This past weekend Al-Shabab, the Al Qaeda affiliate based in Somalia, threatened shopping malls in the United States, Canada, and the United Kingdom. This threat carries a certain amount of weight given the deadly attack Al-Shabab launched against the Westgate Shopping Mall in Nairobi, Kenya a few years ago.
So what to look at today? Well, a few weeks back a colleague sent me a link to a Bloomberg article about the American shopping mall. The article examines the makeup of stores, the people shopping, and the regionalisation in the food court(s). On a personal note, I was glad to see that King of Prussia received a mention.
Credit for the piece goes to Dorothy Gambrell and Patrick Clark.
One of the possible set of sanctions against Russia by the United States and European Union would impact the country’s defence industries. This chart by the Economist shows how that might not have the most impact. Most of Russia’s arms exports go to China, India, and Algeria. None of whom are the United States or European Union.
Clearly I don’t love the pie charts. I would much rather have seen segmentation within the bars. Or a full-on Sankey diagram. But, the story is still worth telling.
Today’s post is not news-related for a change. (Don’t worry, I’ll likely get back to that next week.) Instead, we have a new collection of mobile data visualisations curated by Sebastian Sadowski. You can choose to see either smartphone or tablet visualisations and then filter by visual form.
Credit for the site goes to Sebastian Sadowski, to the various works to the various designers.
Fear not, this graphic makes about as much sense as the title. The concept is actually a worthwhile exploration of the variation in caffeine across cups of coffee from different cafes and coffee shops. But, this visualisation fails at showing it.
Remember, pie charts show the piece amongst the whole. What is the whole in this case? A cup of coffee? No, the data labels indicate milligrams per fluid ounce. It appears as if 60mg./fl. oz. is the whole. A bit arbitrary that. So what happens if you lose the trite pie as a cup of coffee device and simply chart the values. Oh wait, that’s not very hard to do. (I also threw in what I believe to be the benchmark for an average cup of brewed coffee, though I could be wrong.)
Much clearer. More concise (I used less than the original’s dimensions).
Credit for the original piece goes to Dan Gentile.
Happy Friday, everyone. Today’s post comes via colleagues of mine in London, who shared with me the Guardian’s selection of 16 useless infographics. They are shit infographics. Well, at least one is. Check them out and you’ll understand.
Credit for the selection goes to Mona Chalabi. Credit for each infographic belongs to the infographic’s respective designer.
Monday was an odd day, both 1 April and the start of baseball. I had a tough decision to make: Do I post a serious baseball-related piece or a humourous April Fool’s Day one instead? If you recall, I went for the serious baseball option. But that leaves me with Friday, where I try to post work that is a bit on the lighter side of life.
So here is EagerPies, published by EagerEyes on 1 April. It’s in the style of the EagerEyes site, a blog with posts about data visualisation. This selection is EagerPies work to improve upon Minard and the layering of data sets. But if you worry about complexity, fret not for they realised that encoding data in transparency would be a step too far.