With the increase of population, we are reclaiming land from the sea to serve human needs. Have we thought of one day, the sea will take back everything.
This project is inspired by recent news regarding the world’s biggest iceberg A68 has melted away on April 18, 2021. It has broken into countless small fragments that scientists are not able to track anymore. Since July 2017 when it broke away from Antarctica, it had been the largest iceberg, covering an area of nearly 6,000 sq km (2,300 sq miles).
Since the early 1900s, many glaciers around the world have been rapidly melting and the ice extends have been dramatically declining as the global temperature rise. Human activities are at the root of this phenomenon. As the Arctic loses ice and the ocean absorbs more solar radiation, global warming is amplified. That can affect ocean circulation, weather patterns, and Arctic ecosystems spanning the food chain, from phytoplankton all the way to top predators. Specifically, since the industrial revolution, carbon dioxide and other greenhouse gas emissions have raised temperatures, even higher in the poles, and as a result, glaciers are rapidly melting, calving off into the sea and retreating on land.
People all aware that the global temperature rise will eventually harm humans themselves. However, people choose to avoid this issue because they think the rising of temperature and the melting of ice extends won’t impact their lives this soon. In fact, extreme weather started to emerge all around the world, and the consequence brought by temperature rise is gradually revealed. The goal of this project is to analyze the consequences of global warming and the change brought by global warming. Also, I hope this project will bring attention to global warming issues.
My process involved reviewing existing visualizations, brainstorming, learning tableau advanced tutorials, data processing, and user experience research.
At the beginning of the project, my idea is to visualize the change of iceberg A68 since the time it broke away from Antarctica. But after I finished processing the data and import it into Carto, I realized that this visualization is not that meaningful as I thought. The map was showing how A68 is shrinking and melting away in three years. there were not too many insights for me from creating that map. Therefore I chose to visualize the change of ice extent in the world. The dataset is much bigger than A68’s, and I believe I will learn more from creating the visualization based on that.
Most of the data visualization regarding the ice extent I found online are using the linear graph to show the change of ice extent’s area. Some of them are using map to visualize the change.
Those linear graphs online are all showing a similar trend. From the graph, we can obviously see that the area of ice extend is shrinking in the recent decade. Every year, the ice extent reached the peak in the month of February and touched the bottom in the month of September. But every year the peak and the bottom are lower than the year before.
The linear graph cannot clearly show the change through out the decades. Since the trend of the area of ice extend every year is similar which is going down during the summer then going up during the winter. Therefore, it’s hard to tell how much the ice extent has been shrunk at the first glance. Viewers may mistakenly think the ice extent has not changed that much.
For the map graph, I found resources from NASA earth observatory. These image pairs show the average concentration of Arctic sea ice for the month of September (left) and the following March (right) from September 1990 to March 2021. The white areas indicate the greatest concentration, and dark blue areas are open water. All icy areas pictured here have an ice concentration of at least 15 percent (the minimum at which space-based measurements give a reliable measure), and cover a total area that scientists refer to as the “ice extent.”
Brainstorming and the concept
From the map and linear chart, I found online, I didn’t feel they are the best way to show the change of ice extent in the Arctic Sea, so I continue to explore more visualization and try to find if there’s anything that can inspire me. Then I found the work designed by Nicco Cirone on Tableau which made me decide to create a radial graph to better show the change of how ice extends is melting in decades.
Before I started the project, I found this tutorial online to teach you how to create a radial chart on Tableau. Besides the online tutorial, on the tableau’s website, they provided the handed functions about how to turn your linear graph into a radial chart.
Data & Tools
Data: I have three datasets in total. The data I used to create the radial chart is downloaded from NASA.gov and serc.carleton.edu, and the data to create the tab is from Earth Observatory.
OpenRefine: Cleaning and processing the data downloaded from online
Excel: Provided me a basic vision of the data visualization
Tableau: Creating the visualization of data
I have played around with different tools at the beginning of this project such as GIS and Gis Story map. However, I found those data tools is hard to learn within a week so that I chose to develop my project in Tableau.
Processing the data
The first step for me to create the radial chart is to refresh some notions of goniometry to extrapolate the XY coordinates, and this is very difficult and time-consuming. I asked my friend who works as a data analyst in Bloomberg to help me with the coding part.
The X, Y coordinate are defined like:
Month X =
IF [Table Name] = 'Arctic Sea Ice Extent1'
THEN COS((PI() / 2 - (MONTH([Date]) - 1) / 12 * 2 * PI())) * [Year / maximum year] * [Maximum extent (mil km²)]
Month Y =
IF [Table Name] = 'Arctic Sea Ice Extent1' THEN SIN((PI() / 2 - (MONTH([Date]) - 1) / 12 * 2 * PI())) * [Year / maximum year] * [Maximum extent (mil km²)]
Because Cos(X) and Sin(X) would give us coordinates for a circle of 365 points and ray=1, I now want to multiply those by the metric to encode – in our case ‘Arctic Sea Ice Extent’.
