Small-area population forecasting: Borrowing strength across space and time

Guangqing Chi, Paul R. Voss

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


Demographic forecasting techniques do not perform particularly well for small areas. In this study we propose a spatio-temporal regression approach for small-area population forecasting that borrows strength from what has happened nearby and what has happened in the past. In particular, a regression model incorporating temporally lagged neighbour growth and neighbour characteristics is applied to examine population change at the minor civil division (MCD) governmental level in Wisconsin, USA since 1960. For each MCD, the population growth rate for 1980-1990 is regressed on its growth rate for 1970-1980, its various characteristics in 1980, neighbour growth rates for 1970-1980, and neighbour characteristics in 1980. The estimated regression coefficients are then used for projecting population in 2000. Accuracy of the forecasts is measured against the 2000 Census counts. The state's official MCD projections for 2000, which were based on an extrapolation projection - the most often used traditional population forecasting approach for small geographic areas - are taken as the 'gold standard' against which improvements through the regression formulations are sought. The projection evaluations reveal mixed results and do not suggest unambiguous preference for the spatio-temporal regression approach or the extrapolation projection. We discuss several reasons for the inability of our spatio-temporal forecasting model to outshine the simple extrapolation forecast. Although this is disappointing, the proposed approach is more solidly theoretically grounded and provides useful information to policy and decision makers at the community level regarding the consequences of various developmental strategies being adopted by themselves as well as by their neighbours.

Original languageEnglish (US)
Pages (from-to)505-520
Number of pages16
JournalPopulation, Space and Place
Issue number5
StatePublished - Sep 2011

All Science Journal Classification (ASJC) codes

  • Demography
  • Geography, Planning and Development


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