I was reading this Guardian article the other day where they produced maps showing the number of Airbnb listings per 100 dwellings. I thought it was really interesting and I hadn’t seen Airbnb data mapped like that before. I had a few hours to spare yesterday so I set about replicating their method for Ireland. I used the 2016 census electoral divisions (to get the household numbers) and data for Ireland from Inside Airbnb. I think at best this data is questionable because from the reading I’ve undertaken it seems to still list properties that were briefly on Airbnb a number of years ago and have long since been removed however this is the only data available so I went with it.
Below is the map, it was made with a combination of Bash, GDAL, QGIS, LibreOffice Calc and Illustrator.
I’ve been looking at the 2016 census results with the last few years and there is a great deal of suppression of values for relevant SmallAreas. The CSO suppress results or aggregate them depending on the number of people living in a Small Area. If the population is too small and could lead to individuals being identified, the data is suppressed. They are legally required to undertake this exercise under s33 of the Statistics Act, 1993.
I’ve been looking at a selection of variables and after reading this piece on the traveller accommodation crisis by RTÉ I decided to map the percentage travellers per Small Area. I have all this data in a PostGIS database but I’ll quickly run through how to do it without having to use PostGIS. I downloaded the Small Areas shapefile (generalised to 50m) and the CSV of all of the Small Area values from the CSO here. Instead of having to use a spreadsheet or QGIS to manually delete the 802 fields I didn’t need I used the pandas library, the python code below that took 0.3 seconds to run. It opens the relevant CSV and only selects the columns that I need and then strips the first 7 characters from the ‘GEOGID’ string as these are not needed for the join I’ll do in QGIS later.
import pandas as pd, time
start = time.time()
df = pd.read_csv('SAPS2016_SA2017.csv', usecols=['GUID', 'GEOGID', 'GEOGDESC', 'T1_1AGETT','T2_2WIT'])
df['GEOGID'] = df['GEOGID'].str[7:]
end = time.time()
print(end - start)
I then opened the shapefile in QGIS, imported the CSV and joined them. This was subsequently exported to a GeoPackage and I used GDAL’s ogr2ogr library to convert it to a GeoJSON in order to upload it to Carto.
Below is the resultant map with some formatting of headings undertaken to make it more legible. You can make it full screen using the button on the left. What struck me about this was how with a small amount of work it was very easy to visualise accurately the resident locations of one of the most vulnerable groups of society. Obviously this information is useful to local governments, state government agencies, NGOs and so forth but I question whether this data should been available to the general public regardless of it being aggregated to the Small Area geography.
Housing and all its intricacies have come to dominate the media discourse at home over the last few years. We’ve truly come out the other side of the recession and now the conversation is around the shortage of housing and where that has lead us. I’ve been thinking about this recently and in particular social housing. I think that most people assume that we built the majority of our social housing in the 1950s and 1960s. Collectively I think we assume we know when social housing was built but not where. This is where the census data can come in. Part 2 of question H3 in census 2016 asks ‘If renting, who is your landlord?’.
I have all of the census 2016 returns for each geographical unit in a PostGIS database so it was a simple exercise to add the households that rent from a local authority or voluntary/co-operative housing body and divide by the total number of houses. One inherent weakness to this method is that it doesn’t capture the social housing tenants that rent from a private landlord.
I added a new column in PostGIS for the percentage social housing and I then symbolised this in QGIS. I used QGIS’s powerful Atlas generation tool. You’ll have to excuse the basemap, I’m aware that it’s a bit difficult to discern but in the interest of producing this entirely with opensource software I used OpenStreetMap as the basemap.
The next step will be to take the top-ten counties and use the Global Human Settlement Layer as a base to give an approximate indication of what epoch they were built in.
Like a lot of people, I spent a great deal of time following the 2019 federal election results. I was (and still am) very impressed with the Australian Electoral Commission’s Tally Room where results are easily available and downloadable. It was while I was browsing their site that I came across the Western Australian federal seat of Durack, what piqued my interest is that the stated area is 1,629,858km², I looked at its wiki page which states that it’s the largest electoral division in the world that practices compulsory voting. The Guardian have a good article about it which contains a graph that compares it in size to different countries in the world.
I took a quick look online to see if anything already existed, the only thing I could find is the below from Perth’s Wikipedia page.
It’s from 2008 and although a gallant effort, there are a few major problems, most notably the lack of a legend. So I decided to see if I could make something, if not better, than as good as the above.
My first job was to source the data, I knew from previously working with ABS data that their pre-built geopackages or datapacks wouldn’t contain the data I needed (question 12 from census ’16) but the geopackages were useful to download the geometry that I needed.
I needed to use the Tablebuilder in order to collate the data that I needed for the geometry that I was going to use. This was the main learning area for me, I didn’t know enough about which unit of statisitical geography I wanted to use for this exercise. Luckily, the ABS have a website where you can compare and contract each unit.
The ABS already had the hard working done in that one of their staticial units is ‘Greater Perth‘, I used this as my boundary and then chose the SA2 as the statistical unit. I went back to Tablebuilder and tried in vain to make sense of it; I found it very cumbersome and non-intuititve to use at the start and their introductory videos weren’t of any help. Fortuntately, I found an amazing video on YouTube that explained Tablebuilder in great detail and once I’d watched that everything made sense, and I’m a Tablebuilder convert now!
I then used Tablebuilder to build the exact statistics that I needed (Country of Birth by SA2). I saved the table in Tablebuilder and downloaded it as a CSV file. In QGIS I then joined this with the SA2 geopackage file for WA and clipped it using the Greater Perth boundary that I had also downloaded. I then exported this layer as a new geopackage. I had previously found the top 8 nationalities by country of birth (using Tablebuilder) and then created new fields for each one where each number represented 200 persons born in that country. I then used the Random Points Inside Polygons tool to create random points for each nationality.
I then used Adobe Color [sic] to pick a decent colour scheme for the various dots. I used Quick OSM in QGIS to download a layer with the towns in Greater Perth to be used for reference, this took about 10 seconds to do, Quick OSM is really useful.
Lastly, I used Google Fonts to download some nice fonts. I also used some styling effects in QGIS before I exported everything to Inkscape in order to add the text. Below is the finished product, the biggest flaw in what I have done is that there are overlapping points but I still think it gives a good overall understading of where people of different nationalities live in Greater Perth.