Thursday 22 April 2021

Gridded GDP maps for a spiky world

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

Southeast Asia

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.

Monday 5 April 2021

House prices in 2021 (in England and Wales)

A few maps and notes today as I try to catch up with the latest release of the HPSSA house price data from ONS. HPSSA? It stands for 'house price statistics for small areas', and it covers England and Wales. There are loads of different datasets and sometimes finding what you're looking for can be a bit fiddly so I put together a very basic, single page with links to them all. The good news? You can get small area house prices going back a full quarter century now, to the final quarter of 1995. Yes, way back in the mists of time. This is what the house price map of England and Wales looked like then - this is for the four quarters from the final quarter of 1995. Get yourself a time machine and snap up a bargain in central London - treat yourself!

Snap up a bargain while you can!

Fast forward 25 years and, you will be shocked to learn, the pattern is much the same and unless you have access to giant wads of cash, you are not going to be buying a big house in London any time soon. You really did need a map to tell you that, right?

Amazingly surprising patterns

But of course these maps don't change very much over time, save for a few little pockets here and there, or a bit of spread outwards from London or a few other high price areas - which I've tried to label. 

What I find most interesting - apart from this consistency over time, and the eye-wateringly expensive areas in inner London - is the extremes and the spatial patterns. So here are a few maps on that, below. 

By the way, I've chosen a £250k cut-off for the middle category as it's close to the average house price at the end of 2020. Or at least one version of the average. The London average is a bit over £500k now but my last category starts just a little bit below that. If you look at the maps below it's easier to pick out the 'southern bits in the north' (e.g. Wilmslow) or the 'northern bits in the south' (e.g. Portsmouth?) - at least as far as prices and perceptions of them sometimes go. I don't want to start one of those north-south wars that seem very popular on Twitter these days, particularly since all these places are incredibly far south anyway 😉.

The high and low categories together

Just the most expensive areas

Just the least expensive areas

Want the data? Here's my web page list with all the data - take your pick. The data I've used here is from dataset 46 and was last updated at the end of March 2021. How will the Covid-19 situation pan out in relation to house prices over the short, medium and long-term. No idea, but I'll keep following Neal Hudson's latest updates to keep on top of all that.

It's a bit ugly, but it still works

1996, eh? Now that seems like a loooooong time ago. Mind you, so does 2019. Is there any kind of north-south divide in house prices though? Hmm, hard to say.

Again, not exactly surprising

And one of those 'how it started, how it's going' things to end, since the data series goes back so far.

Quite interesting if you look really closely