The Connected States of America

The Connected States of America illustrates the emerging communities based on the social interactions defined by the anonymous cellphone usage data on AT&T’s network. It is a similar idea to the Redrawing Boundaries of Great Britain project we published early this year. One can find that the communities defined by human networks not always coincide with the administrative boundaries.

At the first phase of the project, visualizations were intensely used to help the scientists in our team to explore and validate the data. Comparing to the British dataset, the telecommunication pattern in the States featured many more hubs and more entangled connections, which made it harder to be represented in one static image. In the following map I used reversed coloring (the arc color on the source end is the color of the target county and vise versa. The color gradient helps identify geographic regions) to show which regions a point is most strongly connected to.

Another attempt was an interactive map where the user can click on a county to see its connectivity with all the other counties of the country. The strength of connections is defined by quantiles of total call time. The interactive map is also available on our project website. The following screenshot shows the outgoing connections from San Diego:

The partitioning algorithm showed some very interesting results, such as the split of New Jersey and California, and some other states belonging together.  Some clusters extend through the state lines and claim how people form communities despite of administrative boundaries. Yet the communities still largely correspond to state borders. I think it has something to do with the carrier’s rate policies.

The project has been covered by TIME Magazine and New York Times.

Data source & sponsor: AT&T
Team / Senseable: Carlo Ratti, Franscesco Calabrese(IBM Research), Dominik Dahlem, Xiaoji Chen
Team / AT&T Labs: Alexandre Gerber, DeDe Paul, Christoper Rath, James Rowland

Tools used: R, Processing, Illustrator


Sky Color of 10 Chinese Cities

Sky Color of Beijing 2000-2011

Well, not real colors of the sky – but you get the idea.

The dominant influence factor is the climate. Winter is the most polluted season because of thermal inversion and less rainfall. Spring in northern China suffers from sandstorm. Still, you can easily identify the effect of government intervention, such as the significant improvement in Taiyuan. And look how amazingly Beijing performed in 2008 August through September for the Olympics. Click the image below to zoom in.

Sky Color of Beijing 2000-2011 (small)

Tools used: R

Dedicated to my endearing home city.

Looking at the Forbidden City from Jingshan
[Looking into the Forbidden City from Jingshan – BJNews, March 21, 2011]

Update: Images now available as Flickr photostream

Isochronic Singapore: A Dynamic City Transportation Map

Update: If you are interested in isochronic maps, I have more detailed explaination of the process in my graduate thesis Seeing Differently: Cartography for Subjective Maps Based on Dynamic Urban Data, and the source code (Processing) is on GitHub

Last year I made my first attempt at isochronic map for the City of Paris, where the distance on map is proportional to travel time. Well, maps evolve.

Senseable City Lab is having this exciting exhibition Live Singapore! at Singapore Art Museum. We collaborated with Singapore government and companies of telecommunication, power, seaport, land transportation, etc. to create graphic visualizations that reveal Singapore’s urban dynamics.

In March I received the GPS location record of Singapore taxis of August, 2010. All taxis report their coordinates and availability status every few minutes during operation. Comparing to an animation of dense moving dots all over the map, I’m more interested in the underlying patterns of these activities and how they relate to the structure of land use and road layout.

Our brilliant scientist Chrisian Sommer built a network from this massive data and estimated the shortest travel time between every pair of places on an hourly basis. The data quality this time is far better than what I had for Paris (which was retrieved from Google Directions). It is dynamic, and it reflects real traffic condition. I used 290 control points over the city to distort the map. Selecting any of these points as origin, the other points will move away or towards it according to the travel time it takes to get there.

Isochronic Singapore - Screenshots Collage

The final app runs on a big display controlled by a Magic Trackpad. Visitors can click anywhere on the map to see its animation through the month. This video demos the maps for the central business district and the airport. It is quite interesting to see the response to road density, the expansion of congested area and the travel time explosion when rush hour comes.

This isochronic map is one of the series of cognitive maps I’m developing – beyond objective projections, we are enabled to see what the city looks and feels to its residence. Maps may not be static anymore, but reflect the dynamic nature of contemporary cities. Also, maps can be dependent on the user (location-based in this case) – they are now about individuals.

Followed by an introduction teaser to the whole event. I also did the anthropogenic heating one. The other beautiful visualizations are credited to Aaron Siegel and Oliver Senn.

Tools used: R, Processing, Illustrator

Collaborators: Christian Sommer, Kristian Kloeckl

Visualizing A Real-time Trivia Game

On April 10th, 2011 MIT held the Next Century Convocation as a centerpiece of its 150 anniversary celebration. 10,000 people attended the event at the Boston Convention and Exhibition Center. Before the program began guests participated in a trivia game designed by our team.

The game was a crowdsourcing experience, with participants asking their own questions to the crowd. Participants sent text messages to a short number through Ken’s awesome mSurvey system. Messages ending with “?” were recognized as questions and appeared on the screen with a sequential number. Messages started with “Q+number” followed by a space are recognized as answers to that question. Questions and answers were displayed in real-time, on a 90-foot screen.

My goal in designing this vis was to enable direct feedback to the users, invoke conversation among them and encourage participation. Considering the dimensions of the space, it was critical to keep the interface simple and learnable. Our team came up with this idea of “questions competing for answers” – each question moved from the left edge of screen to the right, gradually accelerating if nobody responded to it, till it was out of sight. When an answer came in, it was attached to the target question and therefore slowed it down. Each question left a trail behind it, whose width was related to speed. So the more popular questions would stay longer on the screen. It was a competition for both good questions and interesting answers.

Screenshot from the game:
Screenshot from the Trivial Game at MIT150 Convocation

Picture at the event:
Live photo at the convocation

I was a bit unhappy that the video staff insisted on adding moving backgrounds to the visualization. Then during the real run, we encountered some technical problem , causing the answers mismatched for a while (in fact a bug in the setup-at-the-last-minute message censorship system – censorship sucks, I knew it better than anyone). Anyway both the team and the guests had a lot of fun with the game. Some of us were sleepless for a few days to make this work, especially our great leader Ken, who’s been suffering from a fever since then – I hope he gets better now. ♥

Tools used: Processing

Visualization: Xiaoji Chen, Yanni Loukissas
Backend: Kenfield Griffith, Reid Williams
And thanks to the rest of the team: Michael Berry, Kristyn Maiorca, Ella Peinovich who made this happen

Power Chart of Chinese Provinces

Economist just posts an interactive visualization Chinese Equivalents on their website. It’s a very interesting approach. (Somehow I feel it has an psychological side-effect by saying one province is equivalent to France while it’s neighbor is equivalent to Kenya, though noted in terms of population.)

I got curious how we can visualize how actually important the Chinese provinces are. I was reading Gastner & Newman’s paper Diffusion-based method for producing density-equalizing maps (PNAS 2004) at the time. So I set out to make my first density-equalizing map.

Newman’s code on his website deals with raster image only. I also tried to implement a diffusion simulator in Processing, but it was hard to preserve all the details of a vector map. Below is my first shot. It took (quite) some manual effort to remove the bad points. Still seeking solutions. Anyway, enjoy.

You can recognize in this map how unbalanced China is – the west is barely occupied due to challenging natural environment, and population keeps flowing from the middle towards the economic centers (Beijing and the southeast coast). They become both productivity and pressure for big cities.

What about looking at the provinces from a social network’s perspective? In the following graph I measured how often each two provinces appear in the same media coverage. It is clear now that Beijing is the absolute, mono-center of all China. The social power is not proportional to a province’s population. The south and the east coast get far more attention than inland provinces, which is a sad fact.

Tools used: Processing, Tulip, Illustrator