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Laws of population growth

Proceedings of the National Academy of Sciences - PNAS, 2008-12, Vol.105 (48), p.18702-18707 [Peer Reviewed Journal]

Copyright 2008 The National Academy of Sciences of the United States of America ;Copyright National Academy of Sciences Dec 2, 2008 ;2008 by The National Academy of Sciences of the USA ;ISSN: 0027-8424 ;EISSN: 1091-6490 ;DOI: 10.1073/pnas.0807435105 ;PMID: 19033186

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  • Title:
    Laws of population growth
  • Author: Rozenfeld, Hernán D ; Rybski, Diego ; Andrade, José S. Jr ; Batty, Michael ; Stanley, H. Eugene ; Makse, Hernán A
  • Subjects: Algorithms ; Cell growth ; Cities ; Datasets ; Demographics ; Economic growth rate ; Humans ; Metropolitan statistical areas ; Models, Theoretical ; Physical Sciences ; Population ; Population Growth ; Population growth rate ; Population mean ; Population size ; Power laws ; Socioeconomic Factors ; Standard deviation ; Urban Population
  • Is Part Of: Proceedings of the National Academy of Sciences - PNAS, 2008-12, Vol.105 (48), p.18702-18707
  • Description: An important issue in the study of cities is defining a metropolitan area, because different definitions affect conclusions regarding the statistical distribution of urban activity. A commonly employed method of defining a metropolitan area is the Metropolitan Statistical Areas (MSAs), based on rules attempting to capture the notion of city as a functional economic region, and it is performed by using experience. The construction of MSAs is a time-consuming process and is typically done only for a subset (a few hundreds) of the most highly populated cities. Here, we introduce a method to designate metropolitan areas, denoted "City Clustering Algorithm" (CCA). The CCA is based on spatial distributions of the population at a fine geographic scale, defining a city beyond the scope of its administrative boundaries. We use the CCA to examine Gibrat's law of proportional growth, which postulates that the mean and standard deviation of the growth rate of cities are constant, independent of city size. We find that the mean growth rate of a cluster by utilizing the CCA exhibits deviations from Gibrat's law, and that the standard deviation decreases as a power law with respect to the city size. The CCA allows for the study of the underlying process leading to these deviations, which are shown to arise from the existence of long-range spatial correlations in population growth. These results have sociopolitical implications, for example, for the location of new economic development in cities of varied size.
  • Publisher: United States: National Academy of Sciences
  • Language: English
  • Identifier: ISSN: 0027-8424
    EISSN: 1091-6490
    DOI: 10.1073/pnas.0807435105
    PMID: 19033186
  • Source: Geneva Foundation Free Medical Journals at publisher websites
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    PubMed Central
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