country joined, just to see if it was worth plotting. You know, something like this:

1 eu filled.png

Except that I’d been having a clumsy day (the kind of day where I spilled coffee on my desk, twice), and accidentally missed the filled map option and clicked line instead:

2 broken line.png

Now, I normally don’t like connected scatterplots, but realised that I could change a couple of things to this accident to make quite a nice connected scatterplot on a map, joining up the central latitude and longitude of each country, so I thought I’d follow through with it and see what happened.

(by the way, the colour palette I use is the Viridis Palette, which I absolutely love. You can find the text to copy/paste into your Tableau preferences file here)

Firstly, I changed my “year joined” field from a discrete dimension into a continuous measure so that I could make it a continuous line with AVG(Year joined):

3 connected line left right.png

This connects all the countries by their central latitude and longitude as generated by Tableau, but it joins them up in order from left to right on the map. So, I then added AVG(Year joined) to the path shelf as well, which means that each country is joined in chronological order, or in alphabetical order when there’s a tie (as with Belgium, France, Germany, Italy, Luxembourg, and the Netherlands, who formed the EU in 1958):

4 connected line year.png

I was pretty happy with this; it shows the EU’s expansion eastwards over time far, far better than the filled map did.

I got talking to Mark and Neil online, who introduced me to the idea of “centre of gravity” plots, which show the average latitude and longitude of something and how it changes with respect to something else (usually time). In this case, a centre of gravity plot of the EU would show the average central point of Belgium, France, Germany, Italy, Luxembourg, and the Netherlands in 1958, then the average central point of Belgium, France, Germany, Italy, Luxembourg, the Netherlands, Denmark, Ireland, and the UK in 1973… and so on. I figured it should be easy enough, I’d just take Country off detail, replace it with Year joined, and average the latitudes and longitudes together.

Sadly, it doesn’t work that way. The Latitude (generated) and Longitude (generated) fields that Tableau automatically generates when it detects a geographic field like country can’t be aggregated, and can’t be used if the geographic field they’re based on isn’t in the view. That meant I couldn’t average the latitudes and longitudes over multiple countries without creating lots of different groups.

But, there’s a simple way around this! You can create a text table of the latlongs, copy/paste them into Excel or whatever, then read that in as another data source. Firstly, drag your geographic field into the view, and put the latitude on text, like so:

5 create table.png

Then copy and paste it all (I just click on there randomly, hit ctrl+A, ctrl+C, switch to Excel, ctrl-V). Now do the same for the longitude. Save the document, and read it in as a separate data source in Tableau. Now you can blend the data on Country, or whatever your geographic field is, and you’ve got actual latlongs that you can use like proper measures.

And so I did. I recreated the line chart with the new fields, but took Country off detail, and made AVG(Latitude) and AVG(Longitude) into moving average table calculations which take the current value and an arbitrarily high number of previous values (I put in 100, just because). This looked pretty good:

6 cog flawed averages.png

…but then I realised that it wasn’t accurate data. Look at the point for 1973, after the UK, Ireland, and Denmark joined. Doesn’t that seem a little far north?

7 cog flawed illustrated

To investigate it fully, I duplicated the sheet as a crosstab, because sometimes, tables are the best way to go. What I found is that I’ve got a bit of Simpson’s Paradox going on; the calculation is taking averages of averages:

8 cog flawed explained.png

Not so great. If we add Country to the view after the Year joined pill, you can see what it should be:

9 what it should be doing.png

But the problem is, how do we put Country on detail but then get the moving average to ignore it? I tried various LODs, but couldn’t get it to work exactly – if you have a solution, I would love to hear it! My default approach is to try to restructure the data in Alteryx – because that generally solves everything – but I feel like I’m becoming too reliant on restructuring the data rather than working with what Tableau can do.

Anyway, I ended up restructuring the data by generating a row for each country and year that the country has been a member of the EU. That means I can create a data table like this:

10 restructured table

…which removes the need for a moving average calculation entirely, because the entire data is moving with the year instead. Just take country off detail / out of the view, and you get the right averages:

11 restructured table 2

Much more accurate:

12 EU cog.png

This is a better way of structuring the data for this particular instance, because the dataset is tiny; 28 countries, 60-ish years, 913 rows in my Excel file. It’s not going to be a good, sustainable solution for a centre of gravity plot over a much bigger dataset though. I did the same thing for the UN – 193 countries, 70-ish years – and ended up with 10,045 rows in my Excel file. It’s easy to see how this could explode with much more data.

It does look interesting, though; I’d never have guessed that the UN’s centre of gravity hadn’t really left the Sahara since its inception:

12 UN cog

Finally, since I was on a roll, I plotted the centre of gravity for the English football champions since the first ever professional season in 1888-89. Conceptually, this was slightly different; unlike the EU and the UN, the champion isn’t a group of teams constantly joining over the years (although it is possible to plot that too). Rather, I wanted to create a rolling average of the centre of gravity over the last N years. If you set it to five years, it’s a bit messy, moving around the country quite a lot:

13 english football 5 years.png

But if you set it to 20 years, the line tells a nice story. You can see how English football started out with the original northern teams being the most powerful, then it moves south after the Second World War, then it moves north-west during the Liverpool/Manchester era of domination, and finally it’s moving south again more recently:

14 english football 20 years.png

Many thanks to Ian, who showed me how to parameterise this. Firstly, put your hard-coded (i.e. not Tableau generated!) latitude or longitude field in the view, and create a moving average over the last ten years. Or two, or thirteen, or ninety-eight, it doesn’t really matter. Next, drag the moving average latitude/longitude pill from the rows/columns into the measures pane in order to store it. This creates a calculated field. Meanwhile, create a parameter to let you select a number. This will change the period to calculate the moving average over. Open up the new calculated fields, and replace the number ten/two/thirteen/ninety-eight with your newly-created parameter, remembering to leave the minus sign in front of it:

15 calc mov avg param

This will let you parameterise your moving average centre of gravity.

It was a lot of fun to play around with these maps this week. I’ve packaged them all up in a Tableau Public workbook here; I hope you find it as interesting as I did!

(title inspiration: Touché Amoré – Gravity, Metaphorically)

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