Most Remote Building in Ireland

Introduction

I read a few excellent blog posts over the last number of years regarding how to calculate remoteness of buildings from each other.

The two that stand out for me are Topi Tjukanov’s Searching for Isolation with GIS and Simon Wrigley’s Finding the most remote buildings in Britain which offer excellent methodologies on how to find the most remote building in Flanders and Britain respectively . I have always been curious as to the most remote building* in Ireland (as measured from another building).  I have been waiting (almost with bated breath) for Microsoft to release a building footprints dataset for Ireland, which they did earlier last year

*Note – I thought long and hard and for this purpose, I am only interested in the most remote building from another building. For the purposes of this analysis, the building cannot be a ruin and must be weathertight. 

Data Download and Processing

I downloaded the data for Ireland (using the provided scripts). As GeoJSONs aren’t an appropriate format to work with, I used GDAL’s ogrmerge.py command to merge all them into one, reproject to the Irish Transverse Mercator (EPSG: 2157) and output it as one GeoPackage file. I then uploaded the GeoPackage to a PostGIS database (using GDAL) in order to be better able to work with it.

Below is an overview of the building footprints — it is obvious that there are two substantial areas missing around Dublin and Limerick. The dataset contains 2,270,112 buildings and the quality in places is dubious due to how it was created. I found a number of instances of silage pits and rocks etc. being mistaken for buildings. It’s a great effort by Microsoft to compile and, frankly, it’s the only dataset available as Tailte Éireann (the newly created public body with responsibility for mapping, property registration and valuation) has not released anything akin to the UK Ordnance Survey’s OpenMap Local Buildings product.

Building Footprints of Ireland from Microsoft

Building Footprints of Ireland from Microsoft

Missing Data

There are two large areas clearly missing from the above dataset, Greater Dublin and Greater Limerick. To get a sense of the problem, I downloaded population data from WorldPop, digitised the missing areas and used Zonal Statistics to calculate that the missing areas contain approximately 206,335 people in Greater Limerick and 2,117,635 for Greater Dublin (as shown below).

Missing Building Footprints - Greater Limerick

Missing Building Footprints – Greater Limerick

Missing Building Footprints - Greater Dublin

Missing Building Footprints – Greater Dublin

The two polygons shown above contain approximately 46% of the population of the country. Ordinarily, missing buildings over an area of the country that includes nearly half of its population would be a terrible outcome. The inverse held true for this analysis though;  the more populated an area is, the more buildings it contains and the less likely it is to contain the most remote building in the country from another building. That’s not to say that the most remote building in the country isn’t located in these are, just that it’s unlikely.

There are two obvious areas within both these polygons that could contain the most remote building, the islands in the Shannon Estuary and the Wicklow Mountains National Park. To give me some idea that the most remote building wouldn’t be in these areas, I manually (rather quickly) used the MapGenie basemap on Geohive to look for buildings. In the missing Limerick polygon, there were only two candidate areas that might contain the most remote building, one on Feenish and one on Inishtubbrid (neither of which was a greater distance to another building than the building identified at the end of this analysis).  For this area I also looked at the Slieve Bearnagh range which didn’t produce any building greater than 500 metres from another building.

For the Wicklow Mountains, I examined the MapGenie basemap in detail but no immediate contenders were apparent.

Shannon Estuary Islands

Shannon Estuary Islands – Contenders for Most Remote Building

Analysis

I am indebted to Topi and Simon, and it’s Simon’s methodology I’m following here (almost to the letter). In order to reduce the computing power and time required, I did two things; I converted the building footprints in PostGIS to points (using ST_Centroid), and I used MMQGIS to create a 3km hex grid in QGIS that I imported into PostGIS using the below command. I then clipped the hex grid to the outline of Ireland and created spatial indexes for all tables.

 ogr2ogr \
-f "PostgreSQL" PG:"dbname='Ireland_Buildings' host=localhost port=xxxx user= 'postgres'" \
-nlt PROMOTE_TO_MULTI \
"Ireland_Admin_Outline.shp"

I adapted Simon’s very helpful building count SQL query and mapped it the buildings per hexagon (shown below).

Building Count - Ireland

Building Count – Ireland

To reduce processing time, Simon counted the building footprints for each hexagon and only focused on the hexagons that had ≤1 building per grid cell. Because of the nature of the geography of both countries (essentially, the remoteness afforded by the Scottish Highlands), this exact approach wouldn’t work for Ireland. After some careful experimentation, I chose ≤5 as the number to use.

 

Less than or equal to 5 buildings per hexagon

Less than or equal to 5 buildings per hexagon

The results accord well with areas of the country that one would consider remote as they don’t have the best quality agricultural land (such as West Connaught). It is important to state here that I ran several iterations of the above with various numbers of buildings and then followed Simon’s step of merging the resultant grid cells and buffering the cluster by 1km in order to find the most remote buildings. This took a considerable amount of time and I think I deleted about 50 contenders through this process.

Method: K-Nearest Neighbours

Simon did some excellent work creating Voronoi polygons to give a visual indication of the most remote building. If I’m being honest, I skipped this step and went straight to a nearest neighbour analysis. I’m not going to reinvent the wheel here so please have a read of Simon’s excellent methodology for the detailed SQL code.

I ran the nearest neighbour analysis (limiting the results to the top 10) and methodically went through all of them to see whether the first entry (being the most remote building listed was correct) and—🥁—it was.

Bearing in mind that Ireland isn’t a particularly large country with vast remote areas etc. I wasn’t expecting the result to be a huge distance. I had discounted a lot of buildings along the way that were clearly not weathertight (such as the below) and, through that process, I knew the final result wouldn’t be numerically impressive.

