It's a bit of a cliché to say that the world is 'spiky', but since I've done a few spiky-type maps in recent years I thought I would take it a step further and look at the world in relation to GDP rather than population. Why? Well, take a look at this part of Hans Rosling's '200 Countries, 200 Years' piece and you'll see that there is much to be gained by separating out different regions from their national contexts if we want to learn more about the economic position of sub-national units. So, using the 'Gridded global datasets for Gross Domestic Product and Human Development Index over 1990–2015' data provided by Matti Kummu, Maija Taka and Joseph H. A. Guillaume in their scientific data paper, I decided to see what it would look like if I mapped GDP in 3D for small grid areas. See below for the world map, and read on for more details about the data.
|This is one way of looking at global GDP|
As a reminder, or something new for those who didn't see my earlier population density graphic in the same style, see below - and you can compare the two to see how different they are - most of Africa and also India are particularly different. Yet we can also see that, below the national level, it's easier to see where statements like 'Europe is rich' or 'Africa is poor' look like false or not-quite-true statements, at least for some grid cells. This kind of insight is not exactly earth-shattering but it's also one of the reasons you'd want to have this kind of data in the first place - to look beneath the surface of country-level stats to see what's going on at a more fine-grained level.
|Global population density|
I've also produced a series of zoomed-in maps of different parts of the world, which you can see below, starting with sub-Saharan Africa. I haven't labelled any of them, on purpose, because I like exploring them visually and trying to figure out what's what based on the obvious spikes - e.g. like Lagos or Cape Town. Call me a nerd, but I like the simplicity of the unlabelled maps and I enjoy trying to figure out which city a particular spike represents.
|This one differs greatly from the population map above|
|Australia and New Zealand + a bit more|
|The Arabian Peninsula has some big spikes here|
|I tried to capture the whole US in this one|
|Most of South America in this one|
|A zoomed-in version showing the lower 48 US states|
With this kind of map it can at times - actually quite a lot of the time - seem a bit gimmicky, but I think the use of 3D in this case adds a useful extra dimension of information that is not possible with standard choropleth maps. Yet with 3D maps like this some of the spikes can easily obscure others so here are a couple more views of the US - one of which is focused on Chicago and blurred everywhere else, just for effect.
|With apologies to Hawaii this time|
|This angle provides a better view, I think|
And one final view, zoomed in to Europe this time.
|A (mostly) European zoomed-in version|
Okay, go on then - two more, but that's it. The first is focused on India and the second is a more zoomed-in version showing East Asia.
|This looks quite different to the population map|
|Manila is in the middle foreground|
That's all for now.
Where can I find the data? You can find it here.
Should I trust this data? That's up to you and of course different methods would produce different outputs, but if you read the Scientific Data paper and the methodology you'll see that it is defensible and robust (well, I think it is). My view is that even if the specific method can be argued with, a different approach would likely produce very similar results and therefore similar maps. There may be some anomalies in the data but that's one reason why a 3D approach like this can be useful - it helps us identify unusual spikes that may or may not make sense, although they usually do after further investigation - e.g. read this and the bit about San Quentin State Prison.
How did you make the maps? I used a Windows tool called Aerialod. Read my previous posts if you want to know how to use it. You can be up and running with it in a very short space of time, but if you want to create large images quickly you'll need a top-end graphics card.
This data looks all wrong, who should I tell? If you spot anything that doesn't look right, feel free to contact the authors of the paper and dataset - see link below, in the Acknowledgements section.
Is this available as a print? No, but I may add this to my existing print store.
Can I use this image? Yes, it's on the internet, which means you can steal it, crop off the author's name and pretend you made it. Just kidding, sort of. Everything on my blog can be used and shared in a non-commercial setting, but it's always nice if people link back and/or give attribution. I post stuff here that I think is interesting, so feel free to share it if you also find it interesting. If you want to use any of this commercially, get in touch with me via my company, Automatic Knowledge, where I do paid consultancy.
What's the resolution of the grid cells? You can see on the first image above that it says '5-arc minutes', which is about 10km at the equator, but less than that as you go further north or south. That's one of the sub-optimal things about this kind of data, unfortunately, but I can't do anything about that.
Hey, there are loads of blank spots with no GDP, what's going on? These maps can't show everything and sometimes the level of economic activity doesn't register as a spike in the maps above. Like all maps and visuals, these are simplifications and abstractions of another reality. Or, to put it another way, this kind of map is intended to provide the general pattern in the data rather than show every individual grain of sand. If you want a specific raw GDP value for anywhere in earth you can download the data and explore it as you wish.
Okay, I know it's an abstraction blah de blah de blah but what numerical value do the very highest spikes actually represent? The very highest spikes represent a value of 100x10⁶ USD and above. How much is that? If my maths is correct that is $100 million USD. And remember that this is total GDP (PPP) in constant 2011 international US dollars, as it says in the paper.
Isn't GDP a load of nonsense though? Yes. No. Spend 5 minutes searching for 'gdp flawed' or 'gdp nonsense' and you'll see lots of great critiques, many of which I agree with. Yet it is still a yardstick and it is still used.
What does FAQ stand for? Frequently asked questions.
What spatial unit does the grid spacing on the ground represent? These are spaced at 100 map units. That means that it's 5-arc minutes x 100, which is equivalent to about 1,000km at the equator. I just put these in to provide a bit of scale, but also because I think they are quite effective for context and drawing our eyes to the main canvas, which I put on a little elevated plinth.
Acknowledgements: I had looked at this before without getting very far, but I decided to look at it again after Michael Gustavson got in touch with me recently to ask about it. I couldn't have mapped GDP like this without the data - obviously - so of course I must acknowledge Kummu et al. (2018) for generating this fascinating dataset - see citation below. Thanks also to ephtracy for making Aerialod free and easy to use.
Kummu, M., Taka, M., & Guillaume, J. H. (2018). Gridded global datasets for gross domestic product and Human Development Index over 1990–2015. Scientific data, 5(1), 1-15.