Average color of each country (using satellite imagery) | Hacker News Reader

Average color of each country (using satellite imagery)

eox.at

339 pointsxk370 comments

The red-brown of South Africa (and I guess Argentina and Australia too) are basically midway points of their lush green regions and dry desert regions.

South Africa is as green as neighbouring Eswatini (formerly Swaziland) in the east and south coast and almost as arid as the Namib desert in the far north west.

I suppose that's stating the obvious but still interesting. And could probably add some comment about how this shows the dangers of averages of any heterogeneous data.

One interesting fact is that the arid regions of Australia look much more red than most other deserts (which look more yellow, look e.g. at the Sahara or the Arabian Peninsula), this is due to the high concentration of iron oxide in the soil of many inland areas of Australia - in other words our sand basically contains a little bit of rust :-D

Edit: a more scientifically correct explanation can be found here: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/200...

Does that mean if you have acid rain, it reduces back to iron?

Acid rain oxydizes. Reduction is the oposite chemical reaction.

Ah, thanks. Then I misremembered my high school chemistry from 25y ago. I thought rust (the verb) is oxidation and that when you add an acid, it would be reduction (to get back the iron).

Acid / base and oxidize / reduce are mostly orthogonal. Acid / base is about moving around protons while oxidize / reduce is about moving electrons.

There's a table here: https://www.vedantu.com/question-answer/acid-base-reactions-...

"Acid" is from Latin acidus (sour, sharp, tart) by way of French.

"Oxygen" is a word invented in 1777 by Antoine Lavoisier, from the Greek oxys (sharp, acid): same thing!

Lavoisier also invented "oxide" from ox(ygène) + (ac)ide.

You're right, averages in mid-sized and large countries are pretty much meaningless

I think the mode (after coercion from millions of colors to a smaller number of buckets) would be more meaningful than the mean.

It seems FTA that the ‘average’ color is found by resizing the color information within the bounds in question to 1:1 and then using that color to fill the bounds. I’m not sure but that seems to be an important algorithmic element of this process that is almost completely delegated to a side effect of an image library and not really discussed in the context of this write up.

I also routinely make visualizations of geographic and weather data (including satellite imagery) as part of my job and it doesn't surprise me. Once you've 1) acquired the data you need (in this case, we're talking about images of countries without clouds and corrected for exposure across the Earth) and 2) fixed your data (because it's never in the shape you want, or the map projection you need), the core of the algorithm can seem pretty trivial sometimes, especially because R and GDAL come with a very large set of ready-to-use algorithms (and I'll never thank the creators of their respective libs enough for that).

FTA?

From The Article

There will be a considerable amount of averaging and re sampling before the final resize. Depending on shape of the region and how samples outside it are handled you may see considerable variation in the results.

What I would be interested in is how much the colour would differ if another colour space were used. I wouldn’t expect a huge change in the imital re projection as adjacent samples are likely to be close in whatever colour space is used, but I think it might change some of the average colours.

The color is seasonal also. Michigan is green in the summer, loses that in the fall, gets whiter in the winter.

I think that most places on Earth get whiter in winter. ;)

You can acheive the same result in three lines of Wolfram Language... https://community.wolfram.com/groups/-/m/t/2431480?p_p_auth=...

A lot of the complexity of the python code is hidden by the Geo* methods you have here; a more representative comparison would show how those methods are implemented (eg with the shape files).

But that is the point of the Wolfram Language. If you are doing work with computation or data, it should be just built-in.

Actually, handling .shp files is also just built-in... https://reference.wolfram.com/language/ref/format/SHP.html

There is a lot of complexity hidden in the R functions as well. The reason of using higher level languages with comprehensive standard libraries is exactly to abstract these things away. So, I think it's fair to say that, for this particular problem, wolfram language is better suited.

That's actually astonishingly impressive to me.

Stephen Wolfram gave the keynote at re:Clojure a few days ago, with an amazing demonstration of the Wolfram language. It's incredible how much data and functionality they've made available with it, and with Stephen's mastery it is bordering on wizardry.

https://youtube.com/watch?v=3C1QQXEg_F8&t=34811s

Time of year for image capture would be worth checking - some places vary a lot. Interesting idea anyway.

Reminds me how Google maps changed to a more blue-green (pine?) for forests which seems jarring for Australian forests or woodlands.

Yes, much of California is green this time of year and yellow-brown in the summer.

Heck, same is true of Washington (the Evergreen State). I think our average color is probably greyish mud?

The brown smudge in Indiana/Ohio is definitely a seasonal thing.

Even more fun would be the same graphic produced as an animation in the time dimension.

A nature lover's tip for Google Maps; old growth primary forest appears a far darker shade of green than secondary forest. I've found this very useful for finding good hiking close to urban areas.

