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Limited Urban Growth: London's Street Network Dynamics since the 18th Century

Overview of attention for article published in PLOS ONE, August 2013
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Title
Limited Urban Growth: London's Street Network Dynamics since the 18th Century
Published in
PLOS ONE, August 2013
DOI 10.1371/journal.pone.0069469
Pubmed ID
Authors

A. Paolo Masucci, Kiril Stanilov, Michael Batty

Abstract

We investigate the growth dynamics of Greater London defined by the administrative boundary of the Greater London Authority, based on the evolution of its street network during the last two centuries. This is done by employing a unique dataset, consisting of the planar graph representation of nine time slices of Greater London's road network spanning 224 years, from 1786 to 2010. Within this time-frame, we address the concept of the metropolitan area or city in physical terms, in that urban evolution reveals observable transitions in the distribution of relevant geometrical properties. Given that London has a hard boundary enforced by its long standing green belt, we show that its street network dynamics can be described as a fractal space-filling phenomena up to a capacitated limit, whence its growth can be predicted with a striking level of accuracy. This observation is confirmed by the analytical calculation of key topological properties of the planar graph, such as the topological growth of the network and its average connectivity. This study thus represents an example of a strong violation of Gibrat's law. In particular, we are able to show analytically how London evolves from a more loop-like structure, typical of planned cities, toward a more tree-like structure, typical of self-organized cities. These observations are relevant to the discourse on sustainable urban planning with respect to the control of urban sprawl in many large cities which have developed under the conditions of spatial constraints imposed by green belts and hard urban boundaries.

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Geographical breakdown

Country Count As %
United Kingdom 2 2%
Korea, Republic of 2 2%
Germany 1 <1%
Italy 1 <1%
United States 1 <1%
Unknown 117 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 19%
Researcher 20 16%
Student > Master 11 9%
Student > Bachelor 10 8%
Student > Doctoral Student 9 7%
Other 25 20%
Unknown 25 20%
Readers by discipline Count As %
Engineering 19 15%
Social Sciences 18 15%
Environmental Science 13 10%
Computer Science 7 6%
Arts and Humanities 7 6%
Other 27 22%
Unknown 33 27%