Skittles for days

This “Nursing Your Sweet Tooth” graphic makes a visual argument that we (Americans) are eating too much sugar by hyperbolically representing the amount of sugar the average American consumes over time with absurd physical objects (i.e. in a lifetime, this much).

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The presentation also gives information about the main sources of sugar in the American diet (focusing specifically on soda), and negative health outcomes associated with high intake. The graphic is on, so it seems likely that the intended audience is white, middle to upper class businessmen, who, interestingly, consume comparatively less sugar than lower-income black or Hispanic consumers.

The goal of the data presentation seems to be fear mongering: awaken people to the ill effects of their high dietary sugar consumption, and they will be so disgusted with their habits that they will never again touch a can of soda or a candy bar. The colors of the graphic – red, black, white and gray – and some of the typology evoke Coca-Cola, which helps drive home the point about soda’s contribution of sugar to the American diet. I think the graphic is effective in conveying a sense that the American diet is too high in added sugar, which is certainly true: 13% of calories in the American diet come from added sugars, which is significantly higher than the amount from every authoritative organization (USDA, WHO, etc). However, in terms of behavior change impact on the intended audience, the graphic seems relatively ineffective. First, the intended audience probably does not consume high levels of sugar, so it doesn’t make sense to target them as a population that needs to cut down on sugar intake. Second, some of the illustrations meant to shock the reader are over-exaggerated and nonsensical (i.e. in one lifetime we eat the amount of sugar in 1,767,900 Skittles), and end up conveying very little substantive information.

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Use of Non-medical Adderall

“College Students Aren’t The Only Ones Abusing Adderall”

This article focuses on non-medical Adderall use, a drug that is believed to frequent college campuses everywhere to keep students more focused while studying. But title implies that it isn’t only frequently used on college campuses like some articles in the news will lead you to believe. In fact, we learn that Adderall is used across many ages. This article is looking to inform a wide range of adults about who is actually using non-medical Adderall most frequently and how that compares to the perception that college student are the biggest abuser.


The first visual aid is a simple bar chart that compares different ages to the used of non-medical Adderall. But it also separates college students from their counterparts in the most common age bracket for college students, 18-22. While we might note that college students age 18-22 do have the highest percentage use of non-medical Adderall at over 14%, we can clearly tell that they are closely followed by 3 other groups that all have more than 10% use. Although this graphic is simple it is effective in showing the all-around use of this drug because it requires little additional explanation to understand the graph.


adderal-collegesLater in the article, there is a comparison to selectivity of the school to non-medical Adderall use, shown by the second visual aid. We can see a clear positive correlation, as the school gets more selective the percentage of students using increases. And a few schools are highlighted to show where they fall on the graph. This graphic is not very effective; the four selected schools seem as though they were chosen at random,  and I don’t think the graph accurately portrays what the rest of the article is trying to say. The graph alone would lead you to believe that the brightest of college students frequent Adderall most often to succeed, but from the reading we learn that it tends to be the students with lower GPAs at their respective college that abuse Adderall.

WSJ World News Data Viz Review

A Divided Libya Struggles Against Islamic State Attacks [Source]

In a recent Wall Street Journal piece about Libya, oil, and the Islamic state, there are two interesting data visualizations. They are both related to the same story, but I will address their merits individually. The first chart, Hot Spots, is very strong. However the story’s second chart is less than ideal.

Hot Spots

Screen Shot 2016-02-04 at 3.01.10 AMThe first visualization is of recent Islamic State attacks in Libya. This is a very strong chart in many ways. In broad-strokes, it displays the data very clearly and in simply. It adds to the reader’s understanding of the story. And it is visually pleasing without being distracting.

More specifically, the author has chosen the obvious way to show a set of locations: a 2D map. Each data point on the map, with is an attack site, is symbolically represented with a black dot surrounded by a red explosion-looking icon. The black dot shows the exact point of the site, while the red icon draws the eye towards the data.

The scale of the map is sufficient in allowing a reader to clearly distinguish the various data points. The light texture on the map’s surface gives some auxiliary information about the geographic terrain, but it does not distract or complicate things.

Since, on a global scale, the data points are relatively close together, the map is fairly zoomed in to northern Libya. However, the mini-map in the bottom left corner of the screen gives the reader context to what part of the region (northern Africa), the up-close map is focused on.

  Libyan Oil Squeeze

The second chart, which attempts to convey Libya’s hurting oil production and revenue, is much weaker.P1-BW243_LIBYAO_16U_20160203172108

oth data sets being displayed in this chart (Oil production and petroleum revenue) are quantities over time. In the left chart, time is (properly) plotted on the horizontal axis. However, in the right chart, time is plotted vertically, which adds confusion and makes it very difficult to quickly scan for trends over time.

