How to make a 3D population density render for any country in the world

This blog post explains how to make a 3D population density render for any country in the world, using open data and free software. The main tool I'll use (Aerialod) is Windows-only at the moment, but it is amazing. I say 'render' rather than 'map' here because the images you'll generate are rendered by Aerialod, which is, technically, a 'path tracing renderer' rather than a piece of mapping software. But feel free to call them 3D maps - either way, this is an example of what they look like. 

Global population density print

But why do this? Well, partly because it's an interesting and often informative way at looking at population and settlement patterns, but also partly because this particular method produces nice-looking graphics. Perhaps the best answer I could give here is that's it's a combination of aesthetics, insight, and interest. In the image below, for example, we can see a lot of what we might already know, but it's often in the comparisons within and between places that we learn new things and that's also part of the point here - it can give us a new perspective on the world.

Singapore, KL, Medan, Ho Chi Minh City, etc

The ingredients

What do we need to make a population density map like this of a single country? Well, it can be done in different ways, and you may have your own method, but the basic ingredients are outlined below. I'm going to use the example of the United Kingdom as a single country, but the data source I use has data for all countries in the world, so you can pick whatever you like and replicate the method. So, here's what we need:

• Some population data, preferably in raster format
• A tool to render it in 3D

For the data, there are a few different sources, including the European Commission's GHSL data and NASA's GPWv4 but here we're going to use WorldPop population density data. These datasets are all free and open (CC BY 4.0), and all have 1km resolution versions, but WorldPop allows you to download individual countries, for each of the years from 2000 to 2020, so I'll use that here. Just a note of caution now to say that '1km' means '1km at the equator', or 30-arc-seconds - so if you're at 50 degrees north then the cell will still be 1km in height but it will only be about 80% of that width. Also, you should read the WorldPop - Mapping Populations page about the data, so that you understand what it is, and how it was created.

For rendering the world as a 3D population surface, there are again lots of options, the best of which is probably Blender. This is free, open source and extremely powerful but it's not what I'm using here. Instead, I'm using Aerialod by @ephtracy. I've previously written tutorials on how to use this for different kinds of data, but here' I'll focus on population density renders. 

About the vertical dimension of population density datasets

You can just download some data from WorldPop for a single country, in tif or asc format, and then just dump it in Aerialod, but when you do that with population data, something like this will probably be the result (below). This is the 2020 UK population density file from WorldPop, opened in Aerialod. I'm showing you this here as a way of explaining what Aerialod does and what you need to understand before you try to map population density in this way. 

What the tif?

The gbr_pd_2020_1km.tif file from WorldPop (2,372KB) is a simple black and white raster image, measuring 1,250 by 1,321 pixels (you can see this in the image properties when you download it). But the individual pixel values have population counts in them. So, when I open the file and inspect it in QGIS (below) I can see that the highest pixel value is 27,500 - and since Aerialod extrudes the vertical dimension according to individual pixel values, what we see in the image above when we open it in Aerialod is a kind of population density tower 27,500 high and only 1,250 wide by 1,321 high. Not exactly very useful. So, we have a 1km area of London with 27,000 people in it and this then gets extruded 27,500 units on the z dimension in Aerialod, while our x and y units max out at 1,250 and 1,321 respectively. Next step is to fix this.

We need to re-scale this file in order to use it

Scaling population density data in QGIS

This is probably the most important bit. I will say now that there are lots of different ways to do this next bit, but ultimately what we are trying to do is arrive at a situation where we can show population density patterns in 3D that provide a useful overview of the settlement patterns in different countries, or the world as a whole. I used a different approach in the image at the top of this blog post, plus different data (GHSL), but it is based on the same principle. The following is a simple workflow to take a tif file from WorldPop and then rescale it so you can use it in Aerialod. If you were mapping something with much lower actual values (e.g. elevation) then you don't need to do this re-scaling.

• Open the population density tif in QGIS. It should just display as a black-to-white image, with low population counts in darker colours and lighter shades for the highest values. You can see this in the screenshot from QGIS above.
• Right-click the layer > Export > Save As... and then you want to change the Output mode to Rendered image, select a folder to save it to, plus a file name, and then click OK. This will add another raster layer to QGIS. You can see a screenshot of this below.
• You'll see that the new raster layer in QGIS looks exactly the same, but it's not. Instead of the actual population values in each pixel, the values go from 0 to 198. This is what the Rendered image save option does - it scales the image using pixel values in the range from 0 to 255, where 0 is black and 255 is white. 

