Truncated lognormal distributions and scaling in the size of naturally defined population clusters

Using population data of high spatial resolution for a region in the south of Europe, we define cities by aggregating individuals to form connected clusters. The resulting cluster-population distributions show a smooth decreasing behavior covering six orders of magnitude. We perform a detailed study...

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Detalhes bibliográficos
Autores: Corral, Á., Udina, F., Arcaute, E.
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2020
País:España
Recursos:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositório:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2072/446112
Acesso em linha:http://hdl.handle.net/2072/446112
Access Level:Acceso aberto
Palavra-chave:51
Descrição
Resumo:Using population data of high spatial resolution for a region in the south of Europe, we define cities by aggregating individuals to form connected clusters. The resulting cluster-population distributions show a smooth decreasing behavior covering six orders of magnitude. We perform a detailed study of the distributions, using state-of-the-art statistical tools. By means of scaling analysis we rule out the existence of a power-law regime in the low-population range. The logarithmic-coefficient-of-variation test allows us to establish that the power-law tail for high population, characteristic of Zipfs law, has a rather limited range of applicability. Instead, lognormal fits describe the population distributions in a range covering from a few dozen individuals to more than 1×106 (which corresponds to the population of the largest cluster). © 2020 American Physical Society.