Creating an English green belt atlas

UPDATE: I've fixed the glitches in version 1 and compiled a spreadsheet with the data. See new download at the bottom of the post.

I've blogged before about green belt, and also written about the underlying data in the press. Now that the data are open, I've finally got round to finishing a little project I meant to complete ages ago. I was prompted to do this during a recent visit to my department by Prof Bob Barr, a legend in the data and GIS worlds. Bob said it would be good to know what percentage of the land area in each local authority in England was covered by green belt. I agree, so here are the results of my analysis (using 2014 green belt data) from Version 1 of my English green belt 'atlas' (actually lots of individual images to keep the file size down). Here's a snapshot of one of the maps...

Green belt land in Cheshire East

And another, this time from Birmingham. You can see that I've dimmed the background so that you can get a sense of other green belt land in the areas I've mapped.

Birmingham green belt land

Finally, a few more from around the country...

There are some glitches in the data but my initial overview suggests the numbers are pretty accurate (see exceptions below). I hope that people might find these maps useful. If you want to use any of them, be my guest.

Download all the files here (154MB): Green Belt Atlas 2014 (version 2) (186 individual map files, plus spreadsheet)

Download just the spreadsheet: percent green belt figures for each of the 186 local authorities:

Contents of the spreadsheet (download above)

Warnings: A couple of issues with version 1... 1. The West Lancashire greenbelt area extends into the sea on the green belt shapefile available from DCLG, so the figures here are incorrect (working on a fix). 2. The figure for Ashfield is clearly wrong - not sure why, so I will fix that too. 3. Some areas have extremely low values and may not actually be in the green belt - it may instead be down to the accuracy of underlying data. 4. Mole Valley currently missing, am looking into why. UPDATE: I looked again at the original Green Belt shapefile from DCLG and found that Mole Valley had the same code as Ashfield, so I fixed that and there's now a map for Mole Valley. New Forest was also assigned two different codes, so I've fixed that too. Also, in the percent figure, I've exluded the part of the West Lancashire green belt that is not on land, so this gives an accurate figure now. You can see from the image below that part of the green belt goes into the Ribble Estuary.

Technical stuff: I did this in QGIS 2.8 (open source GIS software) using the Atlas tool and a very heavy laptop, plus a bit of trickery I picked up here and there. I blogged about this before, with a little tutorial. Perhaps I should actually be using the term 'green belts', as Richard Blyth pointed out, but forgive me for this.

Simple Animations with QGIS - A Long Tutorial

This rather long post explains how you can make images and animations like the ones below using only open source software (mostly QGIS) and open data. I've just used flights from Hartsfield-Jackson International Airport in Atlanta because it's the busiest airport in the world and serves a wide variety of destinations. The methods are relatively straightforward but it does take time to get your head around if you're new to the software and the data. If you're already a competent QGIS user it should be pretty easy. Once you've got the method nailed down you can apply it to all kinds of different scenarios and datasets. First of all, though, here's a static image of outbound flights from ATL projected onto a globe.

Flight destinations from Atlanta (full size)

The first thing you'll need to do is get some data. In this example I've taken some general country boundaries from Natural Earth and for the flights data I've used the OpenFlights dataset. I then created an azimuthal orthographic projection centred on Hartsfield-Jackson International Airport in Atlanta. Hamish Campbell already wrote an excellent tutorial on how to do this in QGIS, so just follow that if you want to use a projection that makes your country layer appear as if it were on a globe. The only extra tips you'll perhaps need to replicate Hamish's method are as follows. To get the lat/long of a place you want to centre your projection on, just search for it on Google Maps and then look in the address bar for the coordinates. The python script on Hamish's page just needs to be copied and pasted into a text document, saved with a .py extension and then placed in the correct folder on your computer (on a PC with QGIS 2.4 it would be something like this: C:\Program Files\QGIS Chugiak\bin). Also, when you clip the layer to a global projection, QGIS will create a clip circle and a new clipped layer. These may not appear at all or if they do they'll maybe be very blocky. If so, go to the properties for the layer in QGIS and on the Rendering tab just untick 'Simplify Geometries'. I normally save new copies of these layers using Save As... from each layer.

A faster animated version of ATL outbound flights (big)

That should be your global projection sorted. You can then apply it via the Project menu in QGIS and then Project Properties... CRS and then 'Project on the fly' as in the tutorial.  To achieve some of the visual effects above, I just duplicated the clipped circle layer (you'll have to Save As... from the temp clipped layer to do this) and applied an inverted polygon style and a shapeburst fill with a blue to black gradient (Nyall Dawson did a great blog post on this, which you might find useful). I also did a similar thing with the land layer, just to make some of the smaller islands stand out. You'll also need to make the outline colour the same as the fill colour in the circle to avoid a line appearing through your earth.

A very slow version, with a pause at the end (big)

So far, so good. But what about the flight paths and animated dots? Well, to create the lines you can follow my blog post on flow mapping in QGIS and use the sample dataset I posted there. You'll need to calculate two new columns for this shapefile (see below) and use the MMQGIS plugin for QGIS (installed, as ever, via the Plugins menu). If you just add this file to your global azimuthal orthographic projection there will be so many lines and it may take a long time to display so there are a few intermediate steps I'd recommend... 

1. Open the new global flights dataset in a blank QGIS project using the default projection and then remove duplicate lines using Modify, Delete Duplicate Geometries in the MMQGIS plugin. Many routes (e.g. JFK-LHR) are served by multiple airlines and I wanted to only show origins and destinations. This also makes the file much smaller. 2. Although the flight connections would appear as straight lines on our global projection, I like them to look a little curved; partly for effect and partly to bring out the curvature of the earth but also because flight paths are not straight lines in reality. So, once I've removed duplicates I then 'densify' the lines in QGIS by adding in 50 intermediate vertices - done via Vector, Geometry Tools, Densify polygons in QGIS. 3. I then added this new flights layer to my ATL-based global projection and I clipped the layer using the Hamish Campbell method, and saving the resulting layer as a new shapefile. You should now have a globe centred on the location of your choice, plus some nice curved airline flight paths.

Same as above, but with labels and pause at end (big)

For the next stage, the way I did it was to open the dbf part of the new flight paths shapefile in Excel and then calculate an 'offset' lat and long column which I could use to animate the dots. You just need to read the Animate Columns part of Michael Minn's MMQGIS page to understand this. Once you've calculated the new lat/long offset columns you can save the csv. Once you've done this, import the csv into QGIS using the Add Delimited Text Layer (comma icon), using the airport origin lat/long as the x,y coordinates in the import dialogue. Filter the new layer so it only shows ATL origins and you'll just see one dot for ATL but actually there are many dots in the same location as they all have the same origin lat/long. Save the filtered layer as a new shapefile and then run the Animate Columns tool in MMQGIS using the appropriate fields and the number of animation frames you want (50 works well with this example). An important point here is that you need to make sure your QGIS window is quite small as the extracted image frames will be the same size as your QGIS map frame and if it's too big it will make a massive GIF.

You now have all you need to create an animation. There are many ways to create an animated GIF, but using GIMP is very simple. You can download this free, open source image manipulation programme in a few minutes. You then just need to go to File, Open as Layers and then select all the frames you just created in QGIS and GIMP will add them to the project and they'll appear in the Layers pane. You don't need to reorder them as they are numbered correctly from the MMQGIS export. From here you can go to File, Export and then select the GIF file format and use the animation options here. Try 50 milliseconds between frames as with 50 frames this will create a nice short 2.5 second animation that isn't too slow. You should use Filters, Animation, Optimize for GIF and then export from that window if you want a much smaller file size. I created another one of these visuals, centred on LHR and showing flows from JFK, LHR and PEK.

Same techniques, different data (and also a bit crazy)

That's quite a lot of information and quite a few steps but if you try this and still can't make it work feel free to get in touch via twitter or e-mail. Why would you want to do this? I'll leave that up to anyone who wants to try it but displaying movements of people and goods is relevant across a number of disciplines so hopefully some will find this useful.


Other tips and information: depending upon which location you're choosing, some of your lines or dots might be going the wrong way round the earth but you can fix this with a bit of simple maths in the offset calculation. In GIMP, you can add a different frame duration by adding a number and then ms to the layer info - e.g. 1000ms - so that it creates a pause effect, as in the examples above. I created an ATL point and a destination airports points layer from the imported csv so that I could manually create a couple of extra frames to add in to the end of my animation. One to show destinatinon airport names and the other just to label ATL. For the glow effect in the flow lines in the static image I used the Feature Blend 'addition' option in layer properties in QGIS. 

