Technicolor Hyper-kinetic Campaign Finance Explosion!


The Louisiana Senate race draws on money from across the nation. The animation above is looking at individual donations – donations that can be tied directly to a particular human (rather than some committee) and shows how, over time, both Landrieu and Cassidy have seen their in-state share of donations shrink. Cassidy ends up with 58% of his donations coming from Louisiana, but if we look at breakdowns by quarter, we find that in the most recent quarter, Cassidy received more out-of-state dollars (as a percent of his total) than Landrieu did.

Click on any graphic to get it full-screen — they look *much* better that way.

Percent from Louisiana Line Chart

Before you become completely offended by so much outside money flowing into Louisiana, it’s important to have a benchmark. Hence, we take a look at Kentucky, where *both* Senate candidates are running at roughly 20% in-state donor levels. Let me know which design you prefer, by the way – I like the Louisiana animation better. It’s more recent, and I think I improved it with time. I’d like to know what everyone else thinks.kengif2







An overall comparison of district 6 congressional candidates — this is pure horse-race coverage stuff, trying to show lead changes in fundraising over time:


Now, let’s kick up the epilepsy-inducing intensity! Here are animations of District 6 house race candidates, and their in-state percentages of donations over time:

edwin-edwards-animated-gif-FIXED       new-animated-gif-graves

new-animated-gif-felder   new-animated-gif-trey-thomas

new animated gif claitor




And, finally, we look at the same candidates, only *this* time we examine their in-district versus out-of-district fundraising. Be sure and check out Edwin Edwards and how he changes once you look at district rather than state.

district-animated-gif---charles-thomas  district-animated-gif---edwin-edwards


The prescription drug epidemic in America

Whether due to pill mills or insatiable appetite, prescription drug use has soared in the United States. At the same time, drug overdoses and treatment admissions for heroin users who got their start using other opiates (like prescription opiates) have also spiked. Using six datasets, I’ve tried to give a glimpse into the problem.

Not all of these datasets are created equal. The drug seizure data, in particular, is problematic — as it’s culled from news reports. That sort of data is, at best, anecdotal. Frankly, I see the greatest value of that set as a way to compare the volume of reports generated for different drugs. In other words, it helps to show just how much attention various substances are receiving in the news.

I like this project, and I think it sheds light on a problem that is receiving plenty of attention, but little objective examination. Hard data on prescription drug abuse is very hard to come by. I know this – because putting this project together was one of the most challenging things I’ve ever done.

A Viz about Vizzes

I’ve committed to Tableau as my primary tool, and I’d like to think I’ve learned how to use it, but just how much progress have I objectively made?

I figured I’d use Tableau and find out.


To me, the thing that jumps out is the audience lag. My “production mountain” shows all the work I’ve done, in terms of the cumulative number of worksheets and dashboards I’ve designed in Tableau over time. Cumulative views, on the other hand, shows how many views I’ve gotten based on the date I published various visualizations. I hit 100 total sheets in October of 2013, yet I didn’t hit my first chunk of views until late January, 2014 – approximately a 3 month delay. Apparently, I needed practice before I properly figured out how to publish tools worth eyeballs.

I also have a bubble map showing my most popular visualizations.This one is colored by time, using a heat map scheme – so the most recent visualizations will have a more brick-red color with older visualizations fading out toward blue.

I already mentioned my two area charts – Production Mountain and Cumulative Views Over Time. These are both running totals, one showing how many sheets I’ve designed in Tableau (individual sheets inside projects I’ve published), and the other showing how many views my visualizations have garnered.

The Monarchist of LSU

One hears about liberal bias in education on a regular basis – but is it true? Just how liberal *is* LSU?

ImageNot very, as it turns out.

After running every name from the Reveille’s database of faculty and staff to retrieve their political party affiliations, I found that the LSU faculty is actually about 10 percentage points less Democrat-leaning than the surrounding Parish. The data says that LSU is no hotbed of liberalism – and it also showed we had a Monarchist. That one came as a surprise.

