So I pulled some data from wisdc.org detailing private donations to both the Scott Walker and Tom Barrett camps in the Wisconsin Gubernatorial election of 2010. Given the recent brew-ha-ha in Wisconsin (read absolute bullshit), I thought it might be worth revisiting with new tools to see if there is anything really interesting tucked away in this public data.
The most interesting insight to be had so far is the specific employers of contributors popping up to the top of the heap. The first look aggregates the data at a listed employer grain and plots the total number of contributions(x-axis) by contribution amount (y-axis). By reversing the x-axis and plotting on a logarithmic scale, the data spreads out and reads from left to right as <more contributions> to <less contributions>. Note that the blue points represent contributions to Democratic candidate Tom Barrett, and the orange points represent contributions to our current governor, Scott Walker.
With all of the plots presented here, click on the graphics to open an interactive data viz.
The points that jump out at me are the larger contributions (higher on the y-axis) and especially the higher contributions that come from a smaller number of contributions (farther to the right – reverse axis).
Click on the image to bring up an interactive data viz powered by Tableau. Hovering over a data point will provide more details including the specific numbers and the data label. Additionally you can control the data selected by interacting with the filter check boxes on the right.
My very first post highlighted significant contributions from Miller, Johnsonville, and Sargento (Beer, Brats and Cheese).
All kidding aside, the Johnsonville point ( ~$37k from 8 contributions) sticks out as big dollars from just a few donors, meaning significant corporate interest. I’m not suggesting that anything is amiss, but holding my personal objections to what’s happened around Wisconsin in 2011 has me thinking that maybe I should start buying Klement’s instead.
Other notable points for Walker un-enthusiasts:
M&I Bank, Aurora Health Care Systems, Hy Cite Corp, Wausau Homes, Miller Brewing, Sargento Inc, Northwestern Mutual Life.
Another interesting plot looks at the same data, grouped at a categorical interest instead of the donor’s employers. This type of categorical interest has been covered in several media outlets, however I like the scatter plot approach to understand the relationship between the contributions and how many people are making these contributions. Again a logarithmic reversed x-axis was used to display the number of contributions.
By far the most noticeable point here is $1.4M contributed to Barrett by interest groups identified as “Lawyers/Law Firms/Lobbyists”. After that its all Walker with heavy interests from Manufacturers, Construction, and Banking.
Perhaps you would like to know more information about how each candidate faired by interest category. The following chart will allow you to compare both candidates total contribution amount, total # of contributions, and average contribution amount by categorical interest.
Here you can see how different each candidates contributions are in each category, sorted by total contributions. The thickness of the bars indicate how large the average contribution in each category is. That information is also displayed on the far right hand side group of bars. Again there’s nothing really new here, but it is a cute way to compare the differences.
Lastly geting to play with these new tools in Tableau, I created a map geographically showing where the money is coming from. The counties are filled by Median Household Income and the size of the data points indicates the total amount of contributions by each candidate.
There isn’t anything really surprising here (at least not that I found). Donor zip code was used to geocode the data. However, I wish there was a way to wiggle the coordinates so that data points in the same zip wouldn’t always lay over the top the of a different data point (ie, Milwaukee and Madison).
"Where's the money?"
As it stands, the plotting will always stick Barrett on top of Walker, so if Barrett won the area, you can’t see what Walker collected. However, there are filters at the right that you can use toggle the specific candidate’s data points.
Predictably, the money follows population density and median household income. Walker did better in areas surround Milwaukee. Barret did much better than Walker in Dane County.
I’m admittedly a novice at Tableau. If you have any tips or questions I would welcome them both in the comments.