Acknowledgements: As ever, I've borrowed ideas and techniques from other QGIS users, including Hamish Campbell, Nyall Dawson, Nathan Woodrow. I decided to have a look at this after an e-mail exchange with Waldo Tobler about migration data. Thanks of course to the excellent OpenFlights team who make their data available under an Open Database Licence.

How we read maps and dataviz - new research needed?

There's a fairly long academic tradition of looking at how humans interact with maps but, in my view, there is a need to revisit such research in relation to the new wave of digital mapping and dataviz currently available online. Some of it is fantastic and some less so, but this isn't about being critical of the bad stuff. Instead, I'm hoping others will share what they've been doing or what they've seen (via @undertheraedar) to try to understand the effect of new dataviz/mapping on how we perceive/read maps - and what impact this might have on cognition/understanding of underlying issues. 

Early last year I had some discussions about this with a very helpful colleague in psychology at Sheffield - Megan Freeth - and I gave her one of my blog images to test with her eye tracking technology. The results are shown below, in sequence (click to enlarge). I've also put them together in a slide show if you want to download them all at once.

The original 3D image

Scan path from first 10 seconds of map viewing

Scan path for one minute of map viewing

Heat map showing areas focused on most

'Region of interest' analysis

I'm aware that I am probably just not up to date with the kind of research being done in this area but before going further I should say that I am aware of people across the world who have done work in these fields - e.g. Alan M. MacEachren and others at the GeoVISTA Center at Penn State and this study from Brodersen et al at Risø National Laboratory in Denmark - but I'm not aware of what's been done in the last 4 or 5 years in particular to help us understand the effects of new approaches to mapping and visualisation on cognition and perception.

Are we understanding more because of the new wave of mapping and dataviz? Are we understanding less? Are we just enjoying how things look and being wowed by the technology more than we are critically engaging with the underlying content? Has the method become the message?

I'm as guilty as anyone of posting maps and images on twitter and this blog without necessarily thinking too much, though my aim is always to inform and engage - but as the protagonist in David Lodge's Changing Places says, "Every decoding is another encoding" and my visual 'decodings' of spatial data will always be 'encoded' by the viewer in ways I might not have expected - or even want. It's always interesting to see how people interpret things and whether this aligns with what we'd hoped. This perception issue might also come up tomorrow when one of my maps appears in the new HS2 report in the UK - we'll see.

Anyway, thoughts and insights welcome via @undertheraedar.

The Urban Fabric of English Cities

[now updated, thanks to @udlondon - scroll to bottom of page]
Inspired by some mapping in the US by Seth Kadish, the availability of new GIS open data, and the fact that I love looking at patterns of urban form, structure and density, I have created a comparative graphic showing the building footprints of nine English cities, with London at the centre (just because it's biggest). I have done this in a very simple way, with all cities mapped at a scale of 1:125,000 in the full size versions (which are massive), plus one small scale bar and a little explanatory text. Here's what it looks like:

The urban fabric of English cities (black/red, medium res)

This graphic does a good job - in my view - of demonstrating the compactness or otherwise of the cities in question. It also illustrates how tightly-bounded some places are and how under-bounded others are. For example, Liverpool is very dense and compact in contrast to Leeds but this really is a boundary effect because the size of the local authorities differs so much. The urban area of 'Liverpool' extends far beyond the boundaries of the local authority area, which is what I show above. I wanted to compare the local authority areas rather than the wider city-region because I wanted to highlight this boundedness issue and compare like with like in terms of formal administrative areas. London is obviously a bit different so I've shown the 33 constituent parts of Greater London.

Take a closer look at the graphic by clicking on the two larger images below - one in white and one in black. They are both just a bit bigger than A0 paper size in their full size versions in the zipped folder below so if you want to take a really close look, download them. I've also uploaded smaller-sized versions in the same folder. I deliberately didn't include more information on the graphic itself, but at the bottom of the post you'll see the population of each city in 2011 (which relates to the individual city images), plus its urban area and metropolitan area population. The population of Greater London in 2011 was 8.2 million (compared to 4.4 million for the other cities shown). The cities I selected are the English members of the Core Cities group, which now also includes Glasgow and Cardiff.

Click here for a full screen white version

Click here for a full screen black version

Download a zipped folder with black and white versions in different sizes.

