Climate changing is a global issue. Since 1751, the first industrial revolution period, the world has emitted over 1.5 trillion tonnes of CO2. People in different countries have experienced extreme weather caused by climate change. For example, Europe has broken the heat records in the summer of 2019. Germany and France recorded their highest temperature ever at 108 F and 114.8 F in July. Also, in the winter of 2021, Texas experienced the biggest snowstorm ever and the temperature was lower to 30 F in the northern area of Texas. Therefore, it’s really urgent for countries to start working on reducing the environmental impact. Here comes the question of how the climate is changing along with the timeline, and what countries contribute the most CO2 emissions in recent decades. For the answers to these questions, I created a data visualization to find out.
National Aeronautics and Space Administration (NASA)
NASA has a specific site for recording all the information regarding changes in climate and temperature. The visualizations there are simple and intuitive. Also, the color code is warm-toned color so that visitors can easily feel the severity of the current climate changing. The visualizations below show that the temperature changed over a year from 1880 to 2021. We can simply tell that the weather is getting warmer and warmer. However, the graph contains too much information which may be difficult to understand at the first glance. To understand this graph, I looked carefully at the legend and the scale label to find out the relationship between the timeline and the linear lines. It would be clearer if the trend can be shown more obviously.
Then I researched more into how professionals create the visualization of climate change. The post on Nature is very inspired. The graph is clear and intuitive and easy to understand the goal of this graph. So from the chart below, viewers can intuitively understand that the global temperature is now 1.5°C warmer. The map below shows the cumulative risks from major climate impacts with 2 °C of warming; the chart estimates how many people would be affected by a selection of those risks.
OpenRefine — I used OpenRefine to clean those datasets and combine some of them together in one spreadsheet. OpenRefine is a standalone open-source desktop application for data cleanup and transformation to other formats. It is a very powerful tool for working with messy data. However, my datasets are not completely raw data, so I delete that unnecessary information and merge datasets together.
Tableau — After cleaning the data, I imported them to the Tableau for creating the data visualization. Tableau is a free interactive data visualization software that helps to represent the data and information. At the beginning, I first drew my idea on the paper to see what kind of chart I want to use. Then I attempted to create those charts in Tableau.
My dashboard contains four charts that show the change of climate from different aspects along with the timeline. A changing climate has a range of potential ecological, physical, and health impacts, including extreme weather events (such as floods, droughts, storms, and heatwaves); sea-level rise; altered crop growth; and disrupted water systems. All these four charts reflect the upward trend of the global temperature and the CO2 emission, and the trends are showing our global warming issue is getting worse and worse.
The first chart is showing the change of atmospheric concentrations of CO2, the timeline is starting from hundreds of years before the common era, but I only filter the time from 1600 to 2000. We can see from the chart that the atmospheric concentrations of CO2 before 1800 were stable and stayed at almost the same level. But the concentrations of CO2 keep mounting up from 1800 which was the end of the first industrial revolution. After the second industrial revolution ended, the concentration of CO2 in the atmosphere is rapidly increasing.
The second chart shows the CO2 emission from selected countries. From the graph, we can easily see that those countries with advanced and developed technology have relatively higher CO2 emissions to the world, such as the united states, China, and Europe. Also, from the graph, we can see which countries contribute the most to global warming.
In the chart we can see the global average temperature relative to the average of the period between 1961 and 1990. The orange line represents the average annual temperature trend through time, with upper and lower confidence intervals shown in light grey.
The chart below shows the relationship between CO2 emission and GDP growth in the United States. Many of those rich countries have high standards of living, but also high levels of emissions; and poor countries have low levels of emissions but poor standards of living. However, countries like the USA have shown an increase in GDP while also reducing CO2 emissions.
I was initially planning to create a chart with prediction and color code it like the visualization graph on “Nature”. However, I found that my knowledge about Tableau is pretty limited. When I played with the software, I couldn’t make my chart look the way I wanted it to be. Therefore, in order to keep my chart intuitive and easy to understand. I eventually stayed with the basic linear graph so that the charts will be more readable. Before using the tableau, I thought it would be really easy, but it actually took a lot of time to learn. Therefore, it’s important for me to be more proficient on using tableau in order to be creative in designing visualizations.
Who has contributed the most to global CO2 emission https://ourworldindata.org/contributed-most-global-co2
The hard truths of climate change — by the numbers https://www.nature.com/immersive/d41586-019-02711-4/index.html