With more than 1,100 candidates, 507 races, top-line campaign finance data and poll averages for select races, the 2014 midterm elections forecast app we launched in early September is probably the most complex mash-up of data, APIs and home-grown content built yet by our Interactive Applications team at Gannett Digital.
We’re happy with the results — even more because the app is live not only at USA TODAY’s desktop and mobile websites but across Gannett. With the rollout of a company-wide web framework this year, we’re able to publish simultaneously to sites ranging from the Indianapolis Star to my alma mater, The Poughkeepsie Journal.
What’s in the forecast? Every U.S. House and Senate race plus the 36 gubernatorial races up in November with bios, photos, total receipts and current poll averages. For each race, USA TODAY’s politics team weighed in on a forecast for how it will likely swing in November. Check out the Iowa Senate for an example of a race detail page.
Finally, depending on whether you open the app with a desktop, tablet or phone, you’ll get a version specifically designed for that device. Mobile-first was our guiding principle.
Building the backend
This was a complex project with heavy lifts both on design/development and data/backend coding. As usual, I handled the data/server side for our team with assists from Sarah Frostenson.
As source data, I used three APIs plus home-grown content:
— The Project Vote Smart API supplies all the candidate names, party affiliations and professional, educational and political experience. Most of the photos are via Vote Smart, though we supplemented where missing.
— The Sunlight Foundation’s Realtime Influence Explorer API supplies total receipts for House and Senate candidates via the Federal Election Commission.
— From Real Clear Politics, we’re fetching polling averages and projections for the House (USAT’s politics team is providing governor and Senate projections).
The route from APIs to the JSON files that fuel the Backbone.js-powered app goes something like this:
- Python scrapers fetch data into Postgres, running on an Amazon EC2 Linux box.
- A basic Django app lets the USAT politics team write race summaries, projections and other text. Postgres is the DB here also.
- Python scripts query Postgres and spits out the JSON files, combining all the data for various views.
- We upload those files to a cached file server, so we’re never dynamically hitting a database.
Meanwhile, at the front
Front-end work was a mix of data-viz and app framework lifting. For the maps and balance-of-power bars, Maureen Linke (now at AP) and Amanda Kirby used D3.js. Getting data viz to work well across mobile and desktop is a chore, and Amanda in particular spent a chunk of time getting the polling and campaign finance bar charts to flex well across platforms.
For the app itself, Jon Dang and Rob Berthold — working from a design by Kristin DeRamus — used Backbone.js for URL routing and views. Rob also wrote a custom search tool to let readers quickly find candidates. Everything then was loaded into a basic template in our company CMS.
This one featured a lot of moving parts, and anyone who’s done elections knows there always are the edge cases that make life interesting. In the end, though. I’m proud of what we pulled off — and really happy to serve readers valuable info to help them decide at the polls in November.