Update: the @udlondon people got in touch via twitter to show their attempt at fitting the core cities inside the London boundary - as below - so this inspired me to try the same with the original data. The first image below is the original @udlondon artwork and the next one is my attempt using GIS. Finally, as a reminder that nothing is ever really new, I have added a similar map which we found as part of the JR James urban image archive which we launched last year. This version has 13 different cities.

A manual approach to GIS!

My attempt at the same thing, using QGIS - full size

Some of the boundaries were a bit different in those days

City	Population	Urban area	Metropolitan area
Birmingham	1,085,400	2,440,986	3,683,000
Bristol	428,200	587,400	1,041,000
Leeds	751,500	1,499,465	2,302,000
Liverpool	466,400	816,216	2,241,000
Manchester	503,100	2,240,230	2,556,000
Newcastle	280,200	879,996	1,599,000
Nottingham	305,700	666,358	1,543,000
Sheffield	552,700	640,720	1,569,000

Totals: the population of the 8 city local authority areas is 4.4 million, for their urban areas it is 9.8 million and for their metropolitan areas it is 16.5 million. I may compare metropolitan areas next time, but mapping this is a little more time consuming.

A national map of cycling to work

I've recently being doing some visualisation work with the newly released Census commuting data from 2011. I've produced maps of all travel to work, and travel by car, train and bus. I've now done a map of cycling to work (below). This map is particularly interesting in relation to the patterns it reveals but also in relation to the strange long-distance flows we can see. I'm certainly not saying that anyone actually commutes by bike between Manchester and Bristol, as the map may suggest. Click on the big version and have a look around to see if you can spot anything interesting or particularly unexpected. A version with some place name labels can be found here.

Go to the full size version

This data comes from Question 41 of the 2011 Census form, which asked people to say how they 'usually' travelled to work in relation to the mode of transport which accounted for the largest part, by distance, of their journey. The results can look quite beautiful on a map, but they can also be confusing. Look closely at the map above and you'll ask yourself why there are so many long distance cyclists in England and Wales. More seriously, you might begin to question the validity of the data, the honesty of respondents or some other aspect of the results. 

The ability to interrogate datasets in this way is one of the strengths of visualising large datasets in that we can often immediately identify anomalous patterns or results that confound expectations or are just plain wrong. I'm not entirely sure what's going on with the long-distance flows. Perhaps some people take their bike on a train so ticked the 'bike' option, despite the train journey being longer. Perhaps some people live in one part of the country during the week and cycle to work there but then live at their usual address during the weekend and this is registered as their residence on the Census forms. I'm only speculating but this could be one possible explanation. 

In the image below, I've filtered the data so that only flows of 2 or more are shown. This significantly reduces the visual clutter, but also draws out stronger long distance connections between places such as Bristol and Manchester, and indeed Manchester and lots of other places. Take a closer look by clicking the link below this map. I've added some place names to this map to help with orientation.

Go to the full size version

I'd be keen to hear different interpretations on the data. You get similar results when you map the 'walk to work' data so there's definitely something interesting going on with how people have answered the Census question and the data we have to work with. I'm certainly not saying it's 'wrong', more that we need to understand what exactly it tells us. For now, I'll leave it at that.

N.B. Why didn't I include Scotland and Northern Ireland? The data are not out yet. It's not some ploy to exclude anyone and I know the blog title says 'national' so forgive me if that threw you. I intend to expand the analysis in due course.

Why you should start using QGIS

I've been a user of GIS since the late 1990s and in that time have mostly used ESRI software, such as ArcView 3.2 and ArcGIS versions 8 to 10. The first piece of GIS software I ever used was MapInfo 5 and I continue to use it now and again (in version 9.5 or above) - mostly for manipulating large datasets with hundreds of thousands or millions of records. I still really like both of these for different reasons so this post is definitely not a proprietary-GIS-bashing piece. It's just an encouragement to current GIS users to take a serious look at QGIS if you haven't already. I've been using it on and off for a couple of years and in that time have seen serious improvements. Most recently, I've done a good bit of mapping with it - as in the example below (commuter flows in Scotland, in case you're asking).

A flow map made in QGIS 2.4

There are many reasons to start using QGIS. The most obvious one might be that it is a cross-platform free and open source GIS that can do many things as good as or better than paid-for software. Take a look at the QGIS Flickr map showcase for some more examples. Of course, it is possible to make stunning maps with other open source packages such as R, but there is a really steep learning curve and many people don't have the time or inclination to get into it.