I need to create a ‘X’ variable, defined as:
IF [Table Name] = 'Arctic Sea Ice Extent' THEN COS([Angle in radians]) * [Extent (mil km²)] * [Lines multiplier] ELSE [Month x]
Similiar to the ‘X’ variable, then I need to creat a ‘Y’ virable which is defined as:
IF [Table Name] = 'Arctic Sea Ice Extent' THEN SIN([Angle in radians]) * [Extent (mil km²)] * [Lines multiplier] ELSE [Month y]
The last step is dragging the coordinates on rows and columns, change the mark to be a line, and drag a field with the actual dates on the ‘path’ shelf.
For this radial chart above, it almost took me one week to understand how the function is working in the Tableau. And I am satisfied with what is turning our at last. Then played around with color to differentiate the lines of different years.
Since my datasets are collected data from 1980 till 2018. My idea here is to use the darkest blue to show the area of ice extent in 1980 and the lightest blue to shows the ice extent in 2018. Then as time goes, the ice extent is gradually shrinking and melting away so the dark blue is faded to light blue.
Along with the radial chart, in order to improve the usability. I also build the simple linear chart with the same color code as the radial one.
I tested the drafts with two participants, with the goal of assessing user experience, specifically, understanding how they interpreted the visualizations, whether there were areas of confusion, and what they learned from the graphics.
I chose two participants who fit my target audience. Both were around 25 and most importantly had an interest in global warming and sustainability. One worked in government, while one worked for Bloomberg philanthropy.
I conducted user tests by video chat, with the participant sharing their screen so that I could see how they interacted with the visualizations, while I took notes on a pad. I used the think aloud method, asking participants to verbalize their thoughts as they interpreted and interacted with the graphics.
Because the graphic is somewhat unconventional, my main concern was determining whether the participants were able to understand them easily. Some of the most important findings were:
- Both the participants could easily tell the graph is somewhat related to the water
- Both the participants need to hover over the chart to see the tooltip for helping them understand what is the chart trying to express.
- One participant said that the radial chart brings her difficulty to interpret since it’s a very unconventional shape. But the other participant said the radial chart helps him to understand the area is gradually shrinking.
- Both the participants think the linear graph is clear to view the change over a year, and the radial chart is clear to view the change over decades.
- Both the participants think the color is too plain and hard to tell the key point.
Conclude by the UX research, I decide to make a few change of my graphics.
I kept the blue as the major color but I change the color of recent years to orange.
The orange can emphasize our ice extent is melting and getting smaller throughout these 40 years. Also, orange can alarm audiences to aware of the consequence brought by the rise of global temperature. Also, warm color can be interpreted as the Arctic is getting warmer.
For clearer visualization, I changed both my graphic to simple lines instead of using dots.
Edited tooltip with more detailed information.
What I did last, I created another graphic which shows the area of ice extent change in every months throughout the 40 years. Form this graph, it’s clearer to view that the area of ice extent is getting smaller year by year.
For the final visualization, I added the interaction feature among these three graphs.
For me, there were several takeaways from exploring the visualizations:
The ice extent is not linearly shrink
From the graphs, some years are overleaping each other which means the ice extend is not linearly shrink. From the monthly graph, there are obvious fluctuations in each month. The ice extent is not constantly declining every year so that there’s still time for us to start protecting the environment and the ice extent will be growing back one day.
The rise of temperature becomes faster and faster
From the monthly ice extend graph, we can see in recent years the average ice extent has huge drop from in the graph. So that we can conclude the temperature in Arctic is rising dramatically.
Summer in Arctic is getting hotter
From the radial chart, by comparing the lines in October and lines in April, It’s obvious to see that the lines in October are looser than lines in April. So that the summer in Arctic is getting hotter and hotter.
The biggest takeaway for my from this project is understanding the process to build a radial chart. At the beginning of the learning phase, although I followed several tutorials and complete the examples, I still couldn’t figure out how to define the X, Y axes based on my datasets. But the good thing is I didn’t give up on this, and I found my friend to help me and tutor me in the coding part.
For future updates of my work, additional user testing would also be helpful. I didn’t conduct user testing after I added an extra monthly chart. The complexity of the graphics could potentially be reduced to be more accessible to a wider audience, although the constant presence of similar graphics in the news makes me optimistic that this could appeal to a range of users.