Ireland, A Country in Motion: Methodology

I promised late last year that I’d do a blog post explaining how I created the ‘Ireland in Motion’ commuting map. Well, this is that post!

The first thing to say is, that until the ’16 census results came out it wasn’t possible (as a member of the public) to create this type of map as the Central Statistics Office (CSO) just didn’t release the data. Before now (and is still the case) in order to access the full Place of Work, School or College data (POWSCAR) you must attend a training program and sign up to be an ‘Officer of Statistics’. The deciding factor for myself was that you have to be resident in Ireland, which I am currently not. You also have to be a ‘bona fide’ researcher.

So, imagine my delight when I found out that they were releasing an aggregated anonymised dataset for the entire country! The data is aggregated by electoral divisions (ED) and county level. The POWSCAR website where the data can be downloaded is located here. There are two important caveats when talking about this data, EDs where fewer than 10 persons commuted have been excluded and records where no work, school or college were able to be geocoded have been removed. Below is an extract from the CSO’s website showing the categories available.

RESIDENCE_ED_GUID Geographic Unique Identification (GUID) Code for origin Electoral Division (ED)
RESIDENCE_CSOED CSO ED code for origin ED
RESIDENCE_CSOED_LABEL Name of origin CSO ED
RESIDENCE_COUNTY County code for origin county
RESIDENCE_COUNTY_LABEL Name of origin county
POWSC_ED_GUID GUID for destination ED
POWSC_CSOED CSO ED code for destination ED
POWSC_CSOED_LABEL Name of destination ED
POWSC_COUNTY County code for destination county
POWSC_COUNTY_LABEL Name of destination county
COUNT Number of persons commuting

The downloaded zip file when extracted was a 42MB CSV file. CSVs are an ideal format because they are supported by a huge number of programs. I knew that for the type of map I was going to create that I wanted to use create straight lines between the centroids of each ED. The basic methodology I followed was as follows:

  1.  Download CSV, inspect and clean the data (remove any extraneous records).
  2. Download the ungeneralised shapefile of the EDs (available here).
  3. Use QGIS to create the polygon centroids of each ED.
  4. Use the VLOOKUP and concatenate functions in Excel to create well-known text linestrings for the commutes between each EDs.
  5. Use python to parse the CSV file and multiply each row by the number (count) of commutes between each ED. Each row represents one commute between two EDs.
  6. Load the CSV into QGIS and save as a shapefile.
  7. Use FME to load the shapefile file into a PostGIS database.
  8. Connect database to QGIS and create the map.

Detailed Methodology:

1 CSV:

The original number of commutes in the CSV was 2, 750, 239. The following records were removed:

A. The destination was within the same ED (478,884)

B. There was no fixed place of work (174,628)

C. Work/school from home (114,189)

D. Commute to Northern Ireland (9,336)

E. Commute overseas(!) (3,531) were removed.

 

This left the grand total of 1,969, 671 Commutes to be mapped.

2  Download Ungeneralised shapefile:

The ungeneralised shapefile was downloaded from here.

3 Use QGIS to Create the Polygon Centroids:

The centroids of each polygon was quickly calculated in QGIS.

4 Vlookup and Concatenate in Excel:

The attribute table of centroids was exported to Excel and the Vlookup and Concatenate functions were used to create the linestrings for individual commutes as shown below:

5 Python:

A simple python script was used to multiply each line string by the count, so that each individual commute would be represented by a separate line on the map.

6 QGIS-Load CSV:

The CSV file was quickly and easily loaded into QGIS and exported as a shapefile. A better method to do this would probably have been to use FME to load the CSV directly into PostGIS and that’s something I will bear in mind for the future.

7 FME Shapefile:

FME 2017 was used to load the shapefile to PostGIS, and a simple reproject was used to get the data into Irish Transverse Mercator (EPSG 2157).

8 Connect PostGIS to QGIS:

A PostGIS layer can be added in a few clicks from within QGIS. The advantage of using PostGIS is that it will load the 1.96 million lines a lot faster than a shapefile for example, shapefiles have their uses (widely supported for example) but they are an archaic format that will hopefully go the way of the Dodo (this is already happening with the support for Geopackage in QGIS 3 for example).

 

The above is a quick overview of how I carried out the data processing for the map. It’s remarkable that almost all the software used to create the map was open-source. I’d be curious to try and do it totally open-source (replace FME with OGR and Excel with LibreOffice Calc) but as I have a home use licence for FME and Office ’16 I decided to use those.

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