I hypothesize that the author knew this chart was weak and did not convey a message alone, so the subtitle of the chart is a straightforward, plain text explanation of what the chart is actually trying to convey.

More than an outcome: Iowa Caucus 2016

From “Iowa Caucus Results” by Wilson Andrews, Matthew Bloch, Jeremy Bowers and Tom Giratikanon for The New York Times

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“Entire Nation Remembers Iowa Exists” is what I imagine headlines the front page of satirical news site The Onion following this week’s Iowa Presidential Caucus. As humorous as it sounds, the joke rings true for one main reason: most Americans probably don’t know much about their own state, much less a random one in 50.

But the data pulled from Iowa on February 1 is greater than winner and loser, and this hidden gem of value requires a grasp of Iowa that extends beyond what the average American knows or cares to Google search.

Questions like “Are most of Hillary’s Iowan supporters considered higher income?”, “Do Iowans in cities vote differently than those in rural areas?”, and “What does any of this mean?” are just a few examples of what the results could tell us, and what their answers offer is a greater understanding of what is happening in the 2016 Presidential Election.

The tricky mix of context, visualization, and clarity that this data demands is exactly what makes the New York Times “Iowa Caucus Results” so brilliant.

In sixteen images and succinct descriptions, the publication manages to illustrate the results of the Iowa Caucus with key factors in mind: population, income, religion (Republicans), age (Democrats), and comparison to previous presidential elections. From here, readers can see which candidates came out on top with these considerations in mind. Percentages give clear presentation, while circles corresponding in size to share of the total percentage and in color to the candidates illustrate the data in a visually appealing way. If the New York Times’s goal was to breakdown the Iowa Caucus results in a captivating, approachable and educational format, they succeed.

Of course, to the New York Times audience, this sort of analysis isn’t new– it’s expected. Their national audience consists mostly of well-educated, middle-aged, relatively well-off readers who can follow these sort of graphics with ease. However, the beauty of data is that with the right communication, it may challenge readers on their preconceived ideas. This presentation leaves the truth of the numbers clear. For example, as a college student witnessing the rise of Bernie Sanders across teenage social media, I expected that Bernie Sanders won college students by a landslide on Monday night, but the graphic clearly tells me this is not the case. If Bernie Sanders were my candidate of choice, this might prompt me to take up campaigning for Bernie Sanders among my fellow college students–to act. Even if he weren’t my candidate of choice, this realization begs analysis, thought and even more questions (“Why?” being one, for example).

Simply put, this presentation works.

Hopefully, we won’t forget Iowa before the 2020 caucus comes around. But hey–if we do, at least we can count on the New York Times to fill us in.

Cell Phones and the Bathroom

phones and bathroom

Cell Phones and the Bathroom is a poster that illustrates the unknown facts about people using their cell phones in the bathroom. The general audience of this poster is anyone who uses a cell phone on a daily basis. The point of this poster is to discourage people from using their phones while they’re in the restroom. It talks about how many people admit to using their phones while in the restroom, while implying that because of this fact, poop can be found on some phones. This poster is organized into two main sections. The top section discusses the three statistics that most people would probably find horrifying. These statistics are highlighted by using larger text and darker font color. The bottom section delves deeper into what people actual do when they use their phones in the restroom. Each activity listed is followed by the percent of people who say they have performed each activity while in the restroom.

I think the poster overall does a decent job at getting the point across since I immediately had the reaction the poster wanted. However, there was one point in the poster that initially made me confused. Similar to the example we discussed in class, the percentages in the bottom section of the poster are misleading. At first I thought the percentages were in reference to how much time is spent on each activity, not what percent of people has done the activity. I think the right choice was made with the fact to put on the top. Knowing that 1 in 6 phones have traces of poop on them is a shocking fact that will convince many people to not use their phones in the restroom.

Gasoline Affordability by Country

Bloomberg created a data presentation titled “The Real Cost of Filling Up: Gasoline Prices by Country,” which includes data on gas prices, gas consumption, and income. Each country is represented by a line ranked in three ways: the numeric gas price, the gas price as a fraction of a day’s wage, and the fraction of income spent on gasoline. This visualization is designed for Bloomberg readers interested in global economics and gas prices. This is reasonable, given that Bloomberg mainly deals with economics and business.

US Gas Prices India Gas Prices

The goal of this data presentation is to represent worldwide gas prices in a different light, which highlights the global differences in the affordability of gas. Simply comparing raw gas prices tells one story, but comparing the fraction of a day’s wage required to buy a gallon of gas tells another story. For example, according to the graphic, gas was $2.74/gallon in the US and $3.95/gallon in India. This makes it seem that gas in India is quite affordable. However, a gallon of gas costs less than 2% of a day’s wage in the US, while it costs almost 80% of a day’s wage in India. This comparison gives a drastically different picture.

