Data Visualization: Assignment #3

Part 1: Slope Chart of Inequality in Income (%)

For our assignment this week, I decided to create a slope chart based on income inequality among five countries: South Africa, China, United States, Hungary, and France. I chose them not only based on my interest in learning which had the greatest amount and greatest change from 2010–2018, but because some of them are where my best friends are currently scattered among the globe.

The two that came as the biggest surprises to me were China and the U.S. — China because I truly hadn’t anticipated a decrease among any of these places (unfortunate, but honest) especially in a population that large, and the U.S. because well, look at how wealth is distributed here. I imagined the initial number to be much larger at least.

Part 2: Hollywood and Gender Graphic Thoughts

From an overall standpoint, I really appreciate all this graphic is aiming to accomplish, but it feels like a whole lot of story vignettes and very little context thrown into one general headline.

As I briefly touched on in class, one of my favorite parts of the story is the graphs that offer an interactive, engaging element, such as the “Films that Fail the Bechdel Test and the Creators’ Gender” one. This element in particular allows users to set their own restraints for the data to display, which to me felt like a really innovative way to think about transforming data. While I know data is far more than numbers on an x and y axis, it felt very creative to frame data around the user’s wants and curiosity. I’m not sure how one even goes about doing that, but I’d like to learn how — and utilize it.

The two parts that were most diffciult for me were the “Percent of Films that Fail the Bechdel Test, Based on Gender Composition of Writers, Producers, and Directors” and “Studios and the Percent of Films that Fail the Bechdel Test” graphs.

Even as a super visual learner, my eyes were going up and down and left and right and upside down. I think one alteration that could benefit these visualizations is separation of color. The use of red and green is very obviously a nod to positive and negative, and to see those separately, as opposed to as one, would really further that separation visually.

The part that GOT me though was “Bechdel Test Results for Notable Producers, Directors, and Writers” — in which Weinstein was lsited at the top (the most “positive”), when in context and really what this story seems to be pushing the idea for, could not be further from the truth. The sentence below it “It’s important to call out the studios . . .” sort of feels like salt in an open wound, too.

Part 3: Midterm Data

Voting by mail —

Based on my last post, I am really interested in the very timely topic of voting by mail, in all different facets. The first dataset I found was based on party divisions on in-person voting: https://www.statista.com/statistics/1134328/share-voters-rather-vote-by-mail-us-election-party/ — which did not feel surprising, based on the current President’s party alignment and his very clear opinions on voting by mail.

As we’re in the midst of an Earth shaking pandemic (and of course, it had to be during an election year because such is life), there’s been a load of talk around voting by mail — that the Postal Service doesn’t have the bandwidth, that voting fraud will soar and the list will go on.

So based on that and Trump’s claims that the Postal Service can’t handle voting by mail, I did a little digging just to get more info on how much mail the U.S.P.S. actually handles a day — 181.9 million pieces of mail, compared to UPS’s 29.9 million and FedEx’s average of more than 16 million.

(sources: https://facts.usps.com/one-day/, https://pressroom.ups.com/pressroom/ContentDetailsViewer.page?ConceptType=FactSheets&id=1426321563187-193, https://www.fedex.com/en-us/about/company-structure.html — respectively)

So in context, quite simply, the Postal Service surpasses two of the largest privately owned mail services by a long, long shot.

While I’m certainly no voting or mail expert, the data I’ve found thus far indicates the following:

In the 2018 primaries and general elections, the U.S.P.S. processed more than 1.2 billion election and political pieces of mail — all of which was done at a 96 percent service performance score. (https://www.uspsoig.gov/sites/default/files/document-library-files/2019/19XG010NO000.pdf)

Voter fraud is still a thing, though. According to figures from The Heritage Foundation (which does not claims to be an entirely comprehensive list, but includes the instances that have been formally prosecuted), there are 1,298 proven instances of voter fraud. However, not all of these are exclusively related to mail-in voting — so does fraud really pose that monumental of a threat, when we look at just how much election related mail the U.S.P.S. actually processes? (https://www.heritage.org/voterfraud/search)

A lot more thoughts on this, and some further links for me to keep in my back pocket: https://trends.google.com/trends/explore?q=save%20the%20usps&geo=US, https://electionlab.mit.edu/research/voting-mail-and-absentee-voting).

(I’m not sure I did this idea 100% correct — a lot of this was partially sifted through data, and the raw data sets weren’t always available, but the below idea I think follows it a lot better. I just went down a bit of a rabbit hole and got excited, to be honest.)

And now . . . an entirely different idea!

I didn’t mention this in my previous post, but one of my favorite groups for anything photo and visual related is Women Photograph, whose mission is to “elevate the voices of women + non-binary visual storytellers,” in a news landscape that is visually dominated by men.

Something I’ve recently discovered is that they actually keep track of the A-1 bylines, in terms of which images were and weren’t created by women, as well as a database of the year in photos.

(https://daniella-zalcman-en45.squarespace.com/data, https://docs.google.com/spreadsheets/d/1Wu6_s2SzB1v5wXeaeVmfxUEs1rG0iGZlJjsu-Lo87Dw/edit#gid=0)

Much like the Thrasher example we were shown in class, I’d love to analyze the photos that were (and weren’t) taken by women, and then do a deeper dive into the photo teams at the listed outlets in the data base to actually analyze what the employee makeup is in term of gender.

As an aspiring visual, multimedia journalist, looking at who controls the visuals in some of the most prominent news orgs is really fascinating (and so frustrating) to me.