{"id":8342,"date":"2017-12-11T19:28:52","date_gmt":"2017-12-12T00:28:52","guid":{"rendered":"http:\/\/research.prattsils.org\/?p=8342"},"modified":"2018-01-06T16:07:27","modified_gmt":"2018-01-06T21:07:27","slug":"8342","status":"publish","type":"post","link":"https:\/\/studentwork.prattsi.org\/infovis\/visualization\/8342\/","title":{"rendered":"The Socio-Political Landscape of Anthropogenic Climate Change: Present, Past, & Future"},"content":{"rendered":"

Introduction<\/strong><\/p>\n

Despite a number of high-profile international climate negotiations since 1992 (with the most recent concluding in Bonn just a few weeks ago), the climate crisis continues to deepen, and the current combined pledges toward mitigation and adaptation are woefully inadequate. With this in mind, I have created visualizations related to data on anthropogenic climate change, as this topic is highly relevant, pressing, and ripe for exploration. Rather than focusing on climate change itself (e.g. temperature and weather patterns), these visualizations explore the socio-political landscape of climate change, highlighting disparities in responsibility and vulnerability. In addition to demonstrating these inequities in the present, I aim to show (one aspect of) their historical roots as well as indications of their likely continuation in the foreseeable future.<\/p>\n

The idea for these visualizations came out of a conversation with a professor of environmental ethics about available teaching tools that address these aspects of climate change. In particular, we discussed the kinds of elements and narrative that would be especially useful for a professor and students in this kind of class. As such, the primary target audience for these visualizations are individuals who are interested in and have at least a minimal background knowledge of climate change, and are looking to learn more about its relationship with social, political, and economic realities. However, while that is my intended audience, my hope is that these visualizations would also be accessible to the general public \u2013 that users do not need<\/em> to have much background knowledge and could get something out of it even on a more superficial level.<\/p>\n

Although several similar visualizations already exist, they can often be a bit overwhelming, even for those familiar with the concepts, vocabulary, and units of measurement, just because there are so many complex and dynamic aspects to the issue (see the Global Carbon Atlas<\/a> for an example). The Carbon Map<\/a> is an example of a (cartogram) visualization that is fairly easily digestible while also being comprehensive. While this served as one of the primary models for my visualizations, I did not simply want to replicate it, and have aimed to include some additional distinguishing elements.<\/p>\n

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Materials & Methods<\/strong><\/p>\n

Data Sources\/Formatting & Visualization Design<\/em>\u00a0<\/em><\/p>\n

The data used for these visualizations was selected for specific reasons and pulled from a variety of sources. These are broken down by each visualization section (Present, Past, and Future) below, along with rationales for the design choices made.<\/p>\n

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In addition to providing information that directly related to climate change responsibility and vulnerability, I wanted to also include some somewhat more indirectly (but still highly relevant) contextual information, such as wealth and population. To demonstrate the current concentration of wealth on an international scale (and, ultimately, how that relates to climate change responsibility), the total gross domestic product (GDP) for each nation from 2016 was included. This data was taken from the US Department of Commerce, Bureau of Economic Analysis<\/a>. Initially, I had hoped to include GDP per capita, as well as greenhouse gas (GHG) emissions per capita, but for reasons I will discuss a bit more below, I decided to just include numbers on a national level to tell an overall more globally-oriented narrative. The population in each country from 2016 was pulled from The World Bank<\/a>.<\/p>\n

To demonstrate each nation\u2019s (more-or-less) current responsibility for contributing to climate change, I included data from the World Resources Institute (WRI) Climate Analysis Indicators Tool (CAIT<\/a>), which \u201cdraws on key climate-relevant data from respected research centers, government agencies, and international bodies.\u201d According to the CAIT website, \u201cto the extent possible, CAIT includes emissions from all greenhouse gases and major emission sources for each country\u201d which may involve \u201cas many as five GHG data sources.\u201d Thus, each number for each country included in the map visualization here is the sum of available greenhouse gas emissions data from individual sectors (including emissions and removals from land-use change and forestry) and gases (i.e. CO2<\/sub>, CH4<\/sub>, N2<\/sub>O, and F-gases). These total emissions are measured in million metric tons of carbon dioxide equivalent (MtCO2) \u2013 a metric measure used to compare the emissions from different greenhouse gases.<\/p>\n

While there are many different kinds of datasets related to emissions, I chose this one because it was the most comprehensive I could find; however, because of this, it is slightly<\/em> old (2014), as it is the most recent data of this kind available, but I think still recent enough to be included in the \u201cPresent\u201d section. It is worth noting that while the inclusion of land-use change and forestry data is important (it factors in carbon emissions as well as removals from these sectors) and is a more holistic approach and in theory provides a more accurate number, there are still \u201chigh data uncertainties\u201d associated with it. It is also worth noting that these national total GHG calculations are very sensitive to data availability. Still, these risks seemed worth taking given the large scale of the visualization and overall narrative, which would still likely accurately reflect general trends. As mentioned above, I had initially wanted to include data on GHG emissions per capita (by dividing the totals by the population), but because of the way the scaling worked in Tableau, it did not produce a meaningful visualization; compared with the range of numbers from the other dataset filters, the GHG per capita numbers were relatively too close together (in any unit), and the circles representing these numbers therefore did not show sufficient variation in size to be visually useful.<\/p>\n

