Using Tableau to Make Informed Decisions
The purpose of this extensive analysis is to gain insight into the effects of climate change in order to make informed decisions about sustainability through the lens of different regions, economic performances and environmental conditions. There is value in visualizing the data analysis because it paints a bigger picture that is otherwise hidden in numerical data. Unique visualization tools, such as Bubble Charts and Heat Maps, allow for and facilitate the absorption of data in constructive ways. This data visualization also allows for faster trend identification and informed action, two things that are extremely important when facing the issue of global climate change.
One of the biggest implications of climate change, as stated by The World Bank, is that it is “expected to hit developing countries the hardest. Its effects—higher temperatures, changes in precipitation patterns, rising sea levels, and more frequent weather-related disasters—pose risks for agriculture, food, and water supplies.” The research question I formulated was designed to test this statement: does climate change disproportionately affect developing countries? If so, is it at the hand of these developing countries?
Overview of Data
One of the biggest implications of climate change, as stated by The World Bank, is that it is “expected to hit developing countries the hardest. Its effects—higher temperatures, changes in precipitation patterns, rising sea levels, and more frequent weather-related disasters—pose risks for agriculture, food, and water supplies.” The research question I formulated was designed to test this statement: does climate change disproportionately affect developing countries? If so, is it at the hand of these developing countries?The information provided in the data set included topics like economic performance, environmental condition, energy use & consumption, and gas emission levels for 131 different countries over six years (2005-2010). Some specific examples include CO2 emissions, GPD, income group and forest area.
Overview of Procedures
My hypothesis was that climate change does disproportionately affect developing countries in terms of economic stability and health standards at the hand of developing countries, assuming that CO2 emissions and energy consumption are contributors to climate change. In order to test this hypothesis in an attempt to answer the research question, I focused most of my attention on five factors: GPD, gross savings, CO2 emissions, energy consumption and improved water source.
Looking at CO2 emissions and energy consumption was meant to give me insight into which countries were contributing the most to climate change, again, assuming that these two measurements are factors of climate change. GPD and gross savings were meant to provide an analysis of which countries were at the lower end of economic performance, and then put alongside income groups and export percentages, as well as clean water levels, so explore any relationships between economic performance, trade and quality of life.
Results & Insights Gained
The first chart I created was a map chart used to emphasize the countries with the most and the least CO2 emissions. It is clear from the chart, Figure 1, that the United States and China have the highest levels of CO2 emissions.
Figure 1. CO2 Emission Levels by Country
The second chart that I created was a bubble chart in order to visualize energy consumption by country and by income group. Once again, the United States and China are the leaders in consumption, both in the high income or upper middle income brackets. Even at a glance, it becomes evident through a clear pattern that the higher the income, the more higher the energy consumption (Figure 2).
Figure 2. CO2 Emission Levels by Country
Next, I wanted to analyze average energy and CO2 emissions by income group using a different visualization chart to see if there was a potential correlation between consumption and income level (Figure 3). It seems as though there is a correlation; energy consumption climbs linearly as income rises, and CO2 emissions rise exponentially
Figure 3. Average Energy and CO2 Emissions by Income Group
The fourth chart was meant to show the relationship between average GDP and average exports grouped by income group and improved water source access (Figure 4). Low income areas have the least improved water source access followed by lower middle, upper middle and high income. Lower GPDs generally mean lower exports of goods and services as a percentage of GPD.
Figure 4. Average GDP vs Average Exports by Income Group
The last chart was used to explore the relationship between time and CO2 emissions (Figure 5). I suspected that for high income levels, there would be an increase over time, while for lower income levels there would be a must slower, if any increase. With the exception of the highest incomes, this was true; low income barely rose while the others increased notably. Perhaps the areas with the highest incomes have the financial means to explore and implement technologies that reduce and eliminate CO2 emissions.
Figure 5. CO2 Emissions per Income Level per Year