Saving as a rendered image

• When we add this new rendered image to Aerialod, and then change some of the settings (as in the screenshot below), we do get quite a good visual representation of population density.
• But, there's no land and the old 0 to 27,500 range has essentially been reclassified from 0 to 198, which means that the full range of classes (up to 250) hasn't been used. We'll fix that in a minute but first here's the result, below. As you can see, this does a pretty good job of illustrating the broad sweep of population density across the UK. If you're struggling with Aerialod here, you may need to go back to the links at the top of the page and see my tutorials on it, but all I did here was drag and drop the tif into the main Aerialod window and then change the settings, as per the screenshot.

This works pretty well - but there's no land

How do we get the land to appear? (don't use black for 0)

This next bit is about how to change the raster image before exporting it so that when we add it to Aerialod we can see the land as well. There are loads of ways to do this, but here's one simple way that works well. This step is only really necessary if you want land to appear. I prefer doing this because it then gives me the option of including the land, or not (i.e. you can make the land disappear by changing the Offset value in Aerialod to something like -2.0 in this case). 

• Go to the original raster layer (in our case the one that goes from 0 to 27,500) and then in the Layer Properties, Symbology section, we change the Render type to Singleband pseudocolor. 
• What you need to do here is edit the colours so that the ramp goes from almost black (but NOT black - so don't use RGB 0, 0, 0 or #000000) to white. Aerialod will interpret the darkest colours as low values and white as the highest values - see below for a screenshot on this.

This is one way to do it

• Once you've done this, you can then save the layer as a Rendered image from QGIS, just like we did above. It will then add a new dark to light raster layer to QGIS, just like before. Unlike before, however, when you click on a cell in QGIS with the Identify Features (i) tool, you'll get a value from just above 0 to 255, instead of the population count. 

• Check: are all your highest population spikes the same height, meaning they look truncated or flat-topped? If so, this means you probably need to adjust the light-dark colour gradient again in QGIS before exporting it and loading it in QGIS. This sometimes happens in the highest density areas so it's worth looking closely at it and then if it does happen, re-scale the data in QGIS using a different kind of classification (e.g. instead of Continuous you could try equal Interval or even Quantile). Also, just remember that this approach is quite a simplified visualisation approach to displaying population density data. The vertical extrusion is not super-precise, and of course depends upon the method you use to create the rendered tif. The idea with the approach outlined here is to give a good general overview of population distribution and density in a country.

• N.B. there are loads of different ways you could do this step, including the use of different colour ramp modes (e.g. continuous, equal interval, quantile) or you could re-classify the raster using a scaling factor (e.g. divide all values by 1,000), or do it in an image program, but here I am aiming at simplicity and a method that allows us to quickly arrive at a result that is both interesting and broadly representative of the spatial patterns of density on the ground. 

• So, what does it look like when we add it to Aerialod now? Well, see below for the new image.

Reduce the Offset (on the right) to remove the land

The point of this is to be able to create a 3D render of population density in any country, and this is one way of achieving that. For things like this, where I am striving for broad-brush patterns and workflow efficiency, rather than precise measurement from pixel values, I will often take shortcuts to achieve what I want - so for example, in the image below I have 'reprojected' the data by manually re-sizing it in IrfanView and then saving it as a png file, which ended up being 289KB in size. Try downloading it and dropping it into Aerialod and then replicate the settings I've used below - I think this one has the makings of something quite nice, and you can replicate it for any country. I have also added a similar file for France (as of 21 Nov 2020).

All this from a 289KB png file + Aerialod

I want to map the whole world this way!

Okay, no problem. You can get global population density layers in a few places, but probably the most convenient are WorldPop or GHSL. The WorldPop ones can be found at the bottom of this page - look for the 'Unconstrained global mosaics' link. For the Global Human Settlement Layer (GHSL) data, published by the European Commission, you'll want to head to their excellent Download page. Once again, I'd advise anyone using this data, or any kind of data, to read about what it is and in this instance the FAQ page is particularly useful. 

Most of my population density mapping in the past has used GHSL data. One great thing about the GHSL download options is the ability to pick different projections, so that you don't just have to use WGS84 - this is still good, but the further you go from the equator, the narrower the area represented by each cell. This is not the case with the Mollweide option, since it is an equal-area projection. But this is something you should look into further yourself if you're interested. 

If you do download a global file, you need to be aware that Aerialod can handle images at a maximum of 16,384 pixels in width or height, and the world is more than 16,384km wide. So, if you do download a global population raster, you will need to re-size it in whatever software you use (I always use IrfanView for this) so that it is less than the maximum pixel size allowed in Aerialod - I normally just re-size to 16,000 pixels. 

