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

Abstract

Small area population forecasts are widely used by government and business for a variety of planning, research and policy purposes, and often influence major investment decisions. Yet, the toolbox of small area population forecasting methods and techniques is modest relative to that for national and large subnational regional forecasting. In this paper, we assess the current state of small area population forecasting, and suggest areas for further research. The paper provides a review of the literature on small area population forecasting methods published over the period 2001–2020. The key themes covered by the review are extrapolative and comparative methods, simplified cohort-component methods, model averaging and combining, incorporating socioeconomic variables and spatial relationships, ‘downscaling’ and disaggregation approaches, linking population with housing, estimating and projecting small area component input data, microsimulation, machine learning, and forecast uncertainty. Several avenues for further research are then suggested, including more work on model averaging and combining, developing new forecasting methods for situations which current models cannot handle, quantifying uncertainty, exploring methodologies such as machine learning and spatial statistics, creating user-friendly tools for practitioners, and understanding more about how forecasts are used. Supplementary Information The online version contains supplementary material available at 10.1007/s11113-021-09671-6.

An Open‐Source Model for Projecting Small Area Demographic and Land‐Use Change

“…The models presented in this paper draw strength from each of these categories, so we consider the broad context here and return to how each have informed our models in the discussion section of the paper. A subsequent review of small area projection methods over the past decade undertaken by Wilson et al (2021) identifies similar broad headings, with the addition of microsimulation and machine‐learning methods. We utilize microsimulation, but not machine learning in our models.…”

“…The key criticism leveled at these approaches by Cameron and Cochrane (2017) is that they lack a strong theoretical basis given their deterministic reliance on past trends, especially when used for areas that exhibit less predictable growth. Nonetheless, they have been found to perform well in terms of overall accuracy (Smith, Tayman, and Swanson 2013), often better than more complex methods such as cohort component models at a small area level (Smith and Tayman 2003) and have the distinct advantage of relatively low data requirements (Wilson et al 2021) so can be applied in a broad range of contexts.…”

“…Wilson et al (2021) refer to as simplified cohort‐component methods, namely cohort change ratios, of which the Hamilton‐Perry method (Hamilton and Perry 1962) is widely used, e.g. by Swanson, Schlottmann, and Schmidt (2010) to project census tract populations in the United States.…”

Small area population denominators for improved disease surveillance and response

“…In these two settings, the choice of modelling approach has a significantly larger impact for the DRC where population counts for massively larger units are being disaggregated to the same size grid squares. Nevertheless, as highlighted above, even in settings with high quality regular censuses with data available mapped to small units, the processes of migration, displacement, urbanization and heterogeneous fertility and mortality can make these data quickly outdated and are hard to accurately forecast at small area scales ( Wilson et al, 2021 ), resulting in potentially major impacts on reliable surveillance and health metrics ( Tatem, 2014 ).…”

“…Subnational changes in population distributions induced by migration and displacement have been shown to be reliably captured by models driven by mobile phone record data ( Lai et al, 2019 , Bengtsson et al, 2011 ), and approaches for incorporating such flow data into small area population estimation models continue to be explored ( Dooley et al, 2020 ). Improved understanding of the processes and dynamics of population changes at small area scales in turn offer the potential for improved forecasting ( Wilson et al, 2021 ).…”