If I was to pick my four favourite features of QGIS, I'd have to go with the following:

1. The high quality map rendering and symbology options available to you - for example, QGIS handles layer and feature transparency in such a way that you can produce really attractive maps. QGIS includes by default so many nice looking, sensible colour schemes that it's much easier to produce quality maps. Anita Graser (QGIS author and guru) highlighted the way QGIS integrated ColorBrewer at version 1.4, for example. With version 2.4, you can also automatically invert colour schemes - which was one of the rare things that frustrated me in previous versions.

Flow map layout created in QGIS 2.4

2. The Processing Toolbox, with which you can access a huge range of spatial analysis and data management tools to perform a massive variety of tasks. See screenshot below for how it looks. If you want to add x,y coordinates to a polygon layer, this can be done really simply here, in addition to so many other geocomputation tasks (e.g. calculating area, line lengths and so on). Beyond the basics there are also so many other more complex tasks you can perform here.

The Processing Toolbox in QGIS 2.4

3. QGIS Plugins - which really are fantastic. The one I probably use the most is OpenLayers, which allows you to add a large number of different base layers to your QGIS map - from Google streets and Bing Roads to OpenStreetMap and Stamen Toner layers. As I write, there are currently 214 available plugins listed in QGIS 2.4. Another fantastically useful plugin is Table Manager, which allows you to very quickly change field headers in attribute tables.

The Plugins menu in QGIS 2.4

4. Flow mapping in QGIS. This is something I've done a lot of over the years but recently I've been blown away by the simplicity and elegance of the way QGIS can convert massive CSV files into large flow maps. MapInfo had served me very well in the past - and is still amazing when you use a single line of MapBasic - and recently ArcGIS has improved, but it still has a way to go. All you need to do in QGIS is format a CSV file and have one Geom field with the LINESTRING command and x, y coordinate pairs formatted as in the image below. Once you import this file using the Add Delimited Text Layer tool the job is done. The results - following a bit of styling - can be amazing. What I love even more about this is that I picked up this tip from a StackExchange post by a 73 year old retiree! Isn't the internet amazing?

This csv file, is easily turned into images like the one below

Travel to work flows - car and train

As I've been writing this I've mentally added several more things to the list but I'll stick with the above for my favourite four right now. I do, however, also love the labelling options, the coordinate system selector options, the vector tools and the fact the user community is so helpful. I still find the Print Composer a bit fiddly for creating maps in but this is a relatively minor issue.

I expect I'll always take a portfolio approach to working with GIS software and continue to use QGIS alongside ArcGIS and MapInfo, but I'd be a bit lost without QGIS now. I'm probably quite behind the curve with all this and I should have got more into QGIS a long time ago but it's still relatively early days in the mass take-up of the software, though some UK councils are now big users. 

So, why should you start using QGIS? Because it's absolutely fantastic, really powerful and pretty straightforward to learn if you already know your way around another GIS. Oh, and it's free - though it costs money to develop so you can always donate here.

Mapping Blight in the Motor City

In my preparations for the launch of our MSc in Applied GIS, I've been putting together lots of case studies of GIS in action. Luckily for me, this has coincided with the launch of the Motor City Mapping project in Detroit; part of a wider attempt by the city to understand and prevent urban blight. One part of this project has produced an amazing survey dataset covering nearly 380,000 land parcels in the city. An overview of this is provided by Motor City Mapping in the following graphic.

Source: www.motorcitymapping.org

This data was generated by survey staff over a short period of time during winter 2013/4 and is probably the most detailed, parcel-level city survey carried out in recent times. For more about the project, take a look at the short video below. One great feature - in addition to all the rest - is that the final dataset contains a link to the photo taken of each land parcel by the survey staff (residents of Detroit surveyed their own neighbourhoods). The entire dataset is pretty big - close to 1GB - but it can be downloaded via this page and used in your GIS. This direct link worked for me.

The image below shows you what it looks like when you map the data using the land use category. 

Link to bigger version

Finally, since they very cleverly included a photo url for each land parcel in a separate column, I decided to extract a small area and put it in a web map using CartoDB so that you can click each land parcel and see what it looks like, in addition to some of the characteristics of the parcel. I extracted the data for Grand Boulevard since it's an important street in Detroit's history, with important locations such as Lee Plaza, Motown Records and Henry Ford Hospital. Click on the image below to go to the full size version. You'll see that I've coloured the map by building condition - mostly good on Grand Boulevard - and when you click on a land parcel you'll see an image of what's on it plus details about condition, occupancy and use. I also included a date of when the survey was carried out.

