Here’s some “behind the scenes” geospatial analysis I used in a recent @PsephoAnalytics post. This was a good excuse to experiment with the sf #rstats 📦 which makes this much easier than my old workflows

Spatial analysis of votes in Toronto

This is a “behind the scenes” elaboration of the geospatial analysis in our recent post on evaluating our predictions for the 2018 mayoral election in Toronto. This was my first, serious use of the new sf package for geospatial analysis. I found the package much easier to use than some of my previous workflows for this sort of analysis, especially given its integration with the tidyverse. We start by downloading the shapefile for voting locations from the City of Toronto’s Open Data portal and reading it with the read_sf function.

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Our predictions for the recent election in Toronto held up well. We were within 6%, on average, with a slight bias towards overestimating Keesmaat’s support. Now we’ll add more demographic richness to our agents and reduce the geographical distribution of errors www.psephoanalytics.ca/2018/11/r…

Reviewing our 2018 Mayoral race predictions

Our predictions for the 2018 mayoral race in Toronto were generated by our new agent-based model that used demographic characteristics and results of previous elections. Now that the final results are available, we can see how our predictions performed at the census tract level. For this analysis, we restrict the comparison to just Tory and Keesmaat, as they were the only two major candidates and the only two for which we estimated vote share.

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4th Axe Pancreatic Cancer fundraiser

Thanks to generous support, the 4th Axe Pancreatic Cancer fundraiser was a great success. We raised over $32K this year and all funds support the PancOne Network. So far, we’ve raised close to $120K in honour of my Mom. Thanks to everyone that has supported this important cause!

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A new approach to predicting elections: Agent-based modeling

It’s been a while since we last posted – largely for personal reasons, but also because we wanted to take some time to completely retool our approach to modeling elections. In the past, we’ve tried a number of statistical approaches. Because every election is quite different to its predecessors, this proved unsatisfactory – there are simply too many things that change which can’t be effectively measured in a top-down view. Top-down approaches ultimately treat people as averages.

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