Health Infoscape


Senseable City Lab partnered with GE to create new ways of understanding human health. Our team created a disease network by analyzing data from over 7.2 million anonymized electronic medical records, taken from between January 2005 and July 2010, across the United States.

Barabasi’s lab has published their disease networks generated by genetic similarity in 2007. In our first attempt, diseases/disorders are considered associated if a patient has got them at the same time or sequentially. The resulting network gives us new insight as to how closely connected some seemingly un-related health conditions might be. Such results force us to re-examine conventional categories of disease classification, as the boundaries between traditional disease categories are thoroughly blurred.

I made this interactive map for the general public to browse the data. You can switch between two layouts – network and circular. Dot sizes are proportional to the percentage of patients who sought medical attention in total population. Width of links shows the strength of connections. Hovering over a disease pops up detailed information. Clicking on a disease highlights its connections. It is also possible to filter the links by gender / category / keywords. Zooming and panning is supported. The aim is to let people locate their disease of interest quickly in a context.

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It is a huge network. The first data files I got was more than 50M in size. It took me some time to get the loading time and real-time performance of the app to an acceptable level. I tried several schemes to filter the links while keeping the look and the structure. The force-directed layouts are pre-calculated. I ended up hard coding the network data instead of loading it from an external file, which helped a lot. The final package was ~1.5M, quite satisfying. And I do like the transition animation a lot.

The network vis was made with Flex and the visualization library Flare, and the user interface with Flash CS4. I’ve had some experience with Actionscript 3, but this is my first time learning Flex. I don’t know if it’s just my computer (Snow Leopard + Firefox 4 + Flash Builder 4), but debugging a Flex application was so much pain. The Flash plugin just crashes at every breaking point. Flare is a really well-done library, I’m truly grateful, but there is so much missing in its documentation. You have to dig into its source to discover the vast possibility of it.

The project was done around the time of my thesis “Seeing Differently: cartography for subjective maps based on dynamic urban data” (and the part of my life that I’m reluctant to look back to). I think the time I really worked on the core part of it was two weeks, then a lot of minor changes over a two months period. This is probably the most polished interactive vis among all projects I’ve posted. Thanks Eric and Dom! ūüôā

Team: (Senseable) Carlo Ratti, Eric Baczuk, Dominik Dahlem, Xiaoji Chen
(General Electric) Camille Kubie, Aimee Atkinson

Tools used: Flash, Flex, Flare, R

 

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

 

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

Redrawing the Map of Great Britain from a Network of Human Interactions


This paper has been published on PLoS ONE: full text

Do regional boundaries defined by governments respect the natural way that people interact across space? The URB team of SENSEable City Lab analyzed 12 billion anonymized landline calls in Great Britain to illustrate the true connections between places. The strength of connection is defined by the frequency and period of phone calls. It is revealed that people tend to communicate with those that are geographically close to them. Therefore, it is possible to identify clusters of connections as regional groups. It is fun to compare these new boundaries with existing ones and see how much people really love each other.

The visualization challenge here is the extra dense connections. An ideal vis solution should show clearer and finer pattern as data accumulates, not the opposite. Mauro Martino worked with the team from the beginning and derived the primary concept. I hopped on board later and finished with the final video to elaborate the whole idea. Processing is not able to handle this scale of objects (especially in animation) so a lot of pre-processing was done exclusively for each scene.

For those who cannot use YouTube, click this instead:

 

The research has also been covered by BBC and The Economist.

Collaborators in visualization: Mauro Martino, Francesco Calabrese
Tools used: Processing, R, Premiere