Full screen version

This is all part of a wider city planning project called 'Time to End Blight', and you can read more about it on their web pages. The report is a great piece of work in a really difficult time in Detroit's history so it's great to see so many people coming together for this. If you have any interest in cities, urban blight, regeneration or revitalisation then I suggest you take a closer look at the report and its recommendations in particular.

Natural Earth for GIS data

People often ask me where they can get GIS data to use for projects, analysis and general mapping. In the UK we now have OS OpenData, which is very nice and very detailed. There's also a new GIS portal from the Office for National Statistics - built using the Geoportal Server. Other GIS datasets are available, from organisations like Natural England, but in this post I thought I'd highlight the excellent - and totally free - Natural Earth site which is very well known in the geodata community but perhaps not more widely. It really is absolutely fantastic.

A little bit of technical information....

• Natural Earth Vector comes in ESRI shapefile format, the de facto standard for vector geodata. Character encoding is Windows-1252.
• Natural Earth Raster comes in TIFF format with a TFW world file.
• All Natural Earth data use the Geographic coordinate system (projection), WGS84 datum +proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs

Whether you're looking for data to make a general world map or a more detailed local area map you will find what you are looking for here. It's not as detailed as OS data for Great Britain but then many GIS users don't need that level of accuracy. Another screenshot below showing the various download options...

Finally, taking my inspiration from the worst website in the world, and Ken Field's blog - I've made an absolutely awful map using some of this data*. Can anyone do a worse one?

*I actually did this as part of my work in developing some new GIS modules. I tried to pack in as much bad practice as possible just as an extreme example of what not to do.

Mapping flows in ArcGIS

This short post is about the process of flow mapping in ArcGIS and not really about the end results - though the maps are quite interesting. I've done quite a bit of flow mapping in the past and am now getting ready to work on the next set of Census flow data in the UK (which should be out in November) so I've been experimenting with some tools. I've written about this in the past in papers in Computers, Environment and Urban Systems and also Environment and Planning B but those papers are a bit long-winded! Other people have produced beautiful flight path maps so I thought I'd experiment with the same data using the relatively new ArcGIS XY to Line tool in version 10.0 (it can be found in ArcToolbox - Data Management Tools - Features - XY to Line at the bottom of the list of tools). For more on other methods and previous iterations of this kind of thing take a look at the work of Nathan Yau, Michael Markieta or James Cheshire.

For anyone wanting to map flows in ArcGIS, Michael Markieta's tutorial is probably a good place to start but be prepared for things to go awry in ArcGIS... When I mapped the 59,000 or so flight paths in the map above using XY to Line and one single dbf file (or csv, etc. - it makes no difference) the resulting shapefile only contained 16,066 rows. This happened every time I tried it and a couple of times my shapefile had only 73 rows. Another time it had ~14,000. That's why Markieta recommends splitting the file up - although I just cut it up into chunks of 16,000. Interestingly, I ran into exactly the same problem with my CEUS paper a few years back using Glennon's flow data model tool - though the limit was about 32,000 before it cut off. 

Another very annoying feature of XY to Line (for me at least) is that when you choose the Great Circle option under 'Line Type' it takes much longer to compute and the resultant shapefile is enormous. The shapefile for flight paths in the above map is about 10MB whereas the great circle version was over 450MB for one 16,000 chunk alone. Not sure if anyone else has run into this but it doesn't seem like a very efficient way of doing things! [Edit - as @baeing has reminded me, it's because shapefiles don't support curves - though geodatabases do.]

Once I had my complete shapefile I moved to QGIS, added in a world layer from Natural Earth and then experimented a little with symbology. I also experimented with different styles and projections to produce some of the maps below. That's all for now - I just hope ESRI are able to improve upon the current version of XY to Line because when it does work it is really fast (on my machine at least) and straightforward.

Very similar to above, minus text

Short haul, different projection

Short haul, different projection, borders

Slightly different symbology

And, yes, I know that flights from Australia or New Zealand typically go over the Pacific rather than the long way round! I'm just showing the XY to Line outputs as they are in this post.