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:
Presentation of the Copenhagen Wheel project at UN Climate Summit for Mayors in December was a huge success. I would like to post a few videos here which is related to my work:
The online user interface:
The idea was that pollutant information collected by the sensors embedded in wheels were transmitted to our server through smart phones. A web interface, accessible with both pc and phones, showed pollutant distribution in real time. One could browse his own data, keep a record of his contribution to a greener city, challenge with friends, and get route suggestions according to pollution level. The city management could keep an eye on the city and react to dangerous level of pollutants right away. The complete and high-definition record of pollutant maps could also cross-reference with land use, weather, transportation etc. in urban research and in long term, support policy decisions.
Tools used: Processing
Here are some following up images of the finally coming data. We are still figuring out the UI elements and color schemes.
Bird view of noise level:
CO level flowing through the streets:
NOx accumulative in one single day:
Urban heat islands:
Transportation and noise level:
The goal of the Copenhagen Wheel project is to create a smart, responsive and elegant emblem for urban mobility. It transforms ordinary bicycles quickly into hybrid e-bikes that also function as mobile sensing units. It allows you to capture the energy dissipated while cycling and braking and save it for when you need a bit of a boost. It also connect your bike to a larger community through smartphone to map pollution levels, traffic congestion, and road conditions in real-time.
I was recruited to visualize the data collected into interactive graphics. In the first review I set myself 3 goals: i) visual impact; ii) insightful interpretation; iii) support for easy reading & decision making.
Embedding sensors in bicycle wheels has obvious advantages that make the visualization interesting, such as mobility and realtime networking. However in current phase of the project, we had no prototypes finished, and could only expect around 15 sensors sent out before the final presentation. That data is too sparse on a city of 4 square km. Moreover, since we have loose control of where the riders go, it is almost impossible to get a filled map at a random time spot.
My first solution was what I called ‘merged time’: users saw data from different time on a merged map, but could also tell which are old and which are new. Data left a fading trace after them. Here is the first demonstration video I made for the concept.