The Yuxi Circle

You may not know it, but you've probably seen the Valeriepieris circle - it's that circle on a map of the world, alongside the text 'There are more people living inside this circle than outside of it'. The name 'Valeriepieris' is from the Reddit username of the person who posted it and in 2015 the circle was looked at in more detail by Danny Quah of the London School of Economics under the heading 'The world's tightest cluster of people'. But of course it's not actually a circle because it wasn't drawn on a globe and it's also a bit out of date now so I thought I'd look at this topic because I like global population density stuff. I'll begin by posting a map of what I'm calling 'The Yuxi Circle' and then I'll explain everything else below that - with lots of maps. As in the original circle, I decided to use a radius of 4,000 km, or just under 2,500 miles. Why Yuxi? Well, out of all the cities I looked at (more than 1,500 worldwide), Yuxi had the highest population within 4000km - just over 55% of the world's population as of 2020. All maps were done in QGIS, which I have been experimenting with a lot more lately because I just launched Map Academy on Udemy, to help more people get up and running with maps and data. 

The Yuxi Circle on a globe

The shape above is a circle because it's plotted on a globe but in the examples below you can see how it looks when drawn on different kinds of flat map projections.

A web Mercator projection

Robinson projection

WGS84

Winkel Tripel

There is more than one Yuxi in China, but the one on the map is in Yunnan province and has a population of about 2.5 million. Obviously, when you draw a simple 4000km buffer you always get a lot of sea, so I also tried to see what kind of ellipse I could draw around Yuxi that still included more than half of the world population - see below for the results of this.

The Yuxi Ellipse with the Yuxi Circle behind it

More than half the world lives in the Yuxi Ellipse

Okay, so what else? Well quite a lot actually but for now let's do something different. 

When looking at this I started to think 'what if someone built a tool like True Size Of but then also combined it with NASA's SEDAC Population Estimation Service?', where you could take the shape of one country, overlay it somewhere else in the world, and then get the population within it. Well, I had a little go at this with the lower 48 US states, which I moved south - and scaled geodesically - and then calculated the population within it when placed over China and India. I also did this with the US flipped horizontally, just because the US west coast is a reasonably close fit for the Chinese east coast when you do this. Quite interesting results I think - and a lot of people in the US this way - and still a lot of empty space!

Please someone build a web tool for this

Nice coastal alignment here

Okay, so then I wondered - because I've been looking at coastal things in the UK but also globally - 'how far in from the coast do you have to go until you capture more than 50% of the world's population? 

Well, I got 200km for that (124 miles). This was done using the very latest WorldPop 1km gridded population data, from 2020, just like the rest of the maps.

More than 50% of the world lives here.

Okay, fine. Now I was thinking even more silly thoughts - like 'where is the world's centre of population'? This is not easy to calculate at a global level, but I did do a bit of research before attempting it and then my first attempt was totally wrong because I didn't do it on on a spherical world. The internet doesn't have any great answers on this, but you can find a few things if you look hard enough and they say something along the lines of Almaty, Kazakhstan or thereabouts. 

Well, I got a world centre of population of just by Aktobe in Kazakhstan, using 1km gridded 2020 population data. Is this correct? Well, it is based on a complete 1km dataset and a spherical world but remember that I'm just some guy on the internet and before you go and locate your global business there because of my analysis you might want to double check the data yourself, although it is a bit tricky. But I'm fairly confident that the global centre of population is either here or close to it - certainly in this region is what I'd say.

Centre of the human world? Possibly.

Hey, what about my city? Why haven't you done a 4000km blob map of my favourite place? Well, it would have taken a long, long time to do everywhere but I did do a few more so see below for that. I calculated over 1,500 4000km population circles overall and 148 had populations of 4 billion or greater within 4000km - including cities in China (loads), Vietnam (e.g. Hanoi), Myanmar (Mandalay), Vientiane (Laos), Bangladesh (Chattogram), India (Agartala) and Bhutan (Thimpu). I have to say, despite taking a while to do all this, I have found it pretty interesting so I hope you do too. Just remember that the white line shapes on all the maps below (and above) are actually circles - but they will only look like circles when plotted on a globe. 

That's all for today. I did all the maps in QGIS (free GIS software) using open data - if you want to learn how to use QGIS, check out my Map Academy course.

Notes: does any of this look different if you use a different data source? Well, I ran the analysis again using the GHSL2015 1km population data and still Yuxi came out on top. I only included capitals plus those cities with 400,000 people or more so you could do this forever with settlements of all sizes but I had to draw the line somewhere otherwise it would have taken forever, plus I thought using larger cities makes some kind of sense. The data I used is WorldPop 2020 1km grid data and that gives us a world population of 7.8 billion. How do we deal with the projection of rasters issue if we need to? Well, we use the GHSL warp tool for that. Why do the shapes seem to differ in size? That's to do with where they are in the world in terms of latitude. The further north or south they are, the more distorted the shapes will be, but they are all circles when plotted on a globe. You can check this out yourself if you measure it manually on Google Maps. Did I use a geodesic buffer? Yes, and I think I got it right but feel free to check my results. The backdrop mapping is NASA's blue marble from August 2004, converted to greyscale. Some places I didn't do maps for because the circle crossed the international date line and so the result was messy, and these include Toyko (1.82 billion within 4,000km), Auckland (26.8 million within 4,000km), Sydney (49.1 million within 4,000km), Melbourne (49.5 million within 4,000km). WorldPop data is open data, licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

