Showing risks, rights & freedoms in visualizations

The tragic events in Ukraine have left me wondering how quantitative visualizations miss showing complex issues such as human rights. One aspect of this conflict mentioned by various media outlets as well as elected officials is the flow of funds to purchase commodities, particularly oil, helps fund the military ambitions of the state. While Russia’s human rights record is terrible, many other oil-exporting nations also have serious human rights issues. How might difficult concepts such as political risk and human rights be shown in a visualization about oil?

In visualization, a quick solution would be to find a metric which encodes risk, rights and freedoms. A metric is needed because:
a. Visualizations encode quantitative (and categorical) data, not unmeasured data;
b. You can’t manage what you can’t measure.

These are commonly-held wisdoms in visualization and management consulting. But is this the right approach? Consider a treemap of oil exports from countries (showing only countries with more than 100,000 barrels per day):

Treemap with size indicating oil exports by country, color indicates a measure of political risk.

The primary encoding of the treemap is oil exports by size. Saudi Arabia is the largest, but also Russia, Iran, Iraq, UAE, Kuwait, Nigeria and Canada are large as well – each exporting more than 1.5m barrels per day. At $100/barrel, that’s more than $150m/day. The dollar amounts are enormous, creating enormous opportunities for sovereign governments to use some portion of that money for state activities.

Not all countries are bad actors. Color in this treemap indicates political risk, as indicated by a risk rating. However, this particular risk rating doesn’t rate some countries such as Norway and Mexico – presumably the level of risk is not similar between these countries.

Thus, we might look a metric with better coverage. The treemap below uses the Corruption Perception Index (from Transparency International) for color:

Treemap with size indicating oil exports by country, color indicates Corruption Perception Index.

In this example there is coverage across all countries. Russia, Iran, Iraq and many others look bad, Libya, South Sudan and Venezuela worse (although this data has not been updated in response to the invasion of Ukraine). The color scale is a diverging scale, copied from a map on the Wikipedia article indicating Corruption Perception Index. Unfortunately, this creates green for countries implying good scores – including for some countries with poor human rights records.

Therefore, we might try to keep searching for a metric (and a color scale), that better captures what we think should this metric should show. This search for metrics is an attempt to capture our real-world knowledge of risks and rights abuses of different countries, but we’re also in danger of simply looking for metrics that confirm our biases. Here’s a nicer version of the treemap perhaps a bit closer to our expectations using the Global Peace Index and the inferno color scale:

Treemap with size indicating oil exports by country, color indicates Global Peace Index.

All of these indexes attempt to capture complex multi-variate data. For example, an American viewer may object the the Peace Index categorizing United States at the same level as Algeria. If no single metric captures these issues, one might turn to a visualization technique that instead shows many variables, such as parallel coordinates. But creating a much more complex visualization, misses the simple immediacy of the treemap – and ignores that all these size-based visualizations (bar charts, pie charts, treemaps, sunbursts, area charts, etc) are highly prevalent and will continue to be popular.

What to do?

Annotations in areas

Many visualizations use size to draw attention to larger objects: bar charts, pie charts, maps, treemaps, etc. In all the treemaps above, Saudi Arabia and Russia are large, Gabon and Vietnam are not. Presumably, the largest exporters should have more scrutiny, not just a larger size.

Interestingly in cartographic maps – such as a roadmap, Google map, etc – large areas end up with more labels. Why shouldn’t visualizations do the same? After all, the largest areas are the items with much larger values, and thus perhaps deserve more attention than the tiny items. Here’s the treemap visualization again, this time with the opening paragraph or lede sentences from Human Rights Watch country pages:

Treemap with size indicating oil exports by country, color indicates Global Peace Index, and prose text indicates some human rights abuses from Human Rights Watch.

In this example, the treemap remains and the color coding remains. Large blocks also have additional text that can be directly read if of interest. Saudi Arabia’s human rights record indicate issues with official accountability for the murder of Jamal Khashoggi; Russia’s record indicates it is the most repressive since the Soviet era (and this is text from before the attack on Ukraine); UAE detains dissidents even after completing their sentences (and UAE is positively biased on both the peace index and corruption index). Even large exporting countries with generally good records, such as Canada and USA, now have enough space to indicate rights issues such as the rights of Indigenous peoples in Canada, or poverty and inequality in USA.

The different kinds of rights issues not visible with a singular metric have the opportunity to become directly visible with the addition of annotations. There is space to shine a light on the details behind the largest exporters. Income inequality and Indigenous issues are human rights issues as are other repressions, but the viewer can make a more informed comparison about the instances, breadth, severity and cruelty of the largest exporters. Abstract concepts such as peace and corruption are made more concrete with instances and examples.

This example helps to turn the concept of a generic commodity (oil) into a more uncomfortable question about where the money goes after you pay to fill up your vehicle, turn on your stove, or take another flight.

About richardbrath

Richard is a long time visualization designer and researcher. Professionally, I am one of the partners of Uncharted Software Inc. I have recently completed a PhD in data visualization at LSBU. The opinions on this blog are related to my personal interests in data visualization, particularly around research interests related to my PhD work- this blog is about exploratory aspects of data visualization not proven principles.
This entry was posted in Alphanumeric Chart, Annotation, Data Visualization, Treemap. Bookmark the permalink.

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