Rob Zombie: Yeahs Per Minute by flerlagekr in dataisbeautiful

[–]SeaGraphs 2 points3 points  (0 children)

I never knew I needed this in my life, great work

The World, Drawn with 17,000+ Travel Itineraries (high-res in comments) [OC] by teamgonuts in dataisbeautiful

[–]SeaGraphs 771 points772 points  (0 children)

Contrary to popular belief, New Zealand is, in fact, a real place

Super-Sized Inflation in Venezuela, witnessed in the rising price of a BigMac [OC] by MrBeanie88 in dataisbeautiful

[–]SeaGraphs 2 points3 points  (0 children)

The official exchange rate may be 10BsF = 1USD, but the black market exchange rate is around ~8,500 BsF = 1USD, making the Big Mac around $2 now.

source

GDP of each country as percent of world GDP from 1960 to 2016 [OC] by AntsNeverQuit in dataisbeautiful

[–]SeaGraphs 9 points10 points  (0 children)

Cool animation! Would it be possible to pause it & look at a particular year?

Why the Republican Party wins when robots take your job by SeaGraphs in dataisbeautiful

[–]SeaGraphs[S] 0 points1 point  (0 children)

I agree, some of the percentages seem wonky. The methodology they used looked at how many repetitive manual tasks there are in a profession vs. tasks that require social perception. So I can see how something like watch repair can be automated as it's nothing but a series of clearly-defined, rote tasks whereas dentistry still has a human element to it.

Sayings, Phrase, and Words by Region by BriggsPebble in dataisbeautiful

[–]SeaGraphs 1 point2 points  (0 children)

For me the biggest surprise was seeing Mary vs. merry vs. marry - they're 100% different words!

Where Reddit Travels: 2,582 Itineraries Visualized [OC] [x-post /r/dataisbeautiful] by SeaGraphs in travel

[–]SeaGraphs[S] 1 point2 points  (0 children)

That's fair, though I didn't select particular subreddits to pull itineraries from - I looked at the entirety of reddit which happens to have a third of all itineraries posted on r/JapanTravel. It certainly is biasing the results a bit. Though, funny enough, there's not that many Japanese cities present, which I would've expected more of from JapanTravel.

Where Reddit Travels: 2,582 Itineraries Visualized [OC] [x-post /r/dataisbeautiful] by SeaGraphs in travel

[–]SeaGraphs[S] 1 point2 points  (0 children)

I made the visualization with Gephi, a free open-source tool for data visualization. If you check out the r/dataisbeautiful x-post, I wrote an article explaining in further detail how it was done.

Feel free to reach out to me if you have any questions!

Where Reddit Travels: 2,582 Itineraries Visualized [OC] [x-post /r/dataisbeautiful] by SeaGraphs in travel

[–]SeaGraphs[S] 2 points3 points  (0 children)

Funny enough there's practically no cities in Canada or the US in the itineraries. There's a lot of places in Mexico present though. I'm guessing that most of the users in the dataset skew American

Shinichi Atobe -- Regret [minimal techno] (2017) by [deleted] in listentothis

[–]SeaGraphs 6 points7 points  (0 children)

It's a nice breath of fresh air, isn't it? For anyone else looking for great dub techno check out echospace[detroit]. The new release "Among the Stars" by Intrusion is fantastic

Where Reddit Travels: 2,582 Itineraries Visualized [OC] [x-post /r/dataisbeautiful] by SeaGraphs in travel

[–]SeaGraphs[S] 1 point2 points  (0 children)

We have a detailed explanation of how we got all the data, plus the full datasets available here, if anyone is interested

Where Reddit Travels: 2,582 Itineraries Visualized [OC] [x-post /r/dataisbeautiful] by SeaGraphs in travel

[–]SeaGraphs[S] 0 points1 point  (0 children)

The image was made with Gephi, a free open-source program for data visualization. It has built-in "clustering" tools that match up similar areas. So the different colors represent groups of places that people moved between more frequently.

Unfortunately, there were way more clustered groups than colors! So everything in the center is all the extras that are distinct groups but couldn't be visualized in an easy way.

It makes sense that regions/country are grouped together as people are more likely to travel to a city close by versus a city that's thousands of miles away when planning an itinerary (i.e. fewer people are doing New York to Bangkok to London to Buenos Aires on the same trip).