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World’s Subways Converging on Ideal Form | Wired Science | Wired.comBy Brandon KeirnMay 15, 2012
After decades of urban evolution, the world’s major subway systems appear to be converging on an ideal form … [Statistical physicist Marc] Barthelemy and National Center for Scientific Research complex systems analyst Camille Roth focused a network analysis lens on the aforementioned cities’ subways, along with Barcelona, Beijing, Berlin, Chicago, Madrid, Mexico, Moscow, Osaka, Paris, Seoul and Tokyo … Patterns emerged: The core-and-branch topology, of course, and patterns more fine-grained. Roughly half the stations in any subway will be found on its outer branches rather than the core. The distance from a city’s center to its farthest terminus station is twice the diameter of the subway system’s core. This happens again and again.
“Many other shapes could be expected, such as a regular lattice,” said Barthelemy. “What we find surprising is that all these different cities, on different continents, with different histories and geographical constraints, lead finally to the same structure.”
Subway systems seem to gravitate towards these ratios organically, through a combination of planning, expedience, circumstance and socioeconomic fluctuation, say the researchers … The convergence “is a sign that there are some basic, profound mechanisms that drive the development of urban systems,” said Barthelemy.](http://25.media.tumblr.com/tumblr_m4hckvYqGV1qzok36o1_500.jpg)
The complexity of cities (a diverse and always changing environment) produces a huge amount of data. The growing availability of tools to generate, capture, store, manage and analyze this data opens up a wide spectrum of possibilities around those big data. The opening up of public data (public transport, traffic flows, water, waste, use of space, business, etc.) offers the possibility of transforming them into far more useful information than just messy and purely statistical aggregation. The result of this in a context of wide spreading of mobile devices helps to understand the social value of creating new apps that use this data to give users greater ability to interact and experience the city from their own needs. Visualization has become a expanding tool in recent years.
Here is a selection of some work I find suggestive as good examples of how to visualize the intensity of urban life in video format or as interactive web tools. I have chosen from the archives only a few examples that seem interesting, so I welcome other contributions that you know (and take a lok at this recent compilations to find other examples: London: A Year in Maps and The best of 2011 from Spatial Analysis):
Traffic accidents in the U.S.
An impressive work that collects all traffic accidents on different roads of the United States by type of accident (pedestrian, driver, year, etc..) And all in one map that has accumulated a huge range of information for the period 2001 - 2009. The same team has prepared one for the UK. Guns of mass destruction? A silent tragedy? The map is shocking.
The long journey of trash
I wrote some lines about this project from MIT some time ago. What is worth watching in the video is how it explains the concept of the project and the result of adding location aware tags to different types of trash and see how each of them travel a huge amount of miles until final disposal. Waste management and removal is an obscure and secret system (throw away and forget about them) and the project helps to visualize and understand there is a much more extensive life than we imagine for the trash we throw away.
A public hire bike system in real time
This map visualizes all bikes of the public hire schemes in London. From the same site, in fact, you can access and check other cities (Zaragoza, Toronto, Lille, etc.). The project displays information on the distribution of all the checkin points, the level of use at any given time, temporal progression of use of each terminal and the availability or not of bicycles at each point.
The intense activity of a subway network
What to do with the data from every user entries in the extensive network of subway in New York? This phenomenal work published by Wall Street Journal is a good example of how to use information from seemingly irrelevant individual data: types of tickets, stations, schedules, fares, etc. Put this in a map and add logic to the data to understand, among other things, the variation in use according to the tariff changes introduced in the price system.
Real-time use of bicycles
Another one about bikes. The video shows the flow dynamically of the bicycles used moving through the 18 hours of the day. I also mentioned this and other projects about London some weeks ago.
A U.S. map block by block
Mapping America: Every City, Every Block
What can you do with the census data? With this map you can reach the level of detail of every building anywhere in the country and see the distribution of population by race, by income, by type of household, type of housing or education, and understand the dynamics of spatial distribution at national, regional, urban or neighborhood level.
Time distance to get around the city
MySociety developed years ago this project that perfectly illustrates the utility of georeferenced data. Mapumental tool displays the travel time to reach a certain point from anywhere in the city, thereby helping to understand the temporal distance mobility, a much more useful and practical information than just physical distance.
The changing city. Day and night
A simple but powerful idea. The population of New York during the day and at night, reflecting the density of different areas.
Singapore real time
Another well-known MIT project from Seansable City Lab. Using different data sets and maps designed to explain the impact of rain on the level of use of the taxi in the city, predicted travel time based on changing traffic conditions , the heat island effect or continuous flow of arrivals and departures of people and goods in a city that serves as a hub of the global economy. The video explains it all.
Understanding air pollution
Make visible the invisible dirty air we breathe, nothing less. That’s what Nerea Calvillo proposed in a dynamic model to visualize and map the footprint of air pollution in Madrid.
