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

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:

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

Uh… This is purely out of boredom. In welcome of the Season 6 of CW show Supernatural, I took a look at the path Sam and Dean have traveled. Guess which state hosted the most monsters?

Congratulations to Illinois! Then there goes South Dakota, Nebraska, Kansas, Pennsylvania, Ohio, Iowa, Indiana, Colorado, Missouri, Wisconsin, Oklahoma, Minnesota and California. The supernatural communities, be them spirits, creatures, mutants, psychics, witches, demons, angels or gods, definitely share taste in choosing playgrounds.

What is your mental map of a city? I bet it’s not measured in miles. This project is part of my work in the SENSEable City’s workshop this semester. In these distorted maps of Paris, the distance between a spot and the city center is not proportional to their geographical distance, but the cost taken to get there.

Standard map vs. driving time map of Paris: the city center expands from congestion, and the edge is denser.

Comparing the isochronic map of Paris under different transportation modes: (unit: minutes, click to zoom in)

Think driving is better? However, if we map the city using carbon footprint as distance: (unit: kg CO2, click to zoom in)

In the workshop I proposed an alternative to Google Maps on smartphone map services. I call it an isogreenic map. This would have a psychological influence on the user when he decides which transportation makes the trip easier:

Made with Processing.
Vector map: openstreetmap.org
Connection data: Google Directions, RATP.com

A demo video that shows how the transformation works:

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When analyzing the web contents of SNCF, I made this realtime tag cloud charter for Twitter. It’s a lot of fun.

Category tree view:

Relevance network view:

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HTML5 Video Player by VideoJS

Click to play with the app:

Tool used: Processing