Adrienne Luk
7 min readApr 6, 2023

project 4: designing information for quick understanding

  1. This data visualization looks at overnight stays at US National Parks. It allows viewers to see when one should pitch their tents, or when it would be better to opt for lodging. This data also shows when one can avoid crowds during their visit. There is a key with four categories of accomodation: lodging, RV, tent, and backcountry. Each category comes with their own characteristics which determine their popularity over time. The data plotted is the number of nights spent per month in each park.

As someone who typically struggles to read data that is presented in graphs, this visualization was direct and relatively easy to understand upon first glance. There seems to be secondary information that is plotted in each graph which I personally find a bit confusing. However, if I put the time and effort to read it properly, I do appreciate the delicate nature of the data visualizations and how they represented a lot of information clearly.

A Night Under The Stars | Jordan Vincent (

2. This graphic looks at how the coronavirus spread in Hong Kong. It is a Sankey diagram. Sankey diagrams are a specific type of flow diagram, in which the width of the arrows is shown proportionally to the flow quantity. Sankey diagrams put a visual emphasis on the major transfers or flows within a system. They are helpful in locating dominant contributions to an overall flow.

I find this infographic to be very successful because it makes complex issues understandable and deeply interesting. The research is simplified such that anyone without expert knowledge can understand what the story is and leaves well-informed. There is a large amount of information on this page, yet it draws the viewer in and the data is clear and simple.

How the coronavirus spread in Hong Kong (

3. The following shows waste of perishable products. This data visualization has the least amount of information to show compared to the first two, and it is also the most boring. Although on first glance it is successful, I personally think that it is a bit arbitrary in the way it is attempting to show the waste in contrast to the amount produced. For example, without the numbers and text, I simply would not be able to make an assumption of the quantity or that it was using one random food item to represent a larger food group. Although at first it seemed interesting, it definitely can be improved significantly.

Among the content, I think that visual cues are important for the audience to grasp. I feel that naturally people are drawn more when something is visually appealing or eye-catching, so minimizing numbers and using lots of words is usually effective. Information can also still be understood in the beginning without the captions and details because it gives a good idea of the analyzed data overall.

Dear Data Postcards

Left: Matthew’s Postcard, Right: My Postcard

The prompt for the postcard that my partner and I chose was “a week of wildlife”.

In this exercise, we recorded the wildlife/animals we encountered throughout one week. My approach was a bit radical in that I did not record the data before I visualized it. I believe that was a big issue and challenge for me and was the reason why I struggled a bit in creating a successful data visualization.

My method began by iconizing the different animals I was encountering on a week based timeline. Based on the animals I saw I also added a different color circle of how I felt that day after encountering that animal. It was clear that I was evidently happier when I interacted with my cat. However, I think that my visualization was a bit confusing because there was no standardization of quantity of animals that I encountered, for example when I saw two dogs in one day, Matthew was confused about the way I represented that information. There also was not a very clear distinguishment of how my mood was correlating with my encountering of other animals.

Data Visualization Sketches

In the visualization below, I wanted to show the relation of my eating habits to the weather.

My initial assumption was that sunny days lead to better appetite. Having dessert is also something that is unusual for me, so it was something I thought would be interesting to track this week since I was consuming a lot more desserts that I usually would.

I think that this visualization was not very successful overall, because there were a lot more sunny days than rainy days in the week I was recording. The information was unclear because the time span was not evident in whether these were tracked over a month, week, or day. Since my eating patterns are a bit irregular, this data might’ve been even more confusing to understand.

In this second sketch, I tried to be more intentional about the dates and how that was represented. I also decided to only show the days on which I snacked, instead of tracking all the meals and desserts I’ve had. I decided to represent the temperature as outlines on the date frames, and I created a range of 4 feelings to track my mood.

I think this is a little better than the beads approach in that, the information is a bit clearer and easily identified as to how I was feeling on specific days, and maybe how the weather could begin to correlate with my mood and snacking habits.

However, I still was not recording my data prior to visualizing it in this iteration, therefore I am not fully happy with how my data is being shown.

In this data visualization sketch, I tried to narrow down the icons and information I was trying to show and draw a clearer picture of the relationships I wanted to convey. I chose to represent the different work hours per day through different colored computer screens. I also decided instead of illustrating my mood, I would convey the number of meals I was consuming by the size of the character’s belly size.

I also don’t think that this visualization is very successful in that the correlation of work hours are unclear with the number of meals I was consuming. I think that choosing different colors as my range of difference is unproductive because a third party would not draw the connections by seeing a different color screen.

In this sketch, I tried to draft a more complete version of what I was trying to achieve. After deliberation, I decided to depict my meals through a scale of small, medium, and large, since I typically only have one meal per day. I chose to represent work hours through stack of books since I felt that was the most visually easy to understand without referencing a key. I also wanted to mark the dates that were irregular in this month since that impacted my eating patterns. I wasn’t sure what the best method to do so was, so I temporarily chose to use brackets. I was then advised that it would also work if I highlighted the blocks of time that were part of a specific time frame.

In my digital iteration, I decided to assign colors to each subject that I was taking this semester. When creating the poster, I realized that the scaling of the books became an issue when I had days of bigger bowls and it would be out of scale on the calendar grid. Adjustments had to be made accordingly. After getting some feedback, it was brought to my attention that the choice of colors I was assigning to the classes were distracting as I chose a bright red color for studio, which was a class that I took often, but because I assigned brighter colors to courses that I spent less time on, it ended up drawing more attention to those days, which was not my intention.

In my final iteration, I made the adjustments that I mentioned previously regarding scale of books and color choices.