Quantifying the uncertainty of regional demographic forecasts

Wilson, Tom (2013) Quantifying the uncertainty of regional demographic forecasts. Applied Geography, 42 108-115. doi:10.1016/j.apgeog.2013.05.006

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Author Wilson, Tom
Title Quantifying the uncertainty of regional demographic forecasts
Journal name Applied Geography   Check publisher's open access policy
ISSN 0143-6228
Publication date 2013-08
Year available 2013
Sub-type Article (original research)
DOI 10.1016/j.apgeog.2013.05.006
Open Access Status
Volume 42
Start page 108
End page 115
Total pages 8
Place of publication Kidlington, Oxford, United Kingdom
Publisher Pergamon
Collection year 2014
Language eng
Formatted abstract
Population forecasts are inherently uncertain, and as a general rule the smaller the population, the greater the uncertainty surrounding its demographic future. Over the last two decades demographers have refined probabilistic forecasting models to produce estimates of uncertainty associated with national demographic forecasts. Since the mid-1960s geographers have progressively developed multi-regional models to produce regional demographic forecasts. However, these two streams of research have remained largely separate. This paper draws on ideas from both literatures. It introduces a probabilistic model which is suitable for large subnational regions and which produces both population and household forecasts. It was created with a view to informing metropolitan planning, and includes a number of simplifications to reduce input data requirements and run-times relative to ‘standard’ probabilistic models. It is illustrated with an application to the Greater Sydney region for the period 2011–51. The paper concludes by arguing that instead of assuming there to be one inevitable future demographic trajectory, regional planning should consider the plausible envelope of demographic futures, and plan desired futures within it.
Keyword Probabilistic
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Geography, Planning and Environmental Management Publications
Official 2014 Collection
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Citation counts: TR Web of Science Citation Count  Cited 4 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 5 times in Scopus Article | Citations
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Created: Sun, 22 Sep 2013, 00:13:57 EST by System User on behalf of School of Geography, Planning & Env Management