PERSON ON TEAM
INFO VIS COURSE
At DePaul University, HCI students can choose to take HCI 512 Information Visualization and Infographics as an elective. The professor required an infographic project at the end of the course, utilizing our newfound knowledge of information and visualizations.
The final project in the course was an individual effort to build an original collection of visualizations for an appropriate dataset chosen by the individual, and then incorporate them into a complete infographic.
I was inspired by a dataset about chocolate bar ratings from kaggle.com. After playing with that data, I realized I did not want to work with subjective ratings from one person and began the hunt for agricultural information about cocoa beans. I found the datasets I needed at Food and Agricultural Organization of the United Nations . For my infographic, Global Production of Cocoa Beans (2013), I focused my exploration on four key questions:
- How many cocoa beans are produced in the world?
- Where do these cocoa beans grow?
- Who grows the most cocoa beans?
- How are these cocoa beans shared around the world?
I surprised myself in creating a great looking and informational infographic! I am not a visual designer, and this project took a lot of time, but I am very proud of the result.
My efforts and final infographic were rewarded with a letter grade of A.
data exploration, ui design
process and methods
understand • define
- Datasets from Food and Agricultural Organization of the United Nations:
- List of countries around the world who produced/exported/imported cocoa beans in 2013.
- Summation lists including yield and production value of cocoa and all agricultural crops.
- Dataset of ISO 3166-1 country lists merged with their UN Geoscheme regional codes. The five major regions of the world as follows:
explore • ideate
I played with various graphs and sketches, and settled on the following options to answer my four research questions (see sketch below).
How many cocoa beans are produced in the world?I wanted to start the infographic with a simple table of numbers, to give the audience an overview of the amount of cocoa beans produced in the world in one year.
Few’s Classification: just a table of numbers, perhaps considered nominal
Variables: acres harvested (ratio), total production (ratio), production value (ratio), percentage of food supply (ratio)
Graph choice: just displaying four summation numbers, a simple table suffices
Where do these cocoa beans grow?I wanted to show which countries grow cocoa beans.
Few’s Classification: geospatial
Variables: country (nominal)
Graph choice: a geospatial map helps the reader visualize the areas where cocoa beans grow, as opposed to a table or list of country names
Who grows the most cocoa beans?Cocoa beans are grown all over the world, so I wanted to show which countries grow the most cocoa beans. An interesting fact came up when working with this data – 38 countries produce cocoa beans, but only 7 countries produce 90% of those cocoa beans. This treemap helps to highlight this fact.
Few’s Classification: hierarchical (not Few’s), nominal, a portion of part-to-whole (not all 100% is shown), ranking, distribution
Variables: region (nominal), country (nominal), percentage of production (ratio)
Graph choice: a treemap brings in a hierarchical perspective to what could have been a simple bar chart
How are these cocoa beans shared across the world?Three more interesting facts include 1) Africa grows the majority of cocoa beans but then exports most of their supply; 2) cocoa beans are the third most traded agricultural product (behind coffee and sugar) and most of this trading happens in Europe; 3) the Americas end up with the largest supply of cocoa beans.
Few’s Classification: nominal, ranking (just not ordered), distribution, deviation (imports versus exports), correlation, part-to-whole (but very hard to see this – production + imports – exports = supply)
Variables: region (nominal), production (ratio), imports (ratio), exports (ratio), final supply (ratio)
Graph choice: I purposefully avoided a stacked bar chart showing deviation because I wanted the reader to see each set of bars on their own, as well as in correlation. I felt the data in the separated bar graphs was more important to show as opposed to all the data smashed into one graph.
explore • prototype
- I created a sketch of my informational choices, to help guide my work on each of the separate graphs.
- I then worked on creating prototypes of each graph, iterating through changes as each one evolved. Here are a few of the rough drafts straight from R.
materialize • test
- After I was happy with each graph, I would run it by a small sample of people for input and suggestions, in other words a guerrilla usability test.
materialize • implement
- Once I was happy with each graph, I would export it from R in PDF format and input it into Adobe Illustrator for final tweaking. Then I imported all of the graphs into one single infographic and created the final PDF file.
- I really enjoyed all the different aspects of this project!
- I have so much more respect for great infographics because of the amount of work required to make them.