I recently came across a detailed analysis by Todd W. Schneider of 1.1 billion New York city cab rides that occurred from 2009– 2015. In addition to this massive dataset provided by the New York City Taxi & Limousine Commission, Schneider also incorporated a public dataset of 19 million Uber rides from April– September 2014 and January– June 2015.
This analysis appears to be intended for New Yorkers, as a lot of the analysis and visualizations assume the audience has basic geographic familiarity with NYC and an understanding of New York lingo and culture. While this presentation would definitely still be interesting for those interested in transportation and unfamiliar with NYC, Schneider focuses less on general transportation analysis (eg. average fare or trip time) and more on New York specific analysis (eg. which neighborhoods are up late and taxi trips taken from Goldman Sachs).
Schneider appears to have multiple goals in this presentation. One is to comprehensively explore a wide variety of questions in this data set. He includes numerous graphs and figures, each addressing a different aspect of the dataset, but it’s almost overwhelming how many figures are presented. Though data junkies would enjoy the comprehensive nature of this presentation, I think most readers will get overwhelmed by sheer number of graphs. In addition, in my opinion, the large number of figures buries some of the most interesting aspects of the data, reducing the efficacy of his analysis. For instance, about halfway down his (long) post, Schneider has a simple bar graph showing that rainstorms don’t appear to affect daily ridership. This was the most surprising conclusion for me personally, as the common thought is that taxis are impossible to get during rainstorms, so the fact that it’s hidden halfway down his post is disappointing.
Intentionally or not, he also appears to be advocating for usage of public transit over taxis in parts of his analysis. In the section dedicated to airports, he concludes that “depending on the time of day and how close you are to a subway stop, your expected travel time might be better on public transit than in a cab, and you could save a bunch of money.” As New Yorkers can then customize the visualizations to show expected travel time to the airports from their own neighborhood, I think this part of the presentation is very effective. It’s much more powerful and relatable to show viewers time averages of taxi trips from their own neighbourhood rather than averages across the whole city, making this section of analysis one of the most powerful in the whole presentation.
The “Our World In Data” website provides a visualization of global energy production over the past two centuries. The data is broken down into specific energy sources and shows how their composition has changed over the years as the primary energy source transitioned from “Biofuels” in the 19th century to “Crude Oil” for much of the 20th century.
This chart shows how our energy production has changed over the last century and shows the gradual adoption of renewable energy sources in recent decades. This visualization is effective in the sense that it presents the data in a clear, understandable form.
In the expanded mode, viewers can see what percentage of the global energy production comes from each energy source, although this view does hide the awe-inspiring near-exponential growth in energy consumption over time. It shows the slight dip in nuclear energy more clearly than in the previous view and makes viewers wonder if this trend is likely to continue – many nuclear reactors have been shut down in recent years due in part to safety concerns and cheaper natural gas.
This graphic appears to be intended for the general public and does not advocate for any particular energy policy. The goal seems to be to simply present the raw data to a wide audience, allowing the viewers to draw their own conclusions regarding what the future for energy sources will look like.
Given my interest in nutrition, I often come across data presentations that address Americans’ consumption of various sorts of food, many of which focus on sugar. This infographic, titled “Nursing Your Sweet Tooth,” includes multiple data presentations that display information about Americans’ sugar consumption. Given the shocking nature of some of the images in the infographic, its intended audience appears to be Americans who either are not aware of the incredible quantity of sugar consumed in the US and the associated health effects or don’t really care about these issues. The shocking images – such as one showing a mouth eating ten strips of bacon, the caloric equivalent of the average American’s daily sugar consumption – are probably intended to persuade people who are not particularly health-conscious that consuming lots of sugar is unhealthy and that they should cut back on their sugar intake.
The infographic shows a variety of data, including how much sugar the average American consumes each year and every five days; how many 12-ounce sodas that equates to; how much sugar people should be consuming compared to what they actually consume; what kinds of foods provide Americans’ with most of their sugar intake; and how many calories from sugar the average American consumes daily. As I said before, the shocking nature of some of the images strikes me as very convincing, although they may turn off some people who don’t want to acknowledge the reality of their sugar consumption. In addition, some of the data presentations are a bit arbitrary, such as the one that compares 5-day average sugar consumption in the modern day with (for some unclear reason) the year 1812. But I do think the use of familiar food items like 12-ounce sodas, pop tarts, and Twinkies to represent certain quantities of sugar, along with bacon to represent the calories in sugar, makes the infographic much more accessible and intelligible to a lay audience than it otherwise would be.