This means that the 1km cells are no longer 1km but closer to 2km, but at a global scale the pattern is very similar. I've created a small-file-size global raster (2.21MB), with the Pacific in the middle rather than the Greenwich Meridian, and shared it via Dropbox so that you can experiment with it as well. See below for what it looks like when you drop it in to Aerialod with the settings as per the screenshots.

This is the whole world, from a 2.2MB png

Zoomed in, and exaggerated heights

Recap

I could say a lot more about methods and how to tweak this and that, but the aim of this post was just to show you how to create a 3D population density render of any country in the world, and I've done that now, using the United Kingdom as the example country. All I would add is that if you're struggling with Aerialod, then you should go back to my original blog post on it, plus the earlier one linked to in it. It's definitely also worth spending time reading about the data sources I have mentioned.

I hope you have found this useful. If you get a lot of use out of Aerialod, and you are able to, perhaps consider making a donation to @ephtracy - the creator of this great piece of software. I have done so in the past and hope to do so again in the future.

Data sources

I've linked to the data sources throughout this post, but I'll do it again here so they're all in the same place. This is all open data, with each one licenced under the Creative Commons - Attribution 4.0 licence (CC BY 4.0). 

• Worldpop.org - WorldPop really is an amazing project and in fact one of the best data resources currently in existence, in my opinion. It has so much beyond population data, so even if you don't want to map population patterns it's 100% worth checking out. Note that in the data section of the website you'll see both 'Population Density' and 'Population Count' but here I've used the population density data. It's worth spending some time on the website to see what's available and also to understand what the data represent and how it is all collected. You can get this data either as a tif, like I've used above, or as a csv file.
• Also one of the best population data resources currently in existence, the Global Human Settlment Layer has the advantage of going further back in time, with a total of four time points - 1975, 1990, 2000 and 2014. The GHSL project is supported by the Joint Research Centre (JRC) and the DG for Regional and Urban Policy (DG REGIO) of the European Commission, together with the international partnership GEO Human Planet Initiative. If you download this data, there are LOTS of options, but what you need if you want to map population density is the GHS-POP option. I've mostly used the 2015 data, at 1km resolution, in Mollweide or WGS84 projection for the whole earth, but you can also just download single tiles. This data is available in tif format.
• NASA's Gridded Population of the World, version 4 - another excellent source of global population data. The GPW v4 home page explains everything about it, and all the different variants available, as well as previous versions. 

Making 3D landscape and city models with Aerialod

This is my second post on Aerialod - the interactive path tracing renderer for height maps, by @ephtracy. It's available in 32 and 64-bit versions on Windows and it's super-lightweight but you can do some really amazing things with it. My previous post covers more of the basics, including controls and interface settings, so here I'll share some more information on how to tweak the settings (of which there are many) to create nice looking vistas. But as a quick reminder, hold down your right mouse button to rotate the map and use the space bar plus the left mouse button to move it around. Scroll wheel to zoom in and out. Keyboard shortcuts are here.

First I'll show you some new visuals I created in Aerialod, then I'll show you some of the settings I used to create them - in screenshots and in a small slide set. All the images I've posted below are in a shared folder, as well as a sample Lidar dataset for Cheddar Gorge in England that you can just drop in to Aerialod and then tweak the settings as you wish. You can of course create these kinds of images in other software (notably Blender) but Aerialod is much easier to use, though can be confusing at first.

Update (3 Nov 2020): here's an alternative shared folder on Dropbox, just in case either one is blocked where you are. The Dropbox folder is the best one to use though. I've left the other one above online as an archive.

So, let's begin with the highest peaks in Scotland, England and Wales, plus one other view that I like.

I did this using a 5m DTM (not open data)

See the curved horizon? - that's the SG lens setting

A very nice looking mountain

I've added Glen Etive because it's so lovely

But of course we don't always have to map things like mountains. If we have good quality Lidar data, as we do in much of the UK, we can create quite interesting cityscapes, as you can see below.

Here's a little Salford Sunset to get things rolling

A Newcastle-Gateshead vista, along the Tyne

The example data I've put in the shared folder is of Cheddar Gorge in the south west of England, and it looks like this once you fiddle with a few settings.

Sun low in the sky, dusky effect

I've tweaked some of the settings here to make it glow

Different angle, light filtering through the gorge

A foggier, early morning effect

So how do you do all this?
If you've not used Aerialod before then you'll really need to read my first blog post on it in order to get to grips with the controls, etc. Once you've done this, look closely at the screenshots below as the settings in them show you how I achieved the effects in the four images above. Study them closely and then see further below for a short slide set with more annotation on the settings options in Aerialod.

