Here’s another visualisation of some data from the 2013 Australian Federal Election.
I wanted to see how consistent voting patterns were across booths within an electorate. From handing out how to vote cards at my local polling booth I had the feeling that not every booth is the same.
Fortunately the Australian Electoral Commission publishes a live feed of all data by booth. Its the same data that the news outlets use, so its pretty good. It follows the Election Markup Language standard, so extracting the data was not too hard. The AEC had added in their own elements, but they used namespaces which made it fairly simple to process using a fairly basic perl script.
Data is based on first preferences only. Because of the way heatmaps work, if booths are closer together the intensity increases, so to some extent the heat map is determined by the layout of the booths. However, distinct patterns are discernible if you explore the data. Only parties that registered votes in at least 200 booths have been included.
Absentee voting at capital cities booths are mapped at the booth rather than in the electorate that the vote was for, so capital cities tend to show high results for every party.
You can see the live demo and the source code is at GitHub.
The Australian Electoral Commission have released the data for preference allocation and makes a good subject for a visualisation.
Using a PERL script I was able to group the ticket preferences into groups and then create a matrix in a format suitable for input to a d3js chord diagram.
Getting useful data is always a compromise. Some parties have multiple tickets (ie they split their preference flows in two or more tickets). Independents do not have parties, but work in a group. I grouped independents by using the highest preferenced candidate for that ticket. The coalition have more than one party, so I had to combine these under coalition.
To measure how highly a party was preferenced, I took the average position on the ballot for each member of the party and average these if there were more than one ticket. For some parties with split tickets this meant that they might end up without preferencing any party particularly highly.
Next was how to visualise the data. A chord diagram seemed the natural way to show how parties preference each other. The problem is that there is too much data to show every preference allocation (by definition every party preferences every other candidate). So I needed to draw the line on how much data to show. I arbitrarily decided that any party that averaged a position in the top 25% of the ballot order was highly preferenced by the other party.
The visualisation shows some interesting things. Whether there is a symmetrical or asymmetrical relationship between parties can easily be seen. Als,o the wider the party is around the circle, the more other parties preference it. The ALP and coalition have fairly narrow widths while the bullet train party and family first are relatively wide.
Of course preferences are a lot more complex than shown here. The order of preferences and how they flow once quotas are allocated can have a subtle and profound affect on the election outcome. If you are considering voting below the line Antony Green has some good advice.
My visualisation is available at http://peterneish.github.io/preferences/ and the code can be found on GitHub.
At the moment it is fairly basic and is not entirely intuitive (no back-button or bookmarking support, inconsistent actions), but these things are fairly easy to add. D3js takes a while to get your head around, but the combination of a well thought out API from the Atlas of Living Australia combined with d3js and twitter bootstrap has allowed me to produce this application relatively easily.
The demo application and source are hosted on GitHub.
The idea for this entry came from the amazing Japanese earthquake map written by Paul Nicholls in which the Japanese earthquakes are shown in a timeline on a google map.
I thought it would be interesting to do something with the Biodiversity Heritage Library data for the Life and Literature Code Challenge. I figured that plotting the place of publication over time might show some interesting patterns.
Continue reading Life and Literature Code Challenge Entry