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Predicting Elections Tutorial!

I’m teaching a workshop at Penn’s Master’s of Urban Spatial Analytics on April 22nd.

I’ve posted all of the materials on github, including RMarkdown walkthroughs. Want to learn how I predicted the 2018 election and only made one bad mistake? Check it out!

Note: As an early tester, by reading the posts you commit to sending me feedback. Preferably before April 22nd. thx.

Post 1: The Relational Database. How I’ve organized the election data.

Post 2: Geographies. How I crosswalked geographies across moving boundaries.

Post 3: Creating the rectangular data.frame. Final steps to get ready to model.

Post 4: Predicting the election. The good stuff! (You can skip the others. This is what I’ll be teaching.)

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The Turnout Tracker is open sourced. And going to Chicago!

The Turnout Tracker: An Introduction

The turnout tracker is a citizen science tool to track election turnout in real time.

In May 2017, I noticed that Philadelphians had organically started sharing on social media where and when they voted, and what number voter they were at their precinct.

I thought “Wow, all that needs is a statistical model to know what turnout is across the city.” So I built it.

We’re going to Chicago

Chicagoans, I need your help! The municipal runoff elections are April 2nd. Let’s track turnout together.

Before Election Day

  • Tell your friends! Share this post!

On Election Day

Open Sourcing the Turnout Tracker

I’m also sharing the code behind the Turnout Tracker with the world. I’ve cleaned it up some, but better engineering and documentation is a work in progress.

Check out the repository at https://github.com/jtannen/turnout_tracker

Programmer/Data Scientist? The codebase may not be fully self-serve yet. So if you want to bring the Turnout Tracker to your city, get in touch. jonathan (dot) tannen (at) gmail (dot) com

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