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Hierarchical scaling in systems of natural cities
Department of Geography, College of Urban and Environmental Sciences, Peking University, Beijing, China.ORCID iD: 0000-0002-7804-9993
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.ORCID iD: 0000-0002-2337-2486
2018 (English)In: Entropy, E-ISSN 1099-4300, Vol. 20, no 6, article id 432Article in journal (Refereed) Published
Abstract [en]

Hierarchies can be modeled by a set of exponential functions, from which we can derive a set of power laws indicative of scaling. The solution to a scaling relation equation is always a power law. The scaling laws are followed by many natural and social phenomena such as cities, earthquakes, and rivers. This paper reveals the power law behaviors in systems of natural cities by reconstructing the urban hierarchy with cascade structure. Cities of the U.S.A., Britain, France, and Germany are taken as examples to perform empirical analyses. The hierarchical scaling relations can be well fitted to the data points within the scaling ranges of the number, size and area of the natural cities. The size-number and area-number scaling exponents are close to 1, and the size-area allometric scaling exponent is slightly less than 1. The results show that natural cities follow hierarchical scaling laws very well. The principle of entropy maximization of urban evolution is then employed to explain the hierarchical scaling laws, and differences entropy maximizing processes are used to interpret the scaling exponents. This study is helpful for scientists to understand the power law behavior in the development of cities and systems of cities. © 2018 by the authors.

Place, publisher, year, edition, pages
MDPI AG , 2018. Vol. 20, no 6, article id 432
Keywords [en]
Allometry, Entropy, Fractals, Hierarchy, Natural cities, Scaling
National Category
Other Engineering and Technologies
Research subject
Sustainable Urban Development
Identifiers
URN: urn:nbn:se:hig:diva-27519DOI: 10.3390/e20060432ISI: 000436275400041Scopus ID: 2-s2.0-85048716908OAI: oai:DiVA.org:hig-27519DiVA, id: diva2:1230647
Available from: 2018-07-04 Created: 2018-07-04 Last updated: 2023-03-28Bibliographically approved

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Jiang, Bin

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CiteExportLink to record
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