Take some time to Google some of the different terms and they'll begin to make a lot more sense - e.g. the Rayleigh setting refers to Rayleigh scattering, which relates to the blue colour we see in the sky. So, using the default Rayleigh setting in Aerialod you'll see a blue sky but if you reduce the number to, say 10, it will become more blue and if you put it up to 90, for example, it will look a lot less blue and instead more like a lovely glowing orange/yellow sunset. 

This relates to the first Cheddar Gorge image

This also relates to the first image

Notice the glowing light at the corners here

This is the fourth Cheddar Gorge image above

In addition to reading this, it's a good idea to check out the @ephtracy Twitter account for other tips, plus the #aerialod hashtag on Twitter. There are also now a few good video tutorials online, including this one by Steven Scott.

Here is the small set of slides, with annotated screenshots, that I made in order to help you get to grips with the settings a little better - direct link here.

All this, including the data, can be found in the Aerialod tutorial folder I made for this short blog post. Just drag and drop the .asc file I provided into Aerialod and start playing around.

I've put everything in here

The information above should be all you need to create more realistic, impressive 3D landscape or city models. It all depends of course upon being able to get good quality data at a high resolution - I've provided an example in the folder, but you can get a lot more on the Defra Lidar download page for England. You can use the OpenDem searcher to find suitable data for other parts of the world.

To end, I'm just going to post a few more images that I haven't shared anywhere else before, before a few final comments.

This is done by keeping the 'Map' option on in Grid settings

In the example above, I have all the options on in the Grid settings (Map, Ground, Vertical) and then I have used a value in the 'Step' settings on the right to match the resolution of the underlying data (in this case 5 metres) so that I get a blocky kind of effect - see below.

This works if you want to Minecraft your map

The Western Highlands of Scotland

In the image above, I just used a 1:50,000 Land-Form dataset shared by Carol Blackwood to render the Western Highlands of Scotland - I added the labels using GIMP. You can get the individual tiles as open data from Ordnance Survey as well (OS Terrain 50).

This city is home to two football teams, as you can see here

In the example above, I've just taken some Lidar open data of Liverpool and created a view looking down on Anfield and Goodison - home to Liverpool and Everton respectively. They are very close together and the densely packed terraced housing nearby also makes for an interesting example use case with this kind of data.

And, last but not least, St Kilda - a very wild and remote archipelago off the Western Isles of Scotland that is now a World Heritage Site.

In this example, I've added some fog

Notes: you can add a single file to Aerialod (it can handle PNG, JPG, TIF, IMG and ASC formats) or you can add multiple files at once using the little button on the top right that looks like a folder. That is covered in my previous blog post. If you try to load humongousbytes of data then it may crash. Sometimes it won't crash but just won't load. Generally I find it just works and the Scotland 1:50,000 terrain model above is over 600MB and worked fine for me. Just remember that when you hover over any of the tools in Aerialod you will get information on what it is and what it does at the bottom of the window. 

Citation: Blackwood, Carol. (2017). Scotland Land-Form PANORAMA® DTM, [Dataset]. EDINA. https://doi.org/10.7488/ds/1929.

Amazing 3D rendering with Aerialod - a tutorial

One of the more exciting developments in recent years for those of us into geospatial things is the arrival at the end of October 2019 of Aerialod by ephtracy. What am I talking about? I'm talking about being able to create the kind of images you see below in only a few minutes using free software and open data. Scroll past the images for some tips on how to do this, and note that this write up was completed on 8 November 2019 and refers to v0.0.1.

The Cuillin ridge in Skye

Part of the City of Belfast in Northern Ireland

This is indeed a little bit of London

A little circular bit of London

This is also London, for Millwall fans

The Cuillins in Skye, featuring Sgùrr Alasdair

This is Sheffield, around the area where I work

Okay, so how do you get up and running? First of all, go to the Aerialod website and download the package you need. It's Windows 64- or 32-bit only for now and you just download and unzip and then run the .exe to launch Aerialod.

You do get some sample raster data in the zipped download (in the 'map' folder) but if you also download the 'Sample Maps' archive next to the software download button you'll get a central Manchester Lidar png and a Mars (yes, the planet) png. This is a nice reminder that Aerialod is able to handle different formats, including .asc, .png. and .tif for example. I haven't tried any other formats though I think you'll be okay with .jpg too.

They don't look like much here, but wait and see!

