Category: assignments

In preparation for Super Bowl LI, I was researching the game to understand the dominance of this sporting event and subsequently stumbled across this infographic produced by WalletHub. The infographic is subdivided into multiple sections; Super Bowl LI, Game Day Notes, Media Extravaganza, Ticket Prices, Super Bowl Ads, Pigging Out at Pigskin Parties, Big-Game Betting, and Super Bowl Economics. Below each section is 3-8 different statistics overlaid on an image.

The audience of this data presentation is sports fans, or at the very least those who have some interest in sports. The statistics present are unconnected, and to be best understood, require some sporting knowledge. This is why I believe the targeted audience is those who are interested in sports.

By providing statistics in a variety of categories that pertain to the Super Bowl, the data presentation aims to convey the magnitude of the game and therefore increase interest in the game. By presenting the massive reach of the game in the form of an infographic readers who do not normally watch the Super Bowl may have a new interest and join the bandwagon by viewing the game. The effectiveness of this data presentation in limited by the lack of comparison for the numbers. For example the infographic lists “70 total cameras used by Fox Sports”. Based on the context of this infographic, one will probably assume this to be a large number, but the reader does not know how many cameras are used in a regular season football game, TV show, blockbuster movie, or another sport. If a comparison was provided, readers can better appreciate the statistic and thus better evaluate the reach of the Super Bowl.

Source: https://wallethub.com/blog/super-bowl-facts/1589/

Note: Apologies for the low-quality image. I did not realize the quality until I made the post. The link provides a high quality visual.

I’m definitely an avid NBA fan and a lot of the accounts I follow on Twitter are geared toward providing me with information about the league and the teams I like to keep track of. One Twitter account that I found just a few weeks ago, NBA Math, tries to provide an objective assessment of the league by crunching numbers, specifically through their TPA model that takes into account a per possession conversion of the events that occur throughout the course of games.

Their TPA model often provides a two-number evaluation of players and teams that can be broken down into an offensive and defensive score. Viewing these computations in a table ordered by greatest difference can be a great way to visualize most of the information one would want, but makes comparing quite difficult and tedious.

Many of the tweets they send overcome this by displaying the teams on a simple x-y coordinate graph where the axes represent the offensive and defensive scores.

Adjusted defensive and offensive scores for each NBA team since 1 January 2017 VIA NBA Math

While the aesthetics of the display may be bland, presenting it this way is incredibly powerful and allows a Twitter user to be able to make the quick assessments that are typically favored on a social media platform. They are on the site to get quick bursts of information and this type of data platform allows a single tweet to streamline a lot of information at an incredibly rapid rate. Seeing how various teams positions differ can be done with great ease, allowing a user to identify where there team stands and how they might need to improve relative to the rest of the league and the teams that, by popular opinion, are considered elite.

It’s very unclear how effective a model that tries to boil down performance into two numbers can be useful and effective. However, the ability of NBA Math to send out bursts of tweets in this variety can capture the attention of Twitter users easily allowing them to show off this model and try and convey the information they’re gathering about the league.

Link to Tweet: https://twitter.com/NBA_Math/status/829369301899567104

For the past two years, Bloomberg has been publishing interactive data graphics. I first found them in 2015 after reading their post on climate change. Since then, they posted many more, mostly about politics and finance. A more recent one is about Trump’s cabinet nominees, titled “Trump’s $6 Billion Cabinet: Mostly Men, Mostly White and Not Much Government Experience”.

The data presentation takes the form of two tables: one that compares Trump’s cabinet nominations to the previous two presidents (top picture), and one that gives more detailed information about each nominee (bottom picture). The graphics take special notice of the race, gender, government experience, military experience, and wealth of each cabinet member/nominee.

As the title suggests, the goal of the presentation is first to highlight the similarities and differences between Trump’s cabinet and previous cabinet (his cabinet has roughly the same diversity as Bush’s, but far less government experience than both Bush and Obama), and also to educate viewers about the nominees and cabinet members (achievements, past boards, recent news, public stance on issues, wealth). The categories it chooses effectively reveal a stark contrast between past and proposed cabinets, and the extra information in the table along with the related links for each nominee make it a useful tool to become informed about each person. The experience is a little diminished by the weird scrolling behavior.