This is such a great tip. Do you have any others? I personally use Strava to find places to walk when I go to new cities. And when I'm looking for a neighborhood to stay in a new cities, I plot out all moderately expensive restaurants to use as a heuristic for safe but not touristy neighborhoods.

The other Google Maps tips I have is a bit less general; when searching for an anchorage for you boat you can often see the direction and magnitude of swell in the satellite photos.

I don't see it mentioned in the article, but I suppose one of the equal-area projections [0] should be used for calculating the average color data.

[0] https://en.wikipedia.org/wiki/Equal-area_map

Good point. Also, in which color space the averaging is done, also strongly affects the results.

In was thinking of this too. For example, the Lab color space is more suitable to interpolation than RGB.

Author forgot the Asian part of Russia, it's included neither to Europe, nor to Asia. But it's still on the general map of the world. Anyway it would be green.

Surpsied at how red Australia is... although we are trying to lighten our complexion by exporting all that iron ore to China.

I think it's fitting, feels like the right colour for it's uniquely ancient geology.

It ain't called The Red Centre for nothing.

It would be interesting to see something similar at a city level, it may be even insightful for some cities.

Very cool, but as a Canadian, I am disappointed that a more detailed view of North America as a whole (Canada+US+Mexico) wasn't included.

Agreed - I think it would be interesting to see it by state/province (or even better some kernel smoothing) instead of country. Country size is kind of arbitrary, as some countries are very large and others are relatively small.

Average colours of Australian states would be interesting. ..and seems about the only part of the world left out so far, besides Antarctica.

I guess people only pay attention to what they want to find, but Canada and Mexico are missing (=North America with the US) https://news.ycombinator.com/item?id=29673305. And the Asian part of Russia https://news.ycombinator.com/item?id=29672321

I was talking whole continents. I did notice some other bits went missing. "I guess people only pay attention to what they want to find" - not sure exactly what you're trying to say there, but it didn't feel good.

Very nice and beautiful! Would print and put on my wall

Is the „A“ from Average in the poster set apart a bit more (letter-spacing) or am I seeing things?

That can happen if you have an A and a v next to each other and no kerning. Although "from" in the (cursive) line below does seem kerned.

Expected to see, essentially, Camo, and wasn’t surprised. But I was surprised by the emphasis of green versus the yellow or brown element.

Was worried that US was going to be “baby poop” yellow/brown.

I’ve always wondered what the average colour of the web is. Probably close to light gray. Maybe Facebook gives it a blue tinge. Dark modes have complicated the question.

It'd be particularly fun to see that change over time - probably very much light grey early on (black text on white or near white or patterned whiteish as was popular) but darker/more colour as 'webapps' became more prominent and took over the whole screen more.

So the "Emerald Isle" of Ireland is basically the same green colour as the UK.

Pretty much. Remember how close they are geographically. IANAG but the gulf stream is probably the only significant difference. Also, they probably should say GB not UK.

https://www.bbc.co.uk/staticarchive/faa977f45fd35ea2f898aaf8...

The chart doesn't capture ... something. Luminous intensity?

I (and others) have noticed that when flying into NZ (Auckland or Wellington), the green is so intense that I feel I have to put on sunglasses. I haven't felt that anywhere else, but I haven't been to Ireland. I imagine Ireland is the same in this respect.

It would also be cool to see the average color of each body of water (ocean, sea, lake…)

Interesting how Turkey and Azerbaijan have the same brown color, while Georgia and Armenia which are in the same geographical location, have a very different deep green color.

They take those images during high summer, so no white countries.

I always knew my home country was dark but, geez…

/ Greetings from Sweden.

These images don't make any sense to me. This map looks all black except for the sahara desert. But the world map before averaging looks mostly greenish.

Try on a different screen. What you're seeing as black is green; there is no black.

One can see the same thing as in regular satellite maps: for example that Spain is the odd color out in Europe. Much more arid, even if it's not a desert.

The map design itself is really beautiful. I wonder if there's a mobile app that lets me skin google maps in this kind of aesthetic?

Note that China is gray with pollution. They claim to be reducing pollution, but as a dictatorship it's probably just lies.

What's going on with the "Sentinel 2" text in the subheading on the US colours by state graphic?

Not unexpected per se, but I love how closely this tracks with various camouflage colors in active use.

Interesting (but not surprising) how the color matches camouflage used in each region.

I'm surprised that the US is that green with all the desertic areas...

The country of Taiwan, a solid dark shade of green

using region in the title is more correct.

Using region in the title is more correct.

Surprised that much of Europe is green, considering that a lot of their forests have been cut down for centuries.

Grass is also green, lots of agricultural land come up as green as well. Actually, Europe is still fairly forested (45%) on average, so I'm not sure what other color it could be seen as.

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