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Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs.

Tom Wilson1, Irina Grossman1, Monica Alexander2

  • 1Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie St, Melbourne, VIC 3010 Australia.

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Small area population forecasting methods are limited but crucial for planning. This review assesses current techniques and identifies key areas for future research to improve accuracy and usability.

Keywords:
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Area of Science:

  • Demography
  • Geographic Information Science
  • Statistical Modeling

Background:

  • Small area population forecasts are vital for governmental and business planning, influencing significant investment decisions.
  • Current methodologies for small area population forecasting are less developed compared to national-level forecasting.

Purpose of the Study:

  • To review and assess the state of small area population forecasting methods published between 2001 and 2020.
  • To identify and suggest avenues for future research in small area population forecasting.

Main Methods:

  • Literature review of small area population forecasting methods (2001-2020).
  • Analysis of key themes including extrapolative, comparative, cohort-component, model averaging, downscaling, microsimulation, machine learning, and uncertainty quantification.

Main Results:

  • The review covers a range of methods, from traditional extrapolative techniques to advanced approaches like machine learning.
  • Identified themes include the incorporation of socioeconomic variables, spatial relationships, and the linkage of population with housing.

Conclusions:

  • Further research is needed in model averaging, developing novel methods for challenging scenarios, and quantifying forecast uncertainty.
  • Exploration of machine learning, spatial statistics, and the creation of user-friendly tools are recommended for practitioners.