False Positive - Cleary not weathertight

False Positive – Cleary not weathertight

Most Remote Building

I think it’s crucial here to reiterate the main caveats to this answer, those are:

  1. The dataset was created using machine learning (limitations being a large number of false positives and potentially missing buildings); and
  2. There are areas around Dublin and Limerick missing that may contain a more remote building.

All that being said, the most remote building is:

Blackhead Lighthouse, Burren Co. Clare - 2,270 metres to Nearest Building
Black Head Lighthouse, Co. Clare

Black Head Lighthouse, Co. Clare

It’s quite interesting that the lighthouse is in the Burren, Co. Clare, which is a karst landscape that is >500km² in area. I’ve inspected it on StreetView and it appears to meet the main criteria (i.e. weathertight). To ensure that there wasn’t any nasty surprises I again examined the MapGenie basemap on Geohive and, thankfully, there were no missing buildings in the vicinity. A very interesting structure to be the most remote (you can read more about it, including a detailed history, on the Commissioners of Irish Lights website).

Black Head Lighthouse by Yair Haklai is licence under a under the Creative Commons Attribution-Share Alike 4.0 International license.

Black Head Lighthouse by Yair Haklai is licence under a under the Creative Commons Attribution-Share Alike 4.0 International license.

CAVEAT

This was the most remote building based on the data available and the methodology used. I will rerun the analysis if and when the entire country becomes available. If you’ve gotten this far, thanks for reading.

North or South of the River

This post is in response to a chat I had a few days ago with someone who was wondering whether there are more people north or south of the river in Perth.

To answer this I’ve taken the ABS’ definition of Greater Perth (being the Greater Capital City Statistical Area [GCCSA]). The overall population of the Perth GCCSA is 2,116,647 (Census ’21). The greater city area is separated by both the Swan and Avon Rivers. I downloaded the polyline of the rivers from https://overpass-turbo.eu/ and then created polygons of both the north and south of the river to quickly select the SA1 areas within each. I then quickly ran a Select by Location to get the figures for the number of people that live north and south of the river.

Perth - SA1 Units with the Greater Capital City Statistical Area

Perth – SA1 Units with the Greater Capital City Statistical Area

Once I had gotten the SA1 units north of the river it was simply a case of repeating the process for the SA1 areas south of the river.

SA1 Areas North of the River

SA1 Areas North of the River

SA1 Areas South of the River

SA1 Areas South of the River

 

The results are as follows:

North of the River –

Area = 1,653.8km² | Population = 962, 257 | Percentage of Perth GCCSA Population: 45% | Population Density = 581.9 Persons/km²

South of the River –

Area = 4,762km² | Population = 1,153,941 | Percentage of Perth GCCSA Population: 55% | Population Density = 242.2 Persons/km²

The above data is out to the tune of 179 people due to what I presume is data suppression in the very sparsely populated SA1 areas by the ABS. An interesting takeaway is that the population density south of the river is only 41.6% that of what’s north of the river.

Put another way, if the southern part of the City had the same density as the north, then there would be 2.784 million people south of the river—really puts it in perspective.

Titanic Wreck Site

A quick map that shows the location of the Titanic wreck site with graticules included:

 

Titanic Wreck Location with Graticules Included

Titanic Wreck Location with Graticules Included

Driving from Perth to Sydney

I’ve driven from Perth to Sydney and back so I was curious as to what the route would look like overlaid on Europe so here it is. If you went the same distance west from Dublin you’d get to a small town in Labrador and Newfoundland called North River (population 579 in 2021).

Driving Route from Perth to Sydney overlaid on Europe

The driving Route from Perth to Sydney overlaid on Europe

 

Ireland’s Population by Altitude

I read Alasdair Rae’s very interesting post where he created a graph of Great Britain’s population by altitude. Using WorldPop data and the EU DEM I used QGIS, Seaborn and Inkscape to create the following graph for Ireland. I must do a bit of digging to see whether the 400 – 500 metre slight bump can be explained by a discrete area…

 

Horizontal bar plot showing Ireland's population by altitude

Horizontal bar plot showing Ireland’s population by altitude

Camino de Santiago

I was talking to someone recently about the Camino de Santiago and whether it plays a role in sustaining the population of the provinces it traverses. It’s a rainy Sunday here so I sat down with a cup of coffee and decided to create a population density map using data from: OSM, WorldPop, Natural Earth and Peter Rukavina’s website ruk.ca.

It proved quite difficult to get GIS data on the route of the Camino through Spain. I first downloaded the Spain.osm.pbf file from Geofabrik and filtered it using Osmium however no matter what combination of tags and names etc. I used, I couldn’t get more than a few thousand disjointed lines that were not fit for purpose. I then found Peter’s website where he had the route as a geojson that I could easily use. I then spent some time in QGIS making the below map. I purposefully didn’t use the minimum and maximum numbers in the legend as I didn’t think it would add much value instead I used low to high.

I also haven’t marked that it’s technically the Camino Francés as it’s the most popular route.

To answer the population question, I buffered the route by 5km and counted the number of people that live within this area. It worked out at 1,316,141 or 2.77% of Spain’s population, I was surprised, I thought the figure would be significantly larger.

Camino de Santiago Population Density

Camino de Santiago Population Density

Knockanore Hill

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.

 

Knockanore, Co. Kerry, Ireland

Knockanore, Co. Kerry, Ireland

 

Knockanore, Co. Kerry, Ireland

Knockanore, Co. Kerry, Ireland

Birthplaces of Irish Taoisigh

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.

 

Birthplaces of Irish Taoisigh

Birthplaces of Irish Taoisigh

Cartogram - Birthplaces of Irish Taoisigh

Cartogram – Birthplaces of Irish Taoisigh