We use a combination of proximity voter theory and statistical techniques (linear regression and principal-component analyses) to undertake two streams of analysis:
- Determining what issues have historically driven votes and what positions neighbourhoods have taken on those issues
- Determining which neighbourhood characteristics might explain why people favour certain candidates
In both cases we use candidates’ currently stated positions on issues and assign them scores from 0 (‘extreme left’) to 100 (‘extreme right’). While certainly subjective, there is at least internal consistency to such modeling.
This work demonstrates that significant insights on the upcoming mayoral election in Toronto can be obtained from an analysis of publicly available data. In particular, we find that:
- Voters will change their minds in response to issues. So, "getting out the vote" is not a sufficient strategy. Carefully chosen positions and persuasion are also important.
- Despite this, the 'voteability' of candidates is clearly important, which includes voter's assessments of a candidate's ability to lead and how well they know the candidate's positions.
- The airport expansion and transportation have been the dominant issues across the city in the last three elections, though they may not be in 2014.
- A combination of family size, mode of commuting, and home values (at the neighbourhood level) can partially predict voting patterns.
We are now moving on to something completely different, where we use an agent-based approach to simulate entire elections. We are actively working on this now and hope to share our progress soon.