World Population by Latitude

If you search online for 'world population by latitude' you'll quickly find quite a few results, including the great analysis by Bill Rankin on his Radical Cartography blog from 2008, which uses population from 2000, and also includes population by longitude. There's also a nice interactive version on Engaging Data, plus similar things on my One Degree of Population piece on here a couple of years ago and my global population density spike maps. In this post I look at world population by single degree of latitude using data from 2020. There are maps and stats below, but let's begin by looking at what appears to be some kind of alien eyeball but is in fact world population by single degree of latitude, where redder areas = more people at that latitude and bluer areas = less people. I used 2020 WorldPop data to do the calculations and all the maps were done in QGIS, as usual.

Weird alien eyeball? Or population map?

It's better viewed as a flat map, obviously, so I've posted that below. I've labelled quite a few places across the world and of course there are places at some highly populated latitudes that have very few people (notably the Sahara Desert) but you get the idea here: redder = more people at a given latitude.

Note the numbers on the left - including population data

If you view this image in full size you should be able to read the population numbers for each single degree of latitude - I've posted a zoomed in extract of this below. The most highly populated single degrees of latitude, according to my analysis? Here are the top 10 that I get:

1. 25-26° North: 278.6 million people
2. 26-27° North: 271.7m
3. 23-24° North: 244.5m
4. 24-25° North: 237.4m
5. 22-23° North: 235.3m
6. 30-31° North: 234.8m
7. 31-32° North: 226.2m
8. 34-35° North: 215.8m
9. 35-36° North: 214.6m
10. 27-28° North: 198.3m

And then down in 28th place we have the first entry in the southern hemisphere with 6-7° South having a population of 117.5 million people, according to my calculations. 

These are numbers I calculated myself

It's difficult to do anything with this colour scale these days without bringing to mind Ed Hawkins' warming stripes, but I think it's useful to use the red/blue colour ramp here because the most highly populated places are generally the warmer ones and the colder latitudes on the whole have fewer people. But of course there's another factor here, and that is about where all the land is. So first let's look at land vs population and then we can look at a density version of the above map - i.e. population density by latitude that presents the stripes based on the population density of land at all the different latitudes.

This is what I get for land by latitude

And population by latitude - people don't like living in the sea

You can do a little crossfaded gif to get the comparison between these two different elements, so that's what I did below. You can see it in the still images above, and in the gif below I have highlighted the single degree of latitude with most people and most land. 

Finally, we have a population/land by latitude gif

Let's hit pause on the gif, mid-fade, and then add some labels so we can make a bit more sense of what's going on. That's what I've done below - click to enlarge in order to read the labels.

Here's another globe-style view of the population data but this time from a different perspective.

Some surprises here perhaps

Okay, so the next three images are similar to the 'world population by latitude' image above but this time they are actually population density by latitude. That is, the redder latitudes are the most densely populated. I did this so that it only takes into account land, which is kind of where people like to live for some reason. You'll note in particular here there is a dark red stripe in the southern hemisphere that cuts across Santiago, Buenos Aires, Cape Town and Sydney, among other places. Note that I experimented by dimming and then removing the latitude colours over the sea in the second and third images because it kind of makes sense to do that.

Density makes a bit more sense

Dimmed the sea because people don't live there

Removed the sea latitudes, but I like this less

Other odds and ends

Once I'd done this I experimented with different map projections and views, hence the weird population latitude eyeball at the top of the piece. I also experimented with views from above and from below, so you can see them below in the first two images. The first one in particular looks like a bloodshot eyeball to me.

Where do most people live? Just eyeball it

Antarctica is pretty big

What else? Well, I did a few other versions, including just coloured lines of latitude, then adding only land, adding a chart and that kind of thing so I've posted them below too. These are more experimental versions - some I like more than others but the one with just colours and land I think is quite interesting.

Population stripes, with land

Just the population stripes

I took away only the land here

In this one I just added a chart of population by latitude

This is the original, as above, but without a white border

Does the final result on all this depend upon what dataset you use? Do you get a different result by using, say GHSL data vs WorldPop data? Or what about NASA's GPWv4, or even another source? Well, probably a bit, but the difference between GHSL and WorldPop wasn't huge and WorldPop is also available for 2020 and GHSL isn't so I used WorldPop. But you can see the difference between GHSL and WorldPop below anyway.

GHSL

WorldPop

GHSL WorldPop GHSL WorldPop GHSL WorldP

This all basically makes sense and is not particularly surprising but then again it's nice to be able to put some numbers to all this and make some maps of it. Plus it's interesting for training data and for working on map methods and techniques in QGIS.

People live here

Here's a screenshot of what this looked like in QGIS before I exported some of the final images. The font is Righteous, by the way.

Print Layout on left, map view on right

So there we have it.