Friday 8 November 2019

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

Saturday 2 November 2019

A deprivation by constituency chart

Yesterday I decided to update a little chart I made after the 2017 General Election. It was inspired by a histogram that Owen Boswarva made and the idea was very simple: put England's 533 constituencies into 10 columns, with the most deprived on the left and least deprived on the right, and then colour it by party. The image below is the result. UPDATE: I have now done a full-UK version of this - see below. Also includes an animation and the individual frames which show one party at a time. Read the notes on the UK chart for more information.

Link to slightly higher resolution version

Full size version

Easier to decipher as a gif




Liberal Democrats


Sinn Féin

Plaid Cymru


Independent (at 2017 General Election)


I did this out of curiosity the last time and then after speaking to my colleague Philip Brown about data, elections and suchlike I decided to update and try to improve the older chart, which was informative but a bit of an assault on the eyeballs. I then saw that the House of Commons Library team re-ran their analysis aggregating the English Indices of Deprivation for the 533 English constituencies, so that was all I needed. And, by the way, the House of Commons Library team are in my opinion doing some of the best data curation, manipulation and analysis out there - really great team. 

Of course, the results are hardly surprising but I didn't expect the sorting to be quite so stark. Naturally I tweeted the graphic and lots of people also found it interesting. So, I've done a slightly different version below which has the constituency names in the boxes - but you'll have to click and zoom to read those.

Full-size version here

Zoomed-in extract of the image above

It's not possible to do a full UK-wide one using a single dataset from the same time point, because the deprivation indices for each country of the UK are slightly different and cover different time period but it would be nice to have been able to - and at least in Scotland the colours would be quite spread across the deprivation spectrum. UPDATE: as you can see above, this last statement is true and I've also done the full-UK chart.

I also did one more version of the graphic, this time in a very long single column vertical one, the original of which is here, with a lower resolution one below. The tends to work great on a mobile but needs a bit of zooming in and out in most browsers. I've added the 'required swing %' figure to this one, showing what percent vote shift it would take for a constituency to change hands.

Click to see full size

Looks like this when full width on screen

There wasn't any great agenda or rhyme or reason behind this, I just wanted to see what it looked like and in particular how much the colours would be grouped and where the obvious anomalies were.

Given what I do for a living I'm duty-bound to point out the following obvious but important things:

  • Correlation is not the same as causation (yawn! but true).
  • I don't believe voting Labour makes you poor, or that voting Conservative makes you rich, as some people online seem to have implied - or even that 'the poor' vote Labour or 'the rich' vote Conservative - and I certainly wasn't trying to demonstrate either of these but understand that's sometimes how people see things.
  • There is a reasonably high amount of variation within most constituencies in relation to levels of deprivation, although at the top and bottom of the scale a lot less than you might imagine - I'm currently working on a project all about this kind of thing.
  • Colours - they are the html values for the party colours from the respective party websites.
  • The aggregation method used to derive the constituency rankings could be done a number of different ways, and therefore can produce slightly different results based on how you do it but the order of places would always be broadly the same.

That's all for now.