The graphic is catered to casual viewers – people who maybe do not follow every political announcement but want a brief overview; a lot of the information would be redundant to people who follow politics vigorously, although the tables give a concise summary that may be good for reviewing.

Overall, Bloomberg’s graphic about the cabinet effectively informs viewers about Trump’s new cabinet – how it’s compares to previous ones, who is on it, and what to expect from the members.

Ventusky is a weather visualization platform developed by ImMeteo, a Czech meteorological company who focuses on weather prediction and meteorological data visualization.

Ventusky presents worldwide weather maps including wind, rain, temperature, air pressure, etc (Figure 1). It has weather data of the past week and can play the timeline as a progress bar to show the weather change. Ventusky supports users looking up for cities/provinces and provides them with detailed information (current weather, weather forecast, sun and moon info, etc) about the cities/provinces (Figure 2).

Audience of Ventusky can be anyone who is interested in knowing the weather information (current or past) in some places. They can be passengers of flights or backpackers checking the weather in destinations, residents who wish to know the affected area of snowstorm, or even researchers who want to see the current weather or recent weather change.

In my view, Ventusky aims to provide users with up-to-date worldwide weather information through an intuitive map interface. It serves as a platform for users to look at current weather, weather change, and weather forecast in a visualization method but not the conventional way – reading text. By providing multiple features (temperature, air pressure, clouds, etc) that can be integrated in the visualization, it also fits users’ need to look at various weather features in one interface.

In fact, the first wind map I saw is not Ventusky, but this one below (Figure 3). The wind map in Figure 3 is concise, beautiful, and does a good job in presenting flow animation, but not as effective as the Ventusky one. Ventusky not only displays wind map but also other weather features such as temperature and clouds, the combination of which makes the visualization more informative. Moreover, the detailed information such as weather forecast for specific cities/provinces not only makes it helpful to a wider range of audience but also makes sure Ventusky has the value to be revisited by users. Last but not least, the history weather data and play-timeline function also add value to Ventusky.

In terms of meteorological data, maps are good presentations since they provide an intuitive view by allowing users to see weathers of different places at one time. Compared to printed map, online maps also do a better job in providing interactive interfaces and allowing zoom-in/out for detail checking. In that way, Ventusky seems to be an effective platform for weather visualization.

However, I feel the color map is a little difficult to read in some circumstances. For instance, when selecting temperature as a feature, the colors for -10°F and 80°F look similar in the map, making some area in Canada and Mexico in almost same color. Another thing may be interesting to consider is whether it’s possible for users to select any two features they are interested in to present in the interface. Currently one feature is fixed to wind and users can change the other feature.

This past IAP, I worked in the new data science group at Maersk Line, the biggest shipping company in the world. One of my coworkers found Shipmap.org, which lets you interact with GPS timeseries data for cargo ships in 2012.

I enjoyed this data representation because it conveys many complexities of global trade without overwhelming its audience: the general internet-using population. On my first glance, the site displayed an aesthetically pleasing bathymetric map that showed global ship movements. However, more aspects quickly began to show through. I zoomed in on choke points, such as the Egyptian Suez Canal, the Panama Canal, and even the area around Singapore. The busiest world ports glowed, which actually helped inform my forecasting work the next week. Even unrelated facts such as the Earth’s curvature show in the ship movement–look at the route from the Vancouver to East Asia. The toggles also allow you to view different cargo types, CO2 emmissions, etc., so I feel like I always learn something new when I visit.

Even though this visualization displays fairly raw data, it does a great job of entertaining and informing: what I perceive to be its two main goals. The map’s interactivity entertains users by letting them discover their own inferences, and the map provides a natural way to deeper explore these inferences. These personal conclusions result in a much higher-impact experience than simply seeing charts showing CO2 emissions, cargo flows, etc. The inferences bring the user into the data, personally connecting them to the data’s story.