In preparing for some PsephoAnalytics work on the upcoming provincial election, I’ve been wrangling the Elections Ontario data. As provided, the data is really difficult to work with and we’ll walk through some steps to tidy these data for later analysis.
Here’s what the source data looks like:
Screenshot of raw Elections Ontario data
A few problems with this:
The data is scattered across a hundred different Excel files Candidates are in columns with their last name as the header Last names are not unique across all Electoral Districts, so can’t be used as a unique identifier Electoral District names are in a row, followed by a separate row for each poll within the district The party affiliation for each candidate isn’t included in the data So, we have a fair bit of work to do to get to something more useful.
The past few years, I’ve delivered an introduction to using R workshop that relied on manipulating Ministry of Finance demographic projections.
Analyzing these data was a great case study for the typical data management process. The data was structured for presentation, rather than analysis. So, there were several header rows, notes at the base of the table, and the data was spread across many worksheets.
Sometime recently, the ministry released an update that provides the data in a much better format: one sheet with rows for age and columns for years.
Sometimes it’s the small things, accumulated over many days, that make a difference. As a simple example, every day when I leave the office, I message my family to let them know I’m leaving and how I’m travelling. Relatively easy: just open the Messages app, find the most recent conversation with them, and type in my message.
Using Workflow I can get this down to just a couple of taps on my watch.
This tweet about the charitable donations by Albertans showed up in my timeline and caused a ruckus.
Albertans give the most to charity in Canada, 50% more than the national average, even in tough economic times. #CdnPoli pic.twitter.com/keKPzY8brO
— Oil Sands Action (@OilsandsAction) August 31, 2017 Many people took issue with the fact that these values weren’t adjusted for income. Seems to me that whether this is a good idea or not depends on what kind of question you’re trying to answer.
The TTC releasing their Subway Delay Data was great news. I’m always happy to see more data released to the public. In this case, it also helps us investigate one of the great, enduring mysteries: Is Friday the 13th actually an unlucky day?
As always, we start by downloading and manipulating the data. I’ve added in two steps that aren’t strictly necessary. One is converting the Date, Time, and Day columns into a single Date column.
Thank you to all the participants, donors, and volunteers for making the third Axe Pancreatic Cancer event such a great success! Together we’re raising awareness and funding to support Pancreatic Cancer Canada.
The day after an historic landslide electoral victory for the Liberal Party of Canada, we’ve compared our predictions (and those of other organizations who provide riding-level predictions) to the actual results in Toronto.
Before getting to the details, we thought it important to highlight that while the methodologies of the other organizations differ, they are all based on tracking sentiments as the campaign unfolds. So, most columns in the table below will differ slightly from the one in our previous post as such sentiments change day to day.
Well, it is now only days until the 42nd Canadian election, and we have come a long way since this long campaign started. Based on our analyses to date of voter and candidate characteristics, we can now provide riding-level predictions. As we keep saying, we have avoided the use of polls, so these present more of an experiment than anything else. Nonetheless, we’ve put them beside the predictions of five other organizations (as of the afternoon of 15 October 2015), specifically:
We’ve started looking into what might be a natural cycle between governing parties, which may account for some of our differences to the polls that we’ve seen. The terminology often heard is “time for a change” – and this sentiment, while very difficult to include in voter characteristics, is possible to model as a high level risk to governing parties.
To start, we reran our predictions with an incumbent-year interaction, to see if the incumbency bonus changed over time.
Political psychologists have long held that over-simplified “rational” models of voters do not help accurately predict their actual behavior. What most behavioural researchers have found is the decision-making (e.g., voting) often boils down to emotional, unconscious factors. So, in attempting to build up our voting agents, we will need to at least:
include multiple issue perspectives, not just a simple evaluation of “left-right”; include data for non-policy factors that could determine voting; and not prescribe values to our agents beyond what we can empirically derive.
Given that we are unable to peek into voters’ minds (remember: we are trying to avoid using polls as much as possible), we need data (or proxies) for factors that might influence someone’s vote. We gathered (or created) and joined data for the 2006, 2008, and 2011 Canadian federal elections (as well as the 2015 election, which will be used for predictions) for Toronto ridings.
We’ll be explaining all this in more detail next week, but for now, here are some basics:
Analyzing the upcoming federal election requires collecting and integrating new data. This is often the most challenging part of any analysis and we’ve committed significant efforts to obtaining good data for federal elections in Toronto’s electoral districts.
Clearly, the first place to start was with Elections Canada and the results of previous general elections. These are available for download as collections of Excel files, which aren’t the most convenient format. So, our toVotes package has been updated to include results from the 2006, 2008, and 2011 federal elections for electoral districts in Toronto.
A number of people have been asking whether we are going to analyze the upcoming federal election on October 19, like we did for the Toronto mayoral race last year. The truth is, we never stopped working after the mayoral race, but are back with a vengeance for the next five weeks.
We have gathered tonnes of new data and refined our methodology. We have also established a new domain name: psephoanalytics.
The first (and long) step in moving towards agent-based modeling is the creation of the agents themselves. While fictional, they must represent reality – meaning they need to behave like actual people. The main issue in voter modeling, however, is that since voting is private we do not know how individuals behave, only collections of voters – and we do not want them all to behave the exact same way. That is why one of the key elements of our work is the ability to create meaningful differences among our agents – particularly when it comes to the likes of issue positions and political engagement.
As the next Federal General Election gets closer, we’re turning our analytical attention to how the election might play out in Toronto. The first step, of course, is to gather data on prior elections. So, we’ve updated our toVotes data package to include the results of the 2008 and 2011 federal elections for electoral districts in Toronto.
