globalisation

State of the World's Fisheries and Aquaculture - UNEP GRID-Arendal
State of the World's Fisheries and Aquaculture - UNEP GRID-Arendal

What Works

I couldn’t end the agriculture week without including a bit about the state of the oceans as a source of food. To be clear, agriculture is often related to farming the land and raising land-based livestock. Aquaculture is the term used to talk about fish farming. Catching fish out of the open water is not considered agriculture. I ought to have used the broader theme of food production.

I like this graphic because it’s got multiple levels of information – aquaculture vs. open water catches by volume, fish catch by country, and the status of the stocks by oceanic region. It can be difficult to figure out how to represent global level data when it isn’t possible to fall back on national boundaries. It’s a little odd to see the oceans chunked into squares, though.

What Needs Work

This graphic has a sort of not-quite-done look to it overall. The treatment of the aquaculture vs. open water catch could have been more elegantly integrated – superimposing the red and blue blocks on one another makes it look a little bit like the kids left their wooden blocks laying around on top of a map. I might have preferred pie charts with two pie pieces – one for the aquaculture bit and one for the open catch bit to communicate relative share. The size of the pies would be determined by absolute value of the total catch + aquaculture.

What I really would have liked to see would have been a more logical representation of the catch volume by oceanic region. The colors chosen don’t indicate much of anything, except perhaps the static areas which are just blue, standard ocean color. What would have been great would have been to indicate the relationship between areas that have decreased yields because they have been overfished and areas that are currently being overfished which will soon have decreased yields even though their current yields are high. This is complicated because the largest increase and the largest decrease are far more closely related to one another than they are to steady areas or areas with only a slight increase. It would be great if the ocean regions could be depicted by the replacement rate with an extra classification for areas that have been severely over-farmed to the point that the concept of replacement rate no longer has the same meaning.

Relevant Resources

United Nations Environment Programme – GRIDA (2009) State of the World’s Fisheries and Aquaculture

Public Service Announcement: The amount of mercury and arsenic found in predator fish is high enough that people who eat these fish recently can suffer the effects of heavy metal poisoning. To figure out what is safe to eat and what should be avoided, check out the Monterrey Aquarium, one of the best, if not *the* best source for guidelines about what to eat when what you’re eating lived in the water. They even have an iPhone app for easy reference at the grocery store and your favorite restaurants.

Monterrey Aquarium Seafood Watch

Click Here to View the Animation by Aaron Koblin
Click Here to View the Animation by Aaron Koblin

What Works

Click on the link in the caption to go to Aaron Koblin’s site and watch the animation. It’s mesmerizing and I ended up watching it more than once, trying to pick out the patterns. And, in fact, what works about this approach is it’s ability to help quickly identify patterns. Generally speaking, data that is dynamic (usually the change is happening over time, as in this case) is data that may lend itself to this sort of pattern recognition analysis via visualization.

As you watch the whole visualization, you’ll see that Aaron Koblin experimented with three different ways of displaying the same data. He starts with impermanent white lines over a dark background, then globs of oil-ish substance over a white background, then he applies color to the original white-on-black version. I like the political implication of using the oily blobs – that is what we are collectively doing when we’re flying – burning up vast quantities of fossil fuels by using just about the least fuel-efficient form of transportation we’ve got. Vehicles for traveling outside earth’s orbit are even less efficient. I still think the white-on-black version works best because I couldn’t figure out what the colors represented.

I love the total flight counter and the running clock. Adds a great deal of contextual information very subtly.

What Needs Work

I think this animation does a great job of showing what it sets out to show – the flight patterns in the US over a 24 hour period. If there was an intention to include data about the environmental cost, I would have liked something that isn’t quite as subtle as showing the patterns using blobs of oil-like substance. But modeling that sort of data would be even more complicated than what was done here because it would count on knowing how big each plane was – jets use more fuel than smaller planes – and some estimate of how heavy it was – full flights use more fuel than empty ones.

I also wanted to know if this represents all passenger and cargo flights, or if it is just passenger flights?

Relevant Resources

Aaron Koblin’s website and a link to the specific animation related to this post.

For more on globalisation, see Saskia Sassen who was interviewed about her work by John Sutherland at the Guardian in 2004.

For more on the relationship between aircraft and climate change see this slightly outdated 2001 report from the Intergovernmental Panel on Climate Change (UNEP)