I’m hopefully going to get a chance to get some decent maps out from the latest census so I’ve started with an easy win.

I was home recently and drove up to the top of Knockanore, a hill just outside Ballybunion, Co. Kerry. Its elevation is 267m and is by far the highest hill in this part of North- Kerry. This got me curious — how much of Kerry is greater or less than this elevation? Below is a quick map I put together from the EU-DEM courtesy of the European Environmental Agency. It turns out 267m is higher than 82.7% of the county, interesting. There’s a story that does the rounds colloquially in North Kerry that, on a clear day, it’s possible to see five counties from this elevation. If I’ve time in the next few weeks I’d like to create a viewshed to test this.

 

 

I made a quick map during the week of the birthplaces of Irish Taoisigh. The below gives a quick overview however I thought putting a cartogram together also might below. No surprise to see Dublin has the most. I know we’re a young country but I found it interesting the number of counties that haven’t had a Taoiseach.

 

 

For Halloween this year I wanted to create a spooky, atmospheric map. I settled on mapping the castles of Europe including Bran Castle. I know that Bran Castle doesn’t actually have any historical links to Bram Stoker but I thought it would be nice to include given it’s reputation. I came across a great website called https://download.osmdata.xyz/. It allowed me to easily download a geopackage of all the historic tags from OSM.

Now the the elephant in the room — the actual castle data. I filtered the data by historic=castle. I has tried filtering it by categories such as castle_type but there just wasn’t enough tagged to make a nice map. People commenting on Reddit have been at pains to point out how inaccurate the map is and by and large they are correct. It’s the best that could be make with the data available and I usually wouldn’t publish something where I know the data wasn’t up to scratch however as this was only meant to be a fun Halloween map I thought an exception could be made!

Anyway, I hope you enjoy it (above data caveat aside) as much as I enjoyed making it. Happy Halloween!

I was having a chat at work recently about the place names in Australia that end in ‘up’. It comes from a dialect of the Noongar Aboriginal language of Australia and means ‘place of’. Below are two quick maps I put together. You can clearly see the concentration in the Noongar region of South-West Western Australia.

I’ve been writing a lot about population density at the moment and it got me thinking about density in Ireland. We all know we’re pitiful in terms of other European countries and we know from census 2016 that we’re sitting at 70 persons per km². What I’m curious about is what’s the densest square kilometre in each county?

It is import to note that the densest square kilometre in each county below is the densest square kilometre from a predefined 1km² grid for the entire country which is manifestly different from the true densest square kilometre in each county but it’s the best data I have access to as a member of the public. If you want to read more about this issue it’s called the modifiable areal unit problem. A simple image to explain this is show below. From a predefined grid that’s draped over the country we can see that cell 3 would be the most dense 1km² however if a cell was placed where cell 5 is we can see that it would be densest 1km² by a large amount, that in a nutshell is the MAUP problem.

 

I used the ’16 census data to generate the below maps to answer the question for each county. What clearly stands out for me is that for a lot of counties it’s housing estates that make up the densest square kilometre. Hopefully with Project Ireland 2040 now in place we can start to do better and go towards sustainable densities throughout the country.

Hyperlinks to maps for each of the other counties:

1. Carlow
2. Cavan
3. Clare
4. Cork
5. Donegal
6. Dublin
7. Galway
8. Kerry
9. Kildare
10. Kilkenny
11. Laois
12. Leitrim
13. Limerick
14. Longford
15. Louth
16. Mayo
17. Meath
18. Monaghan
19. Offaly
20. Roscommon
21. Sligo
22. Tipperary
23. Waterford
24. Westmeath
25. Wexford
26. Wicklow

I realised with the above images that I forgot to include part of the label that showed where in each county the location was. Below are the same maps as above but they now include the location and the lat, long for each square kilometre. Maybe both versions can be used for a very nerdy quiz??

1. Carlow
2. Cavan
3. Clare
4. Cork
5. Donegal
6. Dublin
7. Galway
8. Kerry
9. Kildare
10. Kilkenny
11. Laois
12. Leitrim
13. Limerick
14. Longford
15. Louth
16. Mayo
17. Meath
18. Monaghan
19. Offaly
20. Roscommon
21. Sligo
22. Tipperary
23. Waterford
24. Westmeath
25. Wexford
26. Wicklow

I was looking at this web-map this morning that shows the number of Irish living in the UK. I quickly put together an equivalent for the number of UK born individuals resident in Ireland as reported in the 2016 census.

At some stage in the second half of 2020 the Central Statistics Office released the population for each townland in Ireland for both the 2011 and 2016 censuses. They had a category for both data that has been suppressed ‘-1′ and the townlands that were unpopulated ‘0′. I think it’s a safe assumption that if you read (or have stumbled upon my blog) that you too might be interested in maps of unpopulated places. Below is the static map I posted on Twitter and Reddit and (by popular demand – read as two random people on Reddit) I’ve also included a slippy map that you can examine at your leisure.

 

 

I was reading Alasdair Rae’s street types blog post yesterday and I thought about doing something similar for Ireland. I didn’t have much time today so I said I’d put something quick together for Cork City. Alasdair was lucky that he could use the amazing open data from the UK’s Ordnance Survey. For Ireland, we don’t have anything similar from our national mapping agency but fear not because it’s OpenStreetMap to the rescue. In my humble opinion individuals often spend far too much time trying to get a usable output from overpass-turbo when there’s an easier way. What I did was download the .osm.pbf file for Ireland from Geofabrik. Windows Subsystem for Linux has been a lifesaver since it came along, most of the heavy lifting I can now do through it. The steps I followed are as follows:

1. Download the .osm.pbf file using wget.

2. Install the osmium-tool

 

 

 

 

3.Filter out just the ‘highways’ from the dataset and save it to a new .osm.pbf file

 

 

 

 

4. Convert the .osm.pbf file to a new shapefile ( I don’t normally use shapefile but converting to a geopackage was causing some issues in this instance).

 

 

 

 

 

5. Reproject the shapefile to EPSG:2157 (Irish Transverse Mercator).

 

 

 

 

 

 

I could have made this more efficient again by combining some of these actions (such as save as a new shapefile and reprojecting), I did it this way to make the process easier to explain.

Next I brought the dataset into QGIS and extracted an area just for Cork City. The main issue I then has was how to ascertain what road types occur most frequently. I exported the Cork City extract as a CSV and imported it into Excel. I then used a generic formula from the folks at exceljet to extract the last work from the ‘name’ column. I then counted the frequency of the extracted work in a pivot table to get an idea of the types I’d like to use.

 

 

 

 

 

 

The last thing I did was to create a new field using an expression to find the various road types in the ‘name’ column from the OSM data. The expression I used is the below:

CASE
	WHEN "name" LIKE '%Road%' then 1
	WHEN "name" LIKE '%Park%' then 2
	WHEN "name" LIKE '%Street%' then 3
	WHEN "name" LIKE '%Avenue%' then 4
	WHEN "name" LIKE '%Court%' then 5
	WHEN "name" LIKE '%Drive%' then 6
	WHEN "name" LIKE '%Hill%' then 7
	WHEN "name" LIKE '%Lawn%' then 8
	WHEN "name" LIKE '%Estate%' then 9
	WHEN "name" LIKE '%Place%' then 10
	WHEN "name" LIKE '%Lane%' then 11
	ELSE 0
END

I then symbolised the data and exported it. The final product is below. Obviously this was a pretty quick and dirty way to do it and if I create an atlas for every major town in Ireland I will use PostGIS which will allow me to count the frequencies and create the new field with ease.