Taxi migration in Manhattan

While we’re on the topic of things moving on a map of changing camera angles, class project Taxi, by Tom McKeogh, Eliza Montgomery and Juan Saldarriaga, shows the movements of said vehicles in Manhattan, over 24 hours.

Geographic location data for the origin and destination of each ride is combined with waypoint data collected from the Google Maps API in order to generate a geographically accurate representation of the trip. We used data from taxi rides originating or ending in the neighborhoods of Lincoln center or Bryant Park. The visualization recreates a ‘breathing’ map of Manhattan based on the migration of vehicles across the city over a period of 24 hours, displaying periods of intensity, density and decreased activity.

I hope they do another iteration of this project. I bet they could do a lot more on the temporal side of things.

[Digital Urban via @kennethfield]

12 Comments

  • I love this video and the music..who’s song is this?

  • I’m sorry, but this is kind of a disaster. As someone who just left NYC after living in Manhattan and Brooklyn for 7 years, this was really frustrating to watch. The constantly shifting viewpoint, including sideways (um, we freak out when people do that with a 3-D pie chart, so why is it cool or ok with a map?) made it really hard, if not impossible, to get a read on the geography. I could ‘see’ Central Park some of the time, but not all, I couldn’t always nail down which way was uptown vs. downtown, which bridge was which, etc. So I couldn’t effectively decode the data and learn anything, really, except that taxis move around a lot, there are more of them on the streets during the day than late at night, and that the avenues are further apart than the blocks. I already knew those things!

  • If the map was shown from the top between 4 and 6 PM I would have found keeping the mystery going…

  • err… where do all the cabs go during early rush hour. or half my comment…

  • I appreciate your comments and I agree that at times the moving cameras make it hard to understand where things are happening. These two other animations (with 200,000 cab rides) have fixed cameras:

    Let us know what you think.

  • I did the music! You can find all my music here: http://soundcloud.com/statikluft

  • Can you provide any details on how you made the visualization? Tools used, techniques for changing the camera angle?

    Thanks!

    • The main programs we used were Processing, Excel and After Effects (just to compile the screenshots into a video). Once we cleaned the base data in Excel, we brought everything into Processing and through it we queried the Google Maps API to get more data. So, we used Processing both to get more data (from the API) and to visualize everything. The moving camera was something we wanted to try out. That was also done in Processing. Finally, we exported every frame and compiled them all in After Effects, including the music. If you want more details, for the movement of the camera, for example, let me know, I’ll be happy to provide them.

  • I appreciate what they’re trying to do here, but in the end I’m with Stephanie. Nice as an abstract work of art, but not helpful in providing information about taxi migration in Manhattan. We already knew there were lots of taxis moving around. The questions, or at least my questions, are about changes in routes and volume over time. That information is unavailable because the POV is constantly changing. The second set of links helps more, but most of my attention still gets focused on unraveling the point of view rather than the migration information.

  • Would love to see a fixed-camera version of this; I’m with Stephanie & mjillster. I’m not even looking for “information” per se, but during all the profile shots it just seemed like I was looking at a bunch of dots with no order or meaning whatsoever. Not informative, no, but not even interesting to watch. I like the two fixed-camera vids you provided, but truly the one I’d like to see is the one tracking actual cab movement. It’s so fun to watch, during the moments of this video when it can be seen from overhead.