Shipping Mix

October 20, 2011

(This article was first published on IDV User Experience, and kindly contributed to R-bloggers)

With a fresh pile of historical global shipping data, we came back to the flow visualizations that illustrated tangible supply lines that facilitate global trade.  This time we’ve isolated two types of shipping vessels, cargo and tanker, in order to compare their well worn paths, and heatmapped them individually.  We can generally assume that cargo vessels (the big guys with stacks of shipping containers) are transporting the manufactured goods and materials of international trade, while tankers (enormous floating tubs) carry bulk liquids -almost always petroleum.
So, green means stuff, pink means oil.
Lots of shipping data heatmapped to show de facto shipping lanes.
Lumped together, though, we don’t get a sense for what is being shipped.
Looking at these categorical routes can give us a more nuanced sense of what is flowing where.  It’s not hard to imagine extrapolating this idea to visualize very specific import/export items, or additional vessel types, or by hazardous content flag.  Another idea is to segment this data out by month-of-the-year to get a sense for seasonal fluctuations in global trade flow.  That time data could be mapped individually, like we’ve done here with vessel type, or better yet go full-on stats nerd and run them through a hotspot engine to isolate the time-space intersection of shipping spikes.  Actually, that would be pretty awesome.
Anyways, here are the results of the comparative cargo/tanker traffic mapping…
Southwestern Europe

Aegean Sea 

Gulf Coast 

Hong Kong 

Eastern Mediterranean 

Persian Gulf 



To leave a comment for the author, please follow the link and comment on their blog: IDV User Experience. offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...

If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Tags: , , , , , , , , , , , , , ,

Comments are closed.


Mango solutions

RStudio homepage

Zero Inflated Models and Generalized Linear Mixed Models with R

Quantide: statistical consulting and training



CRC R books series

Contact us if you wish to help support R-bloggers, and place your banner here.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)