In my last post about visualizing Movement in Manhattan I mentioned that it would be interesting to explore a more direct view of the data by using an animation. I have created such a video based on a fresh collection of tweets from Monday, April 30th. I gathered new data because I realized that my previous data set was collected over the weekend and I suspected that a weekday might provide more obvious patterns. It compresses 24 hours of data into 1 minute of video. Here it is:
I was influenced by the 'Fireflies' video showing iPhone traces done by Michael Kreil. In particular, I like the idea of using larger but more transparent graphics to represent the increased uncertainty when drawing interpolated locations. Basically, if a person tweets at location A and then again at location B ten minutes later the model I used assumes they moved at a constant speed in a straight line between those two events. This is an obviously crude approximation and leads to unrealistic paths in many cases. By increasing the transparency in between the two measured events it shows this uncertainty in a visual manner.
Again, as I saw in the original version, the patterns of tweets, both moving and static are quite chaotic. You can easily see the rise and fall of tweets over the changing time of day and some local patterns that look interesting but the patterns are still a bit of a jumble.
The geolocated tweets were collected with the library Twitter4J which was used from code written in Processing. I used this tutorial created by Jer Thorp to get started with the library. Code from this flow field sample by Daniel Shiffman was used as a starting point to create my flow maps. The background map is from OpenStreetMap. Thanks everyone!