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

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

 

Map of Paris: Visualizing Urban Transportation


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.

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: