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Analyzing and interpreting spatial and temporal variability of the United States county population distributions using Taylor's law

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  • Meng Xu
  • Joel E Cohen

Abstract

We study the spatial and temporal variation of the human population in the United States (US) counties from 1790 to 2010, using an ecological scaling pattern called Taylor's law (TL). TL states that the variance of population abundance is a power function of the mean population abundance. Despite extensive studies of TL for non-human populations, testing and interpreting TL using data on human populations are rare. Here we examine three types of TL that quantify the spatial and temporal variation of US county population abundance. Our results show that TL and its quadratic extension describe the mean-variance relationship of county population distribution well. The slope and statistics of TL reveal economic and demographic trends of the county populations. We propose TL as a useful statistical tool for analyzing human population variability. We suggest new ways of using TL to select and make population projections.

Suggested Citation

  • Meng Xu & Joel E Cohen, 2019. "Analyzing and interpreting spatial and temporal variability of the United States county population distributions using Taylor's law," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-25, December.
    • Handle: RePEc:plo:pone00:0226096
    DOI: 10.1371/journal.pone.0226096
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    References listed on IDEAS

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    1. Joel E. Cohen, 2016. "Statistics of Primes (and Probably Twin Primes) Satisfy Taylor's Law from Ecology," The American Statistician, Taylor & Francis Journals, vol. 70(4), pages 399-404, October.
    2. Alessia Naccarato & Federico Benassi, 2018. "On the relationship between mean and variance of world's human population density: A study using Taylor's power law," Letters in Spatial and Resource Sciences, Springer, vol. 11(3), pages 307-314, October.
    3. Michael K. Tippett & Joel E. Cohen, 2016. "Tornado outbreak variability follows Taylor’s power law of fluctuation scaling and increases dramatically with severity," Nature Communications, Nature, vol. 7(1), pages 1-7, April.
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    6. Robert Lang & Paul Knox, 2009. "The New Metropolis: Rethinking Megalopolis," Regional Studies, Taylor & Francis Journals, vol. 43(6), pages 789-802.
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    Cited by:

    1. Federico Benassi & Maria Carella, 2023. "Modelling geographical variations in fertility and population density of Italian and foreign populations at the local scale: a spatial Durbin approach for Italy (2002–2018)," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2147-2164, June.
    2. Kurubaran Ganasegeran & Mohd Fadzly Amar Jamil & Maheshwara Rao Appannan & Alan Swee Hock Ch’ng & Irene Looi & Kalaiarasu M. Peariasamy, 2022. "Spatial Dynamics and Multiscale Regression Modelling of Population Level Indicators for COVID-19 Spread in Malaysia," IJERPH, MDPI, vol. 19(4), pages 1-13, February.
    3. Yang Yang & Han Lin Shang & Joel E. Cohen, 2022. "Temporal and spatial Taylor's law: Application to Japanese subnational mortality rates," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1979-2006, October.
    4. Xu, Meng & Jiang, Mengke & Wang, Hua-Feng, 2021. "Integrating metabolic scaling variation into the maximum entropy theory of ecology explains Taylor's law for individual metabolic rate in tropical forests," Ecological Modelling, Elsevier, vol. 455(C).

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