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      Settlement scaling theory: Bridging the study of ancient and contemporary urban systems

      1 , 2 , 1 , 3
      Urban Studies
      SAGE Publications

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          Abstract

          A general explanatory framework for the social processes underpinning urbanisation should account for empirical regularities that are shared among contemporary urban systems and ancient settlement systems known throughout archaeology and history. The identification of such shared properties has been facilitated by research traditions in each field that define cities and settlements as areas that capture networks of social interaction embedded in space. Using Settlement Scaling Theory (SST) – a set of hypotheses and mathematical relationships that together generate predictions for how measurable quantitative attributes of settlements are related to their population size – we show that aggregate properties of ancient settlement systems and contemporary metropolitan systems scale up in similar ways across time, geography and culture. Settlement scaling theory thus provides a unified framework for understanding and predicting these regularities across time and space, and for identifying putative processes common to all human settlements.

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          Most cited references48

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          Increasing Returns and Economic Geography

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            Location and Land Use

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              Buzz: face-to-face contact and the urban economy

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                Author and article information

                Journal
                Urban Studies
                Urban Studies
                SAGE Publications
                0042-0980
                1360-063X
                March 2020
                October 17 2019
                March 2020
                : 57
                : 4
                : 731-747
                Affiliations
                [1 ]Arizona State University, USA
                [2 ]The University of Chicago, USA
                [3 ]University of Colorado-Boulder, USA
                Article
                10.1177/0042098019873796
                3f91e8ef-0f6c-4f13-8d29-c78762cdfbb9
                © 2020

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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