Archive for the ‘Web’ Category
Soda versus pop on Twitter
Last Updated on Thursday, 23 August 2012 10:15 Written by Celframe Web Team Tuesday, 28 August 2012 04:48
Edwin Chen, a data scientist at Twitter, explored the geographic differences in language usage of soda, pop, and coke. We’ve seen this before, so it shouldn’t be surprising to see that in the United States soda is dominant on the coasts, pop in the midwest, and coke in the southeast. The global view is new, with coke basically penetrating almost all of Europe.
What I think is most interesting though is the idea of tweets and status updates as data that represents cultures. There are applications that keep track of tweet volume, number of replies, and when the best time to share a link is, but in ten years none of that will matter. These miniature data time capsules on the other hand will be worth another look.
Where you measure up against Olympians
Last Updated on Thursday, 23 August 2012 10:15 Written by Celframe Web Team Monday, 27 August 2012 06:18
I think the theme of this year’s Olympic graphics is how you relate to athletes. In this interactive by the BBC (in Spanish), height and weight of medal winners from the last Olympics in Beijing are plotted against each other. The more red, the more athletes with that weight-height combination, and you can click on a square to see the corresponding athlete(s). The twist is that you can enter your own height and weight to see where you are in the mix.
Combine this with the recent age piece from the Washington Post, and you’ve got a more complete picture. Why stop there though? I want country, gender, and hair color breakdowns. [Thanks, Ben]
Movement in Manhattan: Mapping the Speed and Direction of Twitter Users
Last Updated on Thursday, 23 August 2012 10:15 Written by Celframe Web Team Sunday, 26 August 2012 09:48
Inspired by the animated wind map that was posted a little while ago, professional programmer Jeff Clark has explored how people move about in a city. The result, titled Movement in Manhattan [neoformix.com], visualizes the speed and direction of Twitter users in Manhattan, New York.
The visualization is based on a large collection of geo-located tweets that were sent in a 4-hour time-window by the same users. These tweets were used as samples that together construct a vector field representing the average flow of people within a specific area. Particles, representing people, were released at locations where actual tweets were recorded and their subsequent movement was determined by the flow field.
The lines are thus traces of these moving particles, which start out blue and gradually change to red to show the direction of movement.Locations where there is little movement will have blue dots or very short blue traces. Longer traces with more red show a greater speed at that point.
See also Ville Vivante: Tracing the Liveliness of Mobile Phone Usage in Geneva.