As part of the 2016 Irish Census the Central Statistics Office captured data on communing to work and school/college (POWSCAR). The raw data is available for research purposes, but the anonymised data is available to all. This data is aggregated to an Electoral Division (ED) level.
There are 3440 EDs in Ireland, 3409 after amalgamating low population ones. These are legally defined administrative areas. Commutes into Northern Ireland are also captured, but only as one destination (Northern Ireland).
Overall the data consists of a CSV file with 291893 rows of commuting data.
Neighborhood boundaries are a fascinating topic. Where do people see their neighborhood extending to, how clear are those boundaries, and how do they shift with time.
This is a topic that has been tackled a number of times around the world, and with different types of locations (like cities). With the general public asked to draw their own neighborhood, or the ones they are familiar with, on a map. But there are challenges.
Sometimes the results are successful, like in Boston. Boston only has around 60 neighborhoods, which are relatively clearly defined based on the mapping results. And Bostonography clearly had a great readership, with over 2300 responses.
Older cities, like Glasgow are much more complex. In 2016 I did a similar survey, and from 367 responses I received 241 unique neighborhoods. You would need a much bigger set of responses to come to any real conclusions. But I still mapped the results for the West End of Glasgow, where most neighborhoods had multiple responses: Glasgow Regions Mapped
The challenges for Dublin neighborhoods is similar to Glasgow, although not as complex. But anything worth doing would require a lot of responses.
Luckily the Dublin InQuirer decided to run a similar survey, polling their subscribers for responses. The number of submissions was good, not quite beating the Boston yet, but getting close. 2200 responses and 133 unique neighborhoods mapped. They also did a few great things. One was making the data available to download. This meant that anyone could map the results, which is great. Additionally they are reaching out to areas with less responses, which is a great effort on their part.
This year I wasn’t really sure I would take part, as I had no plans and nothing prepared. But it is a great challenge. It challenges your creativity, problem solving, and map making skills. It also sets a time limit so you don’t have to worry about being perfect. And making maps is good fun.
There were a few datasets that I had come across that I thought would be good subjects, and I got a lot of mileage out of them.
Dublin neighbourhoods
These included the Dublin Inquirer neighbourhood survey.
I think this is a great initiative, and hopefully they get a real large set of responses. They are at over 2000 already so a great start.
I did something similar for Glasgow (here) but only got 367 responses in total. So the power of having a well read paper behind the initiative is great. They are also reaching out to areas that have not had many responses, which is really great work.
OpenStreetMap
I also wanted to do some maps around OpenStreetMap in Ireland. The community here has had a large push to map all of the buildings in Ireland, which has progressed well.
Supermac’s is an Irish fast food restaurant chain, who have had a few trademark disputes over the years with McDonald’s over the use of Mc and Mac in burger names.
3D:
This turned out a lot better than I expected.
Was pure QGIS. Create grid (5km x 5km), zonal statistics on CORINE (Majority) and DEM (Median). New field for height rounded up to the nearest 40:
to_int(ceil((“_h_median” / 40))) * 40
Set colors. Create centroids with same colors. These become the Lego nubs.
Rendered in QGIS2threejs plugin. The grid is extruded, with a height of height * 50. The centroids are cylinder rendered height * 50 + 30 * 50, so they come a bit higher, radius of 1800.
Might be better to not use landuse, but elevation for the colours.
No Computer:
Nice to get away from the computer. Definitely promotes some creativity. But I just took it as an opportunity to walk on the beach.
Land:
My favourite of my maps.
Land use vineyard across Europe from CORINE 2020, with the major regions labelled.
GHSL:
Seasonal population of the Balearic Islands.
Data clipping in QGIS/GDAL, rendered in Aerialod, with labels with GIMP afterwards.
This was my second favourite of my maps. I think the topic is interesting and the execution is pretty good. Was however quite manual and probably needed more exaggeration to see the differences.
Historical:
A time lapse of 1,831,044 buildings in Ireland being added to OpenStreetMap.
There are a lot of advantages to this style of map creation and a few down sides.
Advantages
It puts a time limit on the maps. This forces you to put them out there no matter the status. It is easy to leave maps half finished because they are not perfect. I know the internet can be a harsh critic sometimes, but you are making a map a day, it won’t be perfect.
You can revisit some older maps that have been discarded in the past. Perhaps they didn’t look so good, weren’t so interesting. That doesn’t matter, you are posting a map a day they don’t have to be perfect.
There are a lot of ideas out there. I use a Google keep note to store map ideas I have. The list keeps on getting longer and without some pushing it never gets shorter.
Collaboration. There were some great maps made, and every day you could enjoy them as well, feeling part of the community. Sharing ideas and techniques.
Increase your talents. The only way you are going to get better at mapping is by making some maps. No better way to do it than pushing yourself.
Work with new data. 30 maps is a lot of maps. You can explore new datasets and new software.
Grow your following. As the challenge is on Twitter it is a good way to build your following. I got 139 new followers over the month. Now for some people that isn’t much. But I only have 600 in general so it’s a sizeable amount. Although I’m not sure there is that much value to followers. But 197k impressions is good? 1 follower per 1000 who saw a map.
Disadvantages
30 maps is a lot of maps to make. A day is a short time to make one. My tactic, based on my previous experience, was to make a few upfront and ready to go. So by the time November started I had a few maps ready, so I could be ahead of the curve.
I used tweetdeck to schedule the tweets. I didn’t map on the weekend for the most part. I had other commitments, so scheduling and making some easy maps was crucial to completing the challenge.
There are no real rules to the challenge, interpret it how you wish. Do a map a day, or a map a week, but the important part is enjoying it.
Maps
There is a great website that collects all the maps created by theme and creator:
Ireland has a great single transferable election system. It means that every vote is meaningful even for smaller parties and candidates. It also means mapping the results is difficult. As each constituency has more than one seat, ranging from three to five.
One way to map the results is to have the constituencies split up. Either geographically, so split into parts based on how many seats it has. Or into equal sized pieces, like hexes.
Since I had not seen an election hex map for Ireland yet. I thought I would attempt to make one for the February 2020 General Election.
As HEX. One hex per seat. Hex no labels. Constituencies split into equal sized parts based on number of seats.
Postcodes were then created based on the ONS Postcode Directory, filtering for postcodes that were live in 2011 (which is the latest census data). The postcode centroids were turned into polygons using voronoi polygons.
Then we simply select all of the buildings in a postcode from Ordnance Survey, Open Map product, filtering out most schools and hospitals. Then we put a random point in a random building for each person in that postcode.
I would have loved to include Northern Ireland, but the Ordnance Survey of Northern Ireland do not have an equivalent open building outline dataset, like Open Map from the Ordnance Survey.
Average Location of Stanley Cup Champions Since 1915:
The average locations were created using a window function in PostgreSQL. We can utilise the geography type to take into account the curvature of the earth and make the calculation on a spheroid.
So for the average location of the last five years:
select id,
ST_Centroid(st_collect(geom)
over (ORDER BY id ROWS BETWEEN 4 PRECEDING AND CURRENT ROW)::geography,
True)::geometry as five_year_cent
from nhl.winner_1915