Working with Fernanda Zamudio-Suarez, my favorite reporter at the Reveille, we put together a great package showcasing the results and the implications of those results. Fern even knew the Monarchist – Faculty Senate President Kevin Cope – and got him to open up about his party affiliation.


The data was a blast, and it showed all sorts of fascinating things. The most liberal department on campus, unsurprisingly, was Music & Dramatic Arts, where 71.79 percent of the faculty & staff are registered Democrats (of those I was able to retrieve party affiliation for). The least liberal department, was UC Advising & Counseling, where 20 percent of the staff are Democrats, and 46.67 percent are Republicans.


To me, this is the perfect example of how data journalism should be done. We took a dataset we owned – the salary database for the university – and leveraged it into a brand new story, and a brand new insight, simply by running it through a new source of information. Data is all about recombinant information – the mutational evolution of your understanding of the world.

Plus, we found a Monarchist. I don’t think that’s ever going to get old.

Now, I just need to find a new, fun data project to feed Fern – I’m sure she’s disappointed I haven’t kicked anything good her way in quite some time.

One final addendum – after publishing, a friend of a friend asked about what would happen if we had zeroed in on the faculty alone – rather than faculty & staff together. I can’t do that breakdown perfectly, but I can approach it, and when I do it shifts the numbers by a bit less than 3% toward Democrat affiliation. The new numbers, if I’m trying to just grab them for faculty without staff, run 40.77% Democrat, 25.47% Republican, and 33.03% Independent. Don’t let the apparent precision of those percentages fool you — they’re my best attempt at faculty isolation, but they’re not perfect. Even these isolated numbers, however, fail to achieve a strong liberal bias. If you want strong bias, your best bet remains looking at different departments. Music & Dramatic arts, I’m looking at  you.

Special thanks to professor Rosanne Scholl for pointing out that whatever my personal feelings on the matter, the major political parties remain proper nouns and deserving of capitalization. Thanks as well go out to Barbara Clark, who asked the question I failed to consider: How do the numbers change if you look at faculty separate from staff?

Seminoles Most Overhyped CFB Team since 2000

over- and underrated teams by decade

As college football wraps up with all its bowl-ey goodness, it seemed a good time to share my all-time overrated and underrated tool.


The tool shows that for the “decade” since 2000, Florida State has been dramatically overrated – at least by this measure.

Feel free to explore. Here are some tool options:

  • Click on any of the teams in the list to pull up their detailed results, and see *how* they got their score.
  • You can also click on the drop-down to see a team’s results.
  • Select a different decade – or look at the all-time results by choosing “all” decades.
  • Sensitivity helps choose how detailed the ranking list is. Adjust it to fit your tolerance for list crowding.

The Electoral Boogieman: Voter Suppression Dirty Tricks

Lexis/nexis search results for "voter suppression", graphed over time.

Lexis/nexis search results for “voter suppression”, graphed over time.

I set out to look into voter suppression in terms of political communication, and ended up finding something a bit odd — while systematic disenfranchisement is very real, the kind of “I can’t believe they did that!” suppression we hear about in the news appears to be largely a made-up problem. Something which a handful of amateur would-be election riggers engage in, and which professional communicators then seize upon as a resource for fundraising and partisan mobilization.

My storify: http://storify.com/jwkendall/keeping-out-the-vote

How do we know? A simple Google search. When you hunt for “voter suppression” and, say, “flyers”, you get a handful of results, and most of what you find is a lot like the three examples shown here: http://www.solarbus.org/stealyourelection/voter-suppression-flyers.html

Quite honestly, those all seem like the sorts of things some bored, pissed-off crackpot would come up with on a Sunday afternoon. What’s more, I suspect their largest impact on elections comes not through vote suppression, but through the indignation such efforts inspire. Anger is a valuable commodity in the polarized political world.

Turkeys Over the Years

turkeyAs we sit down to feast and avoid picking a fight with Uncle Pete, why not take a moment to reflect on all the great turkeys to go before? America hasn’t always eaten so much turkey, but after a mad dash to the top, the bird has apparently hit some sort of glass ceiling… Or glass coop, at least. Domestic consumption has leveled off, and only increases in exports appear to show much hope for turkey sales to really take flight.