When you launch Aerialod you'll see something pretty much like the image below - and it will have that blocky sample layer in there. This is useful for playing around with so you can get to grips with navigation etc. Just note that when you zoom or move around Aerilod may briefly look pixelly/fuzzy as it re-renders, so don't worry about that. It sharpens up perfectly once it's done, although with more complex layers it takes a bit longer.

You may be a bit bewildered at first, but it won't last long

Before I forget, be sure to look in the config folder and open the hotkey.txt file, which I've shown below. That's really useful. But I find mouse navigation easier, so read on. Also note that the second section of hotkey actions below combine a left mouse button click with keyboard actions too.

Hit D and be amazedx

If you are finding the interface too small and can't actually see the icons easily (e.g. if you have a 4k monitor or something like that) then you can use CTRL + or - to scale the UI but you could also just edit this bit of the config.txt file (in the config folder) so instead of 1.0 it says 2.0, like below:

view :
{
// 0.5 ~ 3.0
ui_scale : '1.0'
}

Okay, we're all set now so here are the basics of moving things around:

• Scroll wheel/middle mouse button - you can scroll forwards and backwards to zoom in and out and with the button pressed down you can position the layer wherever you want. 
• Right mouse button - tilt/pan/rotate etc. Just have a play and you'll see what I mean.
• And of course with keys, as above, W to zoom in S to zoom out, D to rotate clockwise and A to rotate anti-clockwise.

You loaded the software, figured out navigation with the sample data but now want to render some real world stuff. See below for how to do that.

There are tons of sources for this, including things like NASA's 30 metre SRTM but really it's going to look best with high-resolution DSM or DTM data and for this Lidar is ideal. On the Aerialod page they link to two potential sources of this - the UK's Defra Lidar page where you can download a variety of 25cm, 50cm, 1m and 2m Lidar data for England or get NASA's HiRISE data for elsewhere in the universe.

To get data into Aerialod, the easiest method in my opinion is just to drag and drop a raster file straight in, so that's what I just did with the Manchester.png layer you can see in the screenshot below. To be clear, what you see below is just the result of me dragging and dropping a png file into Aerialod. I haven't done anything else yet.

That was easy!

Before I say more about settings, a word on getting data into Aerialod in other ways. In the top right of the window you'll see a save icon. That will save the raster layer (not the 3D render) to a location of your choice. The next button (the open folder icon) will let you open a new single layer instead of dragging and dropping into the window. The next button (the closed folder icon) is a bit different, but it effectively allows you to stitch together all layers in a single folder, and it's amazing. Just click one item in the folder and it will add them all, as you can see below. The final button (blank page) just starts a new Aerialod blank view.

Stitch multiple files!

Okay, see below for where I've used the open folder button to add a single layer. This is for a single tile (of about 7MB) near the University of Sheffield. In the image below this, I've used the closed folder icon to select the first item and then all layers are added. You don't actually have to select the first item though, just any of the files in the same folder. This is really handy as when you download Lidar data it's more than likely going to be comprised of lots of little chunks as individual files.

Single layer from within a folder

All layers from within a folder

If you get too greedy and try to add a gazillion zigabytes of data, Aerialod may crash. I know, I've tried. Anyway, for the rest of this tutorial I'm mainly going to use the Manchester.png sample file provided on the Aerialod download page - and what you see below is the result of me just dragging and dropping it into the viewer. This covers a good chunk of Manchester city centre and also a bit of Salford and Trafford and to the left of the image below you might just spot Old Trafford - both the football and cricket ground.

Just another sunny day in Manchester

Okay, so the rest of this will cover the main things you need to know but I can't cover everything because a) that would take too long and b) I don't actually know enough to tell you everything and I'm kind of learning as I type here. So the two annotated screenshots are my main contribution for now. Look at the images closely though and you should learn enough to produce great visualisations in not much time at all.

Click on the next two images for a little explainer of what does what and then try it yourself.

This covers the basics, click to expand

Some more tips - including on Focus

Offset - actually quite useful for flood viz

A full screenshot so you can see some of the settings here

That's basically it for now. All that's left to say is don't forget to check the #Arielod hashtag on twitter as well as the @ephtracy Twitter feed. Last of all, don't forget that you can drop all kinds of things into Aerialod, not just terrain models and suchlike. I've experimented with adding in photos of people, which often looks sort of cool but also weird, and I've tried all sorts of other stuff but I'll end with one of the MiniScale raster relief maps of Great Britain which are part of their open data offering. It's not intended to be used this way but I think it looks quite interesting.

This actually turned out alright