What Impacts Maternal Mortality?

It’s been a while since my last post—so sorry about that! I have been working on this post for a few days, and it was prompted by a segment I saw on GMA recently about Christy Turlington-Burns’ efforts to bring awareness to maternal mortality throughout the world through her organization, Every Mother Counts. It really hit home with me—if it weren’t for great medical care, my son and I most likely would not be alive right now. (We’re so grateful!) Her work really resonated with me, and so I wanted to share it with you.

So, here’s my agenda for this post, and a few more to follow it: I want to show how easy it is to use data visualization to tell a compelling story about how cultural circumstances can have major impacts on the lives of women and children around the world. I was teaching a class last week, and when I showed a draft of this, people frowned. It’s sad stuff! No doubt about it. But it’s very real.

The first person I talked to about this was my mom. (Hi, Mom!) She’s a nurse practitioner, and she has worked in some of the very remote areas in the map below that have abysmal rates of preventable maternal deaths. I asked her—what do you think the causes are? Without hesitation, she said, “Teenagers giving birth, and the lack of skilled help during births.” That makes a lot of sense to me.

So, I got a hold of the most recent World Bank Indicators, a version of which ships with Tableau, and then spent an eon transforming it in SQL Server so that I could load only the most recent numbers for each country for the metrics in question. (More on that tomorrow!) It’s a very rich data source, and it includes economic measures that, along with literacy and health data, describes some of the living conditions in a country fairly well.

My first question is which areas of the world have higher instances of maternal death. I started with a familiar map—it’s a great way of showing disparities across the world. The countries are ranked in descending order by the likelihood that a woman will die in or after childbirth—countries with high ranks (like #1, South Sudan) are really bad places to be pregnant. (The US is in the middle…below several former Eastern Bloc countries, which is a surprise.) My friend Nelson Davis @nelsondavis recent blogged about the relationship between life expectancy and war—there have been several notable genocides and civil wars in Sub-Saharan Africa, and consequently, they are not places one should expect to live very long or in good health.

The countries are colored by percentiles (great new table calc in Tableau 8.2, along with rank) of maternal mortality rates. When you click on a country, the scatter plots below, which show correlations between the percentage of maternal deaths that are preventable and other public health measures, will highlight. The area map of our aid to those countries also filters.

The scatter plots are significant, and they prove numerically what my mother told me about the correlations between teenaged pregnancies, unattended births, and maternal mortality. I added in literacy rate—notice that it’s trend line is nearly identical to that of unattended births, though the median is a little bit lower. The relationship between percent of GDP spend on health is less significant, though the clustering is obvious—there are some outliers that I would question, like Liberia and Sierra Leone in the upper right—especially what we know about the quick spread of Ebola there recently.

Talk to me about your thoughts on this and what you think I should add in the future.

Join our Online Tableau Viz Contest!

What is an online viz contest? It’s an opportunity for you to share your work with a large community of interested people.

It’s easy to play:

  1. Go to Github (https://github.com/aohmann/Tampa-Bay-TUG) and download the data source. There actually are two, in this case—one on volcanic eruptions, and one on earthquakes.
  2. Play with the data—and join other data sources to it. Did you know that the World Bank (and several other NGOs, like USAID) have tons of data? So does the EPA—emissions data might be fun to add.
  3. Think of awesome ways to spend the $200 gift card that you could win—or, we can donate it to charity on your behalf.
  4. Use Tableau Public to visualize something interesting, something that most of us probably don’t realize but is nonetheless important.
  5. Publish your workbook in Tableau Public before noon on 9/23, and tweet the URL to @ashleyswain with the hashtag, #TampaBayTUG
  6. Register for the Tampa Bay Tableau User Group meet-up at http://www.tableausoftware.com/learn/usergroups/tampa-bay-user-group/09-23-2014. (We need your name and email so that we can track your visualization—and the votes that you get!)
  7. We’ll send you the WebEx if you can’t be there in person. (If you’re there in person, you can enjoy snacks and camaraderie.)
  8. At 5pm EDT on 9/23, login to the WebEx. Check out Jen Underwood’s presentation, and then around 5:45, we’ll give everyone who entered the contest five minutes to present. (So when you login to the WebEx, be sure that your name matches the name you used to register—otherwise we won’t know who you are…)
  9. When everyone has shown their visualizations, we’ll distribute the URL that people can use to vote (yes, you can vote for yourself…)
  10. Hang around for the next presentation, and we’ll present the winners at the end.
  11. Have fun!

Game of Thrones, in Tableau’s Story Points

I know that Game of Thrones Season 4 ended quite a while ago, but I have presented this Story Points (dashboard? story?) a couple of times to different user groups and wanted to post it to my blog for others.

I collected tweets with #GameOfThrones, #GoT, and #GoT Season4 through ScraperWiki, which no longer offers this service, for several months in 2014. You’ll notice that the tweet volume is wildly inconsistent; this is both my fault and ScraperWiki’s. Twitter rate-limited their searches for some weeks, so I am missing a fair amount of tweets. (What better prompt for me to start using the Twitter API?)

Another reason I am missing tweets actually is something I admonish people about when I am training them in Tableau: case sensitivity! Turns out that hashtags are case-sensitive, too. While I searched for #GameOfThrones, I did not search for #Gameofthrones. (Tableau Public limits me to 1mm records, so it probably would not change much in this viz.) And in the Top 5 Hashtags list for each episode, I filtered out the hashtags for which I searched, because that would be redundant, and I normalized the tweets by using UPPER. (You should avoid showing members of a dimension in all upper-case in a visualization, if you can—it looks angry and makes people think that your MDM folks are lame.)

This data set is a good candidate for Story Points in Tableau because it is sequenced over time, and there are many opportunities to comment on the causation of the fluctuation in tweets. For instance, when Mark Gattis joined the cast as a very minor character, there was an unusual spike that had nothing to do with HBO, but rather with his popularity as Mycroft Holmes, on the BBC’s epic hit, “Sherlock”. (Maybe a good topic for using the Twitter API?)

There are some things I like about Story Points—it allows me to guide the user’s navigation very carefully, and it looks great. It does require analysts to think about what their audience really needs to take away from an analysis, which is part of the vocation of data analysis that often gets lost.

I don’t like the inability to modify the appearance of the Story Points controllers, and it doesn’t write changes back to the dashboard or visualization in use. I actually did not need to use it for this dashboard, because the horizontal bar of episode numbers serves as a filter, too. Building a dashboard also was more efficient for me, probably because I have done it so many times. I’m curious what you all think about Story Points.

I’m going to use the dashboard I built previously from this data set, and not Story Points, for my upcoming client demo. Story Points is good for presentations, but it’s not an enterprise analytics tool.

(I did redact some of the spoilers in this Story, for those of you who haven’t seen Season 4–like my husband, who has been ultra-helpful setting up this blog :))