This dataset includes the votes received by each candidate in each district and poll in Toronto.
Turnout is often seen as (at least an easy) metric of the health of a democracy – as voting is a primary activity in civic engagement. However, turnout rates continue to decline across many jurisdictions[i]. This is certainly true in Canada and Ontario.
From the PsephoAnalytics perspective – namely, accurately predicting the results of elections (particularly when using an agent-based model (ABM) approach) – requires understanding what it is that drives the decision to vote at all, instead of simply staying home.
We value constructive feedback and continuous improvement, so we’ve taken a careful look at how our predictions held up for the recent mayoral election in Toronto.
The full analysis is here. The summary is that our estimates weren’t too bad on average: the distribution of errors is centered on zero (i.e., not biased) with a small standard error. But, on-average estimates are not sufficient for the types of prediction we would like to make.
The results are in, and our predictions performed reasonably well on average (we averaged 4% off per candidate). Ward by ward predictions were a little more mixed, though, with some wards being bang on (looking at Tory’s results), and some being way off – such as northern Scarborough and Etobicoke. (For what it’s worth, the polls were a ways off in this regard too.) This mostly comes down to our agents not being different enough from one another.
Our final predictions have John Tory winning the 2014 mayoral election in Toronto with a plurality 46% of the votes, followed by Doug Ford (29%) and Olivia Chow (25%). We also predict turnout of at least 49% across the city, but there are differences in turnout among each candidate’s supporters (with Tory’s supporters being the most likely to vote by a significant margin - which is why our results are more in his favour than recent polls).
As promised, here is a ward-by-ward breakdown of our final predictions for the 2014 mayoral election in Toronto. We have Tory garnering the most votes in 33 wards for sure, plus likely another 5 in close races. Six wards are “too close to call”, with three barely leaning to Tory (38, 39, and 40) and three barely leaning to Ford (8, 35, and 43). We’re not predicting Chow will win in any ward, but will come second in fourteen.
The first (and long) step in moving towards agent-based modeling is the creation of the agents themselves. While fictional, they must be representative of reality – meaning they need to behave like actual people might.
In developing a proof of concept of our simulation platform (which we’ll lay out in some detail soon), we’ve created 10,000 agents, drawn randomly from the 542 census tracts (CTs) that make up Toronto per the 2011 Census, proportional to the actual population by age and sex.
Our most recent analysis shows Tory still in the lead with 44% of the votes, followed by Doug Ford at 33% and Olivia Chow at 23%.
Our analytical approach allows us to take a closer, geographical look. Based on this, we see general support for Tory across the city, while Ford and Chow have more distinct areas of support.
This still based on our original macro-level analysis, but gives a good sense of where our agents support would be (on average) at a local level.
Given the caveats we outlined re: macro-level voting modeling, we’re moving on to a totally different approach. Using something called agent-based modeling (ABM), we’re hoping to move to a point where we can both predict elections, but also use the system to conduct studies on the effectiveness of various election models.
ABM can be defined simply as an individual-centric approach to model design, and has become widespread in multiple fields, from biology to economics.
Based on updated poll numbers (per Threehundredeight.com as of September 16) - where John Tory has a commanding lead - we’re predicting that the wards to watch in the upcoming Toronto mayoral election are clustered in two areas, surprisingly, traditional strongholds for Doug Ford and Olivia Chow.
The first set are Etobicoke North & Centre (wards 1-4), traditional Ford territory. The second are in the south-west portion of downtown, traditional NDP territory, specifically Parkdale-High Park, Davenport, Trinity-Spadina (x2), and Toronto Danforth (respectively wards 14, 18-20, and 30).
As with any analytical project, we invested significant time in obtaining and integrating data for our neighbourhood-level modeling. The Toronto Open Data portal provides detailed election results for the 2003, 2006, and 2010 elections, which is a great resource. But, they are saved as Excel files with a separate worksheet for each ward. This is not an ideal format for working with R.
We’ve taken the Excel files for the mayoral-race results and converted them into a data package for R called toVotes.
In our first paper, we describe the results of some initial modeling - at a neighbourhood level - of which candidates voters are likely to support in the 2014 Toronto mayoral race. All of our data is based upon publicly available sources.
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’).
Political campaigns have limited resources -–both time and financial - that should be spent on attracting voters that are more likely to support their candidates. Identifying these voters can be critical to the success of a candidate.
Given the privacy of voting and the lack of useful surveys, there are few options for identifying individual voter preferences:
Polling, which is large-scale, but does not identify individual voters Voter databases, which identify individual voters, but are typically very small scale In-depth analytical modeling, which is both large-scale and helps to ‘identify’ voters (at least at a neighbourhood level on average) The goal of PsephoAnalytics* is to model voting behaviour in order to accurately explain campaigns (starting with the 2014 Toronto mayoral race).
I recently participated in a panel discussion at the University of Toronto on the career transition from academic research to public service. I really enjoyed the discussion and there were many great questions from the audience. Here’s just a brief summary of some of the main points I tried to make about the differences between academics and public service.
The major difference I’ve experienced involves a trade-off between control and influence.
Our offices will be moving to this new space. I’m looking forward to actually working in a green building, in addition to developing green building policies.
The Jarvis Street project will set the benchmark for how the province manages its own building retrofits. The eight-month-old Green Energy Act requires Ontario government and broader public-sector buildings to meet a minimum LEED Silver standard – Leadership in Energy and Environmental Design. Jarvis Street will also be used to promote an internal culture of conservation, and to demonstrate the province’s commitment to technologically advanced workspaces that are accessible, flexible and that foster staff collaboration and creativity, Ms.