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      Family names, city size distributions and residential differentiation in Great Britain, 1881–1901

      1 , 1 , 1
      Urban Studies
      SAGE Publications

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          Abstract

          Cities have specialised in particular urban functions throughout history, with consequential implications for urban and regional patterns of economic and social change. This specialisation takes place within overall national city size distributions and is manifest in different but often similarly variegated residential structures. Here we develop a novel and consistent methodological approach for measuring macro-scale city size and micro-scale residential differentiation using individual digital census records for the period 1881–1901. The use of family names and neighbourhood classification of dominant economic and social roles makes it possible to relate the changing city size distribution in Great Britain to patterns of urban growth and residential differentiation within urban areas. Together, we provide an integrated and consistent methodology that links the classification of all major urban area growth in Great Britain to attendant intra-urban geodemographic changes in urban residential structures. We suggest ways in which this manifests social and economic change across the settlement system for both new and long-established residents.

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          Rank clocks.

          Many objects and events, such as cities, firms and internet hubs, scale with size in the upper tails of their distributions. Despite intense interest in using power laws to characterize such distributions, most analyses have been concerned with observations at a single instant of time, with little analysis of objects or events that change in size through time (notwithstanding some significant exceptions). It is now clear that the evident macro-stability in such distributions at different times can mask a volatile and often turbulent micro-dynamics, in which objects can change their position or rank-order rapidly while their aggregate distribution appears quite stable. Here I introduce a graphical representation termed the 'rank clock' to examine such dynamics for three distributions: the size of cities in the US from ad 1790, the UK from ad 1901 and the world from 430 bc. Our results destroy any notion that rank-size scaling is universal: at the micro-level, these clocks show cities and civilizations rising and falling in size at many times and on many scales. The conventional model explaining such scaling on the basis of growth by proportionate effect cannot replicate these micro-dynamics, suggesting that such models and explanations are considerably less general than has hitherto been assumed.
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            A century of sprawl in the United States.

            The urban street network is one of the most permanent features of cities. Once laid down, the pattern of streets determines urban form and the level of sprawl for decades to come. We present a high-resolution time series of urban sprawl, as measured through street network connectivity, in the United States from 1920 to 2012. Sprawl started well before private car ownership was dominant and grew steadily until the mid-1990s. Over the last two decades, however, new streets have become significantly more connected and grid-like; the peak in street-network sprawl in the United States occurred in ∼ 1994. By one measure of connectivity, the mean nodal degree of intersections, sprawl fell by ∼ 9% between 1994 and 2012. We analyze spatial variation in these changes and demonstrate the persistence of sprawl. Places that were built with a low-connectivity street network tend to stay that way, even as the network expands. We also find suggestive evidence that local government policies impact sprawl, as the largest increases in connectivity have occurred in places with policies to promote gridded streets and similar New Urbanist design principles. We provide for public use a county-level version of our street-network sprawl dataset comprising a time series of nearly 100 y.
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              Limited Urban Growth: London's Street Network Dynamics since the 18th Century

              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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                Author and article information

                Contributors
                (View ORCID Profile)
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                Journal
                Urban Studies
                Urban Studies
                SAGE Publications
                0042-0980
                1360-063X
                August 2022
                July 19 2021
                August 2022
                : 59
                : 10
                : 2110-2128
                Affiliations
                [1 ]University College London, UK
                Article
                10.1177/00420980211025721
                524e162c-3936-4c42-af81-de0221eac35e
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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