Physics maps city complexity

By Kimberly Patch, Technology Research News

If you're trying to find a city that is both easy to navigate, like Manhattan, and filled with quiet, low-traffic neighborhoods, like Stockholm, don't hold your breath. These two characteristics of city life are a trade-off. This becomes clear when you look at city streets as information networks.

Streets are one of many types of networks that are all around us -- others include the Internet, social networks, and biological food chains. A network is made up of nodes and the links between nodes. For example, streets and intersections, Web pages and links, people and social relationships, and animals and predator-prey relationships.

Researchers from Umea University in Sweden and the Nordic Institute of Theoretical Physics in Denmark have compared city street networks from the standpoint of information handling by looking at road maps in terms of the information needed to locate specific addresses.

The information needed to navigate in a city can be used to quantify and compare the complexity of cities, said Martin Rosvall, a researcher at Umea University. The method could eventually be used to allow city planners to see how street changes affect navigability and could also be used to make other types of networks -- like supermarket aisles and airways -- work more efficiently, he said.

The researchers' model assumes that a person traveling along the streets of the city gets all travel directions in the form of the sequence of roads that lead to the target road. In networking terms, these sequences are sets of nodes linked by intersections. From this perspective, all roads are the same regardless of how long they are. The number of intersections between roads is the measure of the information distance between them. This makes sense intuitively; the more turns there are along a route, the harder it is to follow.

The model shows each main road as a central hub and each crossroad as a peripheral node connected to the hub. A grid of streets appears as a ring of nodes. Connections between streets form a many-pointed star inside the ring, with each of the star's points meeting the ring at a node.

The researchers found that city street networks resemble well-studied models of biological and social networks. They also found that it is more difficult to get from a given street to another given street in a real city street network than in a random network of streets. "We were… surprised that streets in cities on average hide from each other compared to a randomized city," said Rosvall.

The finding was surprising only until the researchers realized that the scenario was reversed on the neighborhood level. This showed that the topologies of real cities reflect a tendency for neighborhoods to avoid being exposed to traffic not destined for the neighborhood. "We simply do not want to live too close to traffic arteries," said Rosvall. "Neighborhoods protect us from the disturbance of traffic."

The researchers' model confirmed the widely-held view that the roads of Manhattan are simpler in terms of information handling than cities with complicated road-construction histories. "Historical cities have an overabundance of short streets that make the cities more complex in the sense that they increase the information distance between streets," said Rosvall.

The researchers' model also showed that ring roads with few exits shortened the information distance in cities, making them easier to navigate.

Many groups of researchers are studying complicated random networks like the Internet to glean information about how they grow and how they handle traffic. There have been fewer studies focusing on communication, which is manifested in the connections between specific nodes. "The main focus has been broadcasting of information like in spreading viruses and advertisements," said Rosvall. "We believe that the specific communication point of view is more important to get an understanding of network structure."

The researchers' next step is to use the method to study the evolution of cities, said Rosvall. "We're looking for historical data with snapshots at different times of the same city."

It would take one to two years for the method to be ready for practical use, said Rossvall. The growth and modification of cities are very slow processes, and so any implementation of the method would take at least five years, he said.

Rosvall's research colleagues were Ala Trusina, P. Minnhagen and Kim Sneppen. The work appeared in the January 21, 2005 issue of Physical Review Letters. The research was funded by the Swedish Research Council.

Timeline:   1-2 years
Funding:   Government
TRN Categories:   Networking; Physics; Applied Technology
Story Type:   News
Related Elements:  Technical paper, "Networks and Cities: An Information Perspective," Physical Review Letters, January 21, 2005


June 29/July 6, 2005

Page One

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