Voting Patterns for Edmonton City Council's 2017-2021 Term

City Council, unlike other levels of government, doesn't rely on party systems for categorizing its members. That being said, there still can be, and in fact are, patterns in how members of council vote, and with the Open Data that's available on council voting records, these patterns can be examined.

There are a lot of different ways to visualize voting patterns, and I've played around with these before (see here and here - unfortunately, since most of the visuals for this blog relied on the now-dead Google Fusion Tables, there's really not much to see). I've settled on three favourite methods for the 2017-2021 Edmonton city council term - let's take a look!

First of all, as in previous years, I've disregarded all motions that were unanimous as they provide no particular differentiating information. That leaves the 2017-2021 term with 921 non-unanimous votes to examine (at time of writing).

The first pattern-finding method I like to use it to simply look at the success rates of each member of council. How often did a vote go the way they wanted it to? This can be a sign of consensus-building, or an indicator of work put in behind the scenes (perhaps at other committees), or potentially a matter of being a part of a majority bloc that tends to vote similarly:

While a direct comparison is perhaps unwise, these number in general follow the same pattern as my similar 2016 analysis. Of members of council who were present both years, Councillor Esslinger and Mayor Iveson are again the top two and Councillor Nickel is again the lowest. Councillors Walters, Knack, and Henderson are all within 5% of their 2016 results as well, with Councillor Caterina showing a slightly larger difference from before.

This of course is not intended to imply anything about the effectiveness of individual members of council, and is performed without a review of the motions themselves (whether they are procedural, multiple readings of the same bylaw, etc.).

Noteworthy from the last analysis was the result that Mayor Iveson had only 'lost' 17 votes out of 358 non-unanimous motions in the previous term. For comparison, at the time of writing, this number is now 94 votes.

A second pattern-finding visualization is how often members of council agree with each other. For the 2017-2021 term so far, that is:

The result from this analysis shows that a group of six members of council agree with each other more than 80% of the time across all pairings, and that a seventh member (Councillor Henderson) is just outside with a 79% minimum agreement rate (with Councillor Hamilton). With a council size of 13, seven members is a winning majority on most motions. Certainly, there is a correlation between the top six council vote winners and this group of six members of council - whether this group is ideologically similar or just more likely to compromise and build consensus is beyond the scope of this analysis though!

A third and final pattern-finding visualization that I quite like is adapted from the NOMINATE system used to scale members of the United States Congress. It is intended to represent ideological similarities and differences between members of council in a spatial manner - members closer to each other agree more, and further apart agree less frequently:

I'd like to stress at this moment that, as it's often tough to assign traditional political ideologies to city council bylaw amendments, this graph does not necessarily represent traditional 'left vs right wing' traits, nor traditional 'authoritarian vs libertarian' traits. The results of the graph are intended to model councillors as though their decisions are made based solely on two non-correlated factors, and the model above is oriented with the most significant factor aligned along the x-axis. 

It's totally cool if you want to stop now, but I actually really love this model system and I want to talk about it a bit more since it gained some interest when I did this for London. Effectively, the NOMINATE system models both councillors and motions along the two axes, then assigns a probability of each councillor voting one way or another based on the relative proximity to each "side" of a debate. The algorithm then iterates thousands of times, tweaking the positions of each councillor and motion in such a way to optimize the probabilities of each decision.

The net result of this is that, using only two dimensions, this use of the NOMINATE algorithm as it stands currently accurately assigns the correct vote to each councillor 93.4% of the time. While to some of you this may not seem perfect, a model that reduces the complexity of council decisions to two factors with over 90% accuracy is something I'm quite astounded by and happy with.

For instance, last week's vote to end the mask mandate effective July 1st broke down like this based on the model. Here, the orange coloring indicates 'voted no', and blue indicates 'voted yes', with clear circles for the locations of the decision-points:

Here, the percentages are the model's prediction at the odds of each councillor voting the way they did. The "yes" and "no" points are shown, and the dashed line indicates the mid-way point between the two positions. In this case, the model managed to accurately capture each member's vote (where accuracy here is defined by a yes vote with more than 50% probability, or a no vote with less than 50% probability). The probability doesn't necessarily reflect the difficulty a given member of council had in making their decision, and is more of the measure of accuracy of the model. 

By looking at all votes together, the model slowly hones in on the best placement for each member of council. Not all votes are as clean cut as this one - for instance, the vote on the solar power plant at EL Smith looked like this:

You can see here that the model was very close with councillor McKeen, and effectively swapped Caterina and Dziadyk. Again, as the model is probabilistic this doesn't mean it got these 'wrong', more that having these councillors and decision points in these locations is optimized over the entire term.

It's not a perfect model, but again I'm quite pleased with how accurately it is able to capture the voting term in only two dimensions!

So that's it - three different ways to look at the data, showing different aspects of what can be learned from it!