I think that this visualization is somewhat effective, but can be better. I like that the visualization portrays multiple ways of comparing gas prices across countries. However, it is significantly lacking in that it takes quite a bit of time and effort to get the most out of this visualization. Since the labels for at most two countries can be highlighted at one time, a substantial amount of clicking around is required to compare more than two countries.

Global Carbon Dioxide Emissions: Causes and Solutions

Link to Data Presentation


Paulina Urbanska’s striking 2011 data visualization of the causes and solutions to carbon dioxide emissions is ambitious but imperfect. The represented datasets are clearly labeled on the top ‘axis’. There is a mix of quantitative and qualitative data for the causes and effects. These include CO2 emissions by continents/countries and proposed solutions accompanied by the potential carbon dioxide savings per inhabitant. This visualization is arguably intended for a typical educated adult civilian, as evidenced by the balance of mild technical jargon such as ‘photosynthesis’ and ‘acid rain’ with humbly practical solutions that include turning off lights at night.

By framing the issue on a per-inhabitant rather than global basis, the presentation is a personal call to action to reduce one’s CO2 footprint by following some of the solutions. Clear, bold visual cues such as line thickness and font size highlight continents and countries that produce a disproportionately high amount of emissions, as well as particularly impactful solutions. However, between the two colored flows, which are informative on their own, is a list of pollution effects that does not visually connect to either flow sub-visualization. A potential connection between them that could have been highlighted is the particular countries for which the solutions are most relevant. For example, the recommendation to reduce car speed would be more applicable to the car-dense United States than Algeria. As it stands, the two colored flows are not well linked story-wise, which impairs the effectiveness of an otherwise informative and visually compelling data presentation.

Divergence between Human State Assumption and Actual Aircraft System State

One of my Graduate Resident Tutors (GRTs), Sathya Silva, gave her thesis defense yesterday on the topic of divergence between human assumptions and actual state in aircraft. In short, the divergence framework she built examines how differences between pilots’ mental models of the state of the aircraft they are flying (i.e. throttle setting, landing gear position, etc.) and the actual state contribute to accidents and postulates mitigations for these divergence events. Her presentation (slides here) drew on a variety of sources, including previous research and aircraft incident reports. I’d like to focus on slide 33 in particular:


This slide comes after more detailed analysis of a number of incidents during flight and shows visually timing information for each incident in the divergence framework. The use of lines that split and come together again, with visual markers for recovery time, loss of control time, and impact times, highlight the extent of divergence in each case, any subsequent re-convergence, and the relative timing in events. One of the main takeaways from this aggregate view is that in many cases the difference between fatal and non-fatal incidents was not that convergence did not happen, but that it it happened without enough time left for a full recovery, proving insight into a possible mitigation strategy.

The audience for this defense was her thesis committee, as well as friends and family attending the defense. In addition to Sathya’s main goal of passing the defense, the presentation was meant to lay out the concepts behind divergence to provide evidence for the thesis; slide 33 in particular highlights patterns in these incidents in the context of the divergence framework. The presentation overall was effective in achieving its goals; the novel divergence visualizations provide at-a-glance comprehension in the divergence framework and demonstrate its effectiveness in highlight patterns. The thesis committee agreed that this was a great presentation – congratulations Dr. Silva on passing your thesis defense!

Why do we have more boys than girls?

Why do we have more boys than girls? is a YouTube video created by Minute Physics. The general audience for this YouTube channel is curious internet browsers who have a few minutes on their hands to learn a little bit more about how the world works. In this particular video, they explain why there are more boys born than girls.

One of the data points they explore is the genetic odds that a child is born as a boy or girl. This is demonstrated in the screenshot captured below. They depict a mother and father tossing each tossing a coin with an X or Y chromosome into the air. The coins meet and combine into either XX or XY and fill their respective sides of the scale with either male or female babies.

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The use of coins emphasizes the point that biologically, there is a 50% chance of a parent donating one of their two chromosomes. The scale further drives home the point that at the end of the process, there should be an equal ratio of boys to girls.

The general style of the video is a simple, well described animation that walks the viewer through a “proof” of how the theoretical ratio of 1:1 boys to girls being born is not true. This method is very well tailored to the target audience as it does not assume much technical knowledge and does not take more than 3 minutes to explain.

Paul Ryan vs. other Speakers of the House

Explore the visualization here!

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Bloomberg Politics put out a beautiful data visualization in light of the recent election of Paul Ryan to Speaker of the House. While their chosen title highlights the recent trend of Speakers no longer working for the government after the gig, there is actually a treasure trove of information to explore here. All past Speakers are listed, and their involvement in various government positions are represented in a timeline. There are also several options at the top to highlight each position, with various callouts providing more facts.

Continue reading Paul Ryan vs. other Speakers of the House