To demonstrate which populations would be most vulnerable to climate change, I followed The Carbon Map\u2019s lead and included data on people who have been and would likely be exposed to extreme weather and temperatures, people who live less than five meters above sea level and are therefore vulnerable to sea level rise, and people living on less than $1.90 a day and therefore less able to easily adapt to and cope with the changing environment. To calculate a current rough estimate of the total number of people in each of these cases per country, I used percentages of the population affected provided by The World Bank and multiplied them by the 2016 population (again, following The Carbon Map\u2019s lead). In the case of people exposed to extreme weather, the original dataset<\/a> provided average percentages of the population exposed between 1990-2009. Likewise, for people living below a five-meter elevation, the original dataset<\/a> provided percentages of each country\u2019s population from 2010. Finally, for people living in poverty, percentages of each country\u2019s population was taken from the most recent year for which data<\/a> was available. For each of these, I chose to include the number of vulnerable people (rather than percentage of the population) to give a better idea of how many actual humans are affected. While the calculations are certainly not perfectly precise, I think they provide a sufficiently accurate estimate on a global scale.<\/p>\n

In order to include all of these datasets on one map and have the ability to filter by each of them, I first compiled all of the data in one table. However, the format of the data as it was provided was not appropriate for Tableau to read it in a way that would allow me to create filters by type of data (each type was its own column). I therefore had to use OpenRefine to \u201ctranspose cells across columns into rows.\u201d Once the data was appropriately formatted and cleaned (e.g. country names edited to be consistent with datasets for other visualizations), it was imported into a single sheet in Tableau to create the map visualization.<\/p>\n

As all of this data is spatially\/geographically-based, using a map to display it was a natural choice. I chose to create a graduated symbol map (using circles as symbols), that allowed users to filter by the above data (i.e. population, wealth, emissions, and the three sets of vulnerability data). A graduated symbol map was preferable to a choropleth map, as the former \u201cavoids confounding geographic area with data values and allows for more dimensions to be visualized (e.g. symbol size, shape, and color)\u201d (Heer et al., 10-11). As a user switches between filters\/datasets, the circles over each country change in size to represent that country\u2019s total number.<\/p>\n

Taking further advantage of pre-attentive processing, I also chose to emphasize the countries with the highest emissions by assigning them colors that would be consistent throughout the other visualizations (the rest of the countries were assigned a neutral dark gray). In accordance with the best practices for color, I limited the number to seven, and tried to avoid placing strong contrasting colors next to each other (although since the data can be filtered, this is not possible to fully control).\u00a0 As I was not satisfied with the colorblind-safe palette provided by Tableau, I used one generated by ColorBrewer<\/a>, which I added to the options in Tableau via the Tableau Preferences file (edited in TextWrangler). I felt these colors (particularly the reds and oranges) were more appropriate for the (dire) subject matter; as Sula points out, \u201cemotions have been found to play an important\u2026role in information processing generally\u201d and color is \u201cwidely regarded as having emotional connotations\u201d (29). Although I initially thought it was obvious which colors corresponded to which countries (based on where they were located on a map), I ultimately included a legend (limited to the top emitters on the dashboard, but users can scroll through the countries if desired), which was prompted by user experience research (discussed more below) and backed up by MacDonald who recommends \u201calways includ[ing] a color key or scale with a color-coded display\u201d (31). This legend corresponds to the other two visualizations as well, and was therefore placed in a somewhat central location on the dashboard. Additionally, I slightly reduced the opacity of the circles on the map so that users are able to see beneath them, as smaller circles can otherwise often be obscured by nearby large circles.<\/p>\n

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To give a sense of the historical trends of how climate change responsibility breaks down by country, I created a stacked area graph of time-based data that does just that. It has been sorted by total emissions, with the highest emitters at the top. The data is from Global Carbon Atlas<\/a>. I chose to include data on territorial emissions as well as consumption emissions (measured in million metric tons of CO2<\/sub>) because they demonstrate different ways of measuring emissions and have different histories. Territorial-based accounting measures emissions within a geographical system boundary and only includes GHG emissions generated by domestic production without consideration for where the product or service is consumed. Consumption-based accounting measures emissions within a product-oriented system boundary and considers all emissions related to the product\u2019s life cycle. However, consistent data for consumption emissions is not available before 1990. I also chose to include data on emissions per capita here (measured in metric tons of CO2<\/sub>), since I was unable to on the map and it is a different and important perspective on the data.<\/p>\n

As with the map visualization, in order to include all of these datasets on graph and have the ability to filter by each of them, I first compiled all of the data in one table and reformatted it in OpenRefine (by transposing cells across columns into rows). Once the data was appropriately formatted and cleaned (e.g. country names edited to be consistent with datasets for other visualizations), it was imported into a single sheet in Tableau to create the graph. The colors used for this graph are consistent with those used for the map.<\/p>\n

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To give a sense of each country\u2019s level of commitment to try to limit global warming, I created a simple dataset with data from Climate Action Tracker<\/a>, which rates countries based on Intended Nationally Determined Contributions (INDCs), 2020 pledges, long-term targets, and current policies, and whether they are consistent with a country\u2019s fair share effort to the Paris Agreement 1.5\u00b0C temperature goal.<\/p>\n

The rankings are as follows:<\/p>\n