@article{TEXTUAL,
      recid = {6080},
      author = {Xu, Meng and Cohen, Joel E.},
      title = {Spatial and temporal autocorrelations affect Taylor's law  for US county populations: Descriptive and predictive  models},
      journal = {PLOS ONE},
      address = {2021-01-07},
      number = {TEXTUAL},
      abstract = {<p>Understanding the spatial and temporal distributions  and fluctuations of living populations is a central goal in  ecology and demography. A scaling pattern called Taylor's  law has been used to quantify the distributions of  populations. Taylor's law asserts a linear relationship  between the logarithm of the mean and the logarithm of the  variance of population size. Here, extending previous work,  we use generalized least-squares models to describe three  types of Taylor's law. These models incorporate the  temporal and spatial autocorrelations in the mean-variance  data. Moreover, we analyze three purely statistical models  to predict the form and slope of Taylor's law. We apply  these descriptive and predictive models of Taylor's law to  the county population counts of the United States decennial  censuses (1790–2010). We find that the temporal and spatial  autocorrelations strongly affect estimates of the slope of  Taylor's law, and generalized least-squares models that  take account of these autocorrelations are often superior  to ordinary least-squares models. Temporal and spatial  autocorrelations combine with demographic factors (e.g.,  population growth and historical events) to influence  Taylor's law for human population data. Our results show  that the assumptions of a descriptive model must be  carefully evaluated when it is used to estimate and  interpret the slope of Taylor's law.</p>},
      url = {http://knowledge.uchicago.edu